sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
95e987fef6408886d17e690d0ac4e859847ecaef2a8e280876ce854eabf21ea9 | R | 6,648 | 192 | #' @tags utils-internal marshalling non-exportable
#' @tags detritus-files
library(future)
find_references <- future:::find_references
reference_filters <- future:::reference_filters
assert_no_references <- future:::assert_no_references
message("*** marshalling ...")
## A connection is a non-exportable reference, w... |
b173a520312a54e6702115f35d3654b9d5618c35ede44818b57ad14027fba856 | R | 6,694 | 196 | #' @tags immediateCondition utils-internal
library(future)
immediateConditionsPath <- future:::immediateConditionsPath
readImmediateConditions <- future:::readImmediateConditions
saveImmediateCondition <- future:::saveImmediateCondition
save_rds <- future:::save_rds
fileImmediateConditionHandler <- future:::fileImmed... |
e6516fef352d2d02c74005884447f32b91490aa391ce75c0baf9e7897c044121 | R | 6,741 | 189 | rm(list=ls())
#### parameters
#### to run
#### Rscript mergeExpansions.R --rscript ~/crg2/scripts/str/ExpansionAnalysis.R --ehdn /hpf/largeprojects/tcagstor/users/btrost/papers/STRs/Qatar_ASD/output_regions.min2.1000G+SSC+MSSNG+QASD.txt
#### --outlier dbscan.tsv --outpath output/ --trf /hpf/largeprojects/ccm_dccforge... |
dae6a880c4c2c6bf7b2d1bece2057e4b1983435638caae06e25b926a2605df26 | R | 6,751 | 153 | library(tidyverse)
setwd("/Users/philippehabets/Dropbox/Git/fMRI-transcriptomics-cortisol/data/")
# read in DEG results
DEG <- read_csv2("differential_expression_results.csv")[,-1]
# read Gene Expression by Cell Type, as medians and trimmed means (source: https://portal.brain-map.org/atlases-and-data/rnaseq/human-mu... |
aa94b6e72df76f9aced4d175ae4358db0e48de8d65e820970e3a8fb0990aa90d | R | 6,752 | 169 | .libPaths(c("/home/che82/R/x86_64-pc-linux-gnu-library/4.1","/usr/local/lib/R/site-library","/usr/lib/R/site-library","/usr/lib/R/library" ))
library(rdist)
library(SCORPIUS)
library(Rmagic)
library(pheatmap)
library(Linnorm)
#######on alignment##########
set.seed(1000)
library(reshape2)
Sys.setenv(RETICULAT... |
887f57f9d02be171b5651c69caf12b49ab4e4c1773c382027773e8d507bd6bcc | R | 6,753 | 181 |
library(dtwclust)
library(dplR)
library(TSdist)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
# Import combined fluorescent data
Data = read.csv('AllDeidentified081921.csv')
Data = Data[1:4799,]
Data = Data[-c(1)]
DataT = t(Data)
#####################
# Data preprocess
#####################
# ---... |
9cfed78422244c77483835f8e17d2be4794af4b5cfe7668cd4ff1e3c6ce61a17 | R | 6,756 | 231 | ---
title: "Time Series Clustering on Fluorescence Data from In Vivo Imaging"
author: "Shan Xu"
date: "3/30/2022"
output: pdf_document
---
```{r setup, include=FALSE}
library(dtwclust)
library(dplR)
library(TSdist)
library(knitr)
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, tidy.opts=list(widt... |
a4278885b2cfb5ba083f150818acc2d5ed0eddd69f889d0dcb24023b51a964c1 | R | 6,775 | 149 | ---
title: "RSC: robust integration of single-cell RNA-seq datasets using a single reference space"
author:
- name: Yang Liu, Tao Wang, Deyou Zheng
affiliation: Albert einstein college of medicine, Bronx, NY, United States
date: "2021"
output: rmarkdown::html_vignette
package: RISC
vignette: >
%\VignetteIndexEntry... |
5b32324e204d77eab581ea35105fb23fc0c9ae2885082f9d7ace899e0dd948cd | R | 6,822 | 220 | # this code reads top toplogically important Genes and and find the percentile rank of these genes in DEGs
library(Seurat)
library(future)
#save each data as seperate Seurat Object (version3)
count <- Read10X("~/public_Data/lambrechts_TME/10x_counts/OvC_counts/")
meta <- read.table('~/public_Data/lambrechts_TME/OvC_m... |
b9c54fd4a8beb78e02dee4e5d4de5d5518f1d2c8fde5cae209f6b7544f922c50 | R | 6,826 | 192 | ##
## Perform gene set enrichment analysis from a data frame of genes and ranks.
##
gse_fgsea = function(stats_df, gene_col, rank_col, species, title = "", pos_label = "Pos", neg_label = "Neg", file_prefix = "gse") {
suppressPackageStartupMessages({
library(magrittr)
library(dplyr)
library(tidyr)
l... |
fb997de4a0a595f0bf1c7cad9d4074dda6402040525a68d51d15eae6e26ad694 | R | 6,826 | 196 | source ("Library.R")
#####melanogaster####
data= NULL
for (i in 1:8) {
sname <- paste("Fly",i, sep="")
.data = read.xlsx("raw_data_imaging_3E_melanogaster.xlsx",sheetName= sname) %>%
dplyr::mutate(Fly = paste(i)) %>%
tidyr::gather(key = Frequency, value = response, -Frame, -Fly)
.data$Frequency = str_s... |
a859034535584679901a5b2887223b924c5fb8308ad5ab4969e066f9ab9ac8be | R | 6,878 | 120 | #' Prune out low-quality assignments
#'
#' Remove low-quality assignments based on the cell-label score matrix returned by \code{\link{classifySingleR}}.
