sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
59bf9040070e4e73298b2cf2ccb67b708788ccf64a5c540d4ea458040d23459b | R | 2,318 | 67 | ### Script to read in many tables and combine them
library(tidyverse)
library(readr)
library(stringr)
library(reshape2)
#adjust the path to the output of script II_filtering.sh!!
files<-list.files(path="/data/share/htp/TRNP1/MPRA_full_300919/MPRA_data/deML/demult/count_summaries/counts/", pattern = ".txt")
meta_data ... |
f6d158b254e34d3edef0ef83372b68c987426ea5a0a1e7fcdb286c587777cf14 | R | 2,320 | 91 |
t2 <- fread("/groups/umcg-wijmenga/scr02/depict2/Height10/Height_genePvaluesNullGwas.txt", data.table=F)
rownames(t2) <- t2[,1]
t2 <- t2[,-1]
collumns2 <- fread("/groups/umcg-wijmenga/scr02/depict2/Height10/Height_geneVariantCount.txt", data.table=F)
t2 <- t2[collumns2[collumns2[,2] > 0, 1], ]
c3 <- cor(t(t2[,1:1... |
fed6464f287235fb6d03a2cdef9fba3f2239cfbc62487ab56c9b784cadad5ade | R | 2,327 | 113 | #' Reset a finished, failed, canceled, or interrupted future to a lazy future
#'
#' A future that has successfully completed, was [canceled][cancel] or interrupted,
#' or has failed due to an error, can be relaunched after resetting it.
#'
#' @param x A Future.
#'
#' @param \ldots Not used.
#'
#' @return
#' `reset()` r... |
0004f5d1a53abb0609707ec3ba1a5cebbe84fa37cefdd5096accf895adc775ba | R | 2,328 | 70 |
# this function orders cell clusters by a column in the seurat metadata. i.e. sort seurat clusters by their developmental stage, with clusters
# shared between stages ordered in between those stages
# col.to.sort (seurat_clusters) is the metadata column which needs to be re-arranged based on the levels in sort.by (or... |
48a30fd7231e2ee0813ed8435e7a60b3f61b9bf01443371b664a4c3a687cedb8 | R | 2,331 | 60 | library("optparse")
library(ggplot2)
library(ggpubr)
library(gridExtra)
library(matrixStats)
library(plyr)
library(data.table)
# Get command line arguments
option_list = list(
make_option(c("-m", "--MetaBrain"), type="character",
help="bin file for MetaBrain", metavar="character"),
make_option(c("-e"... |
9565e1af23ea2277ad6a438a80e60324a585231753bfc9ddfe87bc5f31bfb53d | R | 2,334 | 71 | ```{r}
source("../downstreamer_functions.r")
path <- "~/Desktop/depict2/output/sweep/0kb/"
files <- list.files(path, pattern = "*.xlsx")
kb0 <- lapply(paste0(path, files), read.depict2)
names(kb0) <- files
path <- "~/Desktop/depict2/output/sweep/15kb/"
files <- list.files(path, patter... |
8e1fe3e07b901c3fb9ccbc08ce49c1c92789d1a88a2fcafc8fedbc0f31cbb72c | R | 2,343 | 76 | #' @tags undo-options undo-envvars
#' @tags listenv
#' @tags sequential multisession multicore
library(future)
options(future.debug = FALSE)
message("*** Futures - undo R options and environment variables ...")
strategies <- supportedStrategies()
options(digits = 6L)
Sys.setenv(R_DEFAULT_INTERNET_TIMEOUT = "300")
ol... |
19c0a8516dd77d89252c9f7cba0931b4e41e2188bd613ec7ce9da650f1d484c0 | R | 2,370 | 74 | #!/usr/bin/env Rscript
source <- "./inst/testme"
if (!utils::file_test("-d", source)) {
stop("Source 'testme' folder not found: ", sQuote(source))
}
target <- "./tests"
if (!utils::file_test("-d", target)) {
stop("Target 'tests' folder not found: ", sQuote(target))
}
r_path <- "./R"
if (!utils::file_test("-d", r... |
59933a23e1e7a2a8bb32fb3101adc5187a7ffd254a2b060f4f3c06476be82165 | R | 2,387 | 91 | #!/usr/local/R-4.1.2/bin/R
library(dendextend)
library("optparse")
library(reshape2)
library(ape)
#args = commandArgs(trailingOnly=TRUE)
option_list = list(
make_option(c("-f", "--file"), type="character", default=NULL,
help="dataset file name", metavar="character"),
make_option(c("-o", "--... |
6227d607a0cc4543841bdb505faa361d221060b815b736b142814736f1e0b25e | R | 2,397 | 82 | library(MungeSumstats)
library(tibble)
library(tidyr)
library(readr)
library(purrr)
library(plyr)
library(dplyr)
library(stringr)
library(forcats)
library(data.table)
# Snakemake
## Input
# path_gwas <-
# c("lambert", "kunkle", "marioni", "jansen", "wightman", "bellenguez") %>%
# sapply(function(x) snakemake@inpu... |
6dbcaefdf5930f4347abdbab5c15c3d7434d65181fe5c9d2dd89ab87c463e567 | R | 2,403 | 82 | #' @tags interrupt
#' @tags detritus-files
#' @tags multisession multicore
library(future)
options(future.debug = FALSE)
strategies <- supportedStrategies()
strategies <- setdiff(strategies, "sequential")
## IMPORTANT: Skip when testing with 'covr', because value() cancels the
## still-running futures when one of th... |
fa708cdf85b53e137d43a8aa601f508428bedd4ddb4616434bb46aedcefd97c7 | R | 2,403 | 90 | #' @tags futureAssign
#' @tags sequential multisession multicore
library(future)
message("*** futureAssign() ...")
message("*** futureAssign() - sequential w/ lazy evaluation ...")
