sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c4bd6ba7354b935391c22a582071593aa338455e40c50c09fb51f96065f5bd3a | R | 937 | 18 | #' @import GenomicRanges
#' @import gUtils
#' @import rtracklayer
#' @import GenomeInfoDb
#' @importFrom GenomeInfoDb seqlengths
#' @importFrom data.table data.table fread rbindlist set setkey setkeyv setnames transpose as.data.table
#' @importFrom stats cor loess predict quantile
#' @importFrom skidb read_gencode
#' @... |
732603fda4b292ff2ef03deceffa2cf11977871d05df7b720e7fab20b9259f39 | R | 942 | 34 | ### SHELL> mpiexec -np 4 Rscript mpi_fclust3d.r
library(MixfMRI, quietly = TRUE)
library(EMCluster, quietly = TRUE)
library(pbdMPI, quietly = TRUE)
init()
.FC.CT$MPI.gbd <- TRUE
# .FC.CT$algorithm <- "em"
# .FC.CT$model.X <- "V"
# .FC.CT$ignore.X <- TRUE
#.FC.CT$CONTROL$debug <- 5
set.seed(1234)
### Test toy1.
X.gbd ... |
6d264b693dabada3e17777ed9256875d8e5539035f84430bf24bafbc61642521 | R | 945 | 38 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 16)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_latest_atac_anno_... |
9925954499a54b04addffc4fbb9dc7819f46bd315d29cd1f40efa9942c50fcae | R | 952 | 31 | ### This file contains functions for decomposition of SIGMA of MVN.
### These will be majorly used in m.step() to update PARAM$U and PARAM$U.check.
decompsigma.I <- function(full, sigma.ill = .FC.CT$CONTROL$sigma.ill){
DS <- sqrt(full)
DS.check <- TRUE
if(min(DS) / max(DS) < sigma.ill){
.MixfMRIEnv$cat("Chec... |
70a770d48d2c4bcc2f93315bdb3d77dc7725c8eaf5375a5d2db2a58802dc8f68 | R | 957 | 20 | library(ggplot2)
list<-read.csv("Fig2g_DEG_EpiGene_inf.txt",sep="\t",header = F)
l<-as.character(list$V1)
f<-read.table("Fig2g_DEG_EpiGene_FC_and_FDR.txt",header = T)
mid<-0
ggplot(f,aes(x=Region, y=Gene, size = -log10(FDR), col =logFC ))+
geom_point(alpha=1) +
scale_color_gradient2(midpoint=mid, low="navy", mid="... |
2a43f5e8c975d33c2d339408de94a69cf6bdb41233f4ccfb9df7e7d2942053e4 | R | 977 | 42 | ##
## Garcia (CSDA, 2010)
##
##
gcv.score <- function(s, Lambda, DCT2y, N)
{
Gamma <- 1/(1 + s * Lambda^2)
RSS <- sum((DCT2y * (Gamma - 1))^2)
TrH <- sum(Gamma);
GCVs <- RSS / prod(N) / (1 - TrH/prod(N))^2
## print(c(s,GCVs))
GCVs
}
gcv.smooth2d <- function(y, interval)
{
if (is.nul... |
9672e1e9a75ec2a5be63be14c4904ca2a1b22b79d54aa919f6dff63e60536b70 | R | 983 | 24 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
data_obj <- readRDS("./seurat_objects/snRNA_cellBneder_corrected_postQC_scTransformed_clustered_2ndRoundAnnotated_cleanV2_SeuratV4_object.rds")
filtere... |
5e63750b70ef8578202d20f7849e64965aaca9e701e4473356b7f734fc6c294f | R | 985 | 31 | ### Get the parameters
parser = argparse::ArgumentParser(description="Script to Cluster scATAC data by ArchR")
parser$add_argument('-I','--input', help='input arrow directory')
parser$add_argument('-D','--id', help='tissue ID')
parser$add_argument('-O','--out', help='out directory')
args = parser$parse_args()
library(... |
0abecfe1181ecc658b319f2ff6ed806b391f24f4e27cca2c3d967e15d8702ed2 | R | 990 | 29 | #!/bin/Rscript
library(ggplot2)
library(data.table) # Efficient multiprocessor data reading/writing/processing
library(echolocatoR) # To fo the finemapping
library(tidyverse) # For data wrangling / munging
# Load some functions to make the plotting a bit easier -> https://github.com/AMCalejandro/echor_wrapperPipes
s... |
eb923dbf3051c528c47a16a57b6dce2834e5294234ef4591d53dd166ef5abbc4 | R | 1,004 | 38 | library(dplyr)
library(tibble)
library(readr)
library(tidyr)
library(broom)
library(ggplot2)
library(gridExtra)
library(sjPlot)
library(FSA)
setwd('/Users/mromano/research/mri-surv-dev/mri_surv/')
##########################################
########Now by lobe
tb2 <- read_csv('./metadata/data_processed/shap_with_parce... |
47ac29648f51cea2d6e4c1d434e3acb8f19bb4e6fbcb6d694c312f79461e4393 | R | 1,022 | 33 | library(MixfMRI, quietly = TRUE)
library(MASS, quietly = TRUE)
.FC.CT$model.X <- "I"