#'
#' @param results A \link[S4Vectors]{DataFrame} containing the output generated by \code{\link{SingleR}} or \code{\link{classifySingleR}}.
#' @param nmads Numeric ... |
e4eac29942e74dbaaacd0d3060b8455fed90b5a8e07db1e38a4bfb002acd9cb3 | R | 6,889 | 217 |
source(paste0("C:\\Users\\patri\\Documents\\GitHub\\systemsgenetics\\Downstreamer\\src\\main\\r\\downstreamer_main/downstreamer_functions.r"))
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
mapping <- read.delim("ukbBurden/mapping.txt", stringsAsFactors = F)
ensg <- read.delim("ensgR75_protein_coding.txt", strings... |
831f80a5310175d7b9d3f6f5e5f5bee5f83d532283b5885c5b64f1c9441236d4 | R | 6,897 | 154 | library(RISC)
library(Matrix)
library(irlba)
library(umap)
library(ggplot2)
library(RColorBrewer)
PATH = "/Path to the data/GSE132044"
###################################################################################
### Prepare Data ###
############################################################################... |
40199ff312aa48f414606808b667d10b8597a8bc20139568eff1bc62f26639fa | R | 6,912 | 187 | #' 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... |
06c37797b83cb9bce44554901587bf971404c4aa1d4532d5c02b511a8980e947 | R | 6,913 | 234 | #' @tags futureCall
#' @tags sequential multisession multicore
library(future)
options(future.debug = FALSE)
message("*** futureCall() ...")
message("- futureCall() - sequential + lazy ...")
f1 <- future(do.call(rnorm, args = list(n = 100)), seed = NULL, lazy = TRUE)
f2 <- futureCall(rnorm, args = list(n = 100), se... |
89a1754ade83b95eef5b623d0d9fccd0af7124f463a8045ba820a35219043c48 | R | 6,931 | 144 | ---
title: "DeconCell quick start and tutorial"
author: "Raúl Aguirre-Gamboa and Niek de Klein"
date: "`r Sys.Date()`"
output: rmarkdown::github_document
vignette: >
%\VignetteIndexEntry{DeconCell quick start and tutorial}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
## Introduction
DeconCel... |
f1262070b006ed371bca96a57e6298fa2a6f44a33817a2fe042fdd3c4de6fb1b | R | 6,932 | 144 | ---
title: "DeconCell quick start and tutorial"
author: "Raúl Aguirre-Gamboa and Niek de Klein"
date: "`r Sys.Date()`"
output: rmarkdown::github_document
vignette: >
%\VignetteIndexEntry{DeconCell quick start and tutorial}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
## Introduction
DeconCel... |
b171a842744f415aabcc51440d63f30a63a249de16da39b53bf2494953d0a2c0 | R | 6,938 | 188 | #' Compartment versus compartment scores
#'
#' Produces a compartment score quantile versus quantile analysis in which
#' observed over expected contacts are compared between the different quantiles
#' of the compartment scores.
#'
#' @param explist Either a single GENOVA '\code{contacts}' object or a list of
#' GENO... |
22aa3b9664229ffb4610a509f80f53e6f2a0d0a34ce9715cbcc41d4ca4b648ea | R | 6,941 | 114 | setwd(paste0(figureDirectory, "Figure5-genome-wide-candidates" ) )
library(ggplot2)
library(cowplot)
library(RColorBrewer)
library(grid)
load("numCand.RData")
load("compositionPathDF.RData")
load("compositionCancDF.RData")
load("enrichdf2.RData")
numCand <- numCand[numCand$Level == "Gene",]
enriched <- enrichdf2[en... |
dc10e09119237726afb9dc36a6c2ae5a5af1e7a7251692f59fad6cce7e54e40f | R | 6,945 | 216 | #' @importFrom utils file_test
#' @keywords internal
immediateConditionsPath <- local({
.cache <- list()
function(rootPath = tempdir()) {
rootPath <- normalizePath(rootPath, mustWork = FALSE)
path <- .cache[[rootPath]]
if (is.null(path)) {
path <- file.path(rootPath, ".future", "immediateCondit... |
7654168823dc84a5b8447b26a5cb3183765971d989658099ff1af36bcb50b3c4 | R | 6,954 | 261 | #' Perform batch correction on `ACTIONetExperiment` and `SingleCellExperiment` objects.
