sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
832229dd2255d83f28f2fe832ae9d85965afb2fac2fc194bc915273b9e2459c9 | R | 1,294 | 34 | ### DATA
rpkms <- read.csv("genes_rpkm.csv")
rownames(rpkms) <- rpkms[,1]
rpkms <- rpkms[,2:ncol(rpkms)]
reads <- read.csv("genes_counts.csv")
rownames(reads) <- reads[,1]
reads <- reads[,2:ncol(reads)]
erccs <- read.csv("ercc_counts.csv")
rownames(erccs) <- erccs[,1]
erccs <- erccs[,2:ncol(erccs)]
reads <- rbind(reads... |
44bf8a710a7e441650a74b994a16d5083ac7a05e55a9adb5aee2989e693af3e8 | R | 1,310 | 23 | # Set directory to source file loaction
setwd(paste0(figureDirectory, "Figure2-Autocorrelation" ) )
library(ggplot2)
library(cowplot)
load(file = "allAcfPermutatedPlot.RData")
plotAutocorrelation <- ggplot(data = allAcfPermutatedPlot, mapping=aes(x=lag)) +
geom_hline(yintercept = 0, col = "black") +
geom_ribbo... |
fb3f70732c12773d0c65100cc7559cd81c29d6a25abb9f8845a1cf1785c14ff3 | R | 1,314 | 29 | rm(list = ls())
options(stringsAsFactors = F)
setwd("~/Documents/Virginia Tech/Project_4_VCU_postmortem_SCZ/differential analysis/ChIP-seq/promoters/diff_peaks")
def = read.csv('Differential_SCZ_Control_glia.csv')
def = def[order(def$X),]
def_data = def[,2:5]
setwd("~/Documents/Virginia Tech/Project_4_VCU_postmortem_S... |
0528ef8e30cc4e6489175fb6b88d00b919f3fc313f200bbef4b79e5924fccf85 | R | 1,319 | 17 | K27ac_EB_enhancer<-read.csv("E:/R files/K27ac_EB_enhancer.csv",row.names = 1)
K27ac_EB_enhancer<-as.matrix(K27ac_EB_enhancer)
wss_K27ac_EB_enhancer<-sapply(1:15, function(k){kmeans(K27ac_EB_enhancer,k,nstart = 10,iter.max = 100)$tot.withinss})
plot(wss_K27ac_EB_enhancer,type="l")
K27ac_EB_enhancer_cluster_k5<-kme... |
1a5854ad6d608199250ac921c8739ffea25607593bd9828e72551a68d02f4415 | R | 1,325 | 38 |
# Body Shape Phenotypes
## Load data and select the variables of interest
pheno_data <- data[,c("id", "age_rec", "sex", "centre", "Height", "Weight", "BMI", "Waist", "Hip", "WHR")]
pheno_data_na <- na.omit(pheno_data) #Remove missing values
## Principal component analysis (PCA)
pca1 <- select(pheno_data_na, Height, ... |
c39740a1b2d4bff471826d96b6ce5e375e62f4b386f07448556c4fb0a03cd8a6 | R | 1,325 | 24 | # Configuration for the Package
DESCRIPTION <- list(
package.name = "gruffi"
, version = "1.5.4"
, title = "Gruffi identifies and removes stressed cells from brain organoid single-cell datasets."
, "Author" = c(
person(given = "Julia", family = "Naas", email = "julia.naas@meduniwien.ac.at", role = c("aut",... |
6e9574a79761999c4e75fe1e0edd21c488e07c685e573f519a0251ec4e70732a | R | 1,330 | 25 | # Main outcome variable summaries
# Load data
load('alldata_subj2.Rdata')
load('bbdata2.Rdata')
bbsum <- aggregate(cbind(leneve.ms, tueeve.ms, maxeve)~subj, data=bbdata, FUN=median)
alldata <- merge(alldata, bbsum, by="subj")
######################################################################################
# Su... |
243e7208a0ef2dc7e4e99140ca24fc0fe27277f434ec1c10f5bd413aae30290e | R | 1,331 | 48 | #SR_TAU_CELL
.libPaths("/data/user/jfisher7/.conda/envs/SR_TAU_CELL/lib/R/library")
#make sure the environment is clean
rm(list=ls())
args <- commandArgs(trailingOnly = TRUE)
num1<- args[1]
num2<- args[2]
library(MASS)
library(parallel)
library(signatureSearch)
library(tidyverse)
library(HDF5Array)
library(Summari... |
30776e3163c4dd22cdd327c5674419c07f3378945a2221e6a4d96c56140f805a | R | 1,337 | 30 | dev.off()
rm(list=ls())
library(ggplot2)
library(tidyr)
library(gridExtra)
library(stringr)
library(dplyr)
options(scipen=999)
#do this for both IT and SA
Data_brown <- read.table("WG_5kb_TajimaD_brown_IT.Tajima.D", header = T)
Data_green <- read.table("WG_5kb_TajimaD_green_IT.Tajima.D", header = T)
Data_brown$POS <- ... |
084d8c18c92e20c7ec647b2891318cba21786a790d3ac0114e3cea009f2c6f77 | R | 1,346 | 47 | ### DATA
d1 <- read.table("small_final.txt", header = T)
rownames(d1) <- d1[,1]
d1 <- d1[,2:ncol(d1)]
d2 <- read.table("large_final.txt", header = T)
d2 <- d2[!duplicated(d2[,1]), ]
rownames(d2) <- d2[,1]
d2 <- d2[,2:ncol(d2)]
# merge two tables
d1 <- d1[rownames(d1) %in% rownames(d2), ]
d2 <- d2[rownames(d2) %in% rown... |
cdd3f18a0f6a6096b206ed53f535a6861fa73107b647a504952f78e304c6769a | R | 1,350 | 36 | library(dplyr)
library(ggpubr)
library(rstatix)
library(ggplot2)
modules <- c("response.to.type.I.interferon", "response.to.interferon.beta",
"coagulation", "OXPHOS", "Glycolysis", "MHC.ClassII",
"response.to.interferon.gamma", "translational.initiation")
generate_plot <- function(module_na... |
287eb0dca77b90f832b9e00db3c148de58131235920dfa21a86d61d7ea0b2dde | R | 1,354 | 30 | .libPaths("/home/u23/haozhang1/miniconda2/envs/seurat/lib/R/library")
