sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
070e2682ca55787d5ef126e1ed93497d031f783a3c637e89e71b1df8113af6fd | R | 5,364 | 197 | #' A SequentialFutureBackend resolves futures sequentially in the current R session
#'
#' @inheritParams FutureBackend-class
#'
#' @details
#' The `SequentialFutureBackend` is selected by `plan(sequential)`.
#'
#' @seealso
#' For alternative future backends, see the 'A Future for R: Available Future
#' Backends' vignet... |
1998a7ba7701ca5295bc4e53ac37c049453f768ae3fa94e69568fdbd48814da2 | R | 5,369 | 140 | library(ggplot2)
# library(ggbeeswarm)
source("~/Documents/Personomics_Research_Group/WAPIAW_2/figure_plotting/config.r")
source("~/Documents/Personomics_Research_Group/WAPIAW_2/figure_plotting/utils.r")
# default input ploting csv file
data <- read.csv("~/Documents/Personomics_Research_Group/WAPIAW_2/figure_plotting/... |
53fb7b34693ed148a67e4e3d9d4db278cd22fb6621c3a108d8c5b1306ca8f690 | R | 5,376 | 170 | #' @tags worker-broken
#' @tags detritus-files
#' @tags cluster
library(future)
options(future.debug = FALSE)
message("*** cluster() ...")
message("Library paths: ", commaq(.libPaths()))
message("Package path: ", sQuote(system.file(package = "future")))
types <- NULL
if (!covr_testing) types <- c(types, "PSOCK")
if... |
259bef250ce82038f2cb6cdac884cf657ee74c3b6759cf3a151cc26aaa47dca7 | R | 5,388 | 131 | library(tidyverse)
library(galamm)
library(lme4)
library(memoise)
library(Matrix)
library(mgcv)
library(gamm4)
dat <- readRDS("data/cognition_model/dat.rds")
# Reference levels for identifiability of factor loadings.
# The corresponding loadings are set to 1.
test_ref <- c("cvlt_a1", "digitspan_bwd", "stroop_time_1"... |
208766f5056e9cb8195f00192b31c2f3e7ed0ace8656852f95c6e73fc03fa9ad | R | 5,390 | 161 | setwd('/home/alkallr/Documents/book_chapter/REMBRANDTS/')
# ### RUNNING REMBRANDTS ####
# library(DESeq2); library(gplots); library(tibble);
# library(magrittr); library(data.table);
#
# ### Prepare sample metadata ####
# metadata <- data.table(
# Label = NA_character_, File = list.files(path = './examples/MMB_mat... |
10642f7656701d101221a822e8b9b681156ecff61b94757fbba2a07af10ac3dd | R | 5,393 | 156 | #' Scatterplots of PCA and Gene expression from any 4-node circuit
#' @param rset The sRACIPE object of the simulated circuit
#' @return list(g,p)
#' @import sRACIPE
#' @import SummarizedExperiment
#' @export
plot_RACIPE <- function(rset){
gex = log2(t(assay(rset)))
gex.1 = ggplot(data = as.data.frame(gex), aes... |
2a616a76e3857ddd39090ee0ce2668ffa6e1cab5c53dadce7af0796b0d624d4c | R | 5,400 | 161 | #' @tags backend sequential multisession cluster
#' @tags detritus-connections
library(future)
SequentialFutureBackend <- future:::SequentialFutureBackend
MultisessionFutureBackend <- future:::MultisessionFutureBackend
ClusterFutureBackend <- future:::ClusterFutureBackend
MulticoreFutureBackend <- future:::MulticoreF... |
7d887e86b5e40f593e4ae9321e6b08816e5661d565c33a170a102162d8efc6ff | R | 5,428 | 138 | library(ggplot2)
# library(ggbeeswarm)
source("~/Documents/Personomics_Research_Group/WAPIAW_2/figure_plotting/config.r")
source("~/Documents/Personomics_Research_Group/WAPIAW_2/figure_plotting/utils.r")
# default input ploting csv file
data <- read.csv("~/Documents/Personomics_Research_Group/WAPIAW_2/figure_plotting/... |
c15fc70c143326f944226e829287de22cafa7f3eaafd5099df215ba9ddaca62b | R | 5,429 | 153 | ##' @importFrom mgcv s
##' @export s
##' @name s
##' @rdname sl
NULL
##' @importFrom mgcv t2
##' @export t2
##' @name t2
##' @rdname t2l
NULL
#' @title Set up smooth term with factor loading
#'
#' @srrstats {G1.4} Function documented with roxygen2.
#' @srrstats {G2.3,G2.3b} Argument "factor" is case sensitive, as is ... |
e05a1ee733199c526afc51bd744250058e9cae2b4ac91dc76dd7cb217769f7ed | R | 5,430 | 166 | library(data.table)
library(ggplot2)
library(ggbeeswarm)
library(grid)
library(ggpubr)
source('../config.r')
source('../utils.r')
# text grob
text_annotation <- function(label, x, y, size, color)
{
gtext <- grid.text(label, x = unit(x, "npc"), y = unit(y, "npc"),
gp = gpar(fontsize = size, col = color))
ret... |
c417baa1e6d454321a6d6450983baaa264fd2997afd8e732b0e547956f992e7f | R | 5,435 | 155 | ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------
# This script shows how to compute scrubbing measures directly from the HCP data
# (without using pre-computed CompCor, etc.). It was just used for a... |
900599e911b536fcd352d4097e622901910fcfa554b003a5bc769fb6c4242f8a | R | 5,455 | 154 | #' Results from resolving a future
#'
#' @param value The value of the future expression.
#' If the expression was not fully resolved (e.g. an error) occurred,
#' the the value is `NULL`.
