sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c13649ac83d41f54e2576f5aa9524e4b0535156c80582a2fe6ed9be6542f24fb | R | 4,550 | 138 | #' @name DEPRECATED-read.spc.Kaiser
#' @concept moved to hySpc.read.spc
#'
#' @title (DEPRECATED)
#' Import functions for Kaiser Optical Systems `.spc` files
#'
#' @description
#'
#' These data input functions are **deprecated** and they will be removed in
#' the next release of \pkg{hyperspcc} package.
#' Now f... |
093435916ee2802047cc2547459f9c3ec9e20041bd00766413086c36920ab6f0 | R | 4,552 | 75 | #Load package
suppressMessages(library(qvalue))
suppressMessages(library(limma))
suppressMessages(library(DESeq2))
suppressMessages(library(WGCNA))
suppressMessages(library(preprocessCore))
suppressMessages(library(peer))
#Read command line arguments
args <- commandArgs(trailingOnly = TRUE)
try(if(length(arg... |
f80476d58761bc758900b5dc09c3113fa921145a3090f8b23a253639b7e7792d | R | 4,555 | 119 |
###Script 20
#GSEA on MAST DE results
library(fgsea)
library(data.table)
library(ggplot2)
library(msigdbr)
library(dplyr)
library(ComplexHeatmap)
library(RColorBrewer)
library(future)
library(ggrastr)
library(ggrepel)
gmt.file <- gmtPathways("gene_sets/m5.go.v2023.1.Mm.symbols.gmt")
###############... |
e513086c4cc8fb0664f6f6ebc66286106e1bd09b3c4787f68436ab311ceff45c | R | 4,556 | 120 | ---
title: "Kv_act_inact_curves"
author: "Franz Xaver Mittermaier"
date: "2024-04-21"
output: html_document
---
This script creates the graph for the activation and inactivation curves.
In brief, I performed axonal bleb recordings. In voltage clamp mode I then performed two protocols:
1. Activation protocol: ... |
9a417193a8d0a009cf556ffbdc60b543abff53e591c6dbd0a6c50020265815d3 | R | 4,557 | 125 | is.cran <- testthat:::on_cran()
ncores <- ifelse(is.cran, 1, parallel::detectCores())
# Setup parameters for test cases
R <- ifelse(is.cran, 100, 200)
nreps.null <- ifelse(is.cran, 50, 100)
alpha <- ifelse(is.cran, 0.25, 0.15)
eps <- ifelse(is.cran, 0.2, 0.1)
eff_sz <- 3
n <- ifelse(is.cran, 100, 150)
test_that("rej... |
2a6fb4112fef315d1e1d72a29f802f4aa74e685e6213fed435811f75bebf8be3 | R | 4,560 | 165 | ### ---------------------------------------------------------------------------
###
### read.txt.long: import measurements from .txt file
###
### Format:
### (y x) wl int
###
#' @rdname DEPRECATED-textio
#' @concept moved to hySpc.read.txt
#'
#' @param file filename or connection
#' @param cols the column names spe... |
ad16024bede0f140533d2379cf53c9c3e25b3babe35d95f2f4337e1483557efb | R | 4,565 | 70 | library(DESeq2)
library(Seurat)
library(ggplot2)
library(ggrepel)
load("/media/Scratch_SSD_Voyager/sammi/RNA_editing/Figure3_ADARKO_R1/KO_4ter_project_SO_annot_collapsed_ADARB1-1rm_20230207.rda")
KO_sub
Idents(KO_sub) <- KO_sub$guide
comps <- data.frame(compA = c("AAVS1","ADAR1","ADAR1","ADARB1","ADARB1","ADARB2","A... |
c21cbcd2b5bee59d96172783a02514c611fbacb31387fd9391180818d9b59585 | R | 4,580 | 95 | ### OASIS-3 Retest case for Boney ###
# July 2024
library(dplyr)
library(stringr)
library(ggplot2)
library(psych)
retest_Boney <- read.csv(paste0("/Volumes/OneTouch/MRData/202301_MRIbones/OASIS3/",
"BIDS/sub-OAS30002/ses-d2340/anat/Boney_xm2csv_report_20240427-003047.csv"))
fnames... |
9751b0fa3f0700a4ef91dc69338d2336fe4f33b9e48e2fee462d338a7e3fb821 | R | 4,620 | 120 | library(dplyr)
library(tidyr)
library(stringr)
library(ComplexHeatmap)
library(tibble)
# Tumor
# TME
# CellChat
csv_file <- "gProfiler_Human_LR_allterms.csv"
gProf_res <- read.csv(csv_file, stringsAsFactors = FALSE)
## Get columns
gProf_res <- gProf_res[,c(1,2,4,grep("adjusted_p_value__", colnames(gProf_res)))]
... |
9b10690e3b7a4394e6ca38003d45bb5c17bcdf2da4a85c81e977a781e4c52186 | R | 4,635 | 195 | # ------------------------------------------------------------
# Extract mean framewise displacement per movie and run ezANOVA
# ------------------------------------------------------------
library(tidyverse)
library(ez)
# -----------------------------
# Paths
# -----------------------------
fmriprep_dir <- "/projec... |
986f0560e8c39872417dfca9323972e8bbff37befa49a946b2e5bbdf3385782f | R | 4,655 | 105 | ---