# from https://github.com/satijalab/seurat/issues/5257
# the error in MapQuery() is caused by an update in uwot
# need to install the previous version of uwot and restart the RStudio session to solve this error.
# devtools::install_version("uwot", v... |
76b9a399808051af91c7c13e6eb3d45c7d4033aa52d841abc8df0d3de5d78947 | R | 1,357 | 53 | library(clusterProfiler)
library(enrichplot)
library(dplyr)
library(org.Hs.eg.db)
library(ggplot2)
library(tibble)
library(viridis)
library(forcats)
library(stringr)
dpi <- 300
perform_enrichment <- function(cluster_id) {
degs_ <- markers %>%
filter(gene %in% markers_gene, cluster == cluster_id)
gene_ <- d... |
2ae4eab04cbc9adc35b3299aba6d2794dbb491584dd7ea59ee837a7d33befe4d | R | 1,365 | 26 | library(ggplot2)
library(readr)
setwd('R:/WeijieZheng/Social_defeat_csv')
data <- read_csv('sds_es_corr.csv', show_col_types = FALSE)
ggplot()+
geom_point(data = data, aes(x = Region, y = group, size=abs(corr), fill=pvalue), color="#999999", shape=21)+
geom_point(data = data[which(data$neg_pos == "Pos"), ],aes(x =... |
1a08e2ca8ffad9e34370344be54ba04594839007b19b2722d356a66ede98aaa4 | R | 1,372 | 29 | dev.off()
rm(list=ls())
library(ggplot2)
library(RColorBrewer)
library(tidyr)
library(gridExtra)
library(dplyr)
library(qqman)
options(scipen=999)
Plink_adjusted <- read.table("plink.assoc.linear.adjusted", header=T) #do this for both IT and SA
Plink_nonadjusted <- read.table("plink.assoc.linear", header=T) #do this ... |
f1986d88c9b22677af36caadb83009cc8c9915629c5e8d6394258bdd33eb70db | R | 1,372 | 58 | ---
output: html_document
params:
file:
value: x
set_title:
value: x
title: '`r params$set_title`'
date: '`r strftime(Sys.time(), format = "%B %d, %Y")`'
---
```{r, include=FALSE}
library(knitr)
opts_chunk$set(fig.height = 6, fig.width = 8, fig.align = 'center', echo = FALSE)
library(scater)
li... |
87a5b9f1baf8b2cc04d77b39c4637c681138ff97c9e55c093a88fe160e0e1c44 | R | 1,376 | 30 | # Figure 3B
rm(list = ls())
require("writexl")
require("readxl")
library("dplyr",quietly=T)
library("ggplot2",quietly = T)
library("ape")
library("aplot")
library("pheatmap")
setwd("Source_Data/Figure_3/Figure_3B/")
annotation_col<-read.csv("Figrue_3B.annotation_col.csv",row.names="X")
ERVK_related.NorCounts<-read.cs... |
7f017a6acd253d7c4fb4a4fbe303f35f598b0c357b0ab7de4804fb3acd2a788f | R | 1,394 | 27 | #################
# extraction of MEIS1/2-DLX5 overlapping peak sequences for input in SpaMo
# using MEIS1/2 peak coordinates, summit position +/- 250 bp
genome.mm10 <- getBSgenome("BSgenome.Mmusculus.UCSC.mm10")
library("BSgenome.Mmusculus.UCSC.mm10")
library(seqinr)
merged_peaks.df5 <- read_rds("./Table_S5_ChIP-se... |
1c7fdbfe851aa80acb2091e0989845841837d930be9797d29b41746964f48324 | R | 1,397 | 31 | ### DATA
d <- read.table("NBT_hiseq_linear_tpm_values.txt")
### ANNOTATIONS
cell_type1 <- colnames(d)
cell_type1[grepl("Hi_2338", cell_type1)] <- "2338"
cell_type1[grepl("Hi_2339", cell_type1)] <- "2339"
cell_type1[grepl("Hi_K562", cell_type1)] <- "K562"
cell_type1[grepl("Hi_BJ", cell_type1)] <- "BJ"
cell_type1[grepl(... |
98b0c5ceb4addb841d9440bfffb4597d9a035d70141d8aceb417df3ef50fd47a | R | 1,404 | 52 | #!/usr/env/bin Rscript
## See https://bioconductor.org/packages/release/bioc/vignettes/nullranges/inst/doc/matchRanges.html
library(GenomicRanges)
library(nullranges)
library(tidyrverse)
library(dplyr)
match_cell <- function(input_table, sample_size) {
table <- read.table(input_table, sep="\t", header=FALSE)
coln... |
cd7249d18b09dd42fb94f5eb6f3a37c77c223652e74481dd334399b2c0dc9ea8 | R | 1,419 | 32 | #install.packages("ggplot2")
library(ggplot2)
#########################################################################################
setwd("/Users/plg/Desktop/HaoChing") #set directory to the place you put the
X=read.csv("6C-0728-1.csv", header = T, sep=",") #read the file you want to analyze
Projectname="6C-0728-1... |
5e123a5e0645fe544ab7cc0338e296391c3cd4df1b3f92df2c44b3c1261bccb9 | R | 1,420 | 40 | #!/usr/bin/env Rscript
