sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
cbfcfee98dcbc427853b2eaa7e93b8db1f07479a28974e385216f34430a1c496 | R | 1,285 | 39 | #' Helper function to get normalized spline basis function matrix
#'
#' @description Takes in coordinates and degrees of freedom generates
#' the normalized spline basis function matrix using the `mgcv` `smoothCon`
#' function.
#'
#' @param coords data frame or matrix of coordinates (either 1D or 2D)
#'
#' @param df de... |
50904c73615b046ff0ca86faa32f10be4fffaf38aa6f3f60d3ffd466829f4b02 | R | 1,287 | 55 | ---
title: "Venn diagrams"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
In this document we show the Venn diagrams of the methylated genes captured by MeRIP replicates.
```{r, echo = FALSE, warning=FALSE, message = FALSE}
library(VennDiagram)
library(RColorBrewer)
libr... |
f93bd229cac2958d5569d5330f7fc03555256a642cc10e58a7162c5d135e98d9 | R | 1,290 | 34 | source("R/setup.R")
# read the configuration file
config <- yaml::read_yaml('config/config.yaml')
# load data from csv (produced by R/compute_metrics.R)
file <- "evaluation/tables/error_metrics.csv"
error_metrics <- readr::read_delim(file,
delim = ";",
... |
5c1c3bf6a94d8eb0e68b3415beb567ab7d5357137cc68595d5edaa777bb0e072 | R | 1,293 | 41 | #!/usr/bin/R
# -------------------------------------
# Set up a matrix for the differential
# expression runs using Nebula + RUV
# Only for the selected gene sets
# Updated: 02/23/22
# -------------------------------------
library(cbrbase)
set_proj('DEVTRAJ')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
# -... |
5da9650543d87fb2a0b90420b69db06b8ef1907c0c5663fc5d990858969c2377 | R | 1,295 | 42 | #!/usr/bin/R
# ------------------------------------------------------
# Deidentify the supplementary metadata for multiregion:
# Updated: 12/13/23
# ------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
# Librari... |
ce914d159df4e515354d3d274831eff5eee6551d5846af731face7ee304140fb | R | 1,295 | 38 |
#' archstabms_coupling_heatmap_datasets
#'
#' Coupling scatterplots for multiple datasets.
#'
#' @param dataset_names character vector of dataset names (required)
#' @param base_dir Base directory (required)
#' @param output_dir Output directory (required)
#' @param stagenum stage number (required)
#' @param execute w... |
088ef8653ee96cfb4b50a7436026fae833f6ed4184b56a8437b51a02881964bd | R | 1,301 | 41 | test_that("get analyser object", {
refGen = get("Trace_Analyser")
ana = refGen$new()
expect_equal(ana$ana_name, "Trace")
expect_equal(length(ana$plots), 0)
})
test_that("generates all plots and plot data", {
yaml = get_testyaml_object("dir", analyser = "Trace", changes = NULL)
ana = get_analyser_object("T... |
e00f7e6872cbba72d1846e4c7f6a86f9be0622e6c4fd76937271fdd05ad8ea39 | R | 1,301 | 35 | rm(list=ls())
data<-read.delim("/Users/cazaledl-admin/Library/CloudStorage/OneDrive-UniversityofBirmingham/Post_doc/XP/data/courtscape/Data_courtscape/.txt",h=T,sep="\t",na.strings = "NA")
attach(data)
library(beeswarm)
library(conover.test)
boxplot(data$CI[which(data$behav_before_threat=='orientated')],
... |
febbeb32ed8f2b2af81b187bb30e4d71db3389f0d010766d3ce6f8656679bae0 | R | 1,305 | 27 | ###relatedness matrix heatmap and ibs matrix pca plot
#Relatedness_matrix_heatmap
setwd("C:\\Users\\qjp\\Desktop\\Manuscript\\WGS\\data")
library(pheatmap)
library(magrittr)
library(ggplot2)
cc1 = grDevices::colorRampPalette(c("darkblue","white","red") %>% ggplot2::alpha(0.7))
data <- read.table("allChr.varian... |
c03ef598f7da24df161cdd00f66eae0affb71e3e1304b20afadd03a4435f9228 | R | 1,306 | 35 | library(data.table)
library(spacexr)
library(Matrix)
library(dplyr)
library(tidyr)
library(tibble)
library(spASE)
PATH <- '../inst/extdata/visium/cerebellum_spatial'
counts <- fread(file.path(PATH, 'tagged_bwt2_counts_nm3.csv'))
positions <- fread(file.path(PATH, 'tissue_positions.csv'))
maternal_counts <- counts |>
... |
0ef2d1b16543a940090cd98232b27392cd5d636c80920458d2731aef6687bf6c | R | 1,308 | 42 | library("MAST")
library("edgeR")
setwd("./Step6.DEG")
# load the SCA objects
scaObjs <- readRDS('scaObjs.rds')
for (i in 1:length(scaObjs)) {
CellType <- names(scaObjs)[i]
Sca = scaObjs[[i]]
x = scuttle::summarizeAssayByGroup(x=assay(Sca),
ids = Sca@colData$orig.ident,
... |
17ec2124543e248672e1d7612d3d836d68e5b6223a0f3bebe29584c0ce3fc6f1 | R | 1,310 | 60 |
#### Slice a spectral image ####
## ------------------------------
sliceImage <- function(x, i = features(x, ...), ...,
run = NULL, simplify = TRUE, drop = TRUE)
{
if ( is.null(run) ) {
coord <- coord(x)
runs <- run(x)
xi <- spectra(x)[i,,drop=FALSE]
} else {
if ( !is.character(run) && !is.factor(run) )
... |
abbe735c2644d042324af6ad24cb9ea2f407be00d7f7a92e1647416d06c2457e | R | 1,311 | 33 | test_that("get_spm_status does not treat marker-only runs as complete", {
td <- tempfile("spm_status_")
dir.create(td, recursive = TRUE, showWarnings = FALSE)
on.exit(unlink(td, recursive = TRUE, force = TRUE), add = TRUE)
writeLines(as.character(c(Sys.time(), Sys.time())), file.path(td, ".spm_complete"))
s... |
2be3d135c84594f19983b41af210f344acfc82444236c28754a93716478c3351 | R | 1,312 | 51 | library("Seurat")
library("tidyverse")
setwd("./Step1.DataIntegration/SeuratObj.Ind")
SeuratObjs <- list.files()
# load the files
Seurat.list <- lapply(SeuratObjs, readRDS)
# Merge into one single Seurat object
Seurat.SNC <- merge(x = Seurat.list[[1]], y = unlist(lapply(2:8, function(x){Seurat.list[[x]]})))
rm(Seura... |
d23116ecf8d2b65cd73726da8c7a72d8d33d441dd5154ac74bf931f8befc5433 | R | 1,312 | 45 |
#' archstabms_binding_plots_datasets
#'
#' Coupling scatterplots for multiple datasets.
