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+.idea/material_theme_project_new.xml
+
+*.pyc
+__pycache__/

.idea/.gitignore

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.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
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+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.11 (name1)" />
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.idea/modules.xml

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+<project version="4">
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+</project>

.idea/name1.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
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+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
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+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="py.test" />
+  </component>
+</module>

.idea/vcs.xml

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Procfile

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+web: streamlit run app/main.py --server.port=$PORT --server.headless=true --server.enableCORS=false

R markdowns/MRSS_correlation_plots.Rmd

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+---
+title: "mrss_correlation_plots"
+output: html_document
+date: "2024-09-24"
+---
+
+```{r setup, include=FALSE}
+library(devtools)
+library(readat)
+library(SomaDataIO)
+library(ggplot2)
+library(dplyr)
+library(tidyr)
+library(purrr)
+library(readat)
+library(Biobase)
+library(limma)
+library(magrittr)
+library(tidyverse)
+library(reshape2)
+adat <- readAdat('D:/Data/Data_drive/Data/IS_Protein_data/SS-2342309_v4.1_other.hybNorm.medNormInt.plateScale.adat', keepOnlyPasses = T, keepOnlySamples = T, dateFormat = "%Y-%m-%d", verbose = getOption("verbose"))
+rownames(adat) <- adat$ExtIdentifier
+metadata <- read.csv(file ='D:/Data/Data_drive/Data/IS_Protein_data/somalogic_metadata.csv', row.names = 1)
+SampleDescription <- c("VEDOSS", "Healthy_abdo", "Healthy", "Healthy_abdo", "SSC_low", "SSC_high","SSC_low","SSC_high","Healthy_abdo","Healthy_abdo","VEDOSS","SSC_high","SSC_high","SSC_low","Healthy","VEDOSS", "Healthy_abdo","Healthy")
+adat$SampleDescription<- SampleDescription
+SampleGroup <- c("VEDOSS", "Healthy", "Healthy", "Healthy", "SSC","SSC", "SSC", "SSC","Health
+y","Healthy","VEDOSS","SSC","SSC","SSC","Healthy","VEDOSS", "Healthy","Healthy")
+adat$SampleGroup <- SampleGroup
+adat <- adat[!grepl("PSG", adat$SampleId), ]
+rownames(metadata) ==adat$ExtIdentifier
+metadata$Total_mRss
+adat$SampleNotes <- metadata$Total_mRss
+adat <- adat %>% rename( mrss = SampleNotes)
+
+seq_variance <- getSequenceData(adat)
+seq_variance <- seq_variance %>% filter(Organism=="Human"&Type=="Protein")
+melt_proteins <- melt(adat, na.rm = T)
+proteins_plot <- merge(melt_proteins, seq_variance)
+order <- c("Healthy", "VEDOSS","SSC_low","SSC_high")
+proteins_plot$SampleDescription <- factor(proteins_plot$SampleDescription, levels=order)
+somaEset <- soma2eset(adat)
+somaEset <- subset(somaEset, somaEset@featureData@data[["Organism"]] =="Human"& somaEset@featureData@data[["Type"]]=="Protein")
+protein_IDs <- as.data.frame(somaEset@featureData@data)
+```
+
+```{r}
+# Required libraries
+library(broom)
+library(dplyr)
+
+# Function to calculate R-squared, correlation, and p-value for each gene (SeqId)
+calc_r2_pvalue_correlation <- function(data) {
+  
+  # Initialize an empty list to store results for each gene
+  results <- list()
+  
+  # Iterate over each unique gene (SeqId)
+  for (gene in unique(data$SeqId)) {
+    
+    # Subset data for the current gene
+    gene_data <- data %>% filter(SeqId == gene)
+    
+    # Fit a linear model using Intensity as predictor and mrss as response
+    model <- lm(mrss ~ Intensity, data = gene_data)
+    
+    # Calculate Pearson correlation coefficient
+    correlation <- cor(gene_data$Intensity, gene_data$mrss, method = "pearson")
+    
+    # Extract R-squared and p-value from the model
+    tidy_model <- tidy(model)  # For p-value
+    glance_model <- glance(model)  # For R-squared
+    
+    # Store results in a list
+    results[[gene]] <- data.frame(
+      SeqId = gene,
+      Correlation = correlation,  # Pearson correlation coefficient (r)
+      R_squared = glance_model$r.squared,
+      P_value = tidy_model$p.value[2]  # p-value for the Intensity predictor
+    )
+  }
+  
+  # Combine all results into a single data frame
+  results_df <- do.call(rbind, results)
+  
+  return(results_df)
+}
+
+# Example usage:
+results_table <- calc_r2_pvalue_correlation(proteins_plot)
+
+# Print or plot the results
+ 
+results_table <- results_table %>% filter(P_value <0.05)
+correlation_dataframe <- results_table %>% left_join(protein_IDs, by = "SeqId") %>%  select(Correlation, R_squared, P_value, EntrezGeneSymbol, SeqId)
+```
+
+```{r}
+positive_correlations <- correlation_dataframe %>% filter(Correlation > 0) %>%  arrange(desc(R_squared)) %>% slice(1:10)
+negative_correlations <- correlation_dataframe %>% filter(Correlation < 0) %>%  arrange(desc(R_squared)) %>% slice(1:10)
+correlation_table <- cbind(positive_correlations,negative_correlations)
+write.csv(correlation_table, "D:/Data/Data_drive/Data/IS_Protein_data/correlative_proteins_mrss.csv")
+```
+
+```{r}
+library(ggpubr)
+library(ggpmisc)
+library(cowplot)
+
+CCL18 <- proteins_plot %>% 
+  subset(SeqId =="3044-3")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 5) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+
+
+HNRNPDL <- proteins_plot %>% 
+  subset(SeqId =="10852-114")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+
+TIMP1 <- proteins_plot %>% 
+  subset(SeqId =="25967-34")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+
+SFRP4 <- proteins_plot %>% 
+  subset(SeqId =="17447-52")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+
+SERPINE2 <- proteins_plot %>% 
+  subset(SeqId =="19154-41")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+
+INHBA <- proteins_plot %>% 
+  subset(SeqId =="19622-7")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+
+
+png(file='D:/Data/Data_drive/Data/IS_Protein_data/Tidied_scripts/plots/MRSS_correlation_plots.png', width = 4750, height = 750, res = 300)
+plot_grid(CCL18,HNRNPDL,TIMP1,SFRP4,SERPINE2,INHBA, ncol=6,nrow = 1)
+```
+
+```{r}
+THBS1 <- proteins_plot %>% 
+  subset(SeqId =="3474-19")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+png(file='D:/Data/Data_drive/Data/IS_Protein_data/Tidied_scripts/plots/THBS_MRSS_correlation_plots.png', width = 750, height = 750, res = 300)
+THBS1
+INHBA <- proteins_plot %>% 
+  subset(SeqId =="19622-7")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+png(file='D:/Data/Data_drive/Data/IS_Protein_data/Tidied_scripts/plots/INHBA_MRSS_correlation_plots.png', width = 750, height = 750, res = 300)
+INHBA
+```
+
+```{r}
+###Becky's panel
+CCL2 <- proteins_plot %>% 
+  subset(SeqId =="2578-67")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+png(file='D:/Data/Data_drive/Data/IS_Protein_data/Tidied_scripts/plots/for_becky/CCL2_MRSS_correlation_plots.png', width = 750, height = 750, res = 300)
+CCL2
+dev.off()
+
+CXCL10 <- proteins_plot %>% 
+  subset(SeqId =="4141-79")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+png(file='D:/Data/Data_drive/Data/IS_Protein_data/Tidied_scripts/plots/for_becky/CXCL10_MRSS_correlation_plots.png', width = 750, height = 750, res = 300)
+CXCL10
+dev.off()
+
+CCL19 <- proteins_plot %>% 
+  subset(SeqId =="4922-13")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+png(file='D:/Data/Data_drive/Data/IS_Protein_data/Tidied_scripts/plots/for_becky/CCL19_MRSS_correlation_plots.png', width = 750, height = 750, res = 300)
+CCL19
+dev.off()
+
+CXCL9 <- proteins_plot %>% 
+  subset(SeqId =="11593-21")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+png(file='D:/Data/Data_drive/Data/IS_Protein_data/Tidied_scripts/plots/for_becky/CXCL9_MRSS_correlation_plots.png', width = 750, height = 750, res = 300)
+CXCL9
+dev.off()
+
+CXCL11 <- proteins_plot %>% 
+  subset(SeqId =="3038-9")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 3) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+png(file='D:/Data/Data_drive/Data/IS_Protein_data/Tidied_scripts/plots/for_becky/CXCL11_MRSS_correlation_plots.png', width = 750, height = 750, res = 300)
+CXCL11
+dev.off()
+CCL18 <- proteins_plot %>% 
+  subset(SeqId =="3044-3")%>% 
+  ggplot(aes(mrss, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 5) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Total mRSS")
+png(file='D:/Data/Data_drive/Data/IS_Protein_data/Tidied_scripts/plots/for_becky/CCL18_MRSS_correlation_plots.png', width = 750, height = 750, res = 300)
+CCL18
+dev.off()
+```
+