#'
#' @param visible If TRUE, the value was visible, otherwise invisible.
#'
#' @param conditions A list of zero or more list eleme... |
4c8286bde2146bb05fa035d00f15383b75099c2ea57bcd3df5d2f86de86e5b40 | R | 5,468 | 130 | library(RISC)
library(irlba)
library(umap)
library(ggplot2)
library(RColorBrewer)
PATH = "/Path to the data/GSE131181"
###################################################################################
### Prepare data ###
###################################################################################
library(d... |
f99e326bd45dd686a8c8ce56dcfffab72d33ae3c8ae1ba19ccb18e630f454120 | R | 5,479 | 182 |
rm(list = ls())
# the libraries needed and the actual code for this section is within convenience functions in QCfunctions.R
source("QCfunctions.R")
grp="str"
outname<-grp
# set wd to QC if needed
# setwd(list.dirs(recursive = F)[grep("C_QC",list.dirs(recursive = F))])
# setup directory structure
dirSetup(out.n... |
e16af87cea1ab7b75182af31f7396e52f4e190974cb28e88bed73e21eff7d1a2 | R | 5,481 | 134 | library(dplyr)
library(ggpubr)
library(reshape2)
library(grid)
library(scales)
library(ggrepel)
library(stringr)
#install.packages("remotes")
#remotes::install_github("LCBC-UiO/ggseg", build_vignettes = FALSE)
library(ggseg)
theme_set(theme_classic())
args <- commandArgs(trailingOnly = TRUE)
if(length(args) > 0) {
... |
470dd8d9c60265d072c60ad8397d357aeda61b3a1353d99d260da65f85d3fdfd | R | 5,502 | 135 | library(RISC)
library(irlba)
library(umap)
library(Matrix)
library(matrixStats)
library(ggplot2)
library(RColorBrewer)
PATH = "/Path to the data/GSE96583_PBMC"
###################################################################################
### Preparing RISC Objects ###
##########################################... |
d5aaa93107fa667bf4993eadc6f4f1b74f856611a0f78558329de821babbd746 | R | 5,515 | 139 | #!/usr/bin/env Rscript
# Get all the different covariate/samplesheet/phenotype files and merge them all together
# while harmonizing column names
options(warn=1)
library(data.table)
library(plyr)
library(optparse)
initial.options <- commandArgs(trailingOnly = FALSE)
file.arg.name <- "--file="
script.name <- sub(file.... |
bc6f595e7ab59f0593516fa8387ebb6fd8c4560e9a1a730f398339fe0573a23b | R | 5,518 | 116 | install.packages("PhyloOrchard", repos="http://R-Forge.R-project.org")
BiocManager::install("ggtree")
install.packages("phylobase")
library(PhyloOrchard)
library(phylobase)
library(ggtree)
library(ggplot2)
#load in phylogenetic trees
data(BinindaEmondsEtAl2007)
bestmammaltree <- BinindaEmondsEtAl2007[[1]]
bestmammal... |
e43a22fc585a7a57cd9de50c10d51bec480b08adeb97b8133add49a38732f2c9 | R | 5,532 | 171 |
# this creates scripts that can be run as jobs on a computing cluster
library(Biobase)
library(biomaRt)
setwd(dirname(rstudioapi::callFun("getActiveDocumentContext")$path))
load("WGCNA/cons/inputData.rda")
dc.expr<-t(scale(multiExpr$dc$data))
str.expr<-t(scale(multiExpr$str$data))
metadata.dc<-metadataList$dc
rowna... |
b728ab5f5a816724656ff00eff2024cfbb72ae49f0993a7d6dc85a7788545cc8 | R | 5,561 | 125 | #
# create.tstat.matrix.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 Z s... |
a97d19a80293836764d549c931d2e0c21e57364d9dd0b0cfb5ec15e9b4673415 | R | 5,563 | 208 | ### script to count reads and generate plot for calibration of the 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
# re... |
b97bb950223570bca2a2eb2d5e1458292f81f191e5a6c00ec5283770b4ac11a7 | R | 5,563 | 205 | ---
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... |
e1a717b31442bf4248440f06b8fd84277547a3d8fb659c8a6d42ec5406ffc918 | R | 5,568 | 169 |
library(data.table)
library(ggplot2)
library(GGally)
library(ggrepel)
library(optparse)
library(ggExtra)
library(RColorBrewer)
main <- function(){
options <- get_options()
if (!dir.exists(paste0(options$output,'/figures/'))){
dir.create(paste0(options$output,'/figures/'))
}
if (!dir.exists(paste0(options... |
b4e95a4744cc0c96b0ef2b70b97bb3af5496ced9b49c99f3676e977b84762ac6 | R | 5,571 | 168 | ---
title: "R Notebook"
output: pdf_notebook
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)
```
```{r warning=FALSE, message=FALSE}
library(tidyverse)
library(randomForest)
library(cowplot)
library(gridExtra)
```
```{r}
rents <- read.csv('./data/rent.csv')
glimpse(rents... |
8be2690a4b8285c36a2377844b1ff63676e5acf8f6024ab3f9cb0dc3136b718f | R | 5,579 | 157 |
library(Biobase)
library(pheatmap)
library(RColorBrewer)
library(ggplot2)
library(grid)
library(gridExtra)
setwd(dirname(rstudioapi::callFun("getActiveDocumentContext")$path))
datlist<-readRDS("data/FinalProcessedData.rds")
an<-fData(readRDS("data/rawCountEset.rds"))
hasEGID<-rownames(an[!is.na(an$NCBI.gene.ID),])... |
d12cde89adf5f0faa16fe66ca08ad1aa37b72c4e4c3010da8f9481fdd5cdae39 | R | 5,579 | 162 | #' Tornado plot for aggregated discoveries
#'
#' Takes the bins parallel to the Hi-C diagonal through the anchor locations
#' and perform k-means clustering on these.
