sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
9d512e7c2f03cebbc654c7f18fe26c9b7fc90713406a76e826f6b9952d36f0c9 | R | 530 | 12 | options(timeout = max(300, getOption("timeout")))
if (("Rsubread" %in% rownames(installed.packages()) == FALSE) || (!require("Rsubread", quietly = TRUE))) {
options(repos = c(CRAN="https://cloud.r-project.org/"))
if (!require("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
B... |
e3bf57cb0cdc86cb8810dc2e537e98cc3810cff22121a84c2218b1273e06b01b | R | 540 | 17 | # file for storing variables
filepath <- ''
conditions <- c('AS', 'brain_tumours', 'celiac', 'CHD', 'colon_cancer', 'COPD', 'IBD', 'lung_cancer', 'lyme', 'MS', 'ovarian_cancer', 'pancreatic_cancer', 'parkinsons', 'PCOS', 'RA', 'SBE', 'schizophrenia', 'TB')
results_filepath <- ''
analysis_dt <- '2025-01-01'
# sql... |
2d1da40a5188a29bf9a39d08fa23f2d8c097fedf90d154b8d55d96b3b523578c | R | 548 | 16 | context("generate function")
test_that('generate runs and returns expected output', {
skip_on_cran()
suppressWarnings(RNGversion(vstr = "3.5.0"))
set.seed(42)
vst_out <- vst(pbmc, return_cell_attr = TRUE)
generated_data <- generate(vst_out)
expect_equal(c(1, 0, 0, 4, 1), generated_data['ERP29', 1:5])
g... |
669a63fe395151b966c147ddcf1175e71a16b536073bf19384c2a9da9879690a | R | 580 | 13 | # Migration to Python 3
The tool `2to3` was used as a first step.
The code relied extensively on features deprecated in python3.
* `zip()` now returns an iterable: in most of the code, this got replaced to `list(zip())`.
* `gzip.open` objects (and use in contexts) handled strings in IO. In most places, the calls wer... |
cb55e103c750eff14432705b41c26dcd0d690630580efb53284547c4e594245d | R | 591 | 14 | PanglaoDB <-
read.csv("/Documents/R documents/SeuratExtend_databases/2020-3-27 PanglaoDB/PanglaoDB_markers_27_Mar_2020.tsv",
sep = "\t")
PanglaoDB_data <- list()
Hs <- grepl("Hs",PanglaoDB$species)
PanglaoDB_data[["marker_list_human"]] <-
split(PanglaoDB$official.gene.symbol[Hs], PanglaoDB$cell.type[Hs])... |
b6dae330a527d97922e1d6d4f9d32467fb9d84cee18a8c22bdef6085a956b65d | R | 598 | 18 | ###
pd = read.csv("he_SraRunTable.txt",as.is=TRUE)
## number of unique variables?
uniqueIndex = which(sapply(pd, function(x) length(unique(x))) > 1)
apply(pd[,uniqueIndex], 2, table)
table(pd$Sample_Name, pd$tissue)
fqPath = "/dcs04/lieber/lcolladotor/with10x_LIBD001/HumanPilot/Analysis/he_layers/FASTQ/"
man = data... |
ddd328e03bf40deef33df142e4c4cd757edc6f6ee3eda45d557f76a46eba5eba | R | 616 | 21 | #create separate databases for PTMSigDB based on the main 4 categories: PATH, PERT, KINASE, and DISEASE
library(cmapR)
library(glue)
#database directory
db.dir <- "../db/ptmsigdb/v2.0.0/all"
#get all files
gmt.files <- list.files(path=db.dir,pattern=".gmt")
#for each file, split and save into correct directory
sub.c... |
ac7b95828d5648bee94e5f99ce6c9b4e7c745dc7f1e57bdc665f5cdc0042ac00 | R | 620 | 21 | #!/usr/bin/env Rscript
library(bambu)
library(dplyr)
library(rtracklayer)
library(arrow)
library(readr)
args = commandArgs(trailingOnly=TRUE)
input_rds <- args[1]
bambu_result <- readRDS(input_rds)
do.call(cbind, lapply(bambu_result, function(df) assays(df)$counts)) %>%
as.matrix() %>%
write.csv("bambu_expre... |
3fef937f12d9d1e1708b5390e8a91ba27530a93f234e66f5416d82c65cb87ac8 | R | 626 | 18 | library(IsoformSwitchAnalyzeR)
aSwitchList <- readRDS("data/katherine/isoformswitch_inProg.rds")
aSwitchList_part2 <- analyzeIUPred2A(
switchAnalyzeRlist = aSwitchList,
pathToIUPred2AresultFile = "export/iupred2a_processed_result.txt",
showProgress = T)
aSwitchList <- analyzeSignalP(aSwitchList, pathToSi... |
c1b8dbb0f7adffea9eea8037a39f898fccee54d5f683e8d6b0a49d786a81ef3b | R | 652 | 28 | #!/usr/bin/env Rscript
library(bambu)
library(rtracklayer)
library(arrow)
library(readr)
args = commandArgs(trailingOnly=TRUE)
annotation_gtf <- args[1]
aligned_bam <- args[2]
ref_genome_fasta <- args[3]
ncore <- args[4]
output_rds <- args[5]
bambuAnnotations <- prepareAnnotations(annotation_gtf)
bam_files <- Sys.glo... |
ad2484fca4316cdc9c0ab1bf95d85337ddfed4bff5aaeeb35a297e44c370501c | R | 655 | 18 | library(GenomicRanges)
library(GenomicFeatures)
library(rtracklayer)
library(dplyr)
library(tidyr)
gencode_exons <- makeTxDbFromGFF(paste0(Sys.getenv("GENOMIC_DATA_DIR"), "/GENCODE/gencode.v47.annotation.gtf")) %>%
exonsBy(by = "tx", use.names = TRUE) %>%
unlist() %>%
unique()
SFARI_exons <- rtracklaye... |
4094f675cf13dff3a4b24f5aaca9592420c1ba52a4624c43a0a0028d5a970875 | R | 665 | 22 | ##
f = list.files("Results", pattern = "gsa.out",full=TRUE)
f = f[-5] # drop pgc3
names(f) = c("SZCD", "MDD", "ASD", "BPD")
gsaList = lapply(f, read.table, header=TRUE, as.is=TRUE,row.names=1,comment="#")
magmaTab = sapply(gsaList, "[[", "P")
rownames(magmaTab) = rownames(gsaList[[1]])
magmaTab = magmaTab[!grepl("... |
c20b83054ad452dd9c35ce450f1105fe6dc2f25bbcf11ce83045735cd46322ce | R | 684 | 21 | library(openxlsx)
