sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7670e26c6acabe147f610205ca579e2b68f825b01380b844c1031f86f253387a | R | 3,110 | 90 | # This tests the classic marker detection scheme.
# library(testthat); library(SingleR); source("setup.R"); source("test-markers.R")
library(DelayedArray)
median_by_label <- function(mat, labels) {
ulabels <- SingleR:::.get_levels(labels)
output <- matrix(0, nrow(mat), length(ulabels))
rownames(output) <- ... |
7f4a867915f93cccfd3958da180faa530fc42c78857cf447dbb74676b5e16f87 | R | 3,126 | 89 | ---
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 MGH106 sample from the public dataset of Gliobastoma (GSE131928)
```{r echo=TRUE, message=FA... |
fdbd43592c3082607ddac743b41a13f74189e80f23d6ff235b64888feb2b7915 | R | 3,127 | 124 | #==========================#
# 3. Base (& target) QC #
#==========================#
## Set up ----
filepath <- ""
setwd(filepath)
### Load libraries ----
library(data.table)
library(dplyr)
library(magrittr)
library(purrr)
### Load data ----
base <- fread("dat/genetics/base/Kunkle_etal_Stage1_results.txt", header ... |
b8187e65a48d8ddb9acb08045a08498270afc2d008073224ddac3e5afaa32564 | R | 3,133 | 109 |
write.model<-function(Loadings,S_LD,cutoff,fix_resid=TRUE,bifactor=FALSE,mustload=FALSE,common=FALSE){
Model<-""
if(common == TRUE){
for(f in 1){
u<-1
Model1<-""
for(i in 1:nrow(S_LD)){
if(u == 1){
linestart<-paste("F", f, "=~", colnames(S_LD)[i], sep = "")
u<-u+1... |
7b42eb7228616cdca5ab700237d78f10a51f878bdfc380c79083cae63d7c9586 | R | 3,135 | 58 | #' Match labels from two references
#'
#' Match labels from a pair of references, corresponding to the same underlying cell type or state
#' but with differences in nomenclature.
#'
#' @param ref1,ref2 Numeric matrices of single-cell (usually normalized and log-transformed) expression values where rows are genes and c... |
521777fcfe088a51b767ba6e79778d245d57220cc81b1445ac734a85079149a5 | R | 3,148 | 92 | now <- function(x = Sys.time(), format = "[%H:%M:%OS3] ") {
## format(x, format = format) ## slower
format(as.POSIXlt(x, tz = ""), format = format)
}
debug_indent <- local({
symbols <- rep(c("|", ":", ".", "'", ",", ";", "`"), times = 10L)
function() {
depth <- length(.debug[["stack"]])
if (depth == 0)... |
0209d50a890cc048d7b64924ccddd910e52525208d121ee733aec98bfe4a3eca | R | 3,151 | 106 |
#
## Create geneZscoresNullGwas matrix used to find correlated genes genes x traits
#
#Code to generate matrix from: http://r.789695.n4.nabble.com/Re-Generate-a-serie-of-new-vars-that-correlate-with-existingvar-td822187.html
# generates ndistr vectors of same mean and sd, with various cor.coeffs
# input :
# ... |
190230f171cdaa8f6ae2fdd769dba3d0a26ab5a1963f4558a25ef906488df6de | R | 3,156 | 114 | # AD Gene verification committee
# https://adsp.niagads.org/index.php/gvc-top-hits-list/
library(dplyr)
library(tidyr)
library(purrr)
library(janitor)
library(tibble)
library(glue)
library(stringr)
library(googlesheets4)
library(readr)
library(forcats)
## Snakemake
### Input
path_snps <- snakemake@input[["snps"]]
pat... |
2287cdb9bb61f2b7a96aaaa3250c07212133d441fcba9b25ae5a0a53074b4ecb | R | 3,196 | 82 | ```{r}
setwd("evaluating_coregulation")
source("../downstreamer_functions.r")
tmp <- read.downstreamer.enrichments("../../downstreamer_main/data/pritchard_traits/")
tmp <- read.downstreamer.batch("../../downstreamer_main/data/pritchard_traits/")
```
```{r}
# P1
x <- tmp$pritchard_traits_2021_33587031_Urate_1$GenePri... |
8f78449e3e4277e097f9bbcceea158776a92b8c49dd796f1f885d822a18d6291 | R | 3,202 | 109 | #' @tags FutureRegistry
#' @tags sequential
library(future)
message("*** FutureRegistry() ...")
for (where in c("multicore-dummy-uuid", "rscript")) {
message(sprintf("*** FutureRegistry('%s', 'list') ...", where))
futures <- FutureRegistry(where, action = "list")
print(futures)
stopifnot(length(futures) == 0... |
d503fcb6b8f51b2597434ffb516c66b8d3345b7f07b9265c87af5eab2ce45d91 | R | 3,206 | 79 | #This script takes the output of Orthofinder of the species you wish to run. Both the model training set and the targets.
#Finds Orthofinder/VERSION_RUN/SingleCopyOrthogroups.txt Orthofinder/VERSION_RUN/Orthogroups.csv Orthofinder/VERSION_RUN/Orthogroups.GeneCount.csv
#You need to set the lines below
# Remove all obje... |
1f9a23de1ad11412466fe237a9928bb00c95e01e8d04e54c52f65873124fd771 | R | 3,209 | 152 | #' @export
normalize.scran <- function(
ace,
batch_attr = NULL,
assay_name = "counts",
BPPARAM = SerialParam()
) {
.check_and_load_package(c("scran", "scater"))
batch_attr = .get_attr_or_split_idx(ace, batch_attr, return_vec = TRUE)
ace = scran::computeSumFactors(
ace,
clusters = batch... |
3236783897bc4408d86d6d374bba638897074473a1462288c11439f44d03e0b2 | R | 3,215 | 101 | factor_loadings <- function(object) {
UseMethod("factor_loadings")
}
#' @title Extract factor loadings from galamm object
#'
#' @aliases factor_loadings factor_loadings.galamm
#' @export factor_loadings
#' @export
#'
#' @srrstats {G1.4} Function documented with roxygen2.
