sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
04eef747e96a3010f9a4a070df57c42cb4312d7a7d952f929967279d18f0c5cd | R | 1,224 | 50 | # See http://www.biostat.jhsph.edu/~iruczins/teaching/jf/ch5.pdf
data(longley)
g <- lm(Employed~Population,data=longley)
summary(g, cor=T)
# GLS
y <- as.matrix(longley$Employed)
x <- as.matrix(model.matrix(g)[,2])
# Construct sigma matrix
Sigma <- diag(16)
Sigma <- 0.31041 ^ abs(row(Sigma)-col(Sigma))
rowna... |
08bd695c0ca3bc72c445271adc001db4a6a9a36865b3718421b93419bb1c28d8 | R | 1,228 | 44 | #' Specify globals and packages for a future assignment
#'
#' @usage
#' fassignment \%globals\% globals
#' fassignment \%packages\% packages
#'
#' @inheritParams future
#'
#' @param fassignment The future assignment, e.g.
#' `x %<-% { expr }`.
#'
#' @param packages (optional) a character vector specifying packag... |
d38b17b39734794a6c77f5325c1e428359d01e27d6ecc1d8ac10f589b2f08d5b | R | 1,232 | 54 | ## Extract snpEff annotations
library(tibble)
library(tidyr)
library(readr)
library(purrr)
library(plyr)
library(dplyr)
library(stringr)
library(forcats)
# library(vcfR)
## Snakemake
### Input
file_ann.path = snakemake@input[['ann']]
### Output
outfile = snakemake@output[['outfile']]
## snpEff
raw_snpeff <- file_a... |
09f7efc51b085a0a78cf4d632b3ba5fbb4e82e02b2ddedcff45e55106ca40736 | R | 1,239 | 51 | #' @tags futureAssign
#' @tags environment
#' @tags sequential multisession multicore
library(future)
message("*** %<-% to environment ...")
## - - - - - - - - - - - - - - - - - - - - - - - - - - - -
## Async delayed assignment (infix operator)
## - - - - - - - - - - - - - - - - - - - - - - - - - - - -
z <- new.env(... |
eaefe72fc4b0ad98ba97f3b0e2130b56228d0f4fd268588868f13adae5be13fc | R | 1,241 | 26 | clust_corr_plotting <- function(matrix, save, filename) {
corr_of_matrix <- round(cor(matrix), 2)
p.mat_of_matrix <- ggcorrplot::cor_pmat(corr_of_matrix)
corrplot(corr_of_matrix, method = "color", type = "lower", order = "hclust",
diag = FALSE, tl.col = "black",
addCoef.col = "black... |
bbe0a3738ea9d1efe7f742b74f343f6578cd0cdf0c17f1de6ce11d463b404871 | R | 1,244 | 52 | argnames <- function(..., exclude = "...") {
fcns <- list(...)
names <- lapply(fcns, FUN = function(fcn) {
names(formals(fcn))
})
names <- unlist(names, use.names = FALSE)
names <- unique(names)
names <- setdiff(names, exclude)
}
tweakable <- function(x, ...) {
attr(x, "tweakable", exact = TRUE)
}
u... |
cb29bcbcf3c4248d281608562657033b07e6f66fbdaa57f29bc82fadfacd5381 | R | 1,260 | 8 | rm(list = ls())
for(i in 1:61){
load(paste0("/work/lulab/Ben/distmat/output/ecdfs/rsetlist.ecdf.", i))
print(paste0("loaded ", i))
}
all.ecdf <- c(rsetlist.ecdf.1,rsetlist.ecdf.2,rsetlist.ecdf.3,rsetlist.ecdf.4,rsetlist.ecdf.5,rsetlist.ecdf.6,rsetlist.ecdf.7,rsetlist.ecdf.8,rsetlist.ecdf.9,rsetlist.ecdf.10,rsetlist.e... |
888d34b09dcb7a58bbf3a9544259b1a233dd1a00f5821118e7c5413fc5d3acaa | R | 1,263 | 29 | corr_plotting <- function(matrix, save, filename) {
corr_of_matrix <- round(cor(matrix), 2)
p.mat_of_matrix <- ggcorrplot::cor_pmat(corr_of_matrix)
corrplot(corr_of_matrix, method = "color", type = "lower",
diag = FALSE, tl.col = "black",
addCoef.col = "black", number.cex = .5,
... |
d2498ec601909aec8d1f3e705fb6440a83e12f9fd3f105ab76c1aead2c8caa89 | R | 1,264 | 47 | #' @tags globals packages load attach
#' @tags sequential multisession multicore
library(future)
options(future.debug = FALSE)
message("*** Automatically attaching packages, if attached in parent ...")
for (strategy in supportedStrategies()) {
message(sprintf("- Strategy: %s ...", strategy))
plan(strategy)
... |
8087656ada24fab75001de926db4a0e552c61b103fe748fdf7109ab4178b009b | R | 1,265 | 31 | ff <- list.files( path = "./readcounts", pattern = "*ReadsPerGene.out.tab$", full.names = TRUE )
counts.files <- lapply( ff, read.table, skip = 4 )
counts <- as.data.frame( sapply( counts.files, function(x) x[ , 2,drop=F] ) )
colnames(counts) <- gsub( "_ReadsPerGene\\.out\\.tab", "", gsub( "\\./readcounts/", "", ff ) )... |
c1f9b8a281e22ac3f721bbf2ea80fae076d1d0d9d4444fd0b07499aa8d872667 | R | 1,267 | 59 | #' @tags journals
#' @tags sequential
#' @tags multisession
#' @tags multicore
library(future)
capture_journals <- future:::capture_journals
options(future.debug = FALSE)
strategies <- c("sequential", "multisession")
if (future::supportsMulticore()) {
strategies <- c(strategies, "multicore")
}
message("*** captur... |
49d1f8e5b616f1736eaa0b48715901868ccefba2d7c6726499b612613888b3d2 | R | 1,271 | 36 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("cellline_prediction_model")} else {setwd("../cellline_prediction_model")}
source("../downstreamer_functions.r")
labels <- read.table("../umap/data/lables.txt", sep="\t", header=T)
labels <- rbind(labels, read.table("../umap/data/lablesZoom.txt", sep="\t", h... |
c15350874d7cf0a7593b1c1474c61c7007bf3a5a377fc28ddd484aff05c7ddca | R | 1,292 | 32 |
# gene_annotations is a dataframe from the seurat object misc slot with gene ensembl IDs and corresponding names
# GO:0043565 = sequence specific DNA binding and GO:0003700 = DNA-binding transcription factor activity
modules_to_TFs <- function(gm, gene_annotations, GO_terms = c('GO:0003700', 'GO:0043565')){
gene... |
eb114c9c79955973ce25b9018864e554eb4134450a0b3ee8d394ffabb179da34 | R | 1,292 | 64 | ### script to generate BED (6 column) from GTF/GFF
# load
source('functions.R')
# get args
get_dir <- commandArgs(trailingOnly = TRUE)
# check input file
in_gtf <- get_dir[1]
if(grep('.(gtf|gff[3]{0,1})$', in_gtf, ignore.case = T) != 1){
stop(
paste0(in_gtf, ' without gtf or gff file ending!')
