sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b2ff703b351ae9975c5aafb85b3144bcb1ba552d47017c0da6bec52379f9f7eb | R | 2,665 | 62 | ## ---------- Iso-Seq whole dataset -----------------
IsoSeq_polyAErrorRate <- function(limaPath, RefinePath){
polyA <- lapply(list.files(RefinePath, pattern = "flnc.report.csv", full.names = T), function(x) read.csv(x))
polyACounts <- lapply(polyA, function(x) nrow(x))
names(polyACounts) <- list.files(RefinePat... |
8d186077a48e50c67ea1b462ba92bdd22b78bd4c513d77c5872f2e8a79535797 | R | 2,666 | 68 | ### Ref links
# http://tuxette.nathalievilla.org/?p=1696
# https://swcarpentry.github.io/r-novice-inflammation/05-cmdline/
## -----------------------------------------------------------------------------
# get the input passed from the shell script.
args <- commandArgs(TRUE)
# test if there is at least one ar... |
219f98f7a02132d1bd037b7a681eefea2488f879f2d8ae3edb7181424cb859ea | R | 2,674 | 85 | ---
title: "Torch tutorial"
output: html_document
---
# Packages
```{r}
library(torch)
library(torchvision)
library(tidyverse)
library(reshape2)
library(matlib)
source("./code/utils.R")
source("./code/data_loader.R")
source("./code/modules_2.R")
```
# Preprocessing
```{r}
set.seed(20)
raw <- read.csv("./data/raw.csv"... |
0fbf2cd997b01cc4829ec9a5b06b3700b67e5d7b68a651682e7622cf92519a84 | R | 2,675 | 93 | options(bitmapType='cairo')
plotEigen <- function(x,y,plotTitle) {
ymin<-min(y,na.rm=TRUE)
ymax<-max(y,na.rm=TRUE)
xStart<-min(x)
xEnd<-max(x)
ybound<-max(abs(ymin),abs(ymax))
if(ymin < 0) {
ybounds<-c(-ybound,ybound)
} else {
ybounds<-c(0,ybound)
}
plot(x,y,type="n",main=plotTitl... |
215b64023642416953b8eec5060dc30fa21f3e47a39c9daf0776a3a304a027b9 | R | 2,678 | 79 | library(testthat)
library(dplyr)
# Load the testing set
test_citations <- read.csv("labelled_test_set.csv")
test_citations <- as.data.frame(test_citations)
# Test case: No duplicates detected
test_that("No duplicates detected", {
test_citations_nodups <- test_citations[c(6, 21, 65),]
expect_warning({
a <- de... |
0646c81e9dd4870393c0f76e705b3637520008a21fb49bba2b1de0a96c7dfe38 | R | 2,680 | 64 | library(testthat)
library(sccomp)
test_that("sccomp_identify_covariate_contrasts identifies direct covariates", {
model_input <- list(
X = matrix(0, nrow = 1, ncol = 2, dimnames = list(NULL, c("(Intercept)", "treatment"))),
XA = matrix(0, nrow = 1, ncol = 2, dimnames = list(NULL, c("(Intercept)", "treatment"... |
1c490d9d916930e28e07445efb56cd022dedce3a3c09cf5f1c05d381074361a8 | R | 2,683 | 116 | #' Plot spectra matrix
#'
#' Plots the spectra matrix.
#'
#' If package \pkg{plotrix} is available, a color legend is plotted to the right.
#' The right margin is set to at least 5 lines.
#' @param object hyperSpec object
#' @param y character giving the name of the extra data column to label the y axis.
#' @param ylab... |
3b93142533f985d0c81caa97107f401a13d40af23afe0d059b500931e88531dd | R | 2,684 | 73 | # Description: In this script we obtain the C>U variant lists using replicates. We keep the variants that appear in at least 2/3 replicates and set their freq to the mean of the replicates
# ———————————----------—#
#### Functions ----
read_cims <- function(file) {
data <- read.table(file, header = FALSE) %>%
as... |
33cdbb31f0994495e266a7e4a593d8205fe77904d5fef17f2fceae39803f83b5 | R | 2,689 | 78 | # in
# data/04_DS1.ssce.Rdata
# data/05_DS2_seuratLT.Rdata
# data/07_DS1_pseudot_umap.Rdata
# data/07_DS2_pseudot_umap.Rdata
#
# out
# data_figs/08_density_pseudot.pdf
library(ggplot2)
library(SingleCellExperiment)
options(bitmapType = 'cairo')
# (0) Load Datasets ----
# (0.1) Load DS1 ----
(load('data/04_DS1.ss... |
8a2d821a64ab6d32e264ac8d12c406a6d593763cfd10bf473b7ed736ac4fc803 | R | 2,689 | 105 | library("Seurat")
setwd("./Step8a.SubClusterAnalysis")
## Excitatory neurons
Ex.SNC <- readRDS("Ex/Ex.Harmony.Ind.rds")
Idents(Ex.SNC) <- Ex.SNC$RNA_snn_res.0.2
DimPlot(Ex.SNC)
DimPlot(Ex.SNC, split.by = "orig.ident")
Ex.SNC$seurat_clusters <- Ex.SNC$RNA_snn_res.0.2
Ex.SNC$Subcluster <- paste0(Ex.SNC$MajorCluster, ".... |
ebec98d256bf74bba2cdacaafc1ee5b349793914450769517b8397c75e8022e6 | R | 2,690 | 56 | ## ---------- Script -----------------
##
## Purpose: appending input variables merged datasets
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
##
## ----------------------------------
# source rTg4510_ont_characterise.config.R
## ---------- Appending to existing lists -----------------
# directory paths
dirna... |
2e08344b54ca62e31fce274fbf95ed49be8158fa3e82935109f030e328051f44 | R | 2,693 | 89 | # For the specified k, for pairs of summary scores relating to g or processing
# speed, find the correlations and associated p-values.
# Output:
# olab,'.csv' Correlation, p-value, and p < 0.05 thresholded versions.
#Set libraries.
library(tidyverse)
library(ggplot2)
library(gridExtra)
#Catches arguments.