delayedAssign("a", {
cat("Delayed assignment evaluated\n")
1
})
futureAssign("b", {
cat("Future assignment evaluated\n")
2
}, ... |
78c7419fb6d70143fea77acf37de86cde82b2e233220b3b3be6b81f432fd7f95 | R | 2,404 | 100 | #srun --cpus-per-task=1 --mem=200gb --nodes=1 --qos=priority --time=168:00:00 --pty bash -i
#remoter::server(verbose = T, port = 55556, password = "laberkak", sync = T)
remoter::client("localhost", port = 55508, password = "laberkak")
setwd("/groups/umcg-fg/tmp01/projects/genenetwork/recount3/")
library(readr)
c... |
f2e635296f863224fdeca8ea5cb1c1eebeb22ff57956be00ba08b75a049f0d85 | R | 2,409 | 79 | library(data.table)
library(ggplot2)
library(patchwork)
source('../utils.r')
source('../config.r')
old_age_path <- file.path(
"..", "figure-2", "inputs",
"post_predictive_analysis_all_models_combined_imaging_non_imaging_age.csv")
df_old <- fread(old_age_path)
df_old <- df_old[evaluation == "Generalization"]
ne... |
e2a9e4984fb428530bfce8b8a69ab29f8ef81a45f5ee394c0aed811bfc1a38de | R | 2,417 | 69 |
library(Biostrings)
library(stringr)
library(tidyverse)
library(plyranges)
library(foreach)
library(doParallel)
#what I will need to do:
# 1) cut out blat matches with the same overlap to the human sequence and same blosum scores
#quantify their differences - my guess is that they might be nearly identical and not we... |
085a4092decc75334722341bdf472149f98fd5068426b861bf18411510f50c99 | R | 2,420 | 93 | #' @tags futures resolved value
#' @tags list listenv environment
#' @tags sequential multisession multicore
library(future)
library(listenv)
dims <- list(
NULL,
c(1, 6),
c(2, 3),
c(2, 3, 1),
c(2, 1, 3, 1)
)
message("*** futures() / resolved() / value() ...")
for (cores in 1:availCores) {
message(sprin... |
4cc5fa37040ec70aa47f3fbb6da6de761f808f229f0941c32242fbb4d1ec9604 | R | 2,425 | 97 | # Resolution methods ------------------------------------------------------
#' Get the resolution of GENOVA objects
#'
#' @param x An object from the GENOVA package
#' @param ... Optional arguments (not used).
#'
#' @return A \code{integer} with the resolution
#' @export
#'
#' @note If the \code{ggplot2} namespace is... |
0ed2fcb1bb0384939cb3f974e57573ded92e8aa24e8f402165df80fd1f62abb1 | R | 2,449 | 103 | ### script to calculate read length distribution on aligned reads (BAM files)
# load libs
library(foreach)
library(doParallel)
source('functions.R')
# load tool locations
tool_path <- load_tool_location()
# set max cores
max_cores <- get_max_cores() # from functions.R
registerDoParallel(makeCluster(max_cores))
# ge... |
06cd7ff4c5a8a1f4170b563e51aa0b8f0392a14ca3268f23f84bf475b4ebbd1b | R | 2,451 | 98 | #' @tags dotdotdot listenv
#' @tags sequential cluster multisession multicore
library(future)
library(listenv)
for (cores in 1:availCores) {
message(sprintf("Testing with %d cores ...", cores))
options(mc.cores = cores)
message("*** Global argument '...' ...")
sum_fcns <- list()
sum_fcns$A <- function(x,... |
5aeb4c6070704ca20dedf1906ccc63d16faebb3b61a86a627c5e1a98bff77ec7 | R | 2,455 | 47 | library(WGCNA)
# Load data
load(file='rdata/normalized_data.RData')
load(file='rdata/differential_expression.RData')
d.test <- t(d.norm)
# Check for missing values in data
check.missing <- goodSamplesGenes(d.test, verbose = 3)
check.missing$allOK
# Plot data to check for sample outliers
sampleDend <- hcl... |
6b9aad62b096ab52e0ec39116c24d9c86ff7ef852bdccde97415471e33818176 | R | 2,456 | 54 | #!/bin/env Rscript
#
# Author: Alex Eng
# Date: 1/18/2018
###############################################################################
suppressPackageStartupMessages(library(argparse))
# Defining arguments and parser for command line arguments
parser = ArgumentParser(description = "Calculate community robustness ... |
215c764de4704f6d30e42acd49833dfe719c629fe5c8a98753c31a023f8e08b1 | R | 2,461 | 98 | # Estimate Power curves
## https://www.mv.helsinki.fi/home/mjxpirin/GWAS_course/material/GWAS3.html
library(dplyr)
library(tidyr)
library(purrr)
library(janitor)
library(tibble)
library(glue)
library(stringr)
library(readr)
library(forcats)
library(glue)
## Snakemake
### Input
adgwas_meta.path = "intermediate/gwas_... |
a6e739081208eb22467b273d83eb5cc639a9f16a3102ec98e60e0527df6f6d1f | R | 2,465 | 94 | #' @tags workdir
#' @tags sequential multisession multicore
library(future)
message("*** Futures - working directory ...")
strategies <- supportedStrategies()
owd <- getwd()
for (strategy in strategies) {
message(sprintf("- plan('%s') ...", strategy))
plan(strategy)
## The working directory should be reset, ... |
2eb11e1e33eb9e07deb965620acf440625deb1954da79543562e43adfd89fa1c | R | 2,467 | 88 | #' Place a sticky-globals environment immediately after the global environment
#'
#' @param erase (logical) If TRUE, the environment is erased, otherwise not.
#'
#' @param name (character) The name of the environment on the [base::search]
#' path.