#'
#' @param discovery One of the following \code{discovery} objects: \itemize{
#' \item{An \code{APA_discovery} object}
#' \item{A \code{CSCAn_disc... |
ea008b7b2c1624fa40b3efb8f96a873a08f2112e87375f53965bba89c8e9b4e8 | R | 5,605 | 186 | rm(list = ls())
# the libraries needed and the actual code for this section is within convenience functions in QCfunctions.R
source("QCfunctions.R")
grp="dc1m"
outname<-grp
# set wd to QC if needed
# setwd(list.dirs(recursive = F)[grep("C_QC",list.dirs(recursive = F))])
# setup directory structure
dirSetup(out.n... |
8ae79a8ef38a10b25a111f906e1025bd7574272f688374cbbfd74364b0e94e1a | R | 5,606 | 204 | #' @srrstats {G5.0} Datasets from PLmixed package are used for testing, and
#' results from the functions in this package are precomputed for comparison,
#' in cases where PLmixed and galamm support the same models.
#' @srrstats {G5.4} It has been confirmed that PLmixed returns the same results.
#' PLmixed is not... |
e9c6c8d169bc3efac3bda6e0fd5adcaf89fb958c0f4cc083182566460c640e86 | R | 5,617 | 205 | ---
title: "Permutation testing for VAR in modes 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, include=... |
0657e1482ef00ae29e05b5a5acc0c92bd6f4bef55ea9324ae1aeefb04aaa3863 | R | 5,648 | 165 | library(igraph)
library(SCINET)
library(ACTIONet)
#load entire HNv3
data('HNv3_XC_LLS')
#add LLS to all networks
#################################
cancertype <- 'BC'
#################################
setwd(paste0('~/HumanNetv3/XC_nobatch/',cancertype,'/'))
Celltype.specific.networks <- readRDS(paste0('.... |
63ff9bb2b2f436890ecdf74af9c7c42ea9a290962351214deb1e56185437926a | R | 5,693 | 148 | #library(tidygraph)
#library(ggraph)
library(data.table)
# IO args
args <- commandArgs(trailingOnly=TRUE)
bundle.dir <- args[4]
input.path <- args[1]
phenotype <- args[2]
output.path <- args[3]
input.prefix <- paste0(input.path, "/", phenotype)
# Args
edge.threshold <- 2
edges.within.cols.... |
de5ba06f9d4f0ba9ff86bcc8e3a6aa3bd5a107bddeca23fd2d1f62efeb4d4678 | R | 5,698 | 147 |
#check out and subset the good alignments
aln_QC<-readr::read_tsv("protein/other_protein_alignments/prank_out/Coevol_proteins_aln1 - protein_list_all.tsv")
good<-aln_QC %>% filter(decision=="use") %>% filter(!notes %in% c("short","sus_gap"))
write_tsv(good[,"protein"],"protein/other_protein_alignments/prank_out/goo... |
e22b50023ff05cde7e34c223fee23b03f59f975b9156e1ab8502084c7ebda5d7 | R | 5,710 | 159 |
library(tidyverse)
library(ggtree)
library(Biostrings)
library(readxl)
library(nlme)
library(ape)
library(geiger)
library(stringr)
# put all the results together
# CCDS IDs
#CCDS<-read_delim("protein/other_protein_alignments/CCDS/CCDS.current.txt", delim="\t")
trnp1_ccds<-"CCDS41289.1"
source("protein/scripts/summ... |
c8c4f88b13fb860086b2e70f2b76c1aac25963394d4db549bd0deb98d7d6522a | R | 5,719 | 174 | #' @tags FutureCondition conditions sequential
library(future)
plan(sequential)
message("*** FutureCondition() ...")
## A plain message
c1 <- FutureCondition("some message")
stopifnot(inherits(c1, "FutureCondition"), inherits(c1, "condition"))
stopifnot(grepl("some message", conditionMessage(c1), fixed = TRUE))
##... |
6fd7417f5aebd6dff5e3e38d36b668c1e64012de24469e72021eda7c0a8231bc | R | 5,725 | 187 | ---
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}
---
... |
32054a5fe4da5d2fd529a50c6419093ef604c95a1b7639f62dc5a0679c97abf2 | R | 5,774 | 103 | # This script contains the commands used to identify the longest contig per species, which covers the coding sequences. The input of this script has not been uploaded due to limited git space. But the output of this script can be found under protein/fastas/resequenced_promexon1_primates.fa
# SETTINGS ----------------... |
3ead7302cb44eca9c407f8b3fda6eb1ead35b71480c3dfea19eff8091073e27c | R | 5,793 | 187 | #get B anc T cell specific gene set from GO term
#readdata
data.tcell <- read.table('~/HumanNetv3/gene2go_tcell.tsv', header = F, sep = '\t')
data.bcell <- read.table('~/HumanNetv3/gene2go_bcell.tsv', header = F, sep = '\t')
#filter data by human first
data.tcell <- data.tcell[data.tcell$V1 == '9606', ]
data.bcell <-... |
03c41277bb3518dfa1b58cd1d49fff159f646db811654818e8c38d98f42d8d5d | R | 5,799 | 198 | #' @tags utils-internal options
library(future)
setOption <- future:::setOption
update_package_option <- future:::update_package_option
## A dedicated, non-existing option and its environment variable
name <- "future.test.myoption"
env <- "R_FUTURE_TEST_MYOPTION"
reset <- function() {
opts <- list(NULL)
names(o... |
b715ecb4f68fc56d6729d3bc6b6cc5f9eddbb8338d7952aa065d3c2a9e54c3ad | R | 5,830 | 162 | #!/usr/bin/env Rscript
## COMMAND LINE PARSE ######
library("optparse")
# specify our desired options in a list
# by default OptionParser will add an help option equivalent to
option_list <- list(
make_option(c("-i","--input_dir"), default=NULL,
help="Path to gene count files (.tsv);
input files must be tsv fi... |
76a10d865cb97ccb16ab93badc353645b1f34420c0551d513ec5e408b15c4eae | R | 5,837 | 190 | ```{r}
setwd("../downstreamer_collaborations/biogen/")
source("../../downstreamer_main/downstreamer_functions.r")
# Read excel files
base <- read.downstreamer.batch("/home/work/Desktop/depict2/collaborations/biogen/hand_grip_strength/2021-02-11/")
new <- read.downstreamer.batch("/home/work/Desktop/depict2/collaboratio... |
17f181633c78958a023ff2d85d8bc49981c4dbd19da8d67d0778121bdcdb4177 | R | 5,849 | 178 | #' @tags value futures sequential listenv
library(future)
library(listenv)
plan(sequential)
mkfutures <- function(n = 4L) {
fs <- lapply(seq_len(n), FUN = function(i) future(i))
names(fs) <- letters[seq_len(n)]
fs
}
message("*** value() - 'reduce' validation ...")