)
}
# check ... |
1c34cc06b77bc427af337b75337002793deddf5ba52e3db5a719281524168e8a | R | 1,300 | 60 | #' @export
fastColSums <- function(mat) {
if (is.sparseMatrix(mat) && class(mat) != "dgCMatrix") {
mat = as(mat, "dgCMatrix")
}
out = fast_column_sums(mat)
return(out)
}
#' @export
fastRowSums <- function(mat) {
if (is.sparseMatrix(mat) && class(mat) != "dgCMatrix") {
mat = as(mat, ... |
c6a66cb1f6b67a62a83bc6f1db89c91c44fbcc2c65231ee42af3d3fed9008111 | R | 1,302 | 22 | #load Mouse gene names
mart_export_PVEnsembl_mouseEnsembl_mousegeneName <- read.csv("~/R/Thesis/RNAseq/Data/mart_export_PVEnsembl_mouseEnsembl_mousegeneName.txt", stringsAsFactors=FALSE)
#getting a biomart df that only contains rows that have mouse stable IDs
biomart_mouseE <- filter(mart_export_PVEnsembl_mouseEnse... |
6bb0d28eb4d1df14da41499f12473806995ad2539ff01c2d709da05fd7aa7af3 | R | 1,303 | 35 |
setwd("C:\\Users\\patri\\Documents\\GitHub\\systemsgenetics\\genetica-libraries\\src\\test\\resources\\")
testMatrix <- read.delim("testMatrix.txt", row.names = 1)
write.table(cor(testMatrix), file = "testMatrixColumnCorMatrix.txt", quote = F, sep = "\t", col.names = NA)
write.table(cov(testMatrix), file = "testMatr... |
14881e3999dfb36ebec866dd9d070db7bf38e4144435ab6b46c345f404319316 | R | 1,310 | 35 | table_tmp <- read_delim("/groups/umcg-fg/tmp01/projects/genenetwork/recount3/SRA_Metadata/metadata_sra1.txt", delim = "\t", quote = "", guess_max = 20000)
sraMeta1 <- as.data.frame(table_tmp[,-1])
rm(table_tmp)
table_tmp <- read_delim("/groups/umcg-fg/tmp01/projects/genenetwork/recount3/SRA_Metadata/metadata_sra2.txt"... |
30e09963320eab7a1348f7b1d5c99510bcca9f1d9de7e857df02e05db31439b1 | R | 1,312 | 40 | # The current RNG kind
okind <- RNGkind()
oseed <- globalenv()$.Random.seed
# (a) A L'Ecuyer-CMRG seed based on a numeric-scalar seed
seed1 <- future:::as_lecyer_cmrg_seed(42)
str(seed1)
## int [1:7] 10407 -2133391687 507561766 1260545903 1362917092 -1772566379 -1344458670
# The RNG kind and the RNG state is preserved... |
fedc960288a16e1e3bed8312d180f6a529c313dd403f58b0beb2f6cc5de08843 | R | 1,317 | 29 | # Package definitions for dndscv
#
# Author: Inigo Martincorena
###############################################################################
#' Detection of selection in cancer and somatic evolution
#'
#' The dNdScv R package is a suite of maximum-likelihood dN/dS methods designed
#' to quantify selection in cancer... |
0989c129c6e5eec264645d7f3281bf6e488b290016659c2048d675b1439a13ea | R | 1,318 | 27 | #' Contextualization until all nodes have at least one in degree
#'
#' This function takes 1 network (parent) as input and iteratively removes parentless nodes (nodes with In-degree = 0)
#' and contextualize it. It returns a network which contain every node with atleast an In-degree of 1.
#'
#' @param parentNetwork ed... |
8e3906d01ea7ea726380f5c72f6de0da23945605f91647a36917b1d9fa4857c2 | R | 1,325 | 36 | ## A version of base::sample() that does not change .Random.seed
stealth_sample <- function(x, size = length(x), replace = FALSE, ...) {
## Nothing to do?
if (size == 0L) return(x[integer(0)])
## Nothing to randomize?
if (length(x) == 1L) {
if (!replace && size > 1L) {
stopf("Cannot take a sample (n ... |
f1d18e52cd5b54266b56cb9e7e2ac7ee20151c6689591967bcb4bea6150b1a8e | R | 1,328 | 52 | #' @tags bquote
library(future)
bquote_compile <- future:::bquote_compile
bquote_apply <- future:::bquote_apply
message("*** bquote_compile() & bquote_apply() ...")
exprs <- list(
A = quote(.(a)),
B = quote(1 + .(a)),
C = quote(.(a) + 2),
D = quote({ .(a) }),
E = quote({ 1 + .(a) }),
F = quote(function(... |
f23da277223c17b4f27e4ec4b5243993118eaa2bbfaacaf0e2442e5ddeb0d4b4 | R | 1,338 | 45 | ---
title: "Intratumoral heterogeneity in Glioblastoma"
output:
html_document: default
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Load data
We load the scRNA data of three samples MGH102 , MGH104 and MGH105 from the public dataset of Gliobastoma (GSE131928)
... |
a139c90b5ce8c2d443e88761cc2413a983ebb9040b08b1f83660f6a26e06579a | R | 1,346 | 54 | #' @tags relaying
#' @tags sequential
library(future)
options(future.debug = FALSE)
message("*** withRestart() and muffling ...")
