sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
35f7722b65cc60f42feab99615558f8af1ee6458fb8020afe4d9cae9704c1bf0 | R | 2,618 | 91 | library(ArchR)
library(parallel)
addArchRThreads(threads = 20)
addArchRGenome("hg38")
library(ComplexHeatmap)
library(ggsignif)
library("RColorBrewer")
setwd("/geschwindlabshares/RexachGroup/Xia_Data/atac_cellrangerOut/project0318")
projdir <- "projrmSubset_subPeak"
proj <- loadArchRProject(projdir)
if(T){
projmet... |
adf61a0b60834d26fe8ef7a8d2d09b4f5d407cb1a134ac9ac570969aac730f6a | R | 2,620 | 66 | #' phenocov_mvmr
#'
#' Uses an external phenotypic covariance matrix and summary data to estimate covariance matrices for estimated effects of individual genetic
#' variants on each exposure. The phenotypic covariance matrix should be constructed using standardised phenotype measures. The function returns a number of c... |
4544e5fc2a7d7ba951bf449abbb97825293182f179b3ee661e590fbb29ff52df | R | 2,621 | 73 | # ==============================================================================
# Load Single Nucleotide Variation (SNV) Data
# ==============================================================================
# This script loads and prepares SNV/mutation data from WES
#
# Author: Osama Shiraz Shah
# ====================... |
6e51e12c124988202ad2befa3078632bea7cd72bbb9178be37b713ac667ae42d | R | 2,621 | 48 | library(malevnc)
#library(googlesheets4)
library(neuprintr)
library(Rvcg)
wing_haltere_mns = neuprint_list2df(neuprint_fetch_custom(cypher=paste0("MATCH (a:Neuron) WHERE a.class = 'motor neuron' AND a.subclass IN ['wm','hm'] RETURN a.bodyId AS bodyid, a.class AS class, a.group AS group, a.subclass AS subclass, a.type... |
aca045a0bd65b33f94326ae0732c1663e92d1851812eb7a821bf93423a28b808 | R | 2,622 | 43 | library(R.matlab)
library(ggplot2)
hcp_gar_rss <- readMat("./hcp_fd_rss_rfMRI_REST2_RL.mat");
Data1 = data.frame(Group = hcp_gar_rss$label.all, Value = hcp_gar_rss$coef.all)
Data1$Group = factor(Data1$Group,levels = c(1,2))
color_arr <- array(data = NA, dim = 7, dimnames = NULL)
color_arr[1] <- rgb(120/255, 154/255... |
8d0c918321929f753e615307027c4a6421e7275fcca458c957399fe9cf2cbfd8 | R | 2,624 | 84 | ---
title: "Mixed ANOVAS"
author: "Mona Zimmermann"
date: "2026/04/30"
---
#### Email: m.l.m.zimmermann@amsterdamumc.nl
#### Purpose of script:
Running a type 3 mixed ANOVA.
#### Status:
#### Load libraries
library(tidyverse)
library(ggpubr)
library(rstatix)
library(MASS)
library(dplyr)
fil... |
476f3a4e0b7984d2a8b959c805089a729349da1ded535770f47452b4baf3ffa5 | R | 2,625 | 81 | #setwd("E:/Rworkplace/01-17")
#remove.packages("rlang")
#BiocManager::install(c("lifecycle"))
#install.packages("BiocManager")
#library(BiocManager)
#.libPaths(c("",""))
#BiocManager::install("ggplot2",force=TRUE)
#BiocManager::install("rlang",force=TRUE)
#BiocManager::install(c("ggplot2","ggpubr","ggsignif"))
library(... |
980fd4b655cb6ff848bc3f6a6cb99f16e06732bc2679c5ddcfcbb0bf9248ef3d | R | 2,625 | 59 | ###################################################
## sequencing statistics
## a) total amount of reads
## b) mapping hits
## c) number of splicing spanning reads
###################################################
system('mkdir plots')
expname = paste("_", basename(getwd()),sep='')
## star stats files
files = li... |
d77dea6e170d0725c831774a14aca43a12eb766d2b747855e6ecd135d1e8abc5 | R | 2,626 | 92 | # Load libraries
library(ChIPseeker)
library(org.Hs.eg.db)
library(GenomicFeatures)
library(dplyr)
library(rtracklayer)
library(ggplot2)
library(biomaRt)
library(clusterProfiler)
library(msigdbr)
txdb_ensembl <- makeTxDbFromEnsembl(organism="Homo sapiens", release='113')
# Hallmark gene sets
hallmark <- msigdbr(spec... |
62e46a10aa3a876ce578a1819c42a101c5c02043384b83b502ea41ce19eea6e9 | R | 2,628 | 83 | library(ArchR)
library(parallel)
addArchRThreads(threads = 10)
addArchRGenome("hg38")
library(ComplexHeatmap)
library(ggsignif)
library("RColorBrewer")
setwd("/geschwindlabshares/RexachGroup/Xia_Data/atac_cellrangerOut/project0318")
projdir <- "projrmSubset_subPeak"
proj <- loadArchRProject(projdir)
projmeta <- rea... |
5430c1ba8a558344ec669840351e855b0eb42abeefc0f3d32b05254257eff5c2 | R | 2,630 | 86 | example("registration_pseudobulk", package = "spatialLIBD")
