sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
68e83ae529d7045dd55db180059f142b8741e5a5d9c66e21d371ba1eacd9453b | R | 1,380 | 39 | rm(list=ls(all=T))
library(mvnfast)
source('simulations/mugent_ph/functions.R')
################################################################################
niter=1000
ms=c(50,100,150)
ps=c(2,5,7)
rhos=c(0.9,0.1)
RES=array(dim=c(length(ms),length(ps),length(rhos)))
par(mfrow=c(6,3),mar=rep(1.5,4))
for(i in 1:length... |
8e40bce71109dc63264d98eb86bdedec9026eafdd8a354c1598ee2e431399acd | R | 1,380 | 77 | ---
title: "Nebulosa MSL1"
author: "AF"
date: "`r Sys.Date()`"
output: html_document
---
```{r}
library(Signac)
library(Seurat)
library(tidyr)
library(dplyr)
library(ggplot2)
library(rstudioapi)
library(Nebulosa)
set.seed(17)
```
Nebulosa Density plot for Msl1 expression.
```{r}
seu_Embryo <- readRDS(file = "path... |
ccc5087aafede690f4cbbc99e908afdfc321452386b99c922b3de2df543fc3b2 | R | 1,388 | 24 | #######################################
#
# This function is used to obtain the
# network identification of Schafer 400
ObtainNetID <- function(id_path, anno_path){
net_id <- readr::read_csv(id_path, col_names = 'network_ind')
net_parcel <- readr::read_csv(anno_path, col_names = 'region')
net_ana <- cbind(n... |
c668e80a43c740bb34e1b337360a2bc735669169f88dec0389e1d49c8e969489 | R | 1,392 | 64 | # load example HT data ----
data_ht_file <- system.file("tests", "testthat", "data", "example_HT_data.rds",
package = "OlinkAnalyze",
mustWork = TRUE)
data_ht <- readRDS(data_ht_file)
rm(data_ht_file)
# keep a few of the rows from the dataset ----
## select bri... |
05923af420195800b332d8099f1041cbc81d4f25330f7d66e34070b54c874b4a | R | 1,393 | 35 | bd_tas <- replicate(3000, ape::rlineage(birth = 0.6, death = 0.1, Tmax = 10), simplify = FALSE)
bd_tas_list <- list(tas = bd_tas)
dists_bd <- list(
list(distribution = "uniform", n = 1, min = 0.6, max = 0.6),
list(distribution = "uniform", n = 1, min = 0.1, max = 0.1)
)
ddd_list <- list()
j <- 1
for (i in seq(from... |
b965882fd5269d57d1d2ac1a4265ae740cde6221fd863c964b6095bccf565ae5 | R | 1,395 | 64 | # load example 3k data ----
data_3k_file <- system.file("tests", "testthat", "data", "example_3k_data.rds",
package = "OlinkAnalyze",
mustWork = TRUE)
data_3k <- readRDS(data_3k_file)
rm(data_3k_file)
# keep a few of the rows from the dataset ----
## select bri... |
85532b84ad62482613ca8477da35998f6637c92010cce5aaf9ea13ab4bbf95af | R | 1,397 | 49 | #' Generate example data
#'
#' @return a dataframe containing cell-level information for sample, group and
#' clusters
#'
#' @export
#'
#' @examples
#'
#' speckle_example_data()
#'
speckle_example_data <- function(){
# Make up some data with two groups, two biological replicates in each
# group and three cell t... |
cb37d2a0e6c02fd2fca3f2cae2f627a8aa605f0868aeca942c9c4ea11f1c361c | R | 1,397 | 38 | ################################################################################
# The position of a gene within the genome.
#
# When linking to the UCSC Genome Browser for a gene the position of the gene
# must be passed in the URL including the chromosome, beginning location, and
# ending location.
#
# The location i... |
6cb0f27060fa6e5885ecaae220c2af2897bb4f90fb12136e96c20ef2700a0311 | R | 1,399 | 44 | #' Functional wrapper to get the output of the wonderful iNext library in the format I use to pipe into ggplot2.
#' @export
get_asymptotic_alpha = function(species, verbose = TRUE){
if(any(colSums(species) < 1)){
if(verbose){print("reads seem to be relative abundance. This is not ideal. Applying transformation t... |
8d881987c8c97a0a4ebf883bbbba104550d66896e1b69e49a95a57d9447b690c | R | 1,401 | 41 | args <- commandArgs(TRUE)
name <- as.character(args[1])
data_path <- file.path(name, "EMP_DATA")
export_path <- file.path(name, "EMP_DATA", "EXPORT")
if (!dir.exists(data_path)) {
stop("Empirical data path does not exist")
}
if (!dir.exists(export_path)) {
dir.create(export_path, recursive = TRUE)
}
### Condam... |
771418470f0b8653922c8a10fbda3827c11168894039a83bc453e1ae2ce05523 | R | 1,405 | 65 | test_that("Gamma regression reacts on 'weight'", {
n <- 100L
set.seed(87L)
X <- matrix(runif(2L * n), ncol = 2L)
y <- X[, 1L] + X[, 2L] + runif(n)
X_pred <- X[1L:5L, ]
params <- list(objective = "gamma", num_threads = .LGB_MAX_THREADS)
# Unweighted
dtrain <- lgb.Dataset(X, label = y)
bst <- lgb.trai... |
288fe87201fbd53293d2f1b2eb6a7db99291a085e81fd9509617c1d2643cb7bf | R | 1,409 | 47 | test_that("lgb.plot.importance() should run without error for well-formed inputs", {
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)
params <- list(
objective = "binary"
, learning_rate = 0.01
, num_leaves ... |
1332669c619d27b852f89ed63ed9548486251b5b57a32f348fb8292a67699de6 | R | 1,415 | 39 | # In this script we will be gathering pathology diagnosis
# and pathology free text diagnosis terms to select pineoblastoma (PB)
# samples for following PB subtyping analysis and save
# the json file in PB-subset folder
library(tidyverse)
## Directories
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
... |
bdca09aae0dba033a10d0c736a070b039a0133a54058b9e83561c648cfda39e1 | R | 1,416 | 57 | devtools::build_vignettes()
devtools::document()
devtools::test()
devtools::check()
devtools::build()
