sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
9bd42fbcabcd56038bba146018a713e45d9f419bd37a9f812621b6b4dd0d0855 | R | 2,363 | 68 | library(ggplot2)
library(dplyr)
library(forcats)
library(reshape2)
library(viridis)
# Correct data for each dataset
data_countsV2A <- data.frame(
Category = c(">2.0m", "1.6-2.0m", "1.2-1.6m", "0.8-1.2m", "0.4-0.8m", "<0.4m"),
Count = c(5.69, 0.38, 11.66, 4.31, 2.66, 275.28)
)
data_countsV2B <- data.frame(
Categ... |
36a7e5b9c1e5582cbeee12303bfd95c82c1a079b9c0f2540f3b411dead4c0e94 | R | 2,368 | 93 | # packages
setwd(Sys.getenv("HOME"))
suppressPackageStartupMessages(library(argparse))
suppressPackageStartupMessages(library(Signac))
suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(library(qs))
suppressPackageStartupMessages(library(magrittr))
suppressPackageStartupMessages(library(d... |
e40589c882c2885d321ecd1e9de23116b1f2209c29367cf78871b5fdc61237cc | R | 2,369 | 69 | #' @include model-utils.R
NULL
#' @rdname mlflow_save_model
#' @export
mlflow_save_model.H2OModel <- function(model,
path,
model_spec = list(),
conda_env = NULL,
.... |
015f14dd370739b96bac3679b677be88b02955f80535efc7c33d568e3def776c | R | 2,371 | 70 | #!/usr/bin/Rscript
options(error=utils::recover)
##
## average_coverage
## Given an input coverage file in circos form, ie:
# # coverage stats: mean=4748 std dev=313
# chr1 14400 14404 2
# chr1 14404 14406 3
# chr1 14406 14407 8
# chr1 14407 14408 13
# chr1 14408 14409 21
# chr1 14409 14410 39
# chr1 14410 14411 54
# c... |
85d5bdf6d16b59cc01e0ca0934d2cc4c26398c0f58869ecda327e32358209b0f | R | 2,375 | 53 | #' @title plotTsne_compareSettings
#' @import AUCell
#' @description Plots several t-SNEs coloured by cell properties (to compare different t-SNE settings)
#' @param fileNames t-SNE files to plot
#' @param scenicOptions imports cellInfo and colVars
#' @param varName If a varName is given, it plots all the t-SNEs (not s... |
f1ee1343e3c0e512fb1ea4908ee04f378c8931f3c041d3bfeb7008f4728eefbe | R | 2,375 | 61 | #by Alexander Zizka, 2016-01-30, bugs & questions to speciesgeocodeR@googlegroups.com
setwd("C:/Users/xzizal/Desktop/GitHub/PyRate")
library(maps)
source("pyrate_DES_utilities.R")
#Example 1 - Minimum data
#this generates an DESin R object;
#Presence data = present distribution of all recent taxa also in the fossi... |
7a9d4ce69474d2075af6c605851909eaec967f60bfe06db2584d1177ff224eb5 | R | 2,376 | 71 | rm(list=ls())
#Use for extracting the files from a specific directory
myDir1 <- "/projects/verhaak-lab/GLASS-III/results/mutect2/geno2db/"
myinf1 <- dir(myDir1)
myinf1 <- myinf1[grep("info.tsv",myinf1)]
myinf1 <- myinf1[grep("GLSS-SN-",myinf1)]
myoutf1 <- myinf1
myoutf1 <- gsub(".tsv","",myoutf1)
myinf1 <- paste(myDir... |
34f57b0c0b20010436e7183f8a7470cfa728d79db5c7d952458857d96a1db20e | R | 2,377 | 64 | # Predict the TFBS of each CRE peak
library(rhdf5)
library(data.table)
library(dplyr)
library(parallel)
library(preprocessCore)
library(BSgenome.Hsapiens.UCSC.hg38)
library(memes)
source("pci_snATAC_peakSet_w_Enh.R")
pciATAC_peakset$peakloci <- paste(pciATAC_peakset@seqnames,
pciATAC... |
40f9dcc608a5d682f077aae512f4e5742f244abd62d88cf4e4d1c1120a224e02 | R | 2,379 | 52 | # Load necessary libraries
library(dplyr)
# Define input directory and variable list
directory_path <- "/home/joonho345/1_Epilepsy_RNA/RNA_Human/11.Sample_variables/02.deseq"
variable_list <- c("HS", "withoutHS", "IP", "NO", "IP_Sei", "IP_Other")
all_merged_dfs <- list() # List to store matrices for final merging
f... |
79b78c8b29a22b1b2915d38cbb19211a429579571f996b781737a0b6e63eea6f | R | 2,379 | 81 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Non-specific filtering
# Author: Amanda Zacharias
# Date: 2023-08-07
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 gcc/7.3.0 ... |
296883d14268b3afbe98da4e643ac4af6691bbbacec2cac9f6c93008449e66bd | R | 2,382 | 54 | # 05_go_bubble_heatmap.R
# Combined bubble plot + gene heatmap for selected GO terms
rm(list = ls())
library(ggplot2)
library(reshape2)
library(patchwork)
library(cowplot)
library(openxlsx)
source("scripts/utils.R")
# Load GO results from earlier steps (example for Hip_BP7)
go_df <- read.xlsx("output/Hip_... |
1f770e8d61d57fbd3bcba20c859b59fbdbc44609c81578d0433d4f2c803066b2 | R | 2,386 | 72 | test_that("multiplication works", {
mirroreg = structure(
c(
-0.993507657550687,
-0.0789118732716515,
0.162578427057817,
0,
-0.0415891298774246,
0.998353717776582,
0.00339175766683097,
0,
0.0594215260756958,
0.0023521675167209,
0.9951539398219,
... |
418496b2be72ed6dd7df149b660cd8087f0982507ba807650e901bc5aff2ad22 | R | 2,388 | 73 | ---
title: "Fig_S4G_CD8_Tcell_functional_genes"
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
``... |
5dbff817e28f5ba5550c55100fbdff548ea2691775f31ed70cd70f6b550d0edf | R | 2,388 | 67 | library(ggplot2)
rm(list=ls())
options(digits = 4) # by default, print results to four decimal digits
set.seed(1823) #for replication - the year Trinity College was founded!
