sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2ae98930bb603f5cd8ad01e27d2a67dd3e93d9244a00e380a90f49e32fc5a966 | R | 2,330 | 59 | ###########################################################################
## Author: Youtao Lu <luyoutao@sas.upenn.edu>
##
## Copyright (c) 2021-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2021-2023, James Eberwine, Perelman School of Medicine, University of ... |
b205648ca3ec14708f51ee01dd388928e157b4775e225cf9523f93ff98201349 | R | 2,335 | 48 | # Sconda conda activate TensorFlow_CPU_01; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/results/cross_validation_allsamples; R
#!/usr/bin/R
options(width=240)
library(ggplot2)
library(viridis)
wkdir = '/mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN... |
9f3f492dc123bd272dfb1b75b2ff25f3020c470bf29cfae6e4a7c374187f75d3 | R | 2,343 | 76 | #' Run factR workflow
#'
#' @description This wrapper function will perform the core factR workflow which
#' includes:
#' \itemize{
#' \item{1: }{ Building CDS structures }
#' \item{2: }{ Predicting NMD-sensitive transcripts }
#' \item{3: }{ Translating open-reading frames }
#' \item{4: }{ Quantify sequence conserv... |
e1b3258aee6b9325fad92e008aa04425b8573bf6129f201a00c0f25a19494e20 | R | 2,347 | 49 | #' @templateVar MODEL_FUNCTION choiceRT_ddm_single
#' @templateVar TASK_NAME Choice Reaction Time Task
#' @templateVar TASK_CODE choiceRT
#' @templateVar MODEL_NAME Drift Diffusion Model
#' @templateVar MODEL_CODE ddm
#' @templateVar MODEL_CITE (Ratcliff, 1978)
#' @templateVar MODEL_TYPE Individual
#' @templateVar DATA... |
30974a6c88e5cd8ae9166cd5e265ddf28cafe2a009cea08d362fabd1f666ccc6 | R | 2,348 | 40 | #' Fit generalized linear model under the null hypothesis for imbalanced case-control unrelated samples.
#'
#' The \code{fit_null_glm_Binary_SPA} function is a wrapper of the \code{\link{glm}} function from the
#' \code{\link{stats}} package that fits a regression model under the null hypothesis for
#' imbalanced case-... |
bb18ed69098cd9437caa49f2615ea782978685d713fff34e073628a5343d1913 | R | 2,348 | 76 | library(tidyverse)
library(ggrepel)
limma_coefs <-
readRDS(here::here("results/limma/limma_coefs.Rdata"))
placenta_fetal_tbl <-
limma_coefs %>%
map(.x = ., .f = ~ filter(.x, treatment == "exposureLPS")) %>%
map(.x = ., .f = ~ select(.x, -c(treatment))) %>%
map(.x = ., .f = ~ mutate(.data = .x, gene_id ... |
cc7ff7e0f2b354ed11aefe037c129c60f05552e136abe6e20d68b079a108f434 | R | 2,348 | 61 | #'#################################################################################
#'#################################################################################
#' Prepare GSE179848 dataset
#'#################################################################################
#'#####################################... |
08dbb09257d1e834de9fe3164fabff70ffc80e5508997aef8c6aca1e3ae4d08b | R | 2,353 | 56 | library(readxl)
library(dabestr)
library(dplyr)
library(tidyr)
#1. load excel or csv file containing n-NRF magnitudes "both_sites_magnitudes.csv"
both_sites_magnitudes <- read.csv("both_sites_magnitudes.csv")
#2. create dataframe for Exeter magnitudes separately
exeter_magnitudes <- both_sites_magnitudes[both_sites_m... |
89b3ef0b774bce3da8198ac73122e95809c793921ef3c10e99f7bede4cd9fdd1 | R | 2,354 | 70 | args = commandArgs(TRUE)
eta=as.numeric(args[1])
max_depth=as.numeric(args[2])
gamma=as.numeric(args[3])
min_child_weight=as.numeric(args[4])
nrounds=as.numeric(args[5])
data_folder=as.character(args[6])
sex_split=try(as.logical(args[7]))
BrainAgeFull = function(eta, max_depth, gamma, min_child_weight, nrounds, data... |
c6bed5bde0b482aef94af4ca75f348588b7f6dedff7aee6c5c2517618b33073a | R | 2,357 | 82 | library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
library(dplyr)
library(scatterpie)
library(RColorBrewer)
library(grDevices)
set.seed <- 1234
file_path <- '/data/work/uniPort/Brain_SPOT/'
#-----------------------------------------------------------
# input data for uniPort
#-----------------... |
4a0902df44d36b9d9e3712474ba8c33e9f2da388a9b29e7980cdd2152a3f7d64 | R | 2,360 | 63 | library(ggplot2)
library(tidyverse)
save_plot <- function(p, fn, w, h){
for(ext in c(".pdf", ".svg")){
ggsave(filename=paste0(fn,ext), plot=p, width=w, height=h)
}
}
load_DEG_enrichment <- function(){
return(data.table::fread("clincial_phenotypes_enrichment_analysis_DEGs.tsv"))
}
memDEG <- memoise... |
b958676ff28ba017388b55f69663a2113454f8a988607736e56cfff967dedfe4 | R | 2,363 | 77 | # Adapted from mlbook2_prepare_data.r
# Snijders and Bosker 2012
# This script reads the original data set, does minimal transformations,
# and saves to /data
# Note: this file preserves all information
# read data
library(haven)
library(dplyr)
project <- path.expand("~/Package/mice/multilevel/Snijders")
fn <- file.pa... |
1826b7bce7c0bbfb54e13c1b9789271eefa07e00ecf43fd2291552628c1dc8a8 | R | 2,376 | 68 | #' Evaluate an expression in multiple imputed datasets
#'
#' Performs a computation of each of imputed datasets in data.
#'
#' @param data An object of type \code{mids}, which stands for 'multiply imputed
#' data set', typically created by a call to function \code{mice()}.