R markdowns/Somalogic_deeper_metadata_analysis.Rmd

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+---
+title: "Differential protein expression using Limma"
+output:
+  html_document:
+    df_print: paged
+---
+
+```{r}
+library(dplyr)
+library(tidyr)
+library(ggplot2)
+library(EnhancedVolcano)
+
+metadata <- read.csv(file ='D:/Data/Data_drive/Data/IS_Protein_data/somalogic_metadata.csv', row.names = 1)
+somaEset <- readRDS('D:/Data/Data_drive/Data/IS_Protein_data/somaEset.rds')
+normalised_expression <- as.data.frame(exprs(somaEset))
+protein_IDs <- as.data.frame(somaEset@featureData@data)
+proteins_plot <- read.csv('D:/Data/Data_drive/Data/IS_Protein_data/proteins_plot.csv', row.names = 1)
+
+```
+
+
+```{r}
+### lung fibrosis
+subset_metadata <- dplyr::filter(metadata, grepl("Scleroderma", category))
+subset_proteins <- normalised_expression[, colnames(normalised_expression) %in% rownames(subset_metadata)]
+design <- model.matrix(~ Lung_Fibrosis + 0, data = subset_metadata)
+colnames(design) <- c("No","Yes")
+contrastNames <- c("Yes - No")
+contrasts <- makeContrasts(contrasts = contrastNames, levels=design)
+limmaRes <- subset_proteins %>% lmFit(design = design)%>% contrasts.fit(contrasts) %>% eBayes()
+coeffs <- coefficients(limmaRes)
+
+stats_df <- topTable(limmaRes, number = nrow(subset_proteins))
+stats_df <- merge(stats_df, protein_IDs, by = "row.names")
+write.csv(stats_df, "D:/Data/Data_drive/Data/IS_Protein_data/SSc_lung_fibrosis_allproteins.csv")
+writeable_SSc_lung_fibrosis <- stats_df %>% filter( P.Value <0.05) %>% filter(logFC >0.585| logFC < -0.585) %>% select(SeqId, TargetFullName,Target,UniProt,EntrezGeneSymbol,logFC,AveExpr,P.Value)
+write.csv(writeable_SSc_lung_fibrosis, "D:/Data/Data_drive/Data/IS_Protein_data/SSc_lung_fibrosis.csv")
+
+stats_df <- stats_df %>%
+  arrange(P.Value) %>%
+  mutate(EntrezGeneSymbol = as.character(EntrezGeneSymbol),
+         label = ifelse(P.Value < 0.05 & EntrezGeneSymbol %in% head(EntrezGeneSymbol, 50), EntrezGeneSymbol, NA))
+
+writeable_SSc_lung_fibrosis %>%
+  summarise(
+    logFC_greater_than_0 = sum(logFC > 0),
+    logFC_less_than_0 = sum(logFC < 0))
+
+SSc_lung_fibrosis <- EnhancedVolcano(data.frame(stats_df), x = 'logFC', y = 'P.Value',lab = stats_df$label,selectLab = stats_df$label,
+    title = 'SSc Lung Fibrosis vs SSc no Lung Fibrosis proteins',
+    pCutoff = 0.05,
+    FCcutoff = 0.585,
+    xlim = c(min(stats_df[['logFC']], na.rm = TRUE), max(stats_df[['logFC']], na.rm = TRUE)),
+    ylim = c(0, max(-log10(stats_df[['P.Value']]), na.rm = TRUE)),
+    pointSize = 2.0,
+    labSize = 4.0,
+    labCol = 'grey14',
+    colAlpha = 4/5,
+    boxedLabels = T,
+    legendPosition = 'None',
+    legendLabSize = 8,
+    legendIconSize = 3.0,
+    drawConnectors = T,
+    widthConnectors = 0.6,
+    colConnectors = 'black',
+    max.overlaps = 20,
+    maxoverlapsConnectors = Inf)
+SSc_lung_fibrosis
+png(file='D:/Data/Data_drive/Data/IS_Protein_data/Tidied_scripts/plots/SSc_lung_fibrosis_volcano.png', width = 3000, height = 2000, res = 300)
+SSc_lung_fibrosis
+
+```
+```{r}
+# Required libraries
+library(broom)
+library(dplyr)
+
+# Function to calculate R-squared, correlation, and p-value for each gene (SeqId)
+calc_r2_pvalue_correlation <- function(data) {
+  
+  # Initialize an empty list to store results for each gene
+  results <- list()
+  
+  # Iterate over each unique gene (SeqId)
+  for (gene in unique(data$SeqId)) {
+    
+    # Subset data for the current gene
+    gene_data <- data %>% filter(SeqId == gene)
+    
+    # Fit a linear model using Intensity as predictor and Lung_Fibrosis_binary as response
+    model <- lm(Lung_Fibrosis_binary ~ Intensity, data = gene_data)
+    
+    # Calculate Pearson correlation coefficient
+    correlation <- cor(gene_data$Intensity, gene_data$Lung_Fibrosis_binary, method = "pearson")
+    
+    # Extract R-squared and p-value from the model
+    tidy_model <- tidy(model)  # For p-value
+    glance_model <- glance(model)  # For R-squared
+    
+    # Store results in a list
+    results[[gene]] <- data.frame(
+      SeqId = gene,
+      Correlation = correlation,  # Pearson correlation coefficient (r)
+      R_squared = glance_model$r.squared,
+      P_value = tidy_model$p.value[2]  # p-value for the Intensity predictor
+    )
+  }
+  
+  # Combine all results into a single data frame
+  results_df <- do.call(rbind, results)
+  
+  return(results_df)
+}
+
+# Example usage:
+subset_proteins_plot <- proteins_plot %>% filter(SampleGroup =="SSC")
+results_table <- calc_r2_pvalue_correlation(subset_proteins_plot)
+
+# Print or plot the results
+ 
+results_table <- results_table %>% filter(P_value <0.05)
+correlation_dataframe <- results_table %>% left_join(protein_IDs, by = "SeqId") %>%  select(Correlation, R_squared, P_value, EntrezGeneSymbol, SeqId)
+```
+
+```{r}
+positive_correlations <- correlation_dataframe %>% filter(Correlation > 0) %>%  arrange(desc(R_squared)) %>% slice(1:10)
+negative_correlations <- correlation_dataframe %>% filter(Correlation < 0) %>%  arrange(desc(R_squared)) %>% slice(1:10)
+correlation_table <- full_join(positive_correlations,negative_correlations)
+write.csv(correlation_table, "D:/Data/Data_drive/Data/IS_Protein_data/correlative_proteins_lung_fibrosis.csv")
+```
+
+```{r}
+library(ggpubr)
+library(ggpmisc)
+library(cowplot)
+mat_colors <- c('turquoise2','red')
+
+SCUBE3 <- subset_proteins_plot  %>%
+  subset(SeqId == "16773-29") %>%  
+  ggplot(aes(Lung_Fibrosis, Intensity, label = SampleId))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+  scale_y_log10()+
+  geom_boxplot(fill = mat_colors)+
+  theme_bw() +
+  theme(legend.position = "top")+
+  labs(color = "Sample", x="Lung fibrosis")
+SCUBE3
+ELP1 <- subset_proteins_plot  %>%
+  subset(SeqId == "25102-23") %>%  
+  ggplot(aes(Lung_Fibrosis, Intensity, label = SampleId))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+  scale_y_log10()+
+  geom_boxplot(fill = mat_colors)+
+  theme_bw() +
+  theme(legend.position = "top")+
+  labs(color = "Sample", x="Lung fibrosis")
+ELP1
+
+SNTA1 <- subset_proteins_plot  %>%
+  subset(SeqId == "22946-55") %>%  
+  ggplot(aes(Lung_Fibrosis, Intensity, label = SampleId))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+  scale_y_log10()+
+  geom_boxplot(fill = mat_colors)+
+  theme_bw() +
+  theme(legend.position = "top")+
+  labs(color = "Sample", x="Lung fibrosis")
+SNTA1
+	
+HEXB <- subset_proteins_plot  %>%
+  subset(SeqId == "6075-61") %>%  
+  ggplot(aes(Lung_Fibrosis, Intensity, label = SampleId))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+  scale_y_log10()+
+  geom_boxplot(fill = mat_colors)+
+  theme_bw() +
+  theme(legend.position = "top")+
+  labs(color = "Sample", x="Lung fibrosis")
+HEXB
+	
+plot_grid(SCUBE3,ELP1,SNTA1,HEXB,ncol=4,nrow = 1)
+
+```
+
+```{r}
+### Immunosuppression
+subset_metadata <- dplyr::filter(metadata, grepl("Scleroderma", category))
+subset_proteins <- normalised_expression[, colnames(normalised_expression) %in% rownames(subset_metadata)]
+design <- model.matrix(~ Immunosupression_bin + 0, data = subset_metadata)
+colnames(design) <- c("No","Yes")
+contrastNames <- c("Yes - No")
+contrasts <- makeContrasts(contrasts = contrastNames, levels=design)
+limmaRes <- subset_proteins %>% lmFit(design = design)%>% contrasts.fit(contrasts) %>% eBayes()
+coeffs <- coefficients(limmaRes)
+
+stats_df <- topTable(limmaRes, number = nrow(subset_proteins))
+stats_df <- merge(stats_df, protein_IDs, by = "row.names")
+write.csv(stats_df, "D:/Data/Data_drive/Data/IS_Protein_data/SSc_immunosupressant_allproteins.csv")
+writeable_SSc_immunosupression <- stats_df %>% filter( P.Value <0.05) %>% filter(logFC >0.585| logFC < -0.585) %>% select(SeqId, TargetFullName,Target,UniProt,EntrezGeneSymbol,logFC,AveExpr,P.Value)
+write.csv(writeable_SSc_immunosupression, "D:/Data/Data_drive/Data/IS_Protein_data/SSc_immunosupressant.csv")