args <- ... |
7fe1bf60e9a6f339089f079238f142d83216f48f4fb482dd91cb0b5b16fecbe0 | R | 2,711 | 77 | # https://mllg.github.io/batchtools/articles/batchtools.html
library("batchtools")
setwd("./Step8b.SubCluster.DEG")
reg <- makeExperimentRegistry("DEGRegistry",
packages = c("MAST", "data.table", "Seurat", "lme4"),
seed = 22112019)
reg <- loadRegistry("DEGReg... |
ba9d9eb77a513c4403fbc6f4471268c12b6c274f4bf252252517d8a7861979dc | R | 2,712 | 79 | test_that("scheduler log filenames include scheduler, job id, job name, and extra token", {
log_file <- fmri.pipeline:::scheduler_log_file(
"/tmp/batch",
scheduler = "slurm",
job_name = "3dlmer_l3 session/EV face",
job_id = "%j",
extra = "jobabc"
)
expect_equal(log_file, "/tmp/batch/slurm-%j-... |
c075aa4ace528c58da07bd18c18f617a36913ae012c07fd929fb3f014366d7b4 | R | 2,712 | 96 | library(dplyr)
celltype_list=c("Exc","Inh","Ast","Oli","OPC","Mic")
region_list=c("AG","EC","HC","MT","PFC","TH")
for (r in 1:length(region_list)){
region=region_list[[r]]
#####################################
genes_list <- list()
for (i in 1:length(celltype_list)){
celltype=celltype_list[[i]]
wo... |
cf2616df365887db84aac0317c6053a974b4814b70ca5d5d225d333c2cd5fbf1 | R | 2,716 | 96 | library(dplyr)
celltype_list=c("Exc","Inh","Ast","Oli","OPC","Mic")
region_list=c("AG","EC","HC","MT","PFC","TH")
for (r in 1:length(region_list)){
region=region_list[[r]]
#####################################
genes_list <- list()
for (i in 1:length(celltype_list)){
celltype=celltype_list[[i]]
wo... |
bbd18907dcc530ecaf9a7149a265616500d8327549ca84188a738d38af3c3bd1 | R | 2,726 | 89 | # Gather cognitive tests and confounds into one file, with the addition of
# gPCA, gEFA, and gCFA.
# Output:
# pred_all.csv Cognitive tests and confounds (i.e., Age, Gender), with the addition of gPCA, gEFA, and gCFA.
#Load packages.
library(tidyverse)
#Set outpath.
subgroup <- 'full'
outpath <- paste0('../outputs/c_... |
49473e7e6c00b8a85e4015927779fb0ec3679962e01e31718087005f824ddfe8 | R | 2,731 | 52 | test_that("correct calculation", {
pdf(NULL) # to prevent generating an empty RPlots.pdf
yaml = get_testyaml_object("dir", analyser = "TimeSlots",
changes ="DataSettings$CalculationWindow$Start = 1.5")
ana = get_analyser_object("TimeSlots", yaml)
expect_equal(ana$ana_name, "TimeSlo... |
e436dc15fc8419372bf02d5ce89caa37ef4c803e66d6f84b4eaf4e57a4ac9ac5 | R | 2,739 | 110 | #!/usr/bin/env Rscript
# gnomAD project credits available at http://gnomad.broadinstitute.org/
# Code to perform module 00 (matrix creation) QC across all batches for gnomAD v2
####################################
#####Set parameters & load libraries
####################################
args = commandArgs(trailingO... |
6c3f7de8483ff8df48f7b524e74df0bce06e2d2d1667e3210dbcdb57692dcadd | R | 2,743 | 83 | args <- commandArgs(trailingOnly=T)
miniscan <- args[1] # miniscan output
prefix <- args[2]
# load data
d <- read.delim(miniscan, header=F)
# number of chromosomes/contigs
nchr <- length(unique(d[, 1]))
# plot
plot_height <- 8 * nchr / 10
pdf(paste0(prefix, ".miniscan.pdf"), width=5, height=plot_height)
par(mar=c(4... |
e26096688fa5d6232e99087a0712e7c20d7b13e7b05a46b907c628ae4330c227 | R | 2,746 | 98 | #!/usr/bin/R
# ----------------------
# Turn the cell type matrices into chunked matrices
# Updated: 04/28/2022
# ----------------------
# chunksize=25
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
library(rhdf5)
library(Matrix)
# Function to estimate en... |
c12ddcbf291dff25bd88b9da42bc9a74faa67052a4c4fd954d8fbd2fe7b0436e | R | 2,751 | 52 | source('Analysis/util.r')
library(Seurat)
library(dplyr)
library(magrittr)
library(ggplot2)
x <- readRDS("Data/Processed/alphasyn-triplication-initial-integrated.rds")
meta <- data.table::fread("Results/Initial/Initial integration/doublet-finder-metadata.csv") %>% tibble::column_to_rownames("V1")
meta <- meta[meta$DF.... |
d9a9823dae096df6a36a5f97586f7bb0756e0c7b64f394cc7f9d6a27ef93d2bd | R | 2,751 | 69 | library(pROC)
library(PRROC)
library(tidyverse)
library(writexl)
source("Functions.R")
###########################################################
# This is for the predictions based on metrics.
###########################################################
predictions = read_tsv("Testing_Results_Predictions.tsv") %>%
... |
a62d01af6457faab59e8ded838e6e348b748c1cafed1c01b93a750a84b01531d | R | 2,753 | 75 | ### Ref links
# http://tuxette.nathalievilla.org/?p=1696
# https://swcarpentry.github.io/r-novice-inflammation/05-cmdline/
## -----------------------------------------------------------------------------
# get the input passed from the shell script
args <- commandArgs(TRUE)
## --------------------------------... |
264c2e544f61ce3b9fa32e581c15566e38c9e87a62e32f6439a906496cdd560c | R | 2,757 | 121 | #' Get and set the wavelength axis
#'
#' [wl()] returns the wavelength axis, `wl<-` sets it.