.FC.CT$CONTROL$debug <- 0
K <- 3
### Fit toy1.
set.seed(1234)
X.gbd <- toy1$X.gbd
X.range <- apply(X.gbd, 2, range)
X.gbd <- t((t(X.gbd) - X.range[1,]) / (X.range[2,] - X.range[1,]))
PV.gbd <- toy1$PV.gbd
fcobj <- fclust(X.gbd, PV.gbd... |
755a8d152b088619ac73f460b7030f3e033c50408b3b991d65de28afa47537c0 | R | 1,044 | 43 | W.plus.y <- function(W, y, nrow, ncol){
if(!is.double(W)){
stop("W should be in double.")
}
if(!is.double(y)){
stop("y should be in double.")
}
ret <- .Call("W_plus_y", W, y, as.integer(nrow), as.integer(ncol),
PACKAGE = "MixfMRI")
dim(ret) <- c(nrow, ncol)
ret
} # End of W.plus.y(... |
79be53db07ada21efab83e2a27cd4404cb991743cddb5bc8c05fba1abb7fd5e0 | R | 1,081 | 38 | #------------------
# Folial allometry
# 25 January 2023
#------------------
source("./common.R")
# bivariate scatterplots
# <<Figure 11a>>
# width versus length/area
my_plot_allometry(
df$Log10Area.cb, df$Log10WidthMedian,
df$English_name, 0,
expression("Cerebellar section area (log"[10] * "mm"^2 * ")"),
ex... |
4778b6d4fadefee5382bd0f1456c1cc9afd1ba35bbb3b532ef7ba08b8edc09e9 | R | 1,089 | 24 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(sctransform)
library(scales)
# read in data with 1st round annotations
data_obj_sub <- readRDS("./seurat_objects/snRNA_cellBneder_corrected_pos... |
a4590a162bea16aa09c9e76b0e3196c004faa0181038328cf5212df225b35dea | R | 1,090 | 25 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(pheatmap)
library(viridis)
library(scico)
data_obj <- readRDS("./seurat_objects/snRNA_cellBneder_corrected_postQC_scTransformed_clustered_2ndRo... |
c1af544157071c4dc859fc3006f0fee1ee14a67190466345c76ad5bb221db5af | R | 1,097 | 30 | # read names of individuals
indv.tab <- read.table("example_data/H3K27ac/samples.txt", header=F)
# build matrix of read counts for all target regions
# and individuals. Number of rows is same as number
# of rows in CHT input files. One column per individual
read.count.matrix <- NULL
for(i in 1:nrow(indv.tab)) {
in... |
8b9840a89cc75e9d33b7f610610904b82c7859f1ce187cc2c4c3b00c8e52c0e2 | R | 1,100 | 38 | ### SHELL> mpiexec -np 2 Rscript mpi_toy_test.r
library(MixfMRI, quietly = TRUE)
library(EMCluster, quietly = TRUE)
library(pbdMPI, quietly = TRUE)
init()
.FC.CT$MPI.gbd <- TRUE
# .FC.CT$algorithm <- "em"
# .FC.CT$model.X <- "V"
# .FC.CT$ignore.X <- TRUE
### Test toy1.
set.seed(1234)
X.gbd <- toy1$X.gbd
X.range <- ap... |
a2df7b11cd090ea36faab2fa02e506d777cec07320a47e386bd733d25b9f6706 | R | 1,108 | 40 | ### This file contains functions for log densities of beta and normal.