#'
#' @param pos (integer) The position on the search path where the
#' ... |
e4697a3cfb27636aa0adf50b87be93aa9c8b152936bc6a1ff834936c2d413ea3 | R | 2,473 | 95 | ---
title: "InvCor"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
setwd("D:/UMCG/Genetica/Projects/Depict2Pgs")
library(heatmap3)
library(RColorBrewer)
```
## Genes in arm: 100 number of permutation to determine correlation: 10
```{r }
numberRandomGwas=10
genesInArm=10... |
4f114f6b689422210cc0a106896d88340b452fe52e25df3af9349c5b5d09c688 | R | 2,475 | 64 | #!/usr/bin/Rscript
# Normalize counts to TMM
library(edgeR)
library("optparse")
library(data.table)
# Get command line arguments
option_list = list(
make_option(c("-c", "--countsFile"), type="character", default=getwd(),
help="path to input count file (e.g. merged STAR counts)", metavar="character")... |
f69ed26bd53bb9d0c80ea55da315d95dd42a7a062be6837500ed12a13054e187 | R | 2,476 | 80 | ```{r}
setwd("cellline_prediction_model")
source("../downstreamer_functions.r")
library(caret)
sample.annot <- fread("data/sample_annotations_updated_by_patrick.csv", stringsAsFactors = F, sep="\t", header=T, data.table=F)
rownames(sample.annot) <- sample.annot$Sample
outcome <- sample.annot$CellLine != ""
n... |
fb102bf363b5e1345a916169179f12cf2aeb9ffb6674bb49b4815421d6b91ebf | R | 2,481 | 66 | #' @title Confidence intervals for model parameters
#'
#' @srrstats {G1.4} Function documented with roxygen2.
#' @srrstats {G2.3,G2.3a} match.arg() used on "method" argument.
#' @srrstats {G2.3,G2.3b} Arguments parm and method are case sensitive, as stated in
#' their documentation.
#' @srrstats {G2.1a} Expected data... |
1fb8331bbbee2627c856eb82225748e5b38023742e85705aa5e715cf8158a113 | R | 2,493 | 75 | ```{r}
setwd("../downstreamer_collaborations/biogen")
source("../../downstreamer_main/downstreamer_functions.r")
als.wo <- read.depict2("~/Desktop/depict2/collaborations/biogen/als/without_SOD1_c90rf72/2020-04-28-ALS.replication_enrichtments.xlsx")
als.asia.wo <- read.depict2("~/Desktop/depict2/collaborations/biogen... |
203258f3541ee83e33552689119870a22341dfd4e5e97b0a40a2d317a0fb696c | R | 2,505 | 47 | rm(list=ls())
setwd('tldr_pred_rouge_sig_testing')
# copied from `significance_tests.csv` file:
if(FALSE){
# AO -- \bart vs \ours
R1 multi.max ao ....bart.large.20k.titles.20k.tldr..ctrl. bart.large.tldr 0.47441 0.47804 -0.46438 1.4132 0.321391
R2 multi.max ao ....bart.large.20k.titles.20k.tldr..ctrl. bart.larg... |
644a8454e67a228c0be27393a8d85fb8e305cd63521ac63a890c78db5dd44537 | R | 2,537 | 76 | #!/usr/bin/env Rscript
################################################
################################################
## LOAD LIBRARIES ##
################################################
################################################
library(optparse)
library(UpSetR)
###############... |
f9b5190aeb722bf9d40bac6ac7feaf7a342a573bad092f72f8cb6c663a77a810 | R | 2,550 | 78 | testme <- as.environment("testme")
## Undo options
## (a) Reset
options(oopts0)
## (b) Remove added
local({
added <- setdiff(names(options()), names(oopts0))
opts <- vector("list", length = length(added))
names(opts) <- added
options(opts)
})
## (c) Assert that everything was undone
if (!identical(options(),... |
08cc75c0152ae0034dba0e5fcf4f06575dcd617439c8309b43c93b9b71b05b91 | R | 2,566 | 111 | #' @export
#' @keywords internal
persp.PESCAn_discovery <- function(
x, ...
) {
mat <- x$obsexp
par(mfrow = seq_len(dim(mat)[3]))
# Set default arguments
args <- pairlist(
x = rev(as.numeric(dimnames(mat)[[1]])/1000),
y = as.numeric(dimnames(mat)[[2]])/1000,
z = mat,
ylab = "5' (kb)", xlab... |
64b9ba3c0793adcc4b005e6b669bb4ff1ff9441611f1bec5f4dcc165bc429e17 | R | 2,570 | 71 | #' Discover transcription factors responsible for transition
#'
#' \code{discoverTFs} simulates gene perturbations recursively in order to find the transcription factors that drive the transition.
#'
#' @param max_depth Number of transcription factor (Depth of recursion)
#' @param depth Actual depth of the simulation
... |
135a44b71f51fa13e65e3482448719b874acf9ac3e42a73addd49686c080dfe2 | R | 2,571 | 84 | library(yaGST)
#' Get at most top 30 confident normal cells
#'
#' @param NES
#' @param pValue
#' @param FDR
#' @param pval_filter
#' @param fdr_filter
#' @param pval_cutoff
#' @param nes_cutoff
#' @param nNES
#'
#' @return
#' @export
#'
#' @examples
top30classification <- function(NES, pValue, FDR, pval_filte... |
8839d8c67482f9dacbbe478b1232df986c4261b6bd92a2c1f9d58c100b0ea343 | R | 2,572 | 120 | #' @tags globals
#' @tags local
#' @tags listenv
#' @tags sequential multisession multicore
library(future)
library(listenv)
oopts <- c(oopts, options(
future.debug = FALSE
))
message("*** Globals inside local() environments ...")
for (strategy in supportedStrategies()) {
message(sprintf("- plan('%s') ...", stra... |
56e590f19497c4e8cf19ba990d7f553f5169eccab7cf18bdc0d7cf16aa209d05 | R | 2,580 | 62 | #this code reads HNv3, and celltype specific net, and make edge and node attribute table
library(qpcR)
hnet <- read.table('~/HumanNetv3/HumanNetv3_networks/HumanNetv3-XC_symbol_LLS.tsv', stringsAsFactors = F)
#sort hnet
#sort edge alphabetically
net <- hnet
net <- as.data.frame(t(apply(net,1,sort)))
net <- net[,c(2,3... |
4a9334239b1c3f513d20d031992895c027e2fc5e6de987e3e1c88a0e99c0850c | R | 2,601 | 92 | #' Takes a `ACTIONetExperiment` object and adds the reduced kernel matrix
#'
#' @param ace ACTIONetExperiment object.
