sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
d6dbcef0a602990db6021f5c15a97defd14f94a2d13fb8c682cab7cabd0e1ece | R | 3,750 | 105 |
library(data.table)
library(dplyr)
library(ggplot2)
library(readr)
library(tidyr)
setwd("C:/Users/u0171728/Downloads/FACS")
gene_for_both <- "Atp13a5"
gene_non_myeloid <- NULL
gene_myeloid <- NULL
gene_non_myeloid <- if (is.null(gene_non_myeloid)) gene_for_both else gene_non_myeloid
gene_my... |
b5a68350dd3ed3a6b4966bc0661727dfe9524d4da02d6d9e17e3f3e58be4e2ad | R | 3,753 | 128 | rm(list = ls())
options(stringsAsFactors = FALSE)
# ================================
# Standalone Script 1: Export WAIC to CSV
# ================================
# ---------- 1) Settings ----------
# Set the directory containing the WAIC files
base_dir <- "F:/PIT/model/EEGmodelHC/OCD/results/stan"
# Set the output d... |
2da9879bc32ad25d24fa713f3dcf1af6862d68f5f316bd5c63c4f0ee2af6080e | R | 3,755 | 94 | ## ============== Step 4: Extract subject-level parameters (M9/10/12/13/14) ==============
rm(list = ls())
suppressPackageStartupMessages({
library(rstan)
library(data.table)
})
# ---- Paths (updated to your new directory) ----
rootdir <- "F:/PIT/model/EEGmodelHC/HC"
resultsdir <- file.path(rootdir, "results", ... |
7bb567c910005612bb91559a7e1dac69dc990efeb0965cdb35e3204a9ba157b9 | R | 3,769 | 99 | # =================== Top-level posteriors & summary (M9/10/12/13/14) ===================
suppressPackageStartupMessages({
library(rstan)
})
# ---- Paths ----
rootdir <- "F:/PIT/model/EEGmodelHC/HC"
datadir <- file.path(rootdir, "results")
resultsdir <- file.path(datadir, "stan")
outdir <- file.path(result... |
7c83a936595bd156112cabe14f2c42d27bd64409f3ace5e7679d4b04472159a6 | R | 3,842 | 143 | library(sva)
library(Seurat)
library(magrittr)
unintegrated <- readRDS("PATH_TO_OUTPUT/unintegrated.rds")
layers <- Layers(unintegrated[["RNA"]])
print(layers)
merge_layers <- function(obj, layer_pattern) {
layers <- grep(layer_pattern, Layers(obj[["RNA"]]), value = TRUE)
if (length(layers) > 1) {
merged_matr... |
ebe1321300fbde48db22b556a532538268c33b00ffdfde4a800c52f72d81f706 | R | 3,857 | 116 |
# ---- 0) Setup ----
suppressPackageStartupMessages(library(rstan))
# Your paths
rootdir <- "F:/PIT/model/EEGmodelHC/HC"
datadir <- file.path(rootdir, "results")
resultsdir <- file.path(datadir, "stan")
r2dir <- file.path(resultsdir, "R2")
dir.create(r2dir, showWarnings = FALSE, recursive = TRUE)
# Evalu... |
55b8a687ed530130bdb41a78e610e64c348a180fac6f8e735b5e783fd8f56b72 | R | 3,888 | 76 | #https://github.com/cox-labs/PerseusR ; on windows if "Error in utils::download.file(url, path, method = method, quiet = quiet, :cannot open URL 'https://api.github.com/repos/cox-labs/PerseusR/tarball/HEAD'"
#options(download.file.method = "wininet")
#install.packages("devtools")
#devtools::install_github("cox-labs/Pe... |
1662afffed604e3cfe3b9ab59c17a3e325942598e9cd0bac7844a925618f13d3 | R | 3,891 | 62 | stickle=read.table('bacsticklebac.rmp.txt');
medaka=read.table('bacmedaka.rmp.txt');
branchiostoma=read.table('bacbranchiostoma.rmp.txt');
elephantshark=read.table('bacelephantshark.rmp.txt');
fugu=read.table('bacfugu.rmp.txt');
tetraodon=read.table('bactetraodon.rmp.txt');
zebra=read.table('baczf.rmp.txt');
u150k=c(1,... |
fbc0cd45c7fd37c885c471c504aba100327f63baa94aa1b6d7e99b8ed4f288a0 | R | 3,910 | 74 | #Rscript diffExprDEP.r
#BiocManager::install("DEP")
#BiocManager::install("MSnbase")#MsCoreUtils
library(DEP)
library(dplyr)
#inputAnnotation <-"L:/promec/Animesh/Mathilde/Groups.txt"
#annotation <- read.table(inputAnnotation,sep="\t",header=TRUE)
#annotation<-annotation[annotation$Rem!="Y",]
#annotationDep <- data.fra... |
f778bb483eee9fbad50f50bc301129b21adf2dad85687a75ff88635851d62250 | R | 3,941 | 113 | ```{r data, echo = FALSE}
inpDr <-"F:/promec/Elite/LARS/2018/november/Rolf final/Red/combined/txt/"
inpDw <-"F:/promec/Elite/LARS/2018/november/Rolf final/White/combined/txt/"
dataR <- read.delim(paste0(inpDr,"proteinGroups.txt"),row.names=1,sep="\t",header = T)
dataW <- read.delim(paste0(inpDw,"proteinGroups.txt"),row... |