devtools::install()
devtools::load_all()
# Minimal test run #
devtools::load_all()
# load necessary objects
snps <- fread('./data/hd_1kG_hg19.snppos.filtered.test.gz')
cnvs <- fread('./data/cnvs.txt')
cnvs[, prob... |
dd88920c09c57d04b16e31a5eb490ac98d20fd07e3a068b7b41c330a3f5459de | R | 1,416 | 30 | if (!require(BiocManager)) install.packages('BiocManager', repos='https://www.stats.bris.ac.uk/R/')
if (!require(DirichletMultinomial)) BiocManager::install("DirichletMultinomial")
if (!require(Biobase)) BiocManager::install("Biobase")
if (!require(optparse)) install.packages('optparse', repos='https://www.stats.bri... |
73a1b51986a7f5fd7d6770c29fae5df76b95d3cc8f4e991ce706eedca1f0033a | R | 1,426 | 40 | getTree <- function(rfobj, k=1, labelVar=FALSE) {
if (is.null(rfobj$forest)) {
stop("No forest component in ", deparse(substitute(rfobj)))
}
if (k > rfobj$ntree) {
stop("There are fewer than ", k, "trees in the forest")
}
if (rfobj$type == "regression") {
tree <- cbind(rfobj$forest$leftDaughter[... |
15e582f09133f1c9edd86b0252bbb908aeeffc2392799cc91b8b13c111998ac1 | R | 1,436 | 30 | options("scipen"=999)
library(data.table)
args <- commandArgs(trailingOnly=TRUE)
INDIR = args[1]
SET = args[2]
FDR = args[3]
RESOLUTION = as.numeric(args[4])
#peaks_raw = read.table(args[1], header=T)
#peaks_raw = read.table('../results/mESC/MY_113.MY_115.5k.2.peaks',header=T, stringsAsFactors=F)
inf = paste(INDIR,... |
01561cdef47d801f10fba99f60c555a32dd30527607e9f4beacd6da6a8dc233b | R | 1,438 | 54 | ##-------------------------------------##
##### SETUP OPTIONS #####
##-------------------------------------##
options(timeout = max(1000, getOption("timeout")))
options(download.file.method.GEOquery = "auto")
options(warn = -1)
set.seed(08071993)
options(shiny.maxRequestSize=900000*1024^2)
option... |
9bb978f563bf7113f50b381b2a1711336d14f5d840abef5623772cd318c9da35 | R | 1,438 | 59 | # Load necessary libraries
library(dplyr)
library(ggplot2)
library(car)
library(nlme)
library(lme4)
library(lmerTest)
library(readxl)
library(rstudioapi)
library(reshape2)
library(tidyverse)
library(tidyr)
library(sjPlot)
#Set working directory to local directory
setwd(dirname(getActiveDocumentContext... |
0d9680133d8016402847738e07f1fafb6b9a19821df42485764e1974f8f19ceb | R | 1,439 | 40 | # Function to return points and geom_smooth
PointAndSmooth <- function(data, mapping, method = "loess", ...){
p <- ggplot(data = data, mapping = mapping) +
geom_point() +
geom_smooth(method = method, ...)
p
}
# Functions to aggregate mixed columns
# https://stackoverflow.com/questions/... |
f22a05befb1936ccf5b271fbe424121228ea104948f13f25644640d7be821d3a | R | 1,440 | 38 | #' Plot LRR/BAF for a given CNV
#'
#' This function is mostly needed for developing to check how CNVs looks like
#' "normally" and compare with the PNG form.
#'
#' @param cnv see load_snps_tbx() documentation
#' @param samp see load_snps_tbx() documentation
#' @param snps see load_snps_tbx() documentation
#' @param in_... |
3f03bf7603619a6fba1143018cb9a59d8f6dbfdd2911ca202f7ce56cc9647e78 | R | 1,442 | 49 | ##-------------------------------------##
## BARPLOTS ##
##-------------------------------------##
get_summary_barplot <- function(var1, var2, colors, label){
var1 <- str_sub(var1, 1, 15)
var1_terms <- unique(var1)
var2_terms <- unique(var2)
var1_size <- length(var1_terms)
va... |
1069947d714d33ef9a8857e2453d33391b909029128bce8c8f72664c2d35dbec | R | 1,447 | 38 | library(ggplot2)
library(data.table)
library(cowplot)
source("../Plot_theme.R")
# Load color scheme
colors <- fread("../Plotting/colors.csv", strip.white = F)
color_v <- colors$Color
names(color_v) <- toupper(names(color_v))
# Load TMS differential expression overlap data
c <- read.csv("comp_TMS/diff_exp_loc=all.csv"... |
68d8d1cd423455890d833d409440a385bd217de2280128c1fabad713dc162696 | R | 1,447 | 45 | ################################################################################
# Create an image of a splice junction usage plot.
#
# In order to stay inside the shinyapps.io memory limit this function reads the
# data file, creates the plot as an image, and cleans everything up so that a
# minimal amount of memory i... |
3ab7bde55cf867a22032ddfb09a6798a987aeb2dff3fbc259928cee0319502ea | R | 1,457 | 39 | setwd("/media/user/disk21/completeAnalysis/figure3_2025/")
obj = readRDS('../visium_15Sept/visiumObj_20Sept.rds')
meta = read.csv("../visium_2Jun/meta_12Mar2025.csv", row.names = 1)
identical(rownames(meta), rownames(obj@meta.data))
obj@active.ident = factor(meta$AF_ivy, levels = c('LE_GM', 'LE_WGM', 'LE_WM', 'CT_co... |
5e72c3d3d245ff7f9fdaab5aac0b795cdcc95a7969e62b1d6fe77297082674a2 | R | 1,463 | 36 | library(optparse)
library(dplyr)
option_list <- list(
make_option(c("-d", "--datapath"), type='character', action='store', default='./',
help="Path to where the CSV output files are stored"),
make_option(c("-f", "--filename"), type='character', action='store', default='cell_filter_info.csv',
... |
d1061b00ef107dee22514e48cd6cd8e20f6497557bc957442ae076b5fce7e4fd | R | 1,463 | 39 | Cross_Validation <- function(LabelsPath, col_Index = 1,OutputDir){
"
Cross_Validation
Function returns train and test indices for 5 folds stratified across unique cell populations,
also filter out cell populations with less than 10 cells.