########################################################
#
# Reusable evaluation containing typical columns
# used in experiments using reading latency as a
# dependent variable with memory and syntactic
# predictors.
#
########################################################
##################... |
7d1ec01291972ed7ab59cdb643ded0c626ae25beb55e667158ada1dc10664ba1 | R | 1,421 | 49 | #!/usr/bin/env Rscript
# See https://stephanecastel.wordpress.com/2017/02/15/how-to-generate-ase-data-with-phaser/
args <- commandArgs()
ase_table <- args[6]
output <- args[7]
# load haplotypic counts
ase_counts = read.delim(ase_table)
# select only genes with sufficient coverage
cov = subset(ase_counts, ase_count... |
018a0a911a0703f02484033afb2a6311bb78c1eb433b33eef9e251ee820123ac | R | 1,429 | 34 | library(ChIPseeker)
#BiocManager::install("TxDb.Hsapiens.UCSC.hg38.knownGene")
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(clusterProfiler)
library(stringr)
library(reshape2)
library(ggplot2)
library(ggpubr)
rm(list = ls())
options(stringsAsFactors = F)
txdb <- TxDb... |
47e1804867ab1955a75d3802322cbc3feeeaf7bc143826ca2599fd9938c3b2f3 | R | 1,434 | 46 |
# # Correlation between Accuracy in the Clock Navigation experiment and IPC
library(ggplot2)
library(gridExtra)
library(ggpubr)
library(rstatix)
library(ggprism)
library(car)
# SC -----
# Load the Df
SC_Clock_IPC<-read.csv('/Volumes/ROBBOT/Projects/GridMagination/Tables/03_Correlations/SC_AccuracyCorrelations.cs... |
1641636e68924aa90bc3d7036c11785b357a5a62d59ae23c59ee024ceb570557 | R | 1,442 | 52 | # Hierarchical clustering
#
# Hierarchical clustering to explore patterns of electrophysiological variables
#
# Author: Rafael Naime Ruggiero 2022
# Load required libraries
library(MASS)
library(tidyverse)
library(RColorBrewer)
library(gplots)
# Set working directory
setwd("C:/Users/Rafael Ruggiero/Docu... |
16c901b231f449bc8ee2a0fe9e27412c2bab2d3e8c2ba845a94df5ee707d24ec | R | 1,444 | 32 | #' pGLS plot of a particular grade
#'
#' Plots the pGLS model of a particular subset of a comparative dataset
#' @param Yvar column name of the dependent variable
#' @param Xvar column name of the independent variable
#' @param data a vector of tip values for species; should be in the same order as tiplabels in the tre... |
22498c01eb3ff40f66e6ca3958ca07bb90bd4ab15e6340c427b49a95cb9e836e | R | 1,455 | 51 | #!/usr/bin/env Rscript
# This script uses Bioconductor tools to download ensembl annotation data,
# create a transcript2gene mapping file for use in salmon alevin,
# and a output a list of mitochondrial genes for use in QC.
# Currently hard coded to Ensembl v100 Homo sapiens, mostly because
# AnnotationHub is not as... |
c6d82105d8c82ca556747f92aedae53a615882c749a3c4a9954efd2498c8e5d4 | R | 1,459 | 49 | ## Libraries
library(here)
library(dplyr)
library(parallel)
library(here)
library(tidyverse)
library(DT)
pname <- "AMC"
cname <- "TSC"
## get data
samples <- listSamples(projects = pname , condition = c("TSC_control")) %>%
mutate(cohort = "Control") %>%
bind_rows(listSamples(projects = pname , condition = c("TSC_... |
26835a228557d5c603f09928bb38f1c3f9dbc3e0690f2f532cce16d16f55db8f | R | 1,462 | 41 | library("ggplot2")
rds = list.files("data/rds", pattern = "\\.rds$", full.names = TRUE)
names = c("Jelenia Góra", "Katowice", "Kraków Zachodni", "Kutno",
"Nowy Targ", "Świnoujście", "Tomaszów Lubelski", "Toruń")
names2 = c("Jelenia_Gora", "Katowice", "Krakow", "Kutno",
"Nowy_Targ", "Swinoujscie",... |
098f30d3d46573aa557f9d04bccabceb9ea80f9a3b72133cde88dd3ff40b6b37 | R | 1,470 | 45 | #####################################################
######## Outlier Removal (+/-1.5*IQR) ############
#####################################################
#This script should be run in the directory contianing
#the raw files (starting with "AllCond_Animal...")
#deposited in the companion file repositry.
... |
1c42c980c38bde74a197dc2a648c98accf11ba0319c182803028ae8750c2daf5 | R | 1,473 | 24 | library(umap)
Control_K27me3_all_peaks<-read.csv("E:/R files/Control_K27me3_all_peaks.csv",row.names = 1)
Control_K27me3_all_peaks<-as.matrix(Control_K27me3_all_peaks)
Control_K27me3_all_peaks<-t(Control_K27me3_all_peaks)
Control_K27me3_all_peaks_umap<-umap(Control_K27me3_all_peaks)
#UMAP dimension reduction to 50... |
7be4b1998d1d072b4826457f35a80e13e62445ace6ed7ba9971cf6f3dff24efb | R | 1,473 | 56 | library(Spaniel)
library(scran)
library(scater)
library(batchelor)
library(dplyr)
library(Seurat)
library(SCFunctionsV3)
library(cowplot)
library(RColorBrewer)
library(SCFunctionsV3)
library(harmony)
allData <- "/data/rachel/Linda_Lako/Spatial/spaceranger_outs_2021_102"
tissue <- "AD3"
projDir <- paste0("/data/rache... |
57af623a1a7121cc7bcae7bd016827898fee8ecf910f54377c3bbcb5507b73fd | R | 1,481 | 48 | SparseCCA <- function(x,y,v,penaltyx, penaltyz,niter,trace = FALSE, upos, uneg, vpos, vneg){
vold <- rnorm(length(v))
u <- rnorm(ncol(x))
for(i in 1:niter){
if(sum(is.na(u))>0 || sum(is.na(v))>0){
v <- rep(0, length(v))
vold <- v
}
if(sum(abs(vold-v))>1e-6){
... |
d947db9f825e5b2a8191a5cc9a99ba843d414f4e18b1e51364be7584e011adda | R | 1,489 | 44 | ## Libraries
library(here)
library(dplyr)
library(parallel)
## Load files
load(here("Data/04_QC/Preprocessing.rda"))
sampleInfo <- sampleInfo %>%
filter(PA_Diagnosis %in% c("Control", "FCD 2b") &
Area_of_Resection %in% c("Frontal", "Occipital", "Parietal", "Temporal")) %>%
mutate(log10_seizure_freq = lo... |
b76671b0549a3f90ec60f0df867da68478b6395a7474601b8b99b0cca00e3692 | R | 1,499 | 41 | #' Modify a VCF file to get a row for each variant for each sample and a GT column
#'
#' This function modify a loaded VCF (load_vcf) file.
#'
#' @param VCF_DATA the loaded VCF file
#' @return modified dataframe
model_vcf <- function(VCF_DATA){
VCF_COLNAMES <- colnames(VCF_DATA)
### extract sample names from VCF_DA... |
ff4e80c8a00542a9de4534a9eed9fb9603b3683579461e0389125a8904912676 | R | 1,506 | 42 |
############################################################################################################
########## R script for:
##########
########## Statistical indices of masculinity-femininity: A theoretical and practical framework.