#'
#' @param dataset_names character vector of dataset names (required)
#' @param base_dir Base directory (required)
#' @param output_dir Output directory (required)
#' @param stagenum stage number (required)
#' @param colour_schem... |
6fa3eba61d5c09bc3c60d343540c43c15109dd3d6ec34387fc62771d5727b2b7 | R | 1,314 | 23 | library(dplyr)
dat <- read.table("C:\\Users\\qjp\\Desktop\\POE_AGE_num_direction.txt", header = T)
dat$Group <- factor(dat$Group, levels=c("Pl_F70","Pl_F40","Mu_D168","Mu_D1","Mu_F70","Mu_F40","Li_D168","Li_D1","Li_F70","Li_F40","Br_D168","Br_D1","Br_F70","Br_F40"))
dat1 <- filter(dat, Class=="Maternal"|Clas... |
700de90c78a0e3a2bb107383e969d112e6c0cd1e4ab65ffa12e8d8bd8180ee05 | R | 1,317 | 46 | setwd("/path/")
wes_results = read.csv('WES_results.csv')
cMAC <- c(seq(1, 50, 5),seq(50, 350, 50))
cBETA <- wes_results$BETA_Burden
cSE <- wes_results$SE_Burden
ratio = cBETA/cSE
indx = order(abs(ratio))
#print(ratio[indx])
cBETA = cBETA[indx]
cSE = cSE[indx]
N <- 30620
set.seed(2024)
power <- data.... |
80da995b11044cf0b307eeab808c2eb206a90e78ec52c1f9ad46d983459bb954 | R | 1,321 | 47 | #!/usr/bin/env Rscript
# Simple helper script to iterate over a file containing
# pipeline-formatted genotype columns and returning the
# best non-reference genotype
####Set parameters & read args
options(stringsAsFactors=F,scipen=1000)
args <- commandArgs(trailingOnly=T)
if(length(args)!=2){
stop("Requires two ... |
3bc595c9a7130bc3936359ed1a552792eecba55cea8194b87c1527205eceffcc | R | 1,333 | 33 | ##针对Original genome peaks 的annotation
setwd("F:\\Manuscript\\ATAC\\data")
library(GenomicFeatures)
require(ChIPseeker)
library(clusterProfiler)
#txdb <- makeTxDbFromBiomart(biomart="ENSEMBL_MART_ENSEMBL",dataset="sscrofa_gene_ensembl",host="www.ensembl.org")
txdb2 <- makeTxDbFromGFF("F:\\Resource\\Sus_scr... |
2dab44032dfa9308af643b055cc8108d90884e2ae3943c6a3577fc5abe567d05 | R | 1,339 | 38 |
#' Run Shiny ÉPICo!
#'
#' ShinyÉPICo! Interactive minfi and limma pipeline for Illumina methylation arrays
#'
#' @param n_cores Number of cores to be used in parallelized operations in the application. By default, half of your CPU cores. Parallelization affects only to mean and differences calculation and it does no... |
b135313646b27320cc3c6e850dad3547fa661ea34243f11475f090f6a5c5a3cc | R | 1,344 | 43 | #! /usr/bin/env Rscript
library("caret")
args <- commandArgs(TRUE)
##inputs##
##Note file header should have with SVID,Status,Metric1...MetricX###
##Metric name is unique to the test and can describe the test statistic##
##Status in train file should indicate whether variant is passing or failing##
trainfile=args[1... |
1045586da90eaea0d1693164633e032695ccaac42ff6da844fe88429b5204c53 | R | 1,349 | 49 | #' @name DEPRECATED-raster
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Find an evenly spaced grid for x
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and wi... |
e94b9efa3677c6158c5b0c1b74658edd36f28900b7a2d5b4d1b93f6b05c50be0 | R | 1,350 | 39 | #!/usr/bin/R
# --------------------------------------
# Set up a matrix for DE runs for SI/ACE
# Updated: 10/05/22
# --------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
# ----------------------------------------------------
# List all o... |
f204360c156b480a1cee07ded61046aa901e092132cedefc0962bb05d7cfd777 | R | 1,351 | 25 | setwd("/media/hansruedi/3b32f153-0651-4d56-b863-9b070ac7dd55/home/hansruedi/Brain_region_final")
library(ggplot2)
library(Seurat)
pbmc <- readRDS(file = "In_raw_mscores_integrated2.rds")
addition=read.table("Inh_Metadata_addition_cell_type_highres.txt",header=TRUE,row.names=1,sep="\t")
pbmc=AddMetaData(pbmc,ad... |
af14d510ef85acf0bb8f25a714fd82f4874a2009abcf136a13c8909fec11e233 | R | 1,357 | 30 | # first argument is the chunk of params to run (1-18)
library(spacexr)
library(spASE)
devtools::load_all()
args <- commandArgs(trailingOnly=T)
source('../R/spase_sim_utils.R')
myRCTD <- readRDS('results/results_celltype_cere_3_df_5.rds')
params <- read.delim('simulations_cere3_params.tsv')
df <- 5
cell_types <- c('... |
f7045d2f793a79af8f168fe40ebffb2f0f7358e11c58fe44368f8e3e72b5496d | R | 1,361 | 41 |
#' archstabms__plot_mutation_distributions
#'
#' Plot mutation distributions.