sce$batches <- sample(1:3, ncol(sce), replace = TRUE)
test_that("NA check works", {
expect_error(
registration_pseudobulk(sce, "Treatment", "sample_id", c("age", "batches")),
"has all NAs after pseudo-bulking"
)
expect_error(
... |
e8708e181ee3be6a15488d206a7c5d06a886465a5ba4d2cb5dabe5d59aa60987 | R | 2,641 | 111 | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library("argparse"))
# create parser object
parser <- ArgumentParser()
# specify our desired options
# by default ArgumentParser will add an help option
parser$add_argument("-i", "--input", required=TRUE, help="load clustered RData")
parser$add_argument("-n", "--... |
7a9a49ff4cb857d28ae98ad5c0c27a7cb3b8e47fe7273180da13786fde8f96a6 | R | 2,645 | 74 | #' Inner Nested-CV loop
#'
#' @param
#'
#' @return
#'
#' @export
xgb_inner_loop <- function(data,
group,
outcome,
seed = 42,
n_inner_folds = 3,
eval_metric = "rmse",
... |
57ae21aa4696184f8019f9dfa7cbdd3870a7a2ff90ffe698427e0e0e02753eaf | R | 2,654 | 63 | #########################################
# Plotting variance explained by factor #
#########################################
library(MOFA2)
library(ComplexHeatmap)
library(dplyr)
library(circlize)
library(reshape2)
library(ggsci)
# Retrieving MOFA
load("C:/Users/User/Documents/Drive_PC/Lucas/GitHub/ROSMAP-... |
605337345227f875f8132028753cbeb65dba40dd12b032fd8d99bafef01a29c4 | R | 2,654 | 29 | ## code to prepare `DATASET` dataset goes here
da2ids=c("720575940639337461", "720575940628259407", "720575940611849187",
"720575940622364184", "720575940624106442", "720575940622734835",
"720575940622762995", "720575940626937617", "720575940622311704",
"720575940610052266", "720575940638719104")
aids=da2ids
aids=unio... |
ef2fd570ca7b83f94dcaebe58a1513ed4c75ac5993f4e223db8e7392b4ce30c6 | R | 2,660 | 66 | test_that("key_point_from_neuron picks the principal branch point", {
# synthetic Y-shaped neuron: single branch point at (0, 10, 0)
swc <- data.frame(
PointNo = 1:7,
Label = c(1, 3, 3, 3, 6, 3, 6),
X = c(0, 0, 0, 5, 10, -5, -10),
Y = c(0, 5, 10, 15, 20, 15, 20),
Z = 0,
W = 1,
Parent = c... |
fb7bb963a7aa2610f3172a9ee7c09e840e34d37e9a62e93312fff663de05a6b5 | R | 2,665 | 56 | ####海马体与颞叶皮层细胞类型间差异基因功能富集点图+热图-----------------
temp <- read.table("./temporal_lobe\\enrich\\celltype_EX_IN_Astro_Micro_Oli_Opcs_Endo.go.txt",sep = '\t',header = T)
colnames(temp) <- c(colnames(temp)[1],paste0(colnames(temp),"_Temp","")[2:15])
hippo <- read.table("./Hippo\\enrich\\celltype_EX_IN_Astro_Micro_Oli_Opcs_E... |
b2c32b52d2c4e427e251429bc4f496964302b2e860be101bf5d33373bc79be54 | R | 2,666 | 125 | ---
title: "Further exploration of goseq analysis resutls"
author: "Wei Sun"
date: "`r Sys.Date()`"
output:
html_document:
theme: journal
highlight: tango
toc: true
toc_depth: 3
toc_float:
collapsed: true
smooth_scroll: false
number_sections: false
df_print: paged
---
```{r set... |
bc00ca4aa1e5010046bdeca9194efc6da767997b2779b2d71cc3abebc7bdc105 | R | 2,666 | 70 | output <- "/Users/..." # Output Folder
setwd("/Users/...") # Folder with Images
Proteins <- c("TrkA", "Ncad") # Replace TrkA and Ncad throughout code with proteins of interest
Overlap.Threshold <- c(0, 10, 33)
Folder <- "Explant" # "Explant" "InVivo"
Dirs <- list.dirs(path = paste(Folder,sep = ""), full.names = TRUE, r... |
ada8dc95cb848f68ac11b355475b9a0bf836b09bbb4651972ea0a41d2b9db7b3 | R | 2,668 | 45 | #' Retrieves plot data from a SAHA analysis object
#'
#' This function returns plot data based on the specified plot type and data type.
#' If `data_type` is not provided, it attempts to determine the data type from the `ann` object.
#'
#' Supported `plot_type` values include:
#' - "self-similarity": Retrieves self-sim... |
f223efbbfe9ece3891528633a260199ffb9903a6475825bd158950f6647480ba | R | 2,669 | 78 | #!/usr/bin/env Rscript
################################################
################################################
## LOAD LIBRARIES ##
################################################
################################################
library(optparse)
library(UpSetR)
###############... |
c1c35bd6113b5ab351bb010a5443e7fc953565b751975b14dbd8757a5b481d73 | R | 2,670 | 59 | # wd <- '/net//mraid20//export/tgdata/users/yonshap/proj/mmcortex/'
wd <- '.'