#' @srrstats {G2.1a} Expected data types prov... |
4a77e7ef977886f7c5bf9fb5dec9b54a35bb40dcd61597adfc18e5889944c1fd | R | 3,216 | 108 | #' @tags utils-internal deprecation
library(future)
deprecateArgument <- future:::deprecateArgument
message("*** deprecateArgument() ...")
## Only a known set of functions is accepted
res <- tryCatch(deprecateArgument("<unknown>", name = "a", value = 1L),
error = identity)
print(res)
stopifnot(inher... |
3aec2a37558e3bbb7175bec3a42824d89c19689d74c4fd6a3d92184802b5f87a | R | 3,217 | 113 | #' @tags nested-futures
#' @tags detritus-files
#' @tags sequential multisession multicore
library(future)
options(future.debug = FALSE)
strategies <- supportedStrategies()
message("*** Nested futures ...")
for (strategy1 in rev(strategies)) {
for (strategy2 in strategies) {
message(sprintf("- plan(list('%s',... |
da540341727405726831decfede06614abc73bec827a248a7de39da95a870c49 | R | 3,219 | 142 | # Supplemental Material
## Pre-sale Examination Set
(ref:examination-set) Examination Set.
```{r examination-set, echo=FALSE, message=FALSE, warning=FALSE}
view_full_name %>%
select(`Anatomical Region`, `View (Abbreviation)`, `View`) %>%
mutate(`Anatomical Region` = str_replace_all(`Anatomical Region`, 'Front', ... |
0b507715ce26c804b984a10be09b640a7529525e7bba55c88c7e25bbad39561e | R | 3,243 | 185 | #' @tags globals subassignment
#' @tags sequential multisession multicore
library(future)
oopts <- c(oopts, options(
future.globals.resolve = TRUE,
future.globals.onMissing = "error"
))
message("*** Globals - subassignments ...")
message("*** Globals - subassignments w/ x$a <- value ...")
## Truth:
x <- x0 <- ... |
eec5ddc8cc1e127b0b43f14147bfad9db62c64b382f024aa77b33947c68ab010 | R | 3,247 | 100 | #' @title Calculate variance-covariance matrix for GALAMM fit
#'
#' @srrstats {G1.4} Function documented with roxygen2.
#' @srrstats {G2.3,G2.3b} Argument "parm" is case sensitive, as is documented here.
#' @srrstats {G2.1a} Expected data types provided for all inputs.
#' @srrstats {RE4.6} The variance-covariance matri... |
30877ccedc4975d2dd3d15b73ab8b29bab9f934ec665689e4f99fcdf1e1c7867 | R | 3,248 | 64 | ---
output:
github_document:
pandoc_args: --webtex
bibliography: vignettes/GENOVA.bib
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "vignettes/"
)
```
# GENOVA <img src="vignettes/logo_GE... |
a89a4e9e77eadd7fe9894b348682d314f590e183763b4ef3fc4eaa59c9955a79 | R | 3,255 | 65 | ### Merge protein differential expression values with additional info from Uniprot
#' Merge protein differential expression values with additional info from Uniprot
#' Change the colnames of d.summary (line 14-25) according to the features you would like to add
#'
#' @param d Input a dataframe with the differentia... |
76cac71b72fcb4e7345f2060ea89c55063a58894a30622bd3647835e22922a7d | R | 3,256 | 54 | deg_padj <- function(results.df) {
upDEGS <- results.df %>% dplyr::filter(log2FoldChange>0.30) %>% dplyr::filter(padj < 0.05)
downDEGS <- results.df %>% dplyr::filter(log2FoldChange<(-0.30)) %>% dplyr::filter(padj < 0.05)
allDEGS <- rbind(upDEGS, downDEGS)
upDEGS_genesonly <- as.data.frame(upDEGS$genes)... |
7b4542b744d2a951321057ee096559eb730cde07318879ea7a556a86282e06a0 | R | 3,269 | 112 | #' @tags utils-internal tweakable
library(future)
argnames <- future:::argnames
tweakable <- future:::tweakable
untweakable <- future:::untweakable
`tweakable<-` <- future:::`tweakable<-`
`untweakable<-` <- future:::`untweakable<-`
message("*** argnames() ...")
fcn_ab <- function(a, b, ...) NULL
fcn_bc <- function(... |
43a1aba26e365a19dfd8d83eb8399099509bf2f3d14b7f9522999ba6a625a127 | R | 3,275 | 90 | library(data.table)
simple.qq.plot <- function (observedPValues) {
plot(-log10(1:length(observedPValues)/length(observedPValues)),
-log10(sort(observedPValues)))
abline(0, 1, col = "red")
}
genes <- fread("../../../ensgR75_protein_coding.txt", data.table=F)
expr <- fread("zcat ../exp.tmm.log2.covCorrect.f... |
efb2c9e32dd63e8b15b0701b0274c807dee20c69e43904deb1551557af78d577 | R | 3,281 | 88 | ## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
subjects <- readRDS(file.path(dir_data_misc, "HCP_S1... |
140ee02cdfd013c6ab06c7a2a4f34dc6a588ba80c176a27f159fb9fc819795b8 | R | 3,288 | 98 | #!/usr/bin/env Rscript