ab499fe9ade7b64d91dd0e1ac6a1e7da3fbf6ddad4ec41953128c82ad9bf8a20 | R | 3,954 | 123 | library(dplyr)
library(Seurat)
library(ggplot2)
library(patchwork)
library(SeuratData)
library(SeuratWrappers)
library(Azimuth)
getwd()
setwd("PATH_TO_WORKDIR")
species_seurat <- readRDS("PATH_TO_SPECIES_SEURAT_RDS")
a_seurat <- readRDS("PATH_TO_SPECIES_A_RDS")
b_seurat <- readRDS("PATH_TO_SPECIES_B_RDS")
homolog_ge... |
417c1e631c699628be6e21439b490750f87a10ec8c004480e30add9e43db4a5f | R | 4,000 | 118 | library(data.table)
library(lme4)
library(car)
library(mclust)
library(nlme)
load("/Users/doq/RCode/preprocessed_data.RData")
alldata <- as.data.table(alldata)
alldata[, binary_group := fifelse(grepl("ASD", group, ignore.case = TRUE), "ASD", "TD")]
alldata[, group_verbal := fcase(
is.na(vinabcstd), "flag",
bina... |
ba92fc35defa0f85b9a007f77bdfb1ea2eb619f605265d6c2941ada2ca4b3fc5 | R | 4,002 | 105 | #!/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... |
25e6627840c6985d710cbe36fb72231f1d93d4bbfff2d1ed83472bd3c54aadb3 | R | 4,015 | 104 | #authors Krissy Sakers and Justin Savage
#kristina.sakers@duke.edu
#Version 2.0
#3/31/2023
#install all of these first with install.packages('package name')
library(ggplot2)
library(reshape)
library(car)
library(nlme)
require(multcomp)
library(rstudioapi)
dataFile <- selectDirectory(
caption = "Sele... |
187171e7f12c9022d027d528ef76395b2840e69f4ce268733e21d6037ffa82ab | R | 4,033 | 120 | # =============================================================================
# Put together the package
# =============================================================================
# WORKFLOW: UPDATE EXISTING PACKAGE
# 1) Modify package content and documentation.
# 2) Increase package version in "use_description... |
24880728de4c4e1453d1941564383a7e1f55dac4b10c9ef318859e1448b6159d | R | 4,092 | 74 | #git checkout fe5f9e92f3fe5ca0c75e9d8acfd211feb7358066 diffExprProDA.r
#Rscript diffExprProDA.r
#https://github.com/const-ae/proDA
#remotes::install_github("const-ae/proDA")
#data####
inputMQfile <-"L:/promec/Animesh/Mathilde/rawdata_from animesh 2.txt"
pdf(paste0(inputMQfile,".pdf"))
maxquant_protein_table <- read.del... |
280c6281fe69fcb11ae02d1993eb09eb72fa1c3c842bf549b98e1bef9a0d2c40 | R | 4,100 | 86 | ---
title: "Applying CSIDE to Spatial Transcriptomics Data: Cell-Cell Interactions in Visium of Human Lymph Node"
author: "Luli Zou"
date: "April 15, 2022"
output:
html_document:
keep_md: yes
pdf_document: default
rmarkdown::html_vignette:
keep_md: yes
vignette: |
%\VignetteIndexEntry{visium-cellcell}
... |
7bc797a53c22c5dca7a5a1a5ec66589f8703ccc15ab3d8e30b2cab2442aa4b2e | R | 4,102 | 134 |
# -------- 1) Directory of the original Stan results (unchanged) --------
dir_hc1 <- "F:/PIT/model/EEGmodelHC/HC/results/stan"
file_fit_rda <- file.path(dir_hc1, "M12fits_obsData.Rdata")
# -------- 2) Directory where you want to save the exported data --------
out_dir <- "H:/eegplot/M4b"
if (!dir.exists(out_dir)) di... |
f64bb59fbc8ccf09658328d051a47f43933fba8eb7d2de84b2bdce9940a6e0be | R | 4,108 | 86 | library(SnapATAC)
library(viridisLite)
library(ggplot2)
library(ggdendro)
library(ggsci)
library(ggpubr)
library(chromVAR)
library(motifmatchr)
library(SummarizedExperiment)
library(BSgenome.Hsapiens.UCSC.hg38)
library(rGREAT)
library(GenomicRanges)
library(tidyr)
library(parallel)
library(stringr)
library(umap)
li... |
1d2f7cb917e3880508f9793a1471c243872d3478754a1147933e82a86c2863a3 | R | 4,109 | 139 | library(tidyverse)
library(ggpubr)
library(rstudioapi)
library(ggplot2)
library("car")
library(grid)
library(ggbreak)
#Define all the necessary variables
data_seg<-read.csv("/Volumes/mahamid/Dorothy/Pipeline_after_CNN/20230509_BIF_figures/tomogram_summary_averaged.csv", sep = ',')
group1<-"control"
group2<-"induced"... |
d56988e5067ffd42833a6f5280c76d6e4a32fe6660f3df9bdd360e62adc31414 | R | 4,118 | 97 | ---
title: "Microglia Subclustering Visualization"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
This notebook visualizes **microglia subclustering** results from integrated snRNA-seq analysis.