#replace the empty quotes with the file path to the location where you downloaded the files from Dropbox below
path = ""
#----- Load in data ---... |
1f6ecc0ea4cdd2bf11bf5eb1b3e112f04334d790d998c06d9a7c4b05fae47e9a | R | 2,391 | 61 | ###############################################################################
## Generate DE gene counts plot with FDR < 0.1 ##
###############################################################################
library(here)
library(limma)
library(ggplot2)
library(tidyr)
# Load the checkp... |
f918fa8b4bc79b67650f141bad8e93ba8f2b76c11752b3030017633780f26a4c | R | 2,393 | 53 |
data_selectedCpt_all_CPTPSP <- readRDS("datasets/data_selectedCpt_all_CPTPSP.RDS")
nca_selectedCpt_all_CPTPSP <- readRDS("datasets/nca_selectedCpt_all_CPTPSP.RDS")
pure_selectedCpt_all_CPTPSP <- readRDS("datasets/pure_selectedCpt_all_CPTPSP.RDS")
cmp3_selectedCpt_all_CPTPSP <- readRDS("datasets/cmp3_selectedCpt_al... |
1feb1294056098cc61c8ead439c00aa1577cb338a103f4ba8f23129123d74bbb | R | 2,396 | 66 | library(ggsegGlasser)
library(ggsegSchaefer)
library(ggseg)
library(dplyr)
library(ggseg3d)
library(R.matlab)
library(ggplot2)
library(cifti)
library(stringr)
library(factoextra)
library(matrixStats)
library(scales)
library(Hmisc)
library(tidyr)
library(cocor)
# atlas data.
schaefer200x17_label <- read.csv("G:/datase... |
4a35fcb9ae978d56eb19659c340e71af3381a8ee2397ef6f371349cde7dfbba4 | R | 2,399 | 78 | fanc_cloudvolume <- function(...) {
cv=fafbseg::flywire_cloudvolume(cloudvolume.url = fanc_cloudvolume_url(), ...)
cv
}
fanc_cloudvolume_url <- function() {
rr=with_fanc(getOption("fafbseg.cloudvolume.url"), force = FALSE)
sub("graphene://middleauth+", "graphene://", rr, fixed = TRUE)
}
fanc_api_url <- functi... |
ae2b4666da4a6c6980c3bd390d0c7717e644ea72052c312403b86c83f83b310b | R | 2,402 | 63 | library(DESeq2)
library(ggplot2)
library(pasilla)
# Define all comparisons
comparisons <- list(
list(name = 'HS', data = 'adjusted_df_HS', coldata = 'coldata_df_HS'),
list(name = 'withoutHS', data = 'adjusted_df_withoutHS', coldata = 'coldata_df_withoutHS'),
list(name = 'IP', data = 'adjusted_df_IP', coldata = '... |
cba1c19715a93b63933fc48fbbf9ad0804b55bea762b159d0cbe48c4aa02619e | R | 2,411 | 63 | # Set the output directory
output_directory <- "/home/joonho345/1_Epilepsy_RNA/RNA_Animal/06.Deconvolution/02.IMPORT_GO/"
# Create destination directory if it doesn't exist
if (!dir.exists(output_directory)) {
dir.create(output_directory, recursive = TRUE, showWarnings = FALSE)
}
file_directory <- "/home/joonho345... |
5416c4373c16b4d9e862bc670974098c11982622b7c214dcdb62deeeb5e5d32a | R | 2,412 | 65 | context("preparing a data set")
test_that("ensure that samples and directories are included", {
expect_error(sleuth_prep(study_formula, study_mapping))
expect_error(sleuth_prep(3, study_mapping))
expect_error(sleuth_prep(study_mapping, 3))
})
test_that("normalization factors", {
expect_error(sleuth_prep(stud... |
b429671947324c45ffe5efa685d67a8c16fc340261028082bb33af37bb527fbe | R | 2,412 | 82 | library(methylKit)
library(dplyr)
library(rtracklayer)
library(ggplot2)
library(reshape2)
library(ChIPseeker)
library(org.Hs.eg.db)
library(GenomicFeatures)
library(biomaRt)
library(tidyverse)
library(ggalluvial)
plot_meth_pc <- function(pc, title){
# Add region IDs as a column
pc_df <- as.data.frame(pc)
n_r... |
525bed695c48e58e1381195050cad2571224577af81486775c16b0369e674379 | R | 2,416 | 94 |
# ---- Variance Beta Distribution ---- #
betaVar <- function(alpha, beta){
num <- alpha*beta
den <- (alpha+beta)^2 * (alpha+beta+1)
return( num/den )
}
get_alpha_beta_EV_Var <- function(Pex, NewInfo, lowerQ, upperQ){
out <- matrix(nrow=length(Pex), ncol = 6)
alpha <- 1; beta <- 1
out[1, 1] <- a... |
602cc611e2fa14c007c9478fd72c0675d9f706ea46145a7204b0b07370bc08dc | R | 2,416 | 70 | ###########################################################
### fix-batch-003-duplicates.R
###
### Removes the 3229 duplicate rows (rows 1-3229) from
### batch_000003.parquet that are already present in
### batch_000001 and batch_000002.
###
### Idempotent: skips if .bak already exists.
################################... |
0190a1a5a619311cf4faed3d36572ac98460ef90352cd1c9583a813f2c8b640e | R | 2,419 | 109 | ---
title: "Venn Calculator"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{VennCalculator}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## How to use Venn Calculator?