title: "plot_gctb"
author: "Gokberk Alagoz, L&G Dept., MPI for Psycholinguistics"
date: "`r Sys.Date()`"
output: rmarkdown::github_document
---
```{r}
library(ggplot2)
library(tidyverse)
library(cowplot)
library(reshape2)
```
```{r readData}
gctb_results = read.csv("/data/clusterfs/lag/users/gokala/beat-dyslexia... |
2753729cd6ab4c3161b90e08ca5e63844fa0df6e725ac0b9e89225b7f6c6460b | R | 4,669 | 97 | #----Figure_S02_A --------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure S02 Panel A for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
# set variables:---------------------------... |
2282aae1e583e1ae3b9e862f9bb533c9c4d875115820ef89a6538242a71aca9c | R | 4,672 | 118 | library(Gviz)
library(dplyr)
library(grid)
library(ggplot2)
library(cowplot)
library(magick)
args <- commandArgs(trailing=T)
chromosomeRangePath <- args[1]
bandsPath <- args[2]
circosPicturePath <- args[3]
chromosomeFontsize <- as.numeric(args[4])
chromosomeHeightPerRow <- as.numeric(args[5])
chromosomeMaxColumns <- ... |
811b6fb52a04241211ac8708f54ea2027a58a35defe4e52bfb3bb0cc241ff017 | R | 4,674 | 110 | #' makeBoundaryDistance
#'
#' Generate distance-to-class-boundary grids from mask chips
#'
#' This function reads raster mask chips from the "masks" subfolder of a chips
#' directory created by \code{makeChips} and generates a corresponding distance
#' surface for each mask. Each output raster encodes, for every pixel,... |
915bf8de5be13f9fda51cb5190b75cabc39d16a0eb2c9c6e32840385511a98f1 | R | 4,674 | 67 | source('./scripts/01_Setup.R')
# A treemap R script produced by the Revigo server at http://revigo.irb.hr/
# If you found Revigo useful in your work, please cite the following reference:
# Supek F et al. "REVIGO summarizes and visualizes long lists of Gene Ontology
# terms" PLoS ONE 2011. doi:10.1371/journal.pone.0021... |
1ce80f774f6ed416439b7cc6633f38d572f965de6e97d667b5a7e282b60fc65c | R | 4,679 | 71 | library(Seurat)
library(ggrepel)
load("/media/Scratch_SSD_Voyager/sammi/RNA_editing/Figure3_ADARKO_R1/KO_4ter_project_SO_annot_collapsed_ADARB1-1rm_20230207.rda")
KO_sub
comps <- data.frame(compA = c("ADAR1","AAVS1"), compB = c("AAVS1","NTC"))
adipgenes <- c("FABP7","SULT1E1","DLK1","FRZB","KLF4","KLF6","CD24")
stre... |
6064324aa95581c5ee56617837d750a5b15efef8a3b3a20f69c61c46f35616b9 | R | 4,684 | 133 | require(ks)
test_that("one-hot encoding works with numeric vector", {
Ts <- c(1, 1, 0, 0)
Ts.ohe <- as.matrix(ohe(Ts)$ohe)
expect_true(all(Ts.ohe[1,] == Ts.ohe[2,]))
expect_true(all(Ts.ohe[3,] == Ts.ohe[4,]))
expect_false(all(Ts.ohe[1,] == Ts.ohe[3,]))
})
test_that("one-hot encoding works with character vec... |
f4f81a69d1134150ba9be2aa4544009391827f3a6777847eae22c324029498d8 | R | 4,703 | 134 | # simDupHMM.R
library(expm)
simDupHmm <- function (nsnp, theta, nullr, duplr, nulcov, dupcov, pi, p, seqid='chr1') {
# simulate paralogous data according to HMM parameters
# nsnp: number of SNPs to simulate
# theta: population scaled mutation rate (theta = 4*N*mu)
# nullr: ngsParalog output for non-paralogous s... |
8da781fc0f2f08bc43235c48be649abb41392cbc19960d2ce9c04635104dddde | R | 4,704 | 180 | library(WGCNA)
library(Seurat)
load(file = "/media/Scratch_SSD_Voyager/sammi/RNA_editing/Figure3_ADARKO_R1/KO_4ter_project_SO_annot_collapsed_ADARB1-1rm_20230207.rda")
KO_sub
Idents(KO_sub) <- KO_sub$annot_v1
KO_sub <- ScaleData(KO_sub)
for (i in names(table(KO_sub$annot_v1))){
celltype=i
print(i)
seur<-subs... |
20956e628c5aabcd3baec46f46c4d1cd20c84dfdf10c38f4679430eccc2ea97b | R | 4,707 | 140 | library(data.table)
library(ggplot2)
library(coloc)
bel2022 <-
fread("/data_global/summary_statistics/Bellenguez/GCST90027158_buildGRCh38.tsv.gz")
bel2022 <- bel2022[!which(duplicated(variant_id))]
hs <- fread("output/gwas/mega/saige/hs_saige_results.txt")
hs_pheno <- fread("data/mega/mega_np.pheno", na.strings = "... |
2b7237098c8735a2804da61818759b01bcb2082d523d35f17f68740437dd46ab | R | 4,712 | 135 | #' Get some summary statistics. From library(patchseqtools)
#'
#' This does the summary. For each group return a vector with N, mean, and sd.
#' This is called by qcPlot.