#' @export
reduce.and.batch.correct.ace.fastMNN <- function(
ace,
batch_attr = NULL,
assay_name = "logcounts",
reduced_dim = 50,
MNN_k = 20,
return_V = FALSE,
reduction_slot = "MNN",
V_slot = NULL,
BPPARAM = Serial... |
ab4d2ef5dd0dbf973e72366666e4e4689eb67742e81dcb5e8a12cda247019ce6 | R | 6,980 | 210 | ```{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/final_paper_gfr_network/final_paper_gfr_network... |
e69abb5e2461a8ebab300fb45bdc339c03ecc474087767e8c093f7975db190c8 | R | 6,980 | 248 | ---
title: "Permutation testing for dFC Metrics -without subjects with high FD COBRE"
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_documen... |
2817ff9ba42fb7148f936794c7c9745ea545a799d37de5eac64327b7696fee0e | R | 6,988 | 271 | 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)... |
5cf158e6f60a1c592bf8b1c16f35574dc0c32582406d85db036bdd04571e6a4d | R | 7,009 | 236 | ---
title: "(Post-)Preprocessing of proteomics and RNA"
author: "Fiona Dick"
date: "`r Sys.Date()`"
output: html_document
toc: true
runtime: shiny
---
```{r}
library(ensembldb)
library(ggbiplot)
library(tibble)
library(ggpubr)
library("DESeq2")
require("ggplot2")
library(preprocessCore)
require("kableExtra")
require(... |
5c4c43527ed751be21f518a0c6779180e906c9293062b1b53140bab40d386846 | R | 7,019 | 207 | suppressMessages({
library(tidyverse)
library(progress)
})
theme_set(
theme_minimal()
)
options(scipen=15000000)
options(dplyr.summarise.inform = FALSE)
read_data <- function(filename) {
read_tsv(filename, col_types = cols()) |>
filter(FDR < 0.01 & abs(IncLevelDifference) > 0.2) |>
mutate(event_in_kd ... |
4d03c3929ca71ab18df57f60ff001577d1a9cd5f5ac091d94ed9f35951d8019d | R | 7,031 | 230 | # This script downloads and preprocesses gene-level data and metadata from
# all samples in recount3
#
# Output:
# A tsv file containing an array of gene expression data, and a metadata file
# R version used: 4.1.0
# Some conda issue causes the installation of GenomeInfoDbData to fail silently, preventing
# recount3 ... |
fe2a824405c9de1c5e6c522878276d505ccc12053aa90c16976b6b169249d756 | R | 7,038 | 155 | #!/usr/bin/env Rscript
################################################
################################################
## LOAD LIBRARIES ##
################################################
################################################
library(optparse)
library(ggplot2)
library(reshape... |
6f6fd39310e1425bf32c1ddbe8ed6dd270ecda036a6625ef6e6f3aa77796882c | R | 7,076 | 155 | #!/usr/bin/env Rscript
################################################
################################################
## LOAD LIBRARIES ##
################################################
################################################
library(optparse)
library(ggplot2)
library(reshape... |
71abeb0e8a83e0632fbaee8fac415bdaf1238c0792fd472f4c36de3dad62a1b1 | R | 7,081 | 178 | ---
title: "02_Exploratory.Data.Analysis"
output: html_document
---
## Install packages #
```{r}
rm(list = ls())
options(stringsAsFactors = FALSE)
library(tidyverse)
library(ordinal)
library(ggridges)
```
## Load data #
```{r}
## Load the data
load("ABCD.Release.3.0.C4.Merged.Data.PhewasVars.Rda")
```
## Filter to ... |
bdee9d6e70a8c29f9d4a2b4e0e0b0f0cdeb57dbed442e794fc5af3b2b6826891 | R | 7,086 | 216 | #' Tweak a future function by adjusting its default arguments
#'
#' @param strategy A future backend or the name of one.
#'
#' @param penvir The environment used when searching for a future
#' function by its name.
#'
#' @return a future function.
#'
#' @seealso
#' Use [plan()] to set a future to become the
#' new defa... |
a8da34c9e26148f9a7350fafc4ffc8b4dab22d90a8466d85eee3ea6197ad809b | R | 7,090 | 350 | #' @tags plan %plan%
#' @tags listenv
#' @tags sequential cluster
library(future)
options(future.debug=FALSE)
message("*** plan() ...")
cl <- parallelly::makeClusterPSOCK(1L)
print(cl)
pid <- Sys.getpid()
message("R session PID: ", pid)
message("*** Set strategy via plan(sequential)")
oplan <- plan(sequential)
pr... |
99a1fa3a14d9a73e5b8de64f0624ee7b68f79e6bef99af5554fe8ed893423b72 | R | 7,092 | 225 | rm(list = ls())
# the libraries needed and the actual code for this section is within convenience functions in QCfunctions.R
source("QCfunctions.R")
grp="dc"
outname<-grp
# set wd to QC
setwd(list.dirs(recursive = F)[grep("C_QC",list.dirs(recursive = F))])
# setup directory structure
dirSetup(out.name = outname)... |
932102ec6063907880831b982b7bc66ac0877743abfeb81650f55e06de93f67f | R | 7,133 | 213 | # Genelength and normalized gene scores
```{r}
library(ggplot2)
library(gridExtra)
library(readxl)
library(pheatmap)
library(RColorBrewer)
library(gridExtra)
source("../downstreamer_functions.r")
# Read reference datasets
ensembl <- read.table("~/Documents/data/reference/ensembl/ensembl_gene_position_b37_biotype.txt"... |
1afbd4a2d4cc809915b23b40ce94883e961e05bc58f5479d070749d49eb7c508 | R | 7,140 | 215 | 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... |
fdc05553a7053dd70e253d41db2cd2c24a6f390170d172e56c49730844212f6e | R | 7,146 | 169 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("evaluating_coregulation")} else {setwd("../evaluating_coregulation")}
source("../downstreamer_functions.r")