#' @param reduced_dim Dimension of SVD used for reducing kernel matrix
#' @param max_iter Number of SVD iterations
#' @param assay_name Name of assay to reduce.
#' @param reduction_slot Name of slot to ... |
80f3193853e89b4d6dda83b18ed8646de08625bccb3570eca9523f1b54ee028c | R | 2,604 | 85 | # Jansen 2019
## Jansen, I. E. et al. Nat Genet 51, 404–413 (2019).
library(MungeSumstats)
library(tibble)
library(tidyr)
library(readr)
library(purrr)
library(plyr)
library(dplyr)
library(stringr)
library(forcats)
library(tabulizer)
library(fuzzyjoin)
## Misc functions
calc_se_from_beta_and_l95 = function(or, l95... |
1931875e1dcf1bb9f09e334779b6e3be89c9e1d56cdcd91ddb8abf7c2524e8f9 | R | 2,609 | 68 | ## -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_Shar... |
02709d1740c59b6fa296791590340fccc00528be1aba7e7f3f2f4adfd5b30f81 | R | 2,626 | 115 | #' @tags futureAssign
#' @tags listenv
#' @tags sequential multisession multicore
library(future)
message("*** %<-% ...")
for (cores in 1:availCores) {
message(sprintf("Testing with %d cores ...", cores))
options(mc.cores = cores)
for (strategy in supportedStrategies(cores)) {
message(sprintf("*** %%<-%% ... |
3400aea867f0d8274df4732676459d56e301e1aa502fe854c77ebdc81cd40a66 | R | 2,629 | 83 | library(yaGST)
#' Get at most top 30 confident normal cells
#'
#' @param NES
#' @param pValue
#' @param FDR
#' @param pval_filter
#' @param fdr_filter
#' @param pval_cutoff
#' @param nes_cutoff
#' @param nNES
#'
#' @return
#' @export
#'
#' @examples
top30classification <- function(NES, pValue, FDR, pval_filte... |
da863394c4a868c4892efade5d130be590e32c473ff571b65e9ecbd450d0b7ac | R | 2,630 | 98 | #' @tags globals recursive
#' @tags sequential multisession multicore
library(future)
## Test adopted from http://stackoverflow.com/questions/42561088/nested-do-call-within-a-foreach-dopar-environment-cant-find-function-passed-w
options(future.debug = FALSE)
message("*** Tricky globals requiring recursive search ..... |
2454bc254ee2dc1bc3f31fe1354928707d383c24a0f34421b8953cbb3f0629d3 | R | 2,652 | 68 | #' @title Residuals of galamm objects
#'
#' @description Computes residuals for models fit with [galamm()] using the definitions in
#' Chapter 8 of \insertCite{dunnGeneralizedLinearModels2018;textual}{galamm}.
#' Define \eqn{y} as the response and \eqn{\hat{\mu}} as the model fit. Importantly,
#' \eqn{\hat{\mu}} includ... |
fd0b420d31b1a8691041017c0c974b2c2a2ceace58f82a8df343a90f92ed62c8 | R | 2,660 | 84 | #' @tags cancel
#' @tags sequential
library(future)
options(future.debug = FALSE)
plan(sequential)
## A sequential future is resolved already when created, but canceling it
## still makes it invalid
for (interrupt in c(TRUE, FALSE)) {
message(sprintf("interrupt = %s ...", interrupt))
message(" Cancel a sequent... |
b21c9c276f3ccec36556ca517b633c3f38fe12f6741b6040043202835e0cea59 | R | 2,670 | 85 |
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs\\LudeEigen")
library(readxl)
read.depict2 <- function(path, potential_traits=NULL) {
if (is.null(potential_traits)) {
potential_traits <- excel_sheets(path)
potential_traits <- potential_traits[grep("Overview", potential_traits, invert=T)]
}
output <-... |
4b9cf7073f590f4a80204651193ba5da3e710cae6f0f45a0f254f9ee7d22c3a5 | R | 2,673 | 80 | ```{r}
setwd("evaluating_coregulation")
source("../downstreamer_functions.r")
source("data/source_olivier.r")
library(ggsignif)
# Read reference datasets
ensembl <- read.table(ensembl.file, sep="\t", header=T, row.names = 1, stringsAsFactors = F)
ensembl$gene.length = ensembl$Gene.end..bp. - ensembl$Gene.start..bp.
r... |
fea55d0cf8f9a64c6be76c8aa1ba8bc7c8b88fe3d3aff0c455b3759d9e18fe5a | R | 2,677 | 66 | # This tests the aggregator function.
# library(testthat); library(SingleR); source("setup.R"); source("test-aggregate.R")
library(SingleCellExperiment)
sce <- as(.mockRefData(nreps=50), "SingleCellExperiment")
sce <- scrapper::normalizeRnaCounts.se(sce, more.norm.args=list(delayed=FALSE))
test_that("aggregateReferen... |
a0a3038fa2e1cc87dc7f1717ec1ea77bd2ab8fcfadfa0c63b59263ba8d4e6e32 | R | 2,678 | 107 | #' Title
#'
#' @param theta
#' @param betX
#' @param pi1
#' @param sig1
#' @param w8s
#' @param m0
#' @param nX
#' @param bn
#' @param bins
#' @param M
#'
#' @importFrom stats fft sd
#'
#' @return
#' NOT EXPORTED @export
#' @keywords internal
#'
#' @examples
singleTrait_likelihood <- function(theta,betX,pi1,sig1,w8s,m0... |
81fa754deef40f7c58e389fbe9f769a439c9899493ea500b8d17642c2ba8df70 | R | 2,687 | 90 |
#' Get the percentage cis-contacts genome-wide or from a region
#'
#' @param exp A contacts-objects or a list of them
#' @param bed A bed-dataframe or a (named) list of them.
#'
#' @section Resolution recommendation: 500kb-1Mb
#'
#' @return A data.table with the sample, percentage and region.