#'
#' @param data Annotation data frame
#' @param measurevar what variables to measure
#' @param groupvars what to group by
#' @param na.rm how to t... |
7d66a1ab835437221c0c70042a21fd4149f45b615fdb545bd5d18a5cd621f26c | R | 4,720 | 118 | # If you need the original fragment files for quality control analysis, please contact: wuhaixu@mail.kiz.ac.cn
library(ArchR)
addArchRThreads(threads = 8)
rheMac10_UCSC = createGenomeAnnotation(genome='BSgenome.Mmulatta.UCSC.rheMac10')
#Load rheMac10 Gene Annotation Information
mm10_annotation = read.delim(file = '.... |
d63c44c3c94f3022588c04190524fcd474e014517341aa9927a74445f990de29 | R | 4,721 | 170 | library(tidyverse)
library(lme4)
library(broom.mixed)
# ------------------------------------------------------------------
# Constants
# ------------------------------------------------------------------
n_subj <- 28
n_pairs <- n_subj * (n_subj - 1) / 2 # 378
k_fixed <- 2 # intercept + Beha... |
c58e84174a5b68c3918b45090b0e912571f985c06b4ba7c812f430462d6952e0 | R | 4,726 | 170 | library(tidyverse)
library(lme4)
library(broom.mixed)
# ------------------------------------------------------------------
# Constants
# ------------------------------------------------------------------
n_subj <- 178
n_pairs <- n_subj * (n_subj - 1) / 2 # 15753
k_fixed <- 2 # intercept + B... |
5458c708278391bab2aa3804e282433d0bb3949a625f72965074788b4a57541d | R | 4,727 | 129 | # Single cell RNA-Seq of Tlx3 WT vs KO samples
# Budhaditya Basu
install.packages("BiocManager")
BiocManager::install("AnnotationDbi")
#To install the geneLists package in R, use the devtools package:
# Install from github
devtools::install_github("soderling-lab/geneLists")
devtools::install_github("mont... |
6c01efae1c2f36f60a96497e3b3046a77baa4f8932778a984a24ccb1db721c34 | R | 4,736 | 195 | library(tibble)
library(dplyr)
library(ggplot2)
library(purrr)
library(tidyr)
library(data.table)
# Will be the dataset "wang" or "pbmc"
dataset <- commandArgs(trailingOnly = TRUE)[1]
output_path <- "output"
source("../utils/0-indices.R")
gene_indices <- read_gene_indices(sprintf("data/%s-gene-indices.txt", dataset)... |
d30014a292f707ec5028ba1d40bb5320f8c02f2effe4f2cd01ce0a43624fad7b | R | 4,737 | 133 | ---
title: "Optic Nerve PLP Cre+ vs. Sox10 Cre+ DEG list"
author: "Jonathan Nelson and Katie Emberley"
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
html_document:
toc: yes
toc_depth: 3
toc_float: true
number_sections: yes
theme: bootstrap
df_print: paged
code_folding: hide
highl... |
6e2c8314ae90714dd8df697cdccc90994775931b56f4d3ef385fb7c5e2277ebd | R | 4,738 | 153 |
tissue='Brain_Frontal_Cortex_BA9'
#tissue='Whole_Blood'
st<-read.table(paste0('~/../Dropbox/DansPaper/data/replication/st_',tissue,'.txt'),header = T,stringsAsFactors = F)
st<-st[,-4]
colnames(st)<-c('geneid','r_st','p_st')
length(intersect(which((st$p_st)<0.05),which(st$r_st>0.1)))
length(intersect(which(p... |
02a7b6e0289c9437ef91f1b8dcc024d4bf90466555eba98c0f25f7b2ac0ba38d | R | 4,743 | 127 | #' nested_contingency_table_fun.R
#'
#' Function to create a contingency table - can be used in the nested data-frame
#' for one or two variables of the metadata. Its done to create contingency table in
#' each nested table
#'
#' @param data The data frame containing the metadata.
#' @param ... One or two variables ... |
2e6761b31b10394b65a3ccac24edba8009fa5962d71840ff2c9b011e899cbf90 | R | 4,745 | 115 | library(data.table)
library(ggplot2)
library(forestplot)
make_95CI <- function(or, p, ci=0.95) {
beta <- log(or)
lt_zero <- beta < 0
z <- qnorm(p/2, lower.tail = TRUE)
sd <- beta / z
zrad <- numeric(length = length(p))
for (i in 1:length(p)){
zrad[i] <- qnorm((1 - ci) / 2, lower.tail = lt_zero[i])
}
... |
13a166ccd4e312f8b15d18a5a30bc59906c79e6c538486b20108c93d7fd89649 | R | 4,763 | 123 | args<-as.numeric(commandArgs(TRUE))
#set up sub jobs
run_id<-1
run_list<-list()
for (i in c(13,22)){ #tissues 13:bfc and 22:lcl
for (j in 1:1400){ #subjobs per tissue
run_list[[run_id]]<-c(i,j)
run_id=run_id+1
}
}
run_i<-run_list[[args]]
#tissue list
tissue_list<-dir('/data/c***/z***/data/... |
b15810fe027efbb42b16ad0170110378b07c73bc7681150b71d44bfbba1e9365 | R | 4,772 | 161 | # code Figure 1
# Optional generic preliminaries:
graphics.off() # This closes all of R's graphics windows.
rm(list=ls()) # Careful! This clears all of R's memory!
#-------------------------------------------------------------------------------
# Load rjags library and the wiener module
library(plyr)
library(dplyr... |
bc2d75b6f44ce47a43b74ff2afd6080f57cfb5f15ca4cfe4bb085c074a0c3334 | R | 4,773 | 97 | #' @title Smooth surface
#'
#' @description Smooths surface data at defined full width at half maximum (FWHM) as per the corresponding template of surface data
#'
#' @param surf_data A N x V matrix object containing the surface data (N row for each subject, V for each vertex), in fsaverage5 (20484 vertices), fsaverage6... |
f501f1a83b5c2e9c4f6e5e21ed1eac732c4adbf341fbd98a5e53f466d596af11 | R | 4,775 | 196 | library(tibble)
library(dplyr)
library(ggplot2)
library(purrr)
library(tidyr)
library(data.table)
# Will be the dataset "wang" or "pbmc"
dataset <- commandArgs(trailingOnly = TRUE)[1]
output_path <- "output"
source("../utils/0-indices.R")
gene_indices <- read_gene_indices(sprintf("data/%s-gene-indices.txt", dataset)... |
c8cf189b8599dd9d355f013851aea6dc90dd96603f62b15fe721158fdd5adaad | R | 4,776 | 124 | #alkaslasi has 3 x 2 different matrices. 2 being male and female.
#we will atleast want to merge the matrices belonging to the same groups here, if possible.