It return a 'CV_folds.RData' file which then used as input to clas... |
4a8ddb2f3e8728bae18cb4c2779373989d29735f33a93c70bd1822a6bc50abac | R | 1,464 | 42 | library(pROC)
data(aSAH)
context("ci.sp")
# Only test whether ci.sp runs and returns without error.
# Uses a very small number of iterations for speed
# Doesn't test whether the results are correct.
for (stratified in c(TRUE, FALSE)) {
for (test.roc in list(r.s100b, smooth(r.s100b))) {
test_that("ci.sp with de... |
18f1333b3a010e7cf97d1813a10145c146fd8dfac14a70708e85fba678ba1f6d | R | 1,465 | 60 | context("Testing PCA checking")
n <- 500
p <- 1000
ndim <- 50
nextra <- 100
tol <- 1e-3
data(hm3.chr1)
bedf <- gsub("\\.bed", "",
system.file("extdata", "data_chr1.bed", package="flashpcaR"))
test_that("Testing PCA with stand='binom'", {
S <- scale2(hm3.chr1$bed, type="1")
f2 <- flashpca(S, ndim=ndim, sta... |
97749a921394a864fda92f9d545508180266c1a874103788980b52b7aaa63776 | R | 1,465 | 43 | library(pROC)
data(aSAH)
context("ci.se")
# Only test whether ci.se runs and returns without error.
# Uses a very small number of iterations for speed
# Doesn't test whether the results are correct.
for (stratified in c(TRUE, FALSE)) {
for (test.roc in list(r.s100b, smooth(r.s100b))) {
test_that("ci.se with d... |
b2ad82473e877a15e65ee9aae205035429ee15ce56bff5201054b546a81b8ae9 | R | 1,478 | 38 | library(tidyverse)
library(optparse)
library(Biostrings)
arguments <- parse_args(OptionParser(), positional_arguments = 1)
gencode_fasta_file<-arguments$args[1]
# base_dir<-"/home/jbrenton/nextflow_test"
# gencode_fasta_file<-file.path(base_dir,
# "/output/reference_downloads/gencode.v38.transcripts.fa")
# # gencode_... |
50ff7d8744e383c9f5fa370ba66070d5a87bb9aea1ee819390a9e1a6fa3c1e76 | R | 1,479 | 32 | #GenomicSEM
#Munges sumstats
munge(c("/path/to/MDD.txt", "/path/to/ADHD.txt", "/path/to/BPD.txt"),hm3 = "/path/to/w_hm3.snplist", trait.names = c("MDD", "ADHD", "BPD"), info.filter = 0.9, maf.filter = 0.01)
#Running multivariate LDSC
traits <- c("/path/to/MDD.sumstats.gz", "/path/to/ADHD.sumstats.gz", "/path/to/BPD... |
d732b023fb11b8e2d1e95eca8da5c3a42263ad4d053846fd99fe4a0445acda69 | R | 1,481 | 40 | # The working directory is the directory that contains this test R file, if this
# file is executed by test_dir
#
# testthat package is loaded, if this file is executed by test_dir
context("tests/test_collapse_rp_lists.R")
# import_function is defined in tests/helper_import_function.R and tested in
# annotator/tests/te... |
d4e418596918e2c92854ebde13b3a4f35f2c67ddd742d0423a7c4f047ee651fc | R | 1,482 | 42 | test_that("get_plateform detects EPIC correctly", {
# Create a matrix with EPIC number of probes
mat_epic <- matrix(0, nrow = 866553, ncol = 5)
result <- get_plateform(mat_epic)
expect_equal(result, "IlluminaHumanMethylationEPIC")
})
test_that("get_plateform detects 450k correctly", {
# Create a matrix with ... |
a4c31b4aa16abb029156fad2a6979f53a400316e719f226a976976ce4035e179 | R | 1,484 | 49 | # Get the project directory
project_dir <- getwd()
lockfile_path <- file.path(project_dir, "renv.lock")
cat("Checking for renv.lock in:", lockfile_path, "\n")
# Check if renv.lock exists
if (!file.exists(lockfile_path)) {
stop("Error: renv.lock file not found. Please provide a valid renv.lock to proceed.\n")
}
# I... |
3b84c57c720e818bc6ae69d1e2348a1786eaee594fb65beb61e22ab23751850a | R | 1,486 | 51 | #' Exposure
#'
#' Subset of the original dataset containing the estimated effect of SNPs on the exposure
#'
#' @format A data frame with 750,000 rows and 13 variables:
#' \describe{
#' \item{chr}{chromosome}
#' \item{rsid}{rsid of the SNP}
#' \item{pos}{position}
#' \item{ref}{reference allele for the SNP}
#' ... |
a04227ec15f0e12a6b8ae255eea506824656698da41d5a6e79b7b8503e1428d2 | R | 1,487 | 38 | library(org.Hs.eg.db)
library(AnnotationDbi)
library(tidyverse)
library(biomaRt)
library(GO.db)
setwd("/Users/melis/Documents/GitHub/MRIxST/code")
dirs <- list.files(path = "../raw_data/keyword_search_amiGO/.", pattern = "\\.csv$")
for (i in 1:6) {
# Read in amiGO pathways based on searched keywords
data = read.cs... |
313d1bdefef06f41f070c79ede9cfdf563d284950a86dd8fdfff8f4a7c90d348 | R | 1,489 | 46 | #' @title Get parcel disconnection measures/maps
#' @description This is a wrapper function that calls a series of functions to create various parcel disconnection measures.