+
+stats_df <- stats_df %>%
+  arrange(P.Value) %>%
+  mutate(EntrezGeneSymbol = as.character(EntrezGeneSymbol),
+         label = ifelse(P.Value < 0.05 & EntrezGeneSymbol %in% head(EntrezGeneSymbol, 50), EntrezGeneSymbol, NA))
+
+writeable_SSc_immunosupression %>%
+  summarise(
+    logFC_greater_than_0 = sum(logFC > 0),
+    logFC_less_than_0 = sum(logFC < 0))
+
+SSc_immunosupression <- EnhancedVolcano(data.frame(stats_df), x = 'logFC', y = 'P.Value',lab = stats_df$label,selectLab = stats_df$label,
+    title = 'SSc Immunosupression vs SSc no Immunosupression',
+    pCutoff = 0.05,
+    FCcutoff = 0.585,
+    xlim = c(min(stats_df[['logFC']], na.rm = TRUE), max(stats_df[['logFC']], na.rm = TRUE)),
+    ylim = c(0, max(-log10(stats_df[['P.Value']]), na.rm = TRUE)),
+    pointSize = 2.0,
+    labSize = 4.0,
+    labCol = 'grey14',
+    colAlpha = 4/5,
+    boxedLabels = T,
+    legendPosition = 'None',
+    legendLabSize = 8,
+    legendIconSize = 3.0,
+    drawConnectors = T,
+    widthConnectors = 0.6,
+    colConnectors = 'black',
+    max.overlaps = 20,
+    maxoverlapsConnectors = Inf)
+SSc_immunosupression
+png(file='D:/Data/Data_drive/Data/IS_Protein_data/Tidied_scripts/plots/SSc_immunosupressant_volcano.png', width = 3000, height = 2000, res = 300)
+SSc_immunosupression
+
+```
+
+
+```{r}
+subset_proteins_plot <- proteins_plot  %>%  dplyr::filter(grepl("SSC", SampleGroup))
+
+CFDP1 <- subset_proteins_plot %>% 
+  subset(SeqId == "24706-73") %>%  
+  ggplot(aes(Immunosupression_bin, Intensity, label = SampleId))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+  scale_y_log10()+
+  geom_boxplot(fill = mat_colors)+
+  theme_bw() +
+  theme(legend.position = "top")+
+  labs(color = "Sample", x="Immunosupression")
+CFDP1
+
+KRT1 <- subset_proteins_plot %>% 
+  subset(SeqId == "9931-20") %>%  
+  ggplot(aes(Immunosupression_bin, Intensity, label = SampleId))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+  scale_y_log10()+
+  geom_boxplot(fill = mat_colors)+
+  theme_bw() +
+  theme(legend.position = "top")+
+  labs(color = "Sample", x="Immunosupression")
+KRT1
+```
+
+
+```{r}
+# Required libraries
+library(broom)
+library(dplyr)
+
+# Function to calculate R-squared, correlation, and p-value for each gene (SeqId)
+calc_r2_pvalue_correlation <- function(data) {
+  
+  # Initialize an empty list to store results for each gene
+  results <- list()
+  
+  # Iterate over each unique gene (SeqId)
+  for (gene in unique(data$SeqId)) {
+    
+    # Subset data for the current gene
+    gene_data <- data %>% filter(SeqId == gene)
+    
+    # Fit a linear model using Intensity as predictor and Local_skin_score as response
+    model <- lm(Local_skin_score ~ Intensity, data = gene_data)
+    
+    # Calculate Pearson correlation coefficient
+    correlation <- cor(gene_data$Intensity, gene_data$Local_skin_score, method = "pearson")
+    
+    # Extract R-squared and p-value from the model
+    tidy_model <- tidy(model)  # For p-value
+    glance_model <- glance(model)  # For R-squared
+    
+    # Store results in a list
+    results[[gene]] <- data.frame(
+      SeqId = gene,
+      Correlation = correlation,  # Pearson correlation coefficient (r)
+      R_squared = glance_model$r.squared,
+      P_value = tidy_model$p.value[2]  # p-value for the Intensity predictor
+    )
+  }
+  
+  # Combine all results into a single data frame
+  results_df <- do.call(rbind, results)
+  
+  return(results_df)
+}
+
+# Example usage:
+subset_proteins_plot <- proteins_plot %>% filter(SampleGroup =="SSC")
+results_table <- calc_r2_pvalue_correlation(subset_proteins_plot)
+
+# Print or plot the results
+ 
+results_table <- results_table %>% filter(P_value <0.05)
+correlation_dataframe <- results_table %>% left_join(protein_IDs, by = "SeqId") %>%  select(Correlation, R_squared, P_value, EntrezGeneSymbol, SeqId)
+```
+
+```{r}
+positive_correlations <- correlation_dataframe %>% filter(Correlation > 0) %>%  arrange(desc(R_squared)) %>% slice(1:10)
+negative_correlations <- correlation_dataframe %>% filter(Correlation < 0) %>%  arrange(desc(R_squared)) %>% slice(1:10)
+correlation_table <- full_join(positive_correlations,negative_correlations)
+write.csv(correlation_table, "D:/Data/Data_drive/Data/IS_Protein_data/correlative_proteins_local_skin_score.csv")
+```
+
+```{r}
+library(ggpubr)
+library(ggpmisc)
+library(cowplot)
+
+CHI3L1 <- subset_proteins_plot %>% 
+  subset(SeqId =="11104-13")%>% 
+  ggplot(aes(Local_skin_score, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 5) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Local mRSS")
+CHI3L1
+TPSAB1 <- subset_proteins_plot %>% 
+  subset(SeqId =="9409-11")%>% 
+  ggplot(aes(Local_skin_score, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 5) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Local mRSS")
+TPSAB1
+plot_grid(CHI3L1,TPSAB1,ncol=2,nrow = 1)
+
+```
+
+
+```{r}
+# Required libraries
+library(broom)
+library(dplyr)
+
+# Function to calculate R-squared, correlation, and p-value for each gene (SeqId)
+calc_r2_pvalue_correlation <- function(data) {
+  
+  # Initialize an empty list to store results for each gene
+  results <- list()
+  
+  # Iterate over each unique gene (SeqId)
+  for (gene in unique(data$SeqId)) {
+    
+    # Subset data for the current gene
+    gene_data <- data %>% filter(SeqId == gene)
+    
+    # Fit a linear model using Intensity as predictor and Disease_duration as response
+    model <- lm(Disease_duration ~ Intensity, data = gene_data)
+    
+    # Calculate Pearson correlation coefficient
+    correlation <- cor(gene_data$Intensity, gene_data$Disease_duration, method = "pearson")
+    
+    # Extract R-squared and p-value from the model
+    tidy_model <- tidy(model)  # For p-value
+    glance_model <- glance(model)  # For R-squared
+    
+    # Store results in a list
+    results[[gene]] <- data.frame(
+      SeqId = gene,
+      Correlation = correlation,  # Pearson correlation coefficient (r)
+      R_squared = glance_model$r.squared,
+      P_value = tidy_model$p.value[2]  # p-value for the Intensity predictor
+    )
+  }
+  
+  # Combine all results into a single data frame
+  results_df <- do.call(rbind, results)
+  
+  return(results_df)
+}
+
+# Example usage:
+subset_proteins_plot <- proteins_plot %>% filter(SampleGroup =="SSC")
+results_table <- calc_r2_pvalue_correlation(subset_proteins_plot)
+
+# Print or plot the results
+ 
+results_table <- results_table %>% filter(P_value <0.05)
+correlation_dataframe <- results_table %>% left_join(protein_IDs, by = "SeqId") %>%  select(Correlation, R_squared, P_value, EntrezGeneSymbol, SeqId)
+```
+
+```{r}
+positive_correlations <- correlation_dataframe %>% filter(Correlation > 0) %>%  arrange(desc(R_squared)) %>% slice(1:10)
+negative_correlations <- correlation_dataframe %>% filter(Correlation < 0) %>%  arrange(desc(R_squared)) %>% slice(1:10)
+correlation_table <- full_join(positive_correlations,negative_correlations)
+write.csv(correlation_table, "D:/Data/Data_drive/Data/IS_Protein_data/correlative_proteins_disease_duration.csv")
+```
+
+```{r}
+GPX1 <- subset_proteins_plot %>% 
+  subset(SeqId =="15591-28")%>% 
+  ggplot(aes(Disease_duration, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 5) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Disease Duration (Months)")
+GPX1
+
+PPP1R9B <- subset_proteins_plot %>% 
+  subset(SeqId =="21991-79")%>% 
+  ggplot(aes(Disease_duration, Intensity, label = SampleDescription))+
+  facet_wrap( ~ EntrezGeneSymbol)+
+   geom_point()+
+    geom_label(color = "black", show.legend = FALSE) +
+  stat_poly_line() +
+  stat_poly_eq(use_label(c("R2", "p")), p.digits = 5) +
+  theme(legend.position = "none")+
+  labs( y = "",x="Disease Duration (Months)")
+PPP1R9B
+plot_grid(GPX1,PPP1R9B,ncol=2,nrow = 1)
+
+```
+