##########
########## Marco Del Giudice
##########
########## Script for Fi... |
3285d83b1f6f15b26e36c76ab5ea72299c47f27a3c3839d92bb38be3d0eceec9 | R | 1,518 | 55 | library(harmony)
library(Seurat)
library(cowplot)
library(SCFunctionsV3)
library(dplyr)
library(tidyr)
library(stringr)
library(tidyverse)
library(DoubletFinder)
library(pheatmap)
library(ggplot2)
## read data
sObj_Mapped <- readRDS("rObjects/AD3_individual_reference_map.rds")
## cell cycle genes
s.genes <- cc.ge... |
1c75058bf23b82ec1ee9956e0057c1019869107ba680583131929a65b97aedeb | R | 1,524 | 56 | library(Spaniel)
library(scran)
library(scater)
library(batchelor)
library(dplyr)
library(Seurat)
library(SCFunctionsV3)
library(cowplot)
library(RColorBrewer)
library(SCFunctionsV3)
sce_output <- ""
batch_corrected_seurat_output <- ""
sampleInfo <- data.frame(id = samples,
tissue = "AD3",... |
181e8915e6fa0b2097fafac57104c4046bc12e84559799df69c9951fcddcb75e | R | 1,548 | 24 | # Independently recompute weighted empirical distributions from native inputs.
options(warn=1)
a <- commandArgs(trailingOnly=TRUE)
root <- if(length(a)) normalizePath(a[1]) else normalizePath('.')
d <- read.csv(file.path(root,'qa/distribution_native_inputs.csv'),check.names=FALSE)
s <- read.csv(file.path(root,'results/... |
891e1faf77abaaba7ad246b636aeeaf5ed4404e61203000ca8a9434342ea1ea5 | R | 1,554 | 47 | ## Libraries
library(here)
library(dplyr)
library(parallel)
## Load files
load(here("Data/04_QC/Preprocessing.rda"))
sampleInfo <- sampleInfo %>%
filter(PA_Diagnosis %in% c("Control", "HS") &
Area_of_Resection %in% c("Hippocampus", "Temporal")) %>%
filter(Library_Prep_batch != "A") %>%
mutate(log10_se... |
eecdbf40ee4637b608d8d48ce4cc32bae0df56655fdbe93ebbf5492f91f076b3 | R | 1,576 | 35 | library(Seurat)
library(ggplot2)
sporo=readRDS("Downloads/sporo_dataset.RDS")
sporo@meta.data$batches = ifelse(grepl("2_", rownames(sporo@meta.data)), "batch2", ifelse(grepl("3_", rownames(sporo@meta.data)), "batch3", ifelse(grepl("4_", rownames(sporo@meta.data)), "batch4", "batch1")))
saveRDS(sporo, "sporo_dataset_fin... |
240d0fafb00a7ac9a56d89ad164612ee7b854e50308395f4fc09db87e178514c | R | 1,577 | 31 |
# R-code to recreate the Bayesian analyses performed in JASP for our manuscript "Working memory signals in early visual cortex do not depend on visual imagery" by Weber, Christophel, Görgen, Soch, Haynes.
# Data is stored in "data-export.csv"
# Bayesian t-test for behavioral precision btw. weak and strong imagers.
# ... |
d85fb0bc6c088d5f16aab4f168cae4014fc84b31282d61682947d9cd4707c355 | R | 1,587 | 58 | library(BSgenome.Hsapiens.UCSC.hg38)
library(tidyverse)
library(Signac)
library(Seurat)
library(JASPAR2020)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(TFBSTools)
library(future)
plan()
plan("multicore", workers = 8)
#increase the maximum memory usage
options(future.globals.maxSize = 30 * 1024 ^ 3) # for ... |
a579e9a318ba5c0c3d8c3e6f52dbd7731d5b62e75be922866ce300736cc46cec | R | 1,605 | 55 | ---
title: "Edge labels"
author: "Audrey Luo"
output: html_document
---
# make df's for atlas-specific Edge names
```{r}
# function for creating df of edge names: remove self-connecting edges
# @param nrows_var A numeric var of rows in parcel_labels
# @param atlas_edges An empty vector to be populated by edge nam... |
9fd0ebe1a28ad7d00e148d7bee8f0262638361fa2c55955176eb26094c1fdba1 | R | 1,607 | 44 | library(ggplot2)
library(fgsea)
plotEnrichment2<-function (pathway, stats, gseaParam=1, ticksSize=0.2,linecol=NULL,colticks="#00000099")
{
rnk <- rank(-stats)
ord <- order(rnk)
statsAdj <- stats[ord]
statsAdj <- sign(statsAdj) * (abs(statsAdj)^gseaParam)
statsAdj <- statsAdj/max(abs(statsAdj))
pat... |
cf09c7d2a97a2166c595e731abbaf34429bd12509608ce1332b0b2c353ff9303 | R | 1,607 | 40 | ### DATA
x <- read.table("GSE83139_tbx-v-f-norm-ntv-cpms.csv", header=T, stringsAsFactor=FALSE)
fDat <- x[,1:7]
x<-x[,-1*c(1:7)]
rownames(x) <- fDat$transcript
rownames(x)[!is.na(fDat$gene)] <- fDat$gene[!is.na(fDat$gene)]
rownames(fDat) <- rownames(x)
### ANNOTATIONS
ann1 <- read.delim("GSE83139-GPL11154_series_matri... |
75796769c14326ee517a96cf904c6d35e2ba465246bae4d3e74c7f7dddc4fd3c | R | 1,618 | 58 | setwd('/work/AnneSophiaHartebrodt#6698/ms-project/')
require(rhdf5filters)
require(rhdf5)
require(Rhdf5lib)
renv::activate('/work/AnneSophiaHartebrodt#6698/ms-project/r-single-cell')
renv::repair()
set.seed(11)
require(rlang)
require(Seurat)
library(anndata)
require(data.table)
require(SingleCellExperiment)
require(sc... |
8c5ef78bbd621d83eb591e1ef1c8b9ee83175a1c6ea26ef5a5acd4adc1de001a | R | 1,623 | 51 | # Load necessary libraries
library(Seurat)
library(dplyr)
library(ggpubr)
library(rstatix)
library(ggplot2)
# Load dataset
df <- readRDS('Module_meta_4.rds')
df$Outcome <- factor(df$Outcome,
levels = c("Unknown", "HC", "S", "FT"))
# Define modules to analyze
modules <- c("T.cell.differentiation",... |
87be6875b19d1c1e6dff5456c1f43734991a9f3234cbd141a868bbeb350ad82c | R | 1,642 | 46 | ### DATA
d <- read.table("data.txt", header = TRUE, stringsAsFactors = FALSE)
### ANNOTATIONS
genes <- data.frame(genes = d[,1])
# gene proper gene names from Ensembl Biomart
real_genes <- read.table("mart_export.txt", header=T)
real_genes$genes <- toupper(real_genes$gene_name)
res <- merge(real_genes, genes)
d <- d[d... |