source("data/source_olivier.r")
library(ggsignif)
datasets <- read.downstreamer.batch(main.downstreamer.output.path, USE.CACHE = T)
mr.data ... |
58484646d38760b73a90c9ffe798d09a6f87a0b608710a7924305151c47c6d11 | R | 7,162 | 213 | # Robin's colours ---------------------------------------------------------
#' @name GENOVA_colours
#' @title Colours in GENOVA
#'
#' @description GENOVA comes with a few built-in colour palettes. The gradients
#' in GENOVA's palettes have been carefully chosen to be perceptually linear,
#' which is great for Hi-C vi... |
6bf6b12cf8b84fde103ee863f8661fa491615f8b2d9c5a39b12b96e01d952a5e | R | 7,184 | 216 | ```{r}
setwd("coregulation_clustering")
source("../downstreamer_functions.r")
library(apcluster)
library(tidygraph)
library(ggraph)
# Parameters
z.threshold <- 5
phenotype <- "immune_disease_bonf"
# Read the coregulation data
coreg.matrix <- fread(paste0("data/", phenotype, "/", phenotype, "_c... |
d53d318d0e93ad4804d4284a583e91f264b1abe4b2259bb2eb89faa1dabb46db | R | 7,190 | 155 | source ("Library.R")
#####Figure 1#####
RMTL = NULL
data <- read_excel("copulation_rate.xlsx") %>%
dplyr::mutate(ctime=ctime/60) %>%
dplyr:: mutate(event=ifelse(ctime>30, 0, 1)) %>%
dplyr:: filter(ctime > 0)
mel1535 <- data[(data$species == "mel" & (data$IPI ==15 | data$IPI ==35)),] %>%
dplyr:: mutate(soun... |
36a8f9ae0bb647ff9fa1dcf2eea7e522707d85ac02788475db1c65fa6678fce9 | R | 7,198 | 269 | CPal_default = c(
"#1F77B4", "#FF7F0E", "#279E68", "#D62728", "#AA40FC", "#8C564B", "#E377C2", "#B5BD61", "#17BECF", "#AEC7E8",
"#FFBB78", "#98DF8A", "#FF9896", "#C5B0D5", "#C49C94", "#F7B6D2", "#DBDB8D", "#9EDAE5", "#AD494A", "#8C6D31",
"#E31A1C", "#FFD700", "#771122", "#777711", "#1F78B4", "#68228B", "#AAAA44",... |
6fa50e6129f4bc5ceaf4fb3340abf9b81a8b7949b6b82023bd62819edcba3f70 | R | 7,231 | 173 | #' Fast run
#'
#' High-level wrapper for all functions, can easily get all the results
#'
#' @param reads_1 (Required). The file path to the forward fastq file from paired-end sequence data. Compressed file formats such as .fastq.gz are supported.
#' @param reads_2 (Required). The file path to the reverse fastq file fr... |
76033c9120d8cc4189a357f8129de046b817e499290a1544c0250f559c8d91b1 | R | 7,324 | 245 | # Upset plot of AD Loci
library(dplyr)
library(tidyr)
library(purrr)
library(janitor)
library(tibble)
library(glue)
library(stringr)
library(readr)
library(forcats)
library(glue)
library(ggrepel)
library(plotgardener)
library(GGally)
`%nin%` = negate(`%in%`)
## Snakemake
### Input
ad_loci.path = "results/adgwas_loci... |
28b47e465a18c9ce59e6b4d2a43020045d40051b3666f3065043f4b499d23ad1 | R | 7,335 | 188 | #!/usr/bin/env Rscript
library(data.table)
library(simGWAS)
library(mvtnorm)
library(corpcor)
# Function definitions
#---------------------------------------------------------------
# Function to read haplotype block
gethap <- function(i) {
y=fread(ldd$comm[i], skip="#CHROM")
ha <- simGWAS:::vcf2haps(as.matrix(y[,... |
7a4d92bba6c46421a0cf08c527c5b98d5d815e1442c2479506a3c19260ac0237 | R | 7,370 | 197 |
# SETTINGS ----------------------------------------------------------------
libs<-c("tidyverse","GenomicRanges","data.table","Biostrings","microRNA", 'reshape2')
sapply(libs, require, character.only=T)
system('mkdir -p output/mpra_chunk')
# READ IN SEQUENCES -------------------------------------------------------
... |
5d614fc9d8940265988cd723b6c64db6e643ceb44900fdfe04183f9fcd32d5d6 | R | 7,385 | 138 | .munge_main <- function(i, utilfuncs, file, filename, trait.name, N, ref, hm3, info.filter, maf.filter, column.names, overwrite, log.file=NULL) {
if (is.null(log.file)) {
log.file <- file(paste0(trait.name, "_munge.log"),open="wt")
on.exit(flush(log.file))
on.exit(close(log.file))
} else {
.LOG("\n\... |
d286212488e7f06a453ec09d558f6af843871c562276e789e3c955ca3802fbe4 | R | 7,401 | 226 | source ("Library.R")
#####melanoagster#####
data= NULL
for (i in 1:12) {
sname <- paste("Fly",i, sep="")
.data = read.xlsx("raw_data_imaging_4B_melanogaster.xlsx",sheetName= sname) %>%
dplyr::mutate(Fly = paste(i)) %>%
tidyr::gather(key = IPI, value = response, -Frame, -Fly)
background = .data %>%
... |
6d11332172043da1989bd3c4624df5441c312d4ff222defa0195190ad600d7e7 | R | 7,403 | 182 | #!/usr/bin/env Rscript
require(optparse)
option_list <- list(
make_option(c("--ref2bit"), type="character", default=NULL,
help="genome 2bit file of", metavar="character"),
make_option(c("--other2bit"), type="character", default=NULL,
help="genome 2bit file of", metavar="charact... |
5f328c5a935eaa3a84db5fc7f80341481d96fc64665bcd2cd6fa1782ebfee940 | R | 7,431 | 303 | #Code to generate matrix from: http://r.789695.n4.nabble.com/Re-Generate-a-serie-of-new-vars-that-correlate-with-existingvar-td822187.html
# create the initial x variable
x1 <- rnorm(100, 15, 5)
# x2, x3, and x4 in a matrix, these will be modified to meet the criteria
x234 <- scale(matrix( rnorm(300), ncol=3 ))
# p... |
5fbe1e134bc6d9152cbbf64096acc531920d6b9620aa3f4bc04e322bd23e1823 | R | 7,446 | 167 | #' Annotate scRNA-seq data
#'
#' Returns the best annotation for each cell in a test dataset,
#' given a labelled reference dataset in the same feature space.