#' @param expr An expression to evaluate for e... |
4fe9f262da6c80f9064abf3a4cc6a9f89912bb24f6c884267283a74d8a9a0f5a | R | 2,376 | 51 | #' @templateVar MODEL_FUNCTION cra_linear
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang} <\email{jaeyeong.yang1125@@gmail.com}>
#' @templateVar TASK_NAME Choice Under Risk and Ambiguity Task
#' @templateVar TASK_CODE cra
#' @templateVar MODEL_NAME Linear Subjective Value... |
2ce01c7f522297c2f870ea4eb108a5fcef885a95663f128ffc07364f1e171e0b | R | 2,379 | 49 | #' @templateVar MODEL_FUNCTION igt_pvl_delta
#' @templateVar TASK_NAME Iowa Gambling Task
#' @templateVar TASK_CODE igt
#' @templateVar TASK_CITE (Ahn et al., 2008)
#' @templateVar MODEL_NAME Prospect Valence Learning (PVL) Delta
#' @templateVar MODEL_CODE pvl_delta
#' @templateVar MODEL_CITE (Ahn et al., 2008)
#' @tem... |
460e87f13815330a654341dbd1e64ea2dbf6c26269be61ecdb10b713337195ba | R | 2,382 | 49 | #' Plots highest density interval (HDI) from (MCMC) samples and prints HDI in the R console.
#' HDI is indicated by a red line.
#' Based on John Kruschke's codes.
#'
#' @param sample MCMC samples
#' @param ci_prob A scalar between 0 and 1, indicating the mass within the credible interval that is to be estimated.
#' @pa... |
908e3c98f1cf9b119b6e004e53f436a33f9bdc8b864b04b494ba63a47cf6a22e | R | 2,383 | 47 | #' @templateVar MODEL_FUNCTION banditNarm_delta
#' @templateVar CONTRIBUTOR \href{https://github.com/cheoljun95}{Cheol Jun Cho} <\email{cjfwndnsl@@gmail.com}>
#' @templateVar TASK_NAME N-Armed Bandit Task
#' @templateVar TASK_CODE banditNarm
#' @templateVar TASK_CITE (Erev et al., 2010; Hertwig et al., 2004)
#' @templa... |
7c59247db4d55922991b87cc57f2b2508c7dcb93993d86e447957e530985479e | R | 2,388 | 67 | #2 Merge data and clustering
# Setup---------------------------------------------------------------
rm(list = ls()) ; gc() ; gc()
set.seed(1234)
wdir <- 'set your working directory'
setwd(wdir)
save.path <- 'object folder path'
# load library
easypackages::libraries(c('Seurat','tidyverse','harmony'))
# merge object... |
382632ccfb51f586963f07eaafe8c9da825dbaf8a75e10f216882285313c61c7 | R | 2,390 | 77 | #Computing cell type expression with bMIND, swCAM and TCA
##load bulk-tissue data and cell-pooled reference
load('rosmap_covcorrected_bulk.RData')
load('props_rosmap.RData')
load('rosmap_meanref.RData')
bulk<-as.matrix(combat_expr)
ref<-as.matrix(meanexp)
sdtmp<-apply(ref,1,sd)
ref<-ref[sdtmp!=0,]
tmp<-... |
807b7d42380937f2078b50969a9e3ab6a43f8f581b4325c069a3bd7a0f46036d | R | 2,390 | 97 | #' @export
broom::tidy
#' @export
broom::glance
#' Tidy method to extract results from a `mipo` object
#'
#' @param x An object of class \code{mipo}
#' @param conf.int Logical. Should confidence intervals be returned?
#' @param conf.level Confidence level for intervals. Defaults to .95
#' @param ... extra arguments (... |
858408783f45959257205b1a715a0249fb50e0d4f0fc86c130793c59f8065a76 | R | 2,391 | 47 | #' @templateVar MODEL_FUNCTION peer_ocu
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park} <\email{hrpark12@@gmail.com}>
#' @templateVar TASK_NAME Peer Influence Task
#' @templateVar TASK_CODE peer
#' @templateVar TASK_CITE (Chung et al., 2015)
#' @templateVar MODEL_NAME Other-C... |
fe26f920b6a8e757f5d93e80b783d11b419027dff296209655c1bf31ecb7e824 | R | 2,394 | 51 | #' @templateVar MODEL_FUNCTION cra_exp
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang} <\email{jaeyeong.yang1125@@gmail.com}>
#' @templateVar TASK_NAME Choice Under Risk and Ambiguity Task
#' @templateVar TASK_CODE cra
#' @templateVar MODEL_NAME Exponential Subjective Val... |
25d134e61b0e676e0517afa2fde1ba096461a3dd5f2821eea4bffa4d4e757a91 | R | 2,399 | 84 | #' Cross Geom
#'
#' @param sig.level significance threshold.
