sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
e386547228158198d55c81ceeb0191d7374438ab9f97d8f9e7876f3bf4bec3ab | R | 2,251 | 60 | library(ggpubr)
work_dir = "/Volumes/mahamid/Dorothy/Pipeline_after_CNN/20230516_new_analysis_for_paper/analysis_output/"
setwd(work_dir)
ctr_data_read <- na.omit(read.csv(file = "memb_norm_control_memb_actin_dist_5_nm_bin_noupper_averaged.csv"))
ind_data_read <- na.omit(read.csv(file = "memb_norm_induced_memb_actin_d... |
53ee829241f6613a5529b1d6b5384cc2c219f1c5e4c2542f1a0203e3b9408b72 | R | 2,255 | 59 | replintegrate <- function(means, sds) {
sig_p <- estimate_tau(means, sds)
var_t <- sds^2 + sig_p^2
var_est <- 1/sum(1 / var_t)
mean_est <- sum(means / var_t)*var_est
sd_est <- sqrt(var_est)
return(list(mean_est = mean_est, sd_est = sd_est, sig_p = sig_p))
}
replintegrate_batch <- function(mean_mat, sd_mat)... |
5339a13afce7746af783b2685118cbb0c2fcaaacd13f9a070b34be2bbe2e1620 | R | 2,257 | 83 | LISP Interpreter Run
[[[[[
A LISP expression that asserts that it itself is unprovable!
Let g(x): x -> (is-unprovable (value-of (('x)('x))))
Then (is-unprovable (value-of (('g)('g))))
asserts that it itself is not a theorem!
]]]]]
define (g x)
let (L x y) cons x cons y nil [Makes x and y into list.]
(L... |
ea7ecef04d9be722d24fea3d7ed94a27302df92712a05185fabceed04a269513 | R | 2,281 | 94 | ```{r data}
tt <- "F:/promec/Animesh/RawRead/MSMS.tab"
vals<-read.table(tt,comment.char = "#",sep="\t")
```
```{r plot}
plot(vals$V8,vals$V5)
hist(log2(vals$V8))
```
```{r dist}
n=1000
valSam=sample(vals$V8,n)
hist(log2(valSam))
valsdist=dist(valSam,valSam,method = "euclidean")
valH=hist(abs(valsdist[... |
840606d0b794f07d1538859885bd495ca3365b6f947cbdde2ae07dfd2429255e | R | 2,303 | 28 | #source https://raw.githubusercontent.com/animesh/compbio_tutorials/main/scripts/06_scRNseq_two_lines_from_fastq_to_count_matrix.Rmd
#mamba create -n kb-python python=3.7
#conda activate kb-python
#pip install kb-python gget ffq
#kb ref -i index.idx -g t2g.txt -f1 transcriptome.fa $(gget ref --ftp -w dna,gtf ho... |
6f733877e9c4591f10607f5e5c0ac0d5f37df8f3a4313a2c100361e1a04a15bc | R | 2,307 | 39 | #C:\Users\animeshs>R-4.4.0\bin\Rscript.exe "drive\My Drive\scripts\uniprotIDmap.r" L:\promec\Animesh\Motif\uniprot_sprot.motif.found.txt L:\promec\Animesh\Motif\GOeu.tsv
#setup####
#https://www.uniprot.org/uniprotkb?query=%28reviewed%3Atrue%29+AND+%28taxonomy_id%3A2759%29
#download.file("https://rest.uniprot.org/unipr... |
c1c2fd6162b65a64fc34c31201090e6b04acfcadf1c85df43eafb5ee1392b794 | R | 2,313 | 62 | library(ggpubr)
work_dir = "/Volumes/mahamid/Dorothy/Pipeline_after_CNN/20230527_new_analysis/analysis_output/"
setwd(work_dir)
ctr_data_read <- na.omit(read.csv(file = "memb_norm_control_memb_actin_dist_20_nm_bin_noupper_averaged.csv"))
ind_data_read <- na.omit(read.csv(file = "memb_norm_induced_memb_actin_dist_20_nm... |
1a2a756890cb98d7373494b7f4341d17830a3e5a9bfc6219069c1dff8bf59b23 | R | 2,346 | 48 | plot_to_results <- function(n_bins, plot_df, region_range = 0:1) {
cutoffs <- (0:n_bins) / n_bins
results_df <- matrix(0, nrow=n_bins*length(region_range),6)
for(region in region_range) {
for(bin in 1:n_bins) {
in_bin <- which(plot_df$berg_weight <= cutoffs[bin+1] & plot_df$berg_weight >= cutoffs[bin] &... |
65be1ff5a5d9c51412480d15d027306d7319127510e4659943636c39064e079b | R | 2,352 | 53 | ---
title: "Visualization stable fold changes of IFN-related genes"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