#'
#' @param test A numeric matrix of single-cell expression values where rows are genes and columns are cells.
#' Alternatively, a \link[SummarizedExperiment]{S... |
f85abae3bb716591dce13d07dc50ae57e634b8b2d072c2928ded1d6bf5866620 | R | 7,498 | 188 | library(tidyverse)
library(sf)
library(rlang)
library(reshape2)
library(ggthemes)
library(patchwork)
##function for data processing using imagej cell counter over an image z stack
##you need to know:
#the pixels per micron
#the file path to the folder with 1) manual counts with the ImageJ Cell Counter plug... |
74d786570414b91b28cd42c3b7630f75f9ab6023e1521a8c7acb861ac0ee9375 | R | 7,499 | 185 | library(readxl)
# Loading data
ensembl <- unique(read.table("~/Documents/data/reference/ensembl/ensembl_gene_position_export.bed", stringsAsFactors = F)[,c(4,5)])
rownames(ensembl) <- make.names(ensembl[,2], unique=T)
# Function definitions
simpleQQPlot = function (observedPValues) {
plot(-log10(1:length(observedPV... |
df9390440a76fdd2a2abd3b6631fed6c0d7d6040f8f31211ee98db9f7c963aed | R | 7,513 | 215 | ---
title: '-'
output:
html_document:
css: ~/my_lab/my-lab_folder/resources/rmark.css
date: ''
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
knitr::opts_chunk$set(warning = FALSE)
library(tidyverse)
library(hrbrthemes)
library(ggtext)
theme_set(
# theme_ipsum_rc(grid = 'XY') +
theme_... |
3bbdbbdd151b9ddd2de8892499a76059c69ac76b5d60bb39d3271b86aaddc8a2 | R | 7,523 | 170 | library(readr)
library(rlang)
library(reshape2)
library(tidyverse)
library(readxl)
##this function is for compiling auditory physiology assessed by
##the Eaton-Peabody Laboratory Cochlear Function Test Suite, where
##the ABRs have been thresholded and peaks selected with
##ABR Peak Analysis Version 1.5.5.56... |
627491c9cfe4eee9ae1208aa586270349a6ceb1d9819b555248af2c1b4887da1 | R | 7,548 | 238 | setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(readr)
eqtl <- read_delim("2019-12-11-cis-eQTLsFDR0.05-ProbeLevel-CohortInfoRemoved-BonferroniAdded.txt.gz", delim = "\t", quote = "")
#eqtlFull <- read_delim("2019-12-11-cis-eQTLsFDR-ProbeLevel-CohortInfoRemoved-BonferroniAdded.txt.gz", delim = "\t", quo... |
cfccd2d873f4b5a82845f602a4170db7ea4600a1c3b52bcf065fa6c9a2e7177b | R | 7,565 | 233 | ####################################################################################
#' Utilities Subset data
####################################################################################
#'
#' The "Subset" function can abstract a data subset from the full dataset, this
#' function not only collect the subset ... |
237727ec1e2f993af434e64a2a9043d1fad153a1aaf84391f6cd59a76a717aa5 | R | 7,572 | 172 | #Generate boxplots for s steps backwards up to maxstepsback across all attrs in all 28 networks
# Plot number of predecessors M/2^netsize
# Get mean/median/SD/IQR for each step across nets
if(!require("BoolNet",character.only = TRUE)) install.packages("BoolNet")
if(!require("gtools",character.only = TRUE)) install.pa... |
8e91073a250343a18fec91acef68897179ec54d5c687c3a95c0f51b559291705 | R | 7,614 | 196 | library(igraph)
net.bc <- readRDS('~/HumanNetv3/XC_nobatch/BC/BC_celltypeNet_list_LLS.rds')
net.lc <- readRDS('~/HumanNetv3/XC_nobatch/LC/LC_celltypeNet_list_LLS.rds')
net.crc <- readRDS('~/HumanNetv3/XC_nobatch/CRC/CRC_celltypeNet_list_LLS.rds')
net.ovc <- readRDS('~/HumanNetv3/XC_nobatch/OvC/OvC_celltypeNet_list_LLS... |
35e47418512fbf0930586dabffb88b3c6f1a564fb84c1742cf6e673dedd4aa70 | R | 7,617 | 162 | convert.en2eg <- function() {
require(org.Mm.eg.db)
require(biomaRt)
xx <- mappedkeys(org.Mm.egENSEMBLTRANS2EG)
print(length(xx))
print(xx[1:5])
mart <- useEnsembl(biomart = "ensembl",
dataset = "mmusculus_gene_ensembl",
mirror = "useast")
mart <- useD... |
3d9e8a120937ab4d3a788822dee5125a82a035827b099793dd164de058f9bca2 | R | 7,621 | 162 | #!/usr/bin/Rscript
library(optparse)
library(ggplot2)
library(data.table)
library(reshape2)
library(grid)
library(gtable)
library(ggpubr)
library('scales')
option_list = list(
make_option(c("-e", "--eqtlGenReplication"), type="character",
help="File containing the replication of MetaBrain in eQTLgen a... |
c87217c90739bb10809354ea2e77545751e3dc6bce10b1cf2c247e3e0155fc3d | R | 7,630 | 204 | ```{r}
setwd("umap")
source("../downstreamer_functions.r")
source("data/source_olivier.r")
library(umap)
#pc.file <- "zcat /groups/umcg-wijmenga/prm02/data_projects/Gado/GeneNetwork_V2_01-02-2018/Covariates/PCA/pc-scores1588.txt.gz"
pcs <- fread(pc.file, data.table=F)
rownames(pcs) <- pcs[,1]
pcs ... |
ad1ed464e946c379b3c44ab5b129447aa6fc36cf18a46833600b33608d611807 | R | 7,655 | 211 | ---
title: "Quick_start.Rmd"
author: "Chenxin Li"
date: "2023-01-20"
output: html_notebook
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
This is a workflow for set/intersect visualization using [UpSet plots](https://github.com/hms-dbmi/UpSetR).