## A 'reduce' function given as the name of ... |
e9735f40f20a5ac27ed701e6ec82d8785d8958be63c7564754c1552b84e077a6 | R | 5,945 | 167 | smartSeq <- read.delim("smartseqSamples.txt")[,1]
str(smartSeq)
defaultCol <- adjustcolor("grey", alpha.f = 0.3)
pcsAndMeta$colSmartseq <- defaultCol
pcsAndMeta$colSmartseq[pcsAndMeta$Row.names %in% smartSeq] <- "darkslategray"
pcsAndMeta$colSmartseq[pcsAndMeta$Row.names %in% qseq] <- "orangered"
pcsAndMeta$colSmarts... |
c0a1fa92b67c5e3cb7bf5ce1f7f48535c939b4d7d34dc9dc61a1cd0e063b8099 | R | 5,946 | 175 | #' @title Diagnostic plots for galamm objects
#'
#' @description This function provides diagnostic plots for models fitted with
#' [galamm()]. See the [residuals.galamm()] function for definition of the
#' residuals being used.
#'
#' @param x An object of class \code{galamm} returned from \code{\link{galamm}}.
#' @... |
f5cc4b1ab013d6fb6683ad74282541362e2fc1dcb3836eada6c0aaf84c21cc15 | R | 5,948 | 195 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("evaluating_coregulation")} else {setwd("../evaluating_coregulation")}
source("../downstreamer_functions.r")
source("data/source_olivier.r")
library(pROC)
library(data.table)
library(scales)
# Cached 2021-04-28
datasets <- read.downstreamer.batch... |
916cf312d5514dce65af75ddc7d3937b698c0950cf39ebc33da6ac4f8e061855 | R | 5,965 | 197 | #'---
#'title: "Score models on test set"
#'author: "Denis A. Engemann"
#'date: "7/17/2020"
#'output:
#' html_document:
#' code_folding:
#' hide
#' md_document:
#' variant:
#' markdown_github
#'---
#' First execrise: Load all relevant data:
#' 1. Predictions for all targets fr... |
1ca8edb3205ab1a2923278b23ae9a384fdbc3e9047312b1577a292326dac361f | R | 5,968 | 166 | ```{r}
setwd("evaluating_coregulation")
source("../downstreamer_functions.r")
source("data/source_olivier.r")
cur.data <- read.depict2("~/Desktop/ibd_sub_network_coregulation//inflammatory_bowel_disease_2017_29906448_hg19_enrichtments_exHla.xlsx", potential_traits = c("Coregulation", "Coregulation_eqtlGen"))
cur.data... |
c033b22657e587ada09b89c68352f7184966f51c28f5d6681b09715d3df91c64 | R | 5,980 | 162 | library(ggplot2)
# library(ggbeeswarm)
source("~/Documents/Personomics_Research_Group/WAPIAW_2/figure_plotting/config.r")
source("~/Documents/Personomics_Research_Group/WAPIAW_2/figure_plotting/utils.r")
# default input ploting csv file
data <- read.csv("~/Documents/Personomics_Research_Group/WAPIAW_2/figure_plotting/... |
4a6b3d028404eaf687d5214146cd9eede30dbb91ed1f15ce696b54d9b5995110 | R | 5,996 | 160 | #' Chromosome matrix
#'
#' Computes sums of chromosome interactions genome-wide along with expected
#' values, making it easier to identify enriched trans-contacts that may
#' indicate translocations.
#'
#' @param explist Either a single GENOVA \code{contacts} object or a list of
#' GENOVA \code{contacts} objects.