# setwd(wd)
library(vioplot)
source(file.path(wd, 'scripts/util.r'))
load(file = file.path(wd, 'output/methylation/fig4_meth_data.rda'))
load(file = file.path(wd, 'output/hic/figS5_data.rda'))
mcmd <- readr::read_tsv(file.path(... |
adba7a9bad344d56844283585ce043ef79bcdf6b04a7f15310ee8cb519a29856 | R | 2,672 | 98 | context("rest")
library(withr)
test_that("user-agent header is set", {
config <- list()
config$insecure <- FALSE
config$username <- NA
config$password <- NA
config$token <- NA
rest_config <- mlflow:::get_rest_config(config)
expected_user_agent <- paste("mlflow-r-client", packageVersion("mlflow"), sep... |
1dfd44c1cea9b1f4de2e35ce44e3e3bb92ace0ebbe9113506407e58113c504d7 | R | 2,676 | 56 | #This code builds the atc data frame from finngen data
#projectID: VL20243010
#by: Aleksi Kristian Winstén
#date: 12.11.2024
#University of Turku
setwd("/home/ivm/Projects/VilleLangen/EKG-MachineLearning/")
library(data.table)
library(dplyr)
library(tidyr)
#load the ecg_data and modify it
ecg_data <- fread(file = "./D... |
cc9c92ad1ed54acd8b6c28beab24d662f18235f8f0585ef3303338f4dc31f462 | R | 2,677 | 70 | ### preparing input files for cafe from OrthoFinder 2.x results
### adapted from https://www.biostars.org/p/9553290/
library(ape)
library(data.table)
library(tidyverse)
setwd('/Volumes/laur/data/renckens/projects/genome/gene_families/cafe_2026/')
tre <- read.tree('SpeciesTree_rooted.txt')
stopifnot(is.binary(tre))
s... |
cdb4aa73ef7777adc66ee85f011346c33ff091651b61c978b99b1897e7418cd6 | R | 2,677 | 118 | rm(list = ls())
############################################
# Packages
############################################
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DESeq2")
library(DESeq2)
install.packages("tidyverse")
library(tidyverse)
library(Enhanced... |
6817ea3a5e55b0425a3df65fb3495ff3cbc3594b6c1552762bcd8abfb2b18818 | R | 2,678 | 79 |
###################################################################################################
ISR <- read.csv(here("Data", "Fibroblast_lifespan", "ISR_Gene_Lists_updated.csv")) # using isr list that includes all of the genes for FB data (igtp is IRGM, NARS to NARS1, WARS to WARS1)
#############################... |
0c22c0f23182e5f581ad67ce580fdf4bd6cb32f97b32d7cede1325aad5b495f0 | R | 2,679 | 67 | # This script takes the output of the annotate_vep rule in the Snakemake mutect2-post.smk module reformats it for uploading to the db (variants.vep table)
# Additionally generates a .tsv file for backup
#-----------------------------------------------------
library(VariantAnnotation) # I believe this is only needed fo... |
8c206d2c63f98de9f3bab78da366a3764beda6f46561ef7c919720883ebb8982 | R | 2,679 | 67 | # This script takes the output of the annotate_vep rule in the Snakemake mutect2-post.smk module reformats it for uploading to the db (variants.vep table)
# Additionally generates a .tsv file for backup
#-----------------------------------------------------
library(VariantAnnotation) # I believe this is only needed fo... |
b177326a9f20b73910e93e2b478c45a99bc61922ab8a3d6f157faa6bd9df9129 | R | 2,679 | 67 | # This script takes the output of the annotate_vep rule in the Snakemake mutect2-post.smk module reformats it for uploading to the db (variants.vep table)
# Additionally generates a .tsv file for backup
#-----------------------------------------------------
library(VariantAnnotation) # I believe this is only needed fo... |
c2445d42e952d366b680e738995661f83e28317f2efaefd315c1e8898e10cb59 | R | 2,679 | 82 | # Script: 1.Make_lst_for_LM.R
# Purpose: Prepares testing data for linear modeling analysis by:
# 1. Loading and processing RNA-seq expression data
# 2. Processing predictions from different methods (MODULE, INGENE, EpiXcan, CIS)
# 3. Combining predictions and real data for each brain region
############... |
4cc5be4aa4489c04b7c8fb7523adf3e6c8b4be100182bdefd7a394686b0ec865 | R | 2,683 | 108 | # Load Packages
setwd(Sys.getenv("HOME"))
suppressPackageStartupMessages(library(argparse))
suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(library(qs))
source("R_func/mutate_load.R")
source("R_func/future_load.R")
suppressPackageStartupMessages(library(CellChat))
# load parameters
p... |
37a7825db7ebdc5128592286ebceee102f465ec757c54b2432b2987eb09c7bba | R | 2,686 | 77 | #' Print EMC2 build configuration
#'
#' Prints the compiler flags and options that were active when the package
#' was installed. Useful for debugging performance or numerical issues.
#'
#' @export
emc2_build_info <- function() invisible(.emc2_build_info())
# # Last observation carried forward
# # Replaces NA values ... |
014ff541c4e6842d5e6260fb5bdb4ac3b136383156a7d32037d1d8242f7cff77 | R | 2,690 | 75 | # Install and load dplyr if not already installed
# install.packages("dplyr")
library(dplyr)
GSE <- 'GSE185862'
target <- paste0(GSE, "_10X_scRNA_matrix_DEGs_CellType1")
celltype <- "ExN-M"
celltype <- "InN-M"
celltype <- "Astro-M"
celltype <- "Micro-M"
celltype <- "Oligo-M"
celltype <- "OPC-M"
celltype <- "Endo-M"
... |
54cbfd998eb9c3e3c850ff263a69acfcccc85990822e57881eb071c996f2db2c | R | 2,692 | 66 | here::i_am("scripts/2.validations/2.4.sequence_divergence/2.4.3.calculate_phastCons_scores.R")
library(tidyverse)
library(plyranges)
wd <- here("data/validations/sequence_divergence/")
## Get the longest (C)CDS of each regulator --------------------------------------------------------
# GTF
hg38_gtf <- plyranges::... |
8d622e9bc717ef82b4ac0431b46c30b11a4186ae521c87bd31cbb997625de90a | R | 2,692 | 72 | #' Add analysis data from a 10x Genomics Visium experiment to a SPE object
#'
#' This function adds to a SPE
#' ([SpatialExperiment-class][SpatialExperiment::SpatialExperiment-class])
#' object the output from `read10xVisiumAnalysis()`.