The goal is to explore cluster structure, regional variation, and diagnostic differences (PD vs. ... |
8b43516a5ac225943426613c3e009d6804b88cc0af93e3fc8b0185605d9709c7 | R | 4,182 | 90 | library(Matrix)
library(spacexr)
library(doParallel)
datadir <- '../RCTD/data/SpatialRNA/Puck_190926_11/'
puck <- read.SpatialRNA(datadir)
puck <- restrict_counts(puck, rownames(puck@counts), UMI_thresh = 100)
puck = restrict_puck(puck, colnames(puck@counts))
saveRDS(puck,file.path(datadir,'puck.rds'))
plot_puck_contin... |
6500407287209951eb1eb6100e22d6065b5f12936ae02066fb2dc572dd3754bb | R | 4,214 | 131 | #
# Ensure that factors work in prediction
#
options(na.action=na.exclude) # preserve missings
options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
library(survival)
aeq <- function(x,y, ...) all.equal(as.vector(x), as.vector(y), ...)
tfit <- coxph(Surv(time, status) ~ age + factor(ph.ecog), lu... |
10765f56078c95caa86f5294eaa384d7398e5258df070f4ebd1b22abb4ea997a | R | 4,286 | 96 | #Rscript combineTxtFilesToXlsx.r L:\promec\TIMSTOF\LARS\2024\240207_Deo\combined\txt\ proteinGroups.txtLFQ.intensity.16 0.110.05BioRemGroupsG.txttTestBH.csv MSH2 MLH1 PMS1 PMS2
#install.packages(c("gglot2","svgite"))
args = commandArgs(trailingOnly=TRUE)
print(paste("supplied argument(s):", length(args)))
print(args)
i... |
96930c9de093d8f8544a54123d727fca27d0b47ec0d1820044a32a9f54682f27 | R | 4,321 | 91 | ---
title: "Applying RCTD to Spatial Transcriptomics Data: Visium with multi mode RCTD"
author: "Dylan Cable"
date: "December 20th, 2021"
output:
html_document:
keep_md: yes
pdf_document: default
rmarkdown::html_vignette:
keep_md: yes
vignette: |
%\VignetteIndexEntry{visium-multi}
%\VignetteEncoding{... |
ac487eb4a03f1e39564230ab132a4d3268382a74b3747ebf1e8956f815c699a6 | R | 4,340 | 156 | # ==========================================================
# 02_inspect_adnimerge2.R
# Purpose: Inspect ADNIMERGE2 R Data files and identify useful tables
# ==========================================================
library(tidyverse)
library(janitor)
library(openxlsx)
library(stringr)
project_dir <- "R:/... |
43e8b43b75d310863851b5a6edf07b8a10f2375b2328f730044353520e99feee | R | 4,351 | 104 | # 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... |
995d14e7f24fa510b4fadc773110e6b81eec9c066246a5810319bad468b8243d | R | 4,352 | 104 | # 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... |
b4da3ad75bedbd00dda1c6dc4cff333288bb6563b41b8afce22e3d594418faf5 | R | 4,397 | 150 | # Three-group bootstrap learning curve analysis with KS tests (ASD_high, ASD_low, TD)
# Hard phase: trial > 10
suppressPackageStartupMessages({
library(data.table)
library(dplyr)
library(tidyr)
library(purrr)
library(minpack.lm)
})
load("/Users/doq/Geode_Data/preprocessed_data.RData")
alldata <- as.data.ta... |
7f4fa1b7f6784a30f8d54465078f2d302f1a45e5fc57e90944debcdb737d5934 | R | 4,437 | 115 | ########## EEG model fitting data preparation (R) ##########
# 0) Load function ------------------------------------------------------
root <- "F:/PIT/model/EEGmodelHC" # unified root path
src_file <- file.path(root, "prepEEG4stan.R")
stopifnot(file.exists(src_file))
source(src_file)
stopifnot(exists("prepEEG4stan... |
e8379d388088cef67f6ffa19ad82c77f401d75db3044dddeb311a8664d9968c2 | R | 4,449 | 104 | pathD<-"F:/promec/Animesh/MK/"
inpF<-file.path(pathD,"proteinGroups.txt")
data<-read.table(inpF,header=T,sep="\t",row.names = 1)
summary(data)
colName<-"LFQ.intensity."
