sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b3e739e7e0f3561f6d02f388053ce247e78db07e8cb3a38a8705aef5b74508e5 | R | 745 | 23 | dp <- getOption("defaultPackages")
#dp <- dp[dp != 'datasets'] ## Rscript loads it too
#dp <- dp[dp != 'methods'] ## Rscript (in R 2.6.1) doesn't load methods either
# Count of default packages
cat("int packc = ",length(dp),";\n",sep='')
# List of packages
cat("const char *pack[] = {\n",paste('"',dp,'"',sep='',colla... |
248ff664fe0bcc98dfcd8273ae0c0c75d24ffb2bdcd8f5034eb1cafad57e3141 | R | 751 | 36 | # Function -------------------------------------------------------------------
.subset <- function(x, ...) {
validObject(x)
x@data <- subset(x@data, ...)
validObject(x)
x
}
#' Subset `hyperSpec` object
#'
#' @name subset
#' @aliases subset subset,hyperSpec-method
#' @docType methods
#'
#' @param x `hyperSpe... |
eb1ec9cb376a63b72c29a487730e3e939746b7fa7204145ad5599a2788e2b03c | R | 752 | 35 |
#' mochims__pvalue
#'
#' Obtain P-value from z test or t test
#'
#' @param av sample mean (required)
#' @param se sample standard error (required)
#' @param degreesFreedom an integer degrees of freedom (default:5)
#' @param mu population mean (default:0)
#' @param testType type of test: either "ztest" or "ttest" (defa... |
dd4333f0c7257f1ab805124d97c56db1837eef9b2413be4ee86b6f1de0539e8b | R | 753 | 23 | library(Seurat)
library(sctransform)
library(patchwork)
library(dplyr)
#Load dataset
dt.combined.sct <- readRDS("datasets/1_seurat_integrated.rds")
#cluster
dt.combined.sct <- RunPCA(dt.combined.sct)
dt.combined.sct <- RunUMAP(dt.combined.sct, reduction = "pca", dims = 1:50)
dt.combined.sct <- FindNeighbor... |
418da52e63f05ed485c30915f3230e31742c23ca64dd4614d71507125ba82bac | R | 754 | 21 | n.sims <- 210000
msSimulate <- function(morgans.per.bp, ne, filename, n.chrom = 50, mu = 1.5e-8, bp = 20001){
# converts parameters to hudson style, note nchrom, mu and bp are fixed
total.genetic.distance <- morgans.per.bp * (bp - 1)
rho <- 4 * ne * total.genetic.distance
theta <- 4 * ne * mu * bp
... |
f9b852f93a932f076c1ac3d8fef2f5bc643795d9c18460b9a5781d78108be925 | R | 755 | 27 |
history <- function(accession, index = 1){
res <- data.frame()
for(i in 1:length(accession)){
print(paste('Processing', accession[i], '-', i, 'out of', length(accession)))
xml.url <- paste('https://www.uniprot.org/uniprot/', accession[i], '.rss?version=*', sep = '')
x <- readChar(xml.url, nchars =... |
87b5d6dbcc95ed2bd15dbee85b9ee098b49977039a9fb7d727b61e5b6012dd6a | R | 756 | 26 | #!/usr/bin/env r
#
# a simple example to install one or more packages
if (is.null(argv) | length(argv)<1) {
cat("Usage: installr.r pkg1 [pkg2 pkg3 ...]\n")
q()
}
## adjust as necessary, see help('download.packages')
repos <- "http://cran.us.r-project.org"
## this makes sense on Debian where no packages touch /... |
ac8a966c293385e0e7f2dfd1982f2940a45983b6c07d1947291765913e9795d4 | R | 759 | 19 | options(warn=-1)
a=read.delim("model_comparison.txt", sep=' ')
a
pdf("FigS4A.pdf", height=8, width=10)
par(oma=c(1,20,1,1))
barplot(rbind(a$test_r_squared,a$test_r_squared_withHL),beside=T,horiz=T,
names.arg=a$model,las=1,col=c("red","blue"), border=F, xlim=c(0,0.8))
legend("bottomright", bg="white", bty="n"... |
dc31aa7c13f7bf220fbd2e3f02677d14ec61665798bea224f61351698854f454 | R | 759 | 22 | data.pars <- read.csv('data/rltask_modelparameters.csv')
data.pars$Parameter <- factor(data.pars$Parameter, c('L', 'U', 'm', 'k'))
data.time <- read.csv('data/rltask_timingdata.csv')
data.time$Epoch <- factor(data.time$Epoch, c('Pre-learning', 'Learning', 'Post-learning'))
plot.rlparameters <-
ggplot(data.pars, ae... |
e5bc0ec98c601fd42b9e68a8408dea754f419a3a9c711f177e78d4b99a38dbe6 | R | 764 | 19 |
library(data.table)
ad_gwas <-
fread("/data_global/summary_statistics/GCST90027158_buildGRCh38.tsv.gz")
top_snps <- c("rs6733839", "rs6460900", "rs4721058", "rs2000660", "rs72807981",
"rs769449", "rs12721051", "rs4420638", "rs4803778"
)
ad_gwas_top_snps <- ad_gwas[variant_id %in% top_snps]
jansen_2... |