#'
#' @param input_dt data.table with fitness estimates (required)
#' @param report_outpath output path for scatterplots (required)
#' @param dataset_name dataset name and suffix for output files (required)
#' @param colour_scheme colour sch... |
5e65dfa09b3654ca2cb2ecc68ab99b2923c87ac0a871493594d8c09444e123c6 | R | 1,362 | 36 | require(GenomicSEM)
library(data.table)
NAME <- "MetS"
munge.dir <- "../1_munge/"
ldsc.output.dir <- "./ldscout/"
ld_weights <- "./eur_w_ld_chr/"
logs.dir <- "./logs/"
dir.create(file.path(logs.dir), showWarnings = FALSE)
BMI_GIANT18 <- paste0(munge.dir, "BMI_GIANT18.sumstats.gz")
WC_Watanabe_UKB <- paste0(munge.d... |
9fc474df09711ac0acd3666a86ee0d14ff97876a0850df0d5c384517be9cc8bb | R | 1,362 | 50 | #' @name DEPRECATED-fix_spc_colnames
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Ensure that the spectra matrix has the wavelengths in column names
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are pres... |
845f1d9ad0ba91f0c50185b5a1e5d3b26e90e7bd277d10606bf3bf5fdc98b466 | R | 1,365 | 33 | #' Install all packages in the scrattch suite
#'
#' @param packages A character vector of scrattch packages. If NULL (default), installs all packages. Available packages are:
#' \itemize{
#' \item "scrattch.io": File handling for RNA-seq data storage formats.
#' \item "scrattch.hicat": Hierarchical, iterative clusterin... |
26d62244ccd79be4372211b79cd1137f2101160b31b2c925431a0c1de6521ca3 | R | 1,380 | 34 | require(GenomicSEM)
library(data.table)
NAME <- "MetS_EVEN"
munge.dir <- "../1_munge/"
ldsc.output.dir <- "./ldscout/"
ld_weights <- "./eur_w_ld_chr/"
logs.dir <- "./logs/"
dir.create(file.path(logs.dir), showWarnings = FALSE)
BMI_GIANT18 <- paste0(munge.dir, "BMI_GIANT18.sumstats.gz")
WC_Watanabe_UKB <- paste0(mun... |
64892bdb6ccc2b5f1a97e1e4b9619b1f1d37ea7095668f53479d4a5d33671dfa | R | 1,381 | 38 | library(data.table)
library(dplyr)
df_bim <- fread("EAS.bim",
sep="\t", data.table = F, col.names = c("chr", "snp", "cm", "bp", "ref_1kg_eas", "alt_1kg_eas"),
nThread = 20) %>%
dplyr::select(snp, ref_1kg_eas)
### MetS GWAS KoGES
file_gwas <- "KoGES_MetS_logistic_plink.MetS.glm.... |
80bd7c5452a567c92dd4bd93b9fe06038f4bf45a3acb0d1f43fabef4720adfaf | R | 1,381 | 31 | library(tidyverse)
library(scibet)
# TPM divides counts by transcript length but with UMI-tagged data, it isn't necessarily true that longer length -> more counts. I'd recommend not dividing by transcript length for 10X data.
# Just don't divide by transcript lengths. Just take a gene's UMI count and divide it by the ... |
c783e4d5ce509a9c1f600d8333b03302258305381a6e75c3bb698d41dd9198c7 | R | 1,391 | 53 | ---
title: "DMP in Target Genes"
author: Szi Kay Leung
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
suppressMessages(library("dplyr"))
suppressMessages(library("DT"))
suppressMessages(library... |
163d6205f217b5f2979aa05a14409c6c1819c46064245447396f33dbb29dbafe | R | 1,404 | 41 | library('RColorBrewer')
library('MAST')
library('data.table')
library('Seurat')
library('biomaRt')
library('lme4')
setwd("./Projects/SNCSynuclein")
############ Convert the Seurat object to SCA obejct for Marker calculation #########
# load the seurat object
Seurat.SNC.final <- readRDS("Step5.FinalClustering/Seurat.S... |
1fb34db0a60969101e6881dbfecd891c2e0dc32ced309b615945bf6d0d7daa89 | R | 1,404 | 36 | require(GenomicSEM)
library(data.table)
NAME <- "MetS_ODD"
munge.dir <- "../1_munge/"
ldsc.output.dir <- "./ldscout/"
ld_weights <- "./eur_w_ld_chr/"
logs.dir <- "./logs/"
dir.create(file.path(logs.dir), showWarnings = FALSE)
BMI_GIANT18 <- paste0(munge.dir, "BMI_GIANT18.sumstats.gz")
WC_Watanabe_UKB <- paste0(mung... |
a1256f5e97359b218d4217df29fb452228f6682ebcf1d0048b565601d5f65be1 | R | 1,406 | 50 | require(testthat)
require(Cardinal)
context("summarize")
test_that("summarizeFeatures", {
path <- CardinalIO::exampleImzMLFile("continuous")
mse <- readImzML(path, memory=TRUE)
g <- makeFactor(A=mse$y==1, B=mse$y==2, C=mse$y==3)
mse <- summarizeFeatures(mse)
mse <- summarizeFeatures(mse, groups=g)
expect_equ... |
21e0e6bc01f1b09d3024c78643bc33d54b566c24a082aaa1dedecebbfaefcb32 | R | 1,413 | 39 | #' Main function for detecting spatial allele-specific expression (ASE) patterns
#' considering cell type mixtures.