y<-log2(as.matrix(data[,grep(colName,colnames(data))]))
summary(y)
row.names(y)<-row.names.data.frame(data)
colnames(y)=sub(colName,"",colnames(y))
#co... |
3869f6493d80de956b9c6de13b714064b79ae4c4b9d6e4aa506db7156b45c62f | R | 4,457 | 127 | # Set environment language
Sys.setenv(LANG = "en")
# Set working directory
setwd("D:/project/ndreamer_plot/")
# Load necessary libraries
library(dplyr)
library(fmsb)
library(gridExtra)
library(gt)
library(ggplot2)
generate_radar_plot <- function(df,title) {
# Rename columns
colnames(df) <- gsub("^X1\\.cLISI$", "... |
e13d2596c13bc4e20d690608e0de223f897ae27e901e51e3f7d8af319ea13e41 | R | 4,463 | 170 | need <- c("tidyverse")
to_install <- setdiff(need, rownames(installed.packages()))
if (length(to_install)) install.packages(to_install, Ncpus = 2)
invisible(lapply(need, library, character.only = TRUE))
setwd("C:/Users/u0171728/Downloads")
df <- read.csv(
"fe-wp-dataset-124 (2).csv",
sep = ",",
head... |
4a3e94f0860ab7f669937bdb657e717eb19603643a7ef7762f774bae0e6cf6b7 | R | 4,513 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('Zeller_raw.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
m... |
7b113432f1416912eee5053c0b1d693f6a669e1fe50cc438aaae72d395cf878b | R | 4,513 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('Zeller_raw.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
m... |
b89ad477c0e4725e75a1a9b299e99f317da9cb9437f0db11ea0c7654111ffde1 | R | 4,513 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('Zeller_raw.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
m... |
ef6e37f0b90d1196efffcd75993e9cf51f7475e877c9e63b9952fe09766bcfad | R | 4,513 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('Zeller_raw.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
m... |
ffe0993a121c835f72482dd3543ac2e00bb509da9d26635e91a91f6ebf63d57d | R | 4,514 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('Zeller_raw.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
m... |
702c81d2db70d7bd20e179846314444941d2bedc6686e0fe95083d0eae4a1ae0 | R | 4,522 | 93 | ---
title: "ASAP deep bulk RNAseq of PD and control postmortem samples"
author: "Raquel Garza"
output:
html_document:
df_print: paged
---
## Normalization and PCAs
This small notebook was used to calculate gene size factors to perform normalization over TE counts, and show PCA analysis across the sequenced samp... |
7765b1369435cbc44be7b3445469f6b95f37d054ae878c098497c104ff1640d0 | R | 4,533 | 63 | ---
title: "Vignettes"
author: "Dylan Cable"
data: December 22, 2021
#output: github_document
output:
html_document:
keep_md: yes
pdf_document: default
rmarkdown::html_vignette:
keep_md: yes
vignette: |
%\VignetteIndexEntry{vignette-readme}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdo... |
00a5976d32482eb7e9ace1e30cadeda1465977ce4ee751aa3efcd9fc48ddfa6b | R | 4,535 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data2.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
097e9d03e0c1a3cee499481966d64ed16f2b826afe055649a2958da3a9ba5315 | R | 4,535 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data1.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
0cc13f8736239536f5ca423d8256c6f779ed13f512cdad0dd90018783b490571 | R | 4,535 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data2.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
2b390af4b2ee745717c72d5807397febd42807a75431e027578c01079c898603 | R | 4,535 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data1.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
383546ba84211957a563d2e0758e45966722b88056e9bd09c471cf227cda85b3 | R | 4,535 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data2.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
72cd36753695083e8d9e4e670b3bdb9e37eb18a6f46ba3cb3108c307c76cf4f2 | R | 4,535 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data1.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
7fd0d1f5d48f1a78cd752536bbdbb471e45047f0ae4d837a30daec198bfeba10 | R | 4,535 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data1.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
b2af06dc83a2f72a0d7fa7a6bda5835af028ff59ec68b954af7aeaa8b9ce0ead | R | 4,535 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data2.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