##
## Extract and plot fragment size distribution from a BAM file.
##
## usage: Rscript --vanilla fragment-sizes.R sample_name file.bam
##
# increase output width
options(width = 120)
# print warnings as they occur
options(warn = 1)
# get scripts directory (directory of this file) and load r... |
650eabc7f489bca05897ea7535155e25883b8ff884028389da17fcb87813176a | R | 3,289 | 115 | ---
title: "Posterior Sampling"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Posterior Sampling}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts... |
f8cf9d52229807d89b8e1998988d1bc30e60766c4cf62b14dce52dc2c6cd6e87 | R | 3,289 | 118 | ##
## Replacement for DESeq2::plotPCA() with sample labels and custom condition colors.
## Sample names will be shown next to each dot with minimal overlap.
## The axis will display proportion of variance for each principal component.
## Tested using DESeq2 versions 1.12 to 1.26.
##
deseq2_pca = function(object, intg... |
51138071db271714f9c9b68d935851c28cf8280769d970e649172a21c385f259 | R | 3,305 | 128 | test_that("Heteroscedastic model works", {
mod <- galamm(
formula = y ~ x + (1 | id),
dispformula = ~ (1 | item),
data = hsced
)
expect_snapshot(print(summary(mod), digits = 3))
expect_equal(mod$model$loglik, -2058.14021326104)
expect_equal(
summary(mod)$AICtab,
c(
AIC = 4126.280426... |
1dca3bbe705faa12b3204612e2ead8e4a60ce98c6dabfee5f8049fd701250807 | R | 3,306 | 142 | ## Formula #1:
## expression: ~ rhs
## AST: (~ rhs)
## tweaked expression: rhs; ~ rhs
##
## Formula #2:
## expression: lhs ~ rhs
## AST: (~ lhs rhs)
## tweaked expression: rhs; lhs ~ rhs
tweakFormulaCall <- function(expr) {
if (!is.call(expr)) return(expr)
expr <- unclass(expr)
op <- expr[[1]]
if ... |
0648811d145dae6b19188a9edd6378119eea393a200d798a9ad80ef002d7a563 | R | 3,327 | 87 | ```{r}
source("downstreamer_functions.r")
setwd("evaluating_coregulation")
source("data/source_olivier.r")
eigen <- fread(eigenvectors165.file, data.table = F)
rownames(eigen) <- eigen[,1]
eigen <- eigen[,-1]
ensembl <- read.table(ensembl.file, sep="\t", header=T)
ensembl <- ensemb... |
e6e8d0d7327bab08b723f81b1f21aa07ee8288a27d402d09434fc09ab9f5130c | R | 3,338 | 80 |
projectDir <- getwd()
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
source(paste0(projectDir, "/depict2_functions.r"))
platelets <- read.depict2("excelsForEnrichment/30080_raw.gwas.imputed_v3.both_sexes_enrichtments_exHla_v69.xlsx")
str(platelets$Coregulation)
geneP <- read.de
library(readr)
table_tmp <- re... |
a027497ccc0fc638d569087bf0730a50e941e015a7c0633c08319fdc3764bd3e | R | 3,345 | 97 | ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
baseNames <- c("CC2MP6")
iters2 <- expand.grid(
visit=seq(2),
test=c(TRUE, FALSE... |
9279ac159c3866b450e746ff2ed67c81d5b2ccbc7eeecc3ce994066ea0a2dda8 | R | 3,361 | 65 | setwd(paste0(figureDirectory, "Figure7-expression"))
library(ggplot2)
library(grid)
load("exprMethylConnection.RData")
load("ExpressionComparisonPlot.RData")
plotExpressionMethylation <- ggplot(exprMethylConnection, aes (x=reorder(Gene,medianDiff), y = value, fill = data)) +
geom_hline(yintercept = 0, col = "bla... |
e37a93750b0741bbe760f8659db4ea63e4c65c7a4e4c85b7ea883b4ed2e002b1 | R | 3,381 | 98 |
read_fusion <- function(files,trait.names=NULL,binary=NULL,N=NULL,perm=FALSE){
length <- length(files)
if(is.null(trait.names)){
names.beta <- paste0("beta.",1:length)
names.se <- paste0("se.",1:length)
}else{
names.beta <- paste0("beta.",trait.names)
names.se <- paste0("se."... |
9d18b5238bd64721989a41e9a515a3c369edca910ddfd592256bcdacf510314c | R | 3,387 | 101 | stack_wide2 <- function(DT)
{
DT_wide <- data.table(eid = unique(DT$eid))
DT_tmp <- merge(
DT_wide,
DT[target == 'Age'][, c("eid")])
setkey(DT_wide, eid)
DT_tmp <- DT[target == 'Age'][,
.(age = true, age_pred = predicted, eid = eid)]
setkey(DT_tmp, eid)
DT_wide[DT_tmp, `:=`(age = i.age, age_p... |
6823c319b655703f3354c7aafab37c194bc0ba4401dff2e39fa8329e70140a29 | R | 3,390 | 97 | ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
baseNames <- c("CC2MP6")
iters2 <- expand.grid(
visit=seq(2),
test=c(TRUE, FALSE... |
0fcc774ab0f5da911a8e4251cddaac377f1d4d9ec1e358098c195514e3f0deb0 | R | 3,391 | 126 | ### general functions
# function to summarize into reading frame 0,1,2
frame_counts = function(count, dir = '+', utr_length = 50){
# check length
if(length(count) > (2*utr_length)){
# check if dividable by 3
if( (length(count)-(2*utr_length))%%3 == 0){
# if negative strand --> reverse
if(dir ==... |
482832b296e58d9417600646d3ea2e94f4fbdcb86dd827bd861f786edbf0d264 | R | 3,391 | 99 | ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