#' @inheritParams ggplot2::layer
#' @inheritParams ggplot2::geom_segment
#' @section Aesthetics:
#' \code{geom_cross()} understands the following aesthetics (required
#' aesthetics are in bold):
#' \itemize{
#' \item \strong{\code{x}}
#' ... |
8b7efab99a5642c5352cd44990a34c013965629e3997c076b9ef16d6a052f21b | R | 2,399 | 71 | ```{r}
library(SONAR)
library(here)
library(Matrix)
library(data.table)
library(Seurat)
library(matlabr)
library(R.matlab)
```
```{r}
libd_sc_df = fread("libd_sc_df2.csv")
libd_st_df = read.csv("libd_st_df.csv",row.names = 1)
libd_st_coord = read.csv("libd_st_coord.csv",row.names = 1)
libd_sc_labels = read.csv("libd_sc... |
a699196e994e820da3020ead8e21c07d7a6fd8becda43c566c6dec13c6e8cbdc | R | 2,400 | 50 | #' @templateVar MODEL_FUNCTION prl_fictitious_multipleB
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang (for model-based regressors)} <\email{jaeyeong.yang1125@@gmail.com}>, \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park (for model-based regressors)} <\emai... |
f172641413235bebe626986a4443489501607704d9d6aba565a7d92c52db5a58 | R | 2,403 | 90 | #context("Test factR object")
# setup GRanges
gr1 <- GenomicRanges::GRanges(
seqnames = "1", strand = rep("+", 5),
ranges = IRanges::IRanges(
start = c(1, 401, 801, 1001, 1301),
end = c(100, 500, 900, 1100, 1400)
),
transcript_id = "transcript1",
gene_id = "geneA",
gene_name = "... |
a43091a854a98db6ee5597e82033db55835627cf7260396740cc6a8c84e509d1 | R | 2,406 | 42 | #' @templateVar MODEL_FUNCTION pst_Q
#' @templateVar CONTRIBUTOR \href{https://github.com/munoztd0/}{David Munoz Tord} <\email{david.munoztord@@unige.ch}>
#' @templateVar TASK_NAME Probabilistic Selection Task
#' @templateVar TASK_CODE pst
#' @templateVar MODEL_NAME Q Learning Model
#' @templateVar MODEL_CODE Q
#' @tem... |
e8a426a88040d6daf2f20bf1ea0d89ca4a896a2cf789ac343642327404c13595 | R | 2,410 | 49 | #' @templateVar MODEL_FUNCTION banditNarm_lapse_decay
#' @templateVar CONTRIBUTOR \href{https://github.com/cheoljun95}{Cheol Jun Cho} <\email{cjfwndnsl@@gmail.com}>
#' @templateVar TASK_NAME N-Armed Bandit Task
#' @templateVar TASK_CODE banditNarm
#' @templateVar MODEL_NAME 5 Parameter Model, without C (choice persever... |
41cc5a19e3ee6209a84000598c6ded5c1e0a7ec8db069c064e58f6ef0ca1be7b | R | 2,413 | 90 | ###############################################################################
#
# Create .png files with sequence logos for provided motifs.
#
# AUTHOR: Maciej_Bak
# AFFILIATION: University_of_Basel
# AFFILIATION: Swiss_Institute_of_Bioinformatics
# CONTACT: wsciekly.maciek@gmail.com
# CREATED: 21-03-2020... |
58c8c6c2887d10f44c95cdb8e4ff82b7dcd292af6471b5815d1a45454c0d7419 | R | 2,423 | 68 | #' Walking disability data
#'
#' Two items YA and YB measuring walking disability in samples A, B and E.
#'
#' Example dataset to demonstrate imputation of two items (YA and YB). Item YA
#' is administered to sample A and sample E, item YB is administered to sample B
#' and sample E, so sample E acts as a bridge study.... |
929600e0c52aa76c013f94b8eee623390ee4ab537ff5ffbda3c25b91fd1c2891 | R | 2,431 | 66 | getwd()
library(ggplot2)
library(ggsignif)
library(ggpubr)
# library(scales)
# squash_axis <- function(from, to, factor) {
# trans <- function(x) {
# # get indices for the relevant regions
# isq <- !is.na(x) & x > from & x < to
# ito <- !is.na(x) & x >= to
#
# # apply transformation
# ... |
abc2175dd7351b0416f9b62525ef4c1d84936ec03394c1468d0192a902c4c4ba | R | 2,437 | 64 | library(vroom)
library(dplyr)
library(PLIER)
# Make sure R's working directory is in the correct spot to make relative paths resolve correctly
if (rstudioapi::isAvailable()) {
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
} else{
# If running as a script, finding the file is harder
# https://stacko... |
81aabb60d4c75245d7cf01c149659c8126f8c939644704a6c57814c81c95cff1 | R | 2,438 | 71 | #### This script compares pi1 statistics with CMC
library(ggpubr)
library(dplyr)
save_plot <- function(p, fn, w, h){
for(ext in c('.png', '.pdf')){
ggplot2::ggsave(file=paste0(fn,ext), plot=p, width=w, height=h)
}
}
load_cmc <- function(){
fn <- here::here("input/public_results/cmc_hoffman/_m",
... |
a68f7072011158a943b588f5d0e67ac45427611f9989900e401a8d9b6b384ca5 | R | 2,453 | 66 | # Organize the groups order and combination based on the description
# e.g.
# groupdescription[1] = control
# groupdescription[2] = control
# groupdescription[3] = experimental
# doubleton[1] = control, doubleton[2] = control
# singleton = experimental
# statistical analysis will be: singleton vs doubleton[1] and sing... |
bf8c9ce63dd4f36cedf8e07d80d4fb4895a476f230c46b65fa1cde2fa8cc12fd | R | 2,453 | 62 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/gene_counts_unfiltered.tsv", sep="\t", row.names="gene_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed/04.deseq2/ex... |
d316303171233b65d4b52b0547224ae5ff98141cd20db84ef916a7b581703c0f | R | 2,467 | 57 | #' Cumulative hazard rate or Nelson-Aalen estimator
#'
#' Calculates the cumulative hazard rate (Nelson-Aalen estimator)
#'
#' This function is useful for imputing variables that depend on survival time.