## https://github.com/futureverse/future/issues/485
fcn <- function(...) {
withRestarts({
cond <- simpleCondition("boom")
class(cond) <- c("not_me", class(cond))
signalCond... |
943c3084a6714c164a996f7f4cd035db754b1dd05d89110b4a2ee33439c9b80f | R | 1,351 | 46 | ##' @importFrom lattice qqmath
##' @export qqmath
NULL
#' @title Quantile-quantile plots for galamm objects
#'
#' @srrstats {G1.4} Function documented with roxygen2.
#' @srrstats {G2.1a} Expected data types provided for all inputs.
#'
#' @param x An object of class \code{galamm}, returned from
#' \code{\link{galamm}... |
d4e555431dcacf771df0559c89f090e7d490caeb8c46bb23d3018a153148d5a1 | R | 1,358 | 62 | #' @tags requestNode
#' @tags cluster
#' @tags skip_on_cran
library(future)
message("*** requestNode() ...")
message("*** requestNode() - exceptions ...")
workers <- makeClusterPSOCK(2L)
print(workers)
res <- tryCatch({
requestNode(function() {}, workers = workers, timeout = -1.0)
}, error = identity)
stopifnot(... |
2725b946be62a3e5f49dcb76642b47e0efbed059dcc36deb31e6b1cfffda2832 | R | 1,385 | 68 | #' @tags relaying
#' @tags sequential
library(future)
options(future.debug = FALSE)
message("*** Relaying and split standard output and conditions ...")
message(" - default (implicit split = FALSE)")
plan(sequential)
relay <- recordRelay({
f <- future({
cat("O\n")
message("M")
1L
})
})
message(" ... |
b00ae5f553f1e508acd41b712c032ac2a4af609806bcfd0d77732cf6b575a66e | R | 1,406 | 45 | ```{r}
setwd("evaluating_coregulation")
source("../downstreamer_functions.r")
datasets <- read.downstreamer.batch("data/no_celline_gene_network_test_results/", potential_traits = c("GenePrioritization", "GenePrioritization200"), USE.CACHE = F)
```
```{r}
make.plot <- function(trait, main="") {
curdata.x <- curse... |
40e2f0c4a3fc2809edc51311c58f07273780439d995bb0d028eebe1ac784368b | R | 1,408 | 76 | ### script to prepare annotation BED file to coverage start and stop region
# load libs
source('functions.R')
# load tool locations
tool_path <- load_tool_location()
# get args
get_dir <- commandArgs(trailingOnly = TRUE)
# check input file
in_bed <- get_dir[1]
if(grep('.bed$',in_bed) != 1){
stop(
paste0(in_be... |
068a541b4922bc8f0a8a41d42785bf7ba9821a5f4716eaea74fa7fc5c0676ed5 | R | 1,421 | 43 | ## ------------------------------------------------------
## A single future
## ------------------------------------------------------
x <- sample(100, size = 50)
f <- future(mean(x))
v <- value(f)
message("The average of 50 random numbers in [1,100] is: ", v)
## -----------------------------------------------------... |
81614105bcf1f397a26dad7c3ce152e05af4e67d188629452fdcb8ee6c9f49cb | R | 1,425 | 46 | ---
title: "Intratumoral heterogeneity in Glioblastoma"
output:
html_document: default
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Load data
We load the scRNA data of three samples MGH102 , MGH104 and MGH105 from the public dataset of Gliobastoma (GSE131928)
... |
a8a570fe482bfdd8b3d01d8655ee37e4abf5f219ce056ac6aa4b3f5ab942754e | R | 1,427 | 46 | #' @tags globals
#' @tags where
#' @tags sequential multisession multicore
library(future)
oopts <- c(oopts, options(future.debug = FALSE))
message("*** Globals - sanitizing attribute 'where' (futureverse/future-ideas#1120) ...")
FutureGlobals <- future:::FutureGlobals
## Emulate a future_mapply() call with a 'FUN'... |
ed90b4c7ee85eb9409fec4e220b9bf6fc67735b59125f347d930ebc56f702e95 | R | 1,436 | 53 | #' @tags connections
#' @tags misuse
#' @tags sequential multisession multicore
library(future)
options(
future.debug = FALSE,
future.globals.onReference = "ignore" ## because we return a connection
)
for (onMisuse in c("ignore", "warning", "error")) {
message("onMisuse = ", sQuote(onMisuse))
options(future.c... |
cf9bd102f0b8f86e6454bf6116810453af7aac1193a9007ec806966264b6af81 | R | 1,443 | 47 | # load the data.
gene <- "CLECL1"
ct <- "macrophage"
# gene <- "CYP24A1"
# ct <- "neuron"
setwd(paste0("~/Downloads/export_ieqtl_data/", gene, "/"))
data <- read.csv("data.txt.gz", sep=",", header=TRUE, row.names = 1)
info <- read.csv("info.txt.gz", sep=",", header=FALSE, row.names = 1)
data$genotype[data$genotype... |
51d3c7cd5ea0e17ca08c84396cb127fddb03ccf5f92d0668018b271889e6b607 | R | 1,452 | 52 | #' bindRateData
#'
#' collects data exported from wave
#'
#' @param data.dir character-path of files
#' @param norm.meth character
#' @param out.name character
#' @param returnData Boolean
#' @param batchSelection character vector of file names to include
#' @importFrom data.table fread
#' @importFrom readr write_delim... |
d1d934af013b60dd4bce2201241772a9f3fe3d3253959bd1d1389cf2d821350b | R | 1,454 | 43 | #' @title Extract square root of dispersion parameter from galamm object
#'
#' @srrstats {G1.4} Function documented with roxygen2.
#' @srrstats {G2.1a} Expected data types provided for all inputs.
#'
#' @description
#' Extracts the square root of the dispersion parameter(s) from an object of
#' class \code{galamm}, ret... |
488699a705f7aaa896a5c26d4e583b40c60d8599bf3efd6e00e2325cf9ebcd69 | R | 1,456 | 61 | # Subsetting --------------------------------------------------------------
#' @title subset
#' @param x A \code{contacts}-object
#' @param chrom A vector of chromosomes to subset
#' @param start A single postion in bp.