#' It wraps the 'get_parcel_atlas', 'get_atlas_sspl', 'get_parcel_discon', and 'get_patient_sspl' functions.
#' @param cfg a pre-made cfg structur... |
42ae71b32f6a30c5243914e21aca772a398b149ce10a1ea198b6419b8965c95a | R | 1,489 | 47 | test_that("cpg_na_excl identifies probes with many NAs", {
# Create a test matrix with some NAs
betas <- matrix(runif(100), nrow = 20, ncol = 5)
rownames(betas) <- paste0("cg", sprintf("%07d", 1:20))
# Add NAs to first row (30% NAs)
betas[1, 1:2] <- NA
# Add NAs to second row (40% NAs)
betas[2, 1:2]... |
3f0287b075941a1a00cab6d6a8af8c5e506b8c9a7af4eec2dcd2207ea48bb0c7 | R | 1,499 | 35 | # desc.tab = function(groups, outcome.var, df) {
# factors.dat = df %>% select(where(is.factor)) %>% names()
# df %>% tidyr::drop_na(groups)
# df %>% dplyr::mutate(across(paste0(factors.dat), ~paste(as.numeric(.), .))) %>%
# tidyr::pivot_longer(groups,
# names_to = "key",
# ... |
f997adf5e320888dae2a21b65b09d8e45b59e4d549acd3bf8ec2956df3c1dd1b | R | 1,501 | 38 | rm(list=ls(all=TRUE))
library(mvnfast)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
ar1=function(n,rho) rho^toeplitz(0:(n-1))
atransform=function(Q,null_mean,null_variance) {
# function to transform gene-based test statistics to an asymptotically normal distribution
... |
fbec83ab7aadf6ec65f65fe344a724f5be52038cd8087bd23a164bef75fb317e | R | 1,502 | 60 | calculate_spot_dist <- function(n2, sigma_n, euc_dist2)
{
# calculate spot distance based on gaussian kernal regulated euclidean distance
# n1, n2 is indicies, sigma_n is a vector of N and euc_distance is a matrix of N x N.
# will return a vector of N, which is P_n_n2
tmp = -euc_dist2[,n2]/2/sigma_n[n2... |
7bd482398426888e97bf584bc10d5a0bf0f1114b0044aff48552f92af976bd40 | R | 1,504 | 38 | # [description] List of metrics known to LightGBM. The most up to date list can be found
# at https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric-parameters
#
# [return] A named logical vector, where each key is a metric name and each value is a boolean.
# TRUE if higher values of th... |
8c207479bdbd11a2acc6506262b32ebe78aadcaac7ea5aa8483d00909d6c5c39 | R | 1,508 | 43 | # 2. Copathologies
# Analysis of associations between clinical characteristics and co-pathologies
# Project: Clinical features, genetics, and pathology in a large series of movement disorder cases: a retrospective multi-ancestry brain bank cohort study
# Last updated in November 2025
# Syntax for generating PCs via P... |
831610efc35082ac311dbabe094b0a6e6f12d71455dce768c772f6716eee0a13 | R | 1,509 | 44 |
##-------------------------------------##
## QC TAB ##
##-------------------------------------##
tab_QC_BARPLOTS <- tabItem(
tabName = "Quality Control",
tabPanel("BARPLOTS",
sidebarLayout(
sidebarPanel(width = 2,
selectInput(inputId = "summary_var",
... |
0ac6686321b978e332839e57099dd31c23f68878c64319bcdf99a7451343fa09 | R | 1,510 | 33 | # Theme for all plots
theme_Publication <- function(base_size=15, base_family="Helvetica") {
library(grid)
library(ggthemes)
(theme_foundation(base_size=base_size, base_family=base_family)
+ theme(plot.title = element_text(face = "bold",
size = rel(1.2), hjust = 0.5),
... |
cb94a6cfbc200e9e2f0155032796aec0009818e795057eb7c1c9b5681e1be0f6 | R | 1,515 | 50 | #'---
#' title: Get MAE results
#' author: vyepez, mumichae
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "MAE" / "deseq" / "{vcf}--{rna}.Rds")`'
#' input:
#' - mae_counts: '`sm cfg.getProcessedDataDir() + "/mae/allelic_counts/{vcf}--{rna}.csv.gz" `'
#' output:
#' - mae_res: '`sm cfg.getProcessedResultsDir... |
d0d26b6d0ec0e35ee155e0ba2554bfcbe8606e58ad01ddf1aca24e4626316685 | R | 1,519 | 42 | #!/usr/bin/env Rscript
require(dplyr)
require(data.table)
args=commandArgs(trailingOnly = T)
if (length(args)<2) {
stop("Usage is: ./gtf_to_exons.R input.gtf.gz output.txt.gz")
}
cat("Reading in ",args[1],"\n")
gtf=fread(cmd=paste("zcat <", args[1]), data.table = F, col.names=c("chr","source","feature","start","e... |
b9858d47e4a26d6c9381c4b119030992b9df13433ccd197cd986b7ffac751ba1 | R | 1,528 | 45 | # Test funcs. in reduction.R
library(SummarizedExperiment)
library(SingleCellExperiment)
library(SpatialExperiment)
data(rings)
spe <- rings
spe <- computeBanksy(spe, assay_name = "counts", compute_agf = TRUE)
test_that("runBanksyPCA gives message when seeded", {
expect_message(runBanksyPCA(spe, use_agf = TRUE, ... |
1fd29e50fc1a663ed307f781cd79d12e088ee71bcf9e3948756cd94f7a3f559d | R | 1,533 | 24 | data(aSAH)
r.wfns <- roc(aSAH$outcome, aSAH$wfns, quiet = TRUE)
r.ndka <- roc(aSAH$outcome, aSAH$ndka, quiet = TRUE)
r.s100b <- roc(aSAH$outcome, aSAH$s100b, quiet = TRUE)
r.wfns.percent <- roc(aSAH$outcome, aSAH$wfns, percent = TRUE, quiet = TRUE)
r.ndka.percent <- roc(aSAH$outcome, aSAH$ndka, percent = TRUE, quiet ... |
b9e624560382dae53d7fbeb22acc2f336f58ac446a0519e65bb2180b3c916d38 | R | 1,536 | 36 | #' @title Compute the structural shortest path length (SSPL) for the SC atlas
#' @description This function uses the atlas SC matrix to compute an atlas SSPL matrix
#' containing SSPLs between each pair of brain regions. It assumes that you have already
#' run get_parcel_atlas to obtain the atlas SC matrix.