`ggVennDiagram`... |
a90d82539fc5073623f370adeb4023368f5200455bcfbf22591659739e9946f5 | R | 2,421 | 58 |
setwd(rstudioapi::getActiveProject())
rm(list = ls())
library(tidyverse)
library(ggpubr)
load("./Output_Data/Invitro.SC.analysis.Rdata")
Invitro.SC <- Invitro.SumSC %>%
mutate(Cla_SC = paste0(Class, "(", Sep.SC, ")")) %>%
mutate(Age = ifelse(grepl("^O", Samples), "Old", "Young")) %>%
group_by(Cla_SC, Age) ... |
ef9f1f42cd50a9a4fa15c8ce1e439555119ed5142d1d0a42ccca986ff5137c54 | R | 2,422 | 56 | library(dplyr)
library(tidyr)
#### Create GTF Matrix ####
gtf_path <- "/home/joonho345/resources/Reference/Homo_sapiens.GRCh38.112.gtf"
gtf_table <- read.table(gtf_path, header = FALSE, skip = 5, sep = "\t", quote = "", comment.char = "")
gtf_table <- gtf_table %>% dplyr::select(-c(V1, V4, V5, V6, V7, V8))
gtf_table... |
020cf768894bebc0f83d1117972ccf2d761f8f43fddb2fd95036434de4e6762b | R | 2,423 | 61 | #
# sleuth: inspect your RNA-Seq with a pack of kallistos
#
# Copyright (C) 2015 Harold Pimentel, Nicolas Bray, Pall Melsted, Lior Pachter
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software F... |
605c61b15e416afa5bbb5771d22db0a625033ffacf0d447b8f57c73a25bb30cb | R | 2,425 | 53 | #########################################
### ORGANISE CONNECTOME DATA MATCHING ###
#########################################
source("banc/banc-startup.R")
library(DBI)
library(RSQLite)
library(fs)
####################
### Flywire data ###
####################
# Read from legacy source SQLite
original_file <- "/n/dat... |
3c4d4e191bfac092f3bc2161df4e1713f8041ac721c947dc032447d816047ce4 | R | 2,431 | 51 | #' @keywords internal
"_PACKAGE"
## usethis namespace: start
#' @importFrom DBI dbConnect dbDisconnect dbGetQuery dbReadTable dbWriteTable
#' @importFrom Matrix sparseMatrix solve diag lu
#' @importFrom R6 R6Class
#' @importFrom RSpectra eigs
#' @importFrom RSQLite SQLite
#' @importFrom dplyr filter
#' @importFrom dig... |
d865f3536d354471cf39a6467a07038bbb85fbb83219202cfa93d315f47866bb | R | 2,432 | 83 | #' @importFrom utils unzip
#' @importFrom progress progress_bar
extract_zip_files <- function(files, zipfiles=NULL, ...) {
if(is.character(files)) {
zipfiles=attr(files, 'zip')
files=list(files)
} else {
if(is.null(zipfiles)) zipfiles=names(zipfiles)
}
if(is.null(zipfiles)) {
stop("Please speci... |
54d592cb283a3bb6d558b0de5455ae191879011831ac4ca5e6ee5a78b73a7497 | R | 2,435 | 49 | #' banc-alignment-prep — Prepare data for the cross-dataset alignment algorithm.
#'
#' Dispatcher that loads a region preset and runs the preset's prep logic.
#' Each preset bundles the region filter, the reference-dataset vocabulary
#' rules, the seed-labelling policy, and the per-type capacity formula —
#' those diff... |
149b7279d0ba9cd9beddac1dc74442e3d7b421fc1efd4daebb019ac6ed41d080 | R | 2,438 | 68 | #####################################
## Write TITAN parameters to database
## Author: Kevin Johnson
## Updated: 2023.03.20
#####################################
library(tidyverse)
library(DBI)
library(odbc)
## Current project.
projdir <- "/projects/verhaak-lab/USERS/johnsk/glass4"
setwd(projdir)
## Connect to the ... |
ac1a428d620fbd0bdb24e4d6b80774395a90cb97dea5477cbaba836080213a5e | R | 2,439 | 55 | if(!require(dplyr)) {
install.packages("dplyr")
}
if(!require(ggplot2)) {
install.packages("ggplot2")
}
library(dplyr)
library(ggplot2)
# Test Case #2: CountReadsSpark, no intervals, HDFS input
test_case_2 <- read.csv("/Users/tom/workspace/gatk/q4_spark_eval/test_case_2_results.csv")
test_case_2$Total.cores <- wit... |
36f9fce536f3fd3d33cc6925be134531ff14a31333ee3c2c185fed190c3f9400 | R | 2,441 | 52 | # ----------------------------------------------------------------