#'
#' You might want to use `read10xVisiumWrapper()` instead of using this
#' funct... |
2151eb4fd6fde64fc0a93df8ebee4a959fa5617841a485a8e4c27823f7d41026 | R | 2,694 | 65 | ### Match annotated DESI lipids with In vitro and In vivo LC-MS/MS datasets
rm(list = ls())
library(tidyverse)
library(stringi)
source("./Scripts/Function_scripts/Effect_size_functions.R")
source("./Scripts/Function_scripts/Pre-processing_functions.R")
##====match In vitro ====
Invitro.new.peak <- read.csv("./Input_... |
ac6039d351a6809e8d4a8b7bbe068b0ce3782c51708fac26584e2c1c32475586 | R | 2,695 | 95 | setwd(Sys.getenv("HOME"))
source("R_func/mutate_load.R")
library(tibble)
library(arrow)
# Load background peaks
avail_metric <- read_feather(
"project/multiomics/SCENIC/Data/Regional_DA/agg.avail.mdf.feather"
)
name_key <- 'cluster'
tmp.list <- avail_metric %>% group_split(!!sym(name_key))
avail.list <- tmp.list... |
7593c402b4b946077d7befab5a4791a2d05fb77cdedae41315234a064600b35e | R | 2,696 | 92 | ---
title: "Fig_S3G_MalignantCell_EGFR_expr"
output: html_document
date: "2026-02-17"
---
```{r}
library(Seurat)
library(tidyr)
library(ggplot2)
library(ggpubr)
library(dplyr)
library(harmony)
library(purrr)
library(cowplot)
library(rstatix)
library(ggbeeswarm)
library(ggbreak)
```
# Set up and adjust metadata
```{r... |
3cc46dfa1e8e40a2b9302c55805b31cd4e307fe647f1de96a5098198071d78cd | R | 2,697 | 71 | wigToRangedData <- function(wigfile, verbose = TRUE) {
if (verbose) { message(paste("Slurping:", wigfile)) }
input <- readLines(wigfile, warn = FALSE)
breaks <- c(grep("fixedStep", input), length(input) + 1)
temp <- NULL
span <- NULL
for (i in 1:(length(breaks) - 1)) {
data_range <- (breaks[i] + 1):(br... |
6ab9f91e7320aaa21aaef9188c3fbb01175b4fb2a25e9fc7178603a19f9b3dc6 | R | 2,699 | 69 | #######################################################
# Enumerate cumulative coverage per aliquot for WGS/WXS
# Date: 2018.11.06
# Author: Kevin J., modified by Fred V on 10/14/2020 for GLASSv3
#######################################################
# Directory for GLASS analysis.
datadir = 'results/align/wgsmetr... |
5c219161257f377e2fc3339f4754a9ab748a5adc7ac74d449f47dbd0fa92b0fa | R | 2,700 | 68 | #################################
# Plot the longitudinal change in mutation burden for the cohort cases with matched normal - initial vs recurrence.
# Author: Kevin Johnson
#################################
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(EnvStats)
proj_dir <- "/vast/palmer/pi/verhaak... |
ec5c8ef4fa670141baa0ddef88c6c00eb8126f38feb2299c797bfa8f37cbbb58 | R | 2,702 | 72 | #Code adpated from the STAR package https://github.com/xihaoli/STAAR/blob/dc4f7e509f4fa2fb8594de48662bbd06a163108c/R/CCT.R wtih a modifitcaiton: when indiviudal p-value = 1, use minimum p-value
#' An analytical p-value combination method using the Cauchy distribution
#'
#' The \code{CCT} function takes in a numeric ve... |
aa562e618df2bbd1a38a25b284b9417490f7b2a406074f9e160afb073fbb0a68 | R | 2,703 | 68 |
get_top_taxa <- function(ps, top = 20, method = 'sum', overlap = 'union', threshold_prevalence = 0.75) {
require(phyloseq)
require(data.table)
# Validate input
if (!method %in% c('sum', 'individual')) stop("method must be 'sum' or 'individual'")
if (!overlap %in% c('union', 'intersect', 'core', 'mean')) st... |
a295a36aadbcdffafbeba220c39d993ce0724def344d66d561422dee61e26039 | R | 2,705 | 92 | check_package_available <- function(pkg) {
if(!requireNamespace(pkg, quietly = TRUE)) {
stop("Please install suggested package: ", pkg)
}
}
#' Status report for the malevnc package
#'
#' @description \code{dr_manc} gives a very useful status report for the malevnc
#' package.
#'
#'
#' @return Called for its... |
f0c3c8f44e783e5be029e93985c0b0e6cae15b1c48007898c57c8b754e64dacb | R | 2,705 | 50 | #' Source the Protein-Protein interaction in STRING database
#'
#' This function uses a list of Transcription Factors (TF) of interest to source the Protein-Protein interactions (PPI) from STRING database using all types of interactions not only the physical subnetwork
#' Important: this function produces a simple unw... |
9751fa796e1b3f2097045641c54d2a3f22933f9ec564c3d865e9f0271fed53e0 | R | 2,707 | 88 | library(DESeq2)
library(ggplot2)
cnts <- iPSCsignal #input peak counts
rownames(cnts) = paste(cnts$chr, cnts$start, cnts$end, sep=":")
cnt1 = cnts[,-(1:4)]
# alternative way
rownames(cnts) <- paste(cnts$chr, cnts$start, cnts$end, sep = ":")