1306cc16abb49c76302296e903b5f7e8f55c265e3cc344b416d057746264fec7 | R | 4,536 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data2.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
17ad8d370e3aea49864d4b249134bd9057f67bec81063cfa7b1e123f2614b8e4 | R | 4,536 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data2.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
254732e530606ed141e88fdff7d1607d6ee22813f2ee2dd1150ae0a911e743d9 | R | 4,536 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data2.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
37ccb390a2b1e2eedf031d3a8bc2ff778245a7d588a3237d8e5c30eb53b4baa5 | R | 4,536 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data1.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
6662939a5a670cb6f32f9c80a2c7c2997a88b64861ca8231e1dc46302a348ece | R | 4,536 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data1.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
7589e55565578dadeacc577c933778795b1f13e1e38faa44301a7062b70f0308 | R | 4,536 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data2.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
7fd736e39a648ab099e497a28db1a9c1a2dda49488c5f9ddb45c8ff1981fe077 | R | 4,536 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data2.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
9452b6e9781278c133c3e66a47fd3f6e7641f1d63d8b925f21b74fc6d02b5770 | R | 4,536 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data1.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
959e57748b65d7171dd85cb1371af439d33285da0b1657eb4ab19ae0a993e52d | R | 4,536 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data1.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
d21d643fbe373015b763024537eebc9ffed2a3ac1286d81396b2014decfd8c32 | R | 4,536 | 118 |
rm(list = ls()); gc()
# setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/screening_ipad_rf/')
setwd('/home/yinfeiko/DeepLINK/revision')
dat1 = read.csv('rna_data1.csv', head = T, stringsAsFactors = F)
source('hdknockoff_all_Factor.R')
library(glmnet); library(RSpectra); library(randomForest)
mo... |
b1ed422ae679620999d6bd1365329ddc59955c884eea70e62302946e48098ce0 | R | 4,547 | 154 | #
# utils
#
#' Split BED set by chromosome
#'
#' @param bed data.frame with BED content
#' @param chroms character vector with optional set of chroms to consider (otherwise all chroms in the BED are used)
#' @param cols integer vector with optional set of columns to return (otherwise all columns are returned)
#' @para... |
925d25ebcfbbf2768c3348b7503f9641be9b52828e92c65c4299e2a3c629d96d | R | 4,572 | 168 | ---
title: "Multiple 'shapviz' objects"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Multiple 'shapviz' objects}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
warning = FALSE,
message ... |
8aab489f0f4109dce666b67f1692eed0d4d373f1b59b932ce1096cf81c9dd016 | R | 4,606 | 92 | ---
output:
pdf_document: default
word_document: default
html_document:
keep_md: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, cache = T, results = 'hide')
```
# CSIDE on the MERFISH Hypothalamus
### Load in CSIDE results and calculate significant genes
```{... |
3e946e35789431685e3441724f51a16d4ca4a0e23f7cf527ea0bc697c91bfbe9 | R | 4,615 | 106 | #!/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... |
d93eae2ff381d3a9264ac755f37a800782a4f3c51b8bc89ff33793a5b3dc0614 | R | 4,624 | 98 | #!/usr/bin/env Rscript
# Generate a simulated expression matrix and sample design CSV.
# ── Helpers ───────────────────────────────────────────────────────────────────
parse_int <- function(x, default, min_val = 1L) {
v <- suppressWarnings(as.integer(x))
if (is.na(v) || v < min_val) default else v
}
parse_num <- ... |
6bfb6c4830ae459ee0a1bb84830e0c24657966196751a8e9f8a676f4524cf5af | R | 4,655 | 127 | #!/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... |
d3417e16a3eced407fd0c18de95d501dd3216244f4a32185cf3a3c10fa6923b7 | R | 4,688 | 117 | fitmodel <- function(model2fit, dat4stanfile, stanfitsfile, calcLogLik = TRUE){
# Load prepared data for Rstan.
load(dat4stanfile) # -> dList
stopifnot(exists("dList"))
# 1) More robustly check whether K comes from the parent environment
# (avoids interference from same-named local variables)
if (!ex... |
d679819c94fba8886c40393d4f3765d6616c07ca0ea8734b0e4672e61bd70191 | R | 4,701 | 124 | # Merge E1 + E2 + E3 annotated Seurat objects, SCTransform, and Harmony integration.