#'
#' Fits the intercept-only model (only cell type effects) and the spatial
#' model (non-parametric thin plate smoothing splines)
#'
#' @description This function fits the spatial beta-binomial model wi... |
7709f17ff6b00d0b7daf0319e65f3b5d9bf564a44b60b1a093602d34b62251ea | R | 1,420 | 39 | #!/usr/bin/R
# --------------------------------------------------
# Load the regression estimates of fraction changes:
# --------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists('cellmeta')){
source(paste0(sbindir, 'load_metadata.R'))
}
# Load the regressi... |
8521494cba59f3dfe7b42ff658cbfe0716dc87cd6b91b57fd2f0d2b8f769a6f8 | R | 1,424 | 52 | ## Join PGS with covariates
## Load libraries
library(data.table)
## <--------------- MAIN
## Read GA4K PCs
get_pcs <- fread("/path/to/pcs.txt")
## Read GA4K sex
get_sex <- fread("/path/to/proband/sex.txt")
## Get PGS file names
get_prs_files <- fread("/path/to/prs_id.txt", header=F)[, V1]
## Get list of samples ... |
8850f4aee49758787a9385b4ade506861824ae00610c9995c9367c6c195da5ee | R | 1,425 | 33 | require(GenomicSEM)
library(data.table)
NAME <- "MetS_noUKB_EVEN"
munge.dir <- "../1_munge/"
ldsc.output.dir <- "./ldscout/"
ld_weights <- "./eur_w_ld_chr/"
logs.dir <- "./logs/"
dir.create(file.path(logs.dir), showWarnings = FALSE)
BMI_GIANT15_noUKB <- paste0(munge.dir, "BMI_GIANT15_noUKB.sumstats.gz")
WC_GIANT_no... |
884c4f1122001f9e696a0c98a67e6c2445ce4f9a8461e5261d072f826dbb4f65 | R | 1,427 | 57 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
cwd<-args[1]
inputFile<-args[2]
jobName<-args[3]
yLimit<-as.numeric(args[4])
setwd(cwd)
data<-read.table(inputFile,header=F,skip=1,sep="\t")
x<-data[,2]
if(ncol(data) == 4) {
y<-data[,4]
} else {
y<-data[,5]
}
xStart<-min(x)
xEnd<-ma... |
129e0694b8d7cf369e97f491f8d04b632f9683cb5d814bc724cfa7d0c3e5eca9 | R | 1,428 | 48 | suppressPackageStartupMessages({
require(argparse)
library(EFAtools)
library(data.table)
library(stringr)
})
parser <- argparse::ArgumentParser(description=":: Computes Kaiser-Meyer-Olkin criterion of a Genomic SEM phenotype set ::", formatter_class="argparse.ArgumentDefaultsHelpFormatter")
parser$add... |
bbe580c59679668481671520b35becd1f6232aa5b26e019d0ad6c7f1453694a8 | R | 1,428 | 32 | require(GenomicSEM)
library(data.table)
NAME <- "MetS_noUKB"
munge.dir <- "../1_munge/"
ldsc.output.dir <- "./ldscout/"
ld_weights <- "./eur_w_ld_chr/"
logs.dir <- "./logs/"
dir.create(file.path(logs.dir), showWarnings = FALSE)
BMI_GIANT15_noUKB <- paste0(munge.dir, "BMI_GIANT15_noUKB.sumstats.gz")
WC_GIANT_noUKB <... |
f61a5fa4cadd507cb540fe92873828816a778e497e8648d605e168f8bcc910dd | R | 1,438 | 43 | #' @title read a PLINK bfile file into a matrix
#' @param bfile PLINK bfile (as character, without the .bed extension)
#' @param extract SNPs to extract
#' @param exclude SNPs to exclude
#' @param keep samples to keep
#' @param remove samples to remove
#' @param chr a vector of chromosomes
#' @param fillmissing Whether... |
37e7e336cf68a8969819e273566a9e52af53651a460e2a01e3c3dc5eb5ddd182 | R | 1,439 | 43 |
#' archstabms_ddPCA_linear_model_datasets
#'
#' Biophysical model from shallow ddPCA data for multiple datasets.
#'
#' @param dataset_names character vector of dataset names (required)
#' @param seq_position_offset list of sequence position offsets (required)
#' @param base_dir Base directory (required)
#' @param outp... |
7d990970ee8f113d0f1671a5a9d43991625cb12e16859036b7cc94a22fb9fd08 | R | 1,444 | 68 | ---
title: "TimeSlots Guide for NosaAnalysis"
author: "Julia Thüringer"
date: '`r Sys.Date()`'
output:
html_document: default
pdf_document: null
vignette: >
%\VignetteIndexEntry{TimeSlots Guide for NosaAnalysis}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
### Description
```{r descrip... |
964a3353e7df1ebb43ff6ee2392e256ad854067e1d798a16454f80a7fa1508c1 | R | 1,444 | 43 |
#' archstabms_ddPCA_linear_model_datasets
#'
#' Biophysical model from shallow ddPCA data for multiple datasets.
#'
#' @param dataset_names character vector of dataset names (required)
#' @param seq_position_offset list of sequence position offsets (required)
#' @param base_dir Base directory (required)
#' @param outp... |
06ebabd0892dfb12f8ddd895c5750eab1465cbe1efa1022593c68777b963aee2 | R | 1,446 | 30 | test_that("finalize_pipeline_configuration defaults L3 outlier retry toggle to TRUE", {
finalize_path <- source_tree_file("R", "finalize_pipeline_configuration.R")
lines <- readLines(finalize_path, warn = FALSE)
expect_true(any(grepl("auto_retry_l3_excessive_outliers\\s*=\\s*TRUE", lines)))
})
test_that("finali... |
1973d1abfb5d7c5b0cc584b3c621044fb78a164fce3debd95bffb9b52fadfb07 | R | 1,447 | 33 | require(GenomicSEM)
library(data.table)
NAME <- "MetS_noUKB_ODD"
munge.dir <- "../1_munge/"
ldsc.output.dir <- "./ldscout/"
ld_weights <- "./eur_w_ld_chr/"
logs.dir <- "./logs/"
dir.create(file.path(logs.dir), showWarnings = FALSE)
BMI_GIANT15_noUKB <- paste0(munge.dir, "BMI_GIANT15_noUKB.sumstats.gz")
WC_GIANT_noUK... |
32f9a8cb7f82abfae90378a0de3e6555937f3d34347ba10ccdb5892667880e2c | R | 1,453 | 45 | require(testthat)
require(Cardinal)
context("crossValidate")
test_that("crossValidate", {
set.seed(1, kind="L'Ecuyer-CMRG")
ms <- simulateImage(preset=4, dim=c(10L, 10L), nrun=2,
centroided=TRUE)
ms$class <- makeFactor(A=ms$circleA, B=ms$circleB)
ms$folds <- makeFactor(
fold1=run(ms) %in% c("runA1", "runB1")... |
20ea838ee787ee86e2d9eab7af2ce9489923be8d92d1cae56074c0e021b40849 | R | 1,464 | 59 | # Plot the cluster validity index for each k to produce the elbow plot.