#' @param end A single posttion in bp.
#' @param ... Currently not in use.
#' @examples
#' \dontru... |
bb5973bb719c39407f376d4ac3ed8c322a9ca54e35416a064195ef896d992ce1 | R | 1,460 | 36 | #Create two-column parsed transition tables for all 28 analysed networks, save as csv files
if(!require("BoolNet",character.only = TRUE)) install.packages("BoolNet")
if(!require("gtools",character.only = TRUE)) install.packages("gtools")
library("BoolNet")
library("gtools")
networkpath <- "./28nets_rules/"
nets <- mixe... |
2f5dca75651a00d51b79d4429ba6e3eae0bae3473fc34994985817251e4f81ee | R | 1,464 | 51 | #' @tags utils-internal connections sequential
library(future)
check_connection_details <- future:::check_connection_details
plan(sequential)
f <- future(42L)
v <- value(f)
message("*** check_connection_details() ...")
## A worker without a connection is not checked
stopifnot(is.null(check_connection_details(list... |
5ebc1a2c3ea0bc65dbfbcf436c595427fadbc44efc44d18dc7b2ba9a6af02c2f | R | 1,469 | 31 | setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
source(paste0("C:\\Users\\patri\\Documents\\GitHub\\systemsgenetics\\Downstreamer\\src\\main\\r\\downstreamer_main/downstreamer_functions.r"))
traits <- read.delim("MetaBrain/traits.txt")
i <- 1
pdf("MetaBrain/withAndWithoutEqtls.pdf", height = 20, width = 10)
#png... |
ff4a28e2d310ec403a2d4b5f249d0f2ce5e7f72c6e2b446400deb7f00a6a517b | R | 1,472 | 60 | #' Plot a network
#'
#'
#'
#' @param el Edge List
#' @param bool_data Boolean data
#' @return tring to visualize
#' @family network visualization
plotNetwork <- function(el,bool_data=NULL){
library("DOT")
x = as.data.frame(sapply(el, function(n){ gsub("[^[:alnum:]]","", n) }) )
dot(el2viz(x,bool_data))
}
#' Con... |
78e1a5a7e8ce6f33f4c0686634fc20d6bc20539f138fe9f214afc882abc85b5c | R | 1,474 | 51 | #' Display a part of a Hi-C matrix
#'
#' @name GENOVA_image
#' @param x A \code{contacts_matrix} object, as returned by
#' \code{\link[GENOVA]{select_subset}()}.
#' @param col a list of colors such as that generated by
#' \code{\link[grDevices]{hcl.colors}}, \code{\link[grDevices]{gray.colors}}
#' or similar fun... |
0339cbe6bbd528c53736704a7c7c05f1d8dcccea62ea4ee363fc071b7a82e926 | R | 1,477 | 71 | ### script to merge and sort calibrated reads
# load libs
library(foreach)
library(doParallel)
source('functions.R')
# load parameters
conf <- load_mapping_config()
# load tool locations
tool_path <- load_tool_location()
# set max cores
max_cores <- get_max_cores() # from functions.R
# register max 8 cores because ... |
84a762f35fd4bbebfaf797cbc857878df59d22bca53fecd12da20ec307af6891 | R | 1,481 | 50 | if(!require("BoolNet",character.only = TRUE)) install.packages("BoolNet")
library("BoolNet")
file <- "./28nets_rules/Giacomantonio2010.txt"
network <- loadNetwork(file)
#Running this script required 0.02sec on a MacBook Pro
#with a 2.3GHz Quad-Core processor and 16 GB of RAM
starttime <- Sys.time()
#List the attrac... |
096867d40ce326b6fe7a8210ebeae0d0ef7b456db4a65d9a09ff3c200bd7acca | R | 1,491 | 38 | #' wellsPerInt
#'
#' table of well counts per sample for each interval
#' @import dplyr
#' @importFrom tidyr pivot_wider
#' @param dat data frame
#' @param measure character, what data column should be not na, defaults to "ocr"
#' @param wellVar character, column name for well, defaults to "well"
#' @param groupingVars... |
c0f7e68d046c3055ced6ee62d2298590dd9472f2cdfa8fc0a85ac319c8cea722 | R | 1,491 | 63 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
sqrt_sp <- function(X) {
.Call('_RISC_sqrt_sp', PACKAGE = 'RISC', X)
}
cent_sp_d <- function(X) {
.Call('_RISC_cent_sp_d', PACKAGE = 'RISC', X)
}
multiply_sp_sp <- function(X, Y) {
... |
a8b1a2aa68b01699648d609db12535d945e8ab3c43bc11941dd83a39957f1a03 | R | 1,497 | 52 | library(SingleCellExperiment)
set.seed(100)
###########################################
## Mocking up some example training data ##
###########################################
Ngroups <- 5
Ngenes <- 1000
means <- matrix(rnorm(Ngenes*Ngroups), nrow=Ngenes)
means[1:900,] <- 0
colnames(means) <- LETTERS[1:5]
N <- 100
g... |
0e221f083fd5172a544463d19929fd7561dda9fd58f9f23c5c79fd8f38c4ff04 | R | 1,498 | 53 | #' Signals collected conditions for futures flagged for early signaling
#'
#' @param future A [Future] object.
#'
#' @param \ldots ... Not used.
#'
#' @return The `future` object.