# Run all dataset-specific load_data scripts, found in the
# "load_original_data" directory.
# After completing, each dataset should have a folder with all
# processed data in "data/prelim_data" directory.
# ------------------------------------------... |
28cc15aff21cba474d75de199115ac8668340ddf38089010d0065e2c0a74d8e9 | R | 2,443 | 80 | #DegCre RNAseq input files
# open R
srun -n 10 -p interactive --pty /bin/bash
module load cluster/gsl/1.15
module load cluster/htslib/1.9-devel
module load cluster/build_tools/llvm/8.0
module load cluster/util/gcc/8.2
module load hdf5_18/1.8.21
conda activate r4.4.1_env
#conda deactivate
R
# Load necessary librarie... |
486993842b66c25ffef8e2cfb58a61aaefc84d12d4490801b5ea2504b3bbc0cf | R | 2,446 | 103 |
#' get applicable shape data for Venn object
#'
#' ggVennDiagram stores shapes as internal data. You may see all the shapes by
#' using `plot_shapes()` or `get_shapes()`.
#'
#' @param type type of shape
#' @param shape_id shape id
#' @param nsets number of sets
#'
#' @return a tibble describing specific shape
#' @exp... |
eb7332b9a4d5fb9df6ad7184881f2d98cfbc51b7ac03e0542754d8f6bfb5eb97 | R | 2,446 | 68 | #####################################
## Write TITAN parameters to database
## Author: Kevin Johnson
## Updated: 2022.07.06
#####################################
library(tidyverse)
library(DBI)
library(odbc)
## Current project.
projdir <- "/projects/verhaak-lab/USERS/johnsk/glass4"
setwd(projdir)
## Connect to the ... |
57a0801f87a2b2de79a38e350d1274482794eb96a3f5e94f1665a30f66bf87c9 | R | 2,451 | 78 | local({
correlation_ids <- c(
"undefined_survivor",
"member_b",
"window_survivor",
"index_a",
"hidden_from_sp2",
"overlap",
"r2_survivor",
"index_b",
"ä_member",
"z_member"
)
correlations <- matrix(
0.1,
nrow = l... |
57adf95e506072a3c297cc84027e12fa51160154ba0f7b4d4c381b817666d6a6 | R | 2,455 | 77 | root="C:/Users/Jun/Documents/R/AA_wSignal/" #this is the root folder
###########################you should only need to change the root directory to poinit towards your working directory
#following sub folders rawtrack, rawtrack_ttl, allsigs, sig_ttls
tdir=paste(root,"rawtrack",sep="") #put topscan files in ... |
51868c319ea373325f0b29bba63b9622577d92b3d8e1b834e34062283f4ab002 | R | 2,458 | 73 | suppressMessages({
library(scrattch.hicat)
library(dendextend)
library(dplyr)
library(matrixStats)
library(Matrix)
library(Seurat)
library(RColorBrewer)
library(scrattch.vis)
library(ggdendro)
library(tidyr)
library(ggExtra)
})
args = commandArgs(trailingOnly=TRUE)
obj <- r... |
6bfb63ef0983b70600fc9def5aafa522e008a5df185836291b55feab3af1898b | R | 2,460 | 65 | library(ggplot2)
library(dplyr)
library(forcats)
library(reshape2)
library(viridis)
# Correct data for each dataset
data_countsV2A <- data.frame(
Category = c(">2.0m", "1.6-2.0m", "1.2-1.6m", "0.8-1.2m", "0.4-0.8m", "<0.4m"),
Count = c(5.689655172, 0.3793103448, 11.65517241, 4.310344828, 2.655172414, 275.2758621)
... |
c1c4634626a5435eaa52c624e984027dae5c2ab19bba6a97b2a07d0514aed016 | R | 2,462 | 59 | #####Tanycyte Mapping to Remove Nontanycytic Cells Through MapMyCells (RRID:SCR_024672) #####
#Software were given at https://portal.brain-map.org/atlases-and-data/bkp/mapmycells
#Convert the tanycyte .rds file to .h5ad file
# Extract the raw count matrix
data_matrix <- tanycyte@assays$RNA@counts
# Transpose... |
c668b761199cde84593f78b599b72ddfb0a70afeb55ace51eca77cc7967f73d1 | R | 2,468 | 49 | #################
### Load data ###
#################
rm(list=setdiff(ls(),'params'))
basedir <- params$basedir
setwd(basedir)
savedir <- paste(params$opdir,'processed/',sep='')
dir.create(savedir,recursive=T)
source('code/misc/miscfxns.R')
connectivity.ipsi <- read.csv('data/Connectome_Ipsi.csv',row.names = 1,header... |
7444e2bb80a3c327b2990af22b66da637967e7f2a93527da975c8dc6bd55137f | R | 2,469 | 70 | #!/usr/bin/env Rscript
#-------------------------------
# Title: Check success of scripts
# Author: Amanda Zacharias
# Date: 2022-12-17
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 gcc/7.3.0 r/3.6.0
# If job... |
0dedb23ffd35af2e5f0230588e41a387038230a61e78606565d5f5421519c6ed | R | 2,470 | 76 | # 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 (!re... |
7f98886d278844a406a2a7de970730198983a100b84d7531c3fb644e089a0abe | R | 2,472 | 80 | # Description: This file is used for categorical conversion of trait variables
library(dplyr)
traRfVif2ndEd <- "D:\\work2\\t2dAndComplicationsUkb\\data\\processed\\integComp2ndEd\\traRfVif2ndEd.rds" %>%
readRDS(.)