rownames(cnts) <- gsub(":", "-", rownames(cnts), fixed = TRUE)
rownames(cnts... |
c8101ba5cdb35734903b4ce3ba94f1faeb5e02c21c8ca687269aa577d3f8b980 | R | 2,707 | 79 | context("test CONDOR result")
test_that("Test CONDOR functions", {
set.seed(1619)
r = c(1,1,1,2,2,2,3,3,3,4,4);
b = c(1,2,3,1,2,4,2,3,4,3,4);
reds <- c("Alice","Sue","Janine","Mary")
blues <- c("Bob","John","Ed","Hank")
elist <- data.frame(red=reds[r], blue=blues[b])
condor.object <- createCondorOb... |
0e3322c9c79c20b3a3c0253ffc59e8e7fc802be353b2f3fd714aee22173b93e5 | R | 2,717 | 51 | #!/usr/bin/env Rscript
library(tidyverse)
###################################################################################################
analysis.dir <- "path/to/analysis/dir"
OrthDEG.logFC.unipattern <- readRDS(file.path(analysis.dir, "OrthDEG.logFC.unipattern.RDS"))
##########################################... |
3d11d790bf95c8e9d22e6262d87662f35e015838b3f78b5593d2e4727aa2b2f4 | R | 2,720 | 106 | # Tests for functions in src directory
# change in random number generation in R3.6, this ensures tests will pass under older and newer Rs
# (this was implemented in Seurat's testthat)
library(ArchR)
library(testthat)
library(Matrix)
library(chromVAR)
suppressWarnings(RNGversion(vstr = "3.5.3"))
set.seed(1)
#Context
... |
a6ca683d064595f6ab5d4b5890fa0befeb96239e4011dc0040eef6f2b99d0594 | R | 2,721 | 83 | library(tidyverse)
library(qs)
library(maftools)
maf = qread('data/TCGA_GBM_maf.qs')
surv_df = qread("data/TCGA_GBM_meta.qs")
samps = intersect(maf$Tumor_Sample_Barcode,surv_df$Tumor_Sample_Barcode)
maf = maf[maf$Tumor_Sample_Barcode %in% samps,]
maf_obj = read.maf(maf = maf, clinicalData = surv_df)
somaticIntera... |
07538737385946ff5aaff8a8a5424d4a4606aaa8d37d47a907af15e3b29bc10d | R | 2,723 | 101 | # 加载必要的包
library(readxl)
library(dplyr)
library(tidyr)
# 读取 Excel 文件
data <- read_excel("P60-qRT-PCR-unpaired-t-test.xlsx", sheet = 1)
# 查看实际列名
col_names <- colnames(data)
cat("实际列名:\n")
print(col_names)
# 数据清洗：将基因列转换为字符
data$gene <- as.character(trimws(data$gene))
# 根据实际数据结构提取CON和GCS组的列
# 第1列是gene... |
aa32a935c2ee1accdf4b15f80af3c0303488556bedc126ba56d48a20272484a2 | R | 2,724 | 89 | ---
title: "plot_cpm_CAR_Targets"
output: html_document
date: "2026-01-01"
---
```{r}
library(dplyr)
library(tidyr)
library(readr)
library(tibble)
library(readxl)
library(purrr)
library(ggplot2)
library(cowplot)
```
# load data
```{r}
set.seed(1234)
cpm_df <- read_tsv("/path/to/resources/cpm_gene_symbol_dedup.tsv") #... |
43e7da9bb435208603ec37ef85be63e393d11965d74adaec31e474787222651f | R | 2,730 | 83 | #' Plot spatial weights for cells on a spatial plot
#'
#' Visualizes spatial weights by plotting cell coordinates colored by
#' a numeric weight value.
#' Only cells in `data` that match the names in `weights` will be included.
#' This is useful for visualizing spatial trends such as proximity to
#' boundaries or centr... |
3236a5831aec40ac00c1932a65ecaaa1d15d752fabaf403f3f73af154e8d1244 | R | 2,732 | 81 | #' Sort clusters by frequency
#'
#' This function takes a vector with cluster labels, recasts it as a `factor()`,
#' and sorts the `factor()` levels by frequency such that the most frequent
#' cluster is the first level and so on.
#'
#' @param clusters A vector with cluster labels.
#' @param map_subset A logical vector... |
a47beaac6cf9b4978e0558c2bbb64c6a997940d9662b86c2ae31e61f2a2c3960 | R | 2,734 | 83 | #' Plot spatial weights for cells on a spatial plot
#'
#' Visualizes spatial weights by plotting cell coordinates colored by
#' a numeric weight value.
#' Only cells in `data` that match the names in `weights` will be included.
#' This is useful for visualizing spatial trends such as proximity to
#' boundaries or centr... |
3a9042d5da90e9895e8e75371184876ea73685750769d89496bd20f386dd4b23 | R | 2,735 | 66 | # R script for generating a scatter plot comparing predicted and true atrophy rates
# Author: Maryam Hadji, University of Eastern Finland, Kuopio, Finland (maryamh@uef.fi)
# Last updated: 20-Feb-2025
#
# Description:
# This script loads an RData file, extracts relevant data, and creates a scatter plot
# comparing pred... |
f9b4ffd4d09598278b7eba17604198bce72d83c423e4c0376ab8094d26f915b2 | R | 2,735 | 70 | #' Marker Diversity Function
#'
#' This function calculates the Shannon diversity index for markers within clusters and categorizes the diversity into "High", "Within 95% CI", and "Low" categories.
#'
#' @param ann An object containing annotation data.