#' @param c... |
a7e75ab156d8d78a09025e933180edb38cec16b73f0d9e40f470be94c04c29e0 | R | 1,540 | 35 | # R. Jin 2021
# Filter expression matrix containing all specimens to only normal specimens of specific type
suppressPackageStartupMessages(library("optparse"))
suppressPackageStartupMessages(library("tidyverse"))
option_list <- list(
make_option(c("-e","--expressionMatrix"),type="character",
help="ex... |
810716c24d548fa2d5ed82af2e67c1616e08fa84259cddce85666d14acc45f26 | R | 1,541 | 40 | # The working directory is the directory that contains this test R file, if this
# file is executed by test_dir
#
# testthat package is loaded, if this file is executed by test_dir
context("tests/test_num_to_pct_chr.R")
# import_function is defined in tests/helper_import_function.R and tested in
# annotator/tests/test_... |
aed5c4a9b7274ed88240ca2db270dcec2a4b86aa2a1c0742a2309b2198ba89f2 | R | 1,543 | 32 | # Shared fixtures for the create_mofa / mofa2 tests.
# testthat sources helper-*.R automatically before running the tests.
# Build the miniACC-derived MultiAssayExperiment used by several tests.
# Selects four experiments, makes feature names unique per experiment (so the
# duplicated-feature renaming does not kick in... |
41398770fc89e7d3cff5101a290bc4aa0a32ba18b45998a53d7f3c02a8a68b14 | R | 1,545 | 34 | library(ComplexHeatmap)
library(stringr)
library(ggplot2)
library(data.table)
source("../Plot_theme.R")
# Load color scheme
colors <- fread("../Plotting/colors.csv", strip.white = F)
color_v <- colors$Color
names(color_v) <- colors$ID
# Load DEG data
dereg <- read.csv("results_LAR/deregulated_miRNA/degs_all_formated_... |
2ca00700f366e168ada692c98337abb963d7ba48f2e849092f4095fff67472a1 | R | 1,546 | 47 | margin <- function(x, ...) {
UseMethod("margin")
}
margin.randomForest <- function(x, ...) {
if (x$type == "regression") {
stop("margin not defined for regression Random Forests")
}
if( is.null(x$votes) ) {
stop("margin is only defined if votes are present")
}
margin(x$votes, x$... |
c2ecf1654a075511f520c479e7c5ae99c45ea7894345a5969f431e65b19adaf9 | R | 1,546 | 42 | args <- commandArgs(TRUE)
Cross_Validation <- function(LabelsPath, col_Index = 1, OutputDir){
"
Cross_Validation
Function returns train and test indices for 5 folds stratified across unique cell populations,
also filter out cell populations with less than 10 cells.
It return a 'CV_folds.RData' file wh... |
19d81730c0b9411b149bf6dbea27aa2c8f59571c5ccab2ddeb71fdef932c65e0 | R | 1,550 | 61 | # Model prep: split, preprocessing, CV ------------------------------------
# Train / test split ------------------------------------------------------
set.seed(42)
df_split <- initial_split(
df_select,
prop = 0.80,
# matching age distributions across train and test set
strata = "scan_age"
)
df_train <- trai... |
39426d34c5eda6aa9f011c7112aebde904f19c68972d910b0308579eee4557ff | R | 1,550 | 76 | #!/usr/bin/env Rscript
# Number of CPUs processes
# to use for parallelizing
# the install of N packages
use_ncpus <- max(parallel::detectCores() - 2, 2)
# CRAN packages,
# add any missing/required
# CRAN packages to the list
# directly below. This script
# will install them if they
# are not already installed.
# Ins... |
98fff2b530d412cdcc0c33cd0d0af9f7ac97abe2dd156017c106ce73584fb705 | R | 1,550 | 49 | library(optparse)
library(tidyverse)
arguments <- parse_args(OptionParser(), positional_arguments = 3)
count_file<-arguments$args[1]
metadata_cols_path<-arguments$args[2]
out_dir<-arguments$args[3]
# leafcutter_dir<-"/home/jbrenton/nextflow_pd/output/leafcutter/intron_clustering"
# sample_names <- data.table::frea... |
f330d4e9bb8abe2620d1588809999298400173aa439d9447e040fafd82d0eedb | R | 1,555 | 41 | # Function to calculate CDF of the sum of two distributions at various indices
calc_cdf_sum <- function(v_idx, d_time_1, d_time_2) {
# Calculate predicted CDF at indices
p_pred <- sapply(v_idx, function(t) {
# Calculates sum of the two distributions
integrate(function(u)
query_distr("p", t - u, d_time... |
0be38be7b89eb2bbc4127c37368e95b9aac9e0f753f140f06fe9b8e43ce9a338 | R | 1,559 | 47 | #'---
#' title: Initialize Counting
#' author: Luise Schuller
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AS" / "{dataset}" / "01_0_init.Rds")`'
#' params:
#' - setup: '`sm cfg.AS.getWorkdir() + "/config.R"`'
#' - workingDir: '`sm cfg.getProcessedDataDir() + "/aberrant_splicing/datasets/"`'
#' input:
#... |
ff876d6e63e3c63bedec7da9888c013265c71c377cacd673f05119d5edbb23be | R | 1,569 | 48 | suppressPackageStartupMessages({
library(ggplot2)
library(ggpubr)
library(EDASeq)
})
# create boxplot
box_plots <- function(object, isLog = F, title = "", facet_var, color_var){
if(ncol(EDASeq::counts(object)) <= 1){
stop("At least two samples needed for the PCA plot.")