# Output:
# curr_lab,'.jpg' Cluster validity index elbow plot.
#Load packages.
library(tidyverse)
library(reticulate)
library(ggplot2)
library(gridExtra)
#Set virtual environment.
use_virtualenv('../envs/FlexEnv')
#Load python packages.
h5py <- ... |
ba2cd93d024abb0b0e68403268e6676ac2966a823769c5195adce55c19cfd884 | R | 1,466 | 25 | test_that("run_feat_sepjobs source wires L3 execution to outlier retry wrapper", {
run_feat_path <- source_tree_file("R", "run_feat_sepjobs.R")
lines <- readLines(run_feat_path, warn = FALSE)
expect_true(any(grepl("feat_retry_binary <- system.file\\(\"bash/run_feat_with_outlier_retry\\.sh\"", lines)))
expect_t... |
78aadaa6a2e8cc891c8cf28a04e59e454650f60984d24acf91b2161bd62e8993 | R | 1,475 | 34 | # ********
# 1a-integration.r
# Code for preliminary integration and clustering of the data by subclone.
source("Analysis/util.r")
library(Seurat)
library(dplyr)
library(ggplot2)
library(magrittr)
library(data.table)
x = readRDS('Data/Processed/alphasyn-triplication-initial-filtered-seurat-wo13.rds')
x$percent.mt <- ... |
8de22be8918a903b761786e336af337e9e26b574a89d9f0846ff1760ff66b1a3 | R | 1,484 | 44 | library(tidyverse)
library(DESeq2)
# Read in the raw count pipeline output
data <- read_tsv("~/subread_counts_gene_symbol.txt")
# Clean the data (get rid of lowly expressed genes, make gene names unique)
data_clean <- data %>%
filter(rowSums(.[,-1]) != 0) %>%
mutate(GeneName = make.unique(GeneName)) %>%
column_... |
f8b5a5dc5d2ee80e3be613ca6eab6d43fe548cc1e2856d59d734615ed1f7d7c2 | R | 1,490 | 48 |
#### Perform cross-validation ####
## -------------------------------
crossValidate <- function(fit., x, y, folds = run(x), ...,
predict. = predict, keep.models = FALSE,
trainProcess = peakProcess, trainArgs = list(),
testProcess = peakProcess, testArgs = list(),
verbose = getCardinalVerbose(), chunkopts = list()... |
b87efd133bd890cf7ff92b79dd34bbca374d3ad688e59dacc7434ea1ea9d252d | R | 1,491 | 50 | #!/usr/bin/R
# ----------------------------------------------
# Make average pseudobulk profiles (for website)
# Updated: 04/29/2022
# ----------------------------------------------
# chunksize=25
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
library(rhdf... |
a1a43e94575db0182cb21daba668278b3f234d1613caf67ab7afdb3ffe8f0422 | R | 1,493 | 65 | ####################################################################################################
###
### File: 05_functional_data_analysis.r
### Purpose: Create a simple Functional data analysis model to compare treatments
### Authors: Gabriel Rodrigues Palma
### Date: 04/04/23
###
###########################... |
0c9e7f85bb4806df66997af1aea63d4b85610b209ea63d85464355d54d7841b4 | R | 1,495 | 26 | library("dplyr")
blastOutput <- read.table("/lustre/projects/Research_Project-MRC148213/lsl693/rTg4510/G_Merged_Targeted/4_characterise/BDRBlast/allIsoOntBDRHigh.txt")
blastOutput <- read.table("/lustre/projects/Research_Project-MRC148213/lsl693/rTg4510/G_Merged_Targeted/4_characterise/BDRBlast/allIsoOntBDRLow.txt")
c... |
3c5d638e717057fc46683944259111c619cb8e1b1fca76ae632adcded002cf79 | R | 1,495 | 60 | require(GenomicSEM)
library(data.table)
require(Matrix)
require(stats)
NAME <- "MetS"
N_FACTOR <- 3
ldsc.out <- paste0("../2_ldsc/ldscout/", NAME, "_ldscout.RData")
loadRData <- function(file){
load(file)
get(ls()[ls() != "file"])
}
ldscout <- loadRData(ldsc.out)
ROTATION <- "promax"
EFA.log <- paste0(N... |
186b680e5bbbbc8ab0cab262c31c50637ddf4f1e13c4639e12b58856ec24d7bc | R | 1,497 | 59 | require(GenomicSEM)
library(data.table)
require(Matrix)
require(stats)
NAME <- "MetS_ODD"
N_FACTOR <- 3
ldsc.out <- paste0("../2_ldsc/ldscout/", NAME, "_ldscout.RData")
loadRData <- function(file){
load(file)
get(ls()[ls() != "file"])
}
ldscout <- loadRData(ldsc.out)
ROTATION <- "promax"
EFA.log <- paste0... |
73e98d13d6ae952ac81c58268a8e4c24a240a60ecd19e945e3a5afe54d8acb84 | R | 1,501 | 46 | library(Seurat)
library(SeuratDisk)
library(ggplot2)
library(patchwork)
setwd("/media/hansruedi/3b32f153-0651-4d56-b863-9b070ac7dd55/home/hansruedi/Allen_Institute_06142020")
library(Seurat)
pbmc=readRDS("SeuratObject_AllenData.rds")
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.fact... |
01db449a4f45fabd7483c295c78edee4290f22600e35142e4c5a741126e0de0a | R | 1,502 | 58 | require(GenomicSEM)
library(data.table)
require(Matrix)
require(stats)
NAME <- "MetS_noUKB_ODD"
N_FACTOR <- 3
ldsc.out <- paste0("../2_ldsc/ldscout/", NAME, "_ldscout.RData")
loadRData <- function(file){
load(file)
get(ls()[ls() != "file"])
}
ldscout <- loadRData(ldsc.out)
ROTATION <- "promax"
EFA.log <- ... |
3d40380de3f5ac94302ea5ae4bf434d563a14f11a3e985eb61f95c2c08c1e5ca | R | 1,502 | 29 | class.files$targ_qc %>% filter(isoform %in% class.files$targ_filtered$isoform)