#'
#' @details
#' This function returns immediately ("no-op"), if the future (i) is _not_
#' flagged for early signaling, or (ii) not resolv... |
28954011c74ec06746a21a79343d5ef0e15bdde4a70bf079427b2d163b5012bf | R | 1,500 | 39 | library(optparse)
### The .sh script that calls this R script has this:
# for f in ../8_GeneNetwork_predictions/*AUC.txt; do
# echo $f;
# Rscript calculate_bonferonni.R -i $f -o $(basename ${f%.txt}.bonferonni.txt) -t $(basename ${f%.txt}).bonSigTerms.txt;
# done
option_list <- list(
make_option(c("-i", "... |
6ebf840b8b958a394e0dde14b71437deb2bd69d18ce03d95c9292ae87c31bcc9 | R | 1,504 | 43 |
#selecting candidate genes
#we want to have ~ <1000 AA long proteins, preferentially from 1 exon, present in 30 species, overlap with human sequence at least 50%
setwd("protein/other_protein_alignments/blat_output_full/")
overlap_list<-list.files(recursive = T, pattern = ".rds")
rbb_list<-lapply(overlap_list, functi... |
891a3705d6e61f7e2a36594a9a4a0c2cd29775f3abd13223cd751aaa8a1420e7 | R | 1,520 | 31 | #### compare PCA levels ####
# loop through nested list containing Seurat objects for different filtering thresholds, developmental stages and PCA levels
PCA.level.comparison <- function(data, PCA.levels = c(10, 15, 20, 30), cluster_res = 0.5){
if(class(data) == "list") {
data.stage.PCA <- list()
for (stage i... |
51eabf3e6c5e54558a6536929589ae6b1521ee28f31a8cef435b53e08a6655bf | R | 1,522 | 49 |
#' @importFrom SummarizedExperiment rbind cbind
#' @export
setMethod("cbind", "ACTIONetExperiment", function(..., deparse.level = 1) {
args <- list(...)
# args <- lapply(args, updateObject)
args <- .clear_ace_slots_bind(args)
out <- do.call(callNextMethod, args)
return(out)
})
#' @importFrom Summa... |
907e3ed97a1c544765ca80d9fdd136923b9b5b7c6e411b7e0310604edbdb9e74 | R | 1,530 | 24 | load("/projects/lu-lab/BenStuff/research/projects/oldhome/scripts/networks/clustscore/term2/input/clusterlistNUMBER1")
scorelist_term2_NUMBER1 <- list()
length(scorelist_term2_NUMBER1) <- length(clusterlistNUMBER1)
for (i in 1:length(clusterlistNUMBER1)){
if (!is.null(clusterlistNUMBER1[[i]])){
dist_AB <- dist(cl... |
18378d902b16530f005fcc3a29223111191c359feef3c4483e9da9ab37463139 | R | 1,545 | 44 | #' Test a perturbation solution
#'
#' \code{testPerturb} Identifies a network and tests the simulation of the perturbation until it reaches an attractor
#'
#' @param pkn Prior Knowledge Network
#' @param bool_data list of length two: first element is the starting, second is the desired phenotype
#' @param rule "ID" (d... |
a6913bd292e5bd9cea2e0d7b81c4d7ce406d348c72fd710d69cce6efcbe47a1d | R | 1,547 | 41 | import_from <- function(name, mode = "function", default = NULL, package) {
ns <- getNamespace(package)
if (exists(name, mode = mode, envir = ns, inherits = FALSE)) {
get(name, mode = mode, envir = ns, inherits = FALSE)
} else if (!is.null(default)) {
default
} else {
stop(sprintf("No such '%s' %s: ... |
49809f85bfbb89046d9b057084e750e9f3c94c9499b7825f0a8b8e3f1b5a942d | R | 1,550 | 58 | #### Run Gene Relevance on cell embeddings from siVAE
# args = commandArgs(trailingOnly=TRUE)
# logdir_in = args[1]
logdir_in = 'out/data/siVAE'
logdir_result = 'out/siVAE/gene_relevance'
## Load destiny for gene relevance
library(destiny)
## Save to python numpy
library(reticulate)
library(parallel)
li... |
8bbdc4e5314c411904b52ab07196cbcec62962cbe0e7f38a34190fc4a9dbaed0 | R | 1,550 | 54 | #' Computes quality scores for cells
#'
#' @param ace ACTIONet output
#'
#' @return Updated ace object with dblscore.expression, dblscore.centrality, and dblscore.combined scores
#'
#' @examples
#' ace = compute.ACTIONet.doublet.score(ace)
#' @export
compute.ACTIONet.doublet.score <- function(
ace,
z.threshold = 1,... |
f08939bf23f7f65e3e675b69f27fd3ee585161b0f89ce7baccf9162598fa1c4a | R | 1,555 | 51 | #
# create.tstat.matrix.illumina.R
#
# Copyright (c) 2013-2013 GBIC: Danny Arends, Harm-Jan Westra, Lude Franke and Ritsert C. Jansen
# last modified Jul, 2013
# first written Jun, 2013
#
setwd("~/Github/systemsgenetics/eqtl-mapping-pipeline/src/main/scripts")
source("helper.functions.R")
setwd("~/Github/Juha/")
# Loa... |
51e6c6af230d27f9a931c63976b01b5ae6df42215cdfbcd489bc23e124b3ff41 | R | 1,562 | 40 | plotMPpresZsMr<-function(mpdat,ref,test,filePrefix){
modColors = rownames(mpdat$preservation$observed[[ref]][[test]])
moduleSizes = mpdat$preservation$Z[[ref]][[test]][, 1];
plotMods = !(modColors %in% c("grey", "gold"));
text = modColors[plotMods];
plotData = cbind(mp$preservation$observed[[ref]][[test]][, 2... |
24c59b7d4571bf77b619a10a16e302508aa90219143edebd6b9acd64ebc23b87 | R | 1,569 | 38 | source("../../downstreamer_main/downstreamer_functions.r")