} else {
if(all(is.na(EDASeq:... |
ddb27c11777acb0c19ef402a65b4d28810d2583d822e1634a6ee8d9ad24a60e0 | R | 1,570 | 50 | test_that("format_sample_sheet handles basic input correctly", {
# Create a simple sample sheet
ss <- data.frame(
SampleID = c("Sample1", "Sample2", "Sample3"),
Barcode = c("203021070069_R03C01", "203021070069_R04C01", "203021070069_R05C01"),
Group = c("Control", "Case", "Control"),
stringsAsFactors... |
5aecfe9d2893ada9e3c2aa22c7eb46825f7e0664a7fdfdcbefa3d2362c53a038 | R | 1,575 | 41 | "print.randomForest" <-
function(x, ...) {
cat("\nCall:\n", deparse(x$call), "\n")
cat(" Type of random forest: ", x$type, "\n", sep="")
cat(" Number of trees: ", x$ntree, "\n",sep="")
cat("No. of variables tried at each split: ", x$mtry, "\n\n", sep="")
if(x$type == "classif... |
cc569d0a0a6f520ae7c6f8bc9937abe4640c211a8cb8e0b666e3747d68fb5b77 | R | 1,581 | 32 | #'Adjust p-values using resampling
#'@description Wrapper around `p.adjust`, for simple use will do the same.
#'@param p numeric vector of p-values
#'@param method correction method, a character string. Can be abbreviated. see `p.adjust.methods`
#'@param n.samples Integer. How many times should p-values be resampled
#'... |
36a4cac278c0a9cf4f4d8ccc5cc052b7d3f48ab3d29da4789214ac1ec130a159 | R | 1,582 | 56 | # True cell type proportions for 4 samples
p_s1 <- c(0.5,0.3,0.2)
p_s2 <- c(0.6,0.3,0.1)
p_s3 <- c(0.3,0.4,0.3)
p_s4 <- c(0.4,0.3,0.3)
p_s5 <- c(0.8,0.1,0.1)
p_s6 <- c(0.75,0.2,0.05)
# Total numbers of cells per sample
numcells <- c(1000,1500,900,1200,1000,800)
# Generate cell-level vector for sample info
biorep <- r... |
7d96f11168c3dce4519f7c30fa60f56ee164f6e28cbe37e527561a33aaca7a40 | R | 1,582 | 41 | # File: `scripts/combine_moka_results.R`
combine_skat_results <- function(genotype_prefix, weights_type, result_folder) {
if (weights_type == "" || is.na(weights_type)) {
output_file <- paste0(genotype_prefix, "_combined_association.tsv")
file_pattern <- file.path(result_folder, paste0("*", genotype_prefix, "... |
dd182135e6f11558bc6f6753d857660d28710c2d4b305a51528306e0f35641e1 | R | 1,582 | 43 | geom_polygon_auc <- function(data, ...) {
UseMethod("geom_polygon_auc")
}
geom_polygon_auc.auc <- function(data, legacy.axes = FALSE, ...) {
# Get the roc data with coords
roc <- attr(data, "roc")
roc$auc <- data
df <- get.coords.for.ggplot(roc, ignore.partial.auc = FALSE)
# Add bottom-right point
parti... |
61a134f969d0e9c13b35390e8355b793da8ac760f9d1e1aa983c19eea139dba6 | R | 1,585 | 83 | #' Help function checking if a variable is a vector of integers.
#'
#' @inherit .check_params params author return
#'
#' @keywords internal
#' @noRd
#'
check_is_integer <- function(x,
error = FALSE) {
# check if input error is boolean vector of length 1
check_is_scalar_boolean(x = erro... |
d8087150e0f653074bd8bee1b3de70662c3b32a0e33b2a1bdf9a767a82eb5ba1 | R | 1,586 | 63 |
#' @export
top_percent<-function(inputDF, landmark_col, cols_to_cluster, cutoff){
if(missing(cutoff)){
cutoff=25
}
inputDF<-NoNA.df(inputDF)
invisible(utils::capture.output(inputDF[,landmark_col]<-as.character(inputDF[,landmark_col])))
a=2
b=1
output<-inputDF
rownames(output)<-NULL
output$... |
fb9b246c0fe7bc08bde63a769ac710f6e49b6b0332269c0dc05cadef2b026278 | R | 1,587 | 44 | importance <- function(x, ...) UseMethod("importance")
importance.default <- function(x, ...)
stop("No method implemented for this class of object")
importance.randomForest <- function(x, type=NULL, class=NULL, scale=TRUE,
...) {
if (!inherits(x, "randomForest"))
s... |
b188cf63f21f0d5637c96ec16dbb7c0cff3327299890f1a6d84ed3a77b7e2803 | R | 1,588 | 70 | ---
title: "UpSet Plot of MSLc Primed Genes"
author: "AF"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(UpSetR)
library(dplyr)
library(rstudioapi)
```
```{r}
MSLc_primed_Neuron <- as.data.frame(read.table("../processed_data/genes_... |
9bf2c415829ffe0ec8233bb3292e50dd5fad811f213f57d2fcea7d6e5b9ac144 | R | 1,600 | 48 | cohens.d <- function (vec1, vec0, na.rm = FALSE) {
(mean(vec1, na.rm = na.rm) - mean(vec0, na.rm = na.rm)) / sqrt((var(vec1, na.rm = na.rm)+var(vec0, na.rm = na.rm))/2)
}
cohens.d.2 <- function (mean1, sd1, mean0, sd0) {
(mean1 - mean0) / sqrt((sd1^2+sd0^2)/2)
}
softmax <- function (vec) {
denom <- sum(exp(vec)... |
b4b7a5c12e0982049ada6e115fed75bef40d439a08a307d5dbc45bba5dc78a6c | R | 1,602 | 66 | # Scripts to create MALAT1 ensembl ID gene list
library(dplyr)
library(AnnotationHub)
Create_Ensembl_MALAT1_List <- function(
) {
refreshHub(hubClass="AnnotationHub")
species_list <- c("Mus musculus", "Homo sapiens")
malat_symbol <- c("Malat1", "MALAT1")
ah <- AnnotationHub()
malat_list <- lapply(1:len... |
cb93cf10c181df99352bb8a2e3ad04ae9241fba3c4881bbc4170a773b80a15fc | R | 1,606 | 83 | #' Help function checking if a variable is a vector of characters.