R markdowns/subset_fibroblast_proteomic_overlay.Rmd

@@ -0,0 +1,144 @@
+---
+title: "Fibroblast_Pseudobulk_overlap"
+output: html_document
+date: "2024-09-24"
+---
+
+```{r setup, include=FALSE}
+library(dplyr)
+
+####first set up done with SSc all
+proteomics_df <- read.csv('D:/Data/Data_drive/Data/IS_Protein_data/SSC_all_Healthy_allproteins.csv', row.names = 'Row.names')
+fibroblast_df <- read.csv('D:/Data/Data_drive/GSE138669_RAW/fibroblasts/DESeq2_fibroblasts.csv')
+colnames(fibroblast_df)[1] <- "EntrezGeneSymbol"
+fibroblast_df <- fibroblast_df %>% filter(baseMean >10)
+
+upregulated_fibroblast_df <- fibroblast_df %>% filter(log2FoldChange > 0 & padj <0.05)
+downregulated_fibroblast_df <- fibroblast_df %>% filter(log2FoldChange < 0 & padj <0.05)
+upregulated <- upregulated_fibroblast_df$EntrezGeneSymbol
+downregulated <- downregulated_fibroblast_df$EntrezGeneSymbol
+
+upregulated_proteomics <- proteomics_df %>%  filter(EntrezGeneSymbol %in% upregulated, P.Value <0.05, logFC >0)
+downregulated_proteomics <- proteomics_df %>%  filter(EntrezGeneSymbol %in% downregulated, P.Value <0.05, logFC <0)
+labels <- c(upregulated_proteomics$EntrezGeneSymbol,downregulated_proteomics$EntrezGeneSymbol)
+
+```
+
+```{r}
+library(EnhancedVolcano)
+
+proteomics_df <- proteomics_df %>%
+  mutate(labels = ifelse(
+    P.Value < 0.05 & (logFC > 0.585 | logFC < -0.585) & EntrezGeneSymbol %in% labels,
+    EntrezGeneSymbol, NA
+  ))
+Fibroblast_overlap_volcano <- EnhancedVolcano(data.frame(proteomics_df), x = 'logFC', y = 'P.Value',lab = proteomics_df$labels,selectLab = labels,
+    title = 'SSc all vs healthy proteins',
+    subtitle = 'Fibroblast differentially expressed genes labelled',
+    pCutoff = 0.05,
+    FCcutoff = 0.585,
+    xlim = c(min(proteomics_df[['logFC']], na.rm = TRUE), max(proteomics_df[['logFC']], na.rm = TRUE)),
+    ylim = c(0, max(-log10(proteomics_df[['P.Value']]), na.rm = TRUE)),
+    pointSize = 1.0,
+    labSize = 2.0,
+    labCol = 'grey14',
+    colAlpha = 4/5,
+    boxedLabels = T,
+    legendPosition = 'None',
+    drawConnectors = T,
+    widthConnectors = 0.6,
+    colConnectors = 'black',
+    max.overlaps = 20,
+    maxoverlapsConnectors = Inf)
+Fibroblast_overlap_volcano
+```
+
+```{r setup, include=FALSE}
+####next use SSc high
+proteomics_df <- read.csv('D:/Data/Data_drive/Data/IS_Protein_data/SSC_high_Healthy_allproteins.csv', row.names = 'Row.names')
+fibroblast_df <- read.csv('D:/Data/Data_drive/GSE138669_RAW/fibroblasts/DESeq2_fibroblasts.csv')
+colnames(fibroblast_df)[1] <- "EntrezGeneSymbol"
+fibroblast_df <- fibroblast_df %>% filter(baseMean >10)
+
+upregulated_fibroblast_df <- fibroblast_df %>% filter(log2FoldChange > 0 & padj <0.05)
+downregulated_fibroblast_df <- fibroblast_df %>% filter(log2FoldChange < 0 & padj <0.05)
+upregulated <- upregulated_fibroblast_df$EntrezGeneSymbol
+downregulated <- downregulated_fibroblast_df$EntrezGeneSymbol
+
+upregulated_proteomics <- proteomics_df %>%  filter(EntrezGeneSymbol %in% upregulated, P.Value <0.05, logFC >0)
+downregulated_proteomics <- proteomics_df %>%  filter(EntrezGeneSymbol %in% downregulated, P.Value <0.05, logFC <0)
+labels <- c(upregulated_proteomics$EntrezGeneSymbol,downregulated_proteomics$EntrezGeneSymbol)
+
+```
+
+```{r}
+proteomics_df <- proteomics_df %>%
+  mutate(labels = ifelse(
+    P.Value < 0.05 & (logFC > 0.585 | logFC < -0.585) & EntrezGeneSymbol %in% labels,
+    EntrezGeneSymbol, NA
+  ))
+Fibroblast_overlap_volcano <- EnhancedVolcano(data.frame(proteomics_df), x = 'logFC', y = 'P.Value',lab = proteomics_df$labels,selectLab = labels,
+    title = 'SSc high vs healthy proteins',
+    subtitle = 'Fibroblast differentially expressed genes labelled',
+    pCutoff = 0.05,
+    FCcutoff = 0.585,
+    xlim = c(min(proteomics_df[['logFC']], na.rm = TRUE), max(proteomics_df[['logFC']], na.rm = TRUE)),
+    ylim = c(0, max(-log10(proteomics_df[['P.Value']]), na.rm = TRUE)),
+    pointSize = 1.0,
+    labSize = 2.0,
+    labCol = 'grey14',
+    colAlpha = 4/5,
+    boxedLabels = T,
+    legendPosition = 'None',
+    drawConnectors = T,
+    widthConnectors = 0.6,
+    colConnectors = 'black',
+    max.overlaps = 20,
+    maxoverlapsConnectors = Inf)
+Fibroblast_overlap_volcano
+```
+
+```{r setup, include=FALSE}
+####next use SSc high
+proteomics_df <- read.csv('D:/Data/Data_drive/Data/IS_Protein_data/VEDOSS_Healthy_allproteins.csv', row.names = 'Row.names')
+fibroblast_df <- read.csv('D:/Data/Data_drive/GSE138669_RAW/fibroblasts/DESeq2_fibroblasts.csv')
+colnames(fibroblast_df)[1] <- "EntrezGeneSymbol"
+fibroblast_df <- fibroblast_df %>% filter(baseMean >10)
+
+upregulated_fibroblast_df <- fibroblast_df %>% filter(log2FoldChange > 0 & padj <0.05)
+downregulated_fibroblast_df <- fibroblast_df %>% filter(log2FoldChange < 0 & padj <0.05)
+upregulated <- upregulated_fibroblast_df$EntrezGeneSymbol
+downregulated <- downregulated_fibroblast_df$EntrezGeneSymbol
+
+upregulated_proteomics <- proteomics_df %>%  filter(EntrezGeneSymbol %in% upregulated, P.Value <0.05, logFC >0)
+downregulated_proteomics <- proteomics_df %>%  filter(EntrezGeneSymbol %in% downregulated, P.Value <0.05, logFC <0)
+labels <- c(upregulated_proteomics$EntrezGeneSymbol,downregulated_proteomics$EntrezGeneSymbol)
+
+```
+
+```{r}
+proteomics_df <- proteomics_df %>%
+  mutate(labels = ifelse(
+    P.Value < 0.05 & (logFC > 0.585 | logFC < -0.585) & EntrezGeneSymbol %in% labels,
+    EntrezGeneSymbol, NA
+  ))
+Fibroblast_overlap_volcano <- EnhancedVolcano(data.frame(proteomics_df), x = 'logFC', y = 'P.Value',lab = proteomics_df$labels,selectLab = labels,
+    title = 'VEDOSS vs healthy proteins',
+    subtitle = 'Fibroblast differentially expressed genes labelled',
+    pCutoff = 0.05,
+    FCcutoff = 0.585,
+    xlim = c(min(proteomics_df[['logFC']], na.rm = TRUE), max(proteomics_df[['logFC']], na.rm = TRUE)),
+    ylim = c(0, max(-log10(proteomics_df[['P.Value']]), na.rm = TRUE)),
+    pointSize = 1.0,
+    labSize = 2.0,
+    labCol = 'grey14',
+    colAlpha = 4/5,
+    boxedLabels = T,
+    legendPosition = 'None',
+    drawConnectors = T,
+    widthConnectors = 0.6,
+    colConnectors = 'black',
+    max.overlaps = 20,
+    maxoverlapsConnectors = Inf)
+Fibroblast_overlap_volcano
+```