trait6 <- traRfVif2ndEd
traCla <- trait6 %>%
lapply(., class) %>%
as.data.frame(.) %>... |
fe028a3104f20198bc10ad87b2a88406f816333f444121a6abc2ba4094a5922d | R | 2,472 | 53 | library(dplyr)
library(ggplot2)
library(ggrepel)
studies = c('Ma_human', 'Franjic_human', 'Sepp_human', 'Ma_chimp', 'Ma_rhesus', 'Franjic_rhesus',
'Ma_marmoset', 'Franjic_pig', 'Sepp_opossum', 'Tosches_lizard', 'Tosches_turtle',
'Colquitt_zebrafinch', 'Colquitt_Bengalese_finch', 'Liao_frog', 'L... |
142121d357d14cc77f1ad0498e882dc033788cee03e1cf7c00cc7e36b5ccd051 | R | 2,475 | 58 | require(ggplot2)
require(grid)
# Define the custom geom function
geom_gene_rect <- function(mapping = NULL, data = NULL, stat = "identity", position = "identity",
na.rm = FALSE, show.legend = NA, inherit.aes = TRUE,
rect_height = grid::unit(3, "mm"), ...) {
ggpl... |
50c5039b95ecdb8fc6c123ce4f263e0edb1a1f0e87b2052e6a8afcae9fcf6403 | R | 2,481 | 72 | library(tidyverse)
library(odbc)
library(DBI)
library(GSVA)
library(qusage)
rm(list=ls())
myinf1 <- "/projects/verhaak-lab/GLASS-III/data/res/verhaak_prelim/msigdb_hallmark_enrichment_idhwt.txt"
es <- read.delim(myinf1)
colnames(es) <- gsub("\\.","-",colnames(es))
es <- t(es)
#Establish connection
con <- DBI::dbCo... |
0d7d8539ac215e0c6c5832b598d583ad1a09285f506fe92b5fe1a9652043853f | R | 2,491 | 67 | #' 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 clus... |
d523a2eda743db408d1ed9c68fd8a2d8e1b9d1484bb3207a027f50f79bc51d06 | R | 2,499 | 74 | library(dplyr)
library(readxl)
library(readr)
# Set base directories
# base_dir = "path/to/conditional/results/"
# metadata_path = "path/to/metadata.RData"
base_dir = "./result/conditional.results/"
metadata_path = "./metadata.RData"
check = list.files(file.path(base_dir, "cohort.list"))
check = gsub("\\.r... |
6d8505e5406f63681838f2bcd5e3312580bf86e0daf9eb8ba7cfc5b3a30e313d | R | 2,502 | 84 | # ===============================================================
# 🧭 Stratification helpers (shared across analysis modules)
# ===============================================================
MAX_STRATIFICATION_LEVELS <- 10
stratification_ui <- function(id, ns = NULL) {
ns_fn <- if (is.null(ns)) {
NS(i... |
28028c1707eb0e3bf169e9f390a897164a7a383aa9a29273d15b13746dd665ff | R | 2,503 | 45 | library(dplyr)
library(ggplot2)
library(Hmisc)
library(survival)
library(rms)
library(feather)
#Parse SUPPORT trial data from Vanderbilt Biostatistics web site
#If lab value missing, set to recommended value in: http://biostat.mc.vanderbilt.edu/wiki/Main/SupportDesc?sortcol=1;table=1;up=0#sorted_table
#We do not inclu... |
0d94fc786a0734e23bfca1e099b336760c89c569179f4e86db794c666d0e659d | R | 2,504 | 113 | ---
title: "Get list of triple Aplus samples"
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
... |
1bb255b04deac46329d69396eeaa4d1c0a25e3c89b739c65301ec36e2619b04a | R | 2,508 | 69 | ## ********************
## Create the R package
## ********************
## To get started, install R from https://cran.r-project.org/
## and RStudio Desktop https://rstudio.com/products/rstudio/download/#download
## You can install both of them for free.
## Next, open RStudio as the code that will run benefits from r... |
c0a7722ca74de71178c71f584ce2e1cdbd373e9e194248fbab2fc68f09ea9028 | R | 2,512 | 58 | #' load-keys — Load private Google / Drive / Sheet identifiers into `banc.keys`.
#'
#' Mirrors `BANC-project/R/startup/load-keys.R`. Reads `data/private/keys.csv`
#' (gitignored) into the named list `banc.keys` exposed to the global
#' environment. Each row is `name,value,description`. Downstream scripts
#' reference `... |
2c39383f352e303cb364422f4161a138daa0c6207f7d6a21f0633b67aeec11b0 | R | 2,516 | 120 | ---
title: "Summerize the results from GOseq"
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 setup, include=F... |
5f60da4a869669bfd65b2e5c9eaa1e1c9b1919efda23d3d2ba86132b75ef31cb | R | 2,520 | 75 |
args <- commandArgs(TRUE)
data <- read.table(args[1], fill=TRUE, sep="")
#choose sequences that have less e-value than 1e-5
viruses_sequences <- subset(data, data[,5] <= 1e-5)
#get rid of the last underscore and number of the sequence name
viruse_sequences_name = c()
for (i in 1:length(viruses_sequences[,1])) {
... |
adf674340e71b78b61b6a9069208d90052b5a7212cf1d13f1e100fb6ddd861a2 | R | 2,526 | 73 | library(Seurat)
library(ggplot2)
library(dplyr)
library(cowplot)
library(reshape2)
library(MIN)
library(EnhancedVolcano)
Idents(LG815_TDI) <- "celltype"
levels(LG815_TDI$celltype)
MG <- subset(LG815_TDI,idents = c('MG'))
DefaultAssay(MG) <- 'integrated'
all.genes <- rownames(MG)
MG<- ScaleData(MG, features = all.gene... |
aed112d6a34767b405a23da84999096870a83956087aa6f871d5e77b0071a589 | R | 2,526 | 64 | #!/usr/bin/env Rscript
# Supp10a.export_gsva_modules.R
# ----------------------------------------------------------------------------
# Companion to Supp10.replicate_level_gsva.py — exports the per-sample GSVA
# module scores from the binary RDS checkpoint into a tidy long-format CSV
# that the Python plotting script c... |
f4abbbe384a26c03f3291ef90fb6f5fd15d34b53e6c0e8206564ad8756f48def | R | 2,529 | 70 | suppressMessages(library(monocle))