baseNames <- c("CC2MP6")
iters2 <- expand.grid(
visit=seq(2),
test=c(TRUE, FALSE... |
587d188e192ee299f9046a1a4a0ddb4bef613c27b0b6a52a39bdbd01e35daa0c | R | 3,397 | 124 | library(Signac)
library(Seurat)
library(ggplot2)
library(EnsDb.Mmusculus.v79)
set.seed(1234)
# load processed data matrices for each assay
rna <- Read10X("data/snare-seq/GSE126074_AdBrainCortex_rna/", gene.column = 1)
atac <- Read10X("data/snare-seq/GSE126074_AdBrainCortex_atac/", gene.column = 1)
fragments <... |
e37e00d4bf9a43efb62e4d6be4d7c6d3eb99f9a9f271d295460fd6d88b546848 | R | 3,424 | 61 | #' srr_stats
#'
#' All of the following standards initially have `@srrstatsTODO` tags. These may
#' be moved at any time to any other locations in your code. Once addressed,
#' please modify the tag from `@srrstatsTODO` to `@srrstats`, or `@srrstatsNA`,
#' ensuring that references to every one of the following standard... |
e455ce6a14f2f1edc04274725d7e74a33ae78a734c16fd9c7d466622372185b0 | R | 3,425 | 59 | #!/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 function-specific rob... |
a50c410cc50cc2e0eb90da09b108c60a9fc680b6b40fe94137105cc691b4a999 | R | 3,440 | 105 | ---
title: "Heteroscedastic Linear Mixed Models"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Heteroscedastic Linear Mixed Models}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
`... |
7b1b164cfd6e135e64e228b4edf1d6d5efbda74e1f561421e253d4fa733e1555 | R | 3,441 | 48 | # Set directory to source file loaction
setwd(paste0(figureDirectory, "Figure1-Stability-Dendrogram" ) )
library(RColorBrewer)
library(stringr)
library(pvclust)
library(dendextend)
load(file = "stabilityHclust_wardD2_manhattan.RData")
patientColorManhD2 <- valuesManhD2.pv$hclust$labels[valuesManhD2.pv$hclust$order]... |
6a9833fed6567a3ce87b3e3f1deedcb9231f2096049273913ad4c8286ce3fb40 | R | 3,459 | 96 | library(tidyverse)
library(plotly)
setwd("/Users/philippehabets/Dropbox (Personal)/Git/fMRI-transcriptomics-cortisol/data")
## read in cell_type enrichment for higher and lower DEGs
cells_high <- read_csv("cell_type_enrichment_higherDEGs.csv")%>%
mutate(cellgroup = str_replace(cell_type, "(?s) .*", "")) %>%
mut... |
6986ad0b5ea43fa457931569cc0c5acd511dac22caf933d51eaf14cf28a7555b | R | 3,490 | 103 | #' Synchronising indices across experiments.
#'
#' Particularly when Hi-C data is loaded from different sources, it occurs that
#' the assigned bin indices don't match between datasets. This function
#' re-indexes a list of experiments to a shared set of bin indices.
#'
#' @param explist A \code{list} of GENOVA \code{c... |
1ca842f81d8cae39650ea84b9dc8b9a32fabf10a39a94d751fa82c235f757d48 | R | 3,492 | 87 |
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("traits.txt", stringsAsFactors = F)
pdf("Enrichment_comparisons.pdf", width = 7, height = 7, useDingb... |
eb138e15a9e78a8ac0324e8f4e882d0477273cabc4f3d462c6bf405fd8e4fdeb | R | 3,498 | 98 |
source(paste0("C:\\Users\\patri\\Documents\\GitHub\\systemsgenetics\\Downstreamer\\src\\main\\r\\downstreamer_main/downstreamer_functions.r"))
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
traits <- read.delim("paperPheno.txt", stringsAsFactors = F)
ensg <- read.delim("ensgR75_protein_coding.txt", stringsAsFactor... |
08ba2b194c2a7d36a0372329f6bb827dde7cee28c7f63968f2767cd083a1cf42 | R | 3,516 | 109 |
## Kunkle, B. W. et al. Nat Genet 51, 414–430 (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){
logOR = l... |
b6087eb277e2677dd1f17a4704eeca5ded5ce308db5bf352e1279e8342c31a1b | R | 3,523 | 108 | select.subset.base <- function(exp, chrom, start, end){
sel <- exp$ABS[exp$ABS[,1]==chrom &
exp$ABS[,2] >= start &
exp$ABS[,2] <= end,4]
start.i <- min(sel); end.i <- max(sel)
#create matrix
tmp = exp$ICE[exp$ICE$V1 >= start.i &
exp$ICE$V1 <= end.i &
... |
c78397a019c1a5357cfce5559e08b2cc725c2b04a6804a2d0de47c793d36d677 | R | 3,545 | 114 | library(tidyverse)
library(ggtree)
library(Biostrings)
# try to calculate the average branch length across the sites (per protein)
#functions
calculate_tree_length<-function(df, pos_aln, tree){
vec_sp<-df %>% filter(pos_alignment==pos_aln) %>% pull(., species)
reduced_tree<-ape::drop.tip(tree, tree$tip.label[tree... |
cbfebc8668bc468675859508ced61cd447cff118e1d7088d65d3e746b53116eb | R | 3,556 | 104 | ```{r}
setwd("evaluating_coregulation")
source("../downstreamer_functions.r")
source("data/source_olivier.r")
# Read reference datasets
ensembl <- read.table(ensembl.file,sep="\t", header=T, row.names = 1, stringsAsFactors = F)
ensembl$gene.length = ensembl$Gene.end..bp. - ensembl$Gene.start..bp.