### This markdown produces supplemental figure 5A, to validate the region-specific (SN and PUT) IFN response.
- Load needed libraries
- Load relevant clean data
- Load the heat... |
dfc26175164905eee2a48760ce54d78b9f0830ba970eefedf1e3099ea454650f | R | 2,374 | 45 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
library(geometry)
source('~/Documents/MIT/Research/Rafalab/Projects/slideseq/Cell Demixing/ContentStructure/CSIDE/analysis/personal_utils.R')
#datadir <- '../RCTD/data/SpatialRNA/Puck_210605/24'
datadir <- '../RCTD/data/SpatialRNA/Puck_210605/James_R... |
531e5c8cb18ec6555a0ae7b98c9e7989ad024d53327cac05816206634f4ee208 | R | 2,389 | 60 | # "c:\Program Files\R\R-4.5.1\bin\Rscript.exe" combineCSV.r "F:\promec\HF\Lars\2025\251204_Maren_Gemma\251203_3kda_A9_dda.raw.centroid.MGF.csv" "F:\promec\HF\Lars\2025\251204_Maren_Gemma\251203_3kda_A3_dda.raw.centroid.MGF.csv" "F:\promec\HF\Lars\2025\251204_Maren_Gemma\251203_1kda_A7_dda.raw.centroid.MGF.csv" "F:\pro... |
5d230c4ac7e54dc791e694ecb4051dfb43c940632bdeb31430707be7e6274d2f | R | 2,401 | 40 | df <- read.csv("Fig.4d.csv")
# Blank layer to divide mapping from not mapping
data2 <- data.frame(final_clusters_renamed = rep("MC0.0", 2000),supertype_name = rep("Sst_Gaba_0", 2000), final_clusters_palette = rep("gray", 2000))
data <- rbind(df, data2)
data$final_clusters_renamed <- factor(data$final_clusters_rena... |
acd17a3373b5cad6891b0540abb8938000c50297ed55fbf230eaa3b04a7e63d9 | R | 2,410 | 85 | ---
title: "rustSeqMap"
author: "ani"
date: "12/2/2020"
output: html_document
source: https://github.com/jperkel/gb_read
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(tidyverse)
```
Given a path (no terminating separator, eg '~' or '~/tmp', NOT '~/tmp/'), file base name (eg '... |
dd0ac97a82f5385e01df6bfb2a27ac19ae413015c525bd26cae325665d0a97a4 | R | 2,421 | 44 | #git checkout ebbdd3bf84fdd40e6abd7c353fc101fadcedc87c diffExprTestT.r
#Rscript.exe diffExprTestT.r L:\promec\TIMSTOF\LARS\2024\240319_Nicola\combined\txt\proteinGroups.txt L:\promec\TIMSTOF\LARS\2024\240319_Nicola\combined\txt\GroupsRem.txt Bio Rem Intensity. WT 0.1 1 0.05
#Rscript codonUsageSelect.r "L:\promec\TIMSTO... |
36a69e78e7951b91a1bfdf03d3974042810e2bd0d4ef52ee7f626c5a4534b3d8 | R | 2,433 | 56 | # ..\R-4.5.0\bin\Rscript.exe .\combineTxt.r "L:\promec\TIMSTOF\LARS\2023\230217_Caroline\mRNA\Omego_T1_vs_Control_T1_mRNA_diff_expr_CPM1.txt" "L:\promec\TIMSTOF\LARS\2023\230217_Caroline\mRNA\Omego_T3_vs_Control_T3_mRNA_diff_expr_CPM1.txt" "L:\promec\TIMSTOF\LARS\2023\230217_Caroline\mRNA\Omego_T6_vs_Control_T6_mRNA_di... |
df8be5b74e61596f17773c272432be13b5e4662adabed649f2802982c4ed2bae | R | 2,442 | 31 | #Rscript motifSeqAlign.r L:\promec\Animesh\Motif\uniprot_sprot.motif.found.seq.txt "Enolase "
#setup####
args = commandArgs(trailingOnly=TRUE)
print(paste("supplied argument(s):", length(args)))
inpF <- args[1]
#perl motif.pl uniprot_sprot.fasta "[RK][FWY][ALVI][GALVI][RK]" | awk -F '\t' '$3!=""' > uniprot_sprot.motif... |
bcaa8757325c478b415ea266b11adc134f9da29b4f21461dca3b23d143b03984 | R | 2,480 | 40 | df <- read.csv("Fig.4b.csv")
# Blank layer to divide mapping from not mapping
data2 <- data.frame(final_clusters_renamed = rep("MC0.0", 2000),supertype_name = rep("Sst_Gaba_0", 2000), final_clusters_palette = rep("gray", 2000))
data <- rbind(df, data2)
data$final_clusters_renamed <- factor(data$final_clusters_rena... |
d7b9ca36320a948aacabf85643912a726ad324aac4ea61922744d28580863fca | R | 2,489 | 54 | #..\..\R-4.5.0\bin\Rscript proteinGroupsVSN.r "L:\promec\TIMSTOF\LARS\2026\260518_Sonali\DIANNv2P2.63.260612_140833.64.highacc\report.pg_matrix.tsv" "F..promec.TIMSTOF.LARS.2026.260518_Sonali.260518_Sonali_"
#install.packages(c("BiocManager"),repos="http://cran.us.r-project.org",lib=.libPaths())
#BiocManager::install(c... |
ab9ea5e9baec747b32a0adfa2d0ec701476525fc6d3b299641a227405da3d16e | R | 2,492 | 68 | test_that(".is_discrete() works", {
expect_equal(.is_discrete(1:3), FALSE)
expect_equal(.is_discrete(10:9, n_unique = 2), TRUE)
expect_equal(.is_discrete(c(TRUE, FALSE), n_unique = 3L), TRUE)
expect_equal(.is_discrete(c("a", "b"), n_unique = 3L), TRUE)
expect_equal(.is_discrete(factor(c("a", "b")), n_unique =... |
f2c680051a898818aed8c7b930053a531060a9acc7d4ea000331c3f1f7c4d711 | R | 2,539 | 72 | # ------------------------------------------------------------------------------
# Title: Plotting TF Weights for Target Gene (Meis1)
# Author: Yiran Song
# Date: March 18, 2025
# Description:
# This script loads gene regulatory network (GRN) output from Commot
# and visualizes the weight distribution of transcription... |
eb840947e4472439618a4d1588a44bdc40926aee5a75dddc63d128ff306a9092 | R | 2,556 | 59 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
4d5b4a0acb437aa6f36f6604fbe653b9bb620dde2e4fcec44821fed8a92e8f53 | R | 2,600 | 79 | #!/usr/bin/env Rscript
##import packages
library(readxl)
library(tidyverse)
library(DESeq2)
library(ggfortify)
library(ggplot2)
library(pheatmap)
library(sva)
library(biomaRt)
library(DescTools)
library(ComplexHeatmap)
library(circlize)
# set working dir to the folder containing this script
setwd(dirname(rstudioap... |
e24ac9676b434d2b8f8b0b94201cb82f37fc4cbe69a3ea4eb79a22f9b435fffc | R | 2,611 | 75 | #' @import dplyr
#' @import ggplot2
#' @import scales
#' @importFrom magrittr %>%
#suppressPackageStartupMessages({
# library(dplyr)
# library(ggplot2)
#})
`%>%` <- magrittr::`%>%`
args <- commandArgs(trailingOnly=TRUE)
workingDir <- args[1]
print(paste0("Plotting for ",workingDir))
name = rev(unlist(strsplit(wor... |
ca85a7d67f72ed8ac57dc569df9706cf5f48698e24c27cc77dc202523e8e0ab0 | R | 2,635 | 58 | library(Matrix)
library(ggplot2)
library(STutility)
library(shiny)
library(imager)
library(raster)
library(rgdal)
library(spacexr)
###CONSTANTS
IM_DIR <- '../RCTD/data/Images/Plaque/210715/Processed/'
ALIGN_DIR <- '../RCTD/data/Images/Plaque/210715/AlignmentResults/'
####START CREATE PUCK IMAGES
datadir <- '../RCTD/dat... |
49da6bb227383653fe9ae0caa3169623454ff16a3cebf39c33e1dc035719811f | R | 2,660 | 52 | #!/usr/bin/env Rscript