# Run with working directory set to the project root.
# Prerequisites: Barcode_E1_Clones.Rmd, Barcode_E2_Clones.Rmd, and Barcode_E3_Clones.Rmd
# (annotated objects with clone metadata in Data/E16_barcodes_E{1,2,3}.rds).
library(Seurat... |
0f0d40dabb14c3b6cf30d729f0ada075b0f97ab36eb4288547da72c7d53fe1ad | R | 4,703 | 155 | ```{r data}
install.packages("readxl")
library(readxl)
inpF<-"/home/animeshs/Downloads/For prediction modeling.xlsx"
data<-read_excel(inpF, sheet=1)
summary(data)
```
```{r cycle}
tp<-read_excel(inpF, sheet=3)
summary(tp)
```
```{r test}
#https://cran.r-project.org/web/packages/MetaCycle/vignettes/implementation.html... |
a6202d04f86688ddc2c333753c37da84637567fbc4b83baf55135c3ebb449011 | R | 4,711 | 133 |
setwd("C:/Users/u0171728/Downloads")
library(ggplot2)
metacell_expr <- readRDS("metacells.BrainCellAtlas_Saunders_version_2018.04.01.rds")
annotations <- readRDS("annotation.BrainCellAtlas_Saunders_version_2018.04.01.rds")
groups <- list(
`Exc neurons (sbc)` = annotations$tissue_subcluster[
annotation... |
1815ebbc4889a7b67cf20c2f91419972fe992bcde0c47c3cf355a809a9c2225f | R | 4,834 | 91 | #usage####
#..\R-4.5.0\bin\Rscript.exe flashDeConvMZMS1.r L:\promec\HF\Lars\2026\Solveig_Janus\test\redo\
#mzML####
#generate mzML in windows prompt using cmd
#wget https://mc-tca-01.s3.us-west-2.amazonaws.com/ProteoWizard/bt83/4071095/pwiz-bin-windows-x86_64-vc145-release-3_0_26181_1c640ef.tar.bz2
#tar xvjf pwiz-bin-... |
5c121b72d0353a9274dde6f11f61088877243f58db841436010b55fdd28a424a | R | 4,843 | 137 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
f64a37c217af9d97e357763171d12dd2a67886ff34c32de1a039d44df0168e24 | R | 4,843 | 139 | # Set environment language
Sys.setenv(LANG = "en")
# Set working directory
setwd("D:/project/ndreamer_plot/")
# Load necessary libraries
library(dplyr)
library(fmsb)
library(gridExtra)
library(gt)
library(ggplot2)
generate_radar_plot <- function(df,title) {
# Rename columns
colnames(df) <- gsub("^X1\\.cLISI$", "... |
b62e7dfb11fc630be362fced9cf2e413e95e7d1d55c0fc85493922437bb086f0 | R | 4,905 | 117 | # ------------------------------------------------------------------------------
# Title: SCENIC Integration with Xenium Data
# Author: Yiran Song
# Date: March 18, 2025
# Description:
# This script integrates SCENIC results with Xenium spatial transcriptomics data,
# processes transcription factor activity, and perfor... |
94c9ea9c7b439f86b7f44804f4c7c33ce05543faa295cf64b268747f4a09473a | R | 4,985 | 128 | # ------------------------------------------------------------------------------
# Title: SCENIC Binary Regulon Activity Visualization
# Author: Yiran Song
# Date: March 18, 2025
# Description:
# This script loads and processes SCENIC binary regulon activity for Xenium data,
# generates heatmaps of transcription factor... |
688b2213b49fb03d70e1b24fc74917f4dbb07e7ca3d6eeef73c1e935def79269 | R | 5,008 | 124 | #!/usr/bin/env Rscript
# Compare p-values between two result files (e.g. limma and t-test outputs).
# Usage: Rscript compare_methods.R file1.csv file2.csv
# Output: prints summary and writes scatter plots.
# default result files (same as used by run_limma.R and run_tTest.R)
default1 <- "random_10000gene_seed42_nA3_nB3... |
fee3d4c29e8d6ca158ae5c72b0517ddaa0ab84cb89eef56591ab0be0bc982ab4 | R | 5,024 | 153 | cmeans <-
function(x, centers, iter.max = 100, verbose = FALSE,
dist = "euclidean", method = "cmeans", m = 2,
rate.par = NULL, weights = 1, control = list())
{
x <- as.matrix(x)
xrows <- nrow(x)
xcols <- ncol(x)
if(missing(centers))
stop("Argument 'centers' must be... |
2d569e0f174a7bd32afcb09c6c365d4c04fa105be559dd070490802c7b13e8e6 | R | 5,047 | 186 | ## source template from Arnar Flatberg
#source("http://bioconductor.org/biocLite.R")
#biocLite("BiocUpgrade")
#biocLite("illuminaHumanv3.db")
#biocLite("lumi")
#biocLite("limma")
#install.packages("pls")
#install.packages("RCurl")
#.libPaths()
#remove.packages('limma')
#detach(package:limma, unload=TRUE)
lib... |
13fd6d8dc51aefb29de960316bbefc67e28a53ea919c4fefacbe3779c3e72b82 | R | 5,091 | 149 | WORKDIR <- "C:/Users/u0171728/Downloads/JorstadNL"
setwd(WORKDIR)
genes_file <- "human_MTG_2018-06-14_genes-rows.csv"
samples_file <- "human_MTG_2018-06-14_samples-columns.csv"
exon_file <- "human_MTG_2018-06-14_exon-matrix.csv"
intron_file <- "human_MTG_2018-06-14_intron-matrix.csv"
genes_of_interest... |
a0b66c4352d84d042248c665eac640fce896d1469381174d91b1cf32f39ee72a | R | 5,161 | 112 | print("USAGE:Rscript proteinGroups.r <complete path to proteinGroups.txt file> <LFQ or SILAC if performed else it defaults to raw Intensity columns>")
#example####
#..\R\bin\Rscript.exe proteinGroupsSplit.r "L:\promec\Qexactive\LARS\2022\NOVEMBER\combined\txt\proteinGroups.txt"
#supplying input file for testing
#inpF<-... |
30f35eac5c54eb70507e596ea1e4ad240cf991e810b6d57b114789b0c4548a41 | R | 5,165 | 149 | # common functions for reproducible reporting
# - Bayesian framework R/brms interface to Stan
# - editable vector figures in PowerPoint
# - estimates P-values with emmeans
#source("/opt/fix-rstudio.R")
#Sys.setenv(TZ="CET")
require("tidyverse")
require(readxl) # normal Excel input
require("openxlsx") # for normal w... |
bc6025be3af1b4aadfc92d00424241819d3e7a7bc5a19c0ac52be44f61eacaf6 | R | 5,198 | 121 | @ECHO OFF
setlocal
echo starting simulator ...