rownames(ensembl) <- ... |
5dea2870e5b85b16cbe1a6a20c51747452cfe03b17b36f6d06158d800b6db045 | R | 3,560 | 61 | # PREPARE JASPAR 2020 DATA
library(tidyverse)
library(org.Hs.eg.db)
# 1 DOWNLOAD AND TRANSPOSE,COMBINE PWMs ####
setwd("/data/share/htp/TRNP1/paper_data/")
system("fold=regulation/data/TFs/JASPAR_2020; mkdir -p $fold; mkdir -p $fold/JASPAR_collection;
mkdir -p $fold/JASPAR_collection_transposed; mkdir -p $f... |
8272206fe0d48813ff9e65fbde094596bdc2bb7b744ccfcba1abbe7d2a026726 | R | 3,564 | 123 | #' Get the future of a future variable
#'
#' Get the future of a future variable that has been created directly
#' or indirectly via [future()].
#'
#' @param var the variable. If NULL, all futures in the
#' environment are returned.
#'
#' @param envir the environment where to search from.
#'
#' @param mustExist If TRU... |
c380f210750067a8ad523d76ad75f29fac7c8c31be1f177223452c9575cd083a | R | 3,565 | 103 | #' @keywords internal
"_PACKAGE"
#' @import utils graphics stats grDevices
#' @import methods
NULL
# data.table is generally careful to minimize the scope for namespace
# conflicts (i.e., functions with the same name as in other packages);
# a more conservative approach using @importFrom should be careful to
# import... |
2762a91769fa5c4eb9f97a6690c956e6a2dc18fb8d26b698312df611cd05a0a2 | R | 3,578 | 113 | #' @tags UniprocessFuture sequential
library(future)
message("*** UniprocessFuture ...")
message("*** UniprocessFuture() ...")
## 'substitute = FALSE' takes the expression as-is
f <- UniprocessFuture(expr = quote(6 * 7), substitute = FALSE,
globals = list())
print(class(f))
stopifnot(inherits(... |
d5a9cc0762f09d9d4b4c8dd99c50bbf25c430db5cc632c1dc7c8acc381cd00af | R | 3,582 | 91 | # Title : SN RNA-seq analysis
# Objective : -
# Created by: mvochteloo
# Created on: 22/02/2021
# install.packages('Seurat')
library(Seurat)
library(Matrix)
library(ggplot2)
work_dir <- '/groups/umcg-biogen/tmp01/input/ROSMAP-scRNAseq/2020-10-22-MVochteloo-Copy/'
list.files(work_dir) # Should show barcodes.tsv, ... |
50f4967322abbe1ebc884561c3745795ea12c2f6ffc5ea343cbd6c491caf1090 | R | 3,587 | 102 | library(ggplot2)
library(ggbeeswarm)
source('../config.r')
source('../utils.r')
data <- read.csv("./inputs/imaging.csv")
data$fold <- factor(data$fold)
data$modality <- factor(
data$modality,
levels = c("brain volumes (sMRI)", "white matter (dMRI)",
"functional connectivity (fMRI)", "sMRI, dM... |
41c3a6b461c08f2a9fc0f6994a924180e85520052ee634279939930ec7171b33 | R | 3,601 | 110 | #' Directionality index
#'
#' The directionality index quantifies the degree of bias between upstream and
#' downstream interactions given a bin on the diagonal. Such biases become
#' apparent near the periphery of TADs: the upstream portion of a TAD interacts
#' more with the downstream bins and inversely, the downstr... |
bed5114cfc9507b30dcae4fa366658cdb8fdedf4f6137a44f1ee2f9305f08899 | R | 3,614 | 112 | #' @tags nbrOfWorkers sequential
library(future)
plan(sequential)
## A "legacy" evaluator is one that does not provide a FutureBackend
## factory, in which case nbrOfWorkers() falls back to inspecting the
## 'workers' formal of the evaluator itself
legacy <- function(classes, workers) {
fcn <- eval(bquote(function... |
565c692e5a313c11d3a96d717b62067641a8942a7535ba9d458d258ddb833480 | R | 3,615 | 109 | ##' @importFrom nlme VarCorr
##' @export VarCorr
NULL
#' @title Extract variance and correlation components from model
#'
#' @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{ga... |
57bbc33e517a90d23c7868fabe1228a2187a7f571a481b6c6f06e90973541681 | R | 3,617 | 100 | #' Get an average virtual 4C of 2D-regions (e.g. loops).
#'
#' Extract matrices around a defined region a.k.a. the viewpoint
#'
#' @param explist Either a single GENOVA \code{contacts} object or list of
#' GENOVA \code{contacts}.