#' White and Royston (2009) suggested using the cumulative hazard to the
#' survival time H0(T) rather than T or log... |
32d7852a397a5bf1fb08015035d472dbec6ba575a1544658b18d135ca3edbc86 | R | 2,468 | 61 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcript_counts_unfiltered.tsv", sep="\t", row.names="transcript_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed... |
cb9588cf300d0ce97d7ce98fd406450b7a65ffc976b763e94d04ae25d07ccde3 | R | 2,471 | 49 | #' @templateVar MODEL_FUNCTION igt_pvl_decay
#' @templateVar TASK_NAME Iowa Gambling Task
#' @templateVar TASK_CODE igt
#' @templateVar TASK_CITE (Ahn et al., 2008)
#' @templateVar MODEL_NAME Prospect Valence Learning (PVL) Decay-RI
#' @templateVar MODEL_CODE pvl_decay
#' @templateVar MODEL_CITE (Ahn et al., 2014)
#' @... |
f25223503d38c3cb53cdd5e9fc9141bcce72f026752ff699e238e852e70a5449 | R | 2,471 | 64 | # Differential expression analysis: MMT_D30 vs GF_D30
# per cell type within each tissue (Seurat FindMarkers, Wilcoxon test)
library(Seurat)
library(dplyr)
output_dir <- "./"
scobj <- readRDS("Brain.rds")
stopifnot(all(c("subtype", "group", "Tissue") %in% colnames(scobj@meta.data)))
scobj$Tissue_label <- as.charact... |
654634fe817d32eda5739187bcb8f47c4882abe6c4550cffbc52917e0ec74a92 | R | 2,481 | 85 | library(ggplot2)
library(scales)
library(ggthemes)
# fig 1
set.seed(42)
demog_data_camcan <- data.frame(
age = c(rgamma(300, 90, 2), rgamma(320, 100, 2)),
gender = c(rep("F", 300), rep("M", 320)),
dataset = "cam-can"
)
demog_data_tuab <- data.frame(
age = c(rgamma(700, 100, 2), rgamma(639, 95, 2)),
... |
f3c5adcf0e9983b2e8c5397d5d4199a78fd9be2773ad51a3d5fd984c04530bca | R | 2,481 | 60 | suppressPackageStartupMessages({
requireNamespace("rlang")
requireNamespace("ggplot2")
requireNamespace("cowplot")
})
pc_scores_loadings_plot <- function(pca_model_info, pcx, pcy, color = NULL, color_values = NULL,
loading_colname = "column", loading_label = "Feature") {
sli... |
b027c4e66e1db325244f64dfeceadade886041688de07e92557d9dc262f374a9 | R | 2,489 | 69 | #1 Data preparation
# Setup---------------------------------------------------------------
rm(list = ls()) ; gc() ; gc()
set.seed(1234)
wdir <- 'set your working directory'
setwd(wdir)
save.path <- 'object folder path'
# load library
easypackages::libraries(c('Seurat','tidyverse','patchwork','celda','singleCellTK','... |
08d162bca94152d753a05f9d336615d187a566dc22a628fcfef39a9b62c4a8bf | R | 2,491 | 49 | #' Project on preterm and small for gestational age infants (POPS)
#'
#' Subset of data from the POPS study, a national, prospective study on preterm
#' children, including all liveborn infants <32 weeks gestational age and/or <1500
#' g from 1983 (n = 1338).
#'
#' The data set concerns of subset of 959 children that s... |
67cac020724b5011b8d60b8a583425cb600f02793c9d216978d1ae5bd0ed3632 | R | 2,491 | 79 | #@Time : 2023/7/23 21:23
#@Author : Biao Zhang
#@Email : littlebiao@outlook.com
#@File : transform2seurat.r
#@Description: This file is used to ...
library(Matrix)
library(Seurat)
adata_path_isocortex <- "../../data/srrsc_mouse_human_binary/results_20_1000genes_all_came_selfloop/2023-06-23_20-31-14/embeds/figs/4_s... |
ae5c3473c573d22ecec1be70a19ef750c3a6a6305c0413e7d4b3854fba99d8c6 | R | 2,491 | 49 | #' @templateVar MODEL_FUNCTION banditNarm_kalman_filter
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/yoonseo-zoh/}{Yoonseo Zoh} <\email{zohyos7@@gmail.com}>, \href{https://github.com/cheoljun95}{Cheol Jun Cho} <\email{cjfwndnsl@@gmail.com}>
#' @templateVar TASK_NAME N-Armed Bandit Task (modified)
#'... |
132a9ff0d6fc72f003ebfff4b373f21f6b6cab441d7408ef995af82c5fbb7b95 | R | 2,497 | 88 | library(scatterpie)
library(RColorBrewer)
library(grDevices)
library(Seurat)
library(tidyverse)
library(reshape2)
set.seed <- 1234
file_path <- '/data/work/uniPort/BRCA_SPOT/'
setwd(file_path)
file_path <- '/data/yupines/kai/data/brca/'
#-----------------------------------------------------------
# input data for... |
f6a13be5e4c800dafabbec9627d4b799c849290bf36ed09e7633c08f1bcdfe8d | R | 2,497 | 77 | source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/atac.R')
source('~/scripts/perturbator/de.R')
library(Pando)
library(doParallel)
registerDoParallel(4)
filter <- dplyr::filter
select <- dplyr::select
setwd('~/projects/cutntag/')
#### Read data ####
marks <- read_rds('data/all_marks_list_v3.4lines.... |
5e1a2977caf569bb72fbcbfde415eed81e1b6d19407cdd8e3e26f4688b2c32b5 | R | 2,510 | 68 | #' Imputation by direct use of lasso linear regression
#'
#' Imputes univariate missing normal data using lasso linear regression with bootstrap.
#'
#' @aliases mice.impute.lasso.norm lasso.norm
#' @inheritParams mice.impute.norm.boot
#' @param nfolds The number of folds for the cross-validation of the lasso penalty.
#... |
1e77df2ad0c001637efd55a60f8958acef01041f1e114f43f857c99057920eba | R | 2,514 | 72 | #' Imputation by direct use of lasso logistic regression
#'
#' Imputes univariate missing binary data using lasso logistic regression with bootstrap.
#'
#' @aliases mice.impute.lasso.logreg lasso.logreg
#' @inheritParams mice.impute.pmm
#' @param nfolds The number of folds for the cross-validation of the lasso penalty.... |
059d81fb8820b0eaedfa18b94783e044cbb4ece35998528dc22e905ce986ee99 | R | 2,517 | 77 | ## This script generates plots for GSEA analysis.