library(tidyr)
library(dplyr)
file <- "d:/Documents/R documents/SeuratExtend_databases/2021-5-21 preset colors/2021-8-12 preset colors I want hue.xlsx"
col.space <- c("default","intense","pastel","all","all_hard")
color_presets <- list()
for (i in 1:5) {
col_list <- read.xlsx(file, sheet = i, colNam... |
0e24717f83dddca6a013820cb2bd51b03a839468a73369d88b1560c6f1eaf112 | R | 697 | 18 | library(IsoformSwitchAnalyzeR)
IsoseqsSwitchList <- readRDS("IsoseqsSwitchList.rds")
IsoseqsSwitchList <- preFilter(
switchAnalyzeRlist = IsoseqsSwitchList,
geneExpressionCutoff = 1, # default
isoformExpressionCutoff = 0, # default
IFcutoff = 0.01, # default
removeSingleIsoformGenes ... |
d3eb5463f75ace2bf5ce19239f65131582c8c23336282bc20ddd0863e511c47b | R | 715 | 22 | library(IsoformSwitchAnalyzeR)
library("BSgenome.Hsapiens.UCSC.hg38")
bsg <- BSgenome.Hsapiens.UCSC.hg38
IsoseqsSwitchList <- readRDS("results/long_read/IsoseqsSwitchList.rds")
IsoseqsSwitchList <- extractSequence(
IsoseqsSwitchList,
bsg,
onlySwitchingGenes = FALSE,
removeShortAAseq = FALSE,
remov... |
fb2ff187097f9f3a8bdf79c6fab3d632655decabd0dc1501c5d19d475791b732 | R | 736 | 38 | ---
title: "R Notebook"
output: html_notebook
---
```{r}
library(tidyverse)
library(plotly)
library(pROC)
prl <- read.csv("socialBehaviorPrLdataPIindices.csv",
stringsAsFactors = T) %>%
mutate(ID = as.factor(ID),
Savg = (S1 + S2)/2)
```
```{r}
# ROC analysis
roc_obj <- roc(gt ~ Savg, da... |
f02dadb65dbecbaea40a5621ddabac51918cfd2a303908596f9630142c497f9e | R | 737 | 24 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import metadata ----
meta <- readRDS("Rdata/paper_metadata_v2.rds")
# Sanity check overlap between sets (CV leakage) ----
meta[set %in% c("test", "validation", "training"), {
# Import regions
.c <- .... |
b8ea9652641987e843a9fa40bf9c5ba515f96d0a948116da14049f0e39f17b8b | R | 758 | 27 | library(arrow)
library(dplyr)
library(ggplot2)
library(ggpubr)
peptide_mapping_for_plotting <- read_parquet("nextflow_results/V47/orfanage/peptide_mapping_for_plotting.parquet")
peptide_mapping_for_plotting %>%
ggplot(
aes(x=GENCODE, y=mean_expression)
) +
geom_boxplot() +
theme_minimal()
my... |
10514a15cce0d8136640df1802e0b68f5debab37b8ee437f4aef0b84260041bc | R | 764 | 32 | # Function to compute AUC ----
vl_ROC_AUC <- function(label, predicted, plot.line= F, col= "red", ...)
{
if(!is.logical(label))
label <- as.logical(label)
# Make data table ----
dat <- data.table(label= label,
predicted= predicted)
# Order ----
setorderv(dat, "predicted", -1)
... |
a25cb6939f8f4d61c6179b44633b9fd03d49ccf874c43c5edcd7b03d52afa05c | R | 775 | 30 | context("lots-of-points")
test_that("geom_text_repel works with 10,000 points and 32 labels", {
cars <- mtcars
cars$car <- rownames(mtcars)
set.seed(42)
dat3 <- rbind(
data.frame(
wt = rnorm(n = 10000, mean = 3),
mpg = rnorm(n = 10000, mean = 19),
car = ""
),
cars[,c("wt", "mpg... |
877cd9b88ab3aefc63610ece007c21c37bc0962724a6f66359e6583bdf0e2389 | R | 779 | 17 | context("correcting")
test_that('correcting runs and returns expected output', {
skip_on_cran()
suppressWarnings(RNGversion(vstr = "3.5.0"))
set.seed(42)
vst_out <- vst(pbmc, return_cell_attr = TRUE, res_clip_range = c(-Inf, Inf))
y_smooth <- smooth_via_pca(vst_out$y, do_plot = FALSE)
expect_equal(c(910, 2... |
dbcc3dcb19d91fc7dc1232b847b4ba7a49d7f16c7da7cad0b2761dbb3bac713f | R | 801 | 33 | ##### Create read file #####
require(GenomicSEM)
library(devtools)
args = commandArgs(trailingOnly=TRUE)
tissue <- args[1] #Tissue
setwd("./12_TWAS/5_all_combined/4_im")
# List files
files <- list(paste0("AD_", tissue, "_ALL.dat"), paste0("ASTH_", tissue, "_ALL.dat"), paste0("IBD_", tissue, "_ALL.dat"), paste0(... |
121f173ecb968ddeea417a87d6b86106b93e751756d02b06b963fdd1397504cc | R | 802 | 25 | library(tidyverse)
#Set correct working directory
setwd("D:/RW-PCB 11 Astrocyte Imaging/Files for Cropping and Analysis/Analysis Files/Somatosensory/GFAP_Mask_Measurements")
files <- list.files(pattern = "\\.csv$", full.names = TRUE)
files <- files[file.info(files)$size > 0]
summary_dataset <- map_dfr(
fi... |
627553cb044229d210e986533e31145f82456ec533d9785eff2887a068bf6906 | R | 805 | 18 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite-dev/")
# List observed files ----
dat <- data.table(TL= paste0("TL", c(12, 9, 13)))
dat <- dat[, .(obs.file= list.files(paste0("db/observed/bulkATAC/", TL, "/"), recursive = T, full.names = T)), TL]
dat[, tiss... |
ecf5ddd8f9605947fe0f1babd67cc57b8a699ddcb7143193568ce8d42dc128d1 | R | 807 | 21 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import data ----
dat <- readRDS("db/folds/bulkATAC_folds.rds")
dat <- dat[group=="vista"]
# Add missing labels ----
add <- readxl::read_xlsx("/groups/stark/shenzhi.chen/db/VISTA_enhancer_dataset/VISTA202... |
857b33d6835f4e9dcfed3072aca41604ec819d3d2798a344cc757a82c1258cbc | R | 815 | 33 | ##### Create read file #####
require(GenomicSEM)