# Differential expression analysis using homoscedastic two-sample t-tests.
#
# Preprocessing matches run_limma.R for fair comparison:
# 1. DGEList for integrated count + library-size bookkeeping
# 2. Low-count filtering via edgeR::filterByExpr()
# 3. TMM normalization via edgeR::calcNormFac... |
93faba76a99f20e589d7ae075fbbcdb917275b9c7f0b8905f801fc2e0b909aa3 | R | 2,668 | 76 | library(tidyverse)
library(pheatmap)
setwd("C:/Users/u0171728/Downloads")
df <- read.csv("fe-wp-dataset-120.csv", check.names = FALSE)
rownames(df) <- make.unique(df$gene_id)
df_expr <- df %>% select(-gene_id, -id)
aggregate_celltypes <- function(prefix, df) {
cols <- grep(paste0("^", prefix), colnames(df), ... |
804a9c5787158305985830b8f6d1271fe9f6d4821c8eae37ea5fcac4f7b84ed7 | R | 2,697 | 117 | #' Extractor Functions
#'
#' Functions to extract SHAP values, feature values, the baseline,
#' or SHAP interactions from a "(m)shapviz" object.
#'
#' @name extractors
#' @param object Object to extract something.
#' @param ... Currently unused.
#' @returns
#' - `get_shap_values()` returns the matrix of SHAP values,
... |
4928bc07b093e8a21d6c830a92f954c12e2849c476821c60936ebee09c061296 | R | 2,713 | 63 | #
# collect mappability info in a given chrom or range
#
process.map.values <- function(map.values, strand, step, read.len, read.left.edge, threshold.fraction) {
do.shift = (strand == '-' && read.left.edge) || (strand == '+' && !read.left.edge)
if (do.shift)
map.values = c(rep(0, read.len - 1), map.values[1... |
237e08146a5b8666659c471750b61a5cb1094d4f2db38b0f27f4fd4a262799ec | R | 2,722 | 96 | library(data.table)
library(mclust)
input_path <- "PATH_TO_INPUT/samap_leiden_clusters.csv"
output_path <- "PATH_TO_OUTPUT/samap_ARI.txt"
df <- read.csv(input_path, header = TRUE)
rownames(df) <- make.unique(as.character(df$X))
celltype_label <- "celltype"
batch_label <- "batch"
percent_extract <- 0.8
nbiters <- 20... |
79e957a87c8e0e9c14b576eef07d54400c54e86b0a90bc8f6971454db7164875 | R | 2,722 | 57 | #..\R-4.5.0\bin\Rscript.exe geneGroupsQC.r "Z:/Download/report.gg_matrix.tsv" 1 4 3
#setup####
args = commandArgs(trailingOnly=TRUE)
print(paste("supplied argument(s):", length(args)))
inpF <- args[1]
#inpF<-"Z:/Download/report.gg_matrix.tsv"
#inpD<- gsub("[^[:alnum:]]+", ".", dirname(inpF))
geneC <- args[2]
#geneC<-"1... |
8bc5ba0c553b85fbb8816188cf5f6c2aafbd7b9d499949988b25b26d7e4389a8 | R | 2,722 | 61 | #' Correlation plots
#'
#'
#'
#' @param df_filtered the filtered protein dataset
#' @return Distribution maps for top 3 most correlated proteins, and correlation values
#' @export
correlation_plot <- function(df, target_name, type = 'pearson', location, limit, upperL = 2, lowerL = -2,
col... |
4a75aff4d9fcabdbc25594d091c6c5634cff9429652d67e94fc350f299c7e0b5 | R | 2,741 | 76 | ## By Anoushka Joglekar 2019. Edited 03/2020
#' @importFrom magrittr %>%
#' @import dplyr
`%>%` <- magrittr::`%>%`
args <- commandArgs(trailingOnly=TRUE)
print("Reading in file, this might take a while")
all_info <- data.table::fread(args[1])
if(ncol(all_info) == 9){
all_info <- all_info[,c(1:4,6)]
colnames(all_i... |
d988b8b84992b6793c847769fbb0f5bceb11a7291f7e211f5a335bce0df3db82 | R | 2,742 | 74 | ```{r data}
#setwd('L:/Elite/LARS/2014/desember/christiano/serum/')
#file='W2H.txt'
setwd('L:/Elite/LARS/2014/desember/christiano/plantaris/')
file='W2C.txt'
data <- read.table(file, sep = '\t', header = T)
```
```{r http://stackoverflow.com/questions/17924828/differences-in-heatmap-clustering-defaults-in-r-he... |
256d7ffde3047fa08a6f4b3199ec066b15bf741f15df499e5ce1211e93ff62c0 | R | 2,752 | 110 | library(ijtiff)
my_dir <- '/Users/dcable/Documents/MIT/Research/Rafalab/Projects/slideseq/Cell Demixing/ContentStructure/RCTD/data/Images'
im_ind <- 5
mtif <- read_tif(file.path(my_dir,paste0('HCR/Composite',im_ind,'.tif')))
atif <- read_tif(file.path(my_dir,paste0('HCR/c2_i',im_ind,'.tif')))
if(im_ind == 1) {
purk_t... |
7f9622a5d7d4141f6da12db8e444aa6d418299ce1739f1c8619f254cfa4957cb | R | 2,753 | 78 | ###-- Linear Models, basic functionality -- weights included.