#'
#' @param viewpoint A viewpoint location. One of the following: \itemize{
#' \... |
16c32a0eb6422b721b2242bf418bddd01f83237ab532d098e328074ee4130370 | R | 3,634 | 107 | ---
title: "Permutation testing for META for dFC Metrics - COBRE RAW"
author: "Fran Hancock"
affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
date: "`r format(Sys.time(), '%B %d, %Y')`"
fontsize: 14pt
output: html_document
---
```{r setup... |
1eddaced2e0902304ca952a735dc4afa91cac767703300915aa3f7841148179e | R | 3,656 | 118 |
library(data.table)
library(arm)
library(stargazer)
source('../config.r')
source('../utils.r')
source('../read_data.r')
extra_path <- file.path('inputs')
get_family_model <- function(DT_family)
{
mod <- lm(scale(value) ~ # y
scale(age_pred) + # brain age
scale(age... |
0a199f0003449690de49627ad6e4c0fdf44ac6eaf84de5491d8df25ad69b2440 | R | 3,657 | 131 |
projectDir <- getwd()
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
source(paste0(projectDir, "/depict2_functions.r"))
heightDs <- read.depict2("final_paper/inflammatory_bowel_disease_2017_29906448_hg19_enrichtments_exHla.xlsx")
str(heightDs$Coregulation)
library(readr)
#table_tmp <- read_delim('phenotype_to_... |
651b8d6ad79e576ffc834d20126d9224cfc32f51c9cb57a5ea87a879238f5368 | R | 3,658 | 145 | ---
title: "Categorization of COND based on HCP EP dynamic metrics"
author:
- name: "Fran Hancock"
affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
date: "`r format(Sys.time(), '%B %d, %Y')`"
fontsize: 14pt
output: html_document
---
... |
e33aaa160c1d75908551585f1dbbb12b02fcded4384da5cc66bb991965f6d75d | R | 3,664 | 82 | ## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(RISC)
library(RColorBrewer)
data("raw.mat")
mat0 = raw.mat[[1]]
coldata0 = raw.mat[[2]]... |
1b734d0e65184d91ea57569895021e1cbaf1338322522da6d4489a89e15097bc | R | 3,673 | 108 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("coregulation_clustering")} else {setwd("../coregulation_clustering")}
source("../downstreamer_functions.r")
library(apcluster)
library(tidygraph)
library(ggraph)
nodes <- fread("data/44_traits/44_traits_nodes.tsv", data.table = F)
edges <- fread("data/44... |
6a1ac309bf49fdc58f1e9c83cab75d127c3bc1d9cbcfd14f7bba812d485bb0ee | R | 3,679 | 107 | source("downstreamer_functions.r")
# Read excel files
path <- "/home/work/Desktop/depict2/output/final_paper/excels/"
files <- list.files(path, pattern=".*_enrichtments.*.xlsx")
datasets <- list()
for (file in files) {
name <- gsub("\\_enrichtments\\_exHla\\.xlsx", "", file)
name <- gsub("\\_enrichtments\\_exHla... |
0ee9cdac6eee50ed6589064eca289d8da016d9dbea3297c067870c4b954dde6f | R | 3,696 | 89 | library(RISC)
library(ggplot2)
library(RColorBrewer)
library(irlba)
library(umap)
PATH = "/Path to the data/GSE111113"
###################################################################################
### RISC raw ###
###################################################################################
mat0 = read.t... |
3d7b51b9d6cc64631e45d645f142a6c6ea6c40f9d8c87716a62f90079317e3ab | R | 3,731 | 93 | library(Biostrings)
library(tidyverse)
library(plyranges)
discrepancies_list <- list.files("protein/other_protein_alignments/self_matches_diff", recursive = T, full.names = T)[-25] #exclude procavia
discrepancies_rds<-lapply(discrepancies_list, function(x){readRDS(x)})
discrepancies_df<-bind_rows(discrepancies_rds)
... |
3485c4c0ca0a5bbe151e2da5f4cba57c6a49fcef45e5220b802ea19b56e4e09e | R | 3,732 | 101 | #' @title Predictions from a model at new data values
#'
#' @srrstats {G1.4} Function documented with roxygen2.
#' @srrstats {G2.1a} Expected data types provided for all inputs.
#' @srrstats {G2.3,G2.3a} match.arg() used on "type" argument.
#' @srrstats {G2.3,G2.3b} Argument "type" is case sensitive, which is documente... |
a6c51d92ee36f1fe041889fa3a0991def89ff51433d00d0a58c240dc0e9a7d5f | R | 3,746 | 130 | #' Get future-specific session information and validate current backend
#'
#' @param test If TRUE, one or more futures are created to query workers
#' and validate their information.
#'
#' @param anonymize If TRUE, user names and host names are anonymized.
#'
#' @return Nothing.
#'
#' @example incl/futureSessionInfo.R
... |
2f6356dd228aef9673439244e3d1f99f3515ad27667a699ed7a4277ecd375622 | R | 3,757 | 98 | ## ----Install-------------------------------------------------------------
library(devtools)
#install_github("raguirreg/DeconCell")
## ----Libraries-----------------------------------------------------------
library(DeconCell)
library(edgeR)
library(tidyverse)
library(ghibli)
data("count.table")
dCell.exp <- dCell.... |
0bb472d0cf3e3ab47091fad22cc43fdf7fabb3e3c6147ed9f74d9f9b9badd9d7 | R | 3,767 | 133 | ---
title: "Posterior Sampling"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Posterior Sampling}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
``` r
library(galamm)
```
Sampl... |
9d7a31f964bec5785ee8fa7ee67e72ddb81d512348bedaa298022249ba93baa0 | R | 3,768 | 123 | context("test-workflow")
# Load raw data
# load("../testdata/testdata.rda")
mat0 = as.matrix(raw.mat[[1]])
coldata0 = as.data.frame(raw.mat[[2]])
coldata1 <- coldata0[coldata0$Batch0 == 'Batch1',]
coldata2 <- coldata0[coldata0$Batch0 == 'Batch4',]
mat1 <- mat0[,rownames(coldata1)]
mat2 <- mat0[,rownames(coldata2)]
... |
5d472618400e60ce86b9b408c5123d130d93cbc2e02411b1c940eeec08f5b3d8 | R | 3,790 | 78 | #' buildcodon
#'
#' This function takes a RefCDS object as input and adds to it two fields required to run the codondnds function. Usage: RefCDS = buildcodon(RefCDS)
#'
#' @author Inigo Martincorena (Wellcome Sanger Institute)
#' @details Martincorena I, et al. (2017) Universal patterns of selection in cancer and somat... |
226e191fcfb0ebda4f8a17b9ddbd745a551d28210f9a7f13850321798458a44b | R | 3,808 | 147 | #' @tags globals
#' @tags formula
#' @tags sequential multisession multicore
library(future)
library(datasets) ## cars data set
library(stats) ## lm(), poly(), xtabs()
message("*** Globals - formulas ...")