junc.files.targ_qc <- data.table::fread(paste0(dirnames$targ_root, "/3_sqanti3/reRunValidation/all_iso_ont_collapsed_junctions.txt"), header = T)
class.files$targ_qc <- class.files$targ_qc %>% filter(isoform %in% class.files$targ_filtered$... |
8a62336a648a9bf16c7e0fbe96687d395dde17c1776169338974b4de25b21c20 | R | 1,504 | 16 | utils::globalVariables(c("Abstract", "AlternateName", "Author", "AuthorAddress", "Authors", "ComponentID",
"Custom 1", "CustomId", "DOI", "ISBN/ISSN", "Key", "Keywords", "Label", "Manual.Tags",
"Name of Database", "Number", "Order", "Pages", "PdfRelativePath", "Publicat... |
97133fc1f207edab166ad932adce240f4c6d9349372bbb006af9b81f283c0bc8 | R | 1,505 | 43 | log <- file(snakemake@log[[1]], open = "wt")
sink(log)
sink(log, type = "message")
suppressPackageStartupMessages(library("DESeq2"))
message(paste("log is", log))
parallel <- FALSE
if (snakemake@threads > 1) {
library("BiocParallel")
# setup parallelization
register(MulticoreParam(snakemake@threads))
p... |
522a7123654dcab5038776c7323250ca742c6a731ee059bb6294a81f25d15c9b | R | 1,507 | 48 | # Auxiliary plotting settings:
library(ComplexHeatmap)
library(circlize)
# Global parameters for heatmaps:
# -------------------------------
ht_opt$heatmap_row_names_gp = gpar(fontsize = 5)
ht_opt$heatmap_column_names_gp = gpar(fontsize = 5)
ht_opt$heatmap_row_title_gp = gpar(fontsize = 5.5)
ht_opt$heatmap_column_tit... |
043da4d49c0d21d4886f0d7743de97ac3e954918f85dc48b2715ce61ee503a31 | R | 1,508 | 52 | test_that("run_glm_pipeline tolerates l1 none selection", {
gpa <- create_mock_gpa(include_l1_models = TRUE, include_l2_models = TRUE, include_l3_models = TRUE)
gpa$multi_run <- TRUE
gpa$output_locations$sqlite_db <- NULL
gpa$parallel <- modifyList(gpa$parallel, list(
finalize_time = "0:10:00",
l1_setup... |
f6cfebeb69e00cf57572ff12baf735688714415283fc8ef313936849b0bc4fab | R | 1,510 | 55 |
#' Sorting the wavelengths of a `hyperSpec` object
#'
#' @description
#' Rearranges the `hyperSpec` object so that the wavelength vector is in
#' increasing (or decreasing) order.
#'
#' The wavelength vector is sorted and the columns of the spectra matrix are
#' rearranged accordingly.
#'
#' @param x The `hyperSpec` o... |
4c711cce63499a780923109092988fb19c4fd5083c900e781e8636c27c7e1073 | R | 1,512 | 43 |
#' archstabms_ddPCA_singlebackground_linear_model_datasets
#'
#' Biophysical model from shallow ddPCA data for multiple datasets.
#'
#' @param dataset_names character vector of dataset names (required)
#' @param seq_position_offset list of sequence position offsets (required)
#' @param base_dir Base directory (require... |
81bb0335f9a0486b8d9273d95e1e733c89cf08d58515f92feca202678df0c591 | R | 1,514 | 44 | library(data.table)
library(dplyr)
# IV list
file_in_iv <- "MetS_exposure_IV_list.csv"
df_iv <- fread(file_in_iv, sep=",", data.table = F, nThread = 10)
iv_list <- df_iv$SNP
# Outcome list
file_in_mr_result <- "tsmr_best_sig_result.csv"
df_mr_result <- fread(file_in_mr_result, sep=",", data.table = F, nThread = 10,
... |
f1a273658645f1d1580947470f8e3073c4bd9dc8f69bd82f5eb72153a5b20c44 | R | 1,514 | 53 | library(MEGENA)
library(Seurat)
library(ggplot2)
library(cowplot)
library(Matrix)
n.cores <- 14;
doPar <- TRUE;
method = "spearman"
FDR.cutoff = 0.05
module.pval = 0.05
hub.pval = 0.05
library(DAseq)
library(ggpubr)
cluster_da<-readRDS("cluster_da9.rds")
cnt=cluster_da@assays$RNA@counts
min.cells = ncol(cluster_da)... |
0706a2684c0f2af8b9c34dc786f3534ab6754ab6115817a4326cd3e9da6f0397 | R | 1,518 | 40 | #Library
library(TSA)
library(seewave)
#read raw and dfy
data = read.csv("AB_7dpf_fedeggyolk2days_hindgut20X_G008_Anus_plot_10A.csv") #Replace Fish ID and ROI#
raw <- data$raw
dfy <- data$dfY
#Verify plots
plot(raw, type = "l", lty = 1, lwd = 1, col = "red")
plot(dfy, type = "l", lty = 1, lwd = 1, col = "red")
#Run ... |
06482b76f79bad2059c0c64efa78bfb930b6da8afbff8832ee40ba7b01671f93 | R | 1,524 | 46 | library(dplyr)
library(logger)
library(purrr)
library(yardstick)
## params to specify #####
phenotypes = snakemake@params[["phenotypes"]]
linear_model_res_path = snakemake@params[["input_res_path"]]
phenotype_suffix = snakemake@params[["phenotype_suffix"]]
out_dir = snakemake@params[["out_dir"]]
phenotypes <- phenot... |
5eb638065cda2e78a8e79fec74e19af1e2654f65138b6174f911e803541bf297 | R | 1,530 | 44 |
#' archstabms__reachable_single_AA_mutants
#'
#' Determine single AA mutants reachable from single nucleotide substitutions.
#'
#' @param wt_coding_seq WT coding sequence string (required)
#' @param position_offset residue position offset (default:0)
#'
#' @return List of reachable mutant IDs and a dictionary mapping ... |
320bd06c1bbd1b9cae9157b47974c1a0a7b5252fa174c4e568b5f9439d3d1090 | R | 1,531 | 47 | # on cluster, initiate qlogin session with 16 cores.