d6d4fc837a4bd37f507b5857a81fd010e547b73682c6c5329a31e82cb8e93feb | R | 765 | 13 | library(phytools)
library(ape)
library(data.table)
tree <- read.tree("SpeciesTree.txt")
dist_matrix <- cophenetic(tree)
write.csv(dist_matrix, 'cophenetic_distance_from_all-v-all.csv') ##get cophenetic distance for each branch v. every other on the tree
# Extract distances for species of interest
species_of_interest <-... |
b3dbe13c0fd233235eb002cd04dbedf8ad3a9dd7d1abd22dcc0d32801d70583d | R | 769 | 30 | #' @export
per_lm_variance <- function(shape.data){
variances<-rowSums(apply(shape.data ,c(1,2),var))
cols1<-colorRampPalette(c("#6e016b","#0c2c84","#225ea8","#005a32","#ffff00","#fe9929","#fc4e2a","red"))
cols<-cols1(100)
#calculate log rates:
x=(log10(variances))
xlims<-NULL
tol <- 1e-06
xlims <-... |
ebed96fec1617293b3d318e73324e8eb3e8082918b18f79b410593e9c0c2af95 | R | 770 | 20 | context("spd-interpolate")
# Positive definite matrixTesting data
x <- cov(matrix(rnorm(100), ncol = 5))
y <- cov(matrix(rnorm(100), ncol = 5))
test_that("Functions accurate interpolate", {
# Euclidean
expect_true(compare(x, spd.interpolate(x, y, t = 0, method = 'euclidean'))$equal)
expect_true(compare(y... |
ae119ff12ff42d870788460e14aeef440efb294f2312194f1d684d54fcfd0ca0 | R | 773 | 11 | args <- commandArgs(trailingOnly = T)
filenamex=as.character(args[[1]])
turan_SegNorm = read.table(file.path('./data/processed/', filenamex, 'SegFixed'), header=TRUE, sep="\t")
turan_SegNorm$CHR= as.character(turan_SegNorm$CHR)
turan_ploidy <- readRDS(file.path('./data/processed/rds', paste0('cell_cn_', filenamex, '.r... |
17122d40749e64713214762a88f2002d94ebf2fe38846b64def7b10d915a2dc2 | R | 775 | 15 | library(data.table)
library(ggplot2)
gwas_qtl <- fread("data/tmp/chr2_ENSG00000071575.11_Brain_Cortex_gwas_qtlNPOLD1_bin.tmp")
setnames(gwas_qtl, c("P_gwas", "P_qtl"), c("Cerebral cortex microinfarcts", "TRIB2 expression in cerebral cortex"))
gwas_qtl_long <- melt(gwas_qtl, measure.vars = c("Cerebral cortex microinfarc... |
c0e87965a73f7dc5bd957740e8c38ac5b34ac402a8d2dbbb14f69770d2df9313 | R | 777 | 19 | library(data.table)
library(tidyverse)
# Read sumstats.
# For some reason fread couldn't read the header, so I read it separately
# and added to the sumstats.
header = read.table("/PATH/TO/DIR/rhythym_sumstat.txt", header = TRUE, nrow = 1)
rhy_sumstat = fread("/PATH/TO/DIR/rhythym_sumstat.txt", skip = 1, header = FALS... |
6b13cfa2c78797d7983bf3b578bb122549d7e918d5cd1b2f530ed25251ee4b46 | R | 778 | 18 | #===========================================================================
# select only Chromosome 19 SNP Matrices for APOE-adjusted analysis
#===========================================================================
# nacc
snpmat_list <- readRDS("data/adc/SNPMatrix/SnpMatrix_list.Rds")
snpmat_list <- snpmat_list... |
115e998c30e444f645e8b134e625accc33106c52d127b91019b75f20278b9b87 | R | 785 | 23 | # loading libraries
library(Seurat) # v5.0.3
library(SeuratWrappers)
library(patchwork)
library(dplyr)
library(ggplot2)
library(sctransform)
library(glmGamPoi)
# Load merged and filtered object
stress.merged.filt <- readRDS("~/Downloads/scRNA/stress_merged_filt.rds")
# Normalizing object with scTransform
stress.merge... |
63320f5f641c7e467039fdaccf72642f120046ad98b5ca728371f6636c117535 | R | 793 | 29 | #' Compute (partial) correlations
#'
#' Transforms an SPD matrix into a matrix of partial correlations
#'
#' @param x An SPD matrix to be whitened
#' @param method A string specifying either "correlation" (default) orf
#' "partial" correlation.
#' @return A symmetric, positive-definite matrix.
#' @export
spd.correlati... |
58fc1daf492e17cb7b0f4ec65fc1476e51e02f3964419376d0094bacf15f5d42 | R | 794 | 27 | #' Covariance whitening transform
#'
#' Whitens an SPD matrix using the procedure advocated by Ng, et al. (2014)
#'
#' @param x An SPD matrix to be whitened
#' @param p The SPD baseline whitening matrix
#' @param unwhiten Logical. If TRUE, reverses a previously applied whitening transform.