... |
2af4a854f5a415b6958fd94d3347ff9565c81c6b42b90ec0f0bdba61854daf3f | R | 5,997 | 134 | library("optparse")
library(ggplot2)
library(ggpubr)
library(gridExtra)
library(topGO)
library(ALL)
library(dplyr)
library(data.table)
# Get command line arguments
option_list = list(
make_option(c("-f", "--fdrQTLs"), type="character",
help="DER-08a_hg38_eQTL.significant.txt of psychEncode", metavar="... |
86fa26f63dd7f30955fbd03bfe52daa2b4deea22c7d1879cd40ece9f42580609 | R | 6,000 | 160 | options(warn=-1)
#'# Class definitions
#' @importFrom methods setClassUnion
#' @importClassesFrom Matrix dgCMatrix
library(Matrix)
setClassUnion(name = 'AnyMatrix', members = c("matrix", "dgCMatrix","data.frame"))
setClassUnion(name = 'AnyFactor', members = c("factor", "list",'character'))
my.sapply <- ifelse(
te... |
78e5a9a3bea59f2f4383aaf59fe690ef5b626dfb83b5b91a8e99b7c7453f238e | R | 6,001 | 149 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("evaluating_coregulation")} else {setwd("../evaluating_coregulation")}
source("../downstreamer_functions.r")
source("data/source_olivier.r")
library(Hmisc)
#datasets <- read.downstreamer.batch(main.downstreamer.output.path, USE.CACHE = T)
datasets... |
7d11629cfd0f01221e09f1c97bd50b975834088b5bcaf11b79002df84edaea95 | R | 6,004 | 202 | #' Trans matrix plot
#'
#' This function plots a matrix of contacts data for the intersection of two
#' regions. Using two regions on different chromosomes plots trans data.
#'
#' @param exp1 A GENOVA \code{contacts} object.
#' @param exp2 A GENOVA \code{contacts} object compatible with \code{exp1}
#' (optional) or... |
5cc482f72b94da71acd6942c0041c4ed1ba80144c6c6e85600b6421c7a85539c | R | 6,008 | 195 | ---
title: "Terence Parr Feat Imp"
author: "Christopher Csiszar"
date: "3/22/2018"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)
```
## Biases in RF importance calculations
Simple RF Classification done in R to see how biases their RF feature impor... |
64fc5f6153ea599b4e16d5124e728e614facaf1bd884491d51c0dcf1c37938cc | R | 6,025 | 226 | #' @tags rng
#' @tags listenv
#' @tags sequential multisession multicore
options(future.debug = FALSE)
library(future)
library(listenv)
message("*** rng ...")
okind <- RNGkind()
## A valid regular seed
f <- Future(42, seed = 42L)
print(f)
stopifnot(identical(RNGkind(), okind))
## A valid L'Ecuyer-CMRG RNG seed
se... |
7c75d9f51fef6ff89da5c33048d171683a238807c19b53a52bb9200ee5b72c6a | R | 6,039 | 134 | # Function for plotting gene module heatmap from seurat 10x object
# col_order is a single element or a vector element. By default cells are ordered by the first element of the metadata variable.
# User can specify col_order as a vector, with cell order prioritised based on the order of the col_order variable.
# A cu... |
885fda463643c61328e90b1cfcf40b624e43bb31faac0440de704f9b87573d0a | R | 6,050 | 177 | #=======================#
# 2.Format results #
#=======================#
## Set up ----
filepath <- ""
setwd(filepath)
### Load libraries ----
library(dplyr)
library(magrittr)
library(openxlsx)
library(purrr)
library(tidyr)
## Format results (Module) ----
### Brain volume ----
brainvol_mod <- read.csv("results/... |
64c240cf90c030561d686a383ca163098e95e659214cfba88289353ca6d80544 | R | 6,052 | 223 | #' @tags utils-internal
library(future)
isFALSE <- future:::isFALSE
isNA <- future:::isNA
assert_no_positional_args_but_first <- future:::assert_no_positional_args_but_first
stop_if_not <- future:::stop_if_not
trim <- future:::trim
comma <- future:::comma
commaq <- future:::commaq
hexpr <- future:::hexpr
envname <- f... |
799e57e72e28348a28a671e018b969bea6075099a89e1179e6e31bfd7cc3f151 | R | 6,053 | 142 | sumstats <- function(files,ref,trait.names=NULL,se.logit,OLS=NULL,linprob=NULL,N=NULL,betas=NULL,
info.filter = .6,maf.filter=0.01,keep.indel=FALSE,parallel=FALSE,cores=NULL,ambig=FALSE,direct.filter=FALSE){
if (is.list(files)) {
wrn <- paste0("DeprecationWarning: In future versions a list of... |
3d167fa37db19a25a8358e872166fc7ddf3a32155307034b42da695593f82cd6 | R | 6,058 | 184 |
options(echo=TRUE)
options(stringsAsFactors = FALSE);
library(WGCNA);
allowWGCNAThreads()
library(flashClust);
library(gplots)
library(RColorBrewer)
setwd(dirname(rstudioapi::callFun("getActiveDocumentContext")$path))
# striatum-----
outdir="CSstr"
dir.create(outdir)
load("Striatum/processed_data/inputData.rda")... |
a3783807e8ab3f9a8550a2a7f7146759ab9548f0f6c0d7f8039166b459bf33e5 | R | 6,058 | 151 |
#' summarySE
#'
#' stats for time repeated measures
#' @param data data frame
#' @param measurevar character
#' @param groupvars character
#' @param data a data frame.
#' @param na.rm a boolean that indicates whether to ignore NA's
#' @param conf.interval the percent range of the confidence interval (default is 95%)
#... |
2891f3e521d9d856acb7b7111ce46918b821c3e5e96ca7f89051364da58315da | R | 6,068 | 168 |
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs\\Height_test")
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 ... |
8184c9ce01ad9582e2ef0dcfd61ffb8447f149731599c3de2cc4b101421f2ea5 | R | 6,078 | 211 | loadJuicer = function(juicerPath, resolution, scale_bp = 1e9, scale_cis = F, balancing = T){
try_require("strawr", "loadJuicer", "github")
juicerPath <- normalizePath(juicerPath)
# get metadata of juicer-file
juicer_metadata = get_juicer_metadata(juicerPath)
# check if resolution is found!
if(!any(re... |
2404519fcde6d429d5dd2834147e778ee3f191d57abc277a5b4d32c72174f3bf | R | 6,092 | 174 | #' Calculate insulation scores
#'
#' Insulation scores are calculated by sliding a square along the diagonal of
#' the Hi-C matrix and averaging the values in that square.