#'
#' @inherit .check_params params author return
#'
#' @keywords internal
#' @noRd
#'
check_is_character <- function(x,
error = FALSE) {
# check if input error is boolean vector of length 1
check_is_scalar_boolean(x ... |
98a1b2e639a54f441c04ebf06f23ba6709df7aeaa7b7f222502eb7f198ce2863 | R | 1,609 | 42 | setwd("/media/user/disk21/completeAnalysis/visium_2Jun/Figure4/")
auc = read.csv('AUC.csv')
d1 = cbind.data.frame(auc$orig, auc$AF, auc$celltype, rep('original', nrow(auc)))
colnames(d1) = c('value', 'AF', 'celltype', 'idea')
d2 = cbind.data.frame(auc$shuffle, auc$AF, auc$celltype, rep('shuffle', nrow(auc)))
colnam... |
ca011e4fc40e5828a14c883fed331f84b2a5752365c311cae5f45261747439a4 | R | 1,611 | 44 | # TODO: Add comment
#
# Author: fec
###############################################################################
library(R6)
TrackVariables <- R6Class("TrackVariables",
public = list(
initialize = function(pathToSave=NULL) {
private$trackedVariables = list()
if (!is.null(pathToSav... |
3752dd2d869c767cac566a88690dc225697040b1a112111b729fdfb5e9fe5959 | R | 1,612 | 58 | myData<-read_excel(path = "betaVal_clusterRoi_Tml_rightHemi.xlsx")
View(myData)
#checking assumptions
# checking for normality
# test of normality
# H0: normal distribution; H1: not a normal distribution
#to reject H0 and accept H1, we should have p<0.05
#to accept H0 and reject H1, we should have p>0.05 => it is norm... |
2f4d437e21e3cc18daee171c4aae50ff5a05af592a15154e693c43a579d1d545 | R | 1,619 | 55 | #' @title Modify the legend labels for categorical metadata
#' @description Modify the legend labels for categorical metadata.
#' @param scConf shinycell config data.table
#' @param m metadata for which to modify the legend labels. Users
#' can either use the actual metadata column names or display names. Please
#'... |
2ba0903ddf09062282410ae5cb2b7780af3b6dc673595e47362a246619b20245 | R | 1,623 | 45 | # Master Validation Script for Analytical Pipeline (Scheme 3)
# ==============================================================================
# This script executes the modular unit tests for the core analytical pipeline.
# It uses the downsampled example data to verify:
# 1. Preprocessing & Integration
# 2. Clusterin... |
34da375a1c96319c6d71c186b1d86cab87839f663c35f88c9097b2c3f390ebcf | R | 1,630 | 45 | library(Seurat)
library(SeuratData)
library(SeuratDisk)
library(patchwork)
# See "save_anndata_rds.R" for how to convert AnnData to Seurat RDS
ba9 = LoadSeuratRds("adata_ba9_visium_rawcounts.rds")
# 1. Split dataset by array; log-transform & select HVGs separately
split.by = "visarray"
# use all arrays from one indi... |
beb96df3593b2c264811fcf8182b3b7a4aeefa85759f599deaa50afff6f959b1 | R | 1,631 | 44 | "randomForest.formula" <-
function(formula, data = NULL, ..., subset, na.action = na.fail) {
### formula interface for randomForest.
### code gratefully stolen from svm.formula (package e1071).
###
if (!inherits(formula, "formula"))
stop("method is only for formula objects")
m <- match.call(expand.d... |
939bc0eb42479772ba6d556d7efe2c661411ce120bbe3ce33867a27340d58f5e | R | 1,633 | 57 | setwd("/Volumes/IqraMacFmri/visTac/fMRI_analysis/outputs/derivatives/decoding_pseudoRuns")
#The one sample t-test has four main assumptions:
#The dependent variable must be continuous (interval/ratio).
#The observations are independent of one another.
#The dependent variable should be approximately normally distribute... |
d383cbdb5cbd31e5ed8b9f4b08a9b607482d5058aa69151dd426bb9fece7e1c7 | R | 1,634 | 83 | #' Help function checking if a variable is a vector of numerics.
#'
#' @inherit .check_params params author return
#'
#' @keywords internal
#' @noRd
#'
check_is_numeric <- function(x,
error = FALSE) {
# check if input error is boolean vector of length 1
check_is_scalar_boolean(x = erro... |
aa56a7fc72355f0aff0c897d71be7aba18c2840ae0b30a613912cae6ab6097ca | R | 1,649 | 41 | # Create MTP Open Targets diseases and targets annotation mappings
# David Hill and Eric Wafula for Pediatric OpenTargets
# 02/14/2023
# Load libraries
suppressPackageStartupMessages(library(tidyverse))
# establish base dir
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
# Set path to scratch, module an... |
8756c3f1f0c21dbb9fcfea401fe2bf06ad81e71f1175a53f71e22ac6905beb5f | R | 1,650 | 56 | # pROC: Tools Receiver operating characteristic (ROC curves) with