#' @export
cis_trans = f... |
87e3ecde2f0b8c733cd6538d03ee4c9def8b09b4ef6e118937dcd3962b555d66 | R | 2,690 | 72 | #' Mock data for examples
#'
#' Make up some test and reference data for the various examples in the \pkg{SingleR} package.
#'
#' @param ngroups Integer scalar specifying the number of groups.
#' @param nreps Integer scalar specifying the number of replicates per group.
#' @param ngenes Integer scalar specifying the nu... |
65889b554c61ecf7f792622fd66179d286b73166406b47f71d49555b229872d7 | R | 2,694 | 87 | library(data.table)
library(ggplot2)
library(ggbeeswarm)
library(patchwork)
source('../config.r')
source('../utils.r')
DT_cond_proxy <- fread('mod22_cond_proxy.csv')
DT_cond_proxy_bs <- fread('mod22_cond_proxy_bs.csv')
DT_cond_target <- fread('mod22_cond_target.csv')
DT_cond_target_bs <- fread('mod22_cond_target_bs.cs... |
7f35d513de97311672cd480c41a1ac28472e5499dbfb7109bcee0aa620beaaa3 | R | 2,696 | 88 | library(data.table)
library(ggplot2)
library(ggbeeswarm)
library(patchwork)
source('../config.r')
source('../utils.r')
DT_cond_proxy <- fread('mod23_cond_proxy.csv')
DT_cond_proxy_bs <- fread('mod23_cond_proxy_bs.csv')
DT_cond_target <- fread('mod23_cond_target.csv')
DT_cond_target_bs <- fread('mod23_cond_target_bs.cs... |
3dfa31a1ce2a48e115914f9978bbcd7eb29ffc77f40476e221760eeb69996c8a | R | 2,713 | 84 | #' @tags utils-internal early-signaling sequential
#' @tags deprecated
## plan(..., earlySignal = TRUE) is deprecated as of future (> 1.68.0)
library(future)
signalEarly <- future:::signalEarly
message("*** signalEarly() - no early signaling ...")
plan(sequential)
## A future that is not flagged for early signalin... |
051dd28a82aea20583e66a7903d85ad9a5855effa6f516ccc42c84b8b47f6261 | R | 2,714 | 96 | #' @tags worker-killed
#' @tags cluster
library(future)
library(listenv)
options(future.debug = FALSE)
## IMPORTANT: Since we're killing parallel workers, some of them will not
## get a chance to clean up their R temporary folders. Here we configuring
## them to use temporary folders with this R process temporary fo... |
b9f00b3d2be77af6bae32fa25067af9a18c12945a1e6fd527cb689f6042977b5 | R | 2,734 | 69 | library(readxl)
library(ggplot2)
source("~/Documents/projects/pr_integration/wd_integration/Code/UtillityScripts/PlottingFunctions.r")
library(gridExtra)
make.plot <- function(trait) {
ced <- data.frame(read_excel("/home/work/Documents/tmp/CeD_Z-scores_enrichtments_exHla.xlsx", sheet=trait), row.names=1)
ced.ichip... |
8a843a0e2fdc06e505f0c37f10823e5fe0a2c7016d21e2446079de00a893b16b | R | 2,737 | 65 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' Straw Quick Dump
#'
#' fast C++ implementation of dump. Not as fully featured as the
#' Java version. Reads the .hic file, finds the appropriate matrix and slice
#' of data, and outputs as d... |
1dbe4a030ee1357be188b2efd3b3d43e19b2d30c3c098e048a9cea3a32fdf65e | R | 2,753 | 75 | ```{r}
setwd("evaluating_pvalues")
source("../downstreamer_functions.r")
#source("data/source_olivier.r")
height <- read.depict2("data/height_2018_30124842_hg19/height_2018_30124842_hg19_enrichtments.xlsx")
height.emp <- read.enrichments.as.list(list.files("data/height_2018_30124842_hg19/", pattern="*true*", fu... |
2c13b5d9f16659189bf69b9c8f83a2f0803deff9448e7f75ff387d645fe04f29 | R | 2,753 | 73 | ### Data clustering
#' Create clustered heatmaps of the samples
#'
#' @param d Input a dataframe with expression values, using the Protein IDs as rows, samples as columns. Works best if values are centered and scaled.
#' @param ann_col Dataframe with groupings per sample. Rownames are the sample names, single co... |
8b1f184a31d17d3336ab39b0f29ebc773f82f6cb39e25aa8a7670b047816c684 | R | 2,779 | 77 | library(data.table)
library(matrixStats)
library(DescTools)
# Read expression data from bios
exp.bios <- fread("zcat exp.tmm.log2.covCorrect.freeze2.txt.gz", data.table=F)
rownames(exp.bios) <- exp.bios[,1]
exp.bios <- exp.bios[,-1]
cn <- colnames(fread("zcat exp.tmm.log2.covCorrect... |
ac85a93d0c263cef70903c33e98d7ca5070f099746528b15fcaf828abad93588 | R | 2,802 | 97 | #' @tags reset sequential
library(future)
plan(sequential)
message("*** reset() - default ...")