path <- "~/Desktop/collaborations/lude_cancer_gwas/"
files <- list.files(path, pattern = "*.xlsx")
data <- lapply(paste0(path, files), read.depict2)
names(data) <- files
plot.xy <- function(x.name, y.name, data) {
p1 <- ggplot(data=d... |
1ff5eb0b15b368539dd221243bed4ee4cf9e2f08948dacfa53368187f3194a79 | R | 1,596 | 37 | # library(SingleR); library(testthat); source("setup.R"); source("test-plotMarkerHeatmap.R")
colnames(test) <- sprintf("cell_%i", seq_len(ncol(test)))
pred <- SingleR(test=test, ref=training, labels=training$label, genes="de")
test_that("We can produce heatmaps of markers", {
expect_s3_class(plotMarkerHeatmap(pre... |
bc3e97a70332b4e54312012fce8670dc41c7579653f15d28dc847d8899c937f9 | R | 1,598 | 68 |
args = commandArgs(trailingOnly=TRUE)
if (length(args)<4) {
stop("At least four arguments must be supplied: inputdir eqtlsamples cases controls", call.=FALSE)
}
library(coloc)
inputdir=args[1]
eqtlsamples=args[2]
cases=args[3]
controls=args[4]
#n1<-3780
# MS
#s2<-14802
#n2<-26703+s2
#s2 = s2/n2
# ALS
#s2<-2... |
30b3369b9c693527f982917de1a095cb2aaa2b849c15a00af70506fab8728089 | R | 1,600 | 62 | #' @tags timeouts
#' @tags sequential multisession multicore
library(future)
options(future.debug = FALSE)
## Timeouts are only triggered after Sys.sleep() completes [1],
## so here we split up the sleep into multiple microsleeps
sleep <- function(duration) {
until <- Sys.time() + duration
while (Sys.time() < unt... |
14c268c90df6f4db092632124bd97c8159286e677e2cd5cebb75237181f395e8 | R | 1,603 | 61 | whichIndex <- function(I, dim, dimnames = NULL) {
ndim <- length(dim)
stop_if_not((is.matrix(I) || is.data.frame(I)), ncol(I) == ndim)
if (!is.null(dimnames)) stop_if_not(length(dimnames) == ndim)
if (ndim == 0L) return(integer(0L))
if (is.data.frame(I)) {
## Convert each column to indices
I2 <- arra... |
338c5eabc24a6b9e051cdb050fecec45a3a44e9fa801a5186a2213fbb7aacccd | R | 1,606 | 24 | setwd(paste0(figureDirectory, "Figure2-Autocorrelation" ) )
library(ggplot2)
load(file = "autocorrelationData.RData")
autocorrelationQuantilesData <- autocorrelationData[[1]]
autocorrelationQuantilesPermut <- autocorrelationData[[2]]
autocorrelationQuantilesData$lag <- autocorrelationQuantilesData$lag -1
pdf(file... |
e5d13281f3f94879280897d7967e8fb7799d817c7e1acd73387a719aed1641a3 | R | 1,620 | 66 | #' @title Extract Log-Likelihood of galamm Object
#' @srrstats {G1.4} Function documented with roxygen2.
#' @param object Object
#' @param ... Other arguments
#'
#' @return Object of class \code{logLik}
#' @export
#'
#'
#' @seealso [deviance.galamm()] for a function returning deviance and
#' [logLik()] for the generic ... |
7901f80826089cf821bf603cabb9f1fbffbb4ef3361b15c90a8038c06cb326e2 | R | 1,625 | 60 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("coregulation_calculations")} else {setwd("../coregulation_calculations")}
source("../downstreamer_functions.r")
library(GenomicRanges)
ensembl.file <- "data/ensgR75_protein_coding.txt"
# Read reference datasets
ensembl <- read.table(ensembl.file, sep="\t", ... |
6f2dfacf3543f444241bdebd29c4da35362fa528c584d9751246b791c89192e8 | R | 1,628 | 29 | partner_Latencies <- function(filename, tibble = FALSE) {
#read in files to get the animal names
sheets <- readxl::excel_sheets(filename)
x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X))
if(!tibble) x <- lapply(x, as.data.frame)
#extract animal names from each df
animal_nam... |
9fa6f3d8b4acfdef468d58abfdb9e0972d50e8536a5ff7653ce219b7897330bc | R | 1,630 | 62 | #### Run Gene Relevance on cell embeddings from siVAE
# args = commandArgs(trailingOnly=TRUE)
# logdir_in = args[1]
logdir_in = 'out/data/siVAE'
logdir_result = 'out/siVAE/gene_relevance'
## Load destiny for gene relevance
library(destiny)
## Save to python numpy
library(reticulate)
library(parallel)
li... |
2427c38a4defa06dcdaa67528c3ebd8607e6bdb9ddca558b0ce0a730b5e26b5a | R | 1,634 | 48 | setwd(paste0(figureDirectory, "Figure6-FreqGenes Heatmap"))
library(ggplot2)
library(pheatmap)
library(grid)
library(stringr)
load("mostFreqAnnotationRowList.RData")
mostFreqCpGs <-mostFreqAnnotationRowList[[1]]
annotationRows <- mostFreqAnnotationRowList[[2]]
ann_colorsN = list(
cytokine = c("TRUE" = "darko... |
8d4a1f3f3e21b3771636f13b8a81a582e99c64acafb63c2fa28270f940970ed7 | R | 1,637 | 62 | #' Reference dataset extractors
#'
#' These dataset getter functions are deprecated as they have been migrated to the \pkg{celldex} package
#' for more general use throughout the Bioconductor package ecosystem.
#'
#' @param ... Further arguments to pass to the \pkg{celldex} function of the same name.