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
})
save_plot <- function(p, fn, w, h){
for(ext in c('.svg', '.pdf')){
ggsave(file=paste0(fn,ext), plot=p, width=w, height=h)
}
}
get_top_GO <- function(comp){
new_comp <- to... |
71daf7485c82767bb30be7040d2d19912df8ffa2e1de6f224e20446bb0862836 | R | 2,521 | 74 | rm(list=ls())
options(warn=1)
options(stringsAsFactors=FALSE)
options(digits=15)
require(stringr)
require(magrittr)
require(R.matlab)
require(ggplot2)
require(xml2)
require(scales)
require(foreach)
require(doMC)
require(readr)
require(abind)
dir=""
shelltempt=c(paste0(dir,"am_seg_train.sh"))
infortab=read.table(file="s... |
cf6b96330c082d4337031f69ef70e966ed291a158e02f89f0ca33e87e993dce8 | R | 2,525 | 59 | #' Terneuzen birth cohort
#'
#' Data of subset of the Terneuzen Birth Cohort data on child growth.
#'
#' This \code{tbc} data set is a random subset of persons from a much larger
#' collection of data from the Terneuzen Birth Cohort. The total cohort
#' comprises of 2604 unique persons, whereas the subset in \code{tbc}... |
038092b2d2deba0597d69cd7b5b52b90a698e8ea9e9e27851d92261f0c210beb | R | 2,529 | 75 | options(stringsAsFactors = FALSE)
args <- commandArgs(TRUE)
gene_name<-args[1]
wkdir<-args[2]
library(data.table)
setwd(paste0(wkdir))
# gene_name="ABCA7"
### Load grex.geno and weight files
geno_pred<- fread(paste0(gene_name, "_grex.geno"), header=TRUE)
geno_pred$ID <- paste(geno_pred$CHROM, geno_pred$POS, sep = ":... |
895d00736c60d8c75e66c6d561b36ce6ca1c9dc04c283788d1461a6121577ce2 | R | 2,543 | 72 | ## This script generates plots for GSEA analysis.
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
})
save_plot <- function(p, fn, w, h){
for(ext in c('.svg', '.pdf')){
ggsave(file=paste0(fn,ext), plot=p, width=w, height=h)
}
}
get_top_GO <- function(tissue, sex){
new_tissu... |
16155ae959eea51479061d4b9ef6d336e028f63a26e6ff66433f68412fc8062b | R | 2,544 | 77 | args = commandArgs(TRUE)
eta=as.numeric(args[1])
max_depth=as.numeric(args[2])
gamma=as.numeric(args[3])
min_child_weight=as.numeric(args[4])
nrounds=as.numeric(args[5])
i=as.numeric(args[6])
idx=as.numeric(args[7])
data_folder=as.character(args[8])
sex_split=try(as.logical(args[9]))
HyperParameterSearchCV = functio... |
a256b08772daa49034fe464654b6350e44ead1ba6a579414122ef33fc3e888a4 | R | 2,561 | 51 | #' @templateVar MODEL_FUNCTION igt_orl
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/nate-haines/}{Nate Haines} <\email{haines.175@@osu.edu}>
#' @templateVar TASK_NAME Iowa Gambling Task
#' @templateVar TASK_CODE igt
#' @templateVar TASK_CITE (Ahn et al., 2008)
#' @templateVar MODEL_NAME Outcome-Repr... |
4570aa0550b009e504d1f8ecd65c1cbc56c36b2898a3d793e55ebc17b44d070e | R | 2,563 | 24 | #DTS script for collecting essential data from the dataset file for statistics and plots
dataset.file <- file.choose()
dataset.path = dirname(dataset.file)
dataset.data <- yaml.load_file(dataset.file)
totalflies <- length(paste(dataset.path, unlist(do.call("rbind", lapply(dataset.data$resources, '[', 4))), sep = "/")... |
eb3aadb87d5bb8c825a3600b337f14f8a9771ecf3424f133b207778d8831afe6 | R | 2,564 | 59 | #!/usr/bin/env Rscript
#################### Differential Transcript Usage Analysis with DEXSeq
library(DEXSeq)
source(here::here("src/rnaseq/DTU_Analysis/stageR_DTU_significanceTesting.R"))
DEXSeq_method <- function(sample_info, filtered_dm_object, design_full, sex, organism){
if(organism == 'mmusculus'){
strp <- fu... |
0f584266acc6044357053319fdebfe590d180cb4e5cf2fec4bd27813b0f9c025 | R | 2,578 | 51 | #' @templateVar MODEL_FUNCTION rdt_happiness
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park} <\email{hrpark12@@gmail.com}>
#' @templateVar TASK_NAME Risky Decision Task
#' @templateVar TASK_CODE rdt
#' @templateVar MODEL_NAME Happiness Computational Model
#' @templateVar MODE... |
41526176d59cb0128fb6329a4801e1a235352860fb36045477a23391df0baf66 | R | 2,578 | 53 | #' @templateVar MODEL_FUNCTION igt_vpp
#' @templateVar TASK_NAME Iowa Gambling Task
#' @templateVar TASK_CODE igt
#' @templateVar TASK_CITE (Ahn et al., 2008)
#' @templateVar MODEL_NAME Value-Plus-Perseverance
#' @templateVar MODEL_CODE vpp
#' @templateVar MODEL_CITE (Worthy et al., 2013)
#' @templateVar MODEL_TYPE Hie... |
abce4f496c3fac0adc6996bed5084d4758707fdc39a7279aeba1f63c412e7583 | R | 2,585 | 69 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/gene_counts_unfiltered.tsv", sep="\t", row.names="gene_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed/04.deseq2/e... |
9011cbdf0380089a6528d4b59539b3609b51bec9b91c7f9f7dcb4928d307a12c | R | 2,586 | 65 | ###########################Do ssGESA
library(tibble)
library(magrittr)
library(corto)
library(GSVA)
library(limma)
rm(list=ls())
data<-read.table(file='GSCs.tpm.150',sep='\t',header=T,row.names=1)
minRowFPKM=rowMeans(data)>2 # filter by mean
minNumFPKM=rowSums(data>0)>10 #Screen the number of samples whose ... |
52e8c8573899cc4d6da9e413fe1feb0bd958bad5c00879a99741a522746cda09 | R | 2,589 | 43 | #' @templateVar MODEL_FUNCTION pst_gainloss_Q
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang} <\email{jaeyeong.yang1125@@gmail.com}>
#' @templateVar TASK_NAME Probabilistic Selection Task
#' @templateVar TASK_CODE pst
#' @templateVar MODEL_NAME Gain-Loss Q Learning Model
... |