#' @return A symmetric, posit... |
ed1433ec7d17bcd0a96923b8fc435123a3741299449d3731cd9e65d6d2accc7e | R | 799 | 42 |
library(car)
library(lsmeans)
library(performance)
df = read.csv("data\\coherence\\broadband\\SpectralFlux\\comparison\\SF-brain-CoherencePeaks.csv")
colnames(df)
# Select first peak data only
dflow <- df[1:99,]
m1 = lm(real ~ syll, data=dflow)
summary(m1)
m2 = lm(real ~ phon, data=dflow)
summary(m2... |
66727d770cea1ace576f2e33c930f2b0f5aaf348e36e8290c2625d85dc507107 | R | 804 | 29 | #!/usr/bin/env Rscript
args <- (commandArgs(TRUE))
qual <- read.table(args[1])
mapped_max <- max(qual$V2)
mapped_min <- min(qual$V2)
unmapped_max <- max(qual$V3)
unmapped_min <- min(qual$V3)
jpeg(file="quality.jpg", height=400, width=1300, pointsize = 25)
par(mar=c(4,4,1,4))
plot(qual$V1,qual$V2,type="l",col="blue... |
82a182f5ed7ed1d2026a27e0900e3c8d3cd2a6654e13b587fff6fdc3005c173d | R | 807 | 33 | #' Recode a Time variable to a different granularity
#'
#' A time variable is passed to this function and the corresponding
#' downsampled time bin is returned. One between 'to_ms' and 'to_hz' must be null
#'
#'
#' @param Time A vector variable indicating the elapsed time, in ms, aligned
#' to some origin - i.e., not a... |
21419a7e12c065384f88cfe4a002766721b301450ff6c1b5d030932d8e0c860d | R | 809 | 19 | # ============================================================================
# This code prepares `paracetamol` dataset
# ============================================================================
# Required packages ----------------------------------------------------------
library(hyperSpec)
library(usethis)
# ... |
d39d689e20ced52ee90afa9f2be9c527de16263d4fb7cc5fb71ebbe4d6ef5943 | R | 818 | 23 | source('./scripts/01_Setup.R')
setwd("./data/processed/rds")
aa2=c("dMDA_101_500kb_ConfiScore.rds",
"Pico_76_500kb_ConfiScore.rds",
"Pico_101_500kb_ConfiScore.rds",
"PTA_48_500kb_ConfiScore.rds" ,
"PTA_101_500kb_ConfiScore.rds")
for (i in 1:length(aa2)){
fname = file.path('./dat... |
d222f7b1fa5b32d264d52654efd7c0c022746857b672abfd829e3b43429c3ee0 | R | 821 | 21 | # Copyright 2022 HYBRID Team INCIA, UMR5287, CNRS, Université de Bordeaux
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required b... |
b8e0539200e88bfe391479455fb3829f17cd7af90a2c99ac3cd8380710ba3229 | R | 828 | 35 | library(readr)
library(dplyr)
library(tidyr)
save.image("debug.RData")
clean_table_ld <-
function(path_table_ld,
path_output_ld) {
table_ld <- read_tsv(path_table_ld)
# Get upper triangle of the matrix
mat_r2 <-
table_ld %>%
mutate(rsq = r^2) %>%
select(g1_rsid, g2_rsid, rs... |
208381ed689edfc0219c981a37022db957e0edf3d5b478cae4c16681dbb4cb1d | R | 839 | 29 |
#' mochims__create_dir
#'
#' Create results folder for analysis script plots and saved objects.
#'
#' @param mochims_dir directory path string (required)
#' @param execute whether or not the system command will be executed (required)
#' @param message message string (optional, default: NULL i.e. no message displayed)
... |
1daf5781662f883ce465b5728571dee28b4f871e7f1c6fbe5333b508adb7700d | R | 840 | 32 | library(WRS2)
d<-read.csv("median_gamm_all_groups.csv", header = TRUE, sep = ",", dec = ".")
#-----------------------
# One way Anova
#-----------------------
#perform Welch's ANOVA
t1way(Gamma~Groupe, data=d, tr=0)
#-----------------------
# Mixed design ANOVA
#-----------------------
# subset dataset to remove ... |
4557d66a9389d98970c46bfb5abd9997243dbec3ab6b0a8d742773284b59fd87 | R | 844 | 33 |
library(lme4)
library(sjPlot)
library(performance)
library(ggplot2)
library(lmerTest)
library(multcomp)
#df = read.csv("data\\acoustics\\syllabic_accuracy_scores.csv")
df = read.csv("data\\acoustics\\phonemic_accuracy_scores.csv")
colnames(df)
# declare speed,trial and subject as factors
df$features... |
f3746f17a679d20f8f121e6b1c2ea0b6832114725bbb50889ce3dfb6f04d7866 | R | 861 | 40 | smlab <- function(sm, backtransf, pscale, irscale,
func.backtransf = NULL, forest = FALSE) {
res <- sm
#
if (backtransf) {
if (is_cor(sm))
res <- "correlation"
#
else if (is_mean(sm))
res <- "mean"
#
else if (is_prop(sm) || sm == "proportion") {
if (pscal... |
71c6705f20d1dea6d09554c4b0de66c3f3311081e45a5b9313a50f1034ccd4f4 | R | 867 | 42 |
#' @concept utils
.validate <- function(object) {
ncol <- ncol(object@data$spc)
if (is.null(ncol)) {
ncol <- 0
}
if (length(object@wavelength) != ncol) {
return("Length of wavelength vector differs from number of data points per spectrum.")
}
TRUE
}
#' @title Validation of `hyperSpec` objects... |
dc2f992445da4ffe57d1b124eb72715d9ab0f029c9ac6e95fb738a79b969e2c0 | R | 868 | 30 | library(Seurat)
library(patchwork)
plot_umap <- function(sobj_path, cell_name){
sobj <- readRDS(sobj_path)
sobj <- UpdateSeuratObject(sobj)
P <- DimPlot(sobj, raster = F)
rm(sobj)
gc()
return(P)
}
sobjects <- list.files("ad_knowledge_portal_syn52293433/rds_files/", full.names = T, pattern = "rds")
umap_... |
5e3d6778f496139759c272089aa1c441b8562c2189325b610854acad79fc3e0b | R | 871 | 24 | # For creating categories highlight ----
grouping_helper_category_highligth <-
rlang::expr(
case_when(
inNorway & isWTE ~ "NO: WTE",
inNorway & (!isWTE) ~ "NO: Other Avian",
TRUE ~ "Other Country")
)
category_highligth_levels <- c("Other Country", "NO: Other Avian", "NO: WTE")
# for funct... |
5102dd0735ac5017e938d09885237118f22644c950850183684f6772f28ebed4 | R | 872 | 46 | library(WGCNA)
library(Seurat)
#seur is Seurat object
#genes, the array describing the current module
getEigen<-function(seur,genes,mod="No Name",nam="Cluster1")
{
print("Prep Data")
mn=Matrix::rowSums(seur@assays$RNA@data)
dat=data.frame(t(as.matrix(seur@assays$RNA@data)))
dat=dat[,mn>0]
cols<-r... |
79d0aa489494bdceebfe475ab7da951cecd1a2e75bf5190b380b2342e397ce52 | R | 872 | 29 | #!/usr/bin/env r
#
# a simple example to update packages in /usr/local/lib/R/site-library
# parameters are easily adjustable
## adjust as necessary, see help('download.packages')
repos <- "http://cran.r-project.org"
## or use BioC's repo list if Biobase is installed:
suppressMessages(rc <- require(Biobase))
if (rc) {
... |
452190d7fd674c018ed3fe4bd71dff0151d073330e49eb4c205d51c88cfbe414 | R | 873 | 35 | #' Laser emission dataset
#'
#' A time series of an unstable laser emission.