README.md

@@ -0,0 +1,78 @@
+# Monitoring Progression of Scleroderma
+
+---
+## Project Description
+This is a website for visualising datasets to study protein expression in Scleroderma patients. The website is able to 
+generate the following plots:
+- Correlation Plot
+- Boxplot
+- UMAP plot
+- Volcano plot
+- Violin plot
+
+---
+## Table of Contents
+- [Introduction](#introduction)
+- [Features](#features)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Contributing](#contributing)
+- [License](#license)
+- [Contact/Support](#contact)
+  
+---
+## Introduction
+Scleroderma is an autoimmune disease that can cause thickened areas of skin and connective tissues. To gain a deeper
+understanding of this condition, analysing the expression of different proteins observed in Scleroderma patients is 
+highly beneficial.
+This website utilizes a dataset to generate 4 graphs, enabling researchers to analyse results while requiring
+minimal bioinformatics expertise.
+
+
+---
+## Features
+### Selecting Protein
+Users can search for proteins using multiple naming conventions including:
+- Protein Name
+- Full Protein Name
+- Entrez Gene ID
+- Entrez Gene Symbol
+
+Alternatively, users can utilize the volcano plot on the main page to select a protein by hovering over and clicking 
+on the plot.
+### Volcano Plot
+
+### Correlation Plot
+
+### Box Plot
+
+### UMAP Plot
+
+### Violin Plot
+
+---
+## Installation
+1. Clone the repository:
+```bash
+ git clone https://github.com/ainiimura21/name1.git
+```
+
+2. Install dependencies:
+```bash
+pip install numpy pandas matplotlib seaborn streamlit scanpy
+ ```
+
+## Usage
+To run the project, use the following command:
+```bash
+streamlit run app/main.py
+```
+
+## Contributing
+1. Fork the repository.
+2. Create a new branch: `git checkout -b feature-name`.
+3. Make changes.
+4. Push your branch: `git push origin feature-name`.
+5. Create a pull request.
+
+

app/Correlation.py

@@ -0,0 +1,92 @@
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+from matplotlib.ticker import LogLocator, NullFormatter
+from dataloader import load_data, filter_data
+
+
+def plot_correlation(merged_data: pd.DataFrame, protein_name: str):
+    """
+    Generate a scatter plot of MRSS vs Intensity with hover-over features.
+
+    Parameters:
+    - merged_data: Preprocessed data for plotting.
+    - protein_name: Name of the protein for the plot title.
+
+    Returns:
+    - The Matplotlib figure object containing the plot.
+    """
+    # Extract relevant columns
+    mrss = merged_data["mrss"]
+    intensity = merged_data["Intensity"]
+    condition = merged_data["condition"]
+
+    # Ensure all intensities are positive for logarithmic scale
+    if (intensity <= 0).any():
+        raise ValueError("All intensity values must be positive for a logarithmic scale.")
+
+    # Define custom colours for conditions
+    custom_palette = {
+        "Healthy": "green",
+        "VEDOSS": "violet",
+        "SSC_low": "cyan",
+        "SSC_high": "red",
+    }
+
+    # Initialize plot
+    plt.figure(figsize=(12, 8))
+    ax = plt.gca()
+
+    # Create scatter plot
+    sns.scatterplot(
+        x=mrss,
+        y=intensity,
+        hue=condition,
+        s=100,
+        palette=custom_palette,
+        edgecolor="black",
+        ax=ax,
+    )
+
+    # Set the y-axis to logarithmic scale
+    ax.set_yscale("log")
+
+    # Configure adaptive limits for y-axis
+    y_min = intensity.min() * 0.8
+    y_max = intensity.max() * 1.2
+    ax.set_ylim(bottom=y_min, top=y_max)
+
+    # Configure log ticks and formatter for y-axis
+    ax.yaxis.set_major_locator(LogLocator(base=10.0, subs=None, numticks=10))
+    ax.yaxis.set_minor_locator(LogLocator(base=10.0, subs=np.arange(2, 10) * 0.1, numticks=10))
+    ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f"{int(x):g}" if x >= 1 else f"{x:.1g}"))
+    ax.yaxis.set_minor_formatter(NullFormatter())  # Hide minor tick labels
+
+    # Add hover-over annotations
+    for i in range(len(merged_data)):
+        plt.text(
+            mrss.iloc[i],
+            intensity.iloc[i],
+            condition.iloc[i],
+            fontsize=9,
+            ha="center",
+            va="center",
+            color="black",
+            bbox=dict(
+                boxstyle="round,pad=0.2",
+                edgecolor="black",
+                facecolor=custom_palette.get(condition.iloc[i], "gray"),
+                alpha=0.7,
+            ),
+        )
+
+    # Title and labels
+    plt.title(f"Correlation Plot for {protein_name}", fontsize=16, fontweight="bold")
+    plt.xlabel("MRSS (Linear Scale)", fontsize=14)
+    plt.ylabel("Intensity (Logarithmic Scale)", fontsize=14)
+    plt.grid(which="both", linestyle="--", linewidth=0.5, alpha=0.7)
+    plt.tight_layout()
+    plt.show()
+
+    return plt

app/UMAP.py

@@ -0,0 +1,34 @@
+import scanpy as sc
+import pandas as pd
+from matplotlib.pyplot import rc_context
+from matplotlib.colors import LinearSegmentedColormap
+from dataloader import load_singlecell_data,getEntrezGeneSymbol
+
+from pathlib import Path
+
+def umap_plot(input_data_key,input_data_value):
+    EntrezGeneSymbol = getEntrezGeneSymbol(input_data_key,input_data_value)
+    BASE_PATH = Path(__file__).parent
+    single_cell_data_path = str(BASE_PATH.parent / "Core data")
+    single_cell_data=load_singlecell_data(single_cell_data_path)
+
+    color_vars=['leiden',EntrezGeneSymbol]
+
+    # Define colour map as requested by the client (from grey to blue)
+    cmap = LinearSegmentedColormap.from_list("grey_to_blue", ["#d3d3d3", "blue"])
+
+    with rc_context({"figure.figsize": (6, 6)}):
+        sc.pl.umap(single_cell_data, color=color_vars, # plot UMAP
+        cmap=cmap,
+        # /* Reference 1 - taken from https://scanpy.readthedocs.io/en/stable/tutorials/plotting/core.html */
+        legend_loc="on data", # Place labels on the data
+        frameon=True,
+        legend_fontsize=4.5,
+        legend_fontoutline=1,)
+    # /* end of reference 1 */
+
+    return sc.pl
+
+
+# umap_plot('EntrezGeneSymbol','THBS1') # EXAMPLE OF USAGE WITH GENESYMBOL
+# umap_plot('EntrezGeneID','7057')

app/Violin.py

@@ -0,0 +1,20 @@
+import scanpy as sc
+from pathlib import Path
+import matplotlib.pyplot as plt
+from matplotlib.pyplot import rc_context
+from dataloader import load_singlecell_data,getEntrezGeneSymbol
+
+def violin_plot(input_data_key,input_data_value):
+    EntrezGeneSymbol = getEntrezGeneSymbol(input_data_key,input_data_value)
+    BASE_PATH = Path(__file__).parent
+    single_cell_data_path = str(BASE_PATH.parent / "Core data")
+    single_cell_data=load_singlecell_data(single_cell_data_path)
+
+    with rc_context({"figure.figsize": (5, 10)}):
+        sc.pl.violin(single_cell_data, [EntrezGeneSymbol], groupby="leiden",rotation=90)
+    # plt.subplots_adjust(top=0.02)
+    plt.xticks(fontsize=5)
+    plt.show()
+    return plt
+
+# violin_plot('EntrezGeneSymbol','THBS1') # EXAMPLE OF USAGE WITH GENESYMBOL

app/boxplot.ipynb

@@ -0,0 +1,1629 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ExtIdentifier</th>\n",
+       "      <th>SubjectID</th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>combined</th>\n",
+       "      <th>ANA</th>\n",
+       "      <th>Centromere</th>\n",
+       "      <th>SCL_70</th>\n",
+       "      <th>RNA_Polymerase_3</th>\n",
+       "      <th>Lung_Fibrosis_binary</th>\n",
+       "      <th>Lung_Fibrosis</th>\n",
+       "      <th>Total_mRss</th>\n",
+       "      <th>Immunosupression_bin</th>\n",
+       "      <th>Immunosupression</th>\n",
+       "      <th>overall</th>\n",
+       "      <th>category</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>low_ssc</th>\n",
+       "      <th>Disease_duration</th>\n",
+       "      <th>Local_skin_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EXID40000009256847</td>\n",
+       "      <td>COO-363</td>\n",
+       "      <td>50</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>50_F</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>VEDOSS</td>\n",
+       "      <td>VEDOSS</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EXID40000009257105</td>\n",
+       "      <td>PDAR-0335</td>\n",
+       "      <td>42</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>42_F</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EXID40000009257119</td>\n",
+       "      <td>COO-005</td>\n",
+       "      <td>51</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>51_M</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>SSC_low</td>\n",
+       "      <td>low</td>\n",
+       "      <td>156</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EXID40000009257121</td>\n",
+       "      <td>COO-429</td>\n",
+       "      <td>70</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>70_F</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>33</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>SSC_high</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>EXID40000009257122</td>\n",
+       "      <td>COO-425</td>\n",
+       "      <td>78</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>78_F</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>18</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>SSC_low</td>\n",
+       "      <td>low</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        ExtIdentifier  SubjectID  age  gender combined  ANA  Centromere  \\\n",
+       "0  EXID40000009256847    COO-363   50  Female     50_F    1           0   \n",
+       "1  EXID40000009257105  PDAR-0335   42  Female     42_F    0           0   \n",
+       "2  EXID40000009257119    COO-005   51    Male     51_M    1           0   \n",
+       "3  EXID40000009257121    COO-429   70  Female     70_F    1           0   \n",
+       "4  EXID40000009257122    COO-425   78  Female     78_F    1           0   \n",
+       "\n",
+       "   SCL_70  RNA_Polymerase_3  Lung_Fibrosis_binary Lung_Fibrosis  Total_mRss  \\\n",
+       "0       1                 0                     0            No           0   \n",
+       "1       0                 0                     0            No           0   \n",
+       "2       0                 0                     1           Yes           2   \n",
+       "3       0                 1                     1           Yes          33   \n",
+       "4       1                 0                     1           Yes          18   \n",
+       "\n",
+       "  Immunosupression_bin  Immunosupression      overall     category condition  \\\n",
+       "0                   No                 0  Scleroderma       VEDOSS    VEDOSS   \n",
+       "1                   No                 0      Healthy      Healthy   Healthy   \n",
+       "2                  Yes                 1  Scleroderma  Scleroderma   SSC_low   \n",
+       "3                   No                 0  Scleroderma  Scleroderma  SSC_high   \n",
+       "4                  Yes                 1  Scleroderma  Scleroderma   SSC_low   \n",
+       "\n",
+       "  low_ssc  Disease_duration  Local_skin_score  \n",
+       "0      no                 0                 0  \n",
+       "1      no                 0                 0  \n",
+       "2     low               156                 0  \n",
+       "3      no                 0                 2  \n",
+       "4     low                60                 1  "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "METADATA_PATH = \"../Core data/somalogic_metadata.csv\"\n",
+    "PROTEINS_PATH = \"../Core data/proteins_plot.csv\"\n",
+    "metadata = pd.read_csv(METADATA_PATH)\n",
+    "proteins = pd.read_csv(PROTEINS_PATH)\n",
+    "valid_columns = {\n",
+    "        \"TargetFullName\": \"TargetFullName\",\n",
+    "        \"Target\": \"Target\",\n",
+    "        \"EntrezGeneID\": \"EntrezGeneID\",\n",
+    "        \"EntrezGeneSymbol\": \"EntrezGeneSymbol\"\n",
+    "    }\n",
+    "metadata.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>SeqId</th>\n",
+       "      <th>PlateId</th>\n",
+       "      <th>PlateRunDate</th>\n",
+       "      <th>ScannerID</th>\n",
+       "      <th>PlatePosition</th>\n",
+       "      <th>SlideId</th>\n",
+       "      <th>Subarray</th>\n",
+       "      <th>SampleId</th>\n",
+       "      <th>SampleType</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TargetFullName</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>UniProt</th>\n",
+       "      <th>EntrezGeneID</th>\n",
+       "      <th>EntrezGeneSymbol</th>\n",
+       "      <th>Organism</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Dilution</th>\n",
+       "      <th>PlateScale_Reference</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10000-28</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>C8</td>\n",
+       "      <td>258633888845</td>\n",
+       "      <td>3</td>\n",
+       "      <td>COO-363</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Beta-crystallin B2</td>\n",
+       "      <td>CRBB2</td>\n",
+       "      <td>P43320</td>\n",
+       "      <td>1415</td>\n",
+       "      <td>CRYBB2</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>292.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>10000-28</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>C9</td>\n",
+       "      <td>258633888846</td>\n",
+       "      <td>3</td>\n",
+       "      <td>PDAR-0335</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Beta-crystallin B2</td>\n",
+       "      <td>CRBB2</td>\n",
+       "      <td>P43320</td>\n",
+       "      <td>1415</td>\n",
+       "      <td>CRYBB2</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>292.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>10000-28</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>E8</td>\n",
+       "      <td>258633888845</td>\n",
+       "      <td>5</td>\n",
+       "      <td>COO-005</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Beta-crystallin B2</td>\n",
+       "      <td>CRBB2</td>\n",
+       "      <td>P43320</td>\n",