## Objects that are not futures are returned as-is
stopifnot(identical(reset(42L), 42L))
stopifnot(identical(reset("abc"), "abc"))
stopifnot(is.null(reset(NULL)))
message("*** reset() - default ... DONE")
message("***... |
2b4bee2784e339d08416f706faece2829b9ed2a430df0e876d40016eddeb45b1 | R | 2,805 | 60 | #remoter::server(verbose = T, port = 55557, password = "laberkak", sync = T)
remoter::client("localhost", port = 55557, password = "laberkak")
setwd("/groups/umcg-wijmenga/tmp04/projects/depict2/")
library(readr)
table_tmp <- read_delim("depict2_bundle/reference_datasets/human_b37/pathway_databases/gene_network/hpo_p... |
16d32910c5956cc6671e97a787ca37b507be299dddd5926f848d82d99483d051 | R | 2,819 | 104 | #' @importFrom beachmat initializeCpp tatami.row.nan.counts
#' @importFrom SummarizedExperiment assay
#' @importClassesFrom SummarizedExperiment SummarizedExperiment
#' @importFrom DelayedArray DelayedArray
.to_clean_matrix <- function(x, assay.type, check.missing, msg="x", num.threads=1) {
if (is.null(rownames(x))... |
d2508de291f4bd4303fd6dee8356c42d2e5135fac49169d5116c5c5b743e851c | R | 2,828 | 79 | library(DESeq2)
library(gplots)
library(tibble)
args <- commandArgs(trailingOnly = TRUE)
jobID <- args[1]
tablePath <- args[2]
inputFolder <- args[3]
outputFolder <- paste("./tmp/",jobID,sep="")
sampleTable <- read.csv( tablePath, sep="\t" )
for( readType in c("exonic","intronic") )
{
print( paste("Analyzing ", r... |
3c74badd543aa02052c6f09876b3c6fba96aea98398061de32d1f1f94fa402cc | R | 2,852 | 98 | #' An uniprocess future is a future whose value will be resolved synchronously in the current process
#'
#' @inheritParams Future-class
#'
#' @param \ldots Additional named elements passed to [Future()].
#'
#' @return
#' `UniprocessFuture()` returns an object of class `UniprocessFuture`.
#'
#' @export
#' @name Uniproc... |
f31a7cf1bbce6b6473a9a842af5b3d76ee14e5bcbe3f01cfe90be40daabdac47 | R | 2,858 | 82 | source ("Library.R")
#####melanogaster####
data= NULL
for (i in 1:5) {
sname <- paste("Fly",i, sep="")
.data = read.xlsx("raw_data_imaging_2F.xlsx",sheetName= sname) %>%
dplyr::mutate(Fly = paste(i)) %>%
tidyr::gather(key = Frequency, value = response, -Frame, -Fly)
.data$Frequency = str_sub(.data$Freq... |
a9a9e1ddb8523282cfc09cbc20927df2d47ef663e56553a3035196f918000e2e | R | 2,860 | 90 | #' Validates an ACTIONetExperiment (ACE) object
#'
#' @param object ACTIONetExperiment object
#'
#' @importFrom BiocGenerics NCOL NROW
setValidity2("ACTIONetExperiment", function(object) {
NR <- NROW(object)
NC <- NCOL(object)
msg <- NULL
# 2D
value = object@rowNets
for (i in seq_along(valu... |
3124d2f091b6635063ac28af2b29d9057543f97254d369117a9e7a41f30ce024 | R | 2,872 | 91 | #' @tags resolved
#' @tags sequential multisession multicore
#' @tags skip_on_cran
library(future)
options(future.debug = FALSE)
## Don't poll too frequently in order for these tests to not fail
options(future.wait.interval = 0.1)
message("*** resolved() - assert non-blocking while launching lazy futures ...")
for ... |
6d8a735500a0964cdfcd5709ade989d341fb990c546d6bd420adc0c279604ea3 | R | 2,872 | 73 | #' @tags multithreading
#' @tags multicore
library(future)
plan(multicore)
message("*** multicore() and multi-threading ...")
message("supportsMulticore(): ", sQuote(parallelly::supportsMulticore()))
message("availableCores('multicore'): ", sQuote(availableCores("multicore")))
message("supports_omp_threads(): ", sQ... |
6e437d460098a141728b0b2de806c39ad1ff0b39d2623b9e48cb4069b6d0ccbd | R | 2,873 | 109 | #' @tags futureOf
#' @tags listenv
#' @tags sequential
library(future)
library(listenv)
message("*** futureOf() with listenv ...")
message("*** futureOf() with listenv - future assignments ...")
x <- listenv()
x$a %<-% { 1 } %lazy% TRUE
f1 <- futureOf("a", envir = x)
print(f1)
f2 <- futureOf(a, envir = x)
f3 <- fu... |
5051ff4dcd8618ed1272be087df5de8e92ad069767296e5b8c980357c5bd3ce6 | R | 2,880 | 99 | ---
title: "ML Code Completeness Checklist Analysis"
output:
pdf_document: default
html_notebook: default
---
This notebook contains the ML Code Completeness analysis for NeurIPS 2019 repositories.
For a run & rendered version of this notebook please see: [code_checklist-analysis.pdf](code_checklist-analysis.pdf... |
060d9152dc7998fbce2b40182d2bc6fc7ab9d49801ae33a7e46ea96bcec2843a | R | 2,883 | 31 | ---
title: "Classification of racehorse limb radiographs using deep convolutional neural networks"
author:
- Costa da Silva, Raniere Gaia; Department of Infectious Diseases and Public Health, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR China
- Mishra, Ambika Prasad; Department of Infectious Di... |
025c89287e2038341895c4a1f1c7db0fb72cff96a079579f1bdf065972930468 | R | 2,887 | 105 | #' @tags libPaths
#' @tags detritus-files
#' @tags detritus-connections
#' @tags multisession
#' @tags skip_on_cran
covr_testing <- ("covr" %in% loadedNamespaces())
## These tests requires multisession workers
if (parallelly::availableCores() >= 2L) {
library(future)
options(future.debug = FALSE)
normalize_l... |
6d9e3d84454a4fd1d808870abb59f0cce2d3acbc6ee65a05291478931701e522 | R | 2,893 | 94 |