#'
#' @seealso
#' See the vignette `vignette("laser", package = "hyperSpec")`.
#'
#' @name laser
#' @docType data
#' @format The data set consists of 84 laser emission spectra measured during
#' 95 min. Each spectrum has 36 data points in t... |
3fb4e5f3f704f2990e16b04b58168909178b3760d40fd67a2735a9e2c9dc2a0e | R | 877 | 21 | library(latticeExtra)
crp.rg <- colorRampPalette(c("red","orange","green","cyan","blue","purple","magenta"))
cols <- sample(crp.rg(10))
plotresults = function(dir){
files = paste(list.files(path=dir, pattern='.txt', full.names=T))
pdf("Fig1C.pdf",width=5,height=4)
plot(0, lwd=1, bty='n', type="l",... |
4e35624e714167fdc2745bd2cfe1c098be26fa7ca255b2bd2e19b49e5f98abe5 | R | 883 | 36 | #!/usr/bin/env Rscript
# Load the data.table package
library(data.table)
# Function to count non-missing rows in a specified column
count_non_missing <- function(file_name, column_name) {
# Read the file
data <- fread(file_name)
# Check if the column exists
if (!column_name %in% colnames(data)) {... |
edab91289b38657d0216305cee530e59fb44ce261b83330228174c436c3b6044 | R | 890 | 17 | library(Seurat)
inforDir <- './data/PDAC-A/Infor_Data'
sc.count1 <- t(read.csv(paste0(inforDir,"/ST_count/ST_count_1.csv"), header=T, row.names = 1,check.names=FALSE))
sc.count2 <- t(read.csv(paste0(inforDir,"/ST_count/ST_count_2.csv"), header=T, row.names = 1,check.names=FALSE))
st_counts <- list(sc.count1,sc.count2)... |
db0da8b032e6f955743cb6c81873f91330f0b8a8f6a146bcbcb17218d3f52e50 | R | 892 | 26 | .onAttach <- function(libname, pkgname) {
unlockBinding(".options", asNamespace("hyperSpec"))
desc <- utils::packageDescription("hyperSpec")
first_url <- sub(",.*$", "", desc$URL) # To use the first URL only
packageStartupMessage(
"\n\n",
"Package ", desc$Package, " (version ", desc$Version, ")\n\n",
... |
889f13a0a46c807627c672dd108c67950554f25cb9d7f10fa3ec524180163d79 | R | 900 | 19 | # ============================================================================
# This code prepares `barbiturates` dataset
# ============================================================================
# Required packages ----------------------------------------------------------
library(hyperSpec)
library(usethis)
#... |
e1421465c2c3db3e97c8a3df806339b3ab5e6a7d0c0be450310f8cbc62d328b8 | R | 903 | 39 | #' A convenience function to preprocess pupillometry data
#'
#' The function calls, in order, 'speed_clean', 'interpolate', and 'smooth_vector' from this packages.
#' Parameters can only be changed in the package options, i.e. 'Pupilla::pp_options()'.
#' Warning, the best preprocessing parameters may deviate from the d... |
881962e327ad142f3ad124147a1dc02d09e479fdb3c964febbae5ed2a130e830 | R | 907 | 34 | source('./scripts/01_Setup.R')
library(data.table)
packageVersion("biomaRt")
# Load required libraries
library(biomaRt)
library(GenomicRanges)
library(rtracklayer)
library(biomaRt)
library("RSQLite")
datax <- read.table("./data/processed/txt/Control_PANTHER_MF_GOslim.txt", sep = "\t", header=T, skip=11, quote = "", st... |
39014424963b34498f36d11cff204ac505b95db3a81c9b8a1121eb016aa995a2 | R | 922 | 24 | # ============================================================================
# This code prepares `flu` dataset
# ============================================================================
# Required packages ----------------------------------------------------------
library(hyperSpec)
library(usethis)
# Read the... |
41dadfe95440513ef8045d1438f1d301f5cb04e401d592daca98c6d2024cffc2 | R | 922 | 20 | library(autofocus)
library(magrittr)
library(SummarizedExperiment)
load(data.makepath("ADNI/ADNI_data_for_Annalise/ADNI_data_for_Annalise.RData"))
data.mat <- dfs$bp1[3:ncol(dfs$bp1)]
pheno.mat <- dfs$phe[dfs$phe$RID%in%dfs$bp1$RID,]
mol.mat <- lapply(colnames(data.mat), function(i){names(fset.list)[grep(i,fset.list... |
d0537e15ca9fe7c857bd90adbe1e82712936acd45e0510abba084c91de974290 | R | 922 | 27 | require('plyr')
require('ggplot2')
require('patchwork')
# Plot subjects' estimated implicit and explicit adaptation, along with
# their early error (see figure 1)
source('R/plotELbehaviour.R')
plot.elbehavstats
# Imports and plots the mean loadings for the implicit, explicit, and
# performance axes, as well as the e... |
41295473630b98dcbd902c8fb6a2527959c6918907c146a1739b033a55bd6895 | R | 927 | 32 | ---
title: "Version and necessary libraries"
output: html_notebook
---
```{r}
R.Version()
```
Necessary librairies
```{r}
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
necessary_librairies <- c('ggplot2', 'tidyverse', 'ggVennDiagram', 'clustree', 'biomartr', 'circlize', 'readxl', 'readxl', 'UCell', '... |
5bf72f5186db09451fc9a7ca2b57bc3f8efe40f563adb38a5f50f111a405b976 | R | 928 | 28 | #==============================================================
# perform PC-AiR
#==============================================================
pacman::p_load(data.table, GENESIS, SNPRelate)
#-----------
# PC-AiR
#-----------
# mostly taken from here:
# https://www.bioconductor.org/packages/release/bioc/vignettes/... |
2078135b609a41e66c1dc0c0f4c6f53c5d4f2b8c7ffeac6e4a3329a5358fb3d2 | R | 937 | 21 | ---
title: "Preprocessing your data"
output: html_document
---
Expression matrices derived from mass spectrometry data often contain many missing values. Rows with a high abundance of missing values can be considered uninformative for differential expression analysis and should be removed. The feature presented here l... |
74455059e8aafb0ea324f1a36e03b766ddfab93eab9aae112b179e33331231fa | R | 937 | 42 | ---
title: "News"
#author: "Martin Rydén"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
##### April 15, 2021
* Easier data import
* Improved PCA and heatmap functionality
* Tooltips everywhere
* Volcano plot for differential expression analysis
* Volcano plot for enrich... |
52c13f5ad61e346e5be3bb17e0af2547ccbf6e191fec2a97f0f6a5e1d2e66e7d | R | 939 | 28 | # Original script is taken from Jason Stein's ENIGMA-evo repository.