# Pseudo-time for uncorrected.
expr.matrix <- as.matrix(read.table("data/macrophage/mono_macro_table.txt"))
sample.sheet <- read.table("data/macrophage/mono_macro_meta.txt")
gene.annotation <- read.delim("data/macrophage/mono_macro_genes.txt")
pd <- new("AnnotatedDataFrame", data =... |
7ae084291b17694e8a96d5c082f23368a38d19a40aa2b77af8e845c988cab298 | R | 2,532 | 123 | ---
title: "Add brain and blood analysis results to external cohort (ANDI) results"
author:
- David Lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
rmarkdown::html_document:
highlight: breezedark
theme: lumen
toc: true
number_s... |
253880ec95f3cc3413e1c9f1be089f60c651b0b1b73351b5196ec3df9ffc3e88 | R | 2,534 | 78 | parse_svg <- function(f, outf=NULL, colpal=NULL, colids=names(colpal)) {
if(!requireNamespace('xml2', quietly = TRUE))
stop("Please install")
x <- xml2::read_xml(f)
xmc=xml2::xml_children(x)
ids <- xml2::xml_attr(xmc, 'id')
xmc=xmc[!is.na(ids)]
ids=na.omit(ids)
paths <- xml2::xml_children(xmc)
if(l... |
8822af7435a93ef58f7eed560fd84238332cf1df8ccc153abcf4d5a0a624ae10 | R | 2,536 | 93 | # 加载必要的包
library(readxl)
library(dplyr)
library(tidyr)
# 读取 Excel 文件
data <- read_excel("P0-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，... |
565ad371dfd2d02b807cce09a74f7c99f7aeb99f1b66d43ed25fbc945dced163 | R | 2,537 | 70 | library(ggplot2)
library(dplyr)
library(viridis)
library(cowplot)
# Combine data for global color scale
combined_data <- bind_rows(
horizontal_combined %>% mutate(Type = "Horizontal"),
vertical_combined %>% mutate(Type = "Vertical")
)
# Define a global color scale
shared_scale <- scale_fill_viridis(
option = "D... |
0807fcec4d621aca96e4b0a590b720f1bd7b00cc936dda6915a1c80ac75d2d20 | R | 2,538 | 68 | library(mice); library(readxl)
# IMPUTE BIOMARKERS -------------------------------------------------------
bio <- read_excel("~/Documents/BrainAge/ELS/BioAgingComposite/Biomarkers_raw.xlsx")
# --- Separate ID so it never enters the imputation model ---
subID <- bio[, 1, drop=FALSE] # keep as data.frame,... |
4e65324a07212a8e9fac6981178d490b8db52b5423096869575e2e93b826f00c | R | 2,539 | 39 | ########################################################################################################
#### Trial dataset for this R script can be downloaded from ####
#### https://data.mendeley.com/datasets/454s2vw255/1/files/38438fb8-b4f9-4c3b-8e36-7b7f7ace017e ####
##... |
a52be4cf10f3fbdd3c39ecdc9d293682770a12b957b9167d6d0e4d952ed09985 | R | 2,541 | 78 | #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(... |
3430b0d26d4c614ecf6e3cca507bdb750970bd834e9677ac3ae7adb8ef3d7445 | R | 2,544 | 74 | library(biomaRt)
library(data.table)
library(dplyr)
library(GenomicRanges)
p1 <- "resources"
mpra_pos <- read.csv(file.path(p1,"MPRA_w_pos_GRCh38.csv"))
# load atac peaks
path_mypeak <- "QTL_fsVars/mypeaks"
atac_cts <- c("ast","mg","EX","IN","odc","opc")
bed_files <- read.csv(file.path(path_mypeak,"list"), header = F)... |
a6f1942825fb0aeeea52a29af28fc18131301bf4eb25e2bcf53c2edc736bd8fb | R | 2,544 | 72 | # ==============================================================================
# Load DNA Methylation (DNAm) Data
# ==============================================================================
# This script loads and prepares DNAm data from Illumina 850K arrays
#
# Author: Osama Shiraz Shah
# ======================... |
2563f5278a1af41c6f09cd3ca123b5023747eab06837fb30e5b9f10e5d89126a | R | 2,550 | 66 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# 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... |
cc00bb34ae0e9e7a76f2d6a5ec36f7b7b88aa94f7366a9649bed01b4476936b6 | R | 2,551 | 71 | #-------------------------------------------------
# Title: Check success of stringtie scripts
# Author: Amanda Zacharias
# Date: 2023-01-01
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 gcc/7.3.0 r/3.6.0
# I... |
2d818ffc55cde36752fa2c4c2c342c722ef5b26afd2857539dfe715ee7dc5f8a | R | 2,557 | 71 | # Description: This script uses the mstate package to analyze the risk of
# type 2 diabetes and overall complications
# Shared head ----
library(mstate)
library(dplyr)
integComp2ndEd <- "D:\\work2\\t2dAndComplicationsUkb\\data\\processed\\integComp2ndEd\\integComp2ndEd.rds" %>%
readRDS(.)