library(devtools)
args = commandArgs(trailingOnly=TRUE)
tissue <- args[1] #Tissue
setwd("./12_TWAS/5_all_combined/1_cvd")
# List files
files <- list(paste0("AF_", tissue, "_ALL.dat"), paste0("CAD_", tissue, "_ALL.dat"), paste0("HF_", tissue, "_ALL.dat"), paste0("... |
9b47c45ed45e79c91bec179399168f3a99622940bf0a370f1fa117f9b4ff4077 | R | 818 | 26 | library(ggplot2)
library(readr)
library(dplyr)
library(tidyr)
library(RColorBrewer)
my_theme <- theme_classic() +
theme(
axis.title.x = element_text(size = 13),
axis.title.y = element_text(size = 13),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12)
)... |
bf9e09da3854e5f6fb2583e7662a6de2a98a833368dbeee7592c6dbad98c9f79 | R | 820 | 27 | library(readr)
library(dplyr)
library(ggplot2)
my_theme <- theme_classic() +
theme(
axis.title.x = element_text(size = 13),
axis.title.y = element_text(size = 13),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
legend.position = "none"
)
t... |
5d143ffe91c581761af6155d0ac36bb7e2fe748ad5b4b129d4c7a714a8381f3b | R | 821 | 18 | # Open a connection to a log file
logfile <- file("*PLACEHOLDERPATH*/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("DESeq2")
count_data <- read.table("tests/test_files/big_counted.csv", header=TRUE, sep= ",", row.names = 1)
des... |
1037402bbd6a5314bb58903785b3e1de27a6d938392eab460b3f580652ea5ead | R | 824 | 36 | ###
library(jaffelab)
library(Biostrings)
x = read.csv("../10X/151675/tissue_positions_list.txt",
as.is=TRUE, header=FALSE)
x$barcode = ss(x$V1, "-")
bcs = DNAStringSet(x$barcode)
## hamming
dd_hamming = stringDist(bcs, method = "hamming")
dd_mat_hamming = as.matrix(dd_hamming)
dd_mat_hamming[upper.tri(dd_mat_ham... |
db0c0f30520bc99d5b0d19e7a3023ae425dc2c7d362edf722850d7427b5138f3 | R | 827 | 23 | # Open a connection to a log file
logfile <- file("tests/test_files/featurecounts_tests/outdir/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("Rsubread")
fc <- featureCounts(arg = TRUE,
arg2 = NULL,
arg3 = "string",
arg4 = c("st... |
2ec63138d5b20eb9481c1a07fc3551686d693cccf71d40967961f40ac4c61200 | R | 830 | 22 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import metadata ----
meta <- readRDS("Rdata/paper_metadata_v3.rds")
meta <- meta[ID=="model1_bulkATAC_tsx3Aug_2xBal_noW"]
# Select VISTA tiles and only the center ATAC-Seq bins
meta <- meta[(dataset=="acc... |
d0302874177c08668016effe265b99ad39e20aa9e49cf18c95f6a417bb1b55f6 | R | 835 | 34 | ##### Create read file #####
require(GenomicSEM)
library(devtools)
args = commandArgs(trailingOnly=TRUE)
tissue <- args[1] #Tissue
setwd("./12_TWAS/5_all_combined/3_gastro")
# List files
files <- list(paste0("GORD_", tissue, "_ALL.dat"), paste0("GSD_", tissue, "_ALL.dat"), paste0("IBS_", tissue, "_ALL.dat"), pa... |
e255b120bc02e43d5ae3297165634f5fcb4cef2a3c14374ccf1441f4faf6d32a | R | 841 | 25 | options(timeout = max(300, getOption("timeout")))
pgks <- list("statmod")
for (pkg in pgks) {
tryCatch(
tryCatch(
{install.packages(pkg)
require(pkg)},
warning = function(e) {
install.packages(pkg, type = "binary")},
error = function(e) {
install.packages(pkg... |
4c96fd0c66819e348040f05137b842595f166e25c33da6c4d1d952ba789b019b | R | 842 | 22 | localSRMD <- function(unconstrained, constrained, lhsvar, rhsvar){
print(paste0("calculating localSRMD across h = ",length(unconstrained)/length(lhsvar[[1]])," parameters in g = ",length(lhsvar[[1]])," groups (",length(unconstrained)," parameters total)"))
#convert lists to data frames
lhsframe <- t(data.fr... |
7201b1634205b70413b9cbdb8bc5200973d126ed2b8c76191332de9a3f572d9e | R | 842 | 26 | library(readr)
library(dplyr)
library(ggplot2)
my_theme <- theme_classic() +
theme(
axis.title.x = element_text(size = 13),
axis.title.y = element_text(size = 13),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
legend.position = "none"
)
t... |
a34d9a1df922e2f3b60a99568d0d2a7f0a1f18e57b516249d3c41eb00e4579c5 | R | 861 | 25 | library(tidyverse)
#Set correct working directory
setwd("D:/RW-PCB 11 Astrocyte Imaging/Files for Cropping and Analysis/Analysis Files/Somatosensory/GFAP Morphology")
files <- list.files(pattern = "\\.csv$", full.names = TRUE)
files <- files[file.info(files)$size > 0]
summary_dataset <- map_dfr(
files,
... |
7bf36f4b1faff3118515c90769127ca06ca30203765a3f6b6437136198a97c2d | R | 876 | 24 | setwd("~/Documents/SeuratExtend-database/2024-4-5 color pro from i want hue")
files <- list.files(pattern = ".csv")
files <- files[!grepl("_original",files)]
theme <- c("default","light","red","yellow","green","blue","purple","bright")
lines <- readLines(files[1])
color_palettes <- strsplit(lines, ",")
color_palettes... |
71ae1b04bb02901d3058860f528526c4a13d205c4f89995742c695a0ad6946a8 | R | 877 | 20 | #' Peripheral Blood Mononuclear Cells (PBMCs)
#'
#' UMI counts for a subset of cells freely available from 10X Genomics
#'
#' @format A sparse matrix (dgCMatrix, see Matrix package) of molecule counts.