+       "      <td>1415</td>\n",
+       "      <td>CRYBB2</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>292.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>10000-28</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>F8</td>\n",
+       "      <td>258633888845</td>\n",
+       "      <td>6</td>\n",
+       "      <td>COO-429</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Beta-crystallin B2</td>\n",
+       "      <td>CRBB2</td>\n",
+       "      <td>P43320</td>\n",
+       "      <td>1415</td>\n",
+       "      <td>CRYBB2</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>292.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>10000-28</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>F7</td>\n",
+       "      <td>258633888844</td>\n",
+       "      <td>6</td>\n",
+       "      <td>COO-425</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Beta-crystallin B2</td>\n",
+       "      <td>CRBB2</td>\n",
+       "      <td>P43320</td>\n",
+       "      <td>1415</td>\n",
+       "      <td>CRYBB2</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>292.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94752</th>\n",
+       "      <td>94753</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>A9</td>\n",
+       "      <td>258633888846</td>\n",
+       "      <td>1</td>\n",
+       "      <td>COO-428</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Interferon regulatory factor 6</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>O14896</td>\n",
+       "      <td>3664</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>409.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94753</th>\n",
+       "      <td>94754</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>D8</td>\n",
+       "      <td>258633888845</td>\n",
+       "      <td>4</td>\n",
+       "      <td>COO-403</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Interferon regulatory factor 6</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>O14896</td>\n",
+       "      <td>3664</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>409.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94754</th>\n",
+       "      <td>94755</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>D9</td>\n",
+       "      <td>258633888846</td>\n",
+       "      <td>4</td>\n",
+       "      <td>PDAR-0343</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Interferon regulatory factor 6</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>O14896</td>\n",
+       "      <td>3664</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>409.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94755</th>\n",
+       "      <td>94756</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>G9</td>\n",
+       "      <td>258633888846</td>\n",
+       "      <td>7</td>\n",
+       "      <td>COO-180</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Interferon regulatory factor 6</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>O14896</td>\n",
+       "      <td>3664</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>409.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94756</th>\n",
+       "      <td>94757</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>E7</td>\n",
+       "      <td>258633888844</td>\n",
+       "      <td>5</td>\n",
+       "      <td>PDAR-0344</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Interferon regulatory factor 6</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>O14896</td>\n",
+       "      <td>3664</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>409.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>94757 rows × 43 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0     SeqId   PlateId PlateRunDate               ScannerID  \\\n",
+       "0               1  10000-28  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "1               2  10000-28  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "2               3  10000-28  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "3               4  10000-28  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "4               5  10000-28  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "...           ...       ...       ...          ...                     ...   \n",
+       "94752       94753    9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "94753       94754    9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "94754       94755    9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "94755       94756    9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "94756       94757    9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "\n",
+       "      PlatePosition       SlideId  Subarray   SampleId SampleType  ...  \\\n",
+       "0                C8  258633888845         3    COO-363     Sample  ...   \n",
+       "1                C9  258633888846         3  PDAR-0335     Sample  ...   \n",
+       "2                E8  258633888845         5    COO-005     Sample  ...   \n",
+       "3                F8  258633888845         6    COO-429     Sample  ...   \n",
+       "4                F7  258633888844         6    COO-425     Sample  ...   \n",
+       "...             ...           ...       ...        ...        ...  ...   \n",
+       "94752            A9  258633888846         1    COO-428     Sample  ...   \n",
+       "94753            D8  258633888845         4    COO-403     Sample  ...   \n",
+       "94754            D9  258633888846         4  PDAR-0343     Sample  ...   \n",
+       "94755            G9  258633888846         7    COO-180     Sample  ...   \n",
+       "94756            E7  258633888844         5  PDAR-0344     Sample  ...   \n",
+       "\n",
+       "                       TargetFullName Target UniProt  EntrezGeneID  \\\n",
+       "0                  Beta-crystallin B2  CRBB2  P43320          1415   \n",
+       "1                  Beta-crystallin B2  CRBB2  P43320          1415   \n",
+       "2                  Beta-crystallin B2  CRBB2  P43320          1415   \n",
+       "3                  Beta-crystallin B2  CRBB2  P43320          1415   \n",
+       "4                  Beta-crystallin B2  CRBB2  P43320          1415   \n",
+       "...                               ...    ...     ...           ...   \n",
+       "94752  Interferon regulatory factor 6   IRF6  O14896          3664   \n",
+       "94753  Interferon regulatory factor 6   IRF6  O14896          3664   \n",
+       "94754  Interferon regulatory factor 6   IRF6  O14896          3664   \n",
+       "94755  Interferon regulatory factor 6   IRF6  O14896          3664   \n",
+       "94756  Interferon regulatory factor 6   IRF6  O14896          3664   \n",
+       "\n",
+       "      EntrezGeneSymbol  Organism Units     Type  Dilution  \\\n",
+       "0               CRYBB2     Human   RFU  Protein       2.5   \n",
+       "1               CRYBB2     Human   RFU  Protein       2.5   \n",
+       "2               CRYBB2     Human   RFU  Protein       2.5   \n",
+       "3               CRYBB2     Human   RFU  Protein       2.5   \n",
+       "4               CRYBB2     Human   RFU  Protein       2.5   \n",
+       "...                ...       ...   ...      ...       ...   \n",
+       "94752             IRF6     Human   RFU  Protein       2.5   \n",
+       "94753             IRF6     Human   RFU  Protein       2.5   \n",
+       "94754             IRF6     Human   RFU  Protein       2.5   \n",
+       "94755             IRF6     Human   RFU  Protein       2.5   \n",
+       "94756             IRF6     Human   RFU  Protein       2.5   \n",
+       "\n",
+       "       PlateScale_Reference  \n",
+       "0                    292.15  \n",
+       "1                    292.15  \n",
+       "2                    292.15  \n",
+       "3                    292.15  \n",
+       "4                    292.15  \n",
+       "...                     ...  \n",
+       "94752                409.75  \n",
+       "94753                409.75  \n",
+       "94754                409.75  \n",
+       "94755                409.75  \n",
+       "94756                409.75  \n",
+       "\n",
+       "[94757 rows x 43 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "proteins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TargetFullName\n",
+      "Filtered Data for Interferon regulatory factor 6:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>SeqId</th>\n",
+       "      <th>PlateId</th>\n",
+       "      <th>PlateRunDate</th>\n",
+       "      <th>ScannerID</th>\n",
+       "      <th>PlatePosition</th>\n",
+       "      <th>SlideId</th>\n",
+       "      <th>Subarray</th>\n",
+       "      <th>SampleId</th>\n",
+       "      <th>SampleType</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TargetFullName</th>\n",
+       "      <th>Target</th>\n",
+       "      <th>UniProt</th>\n",
+       "      <th>EntrezGeneID</th>\n",
+       "      <th>EntrezGeneSymbol</th>\n",
+       "      <th>Organism</th>\n",
+       "      <th>Units</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Dilution</th>\n",
+       "      <th>PlateScale_Reference</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>94744</th>\n",
+       "      <td>94745</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>C8</td>\n",
+       "      <td>258633888845</td>\n",
+       "      <td>3</td>\n",
+       "      <td>COO-363</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Interferon regulatory factor 6</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>O14896</td>\n",
+       "      <td>3664</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>409.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94745</th>\n",
+       "      <td>94746</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>C9</td>\n",
+       "      <td>258633888846</td>\n",
+       "      <td>3</td>\n",
+       "      <td>PDAR-0335</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Interferon regulatory factor 6</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>O14896</td>\n",
+       "      <td>3664</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>409.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94746</th>\n",
+       "      <td>94747</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>E8</td>\n",
+       "      <td>258633888845</td>\n",
+       "      <td>5</td>\n",
+       "      <td>COO-005</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Interferon regulatory factor 6</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>O14896</td>\n",
+       "      <td>3664</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>409.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94747</th>\n",
+       "      <td>94748</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>F8</td>\n",
+       "      <td>258633888845</td>\n",
+       "      <td>6</td>\n",
+       "      <td>COO-429</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Interferon regulatory factor 6</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>O14896</td>\n",
+       "      <td>3664</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>409.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94748</th>\n",
+       "      <td>94749</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>F7</td>\n",
+       "      <td>258633888844</td>\n",
+       "      <td>6</td>\n",
+       "      <td>COO-425</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Interferon regulatory factor 6</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>O14896</td>\n",
+       "      <td>3664</td>\n",
+       "      <td>IRF6</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>RFU</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>409.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 43 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0   SeqId   PlateId PlateRunDate               ScannerID  \\\n",
+       "94744       94745  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "94745       94746  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "94746       94747  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "94747       94748  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "94748       94749  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "\n",
+       "      PlatePosition       SlideId  Subarray   SampleId SampleType  ...  \\\n",
+       "94744            C8  258633888845         3    COO-363     Sample  ...   \n",
+       "94745            C9  258633888846         3  PDAR-0335     Sample  ...   \n",
+       "94746            E8  258633888845         5    COO-005     Sample  ...   \n",
+       "94747            F8  258633888845         6    COO-429     Sample  ...   \n",
+       "94748            F7  258633888844         6    COO-425     Sample  ...   \n",
+       "\n",
+       "                       TargetFullName Target UniProt  EntrezGeneID  \\\n",
+       "94744  Interferon regulatory factor 6   IRF6  O14896          3664   \n",
+       "94745  Interferon regulatory factor 6   IRF6  O14896          3664   \n",
+       "94746  Interferon regulatory factor 6   IRF6  O14896          3664   \n",
+       "94747  Interferon regulatory factor 6   IRF6  O14896          3664   \n",
+       "94748  Interferon regulatory factor 6   IRF6  O14896          3664   \n",
+       "\n",
+       "      EntrezGeneSymbol  Organism Units     Type  Dilution  \\\n",
+       "94744             IRF6     Human   RFU  Protein       2.5   \n",
+       "94745             IRF6     Human   RFU  Protein       2.5   \n",
+       "94746             IRF6     Human   RFU  Protein       2.5   \n",
+       "94747             IRF6     Human   RFU  Protein       2.5   \n",
+       "94748             IRF6     Human   RFU  Protein       2.5   \n",
+       "\n",
+       "       PlateScale_Reference  \n",
+       "94744                409.75  \n",
+       "94745                409.75  \n",
+       "94746                409.75  \n",
+       "94747                409.75  \n",