### These will majorly update .MixfMRIEnv$W.gbd.
logbetanorm.I <- function(PARAM, i.k){
# log of beta density.
ret <- dbeta(.MixfMRIEnv$PV.gbd,
PARAM$BETA[[i.k]][1], PARAM$BETA[[i.k]][2], log = TRUE)
# log of normal density.
... |
de05f3c8ddb760472614d4e53e3bc53f70a9f8e8f6ff6e3dcc8d07bdab73f038 | R | 1,123 | 46 | #### q-value
bh.fdr <- function(p){
# This function computes q-values using Benjamini and Hochberg's (1995)
# approach for controlling FDR.
# Author: Dan Nettleton
m <- length(p)
# k <- 1:m
ord <- order(p)
p[ord] <- (p[ord] * m) / (1:m)
qval <- p
for(i in (m - 1):1) {
qval[ord[i]] <- min(c(qval[or... |
53a8489e3c932b5affd27333834946b617fb5ef7bf9bb32af94cda2f53e65d11 | R | 1,138 | 38 | #-------------------
# Folding allometry
# 25 January 2023
#-------------------
source("./common.R")
# bivariate scatterplots
# <<Figure 10a>>
# Cb area versus length
my_plot_allometry(
df$Log10Area.cb, df$Log10Length.cb,
df$English_name, 0.5,
expression("Cerebellum section area (log"[10] * "mm"^2 * ")"),
ex... |
dcaab5adf9fe1d3b68cc382c081b3adc47d06198c3674138f8cae4fc75067223 | R | 1,139 | 42 | #!/usr/bin/env Rscript
library(optparse)
option_list = list(
make_option(c("-i", "--input"), type = "character", default = "qcreport.txt", help = "Input file containing paths to germline "),
make_option(c("-o", "--output"), type = "character", default = "qcreport.pdf", help = "Output panel of normals file"... |
775a4c435d30f620167fa2f3bd567ddc3745db65251d526983c88fd51fce6733 | R | 1,146 | 21 | library(tidyverse)
df <- read_tsv("snATAC_peaks_overlaps_with_ChIPseq_consensus_peaks.txt") %>%
mutate(num_non_overlaps = num_snATAC_merged_peaks - num_overlaps_with_ChIPseq_consensus_peaks) %>%
select(-num_snATAC_merged_peaks) %>%
pivot_longer(-cell_type, names_to = "group", values_to = "count")
p <- df %>% ggplo... |
3b8576c60a6fb3c7e74af37b6ba7ef1a696e50834efc1892aa0ab15391863183 | R | 1,147 | 42 |
# Creates some toy test data
# note that for openvibe .csv you need to manually add the freq value as the last item of the two first lines.
# its not done by this script.
nExamples<-30;
nDim<-50;
# Gaussian data
a<-matrix(data=rnorm(nExamples*nDim),nrow=nExamples);
b<-matrix(data=rnorm(nExamples*nDim),nrow=nExample... |
004a974bc4023f33545b9166188372805304a20427f9ff32ab4b22dcbd9d3808 | R | 1,152 | 41 | plotfclustpv <- function(da, posterior, main = NULL, xlim = NULL, ylim = NULL){
### Force the col.image here.
col.image <- c("#000000",
"#F9070E", "#0063FF", "#01B901", "#C000C5", "#FF7F00",
"#FFFF33", "#A65628", "#F781BF",
"#AF0000", "#009600", "#0000FF", "#FF4500... |
fb7a2c5e357d35fa9dd1722755453892aea082fd46f92329bbcb4f63224fa52b | R | 1,166 | 41 | #!/usr/bin/env Rscript
library(shiny, quietly = TRUE)
library(dplyr, quietly = TRUE)
library(ggplot2, quietly = TRUE)
library(DT, quietly = TRUE)
library(leafcutter,quietly = TRUE)
library(reshape2, quietly = TRUE)
library(gridExtra, quietly = TRUE)
library(intervals, quietly = TRUE) # needed for pretty strand arrow pl... |
d99db8c75a6e9307d68f5cb31150d7c6de8e3d3baeeb485d29b3cb68cb57d6fb | R | 1,176 | 29 | library(Seurat)
###############################################################
#-------------------------regions-----------------------------#
###############################################################
setwd('input path')
obj = readRDS('Adult.rds')
obj$Region = unlist(lapply(strsplit(as.character(obj$Sample),"_")... |
2461576abe8167b9f2df894a2ec0b7d912454e7ca400b68817b8c858c558c3eb | R | 1,181 | 41 | ### Loading Data Sets-----------------------------------------------------------------------------------------
#' Load an excel sheet as a dataframe
#'
#' Uses the openxlsx package function read.xlsx to read the excel sheet
#' The first row is used as column names. These should not have spaces
#' NA cells in excel sho... |
2e0383a4128d82ab45e60165b89cdd75b632f976a5a1a36333ec493d20196b48 | R | 1,184 | 42 | plotfclust <- function(da, posterior, main = NULL, xlim = NULL, ylim = NULL){