#' There are 914 rows (genes) and 283 columns (cells). This is a downsampled
#' version of a 3K PBMC dataset available... |
f35069831e14870553545bdb5ed8f5decf13f655c56a78d2cf4e241b5a5c2c02 | R | 877 | 33 | ##### Create read file #####
require(GenomicSEM)
library(devtools)
args = commandArgs(trailingOnly=TRUE)
tissue <- args[1] #Tissue
setwd(".12_TWAS/5_all_combined/2_metab/")
# List files
files <- list(paste0("GSD_", tissue, "_ALL.dat"), paste0("HDL_", tissue, "_ALL.dat"), paste0("MDD_", tissue, "_ALL.dat"), past... |
313d2f57bcefec58dd97a335e04dddd53a0e6430b1d52012ba6829c95cb8c7d0 | R | 889 | 21 | library(ppcor)
args <- commandArgs(trailingOnly = T)
inFile <- args[1]
outFile <- args[2]
# input expression data
inputExpr <- read.table(inFile, sep=",", header = 1, row.names = 1)
geneNames <- rownames(inputExpr)
rownames(inputExpr) <- c(geneNames)
# Run pcor using spearman's correlation as mentioned in the PNI pa... |
d04a54ffc8e8f70c36ee757be3ea75f01d7401406fea11097b690ed520ffc4c0 | R | 891 | 36 | ---
title: "R Notebook"
output: html_notebook
---
```{r}
library(openxlsx)
library(tidyverse)
# Read in the winsorized data with the additional variables.
dat <- read.csv("winsorizedOutliersRemovedAdditionalVars.csv")
# Read in social behavior data.
wb <- loadWorkbook(file = "PrL_behavior_ML_2025.xlsx")
s1 <- read.... |
fc97b138b989b5e1860a25e98d019d66687e43fe47bd3e71fee1b287ce4d3769 | R | 900 | 16 | context("differential expression")
# test_that('compare expression runs and returns expected output', {
# skip_on_cran()
# options(mc.cores = 2)
# set.seed(42)
# vst_out <- vst(pbmc, return_cell_attr = TRUE)
# # create fake clusters
# clustering <- 1:ncol(pbmc) %/% 100
# res <- compare_expression(x = vst... |
08b1b9a7d21d853cc63b5c46305152cc425dc4ce33f8de92c67721326715b7ad | R | 902 | 29 | library(tidyverse)
library(IsoformSwitchAnalyzeR)
Isoseq_Expression <- read.csv("results/long_read/Isoseq_Expression.csv")
sampleID <- colnames(Isoseq_Expression)[c(-1)]
time_point <- str_split(sampleID, "_", 2) %>% map_chr(~ .x[1])
time_point <- factor(time_point, levels = c("iPSC", "NPC", "CN"))
myDesign <- data.fr... |
e81fda9f5082216ea3c2ff7bccfb059f88d2fcbc9b98c3fe0ece0420753b6134 | R | 903 | 17 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
cmd <- vlite::cmd_mergeBigwig(
bw = c(
"/groups/stark/shenzhi.chen/projects/transferLearningMammalianEnhancerDesign202408/clean_version/db/bw/forebrain_merged.CPM.bigwig",
"/groups/stark/shenzhi.c... |
449e097ba3c4fae0c73b43c7d617cf90e068b067002d0682b061fa2c2c7cd2ee | R | 964 | 51 | setwd("/Users/zhangyuan/Google Drive/2023_math_reading_neurotransmitter/GitHub")
rm(list = ls())
# ------------------------------------------------------------
# Get R package versions for reporting summary
# ------------------------------------------------------------
packages <- c(
"CCA",
"CCP",
"ppcor",
"B... |
a35797bb1474c6692009b8b95a7817789c1d2a181ec342c0b14be182b8198b2a | R | 979 | 21 | # Open a connection to a log file
logfile <- file("*PLACEHOLDERPATH*/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("DESeq2")
count_data <- read.table("tests/test_files/big_counted.csv", header=TRUE, sep= ",", row.names = 1)
des... |
c60727366aa7c60b722bec74d093160de5bcec53a3ce3b8c6b48a749e5d2247c | R | 980 | 36 | #!/usr/bin/env Rscript
library(Gviz)
library(rtracklayer)
library(GenomicFeatures)
library(dplyr)
options(ucscChromosomeNames=FALSE)
args <- commandArgs(trailingOnly=TRUE)
bam_file <- args[1]
gtf_file <- args[2]
gene_name <- args[3]
gencode_gtf <- args[4]
out_pdf <- args[5]
gencode <- import(gencode_gtf)... |
99ec739bbf9f6123ceae81b10b2053e7c9eb81e92efac278a49d6290cf983a7c | R | 982 | 38 | ##### T_SEM Script ######
require(GenomicSEM)
library(devtools)
args = commandArgs(trailingOnly=TRUE)
tissue <- args[1]
setwd("./12_TWAS/T_SEM")
load("LDSCoutput_CVD.RData")
genes <- readRDS(paste0("./6_read_fusion/1_cvd/CVD_", tissue, ".rds"))
model1 <- "
F_CVD =~ HF + CAD + STK + AF
F_CVD_MDD =~ a*MDD + a*F... |
6c18e1bd72b02d29f5207747981597ba4bc103b1b3fd47edd388e02fa20249fa | R | 990 | 31 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import data ----
dat <- readRDS("db/folds/bulkATAC_folds.rds")
dat <- dat[group=="vista"]
# Add missing labels ----
add <- readxl::read_xlsx("/groups/stark/shenzhi.chen/db/VISTA_enhancer_dataset/VISTA202... |