# Gokberk Alagoz - 01/10/2021
library(gprofiler2)
options(stringsAsFactors = FALSE)
#Load in global gene list
GlobalSAHGE = read.csv("/data/workspaces/lag/workspaces/lg-ukbiobank/projects/globularity/evolution/results/VEP/glob_gene_list.txt")
GO <... |
ea5baca9fbd95648aa41128f19cd834036a7c023e8298ce2829ff583f7a0366c | R | 943 | 39 | #' Smooth a time series through cubic splines
#'
#' This can be used as a simple smoothing function, in the case of eyetracking
#' data it can be used as a low-pass filter, useful to correct artifacts,
#' blinks, etc.
#'
#' @param vector A vector variable to be smoothed.
#' @param time A vector variable indicating the ... |
bc48bcdd8271726d6706b262180c350da8035ec5ae36c7cefeabac50ddc957dc | R | 946 | 27 | #' @details
#' Computational methods for the scregclust algorithm
#' @keywords internal
#' @aliases scregclust-package
#' @author Ida Larsson, Felix Held, Sven Nelander
#' @import Rcpp
#' @import cli
#' @import ggplot2
#' @importFrom rlang .data
#' @importFrom prettyunits pretty_dt
#' @importFrom Matrix Matrix sparseMa... |
f9915ad81a197412fb11fceadc3a00527ce14bb4b2ba8b601a8f7116ec92572e | R | 959 | 35 | #!/usr/bin/env r
#
# a simple example to convert miles and times into a pace
# where the convention is that we write e.g. 37 min 15 secs
# as 37.15 -- so a call 'pace.r 4.5 37.15' yields a pace of
# 8.1667, ie 8 mins 16.67 secs per mile
if (is.null(argv) | length(argv)!=2) {
cat("Usage: pace.r miles time\n")
q()... |
8981cf38c927934e0ef01f2274582e4628f44e82014a31a42ce2825ad0dcca7b | R | 960 | 27 | #==============================================================
# perform PC-AiR
#==============================================================
pacman::p_load(data.table, GENESIS, SNPRelate)
study <- 'rosmap'
#-----------
# PC-AiR
#-----------
# mostly taken from here:
# https://www.bioconductor.org/packages/relea... |
ada111a9e63f96ac2a861cbf8c2f65f9a9aa99aa1fa4ce7fd63057f5e1416d93 | R | 962 | 39 | args <- commandArgs(trailingOnly = TRUE)
fasta_file <- args[1]
png_file <- args[2]
log_file <- args[3]
log_con <- file(log_file, open = "wt")
sink(log_con, type = "output")
sink(log_con, type = "message")
library(Biostrings)
tryCatch({
# Read the fasta file
reads <- readAAStringSet(fasta_file)
read_lengths <-... |
3af2a0e65ed104b24a993c87f71cedac30fcba69e5c0244a11a388669286326d | R | 968 | 26 | # ============================================================================
# This code prepares `laser` dataset
# ============================================================================
# Required packages ----------------------------------------------------------
library(hyperSpec)
library(usethis)
# Read t... |
a4a01144e335ef8604f5eca803b2e467e3ade83341de3f9f1b1d62216c31db9c | R | 968 | 39 |
# dichotomize ordinal phenotypes for colocalization analysis
library(data.table)
phenos <- fread("shared/nacc_rosmap_act_np.pheno", na.strings = "")
# braak stage: 0-3 vs 4-6
phenos[braak < 4, braak := 0]
phenos[braak > 0, braak := 1]
# cerad score: 0-1 vs 2-3
phenos[cerad < 2, cerad := 0]
phenos[cerad > 0, cerad :... |
eecd1cad7b8ee5c382b1bcc55ee5a0c7fa0bbfcde9062c677842be817a714c4b | R | 969 | 25 | source('./scripts/01_Setup.R')
filenamesx <- gsub('.rds', '', readRDS("./data/processed/rds/filenamesx.rds"))
aa2= paste0(filenamesx, '_SegFixed_cnv_stat_genome.rds')
aa2= c( "PTA_48_500kb_lift_SegFixed_cnv_stat_genome.rds",
"PTA_101_500kb_lift_SegFixed_cnv_stat_genome.rds",
"Pico_76_250kb_lift_SegFix... |
d26ed2ddcf7ff874ed3fd5dac0dfef0d3fdd2817bfa687ded1fb0929156f4375 | R | 972 | 27 | #! /usr/bin/Rscript --vanilla
#==============================================
# Input text file with SNP IDs as one column
# and output list of SNP IDs
#==============================================
library(data.table)
library(argparse)
parser <- ArgumentParser()
parser$add_argument("-f", "--file", help = "text file... |
87364df1c6664ea3454ed3fdf36e6102986a1190b7c24295ad037ad2e83b5a94 | R | 976 | 36 | #' Thrombolytic Therapy after Acute Myocardial Infarction
#'
#' @description
#' Meta-analysis on Thrombolytic Therapy after Acute Myocardial Infarction
#'
#' @name Olkin1995
#' @aliases Olkin95
#'
#' @docType data
#'
#' @format A data frame with the following columns:
#' \tabular{rl}{
#' \bold{\emph{author}}\tab fi... |
d509cde40dda64662131fca0a2a44086f16db1f22b4696299ebb148a9b7d4bac | R | 978 | 20 | #' NeuroimaGene object for vignette illustration
#'
#' NeuroimaGene data table containing select associations used in the package vignette.