## From John Maindonald :
roller <- data.frame(
weight = c(1.9, 3.1, 3.3, 4.8, 5.3, 6.1, 6.4, 7.6, 9.8, 12.4),
depression = c( 2, 1, 5, 5, 20, 20, 23, 10, 30, 25))
roller.lmu <- lm(weight~depression, data=roller)
roller.... |
97c91c82a5604e3dd7f89d42d08f93f4672866566757b1b9c6b0c584b209aabc | R | 2,776 | 56 | # ..\R-4.5.0\bin\Rscript.exe .\combineXlsx.r "L:\promec\TIMSTOF\LARS\2023\230217_Caroline\combined\txt\proteinGroups.txtLFQ.intensity.110Omego1Cntr1h00.050.5InfBiotTestBH.xlsx" "L:\promec\TIMSTOF\LARS\2023\230217_Caroline\combined\txt\proteinGroups.txtLFQ.intensity.110Omego3Cntr3h00.050.5InfBiotTestBH.xlsx" "L:\promec\... |
af81c81c89b7e3180b6fc26abe85562f8d828ae4ca4be52557fed69f63e53c92 | R | 2,784 | 97 | ---
#title: "Expression GO"
output: html_document
runtime: shiny
---
```{r data,echo=F}
lPep=100
nPep=10000
nTot=lPep*nPep
data<-runif(nTot)
#hist(sample(data,100000,replace=T),breaks=nPep)
sPep=rnorm(lPep)
n=1
i=1
while(n<=lPep){
print(n)
print(i)
sPep[c(seq(n,n+i-1))]=sample(data,i,replace=T)
n=n+i
i=i+1
}... |
bb340dfc59ba670fb02cf4aa6f12d250ccd15bbf79a40ae408a2c434402cb1ce | R | 2,796 | 51 | # ------------------------------------------------------------------------------
# Title: Preparation of Cytospace Input Files from Seurat Objects
# Author: Yiran Song
# Date: March 18, 2025
# Description:
# This script generates input files for Cytospace from Seurat objects.
# It processes single-cell and spatial tra... |
4c53d60d8e20db074f9ea4769f440b94917acacd488fdaec58fde10177b4382d | R | 2,802 | 56 | #Rscript diffExprQC.r "L:\promec\TIMSTOF\LARS\2023\230310 ChunMei\combined\txt\proteinGroups.txtIntensity.0.110.05BioRemoveGroups.txtAMHCtTestBH.combined.xlsx" Log2MedianChange "L:\promec\TIMSTOF\LARS\2023\230310 ChunMei\combined\txt\Exosome and ion channel proteins.csv" "Exosome..1276."
#setup####
args = commandArgs(t... |
48fb06b3b8a046781c4a686eb25a6504a3ba589bc4c783ee801b70c267c7fb35 | R | 2,805 | 71 | ```{r setup, include=FALSE}
knitr::opts_chunk$set(include = FALSE)
#plot
par(mfrow=c(1,2))
#library/packages
.libPaths( c( .libPaths(), "C:/Users/animeshs/GD/R-4.0.1/library") )
.libPaths()
setwd("C:/Users/animeshs/GD/scripts")
getwd()
chkrVector<-c(22.39459,20.48316,21.87155,NA,20.34495)
chkrDF<-as.data.frame(chkrVect... |
6f226348749bc7675957655f1f54b8ed4c0075691894607eea3d47730c8dc4b8 | R | 2,805 | 49 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
#source('~/Documents/MIT/Research/Rafalab/Projects/slideseq/Cell Demixing/ContentStructure/RCTD/PersonalFiles/private_utils.R')
#source('~/Documents/MIT/Research/Rafalab/Projects/slideseq/Cell Demixing/ContentStructure/RCTD/de.R')
#source('~/Document... |
7cc21def3555785be0008e538ff3108b7b41acc566ee213404d854f6b645e882 | R | 2,831 | 71 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
9aa59bf1f36c4176caf100cdc15cf9b0d8967e459912d03b9897075a4f40bd61 | R | 2,831 | 67 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
99b3a9d16fc1dae9db11f2a3e93ce0cd820e3d74d6b32a2a1d17ea224be27536 | R | 2,834 | 128 | ---
source: "https://towardsdatascience.com/ten-random-useful-things-in-r-that-you-might-not-know-about-54b2044a3868"
title: "Check"
author: ""
date: ""
output:
html_document:
code_folding: "hide"
params:
name:
value: n1
choices:
- D
- C
- R
years_of_study:
input: slider
min:... |
be66f4e998fb6fb5d85d48918a2c4690dc0b1f00ee574d89c3127cc8d7901620 | R | 2,845 | 104 | # ==========================================================
# 01_scan_adni_files.R
# Purpose: Scan all ADNI CSV/XLS/XLSX files and find relevant variables
# ==========================================================
library(tidyverse)
library(readxl)
library(openxlsx)
library(janitor)
library(stringr)
# -... |
b66d0f425bf553a5e92bef635447b18c4d2d098d491fba7826846a5fa9a5ad61 | R | 2,867 | 99 | ```{r data}
fileP<-"L:\\promec\\Animesh\\Lymphoma\\"
fileN<-"Sel66.txt"
lym<-read.table(paste0(fileP, fileN),row.names = 1, header=T)
summary(lym)
fileC<-"Code.txt"
code<-read.table(paste0(fileP, fileC),row.names = 1, header=T)
summary(code)
#install.packages('quantable')
library(quantable)
y<-lym
y=robustscale(y)
y$da... |
f7d9741fc05c862849ea95210ed82c41fb6cdfd13107a768ce416bb2b2018575 | R | 2,874 | 54 | #Rscript metaboGroups.r DR_MOHAN_300S_METABOLOMICS_ANALYSIS_NEGATIVE_MODE.xlsx DR_MOHAN_300S_METABOLOMICS_ANALYSIS_POSITIVE_MODE.xlsx
#setup####
args = commandArgs(trailingOnly=TRUE)
print(paste("supplied argument(s):", length(args)))
print(args)
options(nwarnings = 1000000)
warnings()
#data####
inpFN<-args[1]
inpFP<-a... |