#'
#' @return A \l... |
aba3b892be8bc2acc6912f4f7110a11dd5977481efdb009d63454f3d0f4dab49 | R | 1,639 | 35 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
classify_integrated <- function(test, results, integrated_build, quantile, use_fine_tune, fine_tune_threshold, nthreads) {
.Call('_SingleR_classify_integrated', PACKAGE = 'SingleR', test, r... |
04c45b0bc684edc442eff0d9a819a6f1eb9c4631d97c865965911a33970b3742 | R | 1,645 | 61 | #' @tags globals resolve
#' @tags listenv
#' @tags multisession
#' @tags skip_on_cran
library(future)
library(listenv)
## Make sure futures are automatically resolved
oopts <- c(oopts, options(future.globals.resolve = TRUE))
setTimeLimit(cpu = 60, elapsed = 60, transient = TRUE)
message("*** Tricky use cases related... |
bf0e18e25d663d9d3ad1e16020d0e06152c9ba0f2240e64827df4368e8643630 | R | 1,646 | 57 | #!/usr/bin/env Rscript
#$ -cwd
#$ -o makepicardTab.$JOB_ID.out
#$ -j y
# Resources requested:
#$ -l h_data=8G,h_rt=01:00:00
#$ -V
# Email address to notify
#$ -M videlock@mail
#$ -m bea
options(stringsAsFactors=FALSE)
file.patterns=c("*.metricsAlign$","*rna_metrics$","*.metricsDup$")
file.path="picardout"
picList... |
2c099e7684bdc0cd5981d1646e24e77220589553b81f2028db4514564d628986 | R | 1,651 | 56 | library(tidyverse)
theme_set(theme_bw())
theme_update(
panel.grid = element_blank(),
strip.background = element_blank()
)
library(latex2exp)
files <- list.files("results/cognition_model/simulation/model_fits/",
pattern = "opt", full.names = TRUE)
mod <- readRDS("results/cognition_model/cogniti... |
cd71f52af08b903f3d64ee9a052159d750e47e151b49c25ecff3d5b47d902821 | R | 1,655 | 60 | #### Run Gene Relevance on cell embeddings from siVAE
# args = commandArgs(trailingOnly=TRUE)
# logdir_in = args[1]
logdir_in = 'out/data/siVAE'
logdir_result = 'out/siVAE/gene_relevance'
## Load destiny for gene relevance
library(destiny)
## Save to python numpy
library(reticulate)
library(parallel)
li... |
ef3cd42d73aa8e135ca3e732aa2f1bff8ed43cffa8b3ca8592fa28f5b246a7ab | R | 1,657 | 36 | library(Biostrings)
#ok 1 very promising sequence (end), 1 a bit promising (start) --> let's check
ferseq<-readDNAStringSet("/data/share/htp/TRNP1/dna_resequencing/Trnp1_Ferret/trinity_kmer_bwa/trinity-final.Trinity.fasta")
fullest<-ferseq[names(ferseq)=="TRINITY_DN51_c0_g1_i1 len=501 path=[0:0-500]"]
writeXStringSet... |
9e48e9c0de64fb80bf121e0b4e71fefb15e105eb8001dc3535f322c41bc665c6 | R | 1,665 | 59 | # Annotate with gnomAD minor allele frequency
library(dplyr)
library(tidyr)
library(purrr)
library(janitor)
library(tibble)
library(glue)
library(stringr)
library(readr)
library(forcats)
# library(vcfR)
## Snakemake
### Input
path_gnomad <- snakemake@input[["gnomad"]]
path_snps <- snakemake@input[["snps"]]
### Outpu... |
bba0846642c39a0587a6fb20a434299eaae08bc18c1ce23cec405d4977357cf9 | R | 1,676 | 29 | novel_Latencies <- function(filename, tibble = FALSE) {
#read in files to get the animal names
sheets <- readxl::excel_sheets(filename)
x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X))
if(!tibble) x <- lapply(x, as.data.frame)
#extract animal names from each df
animal_names... |
6512253073b5aab8e14c1f903b78ed0a0af074c143e947abbdb0d22b2d61d0a2 | R | 1,681 | 59 | #' Subsets rows/columns of an ACTIONetExperiment (ACE) object
#'
#' @param i,j rows and columns
#'
#' @export
setMethod("[", c("ACTIONetExperiment", "ANY", "ANY"), function(x, i, j, ..., drop = TRUE) {
rn <- x@rowNets
cn <- x@colNets
rf <- x@rowMaps
cf <- x@colMaps
if (!missing(i)) {
if... |
c4551efd55566634e631bf73e8bd83c063666b63696c9cedcb971e08a0e165b8 | R | 1,686 | 45 | #install.packages("BiocManager", repos = "https://cran.r-project.org", dependencies=TRUE)
#install.packages("dplyr", repos = "https://cran.r-project.org", dependencies=TRUE)
#install.packages("tibble", repos = "https://cran.r-project.org", dependencies=TRUE)
#install.packages("tidyr", repos = "https://cran.r-project.or... |
05af4c0ad1bc3b711cb24feff669441670c8bdda58a82bae8addd557f57217b8 | R | 1,687 | 48 | library(data.table)
library(ggplot2)
library(ggbeeswarm)
library(patchwork)
source('../config.r')
source('../utils.r')
data <- fread('mod24_cond_big.csv')
data_swarm <- data[,.(coef = sample(coef, 200, replace = F)),
by = .(target, family, name, type)]
fig <- ggplot(data = data) +
aes(y = ... |
ced5a7f8c468ee28e5e873290fdea96b71b4383c6825ec69cf8afb3758f69485 | R | 1,688 | 67 |
#Code to generate matrix from: http://r.789695.n4.nabble.com/Re-Generate-a-serie-of-new-vars-that-correlate-with-existingvar-td822187.html
# create the initial x variable
x1 <- rnorm(100, 15, 5)
# x2, x3, and x4 in a matrix, these will be modified to meet the criteria
x234 <- scale(matrix( rnorm(300), ncol=3 ))
# ... |
c39cac15b2da60083ec97e4bf3a56f234bd2c8fae5a2332b0c83b6cafd43f423 | R | 1,694 | 58 | #' Shuffle a symmetric matrix
#'
#' ShuffleHiC shuffles a matrix from `select_subset()`` diagonal-wise. That is, every diagonal in the matrix is shuffled independently.
#' @examples
#' \dontrun{
#' ## Shuffle a region on chromosome 1
#' HiCregion <- select_subset(EXP, chrom = "chr1", start = 15e6, end = 20e6)
#' HiCreg... |
bc2c1f25f1f2b9cfa24c0dfaafb2e6adba616f64eea6b98b3399aa590fc03972 | R | 1,699 | 51 | #!/usr/bin/env Rscript
library("optparse")
library("tximport")
# specify our desired options in a list
# by default OptionParser will add an help option equivalent to
option_list <- list(
make_option(c("-t","--tx2gene"), default=NULL,
help="Table with 2 columns: TXNAME and GENEID;",
metavar="FILE.tsv"),
make... |
ba576751fe2167a65390f51c1ac06fcb6db2e6cf29ea172a923f05101686493c | R | 1,701 | 51 | #' Evaluate an expression using a temporarily set future plan
#'
#' @inheritParams plan
#'
#' @param data The future plan to use temporarily, e.g. `plan(multisession)`.
#'
#' @param expr The R expression to be evaluated.