## (i) lm(<formula>):
## From example("lm", package = "stats")
ctl <- c(4.17, 5.58, 5.18, 6.11, 4.50, 4.61,... |
d19f45fef721d36464257ce6a98b0a1478e508c054d496b9cb1dd664c85637a2 | R | 3,812 | 129 | #' @tags early-signal
#' @tags sequential multisession multicore
#' @tags skip_on_cran
#' @tags deprecated
## plan(..., earlySignal = TRUE) is deprecated as of future (> 1.68.0)
## Skip test if option 'future.plan.earlySignal' is "defunct"
if (!identical(getOption("future.plan.earlySignal"), "defunct")) {
library(fu... |
e3a0ffc1624a09c563894f97dac1bd04e7e54fb637455d9e47b9645b5696c45b | R | 3,838 | 150 | #' @tags globals objectSize
library(future)
objectSize <- future:::objectSize
message("objectSize() ...")
env <- new.env()
env$a <- 3.14
env$b <- 1:100
env2 <- new.env()
env2$env <- env
## Namespaces will be skipped
env3 <- getNamespace("utils")
fcn <- function(...) TRUE
objs <- list(
NULL,
TRUE,
1L,
3.... |
5500009b7d2d736c54c5ce39b5c42b405ccd4368d0d113e21ae23eaa6ee72cdc | R | 3,845 | 55 | #' Generate the HTML report including various summary data visualizations
#'
#' Generate the HTML report including various summary data visualizations.
#'
#' @param sample_manifest (Required). The sample barcodes file. The file should include sample_id, barcode_fwd, barcode_rev, sample (sample name, can be the same as ... |
af6ce76c9580d85530ba5ae2c656305e027fe1fe98dabcebbe0c794d01b94357 | R | 3,846 | 124 | #' @tags utils-internal prune
library(future)
with_assert <- future:::with_assert
prune_call <- future:::prune_call
prune_debug <- future:::prune_debug
prune_fcns <- future:::prune_fcns
prune_fcn <- future:::prune_fcn
## NOTE: prune_pkg_code() itself is not tested here, because it rewrites the
## functions of the in... |
156495dc7df5d89fb674d49d1aac0113e5555e49abc206294e6a4f67d9c9f0e1 | R | 3,848 | 135 | SharedCode_version = c(11, 0)
# Change these depending on where files are in your computer -------------------
COMPUTER <- ifelse(
grepl("Carbonate|/N", getwd()),
"RED",
ifelse(grepl("Volume", getwd()), "MPro", "Personal")
)
secret_fnames <- "0_Secret-FilePaths.txt"
if (!file.exists(secret_fnames)) {
if (f... |
cadf2fcf62e7fc9fe1fa9b76057936054b09e9233fcf4b8e6051dcf023bcdd21 | R | 3,854 | 127 | #' @tags utils-internal sticky-globals cluster
library(future)
sticky_globals <- future:::sticky_globals
clusterExportSticky <- future:::clusterExportSticky
name <- "future:sticky_globals"
## Always leave the search path the way we found it
on.exit({
if (name %in% search()) sticky_globals(pos = 0L)
})
message("*... |
e3d40cbdf6a0640e4b944d520dc53b5cf4444b1363fa80413fd7d7d2e23a9ce0 | R | 3,857 | 148 | ### script count reads per base and generate coverage plot around start and stop
# 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
# register max 8 cores because filtering proc... |
a1f8556d9f39b725092ff4578827e6b246b5fe3692d96fe351409f8749e8aefd | R | 3,874 | 131 | library(data.table)
library(ggplot2)
library(ggbeeswarm)
library(patchwork)
source('../config.r')
source('../utils.r')
DT_cond_proxy <- fread('mod_cond_proxy.csv')
DT_cond_proxy_bs <- fread('mod_cond_proxy_bs.csv')
DT_cond_target <- fread('mod_cond_target.csv')
DT_cond_target_bs <- fread('mod_cond_target_bs.csv')
DT... |
2fc7ea84c0a01fdc57d1dbbb3f8df5628f3df637e45b4233c6b56cc8a3f1a1be | R | 3,908 | 115 | library(data.table)
library(ggplot2)
library(ggbeeswarm)
source('../config.r')
source("../utils.r")
data <- read.csv("./inputs/classification.csv")
data2 <- read.csv("./inputs/classification_non_imaging_fresh.csv")
data <- rbind(
subset(data, Type %in% c('Imaging', 'Imaging & Non-imaging')),
data2
)
#data <- sub... |
b13bcfc4848cf5b57290cd1ecc84e95f348e8733504c91639e6e13ef0e71f9ec | R | 3,920 | 120 | #' @inheritParams FutureBackend-class
#'
#' @param wait.timeout Number of seconds before timing out.
#'
#' @param wait.interval Baseline number of seconds between retries.
#'
#' @param wait.alpha Scale factor increasing waiting interval after each
#' attempt.