The upstream segme... |
deefd9820ceb4b6f8f8d3df19a4ce8f64449025e2ed70d1526ffe9512e7bc3ca | R | 7,669 | 350 | #' @tags globals
#' @tags manual
#' @tags sequential multisession multicore
library(future)
options(future.debug=FALSE)
message("*** getGlobalsAndPackages() ...")
FutureGlobals <- future:::FutureGlobals
globals <- structure(list(a = 1), where = list(a = globalenv()))
globals <- FutureGlobals(globals, resolved = TRU... |
63dd8b167d01583a082db2bd7560a26a37703a22ba25681f22bf1d84c1100d7a | R | 7,775 | 161 | #!/usr/bin/env Rscript
################################################
################################################
## LOAD LIBRARIES ##
################################################
################################################
library(optparse)
library(ggplot2)
library(reshape... |
a71acf10ff0abbe5d14996efe3ecf365e96b13afa032c32d67f15ac5a945ed0e | R | 7,825 | 225 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("evaluating_pathways")} else {setwd("../evaluating_pathways/")}
source("../downstreamer_functions.r")
# Cached 2021-01-07 15:15
datasets <- read.downstreamer.batch("../data/final_paper_cov_correct", USE.CACHE = F)
datasets.raw <- read.downstreamer.batch("... |
a6ba5aacab984967b4b0f3fae33f03c916b8c0fa5aade03e28e0673413dd2a4a | R | 7,852 | 232 | ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
subjects <- readRDS(file.path(dir_data_misc, "HCP_S1200_LargeBalancedSample.rds"))
it... |
430c84b83a041cbb8dd45a5d2adfb484ff35640249e93533d19493876140dfe9 | R | 7,859 | 183 | # Organize (re-order, rename, group) the cortical and subcortical labels.
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
### `ParcMat` ------------------------------------------------------------------
# Read in Kong parcellation
parc_cii <- read_cifti(parc_cii_fname)
nVox <- nrow(parc_cii)... |
b2675809369c404bee0f081dff358c9220e581e6796ee6a0beb64d8a188eaac1 | R | 7,876 | 228 | #' Insulation matrix plot.
#'
#' Display a Hi-C matrix with insulation scores annotated in the margins.
#'
#' @param exp1 A GENOVA \code{contacts} object.
#' @param exp2 A GENOVA \code{contacts} object compatible with \code{exp1}
#' (optional) or \code{NULL}. If not \code{NULL}, the \code{exp2} argument
#' will ... |
77b743f4b4bcfd44a5dbd9d4e8709fbe6f02c46d2c6760e9bf8bfa447d6892b8 | R | 7,886 | 206 | #' Plot delta distributions
#'
#' Plot the distribution of deltas
#' (i.e., the gap between the assignment score for the assigned label and those of the remaining labels)
#' across cells assigned to each reference label.
#'
#' @inheritParams plotScoreDistribution
#' @param chosen.only Logical scalar indicating whethe... |
e518965500ac37c73a15d2b5c5f07554dde04adf28d90008c142b20d859461cb | R | 7,922 | 194 | setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(eulerr)
set.seed(1)
humanSkeletal <- read.delim("heightVenn/humanSkeletal.txt")[,1]
mouseLethal <- read.delim("heightVenn/mouseLethal.txt")[,1]
mouseGrowth <- read.delim("heightVenn/mouseGrowth.txt")[,1]
ehlersDanlos <- read.delim("heightVenn/EhlersDanlos.tx... |
02158cd6d508e60b0d731fa0b60da2cb9a7d3dcd4df2c5b611fea65edb0e1085 | R | 7,936 | 220 | # This tests that combineRecomputedResults works as expected.
# library(testthat); library(SingleR); source("test-recomputed.R")
library(testthat); library(SingleR);
set.seed(10000)
# Making up data (using an uneven distribution to avoid symmetry masking problems).
ref <- .mockRefData(nreps=8)
ref1 <- ref[,1:4%%4==0... |
31d4e311096ce9b1a433bedae067661713d901899ebafa70f739192e0a183815 | R | 7,941 | 195 |
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 & ... |
824c20a2c72182d4c29ce7957436d996128d819a7730499a6b7bfcb47fccef73 | R | 7,944 | 272 | library(tidyverse)
theme_set(theme_bw())
theme_update(
panel.grid = element_blank(),
strip.background = element_blank()
)
library(patchwork)
library(ggrepel)
dat <- readRDS("data/cognition_model/dat.rds")
files <- list.files("results/cognition_model/simulation/summaries/",
pattern = "theta0.rd... |
e08ade651ebbbac462f7a9ad5e513db173f386b1235379b47ca7ffe688d0753a | R | 7,946 | 211 | ##########################################################################################
## Rare variant risk enrichment analysis of ASD PPI networks
## using gene-based scores and KS test
##
## Author: Yu-Han Hsu
##########################################################################################
rm(list=ls()... |
f735a841ef6ffb3899eaf7efc7e2f699f1f9b0b00ac887329dcdf58b96e6b56c | R | 7,981 | 253 | #' @title Summarizing GALAMM fits
#'
#' @srrstats {G1.4} Function documented with roxygen2.
#' @srrstats {G2.1a} Expected data types provided for all inputs.