dbce64f465b1f083582839da096a8eb182f9f8deca6a883b61b6fe8bc8d21c83 | R | 1,676 | 38 | library(ggplot2)
library(dplyr)
df <- read.csv("Supp_Table2.csv")
df$Clusters <- factor(df$Clusters,levels= c("C0", "C1", "C2", "C3", "C4", "C5", "C6",
"C7","C8", "C9", "C10", "C11", "C12"))
dpi = 300
png(file = "Fig4D_Severity_cluster_density.png", width = 6, height = ... |
e86697100475f31dbe789d5efcb0f2a69b5879d46620f98488c62512ed4850a9 | R | 1,683 | 46 | # required external packages for SIMLR large scale
library("Rcpp")
library("Matrix")
library("pracma")
library("RcppAnnoy")
library("RSpectra")
# load the igraph package to compute the NMI
library("igraph")
# load the palettes for the plots
library("grDevices")
# load the SIMLR R package
source("./R/SIMLR_Large_Scal... |
b6384189e3221d3cd8754540ba4b110b66c4805b22c76ac57c12fbb511d02a5d | R | 1,689 | 61 | library("ranger")
library("yardstick")
set.seed(1)
df = readRDS("data/dataset.rds")
n = round(0.7 * nrow(df))
trainIndex = sample(nrow(df), size = n)
train = df[trainIndex, ]
test = df[-trainIndex, ]
rm(df, trainIndex)
num_class = length(unique(train$class))
## tuning parameters
num.trees = c(100, 500, 1000)
mtry = c... |
2737b93fa269fa2707726086ca8b8ab358e81d38d98f162357ce004c3fda9897 | R | 1,692 | 67 | # investigation of the association between Foodintake and different treatments
# Test
ancova_post_hoc(file = "../../HKFI-FoodIntake/code/ABCdata.tsv",
name = "Figure1C")
ancova_post_hoc(file = "../../HKFI-FoodIntake/code/ABdata.tsv",
name = "Figure1B")
# investigation of the associ... |
86fdfae57ca8295727a6c8330f74fa6f49cb6fb020829f17e9b15fb497805acc | R | 1,699 | 39 | library(tidyverse)
library(readxl)
###########data processing###############
# load data and set some as factors
filepath <- "pathTomergedPairedData.csv"
data <- read.csv(file = filepath)
data <- data %>% mutate(Condition = recode(Condition,"pCaMKII" = "Ex-Ex", "PV" = "Ex-PV"))
cols <- c("Stain_post","Stain_pre","We... |
761a9ec85b2187c8e3ec457693a349345c2f62ed96fc5b5008c3b2bdf0f2055d | R | 1,700 | 47 | library(ComplexHeatmap)
library(msigdbr)
library(fgsea)
library(tidyverse)
# Load data
res_diet <- readRDS("data/008/002_res_tbl.rds")
res_kd <- readRDS("data/002_res_tbl.rds")
res <- res_kd |>
inner_join(res_diet, by = "ensembl", suffix = c("_kd", "_diet"))
res |>
filter(padj_kd < 0.05, padj_diet... |
c469336f6ae3f36cf0f57b95842493e10f68d208515051575ddd19ffe5c35912 | R | 1,732 | 38 | library(Seurat)
library(ggplot2)
# Assuming dat0 is your Seurat object
dat0 <- readRDS("PlateletsOnly_cluster_Anno.rds")
DefaultAssay(dat0) <- "RNA"
# Assuming marker_Survival and marker_Fatal are predefined
# Ensure Survival_list and Fatal_list are correctly formatted as lists of feature vectors
Survival_list <- c("... |
c106a9d97a80f56e0eea57364a763378e5ba5ba5b1d101ad84cf74ca7bc829ae | R | 1,758 | 46 | #############pre#######
rm(list = ls())
options(stringsAsFactors = F)
library(venneuler)
library(ggplot2)
library(ggpubr)
library(viridis)
library(hrbrthemes)
##########################################MIA_birth_mom#######################################
specie <- c(rep("enh_neun+",2),rep("enh_neun-",2),
rep... |
58aec489fd6af56cb17c37439bb9cefa630564a872d5a214d9b42c895d8467fe | R | 1,766 | 52 | library("stars")
library("ggplot2")
## vector maps must first be rasterized to run the code below
class_tab = read.csv("data/classification_table.csv")
files = list.files("data/raster_maps", pattern = ".tif", full.names = TRUE)
class_vec = integer()
for (f in files) {
class = read_stars(f, proxy = FALSE)
class[[1... |
f601f540e355c61264cd50de7a2736ffbf73982f1c347fb7ec10e33b5fa1aed8 | R | 1,791 | 55 | #===============
# author:
# Li Tang
# Central South University
# tangli_csu@csu.edu.cn
#===============
# Estimate density
#===============
library(foreach)
library(doParallel)
cores=detectCores()
cl <- makeCluster(cores[1]-1) #not to overload your computer
registerDoParallel(cl)
args = commandArgs(trailingOnly=... |
5366bbec923b9390b5de7235ca83ee10599aeb9edb719e193c713bb257748366 | R | 1,799 | 53 | #!/usr/bin/env Rscript
options(width=200)
#########################
#
# Loading Data and Libraries
#
#########################
args <- commandArgs(trailingOnly=TRUE)
cliargs <- args[-(1:2)]
options('warn'=1) #report non-convergences, etc
library(lme4)
# library(languageR)
library(optimx)
# library(ggplot2)
#The bel... |
ebb5e4199f7a8af62b2208dd0996559b39a3ed0db2586ac661a76b535a11a432 | R | 1,801 | 43 | ### This code creates fig. S1e-g
dev.off()
rm(list=ls())
library(ggplot2)
library(gridExtra)
#load all necessary input data files
Data_149ind <- read.csv("WGS_149ind_assignment.csv", sep=",", header=T)
evec <- read.table("populations_PCA.eigenvec")
eval <- read.table("populations_PCA.eigenval")
evec1.pc <- round(ev... |
68ece103eed1cfe524a3af72fb45b25d9da0e4dbd917d169f0f708866c52e211 | R | 1,815 | 75 | CompareMutants <- function(ReadsTable, WhereAreInocula, outputfilename) {
ReadsTable <- read.csv(ReadsTable, row.names = 1)
RefTable <- ReadsTable[,WhereAreInocula]
SampleTable <- ReadsTable[,-WhereAreInocula]
RefVector <- rowSums(RefTable)
RefVectorProp <- RefVector / sum(RefVector)
DivideWithinCo... |
43e8d5fa9a95f8b5b43a3ca50355adfb4f6a0879a5c30a46805b43e8f1fc7a8c | R | 1,820 | 57 | #' Creates a Q-Q plot
#'
#' Creates a quantile-quantile plot from p-values from a GWAS study.