#'
#' @keywords internal
#' @rdname FutureBackend-class
#'
#... |
0f6dfd25b8c3e9ff5fda8234cbb07f39df3a9cbf2c0452db0031cdfef94d78b3 | R | 3,936 | 106 | #' Merge summary statistics into a single input data frame
#'
#' @param input.files - list of data frames, where each data frame contains the summary statistics of a trait to use in the order of Exposure - Outcome
#' @param trait.names - Vector containing the trait names in the order they're found in 'input files'
#' @... |
bc7835db47adf2ec5bdc792bd8b22febdc90c1ca47b4ea316cd038f939d7820f | R | 3,939 | 89 | # Performing Bayesian Linear Regression for Steady-State Rates from the OH family of tasks
# Based on code and analysis from "Doing Bayesian Data Analysis, 2nd Edition",
# John Kruschke, 2014.
# Copyright (C) 2022 Richard Hugh Moulton
#
# This program is free software: you can redistribute it and/or modify
# it under ... |
9a36d6948961d84e4043acbfda8d923134d9c71ac20e7988fd0791d3c531438c | R | 3,941 | 85 | library(ComplexHeatmap)
library(viridis)
library(RColorBrewer)
convert.row.names.to.annot <- function(name_list) {
find.marker.position <- function(contents) {
for (i in 1:length(contents)) {
if (any(contents[i] == c('all', 'SF', 'CU', 'TA', 'TN', 'SC', 'SM')))
return(i)
... |
372355f77676573e778bde3220d3eca1943872a079b1f1c43511374452faa715 | R | 3,947 | 72 | behavior_cleaning_ggplot <- function(ss_ggplot, os_ggplot) {
both_ggplot <- rbind(ss_ggplot, os_ggplot)
both_ggplot <- mutate_at(both_ggplot, vars(Animal, PairingType, PairingTime, Cohort, Timepoint, Stimulus), as.factor)
#refactor the Timepoints so that they plot in the order done in the experiment
both_gg... |
c2e80145ca553555ad82f8469d57818d3aad6ad7dd419282e25e06b48c6f9b3c | R | 3,953 | 119 | #' Intra and inter TAD contacts
#'
#' Calculate the coverage over TADs and between a TAD and its neighbours.
#'
#' @param explist Either a single GENOVA \code{contacts} object or a list of
#' GENOVA \code{contacts} objects.
#' @param tad_bed A BED-formatted \code{data.frame} with three columns
#' containing the chr... |
0948e671ebc2297d52c6e554ab8a195961e6a00d5bbd3c3c160ed7145e00bd71 | R | 3,958 | 134 | #' Filter columns and rows of `ACTIONetExperiment` or `SummarizedExperiment`-like object.
#' @export
filter.ace <- function(
ace,
assay_name = "counts",
min_cells_per_feat = NULL,
min_feats_per_cell = NULL,
min_umis_per_cell = NULL,
max_umis_per_cell = NULL,
return_fil_ace = TRUE
) {
org_dim = dim(ac... |
2c93f64d0b1862802c32f9810cca4e2020a1a6fbc3847205de5ee6d769df2685 | R | 3,959 | 56 | setwd( paste0(figureDirectory, "Figure3-Genomic-Regions") )
library(ggplot2)
library(cowplot)
load("barplotOverviewDF.RData")
load("enhancerGenePlotDf.RData")
load("pathPlotDfAllFreq.RData")
colors <- c(rep("blue3",10),rep("forestgreen",7),rep("gold3",3),rep("darkorchid",3 ),rep("deeppink2",1))
plot1 <- ggplot(ba... |
59f124a105d36d27641871b4d4c1cdf5c24ab615dd5276bbeb84635d005e5ab9 | R | 3,963 | 127 |
#' Test data
#'
#' Retrieves a test \code{contacts} object from a local cache at either 40k or
#' 150k resolution. The test data only contains human chromosomes 21 and 22.
#'
#' @param res The resolution of test data to use. Either \code{"40k"} or
#' \code{"150k"}.
#' @param download A \code{logical(1)}. If \code{FA... |
76e9c4b9ec6e41f817f45a4dfef16cd461baa0214353f82a32bdf07d00663667 | R | 3,980 | 178 | library(MungeSumstats)
library(tibble)
library(tidyr)
library(readr)
library(purrr)
library(plyr)
library(dplyr)
library(stringr)
library(forcats)
library(tabulizer)
library(fuzzyjoin)
data(sumstatsColHeaders)
# Snakemake input & output
bellenguez.path <- snakemake@input[['bellenguez']]
bellenguez_clean.path = snakem... |
2a8d53958ad0aabcff540be84f443165902c4ff29ca4ac8fec6aa8017517a266 | R | 3,981 | 150 | #' A representation of a set of globals used with futures
#'
#' @param object A named list.
#'
#' @param x An object to be coerced to a `FutureGlobals` object.
#'
#' @param resolved A logical indicating whether these globals
#' have been scanned for and resolved futures or not.
#'
#' @param total_size The total ... |
d022e82862b45cf66d62187c73d59d5f82cd4562507424b14d0a12337f637f72 | R | 3,988 | 137 | ---
title: "--"
author: "rbarreiro"
date: "--"
output:
html_document:
theme: journal
css: rmark.css
---
```{r setup, include=FALSE}
suppressMessages({
library(tidyverse)
library(scales)
library(cowplot)
library(ggbeeswarm)
library(ggsci)
library(viridis)
library(knitr)
library(ggpubr)
lib... |
83092bf20b5d3010e0006e56b6ce365914717b4b053a623f3dc1a356f8c53eb4 | R | 3,993 | 98 | ## -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_Share... |
b9dd3077e32332d4f40ffd655ac198c5778418dbf23ce66019d515184413fdc3 | R | 3,993 | 120 | ### script to calibrate reads based on manual inspection
# 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 cor... |
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