#'
#' @param explist Either a single GENOVA \code{contacts} object or list of
#' GENOVA \code{contact} objects.
#' @param window An \code{integer}... |
e0bccc8ef14b0d56a20a8a83c5b2b064eae290431bf52a502e51b8f746ec87c0 | R | 6,102 | 203 | ---
title: "Permutation tests for dFC Mode Metrics - VAR HCPEP 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: axtxtpt
output: html_document
---
```{r setu... |
018f205b038ed58f5e4d73432ad5fc5e84060e0cb65962525bb8f23b06ae62f8 | R | 6,105 | 138 | ## -------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
stopifnot(COMPUTER == "MPro")
# Load list of S1200 subjects.
subjects <- readRDS(file.path(dir_data_misc, "HCP_subj... |
e8c29b14c4cf690bac09f584c1324cd424ef488ab1a1e0be478b5c3501042606 | R | 6,133 | 202 | #' @tags plan tweak
#' @tags sequential cluster
message("*** Tweaking future strategies ...")
message("*** y <- tweak(future::sequential) ...")
sequential2 <- future::tweak(future::sequential)
print(args(sequential2))
stopifnot(identical(sequential2, future::sequential))
stopifnot(!inherits(sequential2, "tweaked"))
... |
786a3fbc959ec1a72a9f6895a9d182f843be9561fec1e064970557023ca1557d | R | 6,135 | 203 | ---
title: "Permutation tests for dFC Mode Metrics - META HCPEP 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: axtxtpt
output: html_document
---
```{r set... |
b61107061a92a9e626b10259522e23dd44e9920b8f0cb6ef532bcbe35cab1254 | R | 6,135 | 188 | ```{r}
setwd("coregulation_clustering")
source("../downstreamer_functions.r")
library(apcluster)
library(tidygraph)
library(ggraph)
# Parameters
z.threshold <- 5
phenotype <- "CeD_2011"
# Read the coregulation data
coreg.matrix <- fread(paste0("data/", phenotype, "/", phenotype, "_coreg_genes.... |
4c1ab1ceb85ce523126343a10234b007d3f83fb76af4364c1dbc1a3d756933f7 | R | 6,149 | 112 | library(RISC)
library(Matrix)
library(R.matlab)
PATH = "/Path to the data/GSE110823"
###################################################################################
### Prepare Data ###
###################################################################################
## All Cells
dat0 = readMat(paste0(PATH, "/... |
5fe280c5e1ab2fc98b490e0f2b598c500e78984927c110cdf6ef11409ca6d000 | R | 6,155 | 176 | ---
title: "Permutation tests for dFC Mode Metrics - META HCPEP 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: axtxtpt
output: html_document
---
```{r set... |
834179a0e578888ce1aaaf7bda3bc4458cb27b837134a6b0af510b4f63670cfd | R | 6,164 | 170 | rm(list = ls())
#### to run
#### Rscript DBSCAN.EHdn.parallel.R --infile /hpf/largeprojects/tcagstor/users/btrost/papers/STRs/Qatar_ASD/output_regions.min2.1000G+SSC+MSSNG+QASD.txt
#### --outpath output/ --a1000g /hpf/largeprojects/ccm_dccforge/dccdipg/Common/annotation/ExpansionHunterDenovo/1000G.samples.txt --sample... |
cc07b88f685a806d446ea23f7b12fdd1396d70eb8041397787af7442d2be8c89 | R | 6,190 | 185 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("evaluating_coregulation")} else {setwd("../evaluating_coregulation")}
source("../downstreamer_functions.r")
source("data/source_olivier.r")
library("ontologyIndex")
library("ontologyPlot")
library("ontologySimilarity")
library(tidygraph)
library(ggraph)
libr... |
403794afcc6f60a1839a184727d48a714f9d10649b01c6ea9ba4ce7a574699af | R | 6,195 | 155 | #write a new summarization function
#1) blast back to the respective region in the respective species to know the exact positions covered by each chunk
#2) make a decoder that spits out per base coverage, aka for each base which chunks that are present in the library are covering and what was their per base activity
#... |
3aa1f8161522d0dd36d8b9b39bad4f29a607b9b47133bd10ef11f177fdb727d8 | R | 6,202 | 218 | #' Get rowNets
#'
#' @param object ACTIONetExperiment object
#' @param ... other parameters
#'
#' @exportMethod rowNets
setGeneric("rowNets", function(object, ...) standardGeneric("rowNets"))
#' Get colNets
#'
#' @param object ACTIONetExperiment object
#' @param ... other parameters
#'
#' @exportMethod colNets
#' @exp... |
d21994cb263b83036bd64bb3f2b16d78347c17641a6410d1f7458ebf5b83ab9a | R | 6,224 | 195 | #' @tags rng rng-utils utils-internal
library(future)
make_rng_seeds <- future:::make_rng_seeds
with_stealth_rng <- future:::with_stealth_rng
parallel_rng_kind <- future:::parallel_rng_kind
is_valid_random_seed <- future:::is_valid_random_seed
next_random_seed <- future:::next_random_seed
is_lecyer_cmrg_seed <- futur... |
3de0c35cdaa90095b656bfee86474575dc3d6985be61205aeb72ae6740304b45 | R | 6,229 | 181 | ## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
# Run this script twice, first without all subjects,... |
5879707649c3b46c6b8ca8b673a9305c8ddb5ab4d87d5acd765ddeca8e30d284 | R | 6,254 | 209 | #' Mandelbrot convergence counts
#'
#' @return Returns an integer matrix (of class Mandelbrot) with
#' non-negative counts (\eqn{C}).