b5e8acf1ba01c85cc099b15bd32d8e1a116cb7fbbe4124641497c22a2b655420 | R | 997 | 32 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite-dev/")
# List peak files (reproducible from rep1 and rep2, see paper) ----
meta <- data.table(
file= list.files(
path = "/groups/stark/shenzhi.chen/projects/accessibility_model_enhancer_design_17112025/d... |
cb4dd3cf4b102345bcb35eacc23a8109ec60c8f69f12431dac4619ae1d81e012 | R | 997 | 25 | library(LEAP)
args <- commandArgs(trailingOnly = T)
inFile <- args[1]
maxLag <- as.numeric(args[2])
outFile <- args[3]
# input expression data
inputExpr <- read.table(inFile, sep=",", header = 1, row.names = 1)
geneNames <- rownames(inputExpr)
rownames(inputExpr) <- c()
# Run LEAP's compute Max. Absolute Correlation... |
b8ebc09e2808255d09050234fa95075bb86a1b6de9a4fbd5948ab62aec4b6a72 | R | 1,037 | 37 | ##### T_SEM Script ######
require(GenomicSEM)
library(devtools)
args = commandArgs(trailingOnly=TRUE)
tissue <- args[1]
setwd("./12_TWAS/T_SEM")
load("LDSCoutput_gastro.RData")
genes <- readRDS(paste0("./6_read_fusion/3_gastro/Gastro_", tissue, ".rds"))
model1 <- "
F_gastro =~ GORD + GSD + IBS + PUD
F_gastro_... |
c3132a8698b1c4f436232e8396f5bdb26d712ba09d926398d1d0a598af5d6206 | R | 1,042 | 38 | #!/usr/bin/env Rscript
library(readxl)
library(dplyr)
library(rtracklayer)
library(GenomicRanges)
library(stringr)
library(readr)
args <- commandArgs(trailingOnly=TRUE)
riboseq_file <- args[1]
riboseq <- read_excel(riboseq_file, sheet = 2) %>%
dplyr::rename(
seqname = chrom,
start = codon5,
... |
d8b0941eae808589fd07f37932468b8411855f156220340d3a82813633620e73 | R | 1,042 | 22 | # Open a connection to a log file
logfile <- file("*PLACEHOLDERPATH*/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("DESeq2")
count_data <- read.table("tests/test_files/big_counted.csv", header=TRUE, sep= ",", row.names = 1)
des... |
dd13de5860a42717fb1fe1de0990918df9171b417ea30e1511bec653d3407312 | R | 1,042 | 43 | ##### T_SEM Script ######
require(GenomicSEM)
library(devtools)
args = commandArgs(trailingOnly=TRUE)
tissue <- args[1]
setwd("./12_TWAS/T-SEM")
load("LDSCoutput_IM.RData")
genes <- readRDS(paste0("./6_read_fusion/4_im/IM_", tissue, ".rds"))
model1 <- "
F_IM =~ ASTH + AD + IBD + MS
F_IM_MDD =~ a*F_IM + a*MDD... |
c84e69b017279226c700ef13a4895b62141cba9dfda74929e510ffbb450fb932 | R | 1,049 | 33 | ##########################################################
#This R script is to analysis the motif enrichment and cluster
##########################################################
Args <- commandArgs()
in_file = Args[6]
out_file = Args[7]
human_motif<-read.table(file=in_file, header = F, sep = "\t")
head(human_mot... |
667e4f35cccb6f31d91d3414f6f327e74508f802ee4856e20d95560ecb71b385 | R | 1,056 | 35 | library(dplyr)
library(purrr)
library(ggplot2)
source("src/utils.R")
novel_splice_site_ClinVar <- read_vcf("export/variant/novel_splice_site_ClinVar.vcf", info=c("CLNVC", "CLNSIG", "MC", "GENEINFO"))
novel_splice_site_ClinVar %>%
mutate(MC = str_remove(MC, ",.*$")) %>%
mutate(MC = str_extract(MC, "(?<=\\|)[... |
2c5488042dbd24165203dc74d3ef95ee3853dcca6d8427ef84c37d5c47008953 | R | 1,086 | 46 | ##### T_SEM Script ######
require(GenomicSEM)
library(devtools)
args = commandArgs(trailingOnly=TRUE)
tissue <- args[1]
setwd("./12_TWAS/T_SEM")
load("LDSCoutput_metab.RData")
genes <- readRDS(paste0("./6_read_fusion/2_metab/Metab_", tissue, ".rds"))
model1 <- "
F_metab =~ MSYN + T2D + TG + HDL + GSD
F_metab... |
82f8c96a8a3d6a222e8d9e2da0cc6d7ac56c2809cd7ef31fda18f9be5443b5c8 | R | 1,087 | 28 | options(timeout = max(300, getOption("timeout")))
if (("DESeq2" %in% rownames(installed.packages()) == FALSE) || (!require("DESeq2", quietly = TRUE))) {
options(repos = c(CRAN="https://cloud.r-project.org/"))
install.packages("png")
pgks <- list("XML", "vctrs", "RCurl")
for (pkg in pgks) {
tryC... |
40f49288b5ca89098aba1f3082eb7ea9c07c14e268c7ed5e6512f8d8a1776f17 | R | 1,091 | 34 | options(width=100)
library(scater)
library(SingleCellExperiment)
## load annotated sce objects
load("SCE_singlet-spots_MNT.rda")
load("SCE_neuropil-spots_MNT.rda")
dim(sce.singlet)
dim(sce.neuropil)
table(sce.singlet$subject_position, sce.singlet$prelimCluster_MNT)
table(sce.neuropil$subject_position, sc... |
83914277ce47cfe20acea175201c02e810954e7377276af087a3bd3aad240f13 | R | 1,098 | 22 | # =============================================================================