# qlogin -q trapnell-login.q -l mfree=4G -pe serial 16
# start R (4.0.0)
basepath = "/net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2022_03_24_reprocessHeartAlone_nobackup/analyze_out/"
out_path = "/net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results... |
7aaeac3fdb91170df7374df4fbc3dd5d07615892201fa7ca7d381e375891b6e3 | R | 1,536 | 23 | library(data.table)
library(dplyr)
for(tis in c("Lung", "Muscle")){
for(dir in c("m6A2me", "me2m6A")){
DNAme_enrichment <- fread(paste0("~/Downstream_analysis/Chrom_states_enrichment/", tis, ".DNAme.", dir, ".txt"), sep = "\t", header = FALSE, col.names = c("Chrom_states", "signif", "background")) %>%
mutate(pro... |
fcd8cab86a6f08d145f45bc7f772a7c55f24d64028618f273bff3c691145f674 | R | 1,548 | 42 | setwd("F:\\Manuscript\\ATAC\\data")
library(dplyr)
dat <- read.table("AllPeakList.merged.count.txt", header = T, row.names = 1)
datFilter <- select(dat, !contains(c("DS70_Li3","LS168_B_Br1","LS168_B_Li1","LS168_B_Mu1","LS168_C_Br2","LS168_C_Li2","LS168_C_Mu2","DS168_M_Br1","DS168_M_Li1","DS168_M_Mu1")))
#TPM (T... |
73e61aaefa40d014449977012e8b1b1e4083b6a4454cf99f6489d0b62984d038 | R | 1,559 | 57 | scrattch_dependencies <- list(
hicat = list(bioconductor = c("impute",
"limma",
"preprocessCore",
"WGCNA"),
github = c("JinmiaoChenLab/Rphenograph")),
io = list(bioconductor = c("rhdf5")... |
14d990d3258181ba93b4713e42cb2a7ff817d711a050020cd71bfac9a96cff48 | R | 1,560 | 60 | require(GenomicSEM)
library(data.table)
require(Matrix)
require(stats)
NAME <- "MetS_noUKB"
N_FACTOR <- 3
ldsc.out <- paste0("../2_ldsc/ldscout/", NAME, "_ldscout.RData")
loadRData <- function(file){
#loads an RData file, and returns it
load(file)
get(ls()[ls() != "file"])
}
ldscout <- loadRData(ldsc.o... |
c3ec6278475eb192fdb26a5965382cde9296ab4d044ca78141628805aa3f32f6 | R | 1,560 | 54 | #!/usr/bin/env Rscript
# Helper script to count median # of filtered variants per trio
# for minGQ optimization filtering workflow
###Set master parameters
options(stringsAsFactors=F,scipen=1000)
################
###RSCRIPT BLOCK
################
require(optparse)
###List of command-line options
option_list <- li... |
fd029683be74efcdc51a378357830b3955ad18cef52ecee98ff032fbf148f9df | R | 1,564 | 42 | #!/usr/bin/env Rscript
# Helper script to return list of outlier samples from an svcounts.txt file
# an example of svcounts.txt these columns: svtype count
# CutoffFile should include these columns: algorithm svtype lower_cff higher_cff
# Read command line arguments
options(stringsAsFactors=F)
args <- c... |
2e202ef7b4402413461fc82e4ac27a61db344e6a2d59d0e7d215a5a6143be9de | R | 1,569 | 42 | library(ggplot2)
library(tidyverse)
## For each dataset
fig.path <- "/home/pauling/projects/04_SEmodel/07_NC_revision/02.figures/01.Classification/01.10x"
res <- readr::read_rds("/home/pauling/projects/04_SEmodel/07_NC_revision/01.res/01.featureSelection/01.10x.classification/02.5.cell.line.rds.gz")
res <- Reduce(rbi... |
26dbe05f3b564af82a7f9c2d92df8f120ea87412cc7b3baf326b985daa1939f8 | R | 1,575 | 55 | library("Seurat")
library("biomaRt")
setwd("./Projects/SNCSynuclein")
mart <- useMart(biomart = "ENSEMBL_MART_ENSEMBL",
dataset = "hsapiens_gene_ensembl")
ProteinEncodingGenes <- biomaRt::getBM(attributes = c("hgnc_symbol",
"transcript_biotype"), ... |
47b2fb24390752ff26eb30ef40d08e9704dd6fda3443f8860e550b72f34c7522 | R | 1,575 | 38 | GeneCodon <-
function(seq,organism="ec",max=TRUE,scale=0.5,numcode= 1){
if(sum(s2c(toupper(seq))%in%c("A","T","G","C")) != nchar(seq)){
stop("The input sequence must be nucleotide sequence only containg 'ATGC'")
}
#data(wSet)
load("input/wSet.rda")
assign("translate",seqinr::translate,envir=... |
fa18141431f4f3e25fd06573ad87be76b83165b03f08229eb929639201f8b0fa | R | 1,580 | 41 | #' @title Performs `pseudovalidation' to select the best \eqn{\lambda} value in
#' lassosum (without PLINK bfile)
#'
#' @param genotype.mat A genotype matrix (coded 0/1/2)
#' @param beta The matrix of estimated \eqn{\beta}s
#' @param cor The vector of correlations (\eqn{r})
#' @details A function to calculate
#' \d... |
f2452014f64c0eaaedc69214a02614f7aaa420a2c587927e78e441c389fd4f1b | R | 1,583 | 48 | library(Seurat)
library(SeuratDisk)
library(ggplot2)
library(patchwork)
setwd("/media/hansruedi/3b32f153-0651-4d56-b863-9b070ac7dd55/home/hansruedi/Allen_Institute_06142020")
library(Seurat)
pbmc=readRDS("SeuratObject_AllenData.rds")
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.fa... |
c00c2a937f27d689e75da76eacb8d8060416d0404828c9606a0ba13aaecc385f | R | 1,585 | 41 | # a example script to calculate the interaction function by R
# First, install `R`(https://www.r-project.org/)
# and install the package `spatstat`(https://spatstat.org/)
# Next, save the x/y locations of points into local files
# For instance, we save the points of the HC mosaic as
# ```python
# from examples.Horiz... |
f4767cc70c774c66893dd63707c78f2a8944da61870b8c3ef04f124bf59c3fae | R | 1,586 | 51 | #' @title Subset a lassosum.pipeline object by lambda and s
#'
#' @param lassosum.pipeline An object returned by lassosum.pipeline
#' @param s Value(s) of s to restrict to
#' @param lambda Value(s) of lambda to restrict to
#' @details This function is usually used to reapply a validated pgs to a new data.set.