#'
#' The wavelength axis of a `hyperSpec` object can be retrieved and
#' replaced with `wl()` and `wl<-`, respectively.
#'
#' When the wavelength axis is replaced, the colnames of `x@@data$spc` are
#' replaced by the rounded n... |
1662108cfec22d28cfccd84fbc3d6a6b0d9a704db001da649778f66a8f80f21e | R | 2,767 | 131 | # Function -------------------------------------------------------------------
.ncol <- function(x) {
validObject(x)
ncol(x@data)
}
#' Number of rows (spectra), columns, and data points per spectrum of a
#' `hyperSpec` object
#'
#' These functions return the number of rows (spectra), columns, and/or
#' data points... |
226838976d658f87057dcef723b652a9e04236db5499f4047f528b6207b9f463 | R | 2,767 | 80 | #!/usr/bin/R
# -------------------------------------
# Set up a matrix for the differential
# expression runs using Nebula + RUV
# Updated: 11/24/21
# -------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
# -------------------------------... |
c73c777967c781ea5644c050849af7fcd0216994f564db1fb25112949c870a06 | R | 2,768 | 105 | #' @name deprecated
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Deprecated and defunct functions
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and will be
#... |
82d012fc84030c5566b4083af55dd3786e84dfaa7a7bf7e0738b8d79bbb52f70 | R | 2,770 | 74 | ### Ref links
# http://tuxette.nathalievilla.org/?p=1696
# https://swcarpentry.github.io/r-novice-inflammation/05-cmdline/
## -----------------------------------------------------------------------------
# get the input passed from the shell script
args <- commandArgs(TRUE)
## ----------------------------------------... |
f79dd17e87bb01c10b1937e9ebea6f847f5e2ba774072d65ddb3a4773b05c928 | R | 2,770 | 87 | # STAT_TABLE.R
# function that creates a summary for all fixed effect of the tested ROI with the probability intervals.
# INPUT:
# - model: brms-model (as created by run_model)
# OUTPUT:
# - ROI_table: summary for all fixed effect of the tested ROI with the probability intervals
stat_table <- function(model) {
# f... |
6ef1ac09982263aad288119446f4a34f752d878779e6138d5d4fec7c3e6d7514 | R | 2,776 | 56 | ####------ Deduplicate citations WITH manual dups added function ------ ####
#'
#' This function performs additional deduplication with the additional of manually flagged duplicates
#' @export
#' @import dplyr
#' @param unique_citations A dataframe containing citations after automated deduplication
#' @param merge_cita... |
787cdec04783ee614b7b5d801c722312dfdb56990061a8f0a21664c18276fac1 | R | 2,777 | 103 | require(testthat)
require(Cardinal)
context("SpectralImagingArrays")
test_that("SpectralImagingArrays accessors", {
sa <- SpectralImagingArrays()
expect_true(validObject(sa))
sa <- SpectralImagingArrays(numeric(0))
expect_true(validObject(sa))
set.seed(1)
n <- 10
i <- rep(list(1:n), n)
a <- replicate(n, ... |
e8eb821d4981bba3ef7e854ccb51a6a62aa8c6912c8698a522de3b4da1e2dc00 | R | 2,777 | 88 | #!/usr/bin/R
# ----------------------------------------------------------
# Get a list of shared DEGs across regions for each ct/path:
# and make plots for amt shared and consistency
# Updated: 12/20/22
# --------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source... |
02064f6a274ccc9bb1708e36a424dcfb9df488134643baa2ee27418ef91cc6f0 | R | 2,779 | 73 | #!R
#plot count of SVs stat
#!/usr/bin/env Rscript
library("optparse")
option_list = list(
make_option(c("-i", "--input"), type="character", default=NULL,
help="input file, should include 5 columns representing sample_id, count_SVs, chr, svtype and size_range", metavar="character"),
make_option(c("-... |
f7d1cd717a3b65e8a7494acd0c052745d40aa64afdd8b06fd45614a06c640884 | R | 2,780 | 76 | ### Ref links
# http://tuxette.nathalievilla.org/?p=1696
# https://swcarpentry.github.io/r-novice-inflammation/05-cmdline/
## -----------------------------------------------------------------------------
# get the input passed from the shell script.
args <- commandArgs(TRUE)
# test if there is at least one argument: ... |
5d605c6a138775b868e1d9dc5b0f6945ee29cf6da0f56439d1bbd55cb0860639 | R | 2,784 | 84 |
dir_MetS_PRS <- "../../PRS-CS_UKBB_target/"
dir_FOF_PRS <- "../../PRS-CS_UKBB_target/firstorder/"
basedir_IP_PRS <- "../../PRS-CS_UKBB_target/individual_phenotypes/"
PRS_files <- c(
paste0(basedir_IP_PRS, 'BMI/', 'score_sum_indiv.profile.reformat'),
paste0(basedir_IP_PRS, 'FG/', 'score_sum_indiv.profile.refor... |
3f520ce5c7fbefe932bdab496453a99819da8ca07257f44bac82d2f353e6590e | R | 2,785 | 79 | ## ANOVA TESTING AGE, POTENTIALLY CONFOUNDING VARIABLES AND BRAIN DATA
#install packages
install.packages("lme4")
install.packages("tidyr")
install.packages("ggplot2")
install.packages("tidyverse")
install.packages("haven")
install.packages("ggpubr")
install.packages("rstatix")
install.packages("broom")
install.packag... |
79a6c02f4f1a4e73f5d620a726a21a90329fe1399683d51639172e9541a77376 | R | 2,789 | 67 | # Title: Nested active zone area analysis
#
# Description:
# This script performs a nested active zone area analysis.
# It calculates the area of active zones within each nested bouton.
#
# Author: Daniel Parthier / Felicitas Brüntgens
# Date: 05.10.2023
# Last modified: 10.04.2024
# Version: 1.0
# Load libraries ###... |
1bf2daf62e98782a6e1e38d33001add29ea0f7dee3c57f69b5475d05e6f3f6ea | R | 2,794 | 87 |
#### Estimate shared domain ####
## ------------------------------
estimateDomain <- function(xlist,
width = c("median", "min", "max", "mean"),
units = c("relative", "absolute"),
verbose = getCardinalVerbose(), chunkopts = list(),
BPPARAM = getCardinalBPPARAM(), ...)