# (partial) area under the curve, confidence intervals and comparison.
# Copyright (C) 2014 Xavier Robin
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the... |
a11eadac26d3a2db34a2829c709c7e2ff2ac39be2a72b6183281bdb172c8ead2 | R | 1,651 | 53 | ##-------------------------------------##
## FEATPLOTS TAB ##
##-------------------------------------##
plot_featplot <- function(mat, genes, dimred, type, x, y, density_lines)
{
plots <- list()
if (type == "pca"){dimred <- dimred$x}
dimred <- dimred[,c(x,y)]
names(dimred) <- c("V1",... |
4756aa44bf95da15ee8d3c5be424c2a8a943480f63050c3c95353cbdf378f1ed | R | 1,658 | 41 | source("./clonal_analysis_common_functions.R")
# Parameters
dataset.name <- "clonal_dataset"
# Seurat object to analyze
seur.objs.proj.ca <- c(readRDS(file = "./STICR.seuratobject.RDS"))
# Clones of size less than min.clonesizes will be filtered out
min.clonesizes <- c(2)
# Lineage barcode calling method, which will ... |
acaa78dfb435da2fc2f61bbf6e2bfb10095caf16ef803a4f490d10f704113359 | R | 1,665 | 38 | context("Creating the model from different objects")
library(MOFA2)
test_that("a model can be created from a list of matrices", {
m <- as.matrix(read.csv('matrix.csv'))
expect_warning(create_mofa(list("view1" = m))) # no feature names provided
rownames(m) <- paste("feature", seq_len(nrow(m)), paste = "", sep = "")... |
a6f8f61c0500b6adc2215e9b0f445aa0e5cdb52ad131574e7bd65360a54d2508 | R | 1,669 | 42 | library(pROC)
data(aSAH)
context("DeLong Placements C++ code works")
for (percent in c(FALSE, TRUE)) {
for (marker in c("ndka", "wfns", "s100b")) {
desc <- sprintf("delongPlacementsCpp runs with %s (percent = %s)", marker, percent)
r <- roc(aSAH$outcome, aSAH[[marker]], percent = percent)
test_that(desc... |
3cdcb8fe4685caf1eec0ed908f3c82b916ac1b51804c5ddd6924322af4d60263 | R | 1,671 | 47 |
##-------------------------------------##
## QC TAB ##
##-------------------------------------##
tab_QC_DOUBLETS <- tabItem(
tabName = "Doublets",
actionButton(inputId = "calculate_doublets", "Calculate doublets"),
actionButton(inputId = "remove_doublets", "Remove doublets"),
b... |
48411496c2178df3f88739bd30d7eedb26f5d2d012385f1ced53bae369ebcaba | R | 1,675 | 38 | ## code to prepare internal dataset goes here
## based on https://r-pkgs.org/data.html#sec-data-sysdata
# rename local names from Olink wide files to match equivalent long export ----
olink_wide_rename_npxs <- dplyr::tribble(
~OA_internal, ~NPXS,
"SampleID", "SampleID",
"Ct",... |
def103f0ae67a208feb93975991909de29e575b426bf46b6eecddd16a63c247a | R | 1,675 | 45 | library(argparse)
parser <- parser <- ArgumentParser(description='Generate tabix file from bedpe-like files')
parser$add_argument('-i', '--bedpe', type="character", help='input bedpe file')
parser$add_argument('-t', '--tabix', type="character", help='output tabix filename')
parser$add_argument('-s', '--score', type="c... |
cbc89e005712f6e96d2713b168d0e2e8252f33c7c897673e1c4057a9fa58bc9f | R | 1,689 | 43 | library(org.Hs.eg.db)
library(AnnotationDbi)
library(tidyverse)
setwd("/Users/melis/Documents/MRI_cortical_layers/MRI_layers/code")
dirs <- list.files(path = "../data/GO pathways/GO_pathways_with_offspring/.", pattern = "\\.csv$")
for (i in 1:6) {
data = read.csv(paste0("../data/GO pathways/GO_pathways_with_offsprin... |
05bd7b574978eae3455fa5766b8b0200993d0c239794aff782e3a736bc6dfe73 | R | 1,691 | 42 | # script to visualize the UMAP results from 01-full_dataset_compute_umap_umap_counts.R
# load libraries
suppressPackageStartupMessages({
library(ggplot2)
library(tidyverse)
library(ggpubr)
})
# generalized function to call for plotting
plot_data <- function(umap_output, title = "", color_var, shape_var, label_v... |
2e9cc8f88fc177b047cec7cebe497e2b7b213afddfeba8f38152db0248583d49 | R | 1,691 | 51 | options(timeout = 3600) # 1 hour for downloads
options(repos=structure(c(CRAN="https://cloud.r-project.org")), warn = -1)
if (!requireNamespace('BiocManager', quietly = TRUE)) {
install.packages('BiocManager')
BiocManager::install("remotes")
}
if (!requireNamespace('data.table', quietly = TRUE)) {
install... |
31b9c6ec55f0bcd5b46f3243388f8e98a3c20bc52c63abb4f99810e810becb15 | R | 1,696 | 53 | #' @title Remove a metadata from being included in the shiny app
#' @description Remove a metadata from being included in the shiny app.
#' @param scConf shinycell config data.table
#' @param m metadata to delete. Users can either use the original
#' metadata column names or display names. For more information regar... |
38b4e6fd1f13383f37415ff4eeff2fdaea20f99f3114b5e085b1877ac0e15567 | R | 1,698 | 51 | # venn_diagram.R
# Create Venn diagrams comparing DEGs between OSNs and Fatbody
library(readr)
library(dplyr)
library(VennDiagram)
library(grid)
# Input files
fatbody_data <- read_csv("InR_Fatbody_All.csv")
osns_data <- read_csv("InR_OSNs_All.csv")
# Venn plot function
generate_venn <- function(set1, set2, filename... |
e146edee21457ef8210123a3eead54e2e3a8af7106b6c0e500624ec341667b7d | R | 1,699 | 82 | ---
title: "Celltype_abundance_MC_sim"
author: "AF"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(Signac)
library(Seurat)
library(tidyr)
library(dplyr)
library(ggplot2)
library(rstudioapi)
devtools::install_github("rpolicastro/scPro... |
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