#'
#' @param pvector A numeric vector of p-values.
#' @param ... Other arguments passed to \code{plot()}
#'
#' @return A Q-Q plot.
#'
#' @keywords visualization qq qqplot
#'
#' @importFrom stats ppoints
#' @import utils
#... |
6f5ac266954fd928ebf7f8e905ee517b2fac8bb6d05d4c543658ab98968e9971 | R | 1,824 | 68 | #clear environment
rm(list=ls())
## set working directory
wdir = "C:/Users/wurmf/Dropbox/GitHub/2023_Wurm_SurpriseMinimization/"
setwd(wdir)
library(lmerTest)
library(lme4)
##########################
# model-free regression #
##########################
file2read = 'staybehavior_exp1-beh.csv'
#file2read = 'staybeh... |
fd295bd30851692e906462efa26e15c4ab30b71cbce973d149d81d03bcf8c1d7 | R | 1,838 | 43 | # Load necessary libraries
library(Seurat)
library(ggplot2)
library(cowplot) # For theme_cowplot
library(reshape2) # For melt function
# Load the Seurat object
identity <- readRDS('PlateletsOnly_cluster_Anno.rds')
# Preparing the data for plotting
# Transposing and selecting features for simplicity
features <- c('MY... |
286620072f3929903742e72beb9fa3b2951ddb507000741ba5d353da84a522a0 | R | 1,856 | 94 | ---
title: "Annotated Eye and Retina"
output: html_notebook
---
## Integration script
integration_from_eye_and_retina_remove_red_blood_cells_increaseMT.Rmd
## Filtering applied :
Any cells which expressed haemoglobin genes were removed from the analysis.
The mitochondrial threshold 10%
Any samples with more th... |
36b303a0dc1fcd70ddf00d74b29bb2fc448b689f9c4f2e4ebc6ff5edf954a21c | R | 1,857 | 50 | library(dplyr)
library(VennDiagram)
library(readr)
library(tidyr)
library(ggplot2)
library(Cairo)
# Function to process DEG files and write filtered results
process_DEG_files <- function(file_path) {
name <- gsub(".txt", "", basename(file_path))
degs <- read_delim(file_path, delim = "\t", col_types = cols(), .def... |
e3d0396a36ea51d7707706040368d46587783205bfa255a7acd9097d4b4db9eb | R | 1,857 | 67 | ### DATA
# human1
h1 <- read.csv("GSM2230757_human1_umifm_counts.csv", header = T)
rownames(h1) <- h1[,1]
labels_h1 <- as.character(h1$assigned_cluster)
h1 <- h1[,4:ncol(h1)]
h1 <- t(h1)
# human2
h2 <- read.csv("GSM2230758_human2_umifm_counts.csv", header = T)
rownames(h2) <- h2[,1]
labels_h2 <- as.character(h2$assigne... |
aff15f98eb9bc2d80a9cb783654b19f29ba445dd1e12eb5cf819459e37c8967b | R | 1,867 | 36 | #################
### Load data ###
#################
rm(list=setdiff(ls(),c('params','grp','treatment','injection.site')))
print(grp)
print(treatment)
basedir <- params$basedir
setwd(basedir)
savedir <- paste(params$opdir,'diffmodel/retrograde/',paste0(injection.site,collapse='-'),'/',sep='')
dir.create(savedir,recur... |
42cebd1150f6829833833ce0628a3142e77f2116f29a6c074b1abc256e4471cb | R | 1,869 | 63 | #!/usr/bin/env Rscript
warnings()
library(ggplot2)
cliargs = commandArgs(TRUE)
# Code for relative path from Suppressingfire on SO
initial.options = commandArgs(FALSE)
file.arg.name <- "--file="
script.name <- sub(file.arg.name, "", initial.options[grep(file.arg.name, initial.options)])
script.basename <- dirname(sc... |
43120a8e107b8bd606b06c5466345b6e3178df4dae0b976a2260420885a7fb6e | R | 1,870 | 70 | library("xgboost")
library("yardstick")
set.seed(1)
df = readRDS("data/dataset.rds")
n = round(0.7 * nrow(df))
trainIndex = sample(nrow(df), size = n)
train = df[trainIndex, ]
test = df[-trainIndex, ]
rm(df, trainIndex)
# class must be numeric, start with 0 and numbering must be continuous
train$class = as.numeric(tr... |
55fc5a0925fce1b03bae5e6ba80e1d41afaf056abb848aca57f91cb0d785b384 | R | 1,872 | 59 | #SR_TAU_CELL
.libPaths("/data/user/jfisher7/.conda/envs/SR_TAU_CELL/lib/R/library")
#make sure the environment is clean
rm(list=ls())
args <- commandArgs(trailingOnly = TRUE)
num1<- args[1]
num2<- args[2]
library(MASS)
library(parallel)
library(signatureSearch)
library(tidyverse)
library(HDF5Array)
library(Summari... |
fea2fdf54e425c92110407c9b53f15f9899417c033a7f8c769fd8e72caf74650 | R | 1,872 | 40 | #' return a GRanges object with gene symbols annotated
#'
#' This function takes in a GRanges object and returns it with genes annotated
#'
#' @param VCF_DATA GRanges object
#' @param COLUMN (optional) the column name of GENES38 to annotate (default: "symbol")
#' @param STRING (optional) get each gene values as comma s... |
8ac5b03e1fd7f3b995cda14cfe068cd8bb1dd03286f5fce68c38b2f98d3a4b14 | R | 1,883 | 39 | # Figure 3A
rm(list = ls())
require("writexl")
require("readxl")