#' See... |
18fac56c8c6454dd128dcc1ff4663c3309d4fea307866a1e14f3ed1dceefcd93 | R | 1,595 | 52 |
get_analyser_object = function(analyser, yaml)
{
ana = get(paste0(analyser, "_Analyser"))$new()
ana$setParams(c(yaml$DataSettings, yaml$Output[[analyser]]))
ana$setPlotSettings(yaml$PlotSettings)
return(ana)
}
get_testyaml_object = function(tmpdir, analyser, changes = NULL)
{
yaml_class = YamlClass$new()
... |
b39bea03467f9e9d84690e8525e4307724d6593b82e6fa0ec370037b945874b8 | R | 1,600 | 71 | #' Distance based on Pearson's \eqn{R^2}{R squared}
#'
#' The calculated distance is
#' \eqn{D^2 = \frac{1 - COR(`x`')}{2}}{D^2 = (1 - COR(x')) / 2}
#'
#' The distance between the rows of `x` is calculated.
#' The possible values range
#' from 0 (perfectly correlated)
#' over 0.5 (uncorrelated)
#' to 1 (perfectly anti-... |
43e548e86f8935002b19611487aecd6ad91fe7c30c2e949a24f05b7017d04280 | R | 1,603 | 57 | #' Guess wavelengths from character vector
#'
#' character vectors used for names (e.g. colnames for matrices or data.frames)
#' are often treated by [base::make.names()] or similar functions that
#' produce suitable names (e.g. by prepending "X" to numbers). Such names
#' cannot be directly converted to numeric.
#'
#'... |
47260572870a52f4e5f7fe4597dcff2c28fccccf700c563bce290e39dfee7927 | R | 1,603 | 23 | source('Analysis/util.r')
x <- load_integrated()
Idents(x) <- x$cell.broadtype
p1 = FeaturePlot(x, 'PRKN', cols=c('gray95', 'red'), min.cutoff = 'q5', max.cutoff='q95', split.by='Genotype', label=TRUE)
p2 = VlnPlot(x, 'PRKN', assay='SCT', group.by='cell.broadtype', split.by='Genotype', pt.size=0)
cowplot::save_plot(plo... |
210d7b27a995cfb6f0d0328664bb101789b79c4a12df47c0b0108c03072c6793 | R | 1,607 | 62 | #' @name DEPRECATED-count_lines
#' @concept moved to hySpc.read.txt
#'
#' @title (DEPRECATED)
#' Count lines (of an ASCII file)
#'
#' @description
#'
#' These data input related functions are **deprecated** and they will be
#' removed in the next release of \pkg{hyperspec} package.
#' Now functions in package \p... |
00cf5b921ec8bc31f9ac56460d77b0bf976cb5c3d0413773a90c7cf7e91c0582 | R | 1,610 | 38 | #!/usr/bin/R
# ----------------------------------------
# Excitatory neuron to region annotations:
# Updated 03/10/2022
# ----------------------------------------
ec.exc.neurons = c("Exc TOX3 POSTN", "Exc DLC1 SNTG2", "Exc AGBL1 GPC5",
"Exc RELN GPC5", "Exc RELN COL5A2", "Exc TOX3 TTC6",
... |
1b4b2b19df593c29d567d85b5d8b5d85240bacde245c5914c7f80cc41d98d8bd | R | 1,612 | 48 |
test_that("valid and invalid yaml parameter input", {
yaml = list()
yaml$Directories = list()
yaml$Directories$nonsense = "hallo"
yaml$Output = list()
yaml$Output$SEM$nonsense = c(1,2)
yaml$Output$SEM$PeakAverage = TRUE
yaml$DataManipulation = list()
yaml$DataManipulation$Normalization = list()
yaml... |
e2e463ccc0c6b1e229c89f592715570baf1f7b2db7e507ff4a9cb887fe6589ed | R | 1,617 | 19 | library("stringr")
fast5Samples <- data.table::fread("/lustre/projects/Research_Project-MRC190311/longReadSeq/ONTRNA/sorted_nuclei/RNA/mouse/NeuN/NeuN/20240207_1339_3D_PAU69826_5fdf4c14/fastq_pass/Samples.txt", data.table = FALSE, col.names = c("Sample"))
demuxSamples <- data.table::fread("/lustre/projects/Research_Pro... |
14abcce8ee022b3b427048738ab23fb8666225a0c6effd9b1bf20384f9e42eb8 | R | 1,621 | 37 | #=============Permutation================
number_of_permutation<- 500000
#max_cnv<- max(chr2_del[,2],chr2_del[,3],chr2_dup[,2],chr2_dup[,3])
#no_case_dup<- nrow(subset(input_case,input_case$CNV.Type=='Del'))
#no_control_dup<- nrow(subset(input_control,input_control$CNV.Type=='Del'))su:774-nu:1081
max_cnv<-179
no_case_d... |
b03af09612379d2be360ac9f3a72bf2009e1c3e5c813a71ac8ff76bb103a3809 | R | 1,623 | 49 | Trace_Analyser = setRefClass(
"Trace_Analyser",
contains = "Analyser",
methods = list(initialize = function()
{
callSuper(
description = "Plots the time sequence for each sample per specified sheet.",
plot_fnc = function(.self, data)
{
plotl = list()
datal = list()
... |
bcef8e2540b5daad4cde99e3f2441891e30dc4c3062ae0fa29b4164f97f73d93 | R | 1,623 | 56 | #first script to launch before building your network with metacore
#Code to preprocess data so that they are usable for metacore and also the Network pipeline
#From the DEGs in all cell types you are getting a file which is ending woth "List.txt" and can be directly copy paste into metacore
#You are also getting the ... |
dc24fb2c942fc06ef78228a4d7dc196e7e763d9ba84a4f887d2be4432278c568 | R | 1,624 | 48 | test_that("infer_spm_microtime reads SliceTiming metadata", {
tmp_dir <- tempfile("spm_timing_")
dir.create(tmp_dir, recursive = TRUE)
run_nifti <- file.path(tmp_dir, "sub-01_task-test_run-01_bold.nii.gz")
json_path <- sub("\\.nii\\.gz$", ".json", run_nifti)
jsonlite::write_json(
list(SliceTiming = c(0,... |
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