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
library(Matrix)
library(dplyr)
cerv_f_dir = "../raw_data_full/alkaslasi/Cervical_F_raw_feat... |
f3552c38f08468e47478850f9998a7a67903a28fcd047aaed46b5196fda65f6c | R | 4,783 | 126 | #################################################################################
###
### plotmap - plot spectral maps
###
### plots intensity or extra data column over 2 extra data columns
## TODO: check whether function should be applied or not
#' Plot a map and identify/select spectra in the map
#'
#' [lattice... |
e752c9207b6cda5b94fb216880816bc58be2c17692778a451c21cb4cb838f5fa | R | 4,786 | 113 | library(Seurat)
library(SingleCellExperiment)
library(Matrix)
library(Matrix.utils)
library(DESeq2)
library(tidyverse)
#specific lamina study
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
#seurat_small <- readRDS("../results/results_small/query.rds")
seurat <- readRDS("../Results/results_smal... |
461c8e059cf5e5c465409c62ff1745dd9312ff5d89241e962738e67f56f21ceb | R | 4,788 | 106 | #this script conducts DiffBind analysis to identify differentially accessible regions
#using R version: 4.1.2
# load libraries --------------------
library(DiffBind) #version 3.4
library(BiocParallel)
datadir<-'DiffBind_outputs/'
# read in data ------------------------------------------------------------
samples <-... |
82c2fce45ee0a1019a9c5f1b0b07764ece276bbc8358554149120281821e0a20 | R | 4,792 | 140 | splatPopSimConditionalEffects <- function (key, means.pop, conditions, cde.groupSpecific, sample_heterogeneity)
{
condition.list <- unique(conditions)
if (is.null(cde.groupSpecific)) {
for (c in condition.list) {
c.samples <- names(conditions[conditions == c])
c.de <- key[[paste0("ConditionDE.", c)]]
if (... |
f6568f4edd415e3c4a8ca856752ea62c6eb004173347a38eb1a7f5c6e3c30072 | R | 4,805 | 95 | #' makeMasks
#'
#' Make raster mask from input vector data
#'
#' This function creates a raster mask from input vector data. The cell value is
#' indicated by the field parameter. A unique numeric code should be provided for
#' each class. In the case of a binary classification, 0 should indicate background
#' and 1 sh... |
3b2d1b652fe25c9ed8547db6cbfdcb0182176ffcb3c2dcd97fbac269b031b42c | R | 4,811 | 122 | #' defineRegressionLoss
#'
#' Define a regression loss function for use in a luz training loop.
#'
#' Returns a \code{torch::nn_module} that computes one of four common
#' regression losses between a prediction tensor and a target tensor. The
#' returned object is passed directly to the \code{loss} argument of
#' \code... |
c490413da9fdfe903449c4ee049ffab9ff9c455ef8ce9e957b5618e46ca8678e | R | 4,833 | 131 |
---
title: "Exploring the brain imaging metrics in association with GA at birth and at scan"
author:
- "Kadi Vaher"
- "This script performs linear regression models testing for the associations of the brain MRI metrics with GA at birth and GA at scan"
date: '`r Sys.Date()`'
output: html_document
---
```{r setup,... |
4458a9d0b9ad35bc43389ae04ead0a11af39421c429ecd34b35b6223137b6b61 | R | 4,834 | 164 | ---
title: "TEBC faecal samples: Clustering of the samples (hierarchical) based on shotgun species data"
author:
- "Kadi Vaher"
- "Hierarchical clustering with all samples together"
- "Based on Bray-Curtis dissimilarity matrix calculated on the species (relative abundances)"
- "The clustering is only used for ... |
bdfac0430c618bdc2ca700a9906fdec503304ec4a4be09fb9efae4a72c414b30 | R | 4,843 | 141 | ---
title: "SupplFigure_Yohimbine_Roche"
author: "Fabienne Rössler"
date: '2023-06-08'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("../DLCAnalyzer_Functions_v4.R")
source("../UnsupervisedAnalysis_Functions.R")
... |
e8741ec8279fa41937a000b53c32b0e063dacb1d2cffb1a6a3cbae2dcf7768cf | R | 4,844 | 197 | #' Reduce time-series to few Principal Components
#'
#' This function is under active development. It is meant to
#' reduce the entire time-series of normalized and baseline-corrected
#' pupillary data in just a few scores obtained by Principal
#' Component Analysis. PCA is an effective way to reduce data dimensionalit... |
07cef7c5073674b576bd11b3b1987024c95ca6087660f2b9e9591105589d94b4 | R | 4,849 | 115 |
#!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
scoreTablePath= args[1]
outDir = args[2]
# .libPaths("/usr/local/lib/R/site-library")
.libPaths("/usr/lib/R/library")
# .libPaths("/usr/local/lib/R/site-library")
library(ggplot2)
library(mixtools)
library(dplyr)
library(robustbase)
library(cluster)
l... |
f0ff332984d19519f67323bf242111dd074316428b53885977f3e0062718ff53 | R | 4,852 | 148 | # APOE-stratified analysis for CAA
library(data.table)
library(ordinal)
###### NACC ######
pheno <- fread("data/adc/adc_np_ord.txt", na.strings = "-1")
covar <- fread("data/adc/adc_np_apoe.covar")
# plink --bfile data/adc/adc_np --recode A --snp rs7247551 --out data/adc/apoe_snp_rs7247551
snp <- fread("data/adc/apoe_... |
0064a556b76560b5900b8380850462727bd993a0b5cea8f898853a255979f91e | R | 4,853 | 79 | #----Figure_04E ----------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure 04 Panel E for the connectivity analysis
# for Reinhard and Fukuda et al. 2024
#-------------------------------------------... |
1549d90f1f76bb49011da37a6ecd566a489059ec0ff97f3ec0b55cfa85532963 | R | 4,857 | 212 | # Function -------------------------------------------------------------------
#
.Summary <- function(x, ..., na.rm = FALSE) {
validObject(x)
if ((.Generic == "prod") || (.Generic == "sum")) {
warning(paste(
"Do you really want to use", .Generic, "on a 'hyperSpec' object?"