traLab2ndEd <- "... |
3a9e1fbd32a930a52dd000100a33287f4047d0e15816a48204ce2e05b19a71b4 | R | 2,559 | 62 | #################################
## Create blocklist for GLSS-LX WGS
## Updated: 2022.07.26
## Author: Kevin Johnson
#################################
library(DBI)
library(tidyverse)
## Connect to the latest version of the database that contains the GLSS-LX samples.
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB4")
... |
19026e8612867eff07f9c60d74f8b2d599fe5b3ec928aacf0d543555303aeafd | R | 2,563 | 73 | #!/usr/bin/env Rscript
# ============================================================================ #
## set_dirs.R
## MGNG-TUS study: Set input and output directories for data processing.
## Copyright (C) Johannes Algermissen, University of Oxford, Oxford, UK, 2024-2025.
set_dirs <- function(rootDir){
#' Initiali... |
9b35dd29345c9eb8ca04bf741a5ae71e16bf7f03e64cbbaa6f8cfc3a2176c3df | R | 2,563 | 61 | library(foreach)
library(doParallel)
library(emmeans)
## Calculate edge-wise significance and Cohen's D for visualization
setwd('/home/huyang/Projects/HuYang/HY_20251111')
## Set parallel cluster to speed up
n.cores <- 20
my.cluster <- parallel::makeCluster(n.cores, type = "FORK")
doParallel::registerDoParallel(cl = my... |
a338aa96bfb5007543ecf744ee96b8e1f07ce05b8a16b8d48cd0e29c2d2d6bae | R | 2,572 | 90 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# Load banctable and save DN metadata, DN skeleto... |
217046827fba404423736da22b3f1646008131e8909d8d808b4d31f531e59461 | R | 2,576 | 70 | # Written by Yapei Xie and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
CBIG_LBC_plot_violin <- function(df_m, outfile, model_order, fill_colors) {
# CBIG_LBC_plot_violin
#
# Generate a violin plot with overlaid boxplot for comparing correlations
# across multiple models and save... |
1cd0af7fe210db4b81f120163f2103919418a71efe319d06ac48fe38fe6475fe | R | 2,578 | 87 | # Convert characters to ordered factors
preprocess_uploaded_table <- function(df) {
df |> mutate(across(where(is.character) | where(is.factor), auto_factor_order))
}
# Convert character/factor to factor with numeric-aware order
auto_factor_order <- function(x) {
if (!is.factor(x) && !is.character(x)) return(x)
... |
3870b3c3fbda2d4fb1100bb9d1e9f31f391d11e607a0a627ad95e1048ec681e2 | R | 2,579 | 76 | rm(list=ls())
#Use for extracting the files from a specific directory
myDir1 <- "/projects/verhaak-lab/USERS/johnsk/glass4/results/mutect2/geno2db/"
myinf1 <- dir(myDir1)
myinf1 <- myinf1[grep("info.tsv",myinf1)]
myinf1 <- myinf1[grep("CARE-TK|CARE-TO",myinf1)]
# Remove CARE-TK-TK06.info.tsv and revisit later since it... |
8199c4167f5660aae90b04233d24c0a51a1d3a0ec46a7f991f3ea564bbeafb79 | R | 2,579 | 68 | #' Tunes marker set based on specified criteria
#'
#' This function filters markers within a specified set (query or database) based on absolute or relative thresholding of a given variable.
#'
#' @param ann An object containing marker data in `ann1$query` or `ann1$db`.
#'
#' @param method The tuning method ("absolute... |
b828edf95255fb46f8035b73ab1016a7c7e2b792127dd9a437141587feb3f754 | R | 2,582 | 124 | ---
title: "Summerize the results from GOseq"
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 setup, include=F... |
03f7cb7a3add22cf448d1faca4bfa960bf0fa1011ff685939d0a740fc0af9d9f | R | 2,583 | 68 | # Load required libraries
library(ggplot2)
library(dplyr)
# Function to plot publication years (horizontal bars) and save as PDF
# order: "older_first" (default) or "newer_first"
plot_publication_year <- function(df, output_file, order = c("older_first", "newer_first")) {
order <- match.arg(order)
# Remove m... |
9ba0305489d116d9adac02175852046eaaa26f665d3cf22190b28933b018acc7 | R | 2,583 | 86 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Write individual multiqc scripts
# Author: Amanda Zacharias
# Date: 2022-12-17
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 ... |
47998b553788b0331486dbeb2bb7eaeb4e22d253949d79b34fde0591bca9f7e7 | R | 2,584 | 54 | library(ggplot2)
library(Seurat)
library(ggrepel)
library(RColorBrewer)
library(dplyr)
library(cowplot)
library(reshape2)
library(MAST)
library(patchwork)
# volcano plot of markers of MG4, focusing on DAM genes (Figure 2E) ======
setwd()
dam <-read.csv("DVvsWT_DE_MAST_RNAassay_MG_pct0.15_dim15.csv")
DV_MG <- read.csv... |
a4d95db9609fc8d9c0737dcd75af5c316feade7355d673486b46d13d1eff8681 | R | 2,586 | 58 | # Read source and libraries
library(SAIGE)
library(data.table)
library(Matrix)
library(optparse)
option_list <- list(
make_option(c("--rdaFile"), type="character", default="",
help="Path to rda file from SAIGE step 1"),
make_option(c("--chrom"), type="character", default="",
help="Chromosome"),... |
efa3e2c236298ddc8e6b99b9c8a4b2ed7583d4b71971c9dca8e4dbb7b60f0fdf | R | 2,588 | 65 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print("************************************ Starting ************************************")
library(Matrix)
## Test parameters:
# in_path <- "/vast/palmer/pi/verhaak/kcj28/care_mut/processed_data/nmf_2026/Myeloid/"
# mat_index <- 1
# out_path <- "/vast/pa... |
cfd73abb242acc514ed73d19658aa785764e01ec1e0f7c4064d147b043d43c2d | R | 2,589 | 67 | library(data.table)
library(dplyr)
library(parallel)
library(limma)
library(purrr)
# Set working directory to project root
# setwd("")
model = "CIS_EpiXcan"
### Params
target_file_path = c("./genotype/")
site = "your_training_genotype.bim" # GTeX.v9.phased.geno.maf.bfile.updated.EUR CMC... |
8f49fd20af14e7ded7b1651a6bb1aac0e980c470348c91d6d583f7ea32940f8d | R | 2,590 | 122 | ---
title: "Summerize the results from GOseq"
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 setup, include=F... |
62625f91a83f167d519973cfa7ebef9e31e16f3ece69705a2d32e275471a9107 | R | 2,594 | 78 | #' banc-obj — Download per-neuron OBJ meshes for proofread BANC neurons.