293ce993e34b8ab5fa629c539a8f14ca30e8d374e03c180328c6e3960cfe4d30 | R | 2,590 | 62 | #!/usr/bin/env Rscript
library(stringr)
library(dplyr)
source(here::here("src/shared/R-helpers/get_settings.R"))
settings <- get_settings(omic = "splicing_analysis_das", stage = "das_prep", dataset_name = NULL)
if(!dir.exists(settings[['output_directory']])){
dir.create(settings[['output_directory']], recursiv... |
f296227e0959a474d46028db415428a6c084b7e6db714e9e69567b337fa9ac4a | R | 2,593 | 58 | #load snRNAseq count matrix (ROSMAP)
library(Matrix)
genenames<-read.delim('filtered_gene_row_names.txt',header=F)
scdata<-readMM('filtered_count_matrix.mtx')
smeta<-read.table('filtered_column_metadata.txt',header=T,sep='\t',row.names=1)
scdata<-as.matrix(scdata)
rownames(scdata)<-genenames[,1]
colnames(scdata)... |
7e8488b39adbddd9203553bb8a4efad911adf7ba4da8848a6b8ba2d431d4b89c | R | 2,597 | 110 | check.data <- function(data, method) {
check.dataform(data)
}
check.dataform <- function(data) {
if (!(is.matrix(data) || is.data.frame(data))) {
stop("Data should be a matrix or data frame", call. = FALSE)
}
if (ncol(data) < 2) {
stop("Data should contain at least two columns", call. = FALSE)
}
d... |
b40c7c176ac06a16f56e48b2947eb6a23d32460593a2ae78b04b79327c0594f6 | R | 2,606 | 81 | args = commandArgs(TRUE)
eta=as.numeric(args[1])
max_depth=as.numeric(args[2])
gamma=as.numeric(args[3])
min_child_weight=as.numeric(args[4])
nrounds=as.numeric(args[5])
data_folder=as.character(args[6])
sex_split=try(as.logical(args[7]))
BrainAgeCV = function(eta, max_depth, gamma, min_child_weight, nrounds, data_f... |
b2ad514599bc97406d9b6f4f2db56cf16a479f2350678cca9d7970bca75ac38d | R | 2,607 | 69 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/gene_counts_unfiltered.tsv", sep="\t", row.names="gene_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed/04.deseq2/e... |
7adffe39b4742546c38042bda84e66386288a420fa4a286545ad3d67bf544019 | R | 2,628 | 77 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcript_counts_multiple_median_cpm_1.tsv", sep="\t", row.names="transcript_id"))
cts_med <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcr... |
ab5921a54c598ea14158466490736fe07b5d9548b73b3006be87828a722aba5b | R | 2,634 | 75 | #'#################################################################################
#'#################################################################################
#' Explore replicability in gene set activity scores from different subsets and random gene sets
#'#####################################################... |
5756903890e4cfdbbe53f1fb9fab36d73b6afcccc7cfb4ed6f883fd1559d2e4f | R | 2,637 | 70 | #### Gene ontology semantic similarity analysis
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
library(GOSemSim)
library(org.Hs.eg.db)
})
get_semData <- function(ont){
return(godata('org.Hs.eg.db', ont=ont))
}
save_img <- function(image, fn, w=7, h=7){
for(ext in c(".pdf", ".... |
85987079c3f42fb168cae9a9db537f629c7ede19f02346dcc63e44d07a6c4290 | R | 2,638 | 51 | #' @templateVar MODEL_FUNCTION prl_fictitious_rp_woa
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang (for model-based regressors)} <\email{jaeyeong.yang1125@@gmail.com}>, \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park (for model-based regressors)} <\email{h... |
b979f6c741e979fb419714c80f0f63d8bc9c51d7344e4cfbd0ea162818652452 | R | 2,642 | 84 | # The purpose of this script is to create all data needed to support the shiny
# application for accessing the data form our 2023 manuscript.
# This script is dependent upon obtaining the raw nextflow nanoseq pipeline
# results directory, which should be stored in data if you are using this dir
# structure. If you do ... |
c54cb1c82f085383d3e89725ba8dd0f51e4f90c25ca7642ecad26fb9bcecdfe4 | R | 2,644 | 77 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcript_counts_multiple_median_cpm_1.tsv", sep="\t", row.names="transcript_id"))
cts_med <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcr... |
647a2c7d296026cd73efb8626bbeca8550a0582d20cf68a354527f41816e7936 | R | 2,652 | 52 | #' @templateVar MODEL_FUNCTION prl_fictitious_rp
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang (for model-based regressors)} <\email{jaeyeong.yang1125@@gmail.com}>, \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park (for model-based regressors)} <\email{hrpar... |
3018338722cacdd9cd2cf4c433dd2b4ca4d640f80327b7e37b7a71fe7a2b43c9 | R | 2,665 | 74 | #!/bin/R
library(MotrpacRatTraining6moData)
library(data.table)
library(sinew)
# for each ome, copy final training-dea tables to GCP bucket
public_bucket = "gs://motrpac-rat-training-6mo-extdata"
# get names of all files
all = system("gsutil ls gs://motrpac-data-hub/pass1b-06/analysis/**/dea/*training-dea-fdr*", inte... |
be21a7af837928ce7ab1029a14712325f698558a9091bddf9aba7d8da04b9db2 | R | 2,667 | 70 | ## Plot contrasts between maternal and fetal placentas
library(tidyverse)
library(ggrepel)
placentas_results <-
read_rds(path = here::here("results/limma_placentas/limma_placenta_results.Rdata"))
gene_names <-
read_rds(here::here("results/download_gene_data/gene_names.Rdata")) %>%
dplyr::select(.data = ., -... |
74c509be688e7585b064f97e84fc599071c4331d2560b3b1701343422233f5bf | R | 2,676 | 74 | ## This script generates the upset plot for mash sig results.