set CONDAPATH=C:\ProgramData\Bruker\Miniconda3
set ENVNAME=timsEngine
if %ENVNAME%==base (set ENVPATH=%CONDAPATH%) else (set ENVPATH=%CONDAPATH%\envs\%ENVNAME%)
call %CONDAPATH%\Scripts\activate.bat %ENVPATH%
simulator -q -l 0 -d 2 -r 0 -i "D:\Data\LARS\240626_Mira\2406... |
06797f3ca49f42d506ce0911de421aaa02203f657d353e5a0052fccbb2b710da | R | 5,201 | 158 | #' Interaction Strength
#'
#' Returns a vector of interaction strengths between variable `v` and all other
#' variables, see Details.
#'
#' If SHAP interaction values are available, the interaction strength
#' between feature `v` and another feature `v'` is measured by twice their
#' mean absolute SHAP interaction valu... |
e550847e8b98fdc92b88e15c694e87420cc2ba62dd89be7189f8e668cd35f785 | R | 5,223 | 115 | #Rscript diffExprDoseCor.r "L:\promec\USERS\SINTEF\20211207_SINTEF_30samples\combined\txt\\" "proteinGroups.txtLFQ.intensity.16" "Clin0.110.05BioRemGroups_filledMEclin.txttTestBH.xlsx"
#Rscript diffExprDoseCor.r "L:\promec\USERS\SINTEF\20211207_SINTEF_30samples\combined\txt\\" "proteinGroups.txtLFQ.intensity.16" "Ctrl0... |
e7ff7327433b1d14f10ecee3c257c0704b6a307dbf7e96fd9cf1476175e34827 | R | 5,291 | 147 | ## ============================================================
## 0) Load required R packages
## If already installed, you can directly use library()
## ============================================================
pkgs <- c("BayesFactor", "dplyr", "tidyr", "readr", "afex")
to_install <- setdiff(pkgs, rownames(insta... |
99c99e5b3eee196718a721a9e8947e8879aa47b166e4c51198d5ec169564a9e2 | R | 5,296 | 160 | library(RCy3)
library(httr)
#install.packages("devtools")
library(devtools)
#install_gitlab("medbio/disgenet2r")
library(disgenet2r)
library(biomaRt)
# set working dir to the folder containing this script
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
# paths relative to the script directory
data_dir <- ... |
a0e9ca84557b8a686477d3a815da475d40d13b3344ab2879299691b154bdd61c | R | 5,342 | 143 | #!/usr/bin/Rscript --vanilla
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation file... |
227dacbe0e53b79b71c916c466c30d067d8568e50d62194729824466315d3452 | R | 5,432 | 132 | #' Pattern selection by SVD
#'
#' Make a location preview plot for user to directly see the distribution of sample slots
#'
#' @param location the formatted location information
#' @return a location preview plot
#' @export
imputation <- function(target, location_corr){
target <- data.frame(Name = names(target), rati... |
4d8ec315effade89954cf8af39baba1cd84164084870a1fd39f9a2e2985fe855 | R | 5,447 | 70 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# Robust decomposition of cell type mixtures in spatial transcripto... |
a0e09be29dc9c5a9f3f5b6eee013649c36423f0ec4726e5c2211b904e0b88b38 | R | 5,455 | 185 | ## =========================================================
## HC + OCD: extract subject-level β, compute PCI / QCI / Φreg,
## and save results tables only
## HC: use M16 + M19 only
## OCD: use M16 + M21 only
## No plotting
## =========================================================
suppressPackageStartupMessages({
... |
7a0ea39f676c2b8957ca1762760a7001260230abae293be9972f2a6b741772ff | R | 5,483 | 246 | ---
title: "Farmand et al 2026 - NeuroFit RCT Neurogenesis Proliferation Analysis Examples"
output: html_document
---
```{r setup, message=FALSE, warning=FALSE}
library(readxl)
library(dplyr)
library(tidyr)
library(glmmTMB)
library(nlme)
library(DHARMa)
library(emmeans)
library(ggplot2)
```
## 1. Import dataset
```{... |
897a290c520f85fea87d6c7f5f4f7c84cb38d7d1b67522ccde8d7d833b4e8d6d | R | 5,506 | 91 | refdir <- 'Data/Reference/DropVizHC'
reference <- readRDS('Data/Reference/DropVizHC/scRef.RDS')
inter_ident <- which(reference@meta.data$liger_ident_coarse == "Interneuron")
inter_ident <- rownames(reference@meta.data)[inter_ident]
counts <- reference@assays$RNA@counts[,inter_ident]
nUMI <- reference@meta.data[inter_id... |
e8beb6588b8615fc8108fe0161615bf41bf395af40fc2d9b0998b5eaef4f085e | R | 5,543 | 148 | # ------------------------------------------------------------
# Convert MATLAB MFpower4Rhc*.mat into RData containing
# "behavior + EEG (transformed)" for Stan
# ------------------------------------------------------------
library(R.matlab)
prepEEG4stan <- function(obs4stanfile, rawfile, zero_mask = TRUE, verbose = T... |
78fd0cdc6c503fc3696aa3930d5869e94c2393d71189766013bf1aa1815dedf8 | R | 5,544 | 193 | [[[[[
Show that a real r is Solovay random
iff it is strong Chaitin random.