#'
#' @section Reads:
#' - SeaTable `banc_meta`
#'
#' @section Writes:
#' - `<banc.obj.save.path>/<root_888>.obj`
#'
#' @section Invoked by:
#' `o2/production/o2_banc_meshes.sh`, `o2/production/o2_banc_split.sh`.
###########################... |
21457c92693929a236e2627bc72e829ffcd7b7fbbfc0c799ae8d52e778223aff | R | 2,597 | 77 | #####################################################################
# Create Prediction Database Pipeline
#####################################################################
# This script creates SQLite databases from the training results
# It performs the following steps:
# 1. Processes summary and weights fi... |
479130fe3cbce389ba54730e543eb686ccbf78271e58ec074561b6325ed22b46 | R | 2,599 | 60 | ##################################
# Plot the proportion of malignant cells cycling per malignant state
# Author: Kevin Johnson
# Date Updated: 2026.04.10
##################################
library(tidyverse)
proj_dir <- "/vast/palmer/pi/verhaak/kcj28/care_idh_mut"
fig_dir <- file.path(proj_dir, "figures/")
se... |
0ebc2672fe2e578468a008979549b818d126a0adef5366f093b3772e7187a041 | R | 2,602 | 80 | ---
title: "Fig_S4B"
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}
srt_merge <- readRDS("~... |
15aee0bc3f3a5e4187e27543063069156cb91d63cab1cf79e9be4ec8cef51455 | R | 2,602 | 95 | rm(list = ls())
library(tidyverse)
library(Seurat)
library(recombine)
# Here we use a scRNA-seq data of the mouse visual cortex from Allen Brain Atlas, which can be downloaded from:
# http://celltypes.brain-map.org/api/v2/well_known_file_download/694413985
# The following files are used after uncompressing the dow... |
83c72a4a4c739b66e2dab83f89e66c74e59715d5929adae5a2776cf22e51acee | R | 2,606 | 66 | ## In vivo sorted cells effect size calculation
setwd(rstudioapi::getActiveProject())
rm(list=ls())
source("./Scripts/Function_scripts/Effect_size_functions.R")
source("./Scripts/Function_scripts/Pre-processing_functions.R")
library(tidyverse)
###########################################################################... |
345748b2c1d65d953b8455b7c6d58967ad20b18896c072870139ffba6db0dd4c | R | 2,610 | 85 | # ============================================================
# Nyström KPCA core routine
#
# This function performs kernel PCA using a Nyström
# approximation to reduce computational complexity.
#
# Key implementation details:
# - A fixed proportion (5%) of the total sample size is used
# as Nyström centers... |
9e5d45fc78dbc93a3a5c0458179c14ef77e7346d5c6b747d9adf9ac6fe01ba1e | R | 2,611 | 67 | # 1-20-10 Qunhua Li
#
# This program first plots correspondence curve and IDR threshold plot
# (i.e. number of selected peaks vs IDR) for each pair of sample
#
# usage:
# Rscript batch-consistency-plot-merged.r [npairs] [output.dir] [input.file.prefix 1, 2, 3 ...]
# [npairs]: integer, number of consistency analyses
# ... |
cc8a4002e6742133a6390a034b27fdf4246808caa631baf71b8912c4432b5a8a | R | 2,611 | 59 | ##################################
### Upload tumor-only files data for GLSS-LX WGS files
### Author: Kevin Johnson
### Date updated: 2022.07.29
##################################
library(tidyverse)
library(odbc)
library(DBI)
library(stringi)
## Establish connection with GLASS 4 database.
con <- DBI::dbConnect(odbc::o... |
2a6f0deac6ec88e27a0dffc69fa00e5c39e32598e59c1c953d90a976e497bc17 | R | 2,615 | 71 | library(dplyr)
example_driver <- readRDS("~/tmp/mhcbinding/example_driver.rds")
example_driver <- example_driver %>%
select(-pos_alter,-transcript,-exon,-index) %>%
select(trans,everything()) %>%
group_by(across(2:aa)) %>%
summarise(contain_trans = paste(sort(trans),collapse = ","))
##文件太大，先把 gene 及其 突变 氨... |
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