#' @srrstats {RE4.11} Goodness-of-fit and other statistics associated such as
#' effect sizes with model coefficients.
#' @srrstats {RE4.18} Summary object.
#'
... |
741b1f754f604595f3e725d6e9d1a20019842ef3e1d332d4f0b16eded7488bb1 | R | 8,038 | 273 | ---
title: 'Analysis of effects of unilateral cortical inactivation on sound localization using logistic
regression '
output:
pdf_document: default
html_document:
df_print: paged
html_notebook: default
---
Version History:
* Created: 2021-09-11 Stephen Town
* Ported to notebook: 2022-05-16
Combine data... |
3d32cdfd26aaf0ba79aa617dc4c7610eae461d78168afe18b106f9e68c29bef4 | R | 8,043 | 166 | library(RISC)
library(irlba)
library(umap)
library(ggplot2)
library(RColorBrewer)
PATH = "/Path to the data/GSE114727"
###################################################################################
### Raw Data ###
###################################################################################
# BC09 rep1
c... |
2c01fc89d7e13b20d2bd23498dde9a5461cdee439d1e81f95a8d9b93228bf921 | R | 8,050 | 233 | # Get the expression matrix
# Sys.setenv(RETICULATE_PYTHON = "/home/rkawaguc/anaconda3/envs/BICCN/bin/python")
library("pROC")
library("reticulate")
py_config()
suppressPackageStartupMessages({
library("ggplot2")
library("SingleCellExperiment")
library("Seurat")
})
source_python("read_pickle_data.py")
l... |
325d065792c6729a10cf38d2f293a35ae9840ab24fedccb3caf046de642628b4 | R | 8,056 | 205 | #' DeconvoluteNet
#'
#' @description Deconvolution of geneset connectivity for each scHumanNet constructed
#'
#' @param network.list network list of edgelist, this is the output of SortAddLLS
#' @param geneset character vector of geneset it can also be a list of multiple geneset
#'
#' @return dataframe of that stores a... |
f91d43dc9bdcaca34e7e561e0664f989dce8447361b04ba695c5ec3677988821 | R | 8,136 | 242 | ####################################################################################
#' Dimension Reduction.
####################################################################################
#'
#' Based on highly variably expressed genes of the datasets, RISC calculates the
#' principal components (PCs) of the cel... |
d4beb7a882732d7e2de7ab898970b31c15b5cf52a84f0c21bd794f471589fd2e | R | 8,140 | 249 |
library(data.table)
library(arm)
source('../config.r')
source('../utils.r')
extra_path <- file.path('..', 'figure-2', 'inputs')
new_path <- file.path('..', 'figure-S2', 'inputs')
#' Get together all input files.
#' First the extra data files. We'll write a function for that.
#'
stack_wide2 <- function(DT)
{
DT_w... |
354abcb5eedc306fb9e1659268047dedcb7536f00e1d5ba8b57db8c26c8934d4 | R | 8,158 | 254 | ---
title: 'Analysis of effects of bilateral cortical inactivation on sound localization using logistic
regression '
output:
pdf_document: default
html_document:
df_print: paged
html_notebook: default
---
Version History:
* Created: 2021-09-11 Stephen Town
* Ported to notebook: 2022-05-16
Combine data ... |
9c8a743f48b5e576edcf910d2eefac0403a8ec9e54fc292f3b08c5816582d67b | R | 8,160 | 148 | #' Aggregate reference samples
#'
#' Aggregate reference samples for a given label by averaging their count profiles.
#' This can be done with varying degrees of resolution to preserve the within-label heterogeneity.
#'
#' @param ref A numeric matrix of reference expression values, usually containing log-expression val... |
9b48c8571b832a7dbd2f555a9b161419480c7a5cfeb3050d5ca2ab933e41c8bb | R | 8,169 | 288 | #' @tags relaying immediateConditions
#' @tags sequential multisession multicore
library(future)
options(future.debug = FALSE)
f_try <- function(f, ...) {
res <- tryCatch(..., error = identity)
if (inherits(res, "error")) {
label <- sQuote(f$label)
if (length(label) == 0) label <- "<none>"
msg <- spri... |
13f346e7ccf30e192bd761e39e72b27084f42e0d48c3ec46f8b7340afc1cd2e1 | R | 8,170 | 192 | ---
title: "Understanding Kurtosis-based PC selection"
author: "Damon Pham"
date: "1/20/2021"
output: html_document
---
# Setup
```{r}
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(6,1))
library(ggplot2)
```
```{r}
# Parameters ---------------------------------
nCC <- c(2, 5)[1]
withCblm <- c(TRUE, FAL... |
a9e94a4d192536b7050188b424e3a6e157a99c1d8693cf757ea3b7e0b79f2426 | R | 8,191 | 201 | library(MASS)
library(cowplot)
library(data.table)
source("/nfs/users/nfs_o/ob7/tools/rscripts/plotting_functions.r")
#source("downstreamer_functions.r")
output.prefix <- "../analysis/simulated_data/sim_data_300_genes"
dir.create(output.prefix, recursive = T)
# -------------------------------------------------------... |
bbbeff78520e2951c91b8294e5a6806338b2ff4258e431b3bf1eea03006e1d35 | R | 8,200 | 197 | #load libraries
libs<-c("tidyverse","cowplot","data.table","reshape2", "broom", "ape", "ggtree", "readr","geiger","nlme","phytools","GGally","RColorBrewer","rr2","purrr")
sapply(libs, require, character.only=T)
setwd("/data/share/htp/TRNP1/paper_data/")
source("regulation/scripts/MPRA/MPRA_helper_functions.R")
####... |
292731d8be76d6872d97ba472f7f0ef128ea4af93b0cfe076d31adba984f4033 | R | 8,216 | 187 | ---
title: "Shuffled ranked genes of RRHO"
output: html_notebook
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
#load libraries
```{r}
library(tidyverse)
library(ggplot2)
library(ggfortify)
library(ggrep... |
00f4cb925a0ad2b0af489795793a83ea8883a9bef48928b9d4352cd8ae4f7a63 | R | 8,217 | 221 | library(tidyverse)
library(furrr)
library(patchwork)
library(latex2exp)
library(gghalves)
options(bitmapType = "cairo")
theme_set(theme_bw())
library(mgcv)
sim_params <- readRDS("data/ses_model/sim_params.rds")
files <- list.files("results/ses_model/simulation/summaries/",
pattern = ".rds", full.na... |
56b4008ace34c34e498b4c6c6b146f04b301758299da328ec84681d2518166a8 | R | 8,217 | 228 | library(MungeSumstats)
library(tidyverse)
library(tabulizer)
library(fuzzyjoin)
library(plotgardener)
data(sumstatsColHeaders)
setwd("/Users/sheaandrews/Dropbox/Research/PostDoc-MSSM/ADGenetics/")
chia_ss.raw <- read_tsv("~/Downloads/GCST90001390_buildGRCh38.tsv.gz")
# ~/Dropbox/Research/PostDoc-MSSM/ADGenetics/reso... |
44a1a399419126f5cc205d2b958850adff9a9f05f30b3043f8427fcc02ef5edb | R | 8,346 | 191 | ##