3237305e2136f9b529777d2fe05136b4a2db9fefb093ff3fbb786f0cb93593b6 | R | 2,877 | 59 | #' Computes cell type profiles in a scRNA-seq dataset
#'
#' Computes averaged normalized expression (summing to 1) for
#' all cells within a cell type
#'
#' @param raw.data a Digital Gene Expression matrix, with gene names as rownames and
#' single cells as columns (barcodes for colnames)
#' @param cell_types a named l... |
3e653f12eb908092fbdddab4b9614f7dcdacdfae9a04bd9b00dfe282f93b69bb | R | 2,904 | 103 | context("Gene Set Co-Regulation Analysis")
test_that("GESECA: is reproducible", {
data("exampleExpressionMatrix")
data("examplePathways")
set.seed(1)
gr1 <- geseca(pathways=examplePathways, E=exampleExpressionMatrix)
set.seed(1)
gr2 <- geseca(pathways=examplePathways, E=exampleExpressionMatri... |
73dcad901557c0f08d2c04ff0f68f3aae1c019b12e570e9e59e61eb50b0bfaf5 | R | 2,910 | 75 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
3e162ce2929f018fdecfc3b2bcb3fd03428452203b4e56331e0131dcbef70520 | R | 2,913 | 72 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
0e25f28564b41fc0ea0a505d0ba96cb6fde8a3316570101285f062cc88a4e67e | R | 2,917 | 69 | ---
output:
pdf_document: default
html_document:
keep_md: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
```
# Platform Effect Prediction (between single-nucleus and single-cell RNA-seq)
```{r libraries}
library(spacexr)
library(M... |
dce7dad126564e63c7c04a0a6e952309b2fa246a954b5718e9acd7e136285fa8 | R | 2,918 | 70 | library(spacexr)
library(Matrix)
library(ggplot2)
library(devtools)
source('~/Documents/MIT/Research/Rafalab/Projects/slideseq/Cell Demixing/ContentStructure/CSIDE/analysis/helper_functions/testes_helper.R')
load_all()
datadir <- '../RCTD/data/SpatialRNA/Testes'
puck <- readRDS(file.path(datadir,'puck.rds'))
### END PR... |
c725d31b2d60fb87135540deee2174a066122f55a3962d8de33f91d4aaa360a4 | R | 2,954 | 114 | library(knockoff)
library(data.table)
library(glmnet)
`+` <- function(e1, e2) {
if (is.character(e1) | is.character(e2)) {
paste0(e1, e2)
} else {
base::`+`(e1, e2)
}
}
source('/home/users/peymanhk/PROJECT_2/Simulated/GOI/KnockoffScreen_updated.R')
prt_result = '/home/users/peymanhk/PROJE... |
f5c305a908d44e667b5edf9b19c0bdb88994484046cc0b6cbd63227be5f8d608 | R | 2,966 | 71 | #' Generate matrices for clustering analysis
#'
#' @description Converts the counts for isoforms
#' into matrices clustered either by cell-type
#' or by barcode.
#' @seealso \code{\link{IsoQuant}}
#' @param isoQuantOutDir output directory of IsoQuant
#' function. Defaults to IsoQuantOutput
#' @param groupBy String indi... |
c18c0cd021dc1d97a6bb478a63465cba4235abf9917e604a6bacca2cc3b00e6c | R | 2,989 | 56 | #' Get single-cell barcodes from long read files
#'
#' @description This is an R-wrapper for a python function. It parallelizes the reading
#' and processing of fastq.gz files, and uses a sliding window approach to
#' identify cell barcodes, assign cluster labels and output useful statistics
#' in one .csv file and one... |
7c01cd0b9e44ec3e9ce7d39902c1e7643277cedcc24d3d893648e3b318146a9a | R | 3,019 | 76 | #8/10/18: fixed a bug for the enr_se column
options(echo=FALSE);
param <- commandArgs(trailingOnly=T)
annotfile = eval(paste(text=param[1]))
resultfile = eval(paste(text=param[2]))
outfile = eval(paste(text=param[3]))
if (length(param)!=3) {
cat("\nError: three arguments must be supplied.\n")
cat("ARG1 : the ... |
9c650afb0dad2f05fc53eb07eae08b31323010d305392b8fb6f46ec47ec4e083 | R | 3,040 | 51 | #c:/Users/animeshs/R/bin/Rscript.exe proteinGroupsTtestCombine.r L:/promec/TIMSTOF/LARS/2021/November/SIGRID/combined/txtNoNQd/ BiotTestBH.csv
args = commandArgs(trailingOnly=TRUE)
print(paste("supplied argument(s):", length(args)))
print(args)
if (length(args) != 5) {stop("\n\nNeeds THREE arguments, the full path of t... |
e7c0f2817bdbf650be71e382840ed6af2a82638a2944da83090aa9cdf1c8d6d3 | R | 3,046 | 63 | #..\R-4.4.0\bin\Rscript.exe markVolcanoXlsx.r L:\promec\TIMSTOF\LARS\2024\240207_Deo\combined\txt\ proteinGroups.txtLFQ.intensity.16 0.110.05BioRemGroupsG.txttTestBH.csv MLH1 PMS1 PMS2
#Overall plot be smaller
#Bigger fonts
#Only top-30 interactors should be named of which, every graph should have the protein itself in... |
4bc7c22e8791efb3aac831bdae5ff5c6fa3cae3b9ec5dbb39d61ee8b90163cad | R | 3,048 | 80 | #' Collapse SHAP values
#'
#' This function sums up SHAP values (or SHAP interaction values) of feature groups.
#' Typical application: SHAP values have been generated by a model with one or multiple
#' one-hot encoded variables, but the explanations should be done using the
#' original factor.