+       "94748                409.75  \n",
+       "\n",
+       "[5 rows x 43 columns]"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id_type = \"TargetFullName\"\n",
+    "column_name = valid_columns[id_type]\n",
+    "print(column_name)\n",
+    "protein_id = \"Interferon regulatory factor 6\"\n",
+    "filtered_data = proteins[proteins[column_name] == protein_id]\n",
+    "print(f\"Filtered Data for {protein_id}:\")\n",
+    "filtered_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ExtIdentifier</th>\n",
+       "      <th>SubjectID</th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>combined</th>\n",
+       "      <th>ANA</th>\n",
+       "      <th>Centromere</th>\n",
+       "      <th>SCL_70</th>\n",
+       "      <th>RNA_Polymerase_3</th>\n",
+       "      <th>Lung_Fibrosis_binary</th>\n",
+       "      <th>Lung_Fibrosis</th>\n",
+       "      <th>Total_mRss</th>\n",
+       "      <th>Immunosupression_bin</th>\n",
+       "      <th>Immunosupression</th>\n",
+       "      <th>overall</th>\n",
+       "      <th>category</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>low_ssc</th>\n",
+       "      <th>Disease_duration</th>\n",
+       "      <th>Local_skin_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EXID40000009256847</td>\n",
+       "      <td>COO-363</td>\n",
+       "      <td>50</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>50_F</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>VEDOSS</td>\n",
+       "      <td>VEDOSS</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EXID40000009257105</td>\n",
+       "      <td>PDAR-0335</td>\n",
+       "      <td>42</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>42_F</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EXID40000009257119</td>\n",
+       "      <td>COO-005</td>\n",
+       "      <td>51</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>51_M</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>SSC_low</td>\n",
+       "      <td>low</td>\n",
+       "      <td>156</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>EXID40000009257121</td>\n",
+       "      <td>COO-429</td>\n",
+       "      <td>70</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>70_F</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>33</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>SSC_high</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>EXID40000009257122</td>\n",
+       "      <td>COO-425</td>\n",
+       "      <td>78</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>78_F</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>18</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>SSC_low</td>\n",
+       "      <td>low</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        ExtIdentifier  SubjectID  age  gender combined  ANA  Centromere  \\\n",
+       "0  EXID40000009256847    COO-363   50  Female     50_F    1           0   \n",
+       "1  EXID40000009257105  PDAR-0335   42  Female     42_F    0           0   \n",
+       "2  EXID40000009257119    COO-005   51    Male     51_M    1           0   \n",
+       "3  EXID40000009257121    COO-429   70  Female     70_F    1           0   \n",
+       "4  EXID40000009257122    COO-425   78  Female     78_F    1           0   \n",
+       "\n",
+       "   SCL_70  RNA_Polymerase_3  Lung_Fibrosis_binary Lung_Fibrosis  Total_mRss  \\\n",
+       "0       1                 0                     0            No           0   \n",
+       "1       0                 0                     0            No           0   \n",
+       "2       0                 0                     1           Yes           2   \n",
+       "3       0                 1                     1           Yes          33   \n",
+       "4       1                 0                     1           Yes          18   \n",
+       "\n",
+       "  Immunosupression_bin  Immunosupression      overall     category condition  \\\n",
+       "0                   No                 0  Scleroderma       VEDOSS    VEDOSS   \n",
+       "1                   No                 0      Healthy      Healthy   Healthy   \n",
+       "2                  Yes                 1  Scleroderma  Scleroderma   SSC_low   \n",
+       "3                   No                 0  Scleroderma  Scleroderma  SSC_high   \n",
+       "4                  Yes                 1  Scleroderma  Scleroderma   SSC_low   \n",
+       "\n",
+       "  low_ssc  Disease_duration  Local_skin_score  \n",
+       "0      no                 0                 0  \n",
+       "1      no                 0                 0  \n",
+       "2     low               156                 0  \n",
+       "3      no                 0                 2  \n",
+       "4     low                60                 1  "
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_ids = filtered_data[\"SampleId\"].unique()\n",
+    "metadata_info = metadata[metadata[\"SubjectID\"].isin(sample_ids)]\n",
+    "metadata_info.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>SeqId</th>\n",
+       "      <th>PlateId</th>\n",
+       "      <th>PlateRunDate</th>\n",
+       "      <th>ScannerID</th>\n",
+       "      <th>PlatePosition</th>\n",
+       "      <th>SlideId</th>\n",
+       "      <th>Subarray</th>\n",
+       "      <th>SampleId</th>\n",
+       "      <th>SampleType</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Lung_Fibrosis_y</th>\n",
+       "      <th>Total_mRss</th>\n",
+       "      <th>Immunosupression_bin_y</th>\n",
+       "      <th>Immunosupression</th>\n",
+       "      <th>overall</th>\n",
+       "      <th>category</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>low_ssc</th>\n",
+       "      <th>Disease_duration_y</th>\n",
+       "      <th>Local_skin_score_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>94745</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>C8</td>\n",
+       "      <td>258633888845</td>\n",
+       "      <td>3</td>\n",
+       "      <td>COO-363</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>VEDOSS</td>\n",
+       "      <td>VEDOSS</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>94746</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>C9</td>\n",
+       "      <td>258633888846</td>\n",
+       "      <td>3</td>\n",
+       "      <td>PDAR-0335</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>94747</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>E8</td>\n",
+       "      <td>258633888845</td>\n",
+       "      <td>5</td>\n",
+       "      <td>COO-005</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>SSC_low</td>\n",
+       "      <td>low</td>\n",
+       "      <td>156</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>94748</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>F8</td>\n",
+       "      <td>258633888845</td>\n",
+       "      <td>6</td>\n",
+       "      <td>COO-429</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>33</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>SSC_high</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>94749</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>F7</td>\n",
+       "      <td>258633888844</td>\n",
+       "      <td>6</td>\n",
+       "      <td>COO-425</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>18</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>Scleroderma</td>\n",
+       "      <td>SSC_low</td>\n",
+       "      <td>low</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 63 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0   SeqId   PlateId PlateRunDate               ScannerID  \\\n",
+       "0       94745  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "1       94746  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "2       94747  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "3       94748  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "4       94749  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "\n",
+       "  PlatePosition       SlideId  Subarray   SampleId SampleType  ...  \\\n",
+       "0            C8  258633888845         3    COO-363     Sample  ...   \n",
+       "1            C9  258633888846         3  PDAR-0335     Sample  ...   \n",
+       "2            E8  258633888845         5    COO-005     Sample  ...   \n",
+       "3            F8  258633888845         6    COO-429     Sample  ...   \n",
+       "4            F7  258633888844         6    COO-425     Sample  ...   \n",
+       "\n",
+       "   Lung_Fibrosis_y Total_mRss Immunosupression_bin_y  Immunosupression  \\\n",
+       "0               No          0                     No                 0   \n",
+       "1               No          0                     No                 0   \n",
+       "2              Yes          2                    Yes                 1   \n",
+       "3              Yes         33                     No                 0   \n",
+       "4              Yes         18                    Yes                 1   \n",
+       "\n",
+       "       overall     category condition low_ssc  Disease_duration_y  \\\n",
+       "0  Scleroderma       VEDOSS    VEDOSS      no                   0   \n",
+       "1      Healthy      Healthy   Healthy      no                   0   \n",
+       "2  Scleroderma  Scleroderma   SSC_low     low                 156   \n",
+       "3  Scleroderma  Scleroderma  SSC_high      no                   0   \n",
+       "4  Scleroderma  Scleroderma   SSC_low     low                  60   \n",
+       "\n",
+       "   Local_skin_score_y  \n",
+       "0                   0  \n",
+       "1                   0  \n",
+       "2                   0  \n",
+       "3                   2  \n",
+       "4                   1  \n",
+       "\n",
+       "[5 rows x 63 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_data = pd.merge(\n",
+    "    filtered_data,\n",
+    "    metadata_info,\n",
+    "    left_on=\"SampleId\",\n",
+    "    right_on=\"SubjectID\",\n",
+    "    how=\"inner\"\n",
+    "    )\n",
+    "merged_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    402.5\n",
+       "1    276.0\n",
+       "2    272.3\n",
+       "3    317.7\n",
+       "4    277.5\n",
+       "Name: Intensity, dtype: float64"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "custom_palette = {\n",
+    "        \"Healthy\": \"green\",\n",
+    "        \"VEDOSS\": \"violet\",\n",
+    "        \"SSC_low\": \"cyan\",\n",
+    "        \"SSC_high\": \"red\"\n",
+    "        }\n",
+    "intensity.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>SeqId</th>\n",
+       "      <th>PlateId</th>\n",
+       "      <th>PlateRunDate</th>\n",
+       "      <th>ScannerID</th>\n",
+       "      <th>PlatePosition</th>\n",
+       "      <th>SlideId</th>\n",
+       "      <th>Subarray</th>\n",
+       "      <th>SampleId</th>\n",
+       "      <th>SampleType</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Lung_Fibrosis_y</th>\n",
+       "      <th>Total_mRss</th>\n",
+       "      <th>Immunosupression_bin_y</th>\n",
+       "      <th>Immunosupression</th>\n",
+       "      <th>overall</th>\n",
+       "      <th>category</th>\n",
+       "      <th>condition</th>\n",
+       "      <th>low_ssc</th>\n",
+       "      <th>Disease_duration_y</th>\n",
+       "      <th>Local_skin_score_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>94746</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>C9</td>\n",
+       "      <td>258633888846</td>\n",
+       "      <td>3</td>\n",
+       "      <td>PDAR-0335</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>94755</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>D9</td>\n",
+       "      <td>258633888846</td>\n",
+       "      <td>4</td>\n",
+       "      <td>PDAR-0343</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>94757</td>\n",
+       "      <td>9999-1</td>\n",
+       "      <td>PLT24684</td>\n",
+       "      <td>2023-08-13</td>\n",
+       "      <td>SG16064525, SG17164580</td>\n",
+       "      <td>E7</td>\n",
+       "      <td>258633888844</td>\n",
+       "      <td>5</td>\n",
+       "      <td>PDAR-0344</td>\n",
+       "      <td>Sample</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>No</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>Healthy</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 63 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0   SeqId   PlateId PlateRunDate               ScannerID  \\\n",
+       "1        94746  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "10       94755  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "12       94757  9999-1  PLT24684   2023-08-13  SG16064525, SG17164580   \n",
+       "\n",
+       "   PlatePosition       SlideId  Subarray   SampleId SampleType  ...  \\\n",
+       "1             C9  258633888846         3  PDAR-0335     Sample  ...   \n",
+       "10            D9  258633888846         4  PDAR-0343     Sample  ...   \n",
+       "12            E7  258633888844         5  PDAR-0344     Sample  ...   \n",
+       "\n",
+       "    Lung_Fibrosis_y Total_mRss Immunosupression_bin_y  Immunosupression  \\\n",
+       "1                No          0                     No                 0   \n",
+       "10               No          0                     No                 0   \n",
+       "12               No          0                     No                 0   \n",
+       "\n",
+       "    overall  category condition low_ssc  Disease_duration_y  \\\n",
+       "1   Healthy   Healthy   Healthy      no                   0   \n",
+       "10  Healthy   Healthy   Healthy      no                   0   \n",
+       "12  Healthy   Healthy   Healthy      no                   0   \n",
+       "\n",
+       "    Local_skin_score_y  \n",
+       "1                    0  \n",
+       "10                   0  \n",
+       "12                   0  \n",
+       "\n",
+       "[3 rows x 63 columns]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_healthy = merged_data[merged_data['condition']== \"Healthy\"]\n",