))
}
## dispatch also ... |
256f439a2bafcbac86ba187f8ef54b236b6c9336f0ff4d4391539fec469f730d | R | 4,863 | 93 | library(ggplot2)
library(gridExtra)
options(stringsAsFactors = FALSE)
args = commandArgs(trailingOnly=TRUE)
celltypes <- args[1] ## Tab delimited file with celltype in first column and number of replicates in the second. Should include DNA/plasmid
count_table <- args[2] ## Barcode level count table from MPRAcount pip... |
4de60f2dc9d2d3a8241f66da6297d5fbc61164b568dab0dad9ca4b5a6aee0ae8 | R | 4,867 | 104 | # FOOTPRINTING ###################
################################################
#hacer lo de diferenciar la orientacion del motivo
neurod2_peaks$dinucleotide_oriented<-substr(neurod2_peaks$ebox_seq, 3, 8)
for (motif in unique(neurod2_peaks$dinucleotide_oriented)) {
motifs<-neurod2_peaks %>% dplyr::filter(dinucl... |
5b16a31bf9c980d3ecf1701c408633afb5c3ac6560c70318df0bdc0da9ad5e5b | R | 4,876 | 118 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
allocate_into_modules <- function(resid_array, resid_var, prior_indicator, k_, update_order, prior_baseline, prior_weight) {
.Call(`_scregclust_allocate_into_modules`, resid_array, resid_va... |
a49e18095d82c51a8edc247e96014348a5564a6c5ecb3ebb6cc87c878b4d1e4e | R | 4,889 | 146 |
library(rstatix)
library(ggplot2)
library(ggpubr)
library(openxlsx)
library(dplyr)
library(RColorBrewer)
library(wesanderson)
library(lme4)
library(nparLD)
library(lmerTest)
library(jtools)
library(readxl)
options("scipen"=100, "digits"=4)
# Load the data and fix the order of the columns
data_sugarf... |
6c57c42d62464311d57db7b149c212af6e846e1600812be5717c8350022782a0 | R | 4,891 | 93 | #' @title CAT12vextract
#'
#' @description Extracts vertex-wise surface-based measures for each subject from a \href{https://neuro-jena.github.io/cat12-help/}{CAT12} preprocessed directory, resampled to a 32k mesh, and stores them all as a single .RDS file.
#' @details The function searches inside the CAT12 preprocesse... |
dbb90a77edefdadafc747f015a15557484e62ae49b3d3681cc6b4171a07b3ee9 | R | 4,906 | 166 | # Set generic ----------------------------------------------------------------
#' `as.hyperSpec`: convenience conversion functions
#'
#' These functions are shortcuts to convert other objects into `hypeSpec`
#' objects.
#'
#' @param X the object to convert. If `X` is:
#'
#' - a `matrix`, it is assumed to contain the s... |
7bf901810d4a826771f90ae75edcbe6bb742a51ffb146216962266a43c172c42 | R | 4,920 | 120 | # Preparation -------------------------------------------------------------
library(geodl)
library(torch)
setwd("Your Working Directory")
device <- torch_device("cuda")
refC <- terra::rast("C:/Users/vidcg/Dropbox/code_dev/geodl/data/metricCheck/multiclass_reference.tif")
predL <- terra::rast("C:/Users/vidcg/D... |
9f0e286453cf5a38bc4c46866a4da099525c98fd828e6dfcde67a87944b24b9a | R | 4,922 | 146 | require(cdcsis)
require(causalBatch)
require(GeneralisedCovarianceMeasure)
require(vegan)
require(RCIT)
require(np)
test.cdcorr <- function(Ys, Ts, Xs, dist.method="euclidean", width="scott", R=1000, normalize=TRUE, ncores=1, ...) {
if (length(width) == 1) {
if (width == "scott") {
width <- apply(Xs, 2,... |
006cc13885fca79351e5e90c2b9bf4999472c72cd18fc37044305c69ab480819 | R | 4,928 | 223 | ---
title: "R Notebook"
output: html_notebook
editor_options:
chunk_output_type: console
---
```{r, echo=FALSE}
library(tidyverse)
library(data.table)
library(scales)
library(ggforce)
library(cowplot)
library(dplyr)
library(splitstackshape)
library(ggridges)
```
```{r}
df = fread("data/calls_and_data.csv.gz")
dim(... |
450c8e4ddbe45490f7b0fed640263ff2dd6df053d60b454baad2c5e5096f0870 | R | 4,934 | 126 | # Load and install missing packages ---------------------------------------
packages <- c("ggplot2", "matrixStats", "reshape2")
package.check <- lapply(
packages,
FUN = function(x) {
if (!require(x, character.only = TRUE)) {
install.packages(x, dependencies = TRUE)
library(x, ch... |
95b6b8fd8d989818ba7b032d6b7db7236c7089806c3968098f29631c14e6ed6c | R | 4,938 | 161 | #' @export
#' @importFrom Matrix sparseMatrix colSums
#' @importFrom SingleCellExperiment SingleCellExperiment counts
#' @importFrom S4Vectors metadata metadata<-
#' @importFrom rhdf5 h5read
#' @rdname readVisium
read10Xh5 <- function(dirname, fname = "filtered_feature_bc_matrix.h5") {
spatial_dir <- file.path... |
48778c001e9f104a08daf41f5bda029d49cc6568f4878d844fcbb7a990b9ed82 | R | 4,946 | 197 | catarg <- function(x, newline = TRUE, end = "") {
xname <- x
x <- gsub(" ", "", x)
#
if (newline)
cat("- ")
#
if (is.null(.settings[[x]]))
cat(paste0(xname, ' = NULL', end, '\n'))
else if (is.character(.settings[[x]]))
cat(paste0(xname, ' = "', .settings[[x]], '"', end, '\n'))
else
cat(p... |
44cb7be7e519003052d8e78cf0970220197e482880461f059ecc27be45823014 | R | 4,951 | 139 |
############
# function to read inputs
getLOC <- function(sub, LOCbasefolder) {