## Lambert, J.-C. et al. Nat Genet 45, 1452–1458 (2013).
library(MungeSumstats)
library(tibble)
library(tidyr)
library(readr)
library(purrr)
library(plyr)
library(dplyr)
library(stringr)
library(forcats)
library(tabulizer)
library(fuzzyjoin)
## Misc functions
calc_se_from_beta_and_l95 = function(or, l95){
logOR ... |
6ca33b8e7f81f02291dd01c2ba22bf88575ea773625ab326c7f129e3e18ec99c | R | 2,895 | 150 | setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
load("keyCisTransRare/key_gene_overlap_files_for_patrick.RData")
row.names(hpo.gwas.link)
str(key.genes)
gwasPthreshold <- 0.05/nrow(gene.pvalues)
gene <- rownames(gene.pvalues)[2]
gene <- "ENSG00000179455"
gene <- "ENSG00000173452"
trait <- row.names(hpo.gwas... |
89e178b8e919a2115de32958b17af42b68d28d633f3a83b8a6498d86655e54c0 | R | 2,898 | 110 | #' Pre-processed genesets from the gProfiler database (Human)
#'
#' @format:
#' SYMBOL: A list of gene x geneset indicator matrices in the sparseMatrix format (rownames are gene symbols)
#' ENSEMBL: A list of gene x geneset indicator matrices in the sparseMatrix format (rownames are ENSEMBL gene IDs)
#'
#' @source ... |
8c89fb58c98b8630aa085ed8e65384cc7ab0c579b975a0dadd887a9891657bc2 | R | 2,900 | 91 |
library(data.table)
library(gridExtra)
library(grid)
library(ggplot2)
library(lattice)
library(viridis)
zscores <- fread('2020-02-26-zsore_table.txt', header=T, na='-')
zscores_topSNP <- zscores[order(zscores$qtl_FDR),]
zscores_topSNP <- zscores_topSNP[!duplicated(zscores_topSNP$gene),]
colnames(zscores) <- gsub(... |
5d444789c295046ee8ee1afa8cd13baef2cab10936308830212fd087317a1e39 | R | 2,904 | 95 | #' @tags cancel
#' @tags detritus-files
#' @tags detritus-connections
#' @tags sequential multisession multicore cluster
library(future)
options(future.debug = FALSE)
## Backends to test, including cluster with interrupts disabled
plans <- list()
for (strategy in supportedStrategies()) {
plans[[strategy]] <- list(s... |
02f19a773f0796ec40b958337f26b05ca62ccbb05b62a30bec973083c27d6f4a | R | 2,909 | 100 | #' @tags invalid-owner
#' @tags detritus-files
#' @tags multisession
library(future)
options(future.debug = FALSE)
## Local functions
usedNodes <- function(future) {
backend <- future[["backend"]]
## Number of unresolved cluster futures
workers <- backend[["workers"]]
reg <- backend[["reg"]]
c(used = length... |
6f3e6aa955c115e340869d4566a5a8e56b2616e55c0a0017afc9b6b5554331d2 | R | 2,913 | 90 | #***********************************************************************************
# Defining a function (neighbourhod) %%%
#***********************************************************************************
neighbour<- function(C_Best, Min_Neuron , Max_Neuron, itrn, t... |
d6e06c69e35de8f59f84bc4b881c44e03027317f697ab71907949db9fd2214f1 | R | 2,940 | 79 |
library(tidyverse)
library(xtable)
#function
summarize_cor_output<-function(path_to_cov_file){
L <- readLines(paste0(path_to_cov_file))
#get the included parameters
params1<-which(grepl("entries are", L)==T)
params2<-which(grepl("covariances", L)==T)
params<-L[(params1+1):(params2-2)]
#correlations & ... |
512c4cdb3b89cb2f01f6482c202993a950720d02af286566f36b64b4407f7883 | R | 2,941 | 78 | #' @tags dotdotdot
#' @tags sequential cluster multisession multicore
library(future)
message("*** Global '...' and ...future.FUN() in custom environments ...")
## BUG FIX: https://github.com/futureverse/future.apply/issues/130
for (cores in 1:availCores) {
message(sprintf("Testing with %d cores ...", cores))
o... |
34b4e4ec3bced1614b38e2991e7f288f8673e251a3f42d4dba307c76bc9dcef3 | R | 2,949 | 110 | # LD Structure
## Assign variants to index snps
library(dplyr)
library(tidyr)
library(purrr)
library(janitor)
library(tibble)
library(glue)
library(stringr)
library(readr)
library(forcats)
# library(LDlinkR)
## Snakemake
### Input
path_snps <- snakemake@input[["snps"]]
### Output
out_ld <- snakemake@output[["ld"]]
o... |
4921032de782d63e73027c99807a02390c9844e7d12aa45e61a0c27db6ff2f44 | R | 2,956 | 97 | library(data.table)
library(ggplot2)
library(ggbeeswarm)
library(patchwork)
source('../config.r')
source('../utils.r')
DT_cond_proxy <- fread('modS3_cond_proxy.csv')
DT_cond_proxy_bs <- fread('modS3_cond_proxy_bs.csv')
DT_cond_target <- fread('modS3_cond_target.csv')
DT_cond_target_bs <- fread('modS3_cond_target_bs.cs... |
73ed4bfceac43893c442bfba9326f0cb657af0d71e6f2b95193067e334963b25 | R | 2,963 | 118 | test_that("Interaction between latent and observed covariates works", {
formula <- y ~ type + x:response + (0 + loading | id)
data <- latent_covariates
load_var <- "type"
lambda <- matrix(c(1, NA, NA), ncol = 1)
factor <- "loading"
factor_interactions <- list(~1, ~1, ~x)
mod <- galamm(
formula = form... |
13602679ad5fe15410d39766f62fe07b15391cdd85c526e05dc77373397aba47 | R | 2,966 | 45 | library(Seurat)
###load the packages
library(SeuratWrappers)
library(velocyto.R)
Idents(rh.combined)<-"celltype"
rhc <- subset(rh.combined,treat=="Ctrl")
rhh <- subset(rh.combined,treat=="HFD")
#####
#####run velocity
rhc <- RunVelocity(object = rhc, deltaT = 2, kCells = 25, fit.quantile = 0.02,ncores = 50,spliced.ave... |
c8116714563c47b93f839eee4b0492387972ff3709353255ebc2f35f8175b937 | R | 2,973 | 56 | ### Perform the beta binomial test to assess significance of differential expression
#' Perform the beta binomial test to assess significance of differential expression
#' Subsequently fits a beta uniform mixture model to match P-values with FDR values
#'
#' @param d Input a dataframe with the Protein IDs as row... |
4fdf4f5e7eab6911063441aac2ca1eb5f509a9d7700a59df983ae138d77e3809 | R | 2,979 | 95 |