# run_all.R - master driver. Runs the full pipeline in order.
# Each script can also be run independently (later scripts read the .rds
# intermediates written by earlier ones into data/processed/).
# Rscript run_all.R
# ==================... |
215dedcef1ee68911b61f943ada5594d083c4822b5c1037395af5b381f515e0c | R | 1,105 | 39 | library(ggplot2)
library(dplyr)
my_theme <- theme_classic() +
theme(
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12),
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 10),
legend.position = "none"
)
theme_set(my_the... |
882d9ba07bde2d0150217802a18d4305d7b741ee7ac31165e1e32aa13732cd4c | R | 1,107 | 16 | suppressPackageStartupMessages({library(data.table);library(IlluminaHumanMethylation450kanno.ilmn12.hg19)})
b <- fread("__MS_GEO_ROOT__/Methylation_Data/New_Datasets/GSE88824_beta_raw.csv")
p <- b[[1]]; B <- as.matrix(b[,-1]); rownames(B) <- p
ann <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)
xp <- row... |
76372459dd0fc39c88af5ecd3796b5a8022cbf256b28064de838eb4c192e3f81 | R | 1,120 | 38 | library(dplyr)
library(rtracklayer)
library(GenomicFeatures)
library(GenomicAlignments)
annotation_gtf <- paste0(Sys.getenv("GENOMIC_DATA_DIR"), "GENCODE/gencode.v47.annotation.gtf")
SFARI_exon <- makeTxDbFromGFF(predicted_cds_gtf) %>%
cdsBy(by = "tx", use.names=TRUE)
# Read in the final_transcript GTF file
gtf... |
0042047e05433e95566cf7b96f695d0cee06703cac22d7c3f5456fa9715b2687 | R | 1,128 | 29 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
#' This functions takes sythetic data to test DSTG's performance
#' @return This function returns files saved in folders "Datadir" & "Infor_Data"
#' @export: all files are saved in current path
#' @examples: load data from folder "syntheic_data"
source('R_... |
9e1791affe225506fa3a311202b15284013433eb11339091c05d93437bf1fa9b | R | 1,140 | 36 | suppressPackageStartupMessages(library(BTR))
suppressPackageStartupMessages(library(doParallel))
num_cores = 32 #specify the number of cores to be used.
doParallel::registerDoParallel(cores=num_cores)
args <- commandArgs(trailingOnly = T)
inFile <- args[1]
inMdl <- args[2]
maxGenesPerRule <- as.numeric(args[3])
allo... |
bbef5e1d15fbe5b9ee77594a1dfac9ed4a396c08d4b7c116aa4348dd901f5fd7 | R | 1,152 | 60 |
#' Run association test uisng longutudinal data
#'
#' @param pheno : data frame of the phenotype
#' @param outcome : as.numeric , outcome to test
#' @param covars_prs : covariates to adjust
#' @param random_effect : e.g Subject ID or FamilyID
#'
#' @return association test results in data frame
#' @export
#'
#' @exa... |
99c3f61644673d094d4bf0e17bba1bed796c21c6d2ac4c616042671370834d8c | R | 1,160 | 32 | # Open a connection to a log file
logfile <- file("tests/test_files/featurecounts_tests/outdir/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("Rsubread")
fc <- featureCounts(files = c("tests/test_files/featurecounts_tests/single... |
0dad47bfd3a6645def0c5c5828df4486802f06bee3d70bc82982afb9d77571ad | R | 1,164 | 80 | packages <- c(
"Cairo",
"CalNetExploreR",
"ComplexHeatmap",
"ComplexUpset",
"NatParksPalettes",
"RColorBrewer",
"UpSetR",
"bigstatsr",
"broom",
"circlize",
"cowplot",
"data.table",
"devtools",
"dplyr",
"factoextra",
"forcats",
"furrr",
"ggVennDiagram",
"ggbiplot",
"ggdendro",
"... |
f8c5ab16a7b1c3261c6cec719a75960fccc15e2176f2109d71eb91325cc84e40 | R | 1,167 | 53 |
#' Title : Genearte AUC
#'
#' @param pheno : data frame of the phenotype
#' @param outcome : as.numeric , outcome to test
#' @param covars_prs : covariates to adjust
#' @return auc
#' @export
#'
#' @examples
#'
generate_auc<-function(pheno,outcome, covars_prs){
pheno_df<-pheno[,c(outcome,covars_prs )]
... |
1c93c0dc48dfb70b1d2698894188bdf0c77460ad54d7569c7cde23fc7e443463 | R | 1,168 | 33 | library(GenomicRanges)
library(GenomicFeatures)
library(rtracklayer)
library(dplyr)
library(VariantAnnotation)
library(tidyr)
library(parallel)
source("src/utils.R")
novel_exonic_regions <- readRDS("export/variant/novel_exonic_regions.rds")
gencode_exons <- makeTxDbFromGFF(paste0(Sys.getenv("GENOMIC_DATA_DIR"), "/GE... |
0145fd1a16c6be18b668087d05c96203a031de636925a08478af851b16ddc906 | R | 1,173 | 48 | library(arrow)
library(stringr)
library(DESeq2)
library(dplyr)
my_theme <- theme_classic() +
theme(
axis.title.x = element_text(size = 13),
axis.title.y = element_text(size = 13),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
)
theme_set(my_theme... |
d71d944fcf1106b81c35366dcff48742d919b7bfe742b6a871215a9535f568a3 | R | 1,202 | 30 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import validated synthtetic enhancer sequences ----
heart <- readRDS("Rdata/final_designed_enhancer_sequences_heart.rds")
heart <- heart[id %in% c(311, 726, 834, 890, 845) & label=="ledidi_12_14"]
limb <-... |
cb594b9f662d4d2c034c73885f4ec004e043d5629882db61a55484604530eea8 | R | 1,225 | 25 |
PEER_plotModel <- function(model){
par(mfrow=c(2,1))
bounds = PEER_getBounds(model)
vars = PEER_getResidualVars(model)
par(mar=c(5,4,4,5)+.1)
plot(bounds, type="l", col="red", lwd=2, xlab="Iterations", ylab="Lower bound")
par(new=TRUE)
plot(vars,,type="l",col="blue",xaxt="n",yaxt="n",xlab="... |
a677c74326f90e89e837202e59944b5e4aa4e2e047c1a8ae38613a77931a07c8 | R | 1,227 | 46 | library(ggplot2)
library(scales)
colorVector <- c(
"FSM" = "#009E73",
"ISM" = "#0072B2",
"NIC" = "#D55E00",
"NNC" = "#E69F00",
"Other" = "#000000"
)
structural_category_labels <- c(
"full-splice_match" = "FSM",
"incomplete-splice_match" = "ISM",
"novel_in_catalog" = "NI... |
5bb76c502bf6e8b521020c3e9f504962e9865467935c4676aedee23e8a6107ca | R | 1,237 | 24 | # Open a connection to a log file
logfile <- file("*PLACEHOLDERPATH*/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("DESeq2")
count_data <- read.table("counted.csv", header=TRUE, sep= ",", row.names = 1)
design_matrix <- read.ta... |
2ee4048fc8ddce4a70980a4c96b496bef7d890a634d685ac74529d5cc37349fd | R | 1,247 | 59 | ## module load conda_R/3.6.x
## ----Libraries ------------------
library(parallel)
library(SummarizedExperiment)
library(Matrix)
library(RColorBrewer)
library(jaffelab)
library(edgeR)
## load rse list
load("Human_DLPFC_Visium_processedData_rseList.rda")
## filter to expressed genes, lets be liberal
exprsMat = sappl... |
4697a2fc024b702fbdc500afe4e83c57ea9e1eac4b400945cbbeea63b8f549a3 | R | 1,247 | 63 | #' ggrepel
#'
#' This package contains extra geoms for \pkg{ggplot2}.