{
units <- match.arg(units)
width <- match.a... |
fa33b063f8e02fba9bb941abea5b1656dba3452af623881e58a5467bf2486e9d | R | 2,801 | 108 | ##################
### PART - 1 ###
##################
rm(list=ls())
data<-read.delim("/Users/cazaledl-admin/Library/CloudStorage/OneDrive-UniversityofBirmingham/Post_doc/XP/data/motioncontrols/tracker/.txt",h=T,sep="\t",na.strings = "NA",stringsAsFactors=T)
str(data)
data$vel[data$vel < 4] =""
is.na(... |
c6e9901d23fa03903bb50e63d344ab3d01ceb8e40adf3595bad07be161f825e3 | R | 2,810 | 100 | ########################################################################## ~
###
### plot_voronoi - plot spectral maps with irregular point pattern
###
### plots intensity or extra data column over 2 extra data columns
###
########################################################################## ~
# Function -----... |
be7532f9b2bcf4908bac050c1749bfbc64f3547ffaba45bf5f7753bd611f0f4a | R | 2,815 | 72 | # Title: Nested bouton complexity analysis
#
# Description:
# This script performs a nested bouton complexity analysis.
# It calculates the bouton complexity within each nested animal.
#
# Author: Daniel Parthier / Felicitas Brüntgens
# Date: 17.04.2024
# Last modified: 19.04.2024
# Version: 1.0
# Loading libraries #... |
908c357709379e0032987cf6c969fdb9f0d7c976fe1f6dded2bd2647800b1075 | R | 2,825 | 87 | make_partial_feat_dir <- function(feat_dir) {
stats_dir <- file.path(feat_dir, "stats")
dir.create(stats_dir, recursive = TRUE)
file.create(file.path(stats_dir, "cope1.nii.gz"))
file.create(file.path(stats_dir, "zstat2.nii.gz"))
writeLines(
c(
"/ContrastName1\tface-house interaction",
"/Contra... |
4f1b80d8c1063cfd58cb96783e6090aadbb15409521864a4b5703496f6295857 | R | 2,833 | 71 | test_that("run_3dlmer_sepjobs wrapper reuses one terminal job status", {
run_3dlmer_path <- source_tree_file("R", "run_3dlmer_sepjobs.R")
lines <- readLines(run_3dlmer_path, warn = FALSE)
expect_true(any(grepl("job_status=\\\"COMPLETED\\\"", lines, fixed = TRUE)))
expect_true(any(grepl("job_status=\\\"FAILED\\... |
9df5776163938f9a0171dbe3abfd3b07a5fec9a88e2c2e7ee4c84bf4b3dacac1 | R | 2,839 | 79 | ### Ref links
# http://tuxette.nathalievilla.org/?p=1696
# https://swcarpentry.github.io/r-novice-inflammation/05-cmdline/
## -----------------------------------------------------------------------------
# get the input passed from the shell script.
args <- commandArgs(TRUE)
# test if there is at least one argument: ... |
226536b4fd72d7e82d967ec3c5b81ca767560520c5af5be7734a378c5639f71d | R | 2,840 | 76 | #!/usr/bin/R
# ---------------------------------------------
# Load published AD DEGs from previous studies:
# Updated: 07/27/23
# ---------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
# source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidyr)
options(width=175)
... |
c77bd77cb827dd72df8a3fccac9c6497d8fd6b4dd41424c1877dac30668c0c38 | R | 2,844 | 70 | ---
title: "gcVAE_processing"
output: html_document
---
# Process data so Alfredo can run gcVAE
```{r}
source("./code/load_packages.R")
all_raw <- read.csv("./data/raw.csv", header = F)
all_cov <- read.csv("./data/covariates.csv")
all_cov$SEX <- as.factor(all_cov$SEX)
all_cov$DIAGNOSIS <- as.factor(all_cov$DIAGNOSIS)... |
33e0140b9bcd830a6ed22367e11747025e4fe9a53d429654283973581d5a4ddb | R | 2,852 | 75 | ## ---------- Script -----------------
##
## Purpose: input variables for differential analysis of Iso-Seq targeted mouse transcriptome datasets
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
## ---------- packages -----------------
suppressMessages(library("dplyr"))
suppressMessages(library("stringr"))
suppr... |
8bb42ae86215c3810b11c59c6d95b4e974f578ecd9632eee38c823102dc79175 | R | 2,855 | 62 | library(SeqArray)
library(SeqVarTools)
library(SNPRelate)
library(Biobase)
library(GENESIS)
##### Inputs #####
seqBED2GDS(bed.fn = "psp.bed", bim.fn = "psp.bim", fam.fn = "psp.fam", parallel=12, out.gdsfn = "psp.gds")
gds <- seqOpen('psp.gds')
# read samples
phe <- read.csv("pheno.csv")
phe$sample.id <- phe$Sample_I... |
efb5078cde34e78d3613e8e0698dd27fb92b1147b7bf9bd32ac67e5218ba931a | R | 2,856 | 88 | library(readr)
library(dplyr)
library(tidyr)
library(data.table)
library(lgr)
library(foreach)
library(doParallel)
library(dependlab)
library(emmeans)
#library(devtools)
#install_github("UNCDEPENdLab/fmri.pipeline")
library(fmri.pipeline)
trial_df <- readRDS(system.file("example_files/mmy3_trial_df_selective_groupfix... |
c07c01188859d7ad8c1a514f5203ff1c2a61aab9cffcf43a6c86e17f67cba35c | R | 2,865 | 91 | ##### ComBat-like:
# Site effect in mean/variance
# Covariate effect in mean only
library(reshape2)
library(mvtnorm)
library(invgamma)
library(randomForest)
library(pROC)
library(ROCR)
library(caret)
library(Matrix)
library(CovBat)
source("covbat_sim_functions.R")
load("animats_add.Rdata")
load("covbat_sim_params.Rda... |
26e3e15da3d4c48cc8fd0b056dba904f63b8ef4a7429d530cb6833913bd6f061 | R | 2,871 | 96 | # For each network, delineate it in brain ROIs by color.