library(magrittr)
library(ComplexHeatmap)
get_sig_de <- function(){
fn <- paste0("../../_m/",
"differential_expression_region_interaction_sex_4features.txt")
return(data.table::fread(fn) %>% dplyr::filter(adj.P.Val < 0.05))
}
mem... |
4c5c51d3b8a8e5994b6e8921b5146ed62a573431967098146d793e4f120e2944 | R | 2,682 | 95 | # Convert annotation txt files to a single detections csv for use in ealuation
#
# To convert xml to txt run Python script annotation_xml_to_txt.py first
library(tidyverse)
ann_dir <- "Z://Adult_Salmon_Scales/Salmon_scale_circuli_detection/SSCD_data/detection_accuracy_analysis/sscd_detections_90_txt/"
ann_path <- li... |
30cd4d74709d85fb9beec25f24c6d98fde5d867209266740d0bff16b754a574f | R | 2,699 | 71 | list.of.packages <- c("jsonlite", "circlize")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(jsonlite)
suppressPackageStartupMessages(library("circlize"))
library(circlize)
pydata = jsonlite::read_json('circli... |
e179874d2168b0c0827b352d3a5ce1be44049a6b444fed7d64c1762a58b11de9 | R | 2,705 | 74 | ## This script generates the upset plot for mash sig results.
library(magrittr)
library(ComplexHeatmap)
get_sig_de <- function(){
fn <- paste0("../../_m/",
"differential_expression_analysis_4features_sex.txt.gz")
return(data.table::fread(fn) %>% dplyr::filter(adj.P.Val < 0.05))
}
memDE <- m... |
15c5039e5e4793ff5a0412fee8b60d540fb9188c0ed25b29acc0234ae90e2077 | R | 2,709 | 82 | library(stringr)
library(readxl)
library(tidyverse)
df <- as.data.frame(readxl::read_xls("IPA_Data_Canon.xls", col_names = TRUE))
coln = c("Canonical Pathways","All Patients", "Females", "Males")
colnames(df) <- coln
df<-df[-1,]
df2 = head(df, 10)
df_long <- pivot_longer(data = df2,
cols = -... |
0f3ac6fd4143c33391eef9802df032b96d03dbd9677842ee3d6ba4ea801fab5a | R | 2,711 | 86 | #' Star Geom
#'
#' @inheritParams ggplot2::layer
#' @inheritParams ggplot2::geom_polygon
#' @section Aesthetics:
#' \code{geom_star()} understands the following aesthetics (required aesthetics are in bold):
#' \itemize{
#' \item \strong{\code{x}}
#' \item \strong{\code{y}}
#' \item \code{n}
#' ... |
b29ffaf84fb1f83f6c28aa7e6589273e8ffc4beea69e174708a4ed2c6eb899b7 | R | 2,711 | 64 | rm(list=ls())
options(warn=1)
options(stringsAsFactors=FALSE)
options(digits=15)
require(stringr)
require(magrittr)
require(ggplot2)
require(dplyr)
require(reshape2)
resdir="/Users/yuewu/Dropbox (Edison_Lab@UGA)/Projects/Bioinformatics_modeling/am_fungi_segmentation/results/run_am_seg_bigdataset/res/"
setwd(resdir)
inf... |
a3d61b0a2e1028a9b06f306a70258b2023401d6fda6e6b4eaeea315a7ac8b206 | R | 2,717 | 67 | library(tidyr)
library(reshape2)
library(plyr)
library(dplyr)
library(grid)
library(gridExtra)
library(Seurat)
library(textTinyR)
library(pbapply)
current = getwd()
folder = "1_calculate_pseudobulk"
setwd(current)
system(paste0("mkdir -p outputs/",folder,"/"))
celltypes <- c("CD4_T","CD8_T","B")
## calculate pseudob... |
b4688f14e0c764f99f9b39218a1f4a656f5f70c5e3a3484bcc195962d0060e6d | R | 2,717 | 78 | ## This script estimates pi1 across brain regions.
suppressPackageStartupMessages({
library(here)
library(dplyr)
library(qvalue)
})
load_eqtls <- function(tissue, sex){
fn <- here("prep_eqtl_analysis/by_sex", tolower(tissue), tolower(sex),
"cis_model/_m/BrainSEQ_TOPMed.signif_variants.t... |
0163c0f599820d521e28a3c81f6c96e43a6da56751ac3bd51ad524e95d9e703e | R | 2,729 | 89 |
### function to plot a small deltapx plot
#' Title
#'
#' @param ffpath
#' @param well
#' @param zhstart
#' @param zhstop
#' @param frstart
#' @param frstop
#' @param colour
#' @param ymax
#' @param width
#' @param height
#' @param xnsecs
#' @param exportPath
#'
#' @return
#' @export
#'
#' @examples
ggDeltaPx <- functi... |
2ab92282c4f0c02f0f8dfdb48eedf7adcc09c8a16b5fa0ed58bda096f48e7ce3 | R | 2,737 | 70 |
# Parse all Rscript arguments
parse_command_args <- function()
{
l <- list()
a <- commandArgs(trailingOnly=T)
for (arg in a)
{
bits <- unlist(strsplit(arg,"="))
name=bits[1]
value=bits[2]
l[[name]] <- value
}
return(l)
}
cargs <- parse_command_args()
# Check all require... |
ee3c06fe9314b3d1e628632b27da4300490c3881fffdbdd9bf37b3f829d8a047 | R | 2,737 | 91 | # This script prepares recount3 data to be run through PLIER. It is based on
# Qiwen and Jackie's script here: https://github.com/greenelab/rheum-plier-data/blob/05822c3a7b2e7c663d79426b1117a36dc2a0b892/recount2/2-prep_recount_for_plier.R
library(tibble)
library(tidyr)
library(vroom)
library(dplyr)
library(biomaRt)
li... |
9e712f90cbb22a3dc19f58e52661af8fdc8ea4a0e1be1db53f74ed0b914ad04c | R | 2,744 | 63 | #' Imputation by linear discriminant analysis
#'
#' Imputes univariate missing data using linear discriminant analysis
#'
#' @inheritParams mice.impute.pmm
#' @param ... Other named arguments. Not used.