fcoord = file.path(LOCbasefolder, paste0(sub, '_shift_coords.csv'))
fnames = file.path(LOCbasefolder, paste0(sub, '_shift_elnames.csv'))
ftypes = file.path(LOCbasefolder, paste0(sub, '_shift_eltypes.csv'))
fpairs = file.path(LOCba... |
07a10370e00440e372b129aa8aa92cca460285de4460091934548a25dfb02419 | R | 4,953 | 204 | #' Reduce time-series to few Independent Components
#'
#' This function is under active development. It is meant to
#' reduce the entire time-series of normalized and baseline-corrected
#' pupillary data in just a few scores obtained by Independent
#' Component Analysis. ICA is an effective way to reduce data dimension... |
7eb56bb5bd48a39116f4cb638902997cc6ca9c100611042d610f8928c429bd46 | R | 4,953 | 79 | library(data.table)
library(ordinal)
library(mediation)
library(MASS)
dt <- fread("data/rosmap_data.csv")
dt[, caa_4gp := ordered(caa_4gp)]
dt[, apoe := factor(apoe, levels = c("apoe33", "apoe22", "apoe23", "apoe34", "apoe44"))]
m_null <- dt[, clm(caa_4gp ~ scale(conversion.efficiency) + scale(pmi) +
... |
1eea23a0e253750ee7c0c6fb23ca4679115d5abf595bed5f7d1af2d5383969df | R | 4,963 | 201 | # Generate permutation distributions of t values for independent groups
## cond1 and cond2 are Nt trials x Nf time frames matrices
permtdist <- function(cond1, cond2, Nt, Nf, nboot = 2000){
st2 <- Nt+1 # index to start condition 2
en2 <- Nt+Nt # index to end condition 2
all <- rbind(cond1, cond2)
perm.tv... |
1bb7f19b8a0221bdffd7bf94b0020652cbf7363943d67e9e47901e19b503eeb9 | R | 4,989 | 99 | ---
title: "GWAMA_Nweighted"
author: "Gokberk Alagoz, L&G Dept., MPI for Psycholinguistics"
date: "`r Sys.Date()`"
output: rmarkdown::github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source("https://github.com/baselmans/multivariate_GWAMA/blob/master/Test_Data/N_weighted_GWAMA.functio... |
f890b930216406a901c6ac3ba4bb03db3664b1d2407060ae24def79345fe1458 | R | 4,989 | 165 | # --------------------------------
# Load libraries and data
# --------------------------------
library(rstudioapi)
library(yaml)
library(dplyr)
library(readr)
library(lme4)
library(emmeans)
library(ggplot2)
library(xtable)
# Set working directory to script location
setwd(dirname(rstudioapi::getActiveDocumentContext()... |
10e3e3d66bdc645d887cc7cc8aa509b662aed2cf947ff2cee91e260ccfee0fce | R | 4,990 | 112 | ## Split ABCD sample into discovery and replication subsamples
# Written by Valeria Kebets, based on the code by Cedric Huchuan Xia
# https://github.com/cedricx/sCCA/blob/master/sCCA/code/final/go1sample_split.Rmd
# https://doi.org/10.1038/s41467-018-05317-y
library('caret')
library('ggplot2')
library('readr')
libr... |
3f1088292de0ff2a3f03f2de928d65f5bcbc0dbb117daaba925b69b72f591e0c | R | 4,992 | 171 |
library(cocor)
df = read.csv("C:\\Users\\Jeremy\\Desktop\\zotero_folder\\data\\acoustics\\cors_sf.csv")
colnames(df)
# low peaks
truelow = cor(df$real_peak_low,as.numeric(df$pred_peak_low), method='pearson')
cor.test(df$real_peak_low,df$pred_peak_low, method='pearson')
fakelow = cor(df$real_peak_low,... |
f3bad10862815e330ed071f028924c4c0df3442fafdf33987180ad0abd0d73e5 | R | 4,992 | 114 | #' Perform threshold analysis
#'
#' Outputs a plot across thresholds from 0.01 to 0.99 at 0.01 increments with the following information:
#' \itemize{
#' \item{Cluster Height Range: The height range of returned clusters at that threshold, normalized to the largest range across all thresholds}
#' \item{Cluster Si... |
e5f86f6ba745d3c352d49bc8cd27c47a9a2f3650c65ac52cd59d8a973341264d | R | 5,023 | 112 | test_that("Matching cComBat makes outcomes for each group more similar", {
sim <- cb.sims.sim_sigmoid(n=200, unbalancedness=1.5)
xbreaks <- seq(from=-1, to=1, length.out=100)
lo.before.0 <- loess(sim$Ys[sim$Ts == 0,1] ~ sim$Xs[sim$Ts == 0])
lo.before.1 <- loess(sim$Ys[sim$Ts == 1,1] ~ sim$Xs[sim$Ts == 1])... |
b44b82b74500743a717c74acf59f8b4b515f5f7e16b352a3e258979f77ea7f0d | R | 5,025 | 180 | ---
title: "Data_prep_for_fastcormics2"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
---
title: "Gene_activity"
author: "Alise"
date: "2019 M09 16"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r... |
2170e214336f7d433c1c9f7b0b48019a1c701fecc96ab61b5516f1ee246cdf9b | R | 5,035 | 140 | # using TCGA lung as demo.
# Usage: Rscript circos.plot.***.R id ( id is a number from 1:ncol(exprs))
#setwd("/infodev1/infoderm/Projects/Naresh/scripts/misc/OmicsFootPrint/sample_data")
suppressMessages(library(circlize))
library(parallel)
args <- commandArgs(trailingOnly = TRUE)
#id <- as.numeric(args[1])
dir <- ... |
c0ca39ef194cc4e8a8c7cd74b0290ba368148645b37c5ff1bc1da71bafa021e4 | R | 5,038 | 181 | #' Cochrane review: Test for funnel plot asymmetry
#'
#' @description
#' Conduct a test for funnel plot asymmetry for all outcomes in a
#' Cochrane review of intervention studies
#'
#' @aliases metabias.rm5 metabias.cdir
#'
#' @param x An object of class \code{rm5} or \code{cdir}.