#'
#' @docType data
#' @name ng_vignette
#' @usage data(ng_vignette)
#' @format A data.table with 6 columns and 3824 rows:
#' \describe{
#' \item{gene}{character: ENSEMBL Gene ID... |
cc687154d856786ff894aeea0ae4bd47d101c2abe6a8ce06f904633727c86e6b | R | 984 | 26 | #! /usr/bin/Rscript --vanilla
#-----------------------------------------------------------------------------
# Check heterozygosty and create a list of IDs that are > +/- 3 SD from mean
#-----------------------------------------------------------------------------
library(data.table)
library(argparse)
parser <- Argum... |
1142d092de2117f8561b9fa3b09097495b0e2dbe51e912211f3e35ea9c677b41 | R | 988 | 48 | #!/usr/bin/env r
#
# Convert miles-per-hour, time-distance measure settable
# on treadmiles, to different race times.
if (is.null(argv) | length(argv)<1) {
cat("Usage: mph.r mph\n")
q()
}
dig <- 5
mph <- as.numeric(argv[1])
cat("Mph : ",format(mph,digits=dig),"\n");
hourminsec <- function(totalsecs){
ret... |
f4da9e0a896914b87fcf90406a4deadcaf84a17d3c927480a0af48c8babbe58e | R | 988 | 31 | # File: deWrapper.R
# Author: Kelly Chang
# Zhihua Li
# Date: Oct 2017
# Version: 1.0
#
# Description: Helper R function which evaluates an objective function for the
# specified subpopulation.
#
deWrapper<-function(chrpernode,pop,objfun){
# returns function... |
51d5d322054c95dbeac0be92c879dba47f91771339d5cd24f7400b3056171917 | R | 991 | 38 | #!/usr/bin/env Rscript
args <- (commandArgs(TRUE))
cov <- read.table(args[1])
c_max <- max(c(max(cov$V2),abs(min(cov$V3))))
c_max <- c_max+floor((c_max/10))
len <- max(cov$V1)
jpeg(file="coverage.jpg", height=400, width=1300, pointsize = 25)
par(mar=c(4,4,1,0))
plot(cov$V1,cov$V2,type="l",xaxt="n",yaxt="n",col="b... |
e7450bab3b41cd96eca2f626b74230c904d6827caf4635e6d2c495dda1ace511 | R | 992 | 27 | #=========================================================================
# Convert plink2 .glm.logistic data.tables to a format that is backwards
# compatible with scripts that use plink1.9 .assoc.logistic data.tables
#=========================================================================
plink2_to_plink_format <... |
d59d2c0fbe0d83f0e657f03e35141dc78f94cb5f5809575d93c5cb9bd1c34f0d | R | 993 | 31 |
#' mochims_structure_metrics_datasets
#'
#' Add 3D structure metrics of multiple datasets.
#'
#' @param dataset_names character vector of dataset names (required)
#' @param pdb_file_list list of paths to PDB files (required)
#' @param base_dir Base directory (required)
#' @param output_dir Output directory (required)
... |
b2a8d6654a84fb3c6cbde8a4aafc38cda47d2d8276bc2248e874ca53b41ecada | R | 994 | 27 | library(ggplot2)
options(stringsAsFactors = FALSE)
args = commandArgs(trailingOnly=TRUE)
stats_out_file <- args[1]
acc_file <- args[2]
id_out <- args[3]
out_dir <- args[4]
stats_out <- read.delim(stats_out_file, stringsAsFactors = F, header=F, sep = "\t")
acc <- read.delim(acc_file, stringsAsFactors = F, header=F)
co... |
5670c04d158a91880b93c7bb1caae113653de638c181ebe0fb6c07925b40ed07 | R | 998 | 37 | #' @importFrom nlshrink linshrink_cov
#' @importFrom huge huge huge.select
spd.estimate.sample <- function(x, ...){
cov(x, ...)
}
spd.estimate.linshrink <- function(x, ...){
linshrink_cov(x, ...)
}
spd.estimate.nlshrink <- function(x, ...){
nlshrink_analytical(x, ...)
}
spd.estimate.glasso <- function(x... |
916a387d170a819fb7dba02eeddd18e984c3266f0207010d2a9a032d72113320 | R | 998 | 20 | #' NIDP freesurfer annotation data
#'
#' Freesurfer names for select cortical and subcortical NIDPs
#' @docType data
#' @name fs_anno
#' @usage data(fs_anno)
#' @format A data.table with 7 columns and 890 rows:
#' \describe{
#' \item{gwas_phenotype}{character: UKB-derived neuroimaging derived phenotype}
#' \item{at... |
bc15d5bf13a9e052768b086ce49ba2f64e13ad03f67625729224ed0b4c257798 | R | 998 | 31 | #' @importClassesFrom kernlab kernel
spddot <- function(sigma = 1, method = 'logeuclidean'){
rval <- function(x, y = NULL){
# Type checking
if (!is(x,"vector")) stop("x must be a vector")
if (!is(y,"vector") && !is.null(y)) stop("y must a vector")
if (is(x,"vector") && is.null(y)... |
19ca88f68a86b8c9539464bb539452efced0f0e5ff81b5ccacaa0dee85c32d75 | R | 1,002 | 48 | # Auxiliary functions to check inputs
#
# Package: meta
# Author: Guido Schwarzer <guido.schwarzer@uniklinik-freiburg.de>
# License: GPL (>= 2)
#
isCol <- function(data, varname)
!is.null(data) & varname %in% names(data)
is_log_effect <- function(x)
x %in% c("PLN", "IRLN", "MLN")
is_relative_effect <- function(x... |
54a24a331773d573defb6abd965ab133349076c635d362073c9f38abaf2fe1bd | R | 1,004 | 31 | #' Get Nested Variable
#'
#' This function extracts a nested variable from a data frame and creates a new column with the concatenated values.