#'
#' @param S Either a ... |
16f55977be327bbae07dceb8b0e1ca9930538426b5415a25263963f6d066ed31 | R | 3,059 | 73 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
4f6c2653aae0b3b299dbd5cfcf2cf46c02f9bb0521407e721e68fddd2cc4bd19 | R | 3,071 | 61 | source('gatc.r')
summary(t(gi.id49175990.gatc))
t(gi.id49175990.gatc)[1,1]
hist(t(gi.id49175990.gatc))
gd=read.table('gatcdist.txt')
hist(gd)
hist(gd$V1)
(gd$V1)
hist(gd$V1)
gd=read.table('gatcdist.txt')
hist(gd$V1)
hist(gd$V1,breaks=100)
hist(gd$V1,breaks=1000)
hist(gd$V1,breaks=10000)
summary(gd$V1)
hist(gd$V1,breaks... |
4d6c45e26fd9315c3b4983d5bcad0dee2240798f380ac54a2e764cb60437b645 | R | 3,080 | 106 | ###################################################
### chunk number 1:
###################################################
library(grDevices)
library(grid)
ps.options(pointsize=12)
options(width=60)
###################################################
### chunk number 2: fig1
###########################... |
41ca403ac28e85827c3c9fd70c5765e33e9d82bd175f59224f7f94371866d4e7 | R | 3,085 | 92 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
36966ccbf9987b23a9e3d445c3af50ea1e06638e35da63413946890b93c9bde5 | R | 3,111 | 72 | ---
output:
pdf_document: default
html_document:
keep_md: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
```
# Platform Effect Prediction (between single-nucleus and single-cell RNA-seq)
```{r libraries}
library(RCTD)
library(Matr... |
8f1ed3f922212157ecd3ced7bd41726f77d283196150b57709ab99cd37bd1d20 | R | 3,125 | 96 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
## scisorseqr - a comparative analysis of alternative splicing patt... |
87a8df7372eed21afa316c9d2d9501dbfb720bf5d2577ee93a2e4947b7ff8a72 | R | 3,150 | 59 | #' Combine all available information per read to a flat file
#' @aliases LongReadInfo
#' @description Function to concatenate all the information per read, i.e
#' gene-name, cellular barcode, UMI, cell-type information, and isoform
#' information into one file. Isoform information includes a string separated list of
#... |
88e1767b41f9d600ffda41cb8a3464e0370ce5d4b1da4200f700927c2cc8db00 | R | 3,156 | 42 | # Load
f1 <- readRDS("Extend Fig.6f_Conserved_clusters_MN.RDS")
f2 <- readRDS("Extend Fig.6f_Differences_MeanExpression_of_finalClustersMarkers.RDS")
f3 <- readRDS("Extend Fig.6f_HausdorffDistance_q50.RDS")
f4 <- readRDS("Extend Fig.6f_Number_DEGs_finalClusters_Atlas.RDS")
f5 <- readRDS("Extend Fig.6f_Number_DEGs_... |
fb98b30955067257f417da3a3c03456272c4561155d68bcd235978902fb50d5c | R | 3,161 | 90 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
5320e15786d3b5893f1ba4917e43f2b7b488862c97c968f531049c781f7ff064 | R | 3,167 | 68 | #' @importFrom magrittr %>%
#' @import parallel
#' @import dplyr
#' @export
`%>%` <- magrittr::`%>%`
args <- commandArgs(trailingOnly=TRUE)
## arguments : AllInfo file, groupingFactor, nThreads, threshold
allInfo <- data.table::fread(args[1],sep="\t")
if(ncol(allInfo) == 11){
allInfo <- allInfo[,c(1:4,9)]
} ... |
029698d25f5cbe42e7b5a29fb21beb715426247856a24acaa07d4b9f311d53e8 | R | 3,193 | 104 | cphyll <- read.table("chlorophyll.txt")
nodule <- read.table("nodule.txt")
wsf <- read.table("weight_sf.txt")
wrf <- read.table("weight_rf.txt")
boxplot(nodule)
boxplot(nodule)
boxplot(wsf)
boxplot(wrf)
wilcox.test(cphyll$Uninoculated,cphyll$GN25)
wilcox.test(nodule$Uninoculated,nodule$GN25)
wilcox.test(w... |
e7a18733dabdd9799e2f69f7c0182aa532b6295ad79725d613e3f1cf0d7c60c3 | R | 3,221 | 67 | #https://github.com/Noble-Lab/casanovo/releases/tag/v4.0.0
#casanovo sequence -o 230508_IRD_37TWTL.mztab desk/IRD/230508_IRD_37TWTL.mgf
#wget https://github.com/Noble-Lab/casanovo/releases/download/v4.0.0/casanovo_nontryptic.ckpt
#casanovo sequence -o 230508_IRD_37GWTL.mztab desk/IRD/230508_IRD_37GWTL.mgf --model cas... |
2e98131b3ca88c39a13043d2a09a6da1acc6222cca33c1c83eccffbe486450e0 | R | 3,226 | 89 | # ==========================================================
# 03_select_adnimerge2_core_candidates.R
# Purpose: Find ADNIMERGE2 tables useful for the clinical core dataset
# ==========================================================
library(tidyverse)
library(readxl)
library(openxlsx)
library(janitor)
librar... |
67dc76d6b07c2858db3dca1145dfacf5966e693dd813a360cc1ab6f2038978ea | R | 3,228 | 81 | ---
output:
pdf_document: default
html_document:
keep_md: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
```
# CSIDE on the Slide-seq KP tumor (immune cell-dependent DE)
### Load in CSIDE Results and calculate significant genes
`... |
41fc8fcc2a5ab20cf5e0c377dc30fbae8c20e1d60e37d7869b3716bf1d7ff12d | R | 3,260 | 92 | # Load necessary library for string manipulation
library(dplyr)
# Define the function
normalize_and_aggregate <- function(dataframe) {
# Rename columns to correct issues
colnames(dataframe) <- colnames(dataframe) %>%
gsub("X1.cLISI", "cLISI", .) %>%
gsub("kBET.Accept.Rate", "kBET", .) %>%
gsub("True... |
60f8cc7a839f62e997c2e94aec52b74f77a938fd8ac46bf9b30b159b76434feb | R | 3,263 | 100 | # Requires: dplyr, ggplot2, ggrepel
# install.packages(c("dplyr","ggplot2","ggrepel"))
aseManhattan_top20 <- function(input,
statvalue = c("pvalue", "qvalue"),
annotation,
gws = 0.05,
filename = ... |
f68917f325edaf1f9a6593d4ef9416fa79b0a91444d3b9a0a395e74d5438d7be | R | 3,265 | 59 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
calcGseaStatCumulativeBatch <- function(stats, gseaParam, pathwayScores, pathwaysSizes, iterations, seed, scoreType) {
.Call('_fgsea_calcGseaStatCumulativeBatch', PACKAGE = 'fgsea', stats, ... |
0d58b566804fd6775266fec6cf32b2a11c7055c8095e7906518e2c0386d6abb0 | R | 3,287 | 76 | #https://github.com/animesh/love-actually-network
library(dplyr)
library(stringr)
library(tidyr)
raw <- readLines("matrix_97_draft.txt")
lines <- data_frame(raw = raw) %>%
filter(raw != "", !str_detect(raw, "(song)")) %>%
mutate(is_scene = str_detect(raw, " Scene "),
scene = cumsum(is_scene)) %>%
filter(... |
98a1e522741b947193bc971b35ade57465f86b77c2efc7506798042e0ee95799 | R | 3,287 | 61 | #simulates doublets for testing on scRNA and snRNA cerebellum reference.