#'
#' @return A ggplot object showing the Shannon diversity by clu... |
fef500ef6897731be49be9d00c9a5fd50ed7364f44a1b6ecaa4329e7808cdfaa | R | 2,736 | 81 | input_folder <- "/Users/jks6575/Dropbox/Jiawan/Analysis/Genetic_enrichment/DEGs_for_fisher/test_gene/CRISPR/"
reference_folder <- "/Users/jks6575/Dropbox/Jiawan/Analysis/Genetic_enrichment/DEGs_for_fisher/reference_gene/"
# List all CSV files in the input folder
csv_files <- list.files(input_folder, pattern = "\\.... |
1a54338c0709967469d4fe1a763a786747064460434822c68349ae185f787e3e | R | 2,741 | 73 | ## Need to run the following script first
### 'Fig3.SideChain_Invitro.vs.GPMV.R'
setwd(rstudioapi::getActiveProject())
rm(list = ls())
library(tidyverse)
library(ggpubr)
library(ggrepel)
### ==== 39 side chain features are too many to label, add text to only top 30% that go up and down with age ====
### ==== color cod... |
196dc1d9f23abc2b43ac4172a72c2cd995a800e8c71116e54db3e1e34d2d036e | R | 2,747 | 97 | ---
title: "Fig_S4T_HallmarkEMT_by_sample_postinfusion"
output: html_document
date: "2026-02-17"
---
```{r}
install.packages("msigdbr")
library(devtools)
install_github("alserglab/fgsea") # dependency for clusterprofiler
devtools::install_github("YuLab-SMU/treeio")
devtools::install_github("GuangchuangYu/ggtree") #... |
19ee13e32712a1f4ffeb35fd86a7b08bb56114566697e9ee3d7acfbaa5be126c | R | 2,747 | 73 | # Written by Yapei Xie and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
# Helper function to install and load packages
personal_lib <- Sys.getenv("R_LIBS_USER", unset = path.expand("~/R/library"))
dir.create(personal_lib, recursive = TRUE, showWarnings = FALSE)
.libPaths(c(person... |
2d0b653d8f4dee451eb0e3270c7b7147ff69cae1beb56a199ab8b9358527f700 | R | 2,748 | 45 | library(R.matlab)
library(ggplot2)
hcp_noise_rss <- readMat("../results_bins/noise/fd_gs_ds_avg1_global_rss_corr.mat");
noise <- "FD"
Data1 = data.frame(Value = hcp_noise_rss$fd.corr.mean.all)
noise <- "GS"
Data1 = data.frame(Value = hcp_noise_rss$gs.corr.mean.all)
noise <- "RV"
Data1 = data.frame(Value = hcp_noise_... |
f88704ba85e0ed550351d92b54786106f0a743747934dc2b4b3a74ba1214f864 | R | 2,750 | 68 | #' Pretrains model for the unsupervised version of MetaNeighbor
#'
#' When comparing clusters to a large reference dataset, this function
#' summarizes the gene-by-cell matrix into a much smaller highly variable
#' gene-by-cluster matrix which can be fed as training data into MetaNeighborUS,
#' resulting in substa... |
15a4067173c87772ab5e0ba43652fa69db778d17a49bd60673b51c8184bfb155 | R | 2,758 | 49 | ########################################################################################################
#### Trial dataset for this R script can be downloaded from ####
#### https://data.mendeley.com/datasets/454s2vw255/1/files/87ff9c3d-9734-47b8-b483-de122e00775d ####
##... |
2e43c9dc1461f7f1f3fa2a17e846ebe92a618073ec1d59deac723f41118ceb61 | R | 2,760 | 60 | options(stringsAsFactors=FALSE)
setwd('~/d/sci/src/mrm_prp/')
# read in list of samples from Parchi & Zerr cohorts, with data from previous ELISA study
samples = read.table('data/samples/samples.tsv',sep='\t',header=T)
# set random seed so this script produces the *same* random result every time
set.seed(1)
# each b... |
d7cc2be4c89596d6b6354fd65ace48606fcff358a1e2ec94743b5006cb81474e | R | 2,760 | 76 | # Description: This script is used to compress the time and status of all
# complications into a complication whole
library(dplyr)
t2dCompMststy2ndEd <- "D:\\work2\\t2dAndComplicationsUkb\\data\\processed\\t2dCompMststy2ndEd.rds" %>%
readRDS(.)
t2dCompMststy <- t2dCompMststy2ndEd
# Some of the study subje... |
c6e8c2ae4e5d9d91d47a23bdbb0a2c991d910d0a578e556667293a85fdae51a8 | R | 2,767 | 68 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# Parameter settings
##### ##### ##### ##### ##### ##### ##### ##### ##### #####
setwd(file.path(getwd(), "R"))
source("00_directorySettings.R") # load baseDir <- "/WORKING_DIR"
source("00_settings.R")
dataDir <- file.path(baseDir, "data")
outDir <- file.path... |
eed165b531ed006fc9b83520e47206d7184fb8d4e1fa668e7fef6038421ce4d2 | R | 2,770 | 149 | ---
title: "Use PCAs to filter out outliers"
author:
- David lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: pygments
theme: yeti
toc: true
number_sections: true
df_print: paged
code_download: false
... |
a9b1d0015e7313821c5b249a020673f7f23db1dce38cfdb1cede577b0ba797ce | R | 2,773 | 61 | #' Combine multiple continuous variables through PCA
#'
#' PCA is performed on \code{cont_mat}, the matrix of multiple continuous
#' features. The first PC is returned, representing the dominant spatial
#' signature of the feature set. Its direction is negated if necessary so that
#' the majority of coefficients across... |
3c317d28240c833c8cf5455836c8373fad983cfb8eff521396652bf2664cc170 | R | 2,774 | 60 | library(stringr)
library(dplyr)
## Merge all measures into one file
setwd('/home/huyang/Projects/HuYang/HY_20250708')
sublist <- scan('./PROCDATA/LIST/t1_sublist_postqc.txt', what = '')
meas_names <- c('thickness', 'area', 'volume', 'sulc', 'meancurv', 'gauscurv', 'curvind', 'foldind', 'lgi')
atlas_names <- c('Schaefer... |
53c8ae0aaa229fbe038333a762ddc6809422140f8d414b00c4cab8253fa764b7 | R | 2,775 | 84 | # Script: 1.Make_lst_for_LM.R
# Purpose: Prepares testing data for linear modeling analysis by:
# 1. Loading and processing RNA-seq expression data
# 2. Processing predictions from different methods (MODULE, INGENE, EpiXcan, CIS)
# 3. Combining predictions and real data for each brain region
############... |