# Output:
# onehemi,'_',onelat,'_colenetwork.jpg' Brain plots for networks for one hemisphere and side.
#Set libraries.
library(tidyverse)
library(ggplot2)
library(ggseg)
library(ggsegGlasser)
library(scico)
#Set outpath.
outpath <- '../outputs/r_network/'
dir.... |
3e2ab5ccff2fb77405a900014f2ad22c2d26aa51024b2389b79f28d24d5174a1 | R | 2,871 | 50 | refMap <- read.table("/lustre/projects/Research_Project-MRC148213/lsl693/rTg4510/H_Sorted_Nuclei/6_merged/bulkSorted.rTg4510SCN_collapsed.filtered_targetgenes_filtered.gtf.refmap", header = T)
srefMap <- read.table("/lustre/projects/Research_Project-MRC148213/lsl693/rTg4510/H_Sorted_Nuclei/6_merged/Sortedbulk.all_iso_o... |
589366bf06781fd7f99862e58501d27e962310bf42d25623634dfbb4e6717317 | R | 2,871 | 45 | ---
title:
output:
html_document:
toc: true
toc_float: true
---
```{=html}
<style>
.list-group-item.active, .list-group-item.active:focus, .list-group-item.active:hover {
background-color: #80CBC4;
}
</style>
```
# About
<img src="updated_logo.png" align="right" width="142"/>
ASySD was developed in t... |
95029a4a2c2cb27f761002088828e46c0d404bbf61ccf4b1be0f1d1cf5b51ecc | R | 2,880 | 56 | library(ggpubr)
library(dplyr)
CR1_geno<-read.csv(file="CR1_geno.csv")
cov<-read.csv(file="covariates.csv")
CR1_geno<-left_join(CR1_geno,cov, by=c("sampleID"="Row.names"))
m1<-lm(CR1~rs679515_dosage+updatedDX + race + gender + expired_age + PMI,data=CR1_geno)
capture.output(summary(m1), file = "rs679515.output.txt")
... |
ba630124631aec6acc155430893b0d77c866d9747ef9dc12cbf3f359e2dd3ead | R | 2,884 | 103 | library(colorspace)
library(doParallel)
library(dplyr)
library(foreach)
library(grid)
library(httr)
library(magick)
library(readr)
library(spacesXYZ)
library(stringr)
library(tibble)
# This file is created by 13_Select_PMC_Images.R.
articles_filepath = "PMC_Selected_Articles.tsv"
# This is where the images downloaded... |
147f679a974284dd226fabb023afee925ee5222bc7fe9978a9a123652c3d5238 | R | 2,892 | 98 | library(dplyr)
celltype_list=c("Exc","Inh","Ast","Oli","OPC","Mic")
celltype_list2 = c("Ex01","Ex02","Ex03","Ex04","Ex06","Ex07","Ex08","Ex10","Ex13")
region_list=c("AG","EC","HC","MT","PFC","TH")
for (r in 1:length(region_list)){
region=region_list[[r]]
#####################################
genes_list <... |
849019b8ba906550be980d603d392c6c1b9b8a171840da7332970ac8644daf6d | R | 2,898 | 98 | library(dplyr)
celltype_list=c("Exc","Inh","Ast","Oli","OPC","Mic")
celltype_list2 = c("Ex01","Ex02","Ex03","Ex04","Ex06","Ex07","Ex08","Ex10","Ex13")
region_list=c("AG","EC","HC","MT","PFC","TH")
for (r in 1:length(region_list)){
region=region_list[[r]]
#####################################
genes_list <... |
cfb41f08d6293bb567a7afffd1a6361d400ef288faab2c4f01dfeb213071c215 | R | 2,901 | 91 | ##### Covariance only
# Site effect in covariance only
# Covariate effect in covariance only
library(reshape2)
library(mvtnorm)
library(invgamma)
library(randomForest)
library(pROC)
library(ROCR)
library(caret)
library(Matrix)
library(CovBat)
source("covbat_sim_functions.R")
load("animats_add.Rdata")
load("covbat_sim... |
a0bfb6d4547045355246a004978351be2dfc59c35c35448533616f4738a7c842 | R | 2,910 | 74 | log <- file(snakemake@log[[1]], open = "wt")
sink(log)
sink(log, type = "message")
# Code adapted from https://github.com/chenlabgccri/CeL-ID/blob/master/Cel-ID_functions.R
# 2023-05-10 Aurélien Ginolhac
# DP and FREQ files from https://github.com/chenlabgccri/CeL-ID
# converted to {fst} by the following:
# ccle_dp <... |
58dd54aad2ab4c97d3a54977604e815211b4ea4f8ba7c28a9cd6bfc1906ab394 | R | 2,912 | 75 | ### Ref links