#' @return Vector with imputed data, of type factor, and of length
#' \code{sum(wy)}
#' @details Imputation of catego... |
92b9a3603b7115e91b82fed7371b93c2e1bd55df9bbfe0b76b7792917d0ff107 | R | 2,747 | 80 | #' Segment2 layer
#'
#' @inheritParams ggplot2::layer
#' @inheritParams ggplot2::geom_segment
#' @section Aesthetics:
#' \code{geom_segment2()} understands the following aesthetics (required aesthetics are in bold):
#' \itemize{
#' \item \strong{\code{x}}
#' \item \strong{\code{y}}
#' \item \stron... |
7882c7b7785fd58644138f08cd3d13ca78ce8c50bb6fabd35dbfb5d94d09d277 | R | 2,757 | 75 | #' An analytical p-value combination method using the Cauchy distribution
#'
#' The \code{CCT} function takes in a numeric vector of p-values, a numeric
#' vector of non-negative weights, and return the aggregated p-value using Cauchy method.
#' @param pvals a numeric vector of p-values, where each of the element is
#'... |
4fc5d2d00fbf7634302278df70a2c18b7265dd8ac4ca6e0b72eb7b30a2234d4f | R | 2,800 | 86 | #@Time : 2023/7/23 21:23
#@Author : Biao Zhang
#@Email : littlebiao@outlook.com
#@File : transform2seurat.r
#@Description: This file is used to ...
library(Matrix)
library(Seurat)
library(magrittr) # needs to be run every time you start R and want to use %>%
library(dplyr) # alternatively, this also loads %>%
a... |
d651c89399b135de215dedddc44a951d2ca22a27b5d631971adba0444b2e68df | R | 2,807 | 79 | # Same seed - multiple cores -
# Result: Imputations not equal between mice and parlmice
test_that("PARLMICE-001: Warning and Imputations between mice and parlmice are unequal", {
skip_if_not(parallel::detectCores() > 2)
expect_warning(A <- parlmice(nhanes, m = 2, seed = 123))
B <- mice(nhanes, m = 2, print = FAL... |
f95f050eacf9deaef5620e4b704ab9e638b5099e95eeb8b2e9a1374873c250d3 | R | 2,810 | 98 | pool.vector <- function(
w,
dfcom = Inf,
custom.t = NULL,
rule = c("rubin1987", "reiter2003")
) {
# rubin1987: Rubin's rules for scalar estimates
# reiter2003: Reiter's rules for partially synthetic data
rule <- match.arg(rule)
present <- hasName(w, c("estimate", "std.error"))
if (!all(present)) {
... |
fe27b239ddcac882aa9435200bf28e468cb5da08984c34bfedda22fc4c35359c | R | 2,815 | 95 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
library(GenomicRanges)
library(plyranges)
# Load data ---------------------------------------------------------------
# PacBio filtered transcripts from Brain (SQANTI output)
Classification <-
list(
GBA... |
93087d6b8e80362431f0ad56da75b47df5550f03d8581a050e0981e46a85391a | R | 2,817 | 62 | # Set working directory ---------------------------------------------------
.libPaths(c("libraries/", .libPaths()))
.libPaths()
libDirectory <- "libraries/"
# Loading libraries -------------------------------------------------------
if (!require("pacman")){install.packages("pacman", lib = libDirectory, repos = "ht... |
852c4b69e7234d09cabddc7c6099061433e5f3799cb32d22374eccb8097c6655 | R | 2,838 | 81 | ## Implementation of RRHO for sex-specific expression
library(RRHO2)
library(dplyr)
get_de <- function(){
err <- 1e-320
fn <- paste0("../../summary_table/_m/",
"differential_expression_schizophrenia_by_sex_4features.txt.gz")
return(data.table::fread(fn) |>
filter(Type == "Gen... |
91e9b71679ed5e6cf3915f7bec1cba2c61f05116cb364ce97d18176f42f763ec | R | 2,848 | 67 | ## Generate circlized plot
suppressPackageStartupMessages({
library(dplyr)
library(circlize)
library(ComplexHeatmap)
})
extract_bed <- function(tissue){
fn <- paste0("../../summary_table/_m/",
"differential_expression_analysis_4features_sex.txt.gz")
bed <- data.table::fread(fn) %... |
6acaf41e56c3afc9e31e9d99ea75726b8ababd54a0501320e549cfdd162bbc91 | R | 2,865 | 51 | StressGeneSets_Mouse_Temp<-list()
StressGeneSets_Rat_Temp<-list()
StressGeneSets_Human_Temp<-list()
names(table(StressGeneSets_RatOrthologs_Symbols_NoNA$Dataset))
for(i in c(1:length(names(table(StressGeneSets_MouseOrthologs_Symbols_NoNA$Dataset))))){
StressGeneSets_Mouse_Temp[[i]]<-StressGeneSets_MouseOrthologs... |
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