## Wightman, D. P. et al. Nat Genet 53, 1276–1282 (2021).
library(MungeSumstats)
library(tibble)
library(tidyr)
library(readr)
library(purrr)
library(plyr)
library(dplyr)
library(stringr)
library(forcats)
library(tabulizer)
library(fuzzyjoin)
## Misc functions
calc_se_from_beta_and_l95 = function(or, l95){
logOR... |
09f4259bddd5537553422614836674b1769798c93e1e88b84bf6d35273d96e96 | R | 2,984 | 81 | #Testing networks for scale-free topology using the poweRlaw package
#Networks are retained given p-values above a threshold of p=0.1,
#meaning that the power law is a plausible hypothesis for the data,
#as described by Clauset et al. (2009), Power-Law Distributions in Empirical Data
#Running this script required 1.0... |
382b4b6839881df21333fe8cf1883c21c35cbbf3048005d3af7d9dc017c49154 | R | 3,000 | 56 | setwd(paste0(figureDirectory, "Figure4-Meta-Information"))
library(ggplot2)
library(cowplot)
load("metaInfoDF.RData")
metaInfoDF$alive <- ""
metaInfoDF$alive[metaInfoDF$Patient == "P39-Blun"] <- "*"
plotLegend <- ggplot(metaInfoDF, aes(shape=TherapyTissue,y=survivalBM , x=numDifCpG,color = negScore ) ) +
geom_p... |
69270c0ee1a86c8c59367f1e592b6648c970f77bc7ec2c119261a5d21dc654c4 | R | 3,004 | 127 | #' @tags globals tricky
#' @tags listenv
#' @tags sequential multisession multicore
library(future)
not_cran <- isTRUE(as.logical(Sys.getenv("NOT_CRAN", "FALSE")))
options(future.debug = FALSE)
message("*** Tricky use cases #2 related to globals ...")
for (cores in 1:availCores) {
message(sprintf("Testing with %d ... |
3fb22ec56c0d2cabdc5ee6aa52b5b70e09ef13d34182a06384846585044c91d1 | R | 3,025 | 85 | #### function to load
ssMwwGst <- function(geData, geneSet, ncore, minLenGeneSet = 15 , regulon = FALSE, filename, standardize = TRUE){
library(yaGST)
if (standardize){
means <- rowMeans(geData)
sds <- apply(geData, 1, sd)
}
# library(doMC)
# registerDoMC(ncore)
# ans <- foreach(ss = 1:ncol(geData)... |
e5d363bd22a49df3d23ade31c41162b8390898a641907ceee5ec9b821cab892b | R | 3,030 | 89 | .columnName <- function (x, i) {
paste0(x$channelNames[i], " [", x$channelUnits[i], "]")
}
# this function can be applied to the return value of readABF
as.data.frame.ABF <- function (x, row.names = NULL, optional = FALSE, ...,
sweep = NULL, type = c("all", "one"),
... |
68cc5f6492afe25637fbc5040b48e9dde2296e44f80f7893f02ca340f59e859e | R | 3,051 | 128 | ### script to pre-process and map/align sequencing reads
# load libs
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
# get args
get_dir <- commandArgs(trailingOnly = TRUE)
... |
02869ce22c783e3b5e04ec17b9cff609f36ad4c4192b218139aa96cfc3daacd9 | R | 3,073 | 83 | #
# rnaseq.analysis.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/")
# Load the RNA seq... |
a080ee15dbdc6df7fbeadf4b9fc28acc066f696010b6094a52cc97ef0d13e498 | R | 3,075 | 115 | library(BioCircos)
library(RColorBrewer)
# adj.dir = 'out/exp3/SEA_AD_BrainAtlas/2000/Oligo/plot/adj/central-100/0.01'
adj.dir = '.'
# adj.dir = "Desktop/Gerald/RitaFetalLiver/central-100/0.01"
file.list = list.files(adj.dir)
file.list = file.list[grep('csv',file.list)]
# file.list = file.list[grep('siVAE',file.list)]... |
81593dcdf1c01e7716964f4a10e87c48473ae9c262ea1f5dddd9bd6f25d77ccd | R | 3,076 | 93 | if (getRversion() >= "4.4.0") {
library(future)
library(parallel)
parse_attach_packages <- future:::parse_attach_packages
message("parse_attach_packages() - supported expressions:")
exports <- list(pkg = "tools", n = 42)
exprs <- list(
quote(library(tools)),
quote(library("tools")),
quote(base... |
1a3f3cf9de2084096779c7fe1d494aa35c8832798d2d9809295467301470ca8f | R | 3,096 | 84 | ##########################################################################################
## Generate seurat tSNE plots for ASD baits using scRNA-seq data from Velmeshev2019
##
## Author: Yu-Han Hsu
##########################################################################################
rm(list=ls())
library(Seura... |
2584a0a41b3cbd3bd3bf0eba6a888db315424e6ddf06a992d056982110cd138e | R | 3,096 | 111 | #' Converts a SummarizedExperiment object to an ACTIONetExperiment object.
#'
#' @param from SummarizedExperiment object
#'
#' @exportMethod coerce
setAs("SummarizedExperiment", "ACTIONetExperiment", function(from) {
ace = ACTIONetExperiment(
assays = SummarizedExperiment::assays(from),
rowData = from@... |
2ea1108c948e563525fc600576b84e981811fd57293efb01ac2dfbd3699ae9ad | R | 3,105 | 87 | #### function to load
ssMwwGst <- function(geData, geneSet, ncore, minLenGeneSet = 15 , regulon = FALSE, filename, standardize = TRUE){
library(yaGST)
if (standardize){
means <- rowMeans(geData)
sds <- apply(geData, 1, sd)
}
# library(doMC)
# registerDoMC(ncore)
# ans <- foreach(ss = 1:ncol(geData)... |
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