library("dplyr",quietly=T)
library("ggplot2",quietly = T)
library("ggtree",quietly = T)
library("ape")
library("aplot")
library("ggsci")
setwd("Source_Data/Figure_3/Figure_3A/")
bed.s.df<-read_excel("Figure_3A.xlsx")
Figure_3A.tre<-read.tree("Figure_3A.... |
441bb6f6794d69de335536a156665f7f717bc17f9e9561e93ad46cc20e0fe092 | R | 1,891 | 55 | ## Manhattan plots for MOSTest project
#install.packages("qqman")
library(ggplot2)
library(data.table)
library(tidyverse)
path = "/path/"
# Load results
fMRI = fread(paste0(path,"rsfmri/rsfmri_eig0.most.orig.csv.gz"))
sMRI = fread(paste0(path,"T1/T1_eig16.most.orig.csv.gz"))
dMRI = fread(paste0(path,"diffusion/diff... |
0d9a4cbe4426d2c0785c68ab25d33ba13c61f12b4734f0615eb4b5862873d96d | R | 1,892 | 46 | # Figure 2BC
rm(list = ls())
require("writexl")
require("readxl")
library("dplyr",quietly=T)
library("ggplot2",quietly = T)
library("ggtree",quietly = T)
library("ape")
library("aplot")
library("ggsci")
setwd("Source_Data/Figure_2/Figure_2BC/")
ERVK_proportion_node<-read_excel("Figure_2BC.xlsx")
### Figure_2B
p.Figu... |
4be05e76921b851f84e411e8952a0541201bcf82f9f99ae0a99301f5a185864f | R | 1,905 | 57 | test_that("edgeExtract() works", {
bam.path <- test_path("fixtures", "sample.bam")
edges <- edgeExtract(c(a = bam.path, b = bam.path))
expect.edges <- readRDS(test_path("fixtures", "sample.bamEdges.rds"))
expect_true(
all(names(edges) == c("a", "b"))
)
expect_true(
all(
... |
bc1ffc1f62bb4e348420a323fec0c4e511478e0d8c527b22bebceafcda25043e | R | 1,924 | 41 | #' Adapt samples specified in COLOR_SAMPLE
#'
#' This function iterates through COLOR_SAMPLE and checks it
#' It the returns a little DF of 2 columns with each specified sample and corresponding group
#'
#' @param COLOR_SAMPLE list with groups and names of sample to color in the final plot
#' @param SAMPLE samples to p... |
211a40dcab559cffbf00267c500994ec1423bd5afde285abf7099410d5a68202 | R | 1,949 | 45 | library(data.table)
require(plyr)
library(ggplot2)
library(RColorBrewer)
rm(list=ls())
setwd("/Users/elleke/SURFdrive/Projects/MOSTest/Results/Discovery/Pleiotropy/Genes/Celltypes/")
# Bhaduri et al
Bhaduri_Cross_modality = fread("Bhaduri_FetalBrain/MOSTest_Bhaduri_FisherExact.txt")
colnames(Bhaduri_Cross_modality)[1... |
56791b3bd78f66e2a84ce2dd89b08f721b54c1cbfcb48f1d84d58cbe27024c6d | R | 1,953 | 37 | #' autocorrelated_genes()
#'
#' Filtering gene expression matrix by spatially auto-correlated genes and correction for multiple-testing
#'
#' @param alternative a character string specifying the alternative hypothesis, chosen from one of "greater", "two-sided" or "less". Default is "greater"
#' @param adjustPv Adjusted... |
b9d0eb363077b1d8bda1bf161fd272a3ec7e094ab5665d466b27ee797b66f67c | R | 1,964 | 53 | require(Seurat)
FindVariableGenesSeurat <- function(data, x.low.cutoff = 0.1, x.high.cutoff = 8,
y.cutoff = 1, y.high.cutoff = Inf, num.bin = 0,
binning.method = "equal_width", sort.results = TRUE,
display.pr... |
1c95360e7da0ed80d5ec2c5358514c1f25d093d2aea4efdd3611536018c655ee | R | 1,975 | 68 | library(dplyr)
library(Seurat)
print("Add Metadata ...")
metadata <- read.csv(paste0("csvFiles/metadata"))
# Run 1
runNo <- "run1"
sampleInfo <- readRDS(paste0("rObjects/sampleInfo_", runNo, ".rds"))
sampleInfo <- sampleInfo %>% left_join(metadata)
sObj_Filtered <- readRDS(paste0("rObjects/sObj_Filtered_", runNo,... |
611bb2fd409e3d0668d1d60c62be7b19a187d186558704522f9e7f630fc75cef | R | 1,977 | 35 | # Code for making dotplot from data.combined
library(Seurat)
library(tidyr)
library(ggplot2)
library(patchwork)
library(ggpubr)
dotPlotMarkers <- read.csv("Markers for Dot plot_Figure 1.csv", header = T, sep = ",", na.strings = "")
colnames(dotPlotMarkers)[1] <- gsub('^...','',colnames(dotPlotMarkers)[1]) ... |
f73d356857946755f709e5cad2f6868a52eaadfd004b2b65c32725936c08ccfb | R | 1,986 | 85 | ---
title: "`r thetitle`"
output:
html_document:
df_print: paged
---
```{r}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r libaries}
library(dplyr)
library(Seurat)
library("ggplot2")
library(monocle3)
library(patchwork)
library(SeuratWrappers)
library(harmony)
library(ggplotify)
libra... |
664461e7ad0c33884ab8524e6609c823fe140f052d4316ba485ec9194fcedbf4 | R | 1,988 | 75 | suppressPackageStartupMessages({
library(Seurat)
library(stringr)
library(sctransform)
library(future)
require(scales)
library(RColorBrewer)
library("readxl")
library(dplyr)
library(dendextend)
library("graphics")
})