#'
#' @examples
#' counts <- mandelbrot(xmid = -0.75, ymid = 0, side = 3)
#' str(counts)
#' \dontrun{
#' plot(counts)
#' }
#'
#' \dontrun{
#' demo("mandelbrot", package = "future", ask ... |
3e4f35c564bf074bb07d08730b0d24c60360572ec92899ca58e8814fc05a25f0 | R | 6,272 | 247 | ---
title: 'Analysis of effects of cortical inactivation on vowel discrimination in noise
using logistic regression '
output:
pdf_document: default
html_document:
df_print: paged
word_document: default
html_notebook: default
---
Version History:
* Created: 2021-09-09 Stephen Town
* Ported to notebook:... |
d6bc6abe9b20dc2eea82d5e71303ba116f474c0850c3ae9911e81bb6769f876d | R | 6,274 | 166 | get1000Freq <- function(outlier, fam.1000g){
outlier <- strsplit(outlier, ";")[[1]]
count <- sum(unique(outlier) %in% fam.1000g)
return(round(count/length(fam.1000g) * 100, digits = 2))
}
mergeLoci <- function(loci, mergeAny = T){
cnv.tmp <- loci
firstIt <- T
olap <- data.frame()
while(nrow(olap) ... |
6ca1eedd0ab78ddfa1955b88713110456cb275df395f4bc2669a8943b36e84dd | R | 6,298 | 202 |
my_quants <- function(x)
{
r <- quantile(x, probs = c(0.025, 0.25, 0.5, 0.75, 0.975))
names(r) <- c("ymin", "lower", "middle", "upper", "ymax")
r
}
my_ggsave <- function(fname, plot, dpi = 300, ...)
{
ggsave(paste0(fname, ".pdf"), plot = plot, useDingbats = F, bg = "#FFFFFF",
...)
embedFonts(file =... |
e1b005d68f75b8ffb39d5f9640f87bdf1c15f6d117188648816c49a7b3432450 | R | 6,320 | 139 | ---
title: "motif4node Tutorial"
author: "Benjamin Clauss & Mingyang Lu"
date: '2023-01-23'
output:
rmarkdown::html_document:
toc: true
theme: readable
number_sections: true
vignette: >
%\VignetteIndexEntry{motif4node Tutorial}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r... |
b0ea59ff92b8e3f2bf4aa3375682a2aec4007161874354d6290e6a2104a2cdf0 | R | 6,337 | 204 |
my_quants <- function(x)
{
r <- quantile(x, probs = c(0.025, 0.25, 0.5, 0.75, 0.975))
names(r) <- c("ymin", "lower", "middle", "upper", "ymax")
r
}
my_ggsave <- function(fname, plot, dpi = 300, savepdf = F, ...)
{
if (savepdf) {
ggsave(paste0(fname, ".pdf"), plot = plot, useDingbats = F, bg = "#FFFFFF",
... |
2e96f71f6b82f2482b15a3869cace21b71747b97fa7c4ccdf5bdc29dd2734318 | R | 6,344 | 211 |
rm(list = ls())
# the libraries needed and the actual code for this section is within convenience functions in QCfunctions.R
source("QCfunctions.R")
grp="dc3m"
outname<-grp
# set wd to QC if needed
# setwd(list.dirs(recursive = F)[grep("C_QC",list.dirs(recursive = F))])
# setup directory structure
dirSetup(out.... |
67accb003fb52e0b8a28e691a898a03b607d97b476ea9a72096c0216ded5f114 | R | 6,377 | 191 | compute.RNA.archetype.to.RNA.archetype.alignment <- function(
reference_ace,
query_ace,
reference_slot_name = "unified",
query_slot_name = "unified",
deflate = FALSE,
reduced_dim = 50,
specificity_filter_threshold = 1
) {
reference_profile = rowMaps(reference_ace)[[sprintf("%s_feature_specificity",
... |
17e6a07b5295f82226288927ade08dad947b1c31656b6b15d3aa059492eabb0f | R | 6,380 | 281 | setwd("D:\\UMCG\\Genetica\\Projects\\GeneNetwork\\compareDecomposeMethods")
load("testExpression.RData", verbose = T)
str(expSub)
genes <- sample(nrow(expSub), 1000)
samples <- sample(ncol(expSub), 2000)
expSub2 <- expSub[genes, samples]
write.table(expSub2, file = "testData.txt", sep = "\t", quote = FALSE, c... |
a8745cffbd614edaef1320528f594c8988bb2488bbc027fa3d6b4a6acd7ff72d | R | 6,407 | 132 | library(tidyr)
library(dplyr)
library(reshape2)
source("utils_Florian.R")
behaviours <- c("rest", "forward_walking", "antennal_grooming", "eye_grooming", "foreleg_grooming", "hindleg_grooming", "abdominal_grooming", "PER_event")
n_repetitions <- 5
base_folder <- paste(root_dir, "Ascending_neuron_screen_analysis_pipeli... |
93f682d7eeeb970237d030e17733b7c2f95a20a81725754b2264d41e59c6335e | R | 6,432 | 185 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("coregulation_clustering")} else {setwd("../coregulation_clustering")}
source("../downstreamer_functions.r")
library(apcluster)
library(tidygraph)
library(ggraph)
library(igraph)
nodes <- fread("data/networks/ibd_for_paper_2021-20-04/ibd_for_paper_2021-20-0... |
0ecb4c9426af706e91eec4b8611695925b22de3aaabb9da2166f9fcd419fbf50 | R | 6,465 | 186 | ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
source("0_Analysis_helper.R")
stopifnot(SharedCode_version == c(11,0))
library(abind)
library(tidyr)
## --------------------... |
7a393963b676fd418f3ad0e1f1623c05f699370c96154cfb41fcad1fe698b400 | R | 6,495 | 178 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("evaluating_coregulation")} else {setwd("../evaluating_coregulation")}