#'
#' Please see the help pages listed below:
#'
#' \itemize{
#' \item \code{\link{geom_text_repel}}
#' \item \code{\link{geom_label_repel}}
#' }
#'
#' Also see the vignette for more usage examples:
#'
#' \code{browseVignettes("ggrepel")}
#'
#' P... |
9a62eca2d7c3d0637b7b293804d47eac9d31bef83469aff99789f843426d71a0 | R | 1,258 | 35 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import data ----
dat <- readRDS("db/folds/bulkATAC_folds.rds")
dat <- dat[group=="vista"]
# Overlap ----
cols <- c("heart", "limb", "forebrain", "midbrain", "hindbrain", "neuralTube")
# Upset plot
pdf("... |
8da6500232900623d6a54ee30ed5ba733c5e2062fb3968d6821ff1c85ecd0085 | R | 1,264 | 24 | # Open a connection to a log file
logfile <- file("*PLACEHOLDERPATH*/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("DESeq2")
count_data <- read.table("tests/test_files/big_counted.csv", header=TRUE, sep= ",", row.names = 1)
des... |
e7c884dd00f1051e074fe5bf5449c6db3d851f82752beca5b380a71a82413ff9 | R | 1,268 | 47 | # Creates 10x1001bp tiles per region (5 + strand, 5 - strand)
augTile <- function(bed, width= 1001, shifts= c(-400, -200, 0, 200, 400))
{
# Hard copy ----
bed <- data.table::copy(bed)
# Augment regions using tiling ----
aug <- lapply(shifts, function(x) {
.c <- data.table::copy(bed)
.c[, start:= (sta... |
b6e0a1b61ff7b7825045bcbe259f9aeb309448b43f7dbebca995db93639366de | R | 1,270 | 33 | ---
title: "Fig 4m - Arp3 KO line profile, NMIIb-focus variant"
output: html_notebook
params:
genotype: "Arp3KO"
final_plot_channel: "Ch2"
pdf_name: "Actin_profile.pdf"
write_csvs: FALSE
---
<!--
================================================================================
Wrapper for Fig 4m, Arp3 KO geno... |
073c190580342781b0cdd937d58d4b9d8f7d896f393b3ad706c3e87ad09e3185 | R | 1,276 | 36 | ---
title: "Fig 4o - brightfield neurite-xcorr (Arp3 KO genotype)"
output: html_notebook
params:
genotype: "KO"
pixel: 0.1081075
freq: 0.8
cell_range_start: 10
cell_range_end: 18
cell_prefix: "Cell"
---
<!--
================================================================================
WRAPPER for Fig ... |
f691efa4ae439898ee58d57c4793f965ecd979acc542b883a4337c7e88bc3638 | R | 1,283 | 38 | # Open a connection to a log file
logfile <- file("tests/test_files/featurecounts_tests/outdir/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("Rsubread")
fc <- featureCounts(files = c("tests/test_files/featurecounts_tests/paired... |
517947a916aa3801e36c2a0a3f47fce1e7de77d3eccadf44e73d0b62e5318702 | R | 1,294 | 38 | library(ggplot2)
#synthetic <- read.csv("~/R/data/DDLS/adni_synthetic_bl_2430.csv", header = TRUE, stringsAsFactors = TRUE)
synthetic <- read.csv("~/Python/WASP-DDLS/DS-synthetic-data/degree3_root_AGE/bn_eps_100.csv", header = TRUE, stringsAsFactors = TRUE)
synthetic$DX[which(synthetic$DX == "")] <- NA
real <- read.cs... |
4c438b9096fed1e54574f9c844cd261767fc824ee9fbf09b494e26d426f72d21 | R | 1,309 | 26 | PEER_plotModel <- function(model){
par(mfrow=c(2,1))
#bounds = PEER_getBounds(model)
bounds = (1:10) + rnorm(10)
#vars = PEER_getResidualVars(model)
vars = -(1:10)/10. + rnorm(10,0,0.1)
par(mar=c(5,4,4,5)+.1)
plot(bounds, type="l", col="red", lwd=2, xlab="Iterations", ylab="Lower bound")
... |
767ef16e4f7b3ea31635f64a6776929871c68f14abc05e3f592a7b3340dbd998 | R | 1,313 | 34 | ---
title: "Fig 4m - WT line profile, actin-focus variant"
output: html_notebook
params:
genotype: "WT"
final_plot_channel: "Ch3"
pdf_name: "Act_profile.pdf"
write_csvs: TRUE
---
<!--
================================================================================
Wrapper for Fig 4m, WT genotype, "actin focu... |
3927ed415407a2e3755bef3d3e2a6f004094e217689e4810d52d2f663c47c7d2 | R | 1,321 | 35 | ---
title: "ED Fig 11d-f - EB3 length under CK-666 (DIV-3, deconvolved images)"
author: "Lin et al., Nature 2026 (Bradke lab, DZNE)"
output: html_notebook
params:
div: 3
image_pipeline: "decon"
summary_variable: "growth_speed_median"
pdf_name: "EB3_length.eps"
d_drive_source: "D:\\DVElite\\CK666_LA_EB3\\24022... |
3358c20884b5103586d02db3c1ae556e17341a68b7e213886f8765aa59030e7b | R | 1,324 | 35 | ---
title: "ED Fig 11d-f - EB3 length under CK-666 (DIV-4, raw images)"