# http://tuxette.nathalievilla.org/?p=1696
# https://swcarpentry.github.io/r-novice-inflammation/05-cmdline/
## -----------------------------------------------------------------------------
# get the input passed from the shell script.
args <- commandArgs(TRUE)
# test if there is at least one argument: ... |
8077374d7648f7de9b52aa3e8e02b1cc1394ab6c36b46f50973b44c0cd8da44d | R | 2,914 | 107 |
library(monocle3)
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjectCode/utility/singleCellUtilFuncs.R")
source("./deHelperFunctions.R")
library(DESeq2)
library(ggplot2)
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-c", "--cdsRNA"), type="character"... |
9cea20cf790085fc25d69f7676407efa402e11d1a80067e2ad044d9909ef424d | R | 2,918 | 64 | # util.r
library(dplyr)
library(magrittr)
library(Seurat)
library(ggplot2)
library(forcats)
library(stringr)
library(patchwork)
library(RColorBrewer)
library(grDevices)
celltype.levels = c('Neuron 0', 'Neuron 5', 'CN 1', 'CN 2', 'CN 3/Photo 6', 'Inhibitory neuron 8', 'proRG 3', 'RG 4', 'RG 9', 'Astro 7')
cell.broadtyp... |
782156b000102b6f1fbfc57c84a82eb3ff66eefeb4fefb8c3413bf463f05e51e | R | 2,920 | 74 | group.blocks <- function(Blocks, parseselect, mem.limit=2^32*8, chunks=NULL,
cluster=NULL) {
#' @title Group blocks into chunks so as not to exhaust memory or hit
#' matrix size ceiling
#'
#' @param parseselect An object returned by parseselect()
#' @keywords internal
ncols <- B... |
2f7ad658ec1ed8f05117930486b870bbfe8e7c4c08b891f6ee9d3df137f7aa25 | R | 2,927 | 79 | #!/usr/bin/env Rscript
#
# Command-line version of 'update_tracked_job_status'
# Used for jobs submitted to 'cluster_job_submit' that are command-line only (e.g., not nested in R_batch_job)
# retreive in code using: upd_job_status_path <- system.file("bin/upd_job_status.R", package = "fmri.pipeline")
# example statu... |
9d056105e4e27b29b62e863f643e58771b92050453a31162a0e017169c2cf8da | R | 2,929 | 82 | ---
title: "MGEnrichment Analysis"
author: "Jenn Kim, Annie Ciernia"
date: "`r Sys.Date()`"
output: html_document
---
# load libraries
```{r}
library(tidyverse)
```
```{r}
# Focusing on all gene lists in database except for WGCNA modules
# tidy up data
setwd("MGEnrichmentResults/") # provided as a zip file in github ... |
cb1185fe8fb1a29a68734eba3663692588756a8b87c483633c22d0c2fc965748 | R | 2,934 | 95 | log <- file(snakemake@log[[1]], open = "wt")
sink(log)
sink(log, type = "message")
suppressPackageStartupMessages(library("Rsubread"))
bams <- snakemake@input[["bams"]]
annot <- snakemake@params[["annotation"]]
# se should come as 0 and 1
se <- as.logical(snakemake@params[["se"]])
# se should be boolean of is each BA... |
05b23e4a536219e2954f4b4c1d67b8e8839038fd970d9ea5505e85819d48cfd2 | R | 2,942 | 64 | setwd("./Projects/SNCSynuclein")
library("Seurat")
library("tidyverse")
# load the DEG files
DEGFileCombind <- readRDS("Step6.DEG/DEGs/DEGFileCombind.rds")
Clusters <- names(DEGFileCombind)
for (i in 1:length(DEGFileCombind)){
dat <- DEGFileCombind[[i]] %>% as.data.frame()
write.csv(dat, paste0("Step6.DEG/DEGs/",... |
d407c4a1bede2fbbd02a4b4484d3cd5dd92658556aaf576918e607f3ee7ee668 | R | 2,943 | 123 | require(testthat)
require(Cardinal)
context("spectrapply")
test_that("spectrapply - SpectralImagingArrays", {
set.seed(1, kind="default")
x <- replicate(9, rlnorm(100), simplify=FALSE)
t1 <- replicate(9, sort(runif(100)), simplify=FALSE)
t2 <- replicate(9, sort(runif(100)), simplify=FALSE)
t3 <- replicate(9, so... |
8ed050b75ea43ef3a5514b8b58c9fa7b896b92a13968d29ff8630fa6523ada2e | R | 2,959 | 113 | #' @rdname spc-loess
#'
#' @title LOESS smoothing interpolation for spectra
#'
#' @description
#' Spectra smoothing and interpolation on a new wavelength axis using
#' [stats::loess()].
#'
#' @details
#' Applying [stats::loess()] to each of the spectra, an interpolation onto a new
#' wavelength axis is performed. At th... |
41545bde139d5283529441d8b597c8d0fec315526a9b4cde36f7a8c382a12eeb | R | 2,960 | 117 |
#' Normalize numbers to interval \[0, 1\]
#'
#' The input `x` is mapped to \[0, 1\] by subtracting the minimum and
#' subsequently dividing by the maximum. If all elements of `x` are equal,
#' 1 is returned.
#'
#' @name normalize_01
#'
#' @param x Object (e.g., vector) with values to transform.
#' @param tolerance ... |
15833f7501b632e99578c949251d9c910ded1c9da73acd2c01d9685dd90b6b49 | R | 2,964 | 96 | # Data integration for replicability analysis
#
# input:
# data/01_DS1x.ssce.Rdata
# data/02_DS2x.ssce.Rdata
# output:
# data/03_DS1x_discardedNuclei.txt : 1071 discarded DS1x nuclei
# data_figs/03_DS1x_DS2_umap.pdf
require(batchelor)
library(BiocParallel)
options(bitmapType = 'cairo')
# (0) Load data ----
... |
cc8c0590089ea8e9cabb142231db4e2272cfbaea672c9e5276eea60201edf9c0 | R | 2,964 | 53 | library(Seurat)
library(dplyr)
library(tidyverse)
library(magrittr)
library(liana)
setwd("F:/Brain_region_project/Brain_region_final_12TB/Data_for_Connectome")
pbmc=readRDS("TH_merge_major_cell_type.rds")
Idents(pbmc)<-"cell_type_high_resolution"
pbmc=subset(pbmc,idents=c("Ast DPP10","Ast GRM3","Ast DCLK1",... |