source("../downstreamer_functions.r")
source("data/source_olivier.r")
library(ggsignif)
# Cached 2021-04-26
#datasets <- read.downstreamer.batch(main.downstreamer.output.path, USE.CA... |
222636c492a9facfad96558603aeb008bd3d4f01612db04173242f3468d2e360 | R | 6,562 | 251 | test_that("Mixed response works", {
dat <- subset(mresp, id < 100)
mod <- galamm(
formula = y ~ x + (0 + loading | id),
data = dat,
family = gfam(list(gaussian, binomial)),
load_var = "itemgroup",
lambda = matrix(c(1, NA), ncol = 1),
factor = "loading"
)
expect_error(
predict(mod, t... |
9c32fde31d12a70124ce3f92d923cbacfda53d8e32c3bfcfd695652bd566ff09 | R | 6,572 | 172 | library(readxl)
library(readr)
library(dplyr)
library(org.Hs.eg.db)
library(EnsDb.Hsapiens.v86)
library(reshape2)
library(plyr)
library(DescTools)
library(abind)
setwd("/Users/philippehabets/Dropbox/Git/fMRI-transcriptomics-cortisol/data")
#Read in DEGs
DEG <- read_csv2("differential_expression_results.csv")[,-1]
DEG... |
ffcb5841ea086290f8380eb51e4a0576583dae223b866e153b4a81bd806c23b5 | R | 6,573 | 196 | ## -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
# Run this script twice, first without all subjects,... |
d80e35a6599067c7f0953d05054863149ed924c536c6562cf8af2ac9df35cb34 | R | 6,575 | 183 | ##########################################################################################
## GTEx tissue enrichment analysis
##
## Author: Kalliopi Tsafou, Yu-Han Hsu
##########################################################################################
rm(list=ls())
library(dplyr)
library(ggplot2)
# ----------... |
112fd479676fe61ace6b206904cedffc5000a253e89cae2c4c4f147e401ce15d | R | 6,584 | 177 | #' annotateGenes Annotate genes with genomic coordinates with reference to hg38 using Ensembl based annotation package
#'
#' @param mtx Count matrix with genes on row names (Ensemble or Symbol)
#'
#' @return Annotated matrix
#'
#' @examples
#' count_mtx_annot <- annotateGenes(count_mtx)
#' @export
annotateGenes <- func... |
ba4f1df9f23916fcd818e3760f9e49c4f1004ce153ff55ba531e89e70fa7d8dc | R | 6,590 | 171 | select.sub <- function( data, start, end ){
# Restrict data.table core usage
dt.cores <- data.table::getDTthreads()
on.exit(data.table::setDTthreads(dt.cores))
data.table::setDTthreads(1)
x <- rep(start:end, end-start+1)
y <- rep(start:end, each=end-start+1)
data.sub <- data[list(x,y)]
data.sub <- da... |
45933cf4109089b520e85e362b3f001388d3c9aa52480e0ecfddeb3fda32ca04 | R | 6,601 | 256 | rm(list = ls())
setwd(dirname(rstudioapi::callFun("getActiveDocumentContext")$path))
options(stringsAsFactors = FALSE);
library(WGCNA);
allowWGCNAThreads()
library(flashClust);
library(gplots)
library(RColorBrewer)
source("../mylabeledHeatmap.R")
#plotpars-------
mar.eigennet=c(1.5,7.5,0,0.5)
oma.eigennet=c(0,0,1,0)... |
618ef0b7a7525083857bdcca7ed7d950b26fb9c471fd896f3f582d4771a20260 | R | 6,624 | 177 | #' @title Control values for galamm fit
#'
#' @description This function can be called for controling the optimization
#' procedure used when fitting GALAMMs using \code{\link{galamm}}.
#'
#' @srrstats {G1.4} Function documented with roxygen2.
#' @srrstats {G2.3,G2.3a} match.arg() used on "method" argument.
#' @srrstat... |
5a68e630f06bb5f60b6a730d235c333e76913fa99ff2435c7d480def2c59ec47 | R | 6,626 | 258 | #' @tags nested-futures
#' @tags listenv
#' @tags detritus-files
#' @tags sequential multisession multicore
library(future)
library(listenv)
options(future.debug = FALSE)
## WORKAROUND: R CMD check --as-cran on Windows will report
##
## * checking for detritus in the temp directory ... NOTE
## Found the following fi... |
f092f377c118cd48f51eba14f70118c455f0647c9060d27bd5cedbe0635d3cf5 | R | 6,642 | 219 | #'---
#'title: "Score models on test set"
#'author: "Denis A. Engemann, Kamalaker Dadi"
#'date: "7/18/2020"
#'output:
#' html_document:
#' code_folding:
#' hide
#' md_document:
#' variant:
#' markdown_github
#'---
#'
#' First execrise: Load all relevant data:
#' 1. Predictions... |
b6f71d777982a57c5a6c2dcbb4aa66f2fd8e53eaa31ce433bded922eaa6da973 | R | 6,647 | 194 | ##########################################################################################
## Compare and plot co-expression z-scores between bait-interactor pairs vs. other
## gene pairs, using co-expression data derived from 4 transcriptomic datasets:
## Stickels2021, Maynard2021, Velmeshev2019, BrainSpan
##
## Autho... |
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