+    "data_VEDOSS =merged_data[merged_data['condition']==  \"VEDOSS\"]\n",
+    "data_SSClow = merged_data[merged_data['condition']== \"SSC_low\"]\n",
+    "data_SSChigh = merged_data[merged_data['condition']== \"SSC_high\"]\n",
+    "data_healthy.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_healthy = data_healthy[\"Intensity\"]\n",
+    "data_VEDOSS =data_VEDOSS[\"Intensity\"]\n",
+    "data_SSClow = data_SSClow[\"Intensity\"]\n",
+    "data_SSChigh = data_SSChigh[\"Intensity\"]\n",
+    "data = [data_healthy, data_VEDOSS, data_SSClow, data_SSChigh]\n",
+    "fig = plt.figure(figsize =(10, 7))\n",
+    "\n",
+    "ax = fig.add_axes([0, 0, 1, 1])\n",
+    "\n",
+    "# Creating plot\n",
+    "bp = ax.boxplot(data)\n",
+    "\n",
+    "# show plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#     # Create scatter plot\n",
+    "#     plt.figure(figsize=(10, 8))\n",
+    "#     sns.scatterplot(\n",
+    "#         x=mrss, y=intensity_log, hue=condition, s=100, palette=custom_palette, edgecolor=\"black\"\n",
+    "#     )\n",
+    "\n",
+    "#     # Add color-coded annotations for each point\n",
+    "#     for i in range(len(merged_data)):\n",
+    "#         plt.text(\n",
+    "#             mrss.iloc[i],\n",
+    "#             intensity_log.iloc[i],\n",
+    "#             condition.iloc[i],  # Text is the condition\n",
+    "#             fontsize=10,\n",
+    "#             ha=\"center\",\n",
+    "#             bbox=dict(\n",
+    "#                 boxstyle=\"round,pad=0.3\",\n",
+    "#                 edgecolor=\"black\",\n",
+    "#                 facecolor=custom_palette[condition.iloc[i]],  # Custom color for tag\n",
+    "#                 alpha=0.7\n",
+    "#             ),\n",
+    "#         )\n",
+    "\n",
+    "#     # Set title and labels\n",
+    "#     plt.title(f\"Correlation Plot for {protein_name}\", fontsize=16)\n",
+    "#     plt.xlabel(\"MRSS (Linear Scale)\", fontsize=12)\n",
+    "#     plt.ylabel(\"Intensity (Logarithmic Scale)\", fontsize=12)\n",
+    "#     plt.grid(visible=True, linestyle=\"--\", alpha=0.6)\n",
+    "#     plt.legend(title=\"Condition\", loc=\"best\")\n",
+    "#     plt.tight_layout()\n",
+    "\n",
+    "#     return plt\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Creating dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # Load necessary libraries\n",
+    "# library(ggplot2)\n",
+    "\n",
+    "# # Define the colors for the boxplot\n",
+    "# mat_colors <- c('turquoise2', 'red')\n",
+    "\n",
+    "# # Filter data for the specific protein of interest\n",
+    "# SCUBE3 <- subset_proteins_plot %>%\n",
+    "#   subset(SeqId == \"16773-29\") %>%  # Select the specific protein\n",
+    "#   ggplot(aes(Lung_Fibrosis, Intensity, label = SampleId)) +  # Map variables to axes\n",
+    "#   facet_wrap(~ EntrezGeneSymbol) +  # Facet by protein name (optional)\n",
+    "#   scale_y_log10() +  # Apply log10 scale to y-axis\n",
+    "#   geom_boxplot(fill = mat_colors) +  # Create boxplot with specified colors\n",
+    "#   theme_bw() +  # Apply a clean theme\n",
+    "#   theme(legend.position = \"top\") +  # Adjust legend position\n",
+    "#   labs(color = \"Sample\", x = \"Lung Fibrosis\")  # Add axis labels\n",
+    "# SCUBE3\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app/boxplot.py

@@ -0,0 +1,62 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import LogLocator, NullFormatter
+
+def plot_boxplot(merged_data, protein_name):
+    """
+    Create a boxplot of the intensity of 4 Scleroderma categories with a logarithmic scale.
+    """
+    # Filter data by conditions
+    conditions = ["Healthy", "VEDOSS", "SSC_low", "SSC_high"]
+    custom_palette = {
+        "Healthy": "green",
+        "VEDOSS": "violet",
+        "SSC_low": "cyan",
+        "SSC_high": "red"
+    }
+
+    # Extract intensity values for each condition
+    data = [merged_data[merged_data['condition'] == condition]["Intensity"] for condition in conditions]
+
+    # Ensure no zeros or negatives for logarithmic scale
+    for i, condition_data in enumerate(data):
+        if (condition_data <= 0).any():
+            raise ValueError(f"Condition '{conditions[i]}' contains zero or negative values, which are invalid for a logarithmic scale.")
+
+    # Create the boxplot
+    fig, ax = plt.subplots(figsize=(12, 8))
+    bp = ax.boxplot(data, patch_artist=True)
+
+    # Set colors for the boxes
+    for patch, condition in zip(bp['boxes'], conditions):
+        patch.set_facecolor(custom_palette[condition])
+
+    # Set median line colors
+    for median in bp['medians']:
+        median.set_color('black')
+
+    # Set logarithmic y-axis
+    ax.set_yscale("log")
+    y_min = min([d.min() for d in data]) * 0.8
+    y_max = max([d.max() for d in data]) * 1.2
+    ax.set_ylim(bottom=y_min, top=y_max)
+
+    # Configure ticks and formatters
+    ax.yaxis.set_major_locator(LogLocator(base=10.0, subs=None, numticks=10))
+    ax.yaxis.set_minor_locator(LogLocator(base=10.0, subs=np.arange(2, 10) * 0.1, numticks=10))
+    ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f"{int(x):g}" if x >= 1 else f"{x:.1g}"))
+    ax.yaxis.set_minor_formatter(NullFormatter())
+
+
+    ax.set_title(f"Box Plot for {protein_name}", fontsize=16)
+    ax.set_xticks(range(1, 5))
+    ax.set_xticklabels(conditions)
+    ax.set_ylabel("Intensity (Logarithmic Scale)", fontsize=12)
+    ax.grid(visible=True, linestyle="--", alpha=0.6)
+    plt.tight_layout()
+
+    # Plot graph
+    plt.show()
+
+    return plt

app/volcano.py

@@ -0,0 +1,48 @@
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+# Updated Volcano Plot Function
+def plot_volcano(data):
+    # Ensure required columns exist
+    required_columns = ["logFC", "P.Value", "Target"]
+    if not all(col in data.columns for col in required_columns):
+        raise ValueError(f"Data is missing required columns: {required_columns}")
+
+    # Calculate -log10(p-value)
+    data['-log10_pvalue'] = -np.log10(data['P.Value'])
+
+    # Define thresholds
+    fold_change_threshold = 0.6  # Adjust as needed
+    pvalue_threshold = 0.05  # Adjust as needed
+    data['Significant'] = (np.abs(data['logFC']) > fold_change_threshold) & (data['P.Value'] < pvalue_threshold)
+
+    # Create the volcano plot
+    plt.figure(figsize=(10, 8))
+    sns.scatterplot(
+        data=data,
+        x='logFC',
+        y='-log10_pvalue',
+        # hue='Significant',
+        palette={True: 'red', False: 'grey'},
+        legend=False
+    )
+
+    # Add vertical and horizontal threshold lines
+    plt.axvline(x=-fold_change_threshold, linestyle='--', color='blue', linewidth=1)
+    plt.axvline(x=fold_change_threshold, linestyle='--', color='blue', linewidth=1)
+    plt.axhline(y=-np.log10(pvalue_threshold), linestyle='--', color='green', linewidth=1)
+
+    # Label the plot
+    plt.title("Volcano Plot", fontsize=16)
+    plt.xlabel("Log2 Fold Change", fontsize=14)
+    plt.ylabel("-Log10 P-value", fontsize=14)
+
+    # Annotate significant points with protein names
+    significant_points = data[data['Significant']]
+    for _, row in significant_points.iterrows():
+        plt.text(row['logFC'], row['-log10_pvalue'], row['Target'], fontsize=9)
+
+    plt.tight_layout()
+    return plt

requirements.txt

@@ -0,0 +1,6 @@
+streamlit
+pandas
+numpy
+seaborn
+pathlib
+scanpy

runtime.txt

@@ -0,0 +1 @@
+python-3.9.21

tests/test_analysis.py

@@ -0,0 +1,30 @@
+import pandas as pd
+import pytest
+from analysis import compute_correlation, differential_expression
+
+def test_compute_correlation():
+    """Test correlation computation."""
+    data = pd.DataFrame({
+        "clinical_score": [1, 2, 3, 4, 5],
+        "protein_expression": [2, 4, 6, 8, 10]
+    })
+    corr = compute_correlation(data, "clinical_score", "protein_expression")
+    assert corr > 0.99, "Correlation should be close to 1 for linear data"
+
+def test_differential_expression():
+    """Test differential expression analysis."""
+    group1 = pd.DataFrame({
+        "Protein1": [10, 15, 12],
+        "Protein2": [5, 7, 6]
+    })
+    group2 = pd.DataFrame({
+        "Protein1": [20, 25, 22],
+        "Protein2": [10, 12, 11]
+    })
+    proteins = pd.concat([group1, group2], axis=1)
+    results = differential_expression(group1, group2, proteins)
+
+    assert isinstance(results, pd.DataFrame), "Results should be a DataFrame"
+    assert "logFC" in results.columns, "Results should include logFC column"
+    assert "p-value" in results.columns, "Results should include p-value column"
+    assert results["logFC"].iloc[0] > 0, "logFC should reflect upregulation"

tests/test_boxplot.py

@@ -0,0 +1,55 @@
+import pytest
+import pandas as pd
+from unittest.mock import patch, MagicMock
+from app.boxplot import plot_boxplot
+
+# Fixture for valid test data
+@pytest.fixture
+def valid_data():
+    """Fixture to provide valid intensity values for all conditions."""
+    return pd.DataFrame({
+        "SampleId": [1, 2, 3, 4],
+        "condition": ["Healthy", "VEDOSS", "SSC_low", "SSC_high"],
+        "Intensity": [10, 20, 15, 25]
+    })
+
+# Fixture for invalid test data
+@pytest.fixture
+def invalid_data():
+    """Fixture to provide invalid intensity values (zero or negative)."""
+    return pd.DataFrame({
+        "SampleId": [1, 2, 3, 4],
+        "condition": ["Healthy", "VEDOSS", "SSC_low", "SSC_high"],
+        "Intensity": [-10, 0, 15, 25]
+    })
+
+@pytest.fixture
+def protein_name():
+    """Fixture for test protein name."""
+    return "Test Protein"
+
+@patch("matplotlib.pyplot.show")
+@patch("matplotlib.pyplot.subplots")
+def test_plot_boxplot(mock_subplots, mock_show, valid_data, protein_name):
+    """Test that the plot_boxplot function initializes the plot and creates a boxplot."""
+    # Mock figure and axes
+    mock_fig = MagicMock()
+    mock_ax = MagicMock()
+    mock_subplots.return_value = (mock_fig, mock_ax)
+
+    # Call the function
+    plot_boxplot(valid_data, protein_name)
+
+    # Verify that plt.subplots is called with correct parameters
+    mock_subplots.assert_called_once_with(figsize=(12, 8))
+
+    # Verify that the boxplot method is called on the axes
+    mock_ax.boxplot.assert_called_once()
+
+    # Verify that plt.show() is called to display the plot
+    mock_show.assert_called_once()
+
+def test_invalid_data_raises_error(invalid_data, protein_name):
+    """Test that plot_boxplot raises a ValueError for zero or negative intensity values."""
+    with pytest.raises(ValueError, match="contains zero or negative values"):
+        plot_boxplot(invalid_data, protein_name)

tests/test_data_loader.py

@@ -0,0 +1,72 @@
+import pytest
+import pandas as pd
+from pandas.errors import ParserError
+from unittest.mock import patch, mock_open
+from app.dataloader import load_data
+
+# Fixture for valid test data
+@pytest.fixture
+def valid_metadata_file():
+    """Fixture to provide valid metadata CSV content."""
+    return """SubjectID,OtherInfo
+1,Info1
+2,Info2
+3,Info3
+"""
+
+@pytest.fixture
+def valid_proteins_file():
+    """Fixture to provide valid proteins CSV content."""
+    return (
+        "SampleId,TargetFullName,Target,EntrezGeneID,EntrezGeneSymbol"
+        "1,Protein A,A,101,GA"
+        "2,Protein B,B,102,GB"
+        "3,Protein C,C,103,GC"
+    )
+
+@pytest.fixture
+def invalid_file_content():
+    """Fixture for invalid file content (non-CSV)."""
+    return "Invalid Content"
+
+def test_load_data_valid_files(valid_metadata_file, valid_proteins_file):
+    """Test that load_data loads valid CSV files correctly."""
+    with patch("pandas.read_csv") as mock_read_csv:
+        mock_read_csv.side_effect = [
+            pd.DataFrame({"SubjectID": [1, 2, 3], "OtherInfo": ["Info1", "Info2", "Info3"]}),
+            pd.DataFrame({
+                "SampleId": [1, 2, 3],
+                "TargetFullName": ["Protein A", "Protein B", "Protein C"],
+                "Target": ["A", "B", "C"],
+                "EntrezGeneID": [101, 102, 103],
+                "EntrezGeneSymbol": ["GA", "GB", "GC"]
+            }),
+        ]
+
+        metadata, proteins = load_data("metadata.csv", "proteins.csv")
+
+        assert not metadata.empty
+        assert not proteins.empty
+        assert list(metadata.columns) == ["SubjectID", "OtherInfo"]
+        assert list(proteins.columns) == ["SampleId", "TargetFullName", "Target", "EntrezGeneID", "EntrezGeneSymbol"]
+
+def test_load_data_invalid_file_path():
+    """Test that load_data raises ValueError for invalid file paths."""
+    with pytest.raises(ValueError, match="Error loading files"):
+        load_data("invalid_metadata.csv", "invalid_proteins.csv")
+
+def test_load_data_invalid_file_content():
+    """Test that load_data raises ValueError for invalid file content."""
+    with patch("pandas.read_csv", side_effect=ParserError("Error parsing file")):
+        with pytest.raises(ValueError, match="Error loading files"):
+            load_data("metadata.csv", "proteins.csv")
+
+def test_load_data_empty_files():
+    """Test that load_data handles empty files correctly."""
+    with patch("pandas.read_csv") as mock_read_csv:
+        mock_read_csv.side_effect = [pd.DataFrame(), pd.DataFrame()]
+
+        metadata, proteins = load_data("metadata.csv", "proteins.csv")
+
+        assert metadata.empty
+        assert proteins.empty

tests/test_visualization.py

@@ -0,0 +1,14 @@
+import pandas as pd
+from app.visualization import volcano_plot
+
+def test_volcano_plot():
+    """Test generating a volcano plot."""
+    results_df = pd.DataFrame({
+        "Protein": ["Protein1", "Protein2"],
+        "logFC": [2.5, -1.2],
+        "p-value": [0.001, 0.05]
+    })
+    fig = volcano_plot(results_df)
+    assert fig, "Volcano plot should return a Plotly figure object"
+    assert "Protein1" in fig.data[0].text, "Hover text should include Protein1"
+    assert "Protein2" in fig.data[0].text, "Hover text should include Protein2"