#'
#' @param local If TRUE, then the future plan specified by `data`
#' is applied temporarily in t... |
2f3bab31bdb61f8b8bd1af05ec872c71412b541c3c2096ef95f7477b48bcf40d | R | 1,708 | 49 | #!/usr/bin/R
library(heatmaply)
library("optparse")
option_list = list(
make_option(c("-f", "--file"), type="character", default=NULL,
help="dataset file name", metavar="character"),
make_option(c("-o", "--out"), type="character", default="out.txt",
help="output file name [default= %de... |
df974dd3af289459d4ed260f7247e39f58b3584a9ac8b36f6d4498383ab0f200 | R | 1,709 | 52 | #!/usr/bin/Rscript
library(optparse)
library(ggplot2)
library(data.table)
library(reshape2)
library(grid)
library(gtable)
library(ggpubr)
library('scales')
option_list = list(
make_option(c("-l", "--ld_scores"), type="character",
help="File containing the LD scores between the top SNPs for each gene o... |
52f9010d6600113f9924e3bf4c73c379f77d13d34b935a3fdd033805c604fa99 | R | 1,711 | 58 | # R version:RStudio
# 2022.02.1 Build 461
# © 2009-2022 RStudio, PBC
# libraries needed:
library(rlang)
library("ggplot2")
library("FactoMineR")
library("factoextra")
library("corrplot")
library("tidyverse")
data.file <- read.csv ("datafile.csv",sep=";", header=T, na.strings="NA", as.is=T)
head(data.fi... |
846dc1d9997df6e481ee04dc1607c58ba5ea0c1d97fc37a110ca880e2900f792 | R | 1,716 | 68 | #' @tags globals
#' @tags nse
#' @tags listenv
#' @tags nested-futures
#' @tags sequential multisession multicore
library(future)
library(listenv)
options(future.debug = FALSE)
message("*** Globals w/ non-standard evaluation (NSE) ...")
data <- data.frame(x = 1:5, y = 1:5)
v0 <- subset(data, x < 3)$y
for (strategy ... |
18a928b855c4a5a5529ec4e23d7a8e2b494d840e3ba8400008dca46f28e3f264 | R | 1,725 | 76 | #' Get all futures in a container
#'
#' Gets all futures in an environment, a list, or a list environment
#' and returns an object of the same class (and dimensions).
#' Non-future elements are returned as is.
#'
#' @param x An environment, a list, or a list environment.
#' @param \ldots Not used.
#'
#' @return An obje... |
005ceea729a648e1e28fed7d3a62d80b9b128a3e207f5f66cf9c5208b644ac30 | R | 1,727 | 51 | #!/usr/bin/env Rscript
library("optparse")
library("tximport")
# specify our desired options in a list
# by default OptionParser will add an help option equivalent to
option_list <- list(
make_option(c("-t","--tx2gene_table"), default=NULL,
help="Table with 2 columns: TXNAME and GENEID;",
metavar="FILE.tsv"),... |
62e40ca5504421afd2204ce7db8e51c4bfec9539018c4d5e14a6c11aa84b0115 | R | 1,735 | 48 | library(sRACIPE)
indexlist_to_rset <- function(index){
tmp <- ALLUNIQUEMODELS[[index]]
colnames(tmp) <- c("A", "B", "C", "D")
rownames(tmp) <- c("A", "B", "C", "D")
tmp2 <- as.data.frame(matrix(ncol = 5))
colnames(tmp2) <- c("from", "to", "arrows.to.type", "color", "type")
k <- 0
for (i in 1:4){
for ... |
7e83979d73dc7c4138fffddbb9c253eab0c33a14dddf99b0a8da233b72241eb9 | R | 1,737 | 39 | #### plot multiple feature plots at once ####
multi.feature.plot <- function(seurat.obj, stage.name = NULL, gene.list, plot.clusters = T, plot.stage = F, cluster.col = "seurat_clusters",
n.col = 4, label = "UMAP plots for GOI on normalised filtered data", legend.pos = "right", plot.cellty... |
8d77f29713ba8ebd8cc4dd43b0b02471e4b305dbec45518785c1e70d99de97aa | R | 1,742 | 63 | get_connections <- function() {
cons <- lapply(getAllConnections(), FUN = function(idx) {
tryCatch({
con <- getConnection(idx)
as.data.frame(c(index = idx, summary(con)))
}, error = function(e) {
NULL
})
})
do.call(rbind, cons)
}
diff_connections <- function(after, before) {
index... |
bee0f1a6acb6d6c5602bd5231adbb2cb0b421dc3fb6a0cb5440bcb1083f79d60 | R | 1,753 | 60 | # Require reticulate
# Use reticulate to save
# library(reticulate)
# library(renv)
# renv::install("reticulate")
# renv::use_python()
# 3
library(reticulate)
sc <- import("scanpy")
days = c('D11')
# celltypes = c('FPP','P_FPP')
celltypes = c('P_FPP')
df.eff = read.table('diff_efficiency_neur.csv',sep=",",header=T)... |
0c99e73699c799c172d477a7f608eb1927932ad757f9172325372c3fae7bc5a3 | R | 1,754 | 67 | #' @tags globals non-exportable
#' @tags detritus-files
#' @tags cluster
library(future)
message("*** Non-exportable globals ...")
options(future.debug = FALSE)
options(future.globals.onReference = "warning")
plan(cluster, workers = 1L)
message("* R connections ...")
## Create a dummy, global connection with a hi... |
2f494f4116b79daa212095ff56ce465e307b6b481bd9bd55e31e0fd72b201e1e | R | 1,757 | 53 | #' @usage
#' x \%<-\% value
#' value \%->\% x
#'
#' @aliases %<-% %->%
#' @rdname futureAssign
#'
#' @export %<-% %->%
`%<-%` <- function(x, value) {
target <- substitute(x)
expr <- substitute(value)
envir <- parent.frame(1)
futureAssignInternal(target, expr, envir = envir, substitute = FALSE)
}
`%->%` <- func... |
27200b8f8bdbdbaffc30302d1448e064c6dfb069651932458791efc79d8cf780 | R | 1,760 | 66 | setwd("C:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(GEOquery)
GSE8759 <- getGEO("GSE8759", GSEMatrix=TRUE, AnnotGPL=TRUE)
show(GSE8759)
if (length(GSE8759) > 1) idx <- grep("GPL538", attr(gset, "names")) else idx <- 1
GSE8759 <- GSE8759[[idx]]
str(GSE8759)
experimentData(GSE8759)
pheno <- pData(phenoData(... |
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