#'
#' @param data The input data frame. (plot_data)
#' @param nested_col The name of the column containing the nested variable.
#' @param var The name of the nested variable to ... |
8ee263119d7b07d8cb6f8916ecee492f6501e960538c517d859a87108fbd34ae | R | 1,005 | 29 | source('./scripts/01_Setup.R')
setwd("./data/processed/rds")
aa= grep("MAD",list.files(), val=T)
aa2= aa[aa%in%c("MAD_dMDA_101_5Mb_t2t.rds",
"MAD_PTA_101_500kb_t2t.rds",
"MAD_Pico_101_250kb_t2t.rds",
"MAD_Pico_76_250kb_t2t.rds",
"MAD_PTA_48_500kb_t2t.rd... |
be91feb1c66bd861e7a72b40f07728155882970ec6820360964aa09661f559ee | R | 1,008 | 34 |
#' mochims__predict_fitness_2state
#'
#' Predict fitness from energies for 2-state model.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param phenotype_name phenotype name (required)
#' @param energy energies for 2-state model (required)
#' @param RT constant (default:0.001987*(273+2... |
7c50008bf67b4eeadf8bdf770e40e601ab3e73a82b1755a207ebe9ef0b1cb6c6 | R | 1,013 | 22 | plotFit <- function(fitDat, readBw){
for(reg in unique(fitDat$Region)){
regDat <- fitDat[fitDat$Region == reg,]
p1 <- ggplot(regDat) +
geom_line(aes(x=EventPos, y=EventScore), size=0.1) +
ggtitle(regDat$Region) + theme_minimal() +
#geom_hline(aes(yintercept=yint),... |
e00096d6f5e6ac781bc16e156a0ed711187d531ef03dd1b0840a1427a60d64b1 | R | 1,014 | 37 | #' @title Command line completion for `$`
#' @description
#' Command line completion for `$`.
#' This function is not intended to be used directly by users but provides
#' auto-completion capabilities.
#'
#' @aliases .DollarNames .DollarNames,hyperSpec-method
#' @author C. Beleites
#' @seealso [utils::.DollarNames()]
#... |
97f6268c455eb50a562a4a42f2098b9fe1a17d5520dad7d05f80abb34c31f10c | R | 1,015 | 37 | #cross validation of exploratory best measure
library(caret)
library(party)
library(flexplot)
if (exists("bones_ukb")) {
bones_ukb$eid <- as.numeric(bones_ukb$eid)
bones_ukb <- select_if(bones_ukb, is.numeric) #get only numeric vars
bones_ukb<- bones_ukb[,-c(nearZeroVar(bones_ukb, freqCut = 100/0))]#remove ... |
1f24fb4e07a8586d1c92a6a0ac78515456024da0e19ce62d9675c0965bd4726d | R | 1,019 | 28 | source('./scripts/01_Setup.R')
setwd("./data/processed/rds")
standardize_name <- function(name) {
return(gsub('[^0-9a-zA-Z]+', '_', name))}
aa= grep("MAD",list.files(), val=T)
aa2= aa[aa%in%c("MAD_dMDA_101_5Mb.rds", "MAD_Pico_76_250kb.rds", "MAD_Pico_101_250kb.rds","MAD_PTA_48_500kb.rds",
"MAD_PTA_1... |
6f8a3960aa1e883ed7f44035273e0f29b971574ae55b3c4ad721a7fe7935b869 | R | 1,019 | 30 | #### Notes ####
#### Libraries ####
library(ggplot2)
library(viridis)
#### Sourced functions ####
#source("../functions/annotate_boxplots.R")
#### Plotting functions ####
plot_triangle_correlation_matrix <- function(corr.matrix.diag, title="") {
ggplot(data=corr.matrix.diag,
aes(x=Condition1, y=Condition... |
754674aa0530d9a3ebf9a105214d8b047db1a13247d8bc48ae0c3ddc65b90b93 | R | 1,027 | 29 |
install_libraries <- function() {
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!require("SingleCellExperiment", quietly = TRUE))
BiocManager::install("SingleCellExperiment")
if (!require("scater", quietly = TRUE))
BiocManager::install("scater")
if (!r... |
82ae4e434b6c2fffaec0ca67738595451b6d5ea6bac6cb5d3ec5734693b7e4bc | R | 1,037 | 29 | source('./scripts/01_Setup.R')
setwd("./data/processed/rds")
aa= grep("MAD",list.files(), val=T)
aa2= aa[aa%in%c( "MAD_dMDA_101_5Mb_lift.rds",
"MAD_Pico_76_250kb_lift.rds",
"MAD_Pico_101_250kb_lift.rds",
"MAD_PTA_48_500kb_lift.rds",
"MAD_PTA_101_500... |
c71b9a769b076e286bc7258bed8ea61949edd0a3e2c768bd7130d34a6610aa69 | R | 1,044 | 30 | #==================================================
# compare imputed APOE e diplotype vs synapse data
#==================================================
library(data.table)
rosmap_clin <- fread("data/synapse/ROSMAP_clinical.csv")
covar <- fread("~/projects/np_phewas/data/mega/mega_np_apoe.covar")
# harmonize varia... |
fc87929564fafa7042fe04e5824a929930a3ba0a156238843fee02f2f2695241 | R | 1,044 | 32 | ##---------------------------------------------------------------------
## make lists of snps of length 30K for analysis blocks
##---------------------------------------------------------------------
library(pacman)
p_load(data.table, magrittr)
# args <- commandArgs(trailingOnly = TRUE)
# load snps from .bim file
bi... |
12644177f69c6b008bf297d2d614d6ecaeb53b0bff502f97c4da06463948a0de | R | 1,046 | 30 | source('./scripts/01_Setup.R')
setwd("./data/processed/rds")
aa= grep("ConfiScore",list.files(), val=T)
aa2= aa[aa%in%c("dMDA_101_5Mb_ConfiScore.rds",
"Pico_76_250kb_ConfiScore.rds",
"Pico_101_250kb_ConfiScore.rds",