4ac2f4389579f089fbf338b8b664ac5994ac77309da0335beae2332aed43c249 | R | 2,967 | 89 | ---
title: "Methylation Query"
author: Szi Kay Leung
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Files
1) rTg4510_RRBSbetaComplete.Rdata
2) Phenotype file
(Transferred from "/gpf... |
4ce25ca0c25c0976e3832ca5ceddd828c76547b6cfd5f2dfca882d47e4d6c65c | R | 2,981 | 85 | library('RColorBrewer')
library('MAST')
library('data.table')
library('Seurat')
library('biomaRt')
library('lme4')
# load the seurat object
Seurat.SNC <- list()
MajorCluster <- c("Ex", "In", "Olig", "OPC", "Ast", "Mic", "Vascular")
for (i in 1:length(MajorCluster)){
setwd("./Step8a.SubClusterAnalysis")
setwd(Majo... |
ed3ad10e4fc989e75f935c77c10647d2c0261f777db8103f384e6ea9e2bbd04a | R | 2,984 | 88 | #!/usr/bin/R
# -----------------------------------------------------------
# Load the UMAP coordinates parameters for modules / subsets:
# Updated 11/27/2021
# -----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(bind... |
160deeaa60b193e63d48364c811a9c40c7b136c2496c196556b508ae21cb04d0 | R | 2,989 | 106 | # Set generic ----------------------------------------------------------------
#' Function evaluation on `hyperSpec` objects
#'
#' Function `vanderMonde()` generates Vandermonde matrices, the `hyperSpec`
#' method generates a `hyperSpec` object containing the Vandermonde matrix
#' of the wavelengths of a `hyperSpec` o... |
f56bf1c2e1a1cf91d22f81e2f3fad188b3be412643e35176eefb7694539d4d12 | R | 3,001 | 77 | ###############################################################################
# #
# GECCO 2018 Industrial Challenge - Main Evaluation #
# #... |
ff231098fe863ae21afa0842113ad0c718205eb5c06e3462b643946f9190eb22 | R | 3,005 | 94 | ##Difference analysis of chromoatin accessibility
setwd("F:\\Manuscript\\ATAC\\data\\Tissue_stage_specific")
library(dplyr)
library(edgeR)
library(ggplot2)
library(stringr)
dataCount <- read.table("F:\\Manuscript\\ATAC\\data\\AllPeakList.merged.TPM.txt", header = T, row.names=1)
datFilter <- select(dataCoun... |
572e0e99aa184aaadd224e273371133f238f9da514acd361abbb6ae275a22dba | R | 3,008 | 87 | library(Seurat)
setwd('/ArchR/scRNAseq')
dir <- 'CN6_E11-5A/mm10_cur'
ob <- Read10X(dir)
seu <- CreateSeuratObject(ob, project = 'CN6-e115')
seu <- NormalizeData(seu)
seu <- FindVariableFeatures(seu)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
seu <- RunUMAP(seu, reduction = 'pca', dims = 1:50)
hist(as.numeric(seu['g.eGF... |
668996c08c6ad678365ac10a3bc2f39711c11e3750c4e4ce1c46cbbf4a3c3408 | R | 3,009 | 94 | setwd("./Projects/SNCSynuclein")
library("Seurat")
library("tidyverse")
library("DESeq2")
library("vsn")
# load the Seurat object
Seurat.SNC.final <- readRDS("Step5.FinalClustering/Seurat.SNC.final.rds")
# normalize the data before plotting
DefaultAssay(Seurat.SNC.final) <- "RNA"
Seurat.SNC.final <- NormalizeData(Seu... |
04f84899924ab6991a059d7a5feab1a9ea3165498d2fa1c076c6685bf6eec4c3 | R | 3,012 | 80 | library(spacexr)
library(spASE)
library(tibble)
library(data.table)
library(Matrix)
library(dplyr)
library(tidyr)
cerebellum_ref <- readRDS('../inst/extdata/reference_scrna/cerebellum_sn_ref_spacexr.rds')
PATH <- '../inst/extdata/slideseq/cere3'
positions1 <- fread(file.path(PATH, 'Puck_221014_23_barcode_matching.tx... |
3ecd84d986b5ca9ea7e36bead3accbaa3c9fdcda3e3bab4b3d016696c0ea732e | R | 3,016 | 89 |
#### VIRTUAL class for a DataFrame with key columns ####
## -----------------------------------------------------
setClass("XDataFrame",
contains = c("VIRTUAL", "DataFrame"),
slots = c(keys = "list"))
setClass("XDFrame", contains = c("XDataFrame", "DFrame"))
#### DataFrame with position information ####
## -------... |
1f4c9fb82bc42d9b8261671d8da18b27b345f29fc694d0fec54da41fd7869da5 | R | 3,020 | 83 | ImpulseDE2_ATACSeq <- function(comparisons, conditions, results, padj_threshold) {
# Load Comparisons
matCountData = read.csv(comparisons)
rownames(matCountData) <- matCountData[,1]
matCountData$ATACSeq <- NULL
matCountData <- data.matrix(matCountData)
colnames(matCountData) <- substr(colnames(... |
8c43b2258d585f7616c835344e5d7f0ce21ffc42adb790b403a48dfe9a6f2dc2 | R | 3,024 | 89 | calculate_performance_1 <- function(data){
asysd_result <- dedup_citations(data, keep_label = "Unique", merge_citations = FALSE)
unique <- asysd_result$unique
unique_to_match <- unique %>%
rename(asysd_label = label) %>%
mutate(asysd_label = "Unique") %>%
select(asysd_label, record_id)
check <- l... |
7f85db1ce196ff08e767b69e66b09e15fedcf373259b60f36a0d08a5d913eebe | R | 3,025 | 81 | rm(list=ls())
library("ggalluvial")
library(dplyr)
load("../NMF_res/NMF_LOOV_cluster.RData")
load("Wang_subtype.RData")
load("Neftel_subtype.RData")
load("Garafano_subtype.RData")
dat <- data.frame(Wang=Wang_subtype[names(groups)],NMF=groups,stringsAsFactors=F,row.names=names(groups))
dat %>% filter(NMF != "other") ->... |
4bbd2a01c245dea8f03256d0bf52460a27a83458e81033ab991d419d661921d0 | R | 3,026 | 59 |
#setwd("/proj/mnhallqlab/users/michael/fmri.pipeline/example_files")
#load("MMClock_aroma_preconvolve_fse_groupfixed_sceptic-clock-feedback-pe_max-preconvolve_fse_groupfixed_lvl2_inputs.RData")