#' @param comp.no Comparison numbe... |
3f0ad57a2c4b2fcbbe3fe3cad5616a8b321b8b6c000800c9c68527fd1fa8e62a | R | 5,041 | 106 |
###Script _
#Pseudo Bulk DE analysis
library(Seurat)
library(SingleCellExperiment)
library(scater)
library(MAST)
library(dplyr)
library(future)
library(future.apply)
dt.sct <- readRDS("datasets/2_seurat_integrated_clustered.rds")
Idents(dt.sct) <- "subclusterAssignment"
levels(Idents(dt.sct))
calc_m... |
06d3dc0b5f1339f683196e6d60b39acc543dc4d9d52348801357ceee0cd4f340 | R | 5,063 | 87 | #----Figure_03_E----------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure 03 Panel E for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
#-------------------------------------------... |
fb10813b4b0b5bfdf262549b50a74e442d8d75315c11f3dd78529d7260509fe5 | R | 5,077 | 148 |
library(tidyverse)
#net_cols <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") # original order
net_cols <- c("#0072B2", "#009E73", "#D55E00", "#56B4E9", "#E69F00", "#F0E442", "#CC79A7") # reordering for 3 colours only
plt_in = '/home/mgell/Work/reliability/input/'
plt_out = '/hom... |
86272b02bc5702db744a91ccd4e2fb8ef43e464b4e2181cee7904583e4ba0b4c | R | 5,083 | 150 | # Aim of script
# Examine output from Baypass (C2 model): Manhattan plot & write out significant SNPs
library(optparse)
library(ggplot2)
library(dplyr)
library(tidyr)
option_list <- list(make_option(c('-i','--input'), action='store', type='character', default=NULL, help='input'),
make_o... |
e18a347483a1f733758f47ee56c5b387756b52de5ec5c8b4186abab0841f9869 | R | 5,088 | 146 |
#' @title
#' Predict DNA methylation age using CTS clocks
#'
#' @description
#' This is the function for computing DNA methylation age using CTS (cell type
#' specific) clocks. The inputs including DNAm matrix, the CTS clock you want to
#' use, is your DNAm data from bulk tissue sample or sorted cell sample, cell
#' ... |
0c4491b8e20774b7a165df29fa745d7fa070a8273d334fd71d36cbd82db378ca | R | 5,096 | 106 | library(neuprintr); library(hemibrainr); library(nat); library(natverse); library(nat.jrcbrains); library(fafbseg);
library(reticulate)
library(R.matlab)
library(RConnectomicsAnalysis)
library(progress);library(bit64);library(pracma);
# ### plotting packages
# library(pheatmap);library(ComplexHeatmap);library(zoo);lib... |
126a036f8d27124b7a1bd643abe3940cbc01a9c4bf8e73c87ea75af403dd951f | R | 5,097 | 128 | #Bone and Fat Measures Validation (using UKB data)
#April 2024
library(dplyr)
library(party)
library(flexplot)
library(psych)
library(stringr)
library(readr)
library(caret)
out_dir <- "/Volumes/OneTouch/MRData/202301_MRIbones/derivatives/"
rm("spm_boney", "ukb_bone", "ukb_1000", "bones_ukb",
"boney_measures", ... |
ddea695cc71818904031599db058d4b937ffb6f177ae04d30fb009d2c9d7464b | R | 5,097 | 147 | # Compares b/se using z score method - doi: 10.1038/s41380-019-0596-9
fzscore <- function(b1, b2, se1, se2){(b1 - b2)/sqrt(se1^2 + se2^2)}
farrer_apoe.raw <- read_csv("~/Dropbox/Research/UCSF/ABA/Farrer1997apoex.csv")
farrer_apoe <- farrer_apoe.raw %>%
mutate(
b = log(or),
se = (log(uci) - log(lci)) / 3.92... |
e9c5bc70aa078da26a27b3efc94b8928f43576e7c4f7169485432b0911cde61e | R | 5,101 | 108 | #' Check neuroimaGene database downloaded
#'
#' Check if the NeuroimaGene database exists in the proper location prior to running the query and prompt user to download if not.
#' @keywords installation
#' @param timeout time to spend downloading the NeuroimaGene database in seconds (default = 900)
#' @param localdb pat... |
0e311b52de7f5b9391fb0a406c6f7872c6d6e2d9ec4d6d1f5ba1cc9988122cb1 | R | 5,107 | 143 | source("/media/Scratch_SSD_Voyager/sammi/RNA_editing/scripts/PertLM.R")
source("/media/Scratch_SSD_Voyager/sammi/RNA_editing/scripts/WGCNA.Get.Eigen.R")
source("/media/Scratch_SSD_Voyager/sammi/RNA_editing/scripts/BatchSample.v2.R")
library(Seurat)
load(file = "/media/Scratch_SSD_Voyager/sammi/RNA_editing/Figure3_ADA... |
d5a82f72e7016cedce38abface9429073d31fad46653528c25925ca0642df544 | R | 5,112 | 204 | library(ggplot2)
library(dplyr)
library(tidyr)
library(purrr)
library(tibble)
library(readr)
library(vctrs)
library(data.table)
# Will be the dataset "wang" or "pbmc"
dataset <- commandArgs(trailingOnly = TRUE)[1]
output_path <- "output"
# Load data
source("../utils/0-indices.R")
gene_indices <- read_gene_indices(sp... |
f021f783c0a862d54416aa79e3ca189fd60e35f57f938207c4e4738c0a48ed52 | R | 5,115 | 92 | #----Figure_S06_F---------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure S06 Panel F for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
#------------------------------------------... |
42baf210b2b69784dc986d0f7f581f917c849bc084444437b105301656bfd97a | R | 5,125 | 142 | library(data.table)
library(magrittr)
library(kableExtra)
source("code/functions/strip_alleles.R")
# rosmap_phenos <- c('arteriol_scler_bin', '')
rosmap_path <- "output/gwas/rosmap"
adc_path <- "output/gwas/adc"
act_path <- "output/act"
get_file_name <- function(path, pheno) {
file_name <- paste0(path, '/', pheno, ... |
b771ff92582cac734f088bdd969b0fb45faf6343b67988bb80c8608138b5e75c | R | 5,149 | 179 | text_meth <- function(x, i, random, method) {
#
if (x$method[i] == "MH" & x$model[i] == "common")
txt <- text_MH(x, i, random)
else if (x$method[i] == "Peto")
txt <- text_Peto(x, i, random)
else if (x$method[i] == "Inverse" |
(x$method[i] == "MH" & x$model[i] == "random"))
txt <- text_Inv... |
235c255bab7d39433ce31926105e1dd960417131cb61cfb1c7d67b7b62028c04 | R | 5,156 | 173 |
library(tidyverse)
initial.options = commandArgs(trailingOnly = FALSE)
file.arg.name = "--file="
script.name = sub(file.arg.name, "", initial.options[grep(file.arg.name, initial.options)])
source(paste0(dirname(script.name), "/Functions/functions.R"))
set.seed(1234)
args = commandArgs(trailingOnly = TRUE)
ref_pat... |
82986198cddc2a856727ac1aaa99b7814f5f794039b3bd5c78433346de028c7e | R | 5,158 | 119 | ---
title: "depol_amplitude_analysis"
author: "Franz Xaver Mittermaier"
date: "2024-05-21"
output: html_document
---
This script plots the relationship between the depolarization amplitude and the subthreshold modulation effect on synapses.