author: "Lin et al., Nature 2026 (Bradke lab, DZNE)"
output: html_notebook
params:
div: 4
image_pipeline: "raw"
summary_variable: "growth_speed_median"
pdf_name: "EB3_length.eps"
d_drive_source: "D:\\DVElite\\CK666_LA_EB3\\240226_DIV4_EB3... |
63c51f11f0b4d90768f277a0d0309dac4e11b0a392968c7cce1540cd79188096 | R | 1,332 | 34 | ---
title: "Fig 4m - WT line profile, NMIIb-focus variant"
output: html_notebook
params:
genotype: "WT"
final_plot_channel: "Ch2"
pdf_name: "Act_profile.pdf"
write_csvs: FALSE
---
<!--
================================================================================
Wrapper for Fig 4m, WT genotype, "NMIIb foc... |
67079078136f8b34e85cbf785e167afa4b0243257468470bbf3edf8b3d1f2da2 | R | 1,334 | 50 | data <- read.csv("temp.csv", header =FALSE)
D<-data[2]
#head(D)
block_length<- ceiling(nrow(D)^(1/3))
blocks <- D[1:block_length, 1]
for (i in 2:(length(D[, 1]) - (block_length-1))) {
blocks <- rbind(blocks, D[i:(i + (block_length-1)), 1])
}
mbb_size<-floor(nrow(D)/block_length)
# MOVING BLOCK BOOTSTRAP
xbar <- NULL
fo... |
caa8eb7793e707e88278b55f878288a95040cef0bceaff3ac7f48b756b8e5f9b | R | 1,341 | 38 | library(GenomicRanges)
library(dplyr)
library(readr)
library(rtracklayer)
library(arrow)
detected_peptides <- import("nextflow_results/V47/orfanage/UCSC_tracks/detected_peptides.gtf")
tx_classification <- read_parquet("nextflow_results/V47/final_classification.parquet")
protein_classification <- read_tsv("nextflow_res... |
689cf639a5903f1e631c94d046cdf1ff77f237160bf694321ac6bc0504cf4735 | R | 1,342 | 34 | ---
title: "Fig 4m - Arp3 KO line profile, actin-focus variant"
output: html_notebook
params:
genotype: "Arp3KO"
final_plot_channel: "Ch3"
pdf_name: "Actin_profile.pdf"
write_csvs: TRUE
---
<!--
================================================================================
Wrapper for Fig 4m, Arp3 KO genot... |
4043a53d0265677e95e894726dee191bfea4b68a047523814fe5dd299aec8c94 | R | 1,356 | 37 | ---
title: "Fig 4o - brightfield neurite-xcorr (WT genotype)"
output: html_notebook
params:
genotype: "WT"
pixel: 0.3243227
freq: 0.2
cell_range_start: 11
cell_range_end: 13
cell_prefix: "Cell_"
---
<!--
================================================================================
WRAPPER for Fig 4o, ... |
00a6b707779c4cd52ef222cb7532dfc257a693ad8026d14fa90402bae34e375e | R | 1,357 | 36 | ---
title: "ED Fig 1b - 2D-culture pooled-replicate neurite-xcorr"
output: html_notebook
---
<!--
================================================================================
WRAPPER for ED Fig 1b (2D culture, pooled-replicate CCF).
================================================================================... |
a2bcf5ebc53982b69256a52f03c45c71b95a5f7746829a0e0b325d44c980d0b2 | R | 1,362 | 45 | ###
library(jaffelab)
dir.create("S3")
## mv loupe files
path = "/dcs04/lieber/lcolladotor/with10x_LIBD001/HumanPilot/10X/"
## copy barcodes etc
h5 = list.files(path, pattern = "feature_bc_matrix.h5", recur=TRUE,full=TRUE)
file.copy(h5, "S3/")
hi = list.files(path, pattern = "image.png", recur=TRUE,full=TRUE)
newhi ... |
8d3a1a84b640ee4c04a383abe71ee68d8c17abe468a03b6532c9dbdadc6ede45 | R | 1,364 | 26 | # Open a connection to a log file
logfile <- file("*PLACEHOLDERPATH*/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("DESeq2")
count_data <- read.table("tests/test_files/big_counted.csv", header=TRUE, sep= ",", row.names = 1)
des... |
9874cc5fe2514322aecef372f1eb45f49eb591d7cd5cb1c542d0c8083279033c | R | 1,375 | 30 | # Open a connection to a log file
logfile <- file("*PLACEHOLDERPATH*/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("limma")
design_matrix <- read.table("tests/test_files/test_design_matrix.csv", header=TRUE, sep= ",")
design_ma... |
871c84bae46569c1b60b27d0406e9e4f17d1c6c8ab23651c341bf8b98306a3c1 | R | 1,380 | 51 | ###
library(jaffelab)
library(filesstrings)
## unpack
system("tar xvf Lieber_Visium_Transfer_1.tar")
system("tar xvf Lieber_Visium_Transfer_2.tar")
## move to common folder
system("mv Lieber_Visium_Transfer_1/ 10X")
system("mv Lieber_Visium_Transfer_2/ 10X")
system("mv Lieber_Visium_Transfer_2/Lieber/ Analysis/")
##... |
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