#load("MMClock_aroma_preconvolve_fse_groupfixed.RData")
setwd("/proj/mnhallqlab/projects/clock_analysis/fmri/fsl_pipeline/con... |
644be0a8f0d144ff571a21afe70034cc25a05f59d867cf0c9fba2c1637a1b751 | R | 3,030 | 89 | require(arrow)
require(plyr)
require(dplyr)
require(stringr)
require(tidyr)
cct_vtypes = c("missense", "spliceai")
pval_cols = c(score = "pv_score")
tests_to_exclude = c("plof-skat", "deepripe-burden") #plof_skat and deepripe_burden is not done by Monti
config = snakemake@params[["config"]]
phenotype = snakemake@p... |
3a2877e9ac70a7bab8292439722c1e0e91a0e647e62559e7deecfefcf461af9e | R | 3,032 | 99 | require(GenomicSEM)
library(data.table)
sumstat.dir <- "../../gwas/EasyQC/results/"
munge.dir <- "./"
log.dir <- "./logs/"
dir.create(file.path(log.dir), showWarnings = FALSE)
reference_file <- 'w_hm3.snplist'
TG_GLGC <- paste0(sumstat.dir, "CLEANED.TG_GLGC_UKB.txt")
TG_GLGC_noUKB <- paste0(sumstat.dir, "CLEANED.TG_... |
36e2d15153c8f23c779d75d45cca40de5217fda7488602a1c82c4f1a71663a88 | R | 3,034 | 97 | source("R/setup.R")
source("R/shinyforecasts/plots.R")
config <- yaml::read_yaml("config/config.yaml")
dataset <- "xxx"
case_id <- "xxx"
case_pattern <- paste0("case_", case_id, ".parquet$")
univ_folder <- paste0("data/predictions/", dataset, "_predictions_univariate/")
ml_folder <- paste0("data/predictions/", datase... |
7923c71ea25056e2abf23a6ac6164ee6150b4cedc0f427963577adb3c19d6384 | R | 3,035 | 106 |
enrichment_analysis2 <- function(geneUniverse,genesOfInterest)
{
#STEP1: select genes of interest
geneList <- factor(as.integer(geneUniverse %in% genesOfInterest))
names(geneList) <- geneUniverse
print(geneList)
#suppressMessages(topGO::groupGOTerms())
#topGO::groupGOTerms(environment())
#STEP2: Ma... |
342400fcfd9fbe084adb0276314750963d88f96e53d6a6e2aca652aa877da768 | R | 3,039 | 77 | required_libs <- c("rjson", "tidyverse", "magrittr", "data.table",
"ggpubr", "kableExtra", "rmarkdown", "argparser",
"lme4", "lmerTest", "multcomp", "xtable",
"tidyselect", "ggthemes", "RColorBrewer", "openxlsx")
for(libb in required_libs){
lib_test <- eval(p... |
a8267e54523a00b2fcce732272d2b5cca62b672092105c8031da16e48ff6f31d | R | 3,043 | 59 |
#' archstabms_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 (require... |
9df2c79428ad9bc485d8e897eae9835faff205611332f0f50e0becf6591a23a8 | R | 3,048 | 88 | # Title: Nested active zone distribution analysis
#
# Description:
# This script performs a nested active zone distribution analysis.
# It calculates the 3D distance of active zones within each nested bouton.
#
# Author: Daniel Parthier / Felicitas Brüntgens
# Date: 09.04.2024
# Last modified: 19.04.2024
# Version: 1.... |
e47ed8324ecf05ffb932ee07d659b30d6d78883ccd272ddcb1bd40b2f09eab87 | R | 3,048 | 105 | ### -----------------------------------------------------------------------------
###
### write.txt.wide
###
###
#' @rdname DEPRECATED-write_txt
#' @concept deprecated
#'
#' @importFrom utils write.table
#' @export
write.txt.wide <- function(object,
file = "",
col... |
22f4b7a7ce640cbfb0f7f97161d92dcaf3945e947b2f28cdfc425df4fb56d633 | R | 3,053 | 93 | library(dplyr)
library(emmeans)
library(fmri.pipeline)
library(readr)
source("/proj/mnhallqlab/projects/clock_analysis/fmri/keuka_brain_behavior_analyses/dan/get_trial_data.R")
trial_df <- get_trial_data(repo_directory = "/proj/mnhallqlab/projects/clock_analysis", dataset = "mmclock_fmri", groupfixed = TRUE) %>%
dpl... |
e0356b309f0fde5dabec58813ce768900836caccedde53a81426f6ea203261a5 | R | 3,054 | 84 | ## ----------Script-----------------
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
## Date: Jan 2023
##
## Explore the output from cupcake collapse after parameter optimisation of --max_5_diff and max_3_diff
## Output of 4_collapse_further.sh
## Format of group.txt, generated from cupcake collapse (v8.6)
## ... |
ef0c5ab0b4032f32a3b6994f9fba4fc8f5ba0dfeef14d3e91768015567563c84 | R | 3,068 | 88 | #' Batch deduplication for large searches
#'
#' This function splits citations into batches, deduplicates each batch, and binds results together into one dataframe of unique citations.
#' @details The following fields will be used in `citations` (if provided): record_id, author, year, journal, doi, title, pages, volume... |
00ec72a14e7a4b37d89229f47da10225258204d1970edb4b06117b9053e1573f | R | 3,070 | 62 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
dir<-args[1]
inputFile<-args[2]
inputFileArray<-unlist(strsplit(inputFile, "\\/"))
inputFileName<-inputFileArray[length(inputFileArray)]
wd<-paste(dir,sep='')
setwd(wd)
data<-read.table(inputFile,header=T,sep="\t")
data<-subset(data,data$loessExpect... |
7815f856c844c48d9c51a6628841280d5f5dc32320416dfb6b5265f6ed96e058 | R | 3,070 | 102 | #!/usr/bin/env Rscript
# Helper script to clean final table of vcf vs vcf comparisons
###Set parameters
options(stringsAsFactors=F)
###Helper function to clean an input overlap file
cleanData <- function(dat){
#Get list of repeated VIDs
repeat.IDs <- names(which(table(dat[,1])>1))
#Iterate over repeated VIDs a... |
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