9683ab38fda98191cb398ea45716ee6500d9a6d14fc488862a7d42467c5e23ae | R | 2,776 | 71 | test_that("Python wrapper functions are available", {
# These tests only check function availability, not execution
# since Python environment may not be set up in testing
expect_true(exists("set_python_env"))
expect_true(exists("influence_calculator_py"))
expect_true(exists("calculate_influence_py"))
})
... |
86f1df8398261d44cb2555406df649c394530d4de8b5cf6f1d22701343351124 | R | 2,777 | 91 | library(dplyr)
library(tibble)
library(readr)
library(ggplot2)
library(lme4)
library(rstatix)
data <- read_csv("data/TREC_KREC_combine_Baby_4.csv") %>%
filter(type == "Baby", baby_age > 0) %>% # remove cord blood
mutate(log10age = log10(baby_age))
m <- glm(log10TREC ~ log10age + SNP_merge + sex + mode_delivery... |
e2e963f87881bcd96b488f392685535d61ecce6ef8eff8068f4062a52a2a4000 | R | 2,779 | 68 | # This script takes the output of the annotate_vep rule in the Snakemake mutect2-post.smk module reformats it for uploading to the db (variants.vep table)
# Additionally generates a .tsv file for backup
#-----------------------------------------------------
library(VariantAnnotation) # I believe this is only needed fo... |
10cd584844305261ea8f75971e211ead61169e2aff0631fdb8d7e18c28fee09b | R | 2,780 | 69 | # This script takes the output of the annotate_vep rule in the Snakemake mutect2-post.smk module reformats it for uploading to the db (variants.vep table)
# Additionally generates a .tsv file for backup
#-----------------------------------------------------
library(VariantAnnotation) # I believe this is only needed fo... |
931ca03ee8dcffea3187bbcf64ef828012f6c81f40259e0344a6da9288d8c01f | R | 2,780 | 81 | # R script for identifying stable, converter, and unsure MCI and CN subjects based on longitudinal ADNI data
# Author: Maryam Hadji, University of Eastern Finland, Kuopio, Finland (maryamh@uef.fi)
# Last updated: 21-Feb-2025
#
# Description:
# This function loads baseline and longitudinal ADNI data, identifies stable s... |
8fa4b997756be18dfb01e067429fb14236ab277cca5d395f82bbf2b65cb449d3 | R | 2,784 | 85 | #conda activate cistopic
# ***!!!! mutate .bashrc R 4.2.2 !!! all versions of R
# Identify modules for CREs contain frVar+eQTL
# XH Sep 8 2025
library(data.table)
library(dplyr)
library(GenomicRanges)
library(cisTopic)
outp <- "/geschwindlabshares/RexachGroup/Xia_Data/atac_cellrangerOut/project0318/cisTopic"
dxs <- ... |
2d9e9dc41716d62c043e8c5958547ba12ff40aed606243cc4234d0dc6fd73ba8 | R | 2,787 | 70 | library(tidyverse)
library(lme4)
library(DHARMa)
library("visreg")
get_ran_df <- function(intro){
ran_pi <- data.frame()
for (n in 0:99){
t <- read.table(paste0("data/introgression_scans/", intro, "_randomwins/random_windows.", n, ".diversity.bed"),
col.names = c("chrom", "start", "end", "p... |
fb8da660dbb96a18fafa223a27c74d7d865a4a80d02bd5f668df91ce3854b452 | R | 2,791 | 78 | #####################################################################
# Brain Tissue Module coeQTL Analysis Pipeline
#####################################################################
# This script performs co-expression QTL analysis for brain tissue modules.
# It processes the output from the mapping pipeline t... |
b5f9f5f098e5d76ceb3ca8fad10a62b906897ef454fae74806aad910567812e2 | R | 2,792 | 91 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Download genes in KEGG pathways of interest
# Author: Amanda Zacharias
# Date: 2023-08-10
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nix... |
20b836b6fd4d55790aa7a97362e8953353fb7b3d511b0c05991301036668b51d | R | 2,794 | 72 | #!/usr/bin/env Rscript
# Import TPM matrix for CIBERSORTx
# Copy TPM matrix from 03.Normalization, add GeneSymbol header, and save to 01.IMPORT_MIXTURE
# Source and destination directories
source_dir <- "/home/joonho345/1_Epilepsy_RNA/RNA_Animal/03.Normalization"
dest_dir <- "/home/joonho345/1_Epilepsy_RNA/RNA_Animal/... |
c6046d97a29901ccc1c58e946ef0dea504aba2e919527097f0e811857d05ec1f | R | 2,796 | 103 | #!/usr/bin/env Rscript
###############################################
# Manoj M Wagle (USydney, MIT CSAIL)
###############################################
suppressPackageStartupMessages({
library(ggplot2)
library(data.table)
})
args <- commandArgs(trailingOnly = TRUE)
dataset_path <- args[1]
modality <- a... |
7ae0f6fdb29f7d369a24622d3c520ddd0a87b5721851e471daa34834c7fa2850 | R | 2,797 | 91 | library(dplyr)
library(tibble)
library(readr)
library(ggplot2)
library(lme4)
library(rstatix)
data <- read_csv("data/TREC_KREC_combine_Baby_4.csv") %>%
filter(type == "Baby", baby_age > 0) %>% # remove cord blood
mutate(log10age = log10(baby_age))
m <- glm(log10KREC ~ log10age + SNP_merge + sex + mode_deliver... |
25df9138a848acf55fcd5a706aeb0b8f3cb135de638c6879b227caa27f509939 | R | 2,800 | 62 | ###细胞活性基因在细胞中的表达------------------
Neuronal_activation <- toupper(c("Arc", "Btg2", "Coq10b", "Crem", "Dusp1", "Dusp5", "Egr1", "Egr3", "Fbxo33", "Fos",
"Fosl2", "Gadd45g", "Gmeb2", "Grasp", "Junb", "Nr4a1", "Nr4a2", "Nr4a3", "Per1", "Rgs2",
"Sertad1","Ti... |
49711d65c21f111c7b3db56a02157e2e98ad1ee3ee81aff4d20e2efd5d9c84d7 | R | 2,800 | 76 | #' Spatial registration: model
#'
#' This function defines the statistical model that will be used for computing
#' the block correlation as well as pairwise statistics. It is useful to check
#' it in case your sample-level covariates need to be casted. For example, an
#' `integer()` variable might have to be casted in... |
b2ef4138e538b98f50e2452fc60c98d82ba4ba200a2cf2ff5d2cd92a8993f451 | R | 2,802 | 119 | # Tests for ArchR