library(RCTD)
library(Matrix)
DropViz <- T
if(DropViz) {
test_reference <- readRDS("Data/Reference/DropVizCerAnnotated/scRefSubsampled1000.RDS")
common_cell_types = c("Astrocytes", "Bergmann", "Endothelial", "Fibroblast", "Golgi", "Granule", "... |
c398e2f32d18d5166c7292eae84433d7a909a92830b8479d1313e9ff7bef2683 | R | 3,305 | 99 | S <- cbind(
x = c(0.1, 0.1, 0.1),
`age low` = c(0.2, -0.1, 0.1),
`age mid` = c(0, 0.2, -0.2),
`age high` = c(1, -1, 0)
)
collapse <- list(age = c("age low", "age mid", "age high"))
out <- collapse_shap(S, collapse)
test_that("collapse_shap work", {
expect_equal(colnames(out), c("x", "age"))
expect_equal(ou... |
cedb5a918da469cdfb0a320f5b0cb3d3b10aa9512176cee839fcaac57b7d0123 | R | 3,311 | 149 | ---
title: "Farmand et al 2026 - NeuroFit RCT Blood-Based Biomarker Analysis Examples"
output: html_document
---
```{r setup, message=FALSE, warning=FALSE}
library(readxl)
library(dplyr)
library(tidyr)
library(nlme)
library(emmeans)
library(ggplot2)
```
## 1. Import dataset
```{r import-data}
df <- read_excel("Farma... |
507ccd08b4fa4a84abf4aadd56f62803fcc84ea15499c43382a04199e78c4ff1 | R | 3,331 | 74 | #!/usr/bin/env Rscript
## kernlab regression using nifti files
## James Cole 24/09/2019
rm(list = ls())
args <- commandArgs(trailingOnly = TRUE)
## test if there are two argument: if not, return an error
if (length(args) < 6) {
stop("Six arguments must be supplied (brainageR directory full path; list of GM nifti file... |
f6997444ce6ead74dbf49149943de84434d1bfd6a41da2b5d99af3feebb3d6d5 | R | 3,469 | 115 | ```{r setup}
inpD <-"L:/OneDrive - NTNU/PROMEC01/IDH1/"
.libPaths(c(paste0(inpD,"/R/")),.libPaths())
.libPaths()
#data
thr<-20
selThr=0.3
selThrFC=0.5
hdr<-gsub("[^[:alnum:] ]", "",inpD)
hdr<-gsub(" ", "",hdr)
setwd(inpD)
getwd()
```
```{r library}
install.packages("BiocManager")
install.packages("scal... |
c5e2e458a21638efd973515ca58891a55f5934dc643581f1e67829c11dc72913 | R | 3,488 | 126 | suppressPackageStartupMessages({
library(Seurat)
library(conos)
library(ggplot2)
})
unintegrated <- readRDS("PATH_TO_INPUT/unintegrated.rds")
species_levels <- sort(unique(as.character(unintegrated$species)))
letters_map <- setNames(letters[seq_along(species_levels)], species_levels)
unintegrated$species <- unn... |
fab4dd1ae3cfb9fb3f5703e6ded4e7af3f80f2c6ebcf8b8b0c5f9603a9f81ea8 | R | 3,490 | 81 | library(Matrix)
library(ggplot2)
library(STutility)
library(shiny)
library(imager)
library(raster)
library(rgdal)
library(spacexr)
###CONSTANTS
IM_DIR <- '../RCTD/data/Images/Plaque/210715/Processed/'
ALIGN_DIR <- '../RCTD/data/Images/Plaque/210715/AlignmentResults/'
####START CREATE PUCK IMAGES
datadir <- '../RCTD/dat... |
812b758144a3f3144bbfd8bcdbaa4461ed6eeb02ac16bcb4ebf7029b4f4574e4 | R | 3,500 | 113 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(data.table)
})
setwd('/Users/doq/RCode')
load('preprocessed_data.RData')
# Match core preprocessing used in analysisCode.R
alldata[, binary_group := fifelse(grepl('ASD', group, ignore.case = TRUE), 'ASD', 'TD')]
alldata[, group_verbal := fcase(
is.n... |
be403b59ca1620f297205c42823bab29bef33dd43a8e54b4704e1af12769f709 | R | 3,507 | 69 | #..\R\bin\Rscript.exe proteinSequenceProperties.r
#setup####
#https://github.com/dosorio/Peptides/
#install.packages("Peptides", dependencies=TRUE)
#https://github.com/jedick/canprot
#install.packages("canprot", dependencies=TRUE)
#http://chnosz.net/canprot/doc/canprot.html
#proteins <- c("LYSC_CHICK", "RNAS1_BOVIN", "... |
8baf4d1c483409aa04fb6efa422279afa97ac952ff08b991e882b93bb6854684 | R | 3,511 | 79 | #!/usr/bin/env Rscript
library(optparse)
ref_reselectpc <- function(pcadir, ref) {
pcafiles <- unlist(strsplit(list.files(pcadir,pattern=".pc.txt"),".pc.txt"))
for(j in 1:length(pcafiles)) {
pcadf <- read.table(paste0(pcadir,"/",pcafiles[j],".pc.txt"), h=T, as.is=T)
rownames(pcadf) <- paste0(pcadf$chr,"_",pcad... |
a60aac558f52868fba5cf77fc1c513992500d8bfee41c06a369898ff0834ad80 | R | 3,534 | 74 | #' Perform differential splicing analysis
#' @aliases DiffIsoTest
#' @description This function allows you to test alternative
#' splicing differences between any two groups
#' based on the full splice-form, exons,
#' the transcription start site (TSS) or end site (polyA).