## Calculate DESeq2 results, generate tables, heatmaps, and gene set enrichment.
##
deseq2_compare = function(deseq_dataset, contrast = NULL, name = NULL, genome = NULL) {
suppressPackageStartupMessages({
library(magrittr)
library(dplyr)
library(tidyr)
library(glue)
library(DESeq2)
libra... |
224457e98bc3f3f5b37091cbee38b6981bc11cc3ed3a8923ef1d3a0fd84b2e14 | R | 8,354 | 193 | if(!require("BoolNet",character.only = TRUE)) install.packages("BoolNet")
if(!require("ggplot2",character.only = TRUE)) install.packages("ggplot2")
library("BoolNet")
library("ggplot2")
starttime <- Sys.time()
every_nth = function(n) {
return(function(x) {x[c(TRUE, rep(FALSE, n - 1))]})
}
update_geom_defaults("poin... |
96e4579c239fcc8460bbffe89bd3c5c4f991f5d1d27f7ce308ea0f22616a0610 | R | 8,413 | 291 |
getCorrelationCN <- function(CNVref, CNVcomp){
df_1 <- as.data.frame(approx(CNVcomp$Pos,CNVcomp$Mean, seq(min(CNVref$Pos,CNVcomp$Pos),max(CNVref$Pos,CNVcomp$Pos), by = 1), ties = "ordered"))
df_2 <- as.data.frame(approx(CNVref$Pos,CNVref$Mean, seq(min(CNVref$Pos,CNVcomp$Pos), max(CNVref$Pos,CNVcomp$Pos), by = ... |
448e3c2b6902f7efcb66f2ca9fd86445c5846421bd652dace99be86d442eb5cf | R | 8,414 | 251 | ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
## ---------------------------------------------------------------------------------... |
e794b0fe0b13392b089e6d5b6bbf7be12892574f8162e182b285c9b766ed08f6 | R | 8,428 | 186 | # Jags-Ymet-Xnom1grp-Mnormal-OHOHA.R
# Based on code accompanying the book:
# Kruschke, J. K. (2015). Doing Bayesian Data Analysis, Second Edition:
# A Tutorial with R, JAGS, and Stan. Academic Press / Elsevier.
# Adapted for steady-state rate specific analysis for the OH family of tasks.
source("../DBDA2E-utili... |
6fc95a6a2856ed292ab32ec59ee961f8c357c384cea9d2347a620a9d514bc7ab | R | 8,444 | 251 | ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
## ---------------------------------------------------------------------------------... |
d76daeb4a7a61c6ac909c8773df9d7d44c6bbedc2be2581f986ec0a5703129f6 | R | 8,465 | 193 | library(RISC)
library(irlba)
library(umap)
library(ggplot2)
library(RColorBrewer)
###################################################################################
### Prepare Data ###
###################################################################################
PATH = "/Path to the data/GSE85241_GSE81076_GSE... |
5c0bf79655cfad8c78a7db7f9ba23d99efe9e86ae177da446555435fcb2b7f2b | R | 8,479 | 252 | ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
subjects <- readRDS(file.path(dir_data_misc, "HCP_S1200_LargeBalancedSample.rds"))
it... |
92fe0870b8b2ea279aa674a40776afa0b76cf89fd7b1226efac8900d87a5c196 | R | 8,492 | 283 | compute.pairwise.alignment <- function(
reference_profile,
query_profile,
reduced_dim = 50,
log_transform = FALSE,
deflate = TRUE,
seed = 0
) {
set.seed(seed)
if (log_transform == T) {
if (max(query_profile) > 100) {
query_profile = log1p(query_profile)
}
if (ma... |
a3ee02806578d28fd2b4f4b38bf9819650b75deb64e946601e6723938ea8f8ed | R | 8,516 | 321 | #' @tags resolve
#' @tags list listenv environment
#' @tags sequential multisession multicore
library(future)
library(listenv)
options(future.debug = FALSE)
sleep <- function(n) Sys.sleep(0.01*n)
strategies <- supportedStrategies()
message("*** resolve() ...")
message("*** resolve() for default ...")
x <- 1
y <- ... |
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