# change in random number generation in R3.6, this ensures tests will pass under older and newer Rs
# (this was implemented in Seurat's testthat)
library(ArchR)
library(testthat)
library(Matrix)
suppressWarnings(RNGversion(vstr = "3.5.3"))
set.seed(1)
#Context
context("test_workflow")
#Genome
addAr... |
e667c83cb89d9b59bb2547f46ff81bd6702333653eca1b30da5b644e0611c26e | R | 2,804 | 124 | rm(list = ls())
############################################
# Packages
############################################
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DESeq2")
library(DESeq2)
install.packages("tidyverse")
library(tidyverse)
BiocManager::in... |
604fd91ac24796c4dc7449ca4972e37ae44452422024d120c90bb667d73f7f4b | R | 2,805 | 88 | rm(list = ls())
############################################
# Packages
############################################
pkgs_cran <- c("readxl", "openxlsx", "ggplot2", "reshape2")
for (pkg in pkgs_cran) {
if (!requireNamespace(pkg, quietly = TRUE)) {
install.packages(pkg, dependencies = TRUE)
}
library(pkg, c... |
fd932133b95985d5ce4fa750bf37a90d56e6d0607e1de72fbdcab44d8768ecbe | R | 2,806 | 78 | ---
title: "Volcano Plots"
author: "Hannah Serio"
date: "2025-04-15"
output: html_document
---
```{r libraries, include=FALSE}
library(ggplot2)
library(readxl)
library(openxlsx)
library(dplyr)
```
```{r volcanoPlots, warning=FALSE, fig.width=9, fig.height=6, fig.path='R_plots_volcanos/', dev=c('png', 'pdf')}
setwd... |
fed322bfca5489619486e9d1862e413af410a96a5a93d53e0609028d57fd0ba3 | R | 2,807 | 69 | library(data.table)
rm (list=ls ())
gc()
gc()
# Read in list of RDat files
rdat_list<-list.files(path='D:/Work/Ten/brain/brain_cell_TWAS_Fujita et al/Astrocytes', pattern='.RDat')
# Start making pos file
pos<-data.frame(PANEL='Astrocytes',
WGT=paste0('Astrocytes/',rdat_list),
... |
760561fb6498ebc5dd55a8b445471b4a9b3ceb85dac45367e361d6771ae750db | R | 2,809 | 79 |
# This script identifies SNPs that are present in the EpiXcan database but missing from the target genotype data
# It is typically used as a preprocessing step for genetic analysis to identify variants that need proxy SNPs
# Load required packages
if (!require("data.table")) install.packages("data.table")
if (!... |
fc12fab5e6d0a3e2bda0912c6a82f427c430fda1ba91b4e31abad4699b1cf493 | R | 2,809 | 75 | library(dplyr)
library(survival)
library(stringr)
all_file_paths <- "D:\\work2\\t2dAndComplicationsUkb\\data\\processed\\LassoCox" %>%
list.files(path = ., pattern = ".*", full.names = TRUE, recursive = TRUE)
compIndex <- which(str_detect(all_file_paths, "Comp.rds"))
comp_path <- all_file_paths[compIndex]
... |
9e4fa389843c6eac674b7be089beb6e680c9908723042af77ee692ee4e8698b6 | R | 2,814 | 61 | library(ggplot2)
library(tidyverse)
library(ggtext)
library(dplyr)
####read mean spreadsheet##########
mean = read.csv("/Users/guangli/Library/CloudStorage/OneDrive-UniversityofPittsburgh/Haoting/Curioseeker_output/code/update-for-normalized_e14_1/E14_1_eGFP_upk3b/lig_rec_means.csv",skip = 2)
mean = mean[,c(1,2,5)]
col... |
82a12a876a1e3cb12af9805b35c3dbb01434fe7fa4c476a3d21f336344358357 | R | 2,815 | 68 | # This script takes the output of the annotate_vep rule in the Snakemake mutect2-post.smk module reformats it for uploading to the db (variants.vep table)
# Additionally generates a .tsv file for backup
#-----------------------------------------------------
library(VariantAnnotation) # I believe this is only needed fo... |
d09444816484ceee59b58c6f71a459d209b7319de9055b9a15d25292ea2ce1cf | R | 2,833 | 81 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# Parameter settings
##### ##### ##### ##### ##### ##### ##### ##### ##### #####
setwd(file.path(getwd(), "R"))
source("00_directorySettings.R") # load baseDir <- "/WORKING_DIR"
source("00_settings.R")
dataDir <- file.path(baseDir, "data")
nmfDir <- file.path... |
2023a35c61c26064cffe884e65a4a1f0bac09b9e58ceb581d4fe0cc2f552663e | R | 2,834 | 94 | # -----
# UMS IPSIT BIOINFO - Licence GPL-3
# https://github.com/fdumbioinfo/moal
# -----
#
# moal install
source("https://raw.githubusercontent.com/fdumbioinfo/rtools/main/moal-demo/0-moal-install-r-universe.r")
library(moal)
# DESeq install
if(!require("BiocManager",quietly=TRUE)){install.packages("BiocManager")}
if(... |
8940382a66942b6660208cc4ce83692c726613de4cba4652b381554cae0c6eff | R | 2,838 | 74 | ### In vitro #2 effect size calculation on lipids and double bond composition
setwd(rstudioapi::getActiveProject())
rm(list=ls())
source("./Scripts/Function_scripts/Effect_size_functions.R")
library(tidyverse)
#########################################################################################
## ====calculate e... |
857064ca6e93561d650ed28d97d07308ffbb645428d9af91f0f7445cb84e11ed | R | 2,842 | 88 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
if(!require(data.table)){ install.packages("data.table"); library(data.table) }
if(!require(tidyverse)){ install.packages("tidyverse"); library(tidyverse) }
if(!require(optparse)){ install.packages("optparse"); library(optparse) }
})
#######################... |
e2a169905edff479adbae367e667a28f26c6b0774e4bb8ef1fdc56a1091d6dd8 | R | 2,845 | 83 | # R script for identifying MCI converters based on longitudinal data
# Author: Maryam Hadji, University of Eastern Finland, Kuopio, Finland (maryamh@uef.fi)
# Last updated: 21-Feb-2025
#
# Description:
# This script identifies stable and converting individuals in the MCI group
# based on their longitudinal diagnosis da... |
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