#' @param configFile File in a .tsv format wit... |
f93337f3f06bec34b7bd76524350de08cee3f7adf8e5bf180d74ea5deb1a5f7a | R | 3,534 | 110 | Synergy check
========================================================
```{r load file}
#syn=read.csv('L:/Elite/kamila/FA-UDC-IM-M-6.csv')
#syn=read.csv('L:/Elite/kamila/flufenamic acid for Animesh.csv')
#syn=read.csv('L:/Elite/kamila/UDCIMrawLR5.csv')
#syn=read.csv('L:/Elite/kamila/UDCIMraw.csv')
#syn=read.cs... |
82b38bf695e1ee477f24b749272c03e88d99d0fec31499e83baaee6f01630ebe | R | 3,540 | 93 | # Set environment language
Sys.setenv(LANG = "en")
# Set working directory
setwd("D:/project/ndreamer_plot/")
# Load necessary libraries
library(ggplot2)
library(dplyr)
library(tidyr)
library(scales)
# Define a function to create the plot
generate_plot <- function(input_df) {
# Rename columns to correct issues
c... |
2ac2a9d2e885574424b09e1b0903afbb817e1bac30eb4134b9993e2dc5c5538a | R | 3,551 | 122 | library(Seurat)
library(SeuratObject)
library(Matrix)
library(data.table)
library(lisi)
source('.../benchmark_script/evaluation/ARI_LISI/evaluation_ARI_LISI_R_F.R')
pre_dir <- "PATH_TO_PRE_DIR/"
save_dir <- "PATH_TO_SAVE_DIR/"
temp_dir <- paste0(save_dir, "/temp_files/")
dir.create(temp_dir, showWarnings = FALSE,... |
ac73119c25574f400902373bc2fa7fb3b56368d19f3240b5ad6587e99541e508 | R | 3,556 | 51 | gene_list1<-c('FTH1', 'PCDH9-AS2', 'FTL', 'P2RY14', 'IPO9-AS1', 'ANGPT2', 'FN1', 'SHOC1', 'PDE8A', 'HHIP', 'LINC02899', 'TIMP3', 'B3GNT5', 'ENO4', 'PPP1R9A-AS1', 'SLC7A14-AS1', 'PDE7B-AS1', 'TMEM165', 'RFX4', 'CFTR', 'CTNNA3', 'BACH1', 'LINC02930', 'ABCB1', 'FGF1', 'CPB2', 'IL18', 'TGFB1', 'MARCKSL1', 'CCK', 'HIF1A-AS3... |
903a4982b90b1a865836c309e392ad61790c7f911fc2e2ce5006ef8c470ddf85 | R | 3,648 | 58 | #Everything Is Correlated -> https://www.gwern.net/Everything
#Common statistical tests are linear models (or: how to teach stats) -> https://lindeloev.github.io/tests-as-linear/
#books:
#rmarkdown->https://bookdown.org/yihui/rmarkdown/
#plot->https://plotly-r.com/introduction.html#data-visualization-best-practices , h... |
e5a1687cc39e7a6dd512914133d058c891a7aea641baa19ac3a13b38a1670094 | R | 3,660 | 106 |
solveOLS<-function(S,B, solution, constrain = T){
D<-t(S)%*%S
d<-t(S)%*%B
norm_factor <- norm(D,"2")
D <- D / norm_factor
d <- d / norm_factor
epsilon <- 1e-7; D <- D + epsilon * diag(length(d))
A<-cbind(diag(dim(S)[2]))
bzero<-c(rep(0,dim(S)[2]))
if(constrain) {
A_const = t(rbind(1,A))
b_con... |
4ce443ec48326a6c51fa3f37a986eb2264b9f41694810e25873b47ecf6f2388b | R | 3,661 | 38 | df <- read.csv("Extend Fig.9a,b,c,d.csv")
df <- df %>% mutate(Category = ifelse(subclass_bootstrapping_probability >= 0.9, ">=0.9", "<0.9")) %>% mutate(SubclassClean_Other = ifelse(subclass_name %in% c("052 Pvalb Gaba","053 Sst Gaba"), subclass_name, "Other"))
percentage_data <- df %>% group_by(Major, Category) %>% s... |
240e152864b19b5d8e740b878a60a7e94d6b7a086caa180d1d2f4d2b423ab519 | R | 3,690 | 95 | # ============================================================
# Helper functions for Perturb-seq module-score analysis
# (minimal subset extracted from original functions.R)
# ============================================================
#' Count cells expressing a gene above a threshold
#' @param object Seurat object... |
293ea81d276f2b5cb32c974a136760479038675b9c9355aea1ab0a351590a6ed | R | 3,700 | 85 | #' Extract isoform information from aligned reads
#'
#' @description This function follows the alignment function.
#' It is a wrapper for several scripts that impose checks and
#' balances on the mapping quality, consensus nature of splice
#' sites, and annotated start and end sites per transcript.
#' @seealso \code{\l... |
79d177953bedfc149450c2990655cbb73f96e1fc51b858bb820c2832a9125249 | R | 3,719 | 248 | #"c:\Program Files\R\R-4.5.2\bin\Rscript.exe" diffExprTestCor.r "L:\promec\TIMSTOF\LARS\2026\260518_Sonali\DIANNv2P2.63.260612_140833.64.highacc\report.pg_matrix.tsv" "L:\promec\TIMSTOF\LARS\2026\260518_Sonali\DIANNv2P2.63.260612_140833.64.highacc\Groups.txt" "F..promec.TIMSTOF.LARS.2026.260518_Sonali.260518_Sonali_" "... |
e9659fae0f5df481ca45369b3391960b7a8293b0a561f78a0f89e95dea2c9edd | R | 3,739 | 97 | #' Returns a list of Reactome pathways for given Entrez gene IDs
#' @param genes Entrez IDs of query genes.
#' @return A list of vectors with gene sets.
#' @export
#' @examples
#' data(exampleRanks)
#' pathways <- reactomePathways(names(exampleRanks))
reactomePathways <- function(genes) {
stopifnot(requireNamespace... |
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