"PTA_48_500kb_ConfiScore.rds",
"PTA_1... |
47a4f1038d5b2becd5be084cf479ab6e1dc7aac6e59c96fa42fd26fc4b65fc9a | R | 1,050 | 27 | library(dplyr)
library(tidyr)
library(bio3d)
template_file <- unlist(snakemake@input)
output_file <- unlist(snakemake@output)
templates <- read.table("pdb/template.txt", col.names = c("ID", "_P_", "fname")) %>%
mutate(ID = substring(ID, 2))
data <- lapply(templates$fname, read.pdb) %>%
setNames(templates$ID)
... |
b9a3214458a49b55e76f03b8854cc01a88d629077d3c0bf338e189f37d67d773 | R | 1,052 | 27 | ##################################
#
# This function is used to add the
# network score to the TDp
#
# Net_annotation is the dataframe obtained
# from ObtainNetID
#
AddNetworkScore_Schaefer <- function(dat_TDp, net_anna, prefix=1){
dat_tmp <- dat_TDp
net_annotation <- net_anna
network_name <- unique(net_annotat... |
e07bece8d0f6d24ca3461bfd0b4251d1c31cfaefb3a2d624cd90e92af72e1c81 | R | 1,054 | 32 | library(Seurat)
library(dplyr)
library(rhdf5)
# Read in the data
h5_file = h5read("./mouse_leukocyte.h5", name="dge")
sample.data = t(h5_file$block0_values)
colnames(sample.data) = h5_file$axis0
rownames(sample.data) = h5_file$axis1
# Make seurat object
sample <- CreateSeuratObject(raw.data = sample.data)
# Normaliz... |
6fe68a076ae8a352a814a8b63ececba26860b3fac17732302696e0ca8906b09c | R | 1,056 | 30 | source('./scripts/01_Setup.R')
setwd("./data/processed/rds")
aa= grep("Confi",list.files(), val=T)
aa2= aa[aa%in%c("dMDA_101_5Mb_t2t_ConfiScore.rds",
"Pico_76_250kb_t2t_ConfiScore.rds",
"Pico_101_250kb_t2t_ConfiScore.rds",
"PTA_48_500kb_t2t_ConfiScore.rds",
... |
9442d77afaa6c1b6c457bae86443f503d495b225b5e750fd445cd32419505e6d | R | 1,057 | 32 | ##--------------------------------------------------
## Single-CpG analysis for genes in gene list
#---------------------------------------------------
library(data.table)
library(synapser)
cpg.meta <- fread("/data_global/ROSMAP/DNA_methylation/ROSMAP_arrayMethylation_metaData.tsv")
genes <- fread("/data_global/UCSC... |
1fd93d848437b38f826a13c46ed56cdf6a994a678a02a2260ff395b004f389a9 | R | 1,064 | 3 | #!../r
x<-"012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678... |
2b42bcfd91eeb7b411fa81d3d1c166043757888407ac88c936566ef4af7d9088 | R | 1,064 | 35 |
#' List installed *R hyperSpec* family packages
#'
#' Convenience function that lists all installed
#' [*R hyperSpec*](https://r-hyperspec.github.io/) family packages.
#' These include **hyperSpec** and all packages that share common
#' **`hySpc.`_something_** package name pattern.
#'
#' @return Character vector with ... |
ffdb3372d1e54292c18c100aeb7729cab90047ab44301e5457d364ff42304872 | R | 1,070 | 23 | ---
title: "Inspect data"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Principal Component Analysis
A common goal in many of ProteoMill’s functionalities is to reduce data complexity, and to provide a framework for extracting elements of biological relevance. PCA is ... |
7e235e6e92de515561a2966b99e47599a50d54c5629c6d63639ab598ec587ad7 | R | 1,072 | 23 | ## should check if the stat is p-values, but this won't effect that
## so not fixing it now
lhRatioMax <- 25
plotROCPerRead <- function(rocDat, denStats){
print(ggplot(rocDat) + geom_abline(slope=1, intercept=0) +
geom_path(aes(x=FP, y=TP, color=Comparison)) + theme_bw() +
xlab('False Positive ... |
0db1998a61ee5ca497d4042b7bf6859c04525ed9b9428cce7efcdfe6e855f234 | R | 1,073 | 22 | #----Figure_S06_A---------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure S06 Panel A for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
#------------------------------------------... |
4b9ac66d76d3502aa74f7e5e551f84f7da3085eb9e826767572709a8c1391223 | R | 1,083 | 29 | source('./scripts/01_Setup.R')
setwd("./data/processed/rds")
aa= grep("Confi",list.files(), val=T)
aa2= aa[aa%in%c("dMDA_101_5Mb_lift_ConfiScore.rds",
"Pico_76_250kb_lift_ConfiScore.rds",
"Pico_101_250kb_lift_ConfiScore.rds",
"PTA_48_500kb_lift_ConfiScore.rds",
... |
7dcafd69fd574ee9036f9b1e178160c5fcb8c248fc6371d53c0f766581dd8a6e | R | 1,086 | 35 | #######################################
#
# This function is used to obtain the
# time delay projecton maps from inputs
# while matching the selected participants
#
# This function can load the data of TDp and
# ETS. Indicates what kinds of data you want to
# load in [type] parameter
ObtainBrainData <- function(sbj_... |
50df68f31aacc699589c139ca000a7076689917013c98cb3d1be06992df66708 | R | 1,094 | 37 |
#' mochims_coefficient_size_distribution_datasets
#'
#' Coefficient size distribution plots 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 (requi... |
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