### figure 5 + supplements
# import the Dopaminergic nuclei (mDA) dataset.
sobj ... |
53f52be346eeabbdfb7a6860f914e1196c519c439c54f624ba83fc170d227635 | R | 1,997 | 13 | HG37_FASTA_FILE <- '/home/enrico/columbia/FASTA/hs37d5.fa'
CHR_ORIGINAL_NAMES <- c( "1 dna:chromosome chromosome:GRCh37:1:1:249250621:1", "2 dna:chromosome chromosome:GRCh37:2:1:243199373:1", "3 dna:chromosome chromosome:GRCh37:3:1:198022430:1", "4 dna:chromosome chromosome:GRCh37:4:1:191154276:1", "5 dna:chromosome ... |
91959099ffb9ac4444b112b887e87c5dc5d76419b9f01aace257a5f1a1c22d54 | R | 2,003 | 69 | library(BSgenome.Hsapiens.UCSC.hg38)
library(tidyverse)
library(Signac)
library(Seurat)
library(JASPAR2020)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(TFBSTools)
pfm <- getMatrixSet(
x = JASPAR2020,
opts = list(collection = "CORE", tax_group = 'vertebrates', all_versions = FALSE)
)
motifs <- sapply... |
4337b9fbf3d8b816950100ad4cdff6252500c94127fa9a4bd376b2e92c925ec0 | R | 2,020 | 27 | ---
title: "Home"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
1. System requirements
- All software dependencies and operating systems (including version numbers) are available in the [methods section][methods]
- Archived version of thi... |
4d9c16d9b6eff26b2ce4a023d807b252d3dca44b4716f0ff3d1b883c100880df | R | 2,065 | 49 | #' get_model_stats()
#'
#'Get Model Statistics
#'
#' This function calculates the model statistics for a given effect plot and data frame.
#'
#' @param effect_plot The effect plot containing the predicted values from different models.
#' @param dt The data frame containing the observed values.
#' @param models A charac... |
306b0e689dc9b80e48e542e7d21dda9e9cc96df2fd464b9a9f00af4378e7c765 | R | 2,073 | 52 | library("stars")
library("ggplot2")
maps = list.files("data/raster_maps", pattern = "\\.tif$", full.names = TRUE)
rds = list.files("data/rds", pattern = "\\.rds$", full.names = TRUE)
df_output = data.frame(x = double(), y = double(), var = factor(), class = double(),
class2 = factor())
freq_out... |
1a6cf921a65092c07209edf384f7fab7b6ef3b511278683f6373a4db0cce3983 | R | 2,079 | 67 | run_DAseq_comparison <- function(object,
groupA = "",
groupB = "",
labels.1 = "",
labels.2 = "",
path = "",
outputname =... |
3175cc121ec49c10a63bf0c3798fc5825020cc0b43b6371fe7a498a9d69cf1b5 | R | 2,081 | 56 | ## Correlation analysis: all-to-all correlation between variables
#
# Vinding, M. C., Eriksson, A., Low, C. M. T., Waldthaler, J., Ferreira, D., Ingvar, M.,
# Svenningsson, P., & Lundqvist, D. (2021). Different features of the cortical sensorimotor
# rhythms are uniquely linked to the severity of specific sympt... |
46cc34765e40e98dfb9a46c923b3511338c7cb2680be3602ad175dd048f096be | R | 2,099 | 40 | #' Create a list of SingleCellExperiment objects using scpcaTools::import_quant_data.
#'
#' @param info_df data.frame containing information for each sample to create a SingleCellExperiment object for.
#' Each row corresponds to an individual sample and columns contain information for that sample:
#' data_dir - ... |
76c6198bfd99e3db5c63bbd4798f44998658b9784ddcc10fb542c39ecfdb13ca | R | 2,107 | 77 |
combinereplicates <- function(listoffiles, pvalcutoff, fccutoff, outputfilename) {
combinetolist <- function(filename) {
Finaltable <- read.csv(filename, row.names = 1)
}
combinedlist <- lapply(listoffiles, combinetolist)
combined <- unlist(combinedlist)
numrows <- dim(as.data.frame(combinedlist[1]))[1]
dim(combined... |
58ee7e28158eb3a0b4ee3d1d689cc1545bd75a6d3f9e4cc340139822f6e87218 | R | 2,113 | 93 | ### _go.R ###
suppressPackageStartupMessages({
library(AnnotationDbi)
library(org.Hs.eg.db)
library(org.Mm.eg.db)
library(org.Rn.eg.db)
library(clusterProfiler)
library(GO.db)
})
gene_loader <- function(path){
stopifnot(is.character(path))
return(rownames(read.csv(path, header=T, row.... |
7eed5f5043c9626a724aef4610cc2b46c680146b76e274c0f47f7bc43c8ea888 | R | 2,149 | 48 | ################
#load libraries#
################
library(Seurat)
library(ggplot2)
library(utils)
library(DESeq2)
library(grDevices)
library(cowplot)
library(dplyr)
library(patchwork)
###########################
#Basic pipeline for Seurat#
###########################
no_CDSUTR_Sporo_FUGI_R_D7465034=readRDS("no_CDSUT... |
4b55338abda646baf29b460b580b573f698ceee2bd815cc509dacbcdc8966d7b | R | 2,151 | 58 | ### DATA
# http://genome-tech.ucsd.edu/ZhangLab/index.php/data/epigenomics-and-transcriptomics/sns/
# Lake et al. (2016) Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science. 352 (6293): 1586-1590
# Own website
#Prefrontal cortex - BA10
#Frontal cortex (eye fields) - BA8... |
9895ecd6eb30cdd38189747464572610378e3d7926bb8a61f3bbf52dd08a7afa | R | 2,180 | 51 | #' Import Alevin-fry and Spaceranger output for Spatial Transcriptomics library into a SpatialExperiment object.
#'
#' @param fry_dir Path to alevin-fry outputs directory.
#' @param spaceranger_dir Path to folder output by Spaceranger.
#' @param sample_name Sample id.
#'
#' @return SpatialExperiment object with shar... |
98841ba77904fedd587822963b987d1f26aba15383d9019446ad45f9b64d13a1 | R | 2,191 | 66 | args = commandArgs(TRUE)
report = read.table(args[1],header=T)
indextable = args[2]
output = args[3]
if(indextable != "None"){
indices = read.table(indextable)
}
#This code is directly adapted from the picard source code: http://picard-tools.sourcearchive.com/documentation/1.25-1/classnet_1_1sf_1_1picard_1_1sam_1_1D... |
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