Three tables are needed:
- The basic experiments (t_basic)
- The dep... |
71a07f79151dc2c15b91363b193b2aa0398556417c57effe3f25117929f3aebe | R | 5,163 | 137 | library(Seurat)
library(tidyverse)
library(Matrix.utils)
library(dplyr)
library(Matrix)
library(purrr)
library(tibble)
library(SingleCellExperiment)
library(apeglm)
library(DESeq2)
library(DEGreport)
library(magrittr)
library(sva)
#to be ran after updated SeqSeek Pipeline. With updated 'query.rds' object... |
63bed351c799560e6257a84f3b188e931d165c3c22143a3234e50ae7f4a7b116 | R | 5,167 | 148 | #### Notes ####
#### Libraries ####
library(tibble)
library(dplyr)
#### Sourced functions ####
#### Functions ####
# Merge variability rank
merge_ev_rank <- function(jack.summary, metadata, by=c("local","global"), join=c("inner","full"), method=c("sd","cv")) {
Tissue <- as.factor(metadata$Tissue)
if ("Sex" %in%... |
668efd6a54a6ef601b8744e8252f17ecbae619d2c49aecd87808f8a90159735a | R | 5,172 | 109 | ---
title: "LAVA"
author: "Gokberk Alagoz, L&G Dept., MPI for Psycholinguistics"
date: "`r Sys.Date()`"
output: rmarkdown::github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(LAVA)
library(stringr)
library(kableExtra)
```
```{r processInput}
### Read in summary statistics and re... |
10f9bc339111131cd2ee9671ca76bdd51905d8a2646a0b25303b070a78324e1f | R | 5,177 | 116 | plotKmerDist <- function(dat, baseDat, saveDatFn, dontPlot){
if (!is.na(saveDatFn)){
save(dat, file=saveDatFn)
}
if (dontPlot){ return() }
mainP <- ggplot(dat) +
##geom_boxplot(aes(x=Kmer, y=Signal, color=Base)) +
##scale_color_manual(
## values=c('#00CC00', '#0000CC',... |
19831bbe69d76590c608865127b93a5f7c7a936a4d08dfdd7daa471acb4fdc59 | R | 5,185 | 140 |
library(Seurat)
library(SoupX)
library(sctransform)
library(dplyr)
library(HGNChelper)
library(DoubletFinder)
pre_process <- function(srat, x) {
print(paste0("Original data dimensions... ", x))
print(dim(srat))
#percent mitochondrial reads to regress out
srat[["percent.mt"]] <- PercentageF... |
93159b81e5d1b220ebab21ffde99d010ea93378b0111e372655c0692123092fd | R | 5,188 | 136 | ### --------------------------------------------------------------------------~
###
### read.txt.wide
###
### Format:
### x y ... int (wl1) int (wl2) ... int (wl p) z ...
###
#' @rdname DEPRECATED-textio
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Import `hyperSpec` objects from ASCII (text) files
#... |
adce8c849a710352d3576ffbd7159e7eca0be4ff0d2a907fe2acd61d868ffd16 | R | 5,189 | 176 | #' Expand scalar, vector, matrix or similarly shaped `hyperSpec` object to matrix
#'
#' Helper function for `hyperSpec` arithmetics.
#'
#' A row- or column vector or
#' a matrix of size 1 x n or n x 1 or
#' a hyperSpec object with 1 row or 1 wavelength will be expanded
#' along the size-1-dimension.
#'
#' Dimensions th... |
d9d9930b5b26dca4c8d9c58d9ff244d1de1a601030021cd8c148d1887685723c | R | 5,191 | 63 | data <- data.frame(Country = as.numeric(ases$Country), Continent = as.numeric(ases$Continent))
# "neither agree nor disagree" coded as a class
data$Protectionism.d <- recode(as.numeric(ases$Protectionism), '1:2=1; 3=2; 4:5=3; 6:7=NA')
data$Antiforeigner.d <- recode(as.numeric(ases$Antiforeigner), '1:2=1; 3=2; 4:5=3; 6... |
586c0864832b41b38477f71fed05d9b20f00a4602d8c32942f24e326a46a38d3 | R | 5,200 | 178 |
#from: https://coolbutuseless.github.io/2020/08/26/run-length-encoding-and-the-problem-of-nas/
rle2 <- function (x) {
stopifnot("'x' must be a vector of an atomic type" = is.atomic(x))
n <- length(x)
if (n == 0L) {
return(structure(list(
lengths = integer(), values = x)
), class = 'rle')
}
... |
89c4412f4987c360247bb028df0fc130e649cb499821d4fb5fe2a65abc0150ae | R | 5,218 | 100 | #----Figure_S04_S05-------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure S04/ S05 for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
#---------------------------------------------... |
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