An effective covering A_k of k is a function
of k that enumerates bit strings s,
which are the initial bits of the covered
reals. We assume that no s in A_k is a
proper prefix or extension of another.
Thus the measure ... |
97d17073e4a2ccd77bc8fe0f253b8ff3be39775a309014038701b3c9f9462529 | R | 5,590 | 162 | ```{r proteinGroups}
#inpD<-"F:\promec\Qexactive\LARS\2020\mars\Heidi nano\combined\txt"
inpD <-paste0(getwd(),"/Heidi/")
inpF<-paste0(inpD,"proteinGroups.txt")
data <- read.delim(inpF,sep="\t",header = T,row.names = 1)
#clean
data = data[!data$Reverse=="+",]
#data = data[!data$C..Potential.contaminant=="+",]
row.names... |
b4e75e69907f663426dab3931f0b893715636eb8e6302d285cdac701949276f7 | R | 5,609 | 203 | dtrain <- xgboost::xgb.DMatrix(
data.matrix(iris[, -1L]), label = iris[, 1L], nthread = 1L
)
fit <- xgboost::xgb.train(params = list(nthread = 1L), data = dtrain, nrounds = 10L)
x <- shapviz(fit, X_pred = dtrain, X = iris[, -1L])
test_that("nbins has no effect for factor v", {
expect_equal(
potential_interacti... |
eaf79b5829afb436b3129218636bdf50b632e594706d6060a8fe9a67deaec114 | R | 5,631 | 100 | #!/usr/bin/env Rscript
# Differential expression analysis using the limma-voom pipeline.
#
# Best practices applied:
# 1. DGEList for integrated count + library-size bookkeeping
# 2. Low-count filtering via edgeR::filterByExpr()
# 3. TMM normalization via edgeR::calcNormFactors()
# 4. Precision weights via limm... |
882da0ff09ffb0e88b9893db39aeaab36834b5ecb4361bd4286317a2f28861ff | R | 5,666 | 187 | #' SHAP Force Plot
#'
#' Creates a force plot of SHAP values of one observation. If multiple
#' observations are selected, their SHAP values and predictions are averaged.
#'
#' f(x) denotes the prediction on the SHAP scale, while E(f(x)) refers to the
#' baseline SHAP value.
#'
#' @inheritParams sv_waterfall
#' @param ... |
c46658fea7b386428695a8f27e3c3952b9d08f7bd98091434e482f180766f400 | R | 5,674 | 151 | #' Runs simple variant for performing gene sets co-regulation analysis
#'
#' This function is based on the rude Monte Carlo sampling approach
#' and P-value calculation accuracy is limited to `1 / nperm` value.
#' @param pathways List of gene sets to check.
#' @param E expression matrix, rows corresponds to genes, colu... |
31bd3201cb73cb03a1aa20efe0ccdeadf4b5c85f372975635d49e4c43797b200 | R | 5,733 | 82 | #..\R-4.5.0\bin\Rscript.exe mapKEGGpathview.r "L:/promec/TIMSTOF/LARS/2023/230217_Caroline/mRNA/CPM1.txt.Omego_T3_vs_Control_T3_mRNA_diff_expr_CPM1.Omego_T6_vs_Control_T6_mRNA_diff_expr_CPM1.Omego_T12_vs_Control_T12_mRNA_diff_expr_CPM1.logFC.genesselect.IDmap.absmax.csv" "hsa00100,hsa03050,hsa03320,hsa04068,hsa04066,h... |
5875076617972f54861097793b4a04c5aa273910bcb8b62969fca0f61e323f07 | R | 5,744 | 123 | #script F:\GD\OneDrive\Dokumenter\GitHub\scripts\proteinGroupsANOVA.r
#R C:\Users\animeshs\R\bin\Rscript.exe
#options(download.file.method = "wininet")
#install.packages("devtools")
#devtools::install_github("cox-labs/PerseusR")
library(PerseusR)
args = commandArgs(trailingOnly=TRUE)
if (length(args) != 2) {stop("NOTE:... |
9d9f94e9c8b5c957df30c4c2206a2014096b7dc648ec569994509978136ff053 | R | 5,755 | 144 | ## 0) Libs & Stan options ------------------------------------------------------
suppressPackageStartupMessages({
require(rstan)
require(data.table); require(parallel); require(boot)
require(R.matlab); require(devtools); require(corrgram)
require(ggplot2); require(psych)
})
options(mc.cores = parallel::det... |
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