### Force the col.image here.
col.image <- c("#000000",
"#F9070E", "#0063FF", "#01B901", "#C000C5", "#FF7F00",
"#FFFF33", "#A65628", "#F781BF",
"#AF0000", "#009600", "#0000FF", "#FF4500",... |
1d89bfbf5a24dfc81310bb99624b855e79115d1010085e2c12bc8cde67d56b1e | R | 1,202 | 23 | data<-read.table("S-Fig2b_DEG_shared_3tissue_logFC_FDR.txt")
f<-c()
da1<-data[,c(1,2,6)];colnames(da1)<-c("Gene","LogFC","FDR");da1$Region<-"CA"
da2<-data[,c(1,3,7)];colnames(da2)<-c("Gene","LogFC","FDR");da2$Region<-"DG"
da3<-data[,c(1,4,8)];colnames(da3)<-c("Gene","LogFC","FDR");da3$Region<-"NAc"
da4<-data[,c(1,5,9)]... |
43603c15e007b58b2052f10a54ad7fc5072216b8ae0cfc01ae9d97e51c1fc0fe | R | 1,203 | 31 | #box plot expression of Ptprn2 gene
data<-read.table("Fig3f_gene_Ptprn2_expression.txt",header = T)
par(las=1)
a<-boxplot(data,ylim=c(250,500),main="Ptprn2",
at=c(1,2,4,5),axes=F,col=c("darkgreen","purple"))
axis(2)
axis(1,at=c(1.5,4.5),labels = c("DG","CA"))
legend(0.5,500,"METH",bty="n",pch=15,col="purple"... |
606fe6a94163fe1e8ffeb0a0a215b1dca014636c75f1985974722c1f0c483f30 | R | 1,203 | 57 | library(pbdMPI, quietly = TRUE)
init(set.seed = FALSE)
library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
.FC.CT$MPI.gbd <- TRUE
### Test 2d data.
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
# ret.PARA... |
c297617ab4fac3f6da6a4c47d8f19338b265807588886010d7d9ff3e1fa43a17 | R | 1,204 | 57 | library(pbdMPI, quietly = TRUE)
init(set.seed = FALSE)
library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
.FC.CT$MPI.gbd <- TRUE
### Test 3d data.
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
# ret.PARA... |
bb4494b0f3a142bfc4b9baf9495c526870ebd0188010901394d10818e3ed7e55 | R | 1,242 | 33 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(sctransform)
## read the seurat object subset by major cell types in 1st round annotations
clusters <- c("Exc", "Inh", "NonNeu")
run_sctransfo... |
adc72c63884d690b4855333fff4ca207d929bfac3816d479e16835cbb1428658 | R | 1,244 | 42 |
#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
#
# Script performing Gene Set Enrichment Analyses from GO gene sets and HGNC gene sets
#
#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
source("src/julien/lib/go_hgnc_lib.R")
go <- function(universe,genelist) {
... |
c3f0dde81d94c2de7a1e0b774eb3ac6878cb21f0570e3eb601500b290e1a3503 | R | 1,244 | 41 | ### SHELL> cd ../; Rscript simu_2d/create_rep.r
seed <- 1234
for(i.rep in 1:25){
# mkdir rep
cmd <- paste("cp -R simu_2d/ ",
"simu_2d_", sprintf("%02d", i.rep),
sep = "")
system(cmd)
# change seed
cmd <- paste("cd simu_2d_", sprintf("%02d", i.rep), "/; ",
"sed -... |
8806b7ecf82902d60786156c6ced0f638c15e3e825d4f0f5aa7e301d3d7190d3 | R | 1,258 | 30 | #' gGnome: An R API to manipulate genome graphs
#'
#' @importFrom parallel mclapply
#' @importFrom reshape2 melt
#' @importFrom VariantAnnotation readVcf info geno
#' @importFrom RCurl url.exists
#' @importFrom S4Vectors cbind.DataFrame
#' @importMethodsFrom S4Vectors do.call elementNROWS Rle mcols mcols<- values valu... |
d2a18576e56f2189f7eb97fa2eb88a11d3baec6c2d76727d8eab4afc7d3038d3 | R | 1,258 | 41 | ### SHELL> cd ../; Rscript simu_2d/create_rep.r
seed <- 1234
for(i.rep in 1:25){
# mkdir rep
cmd <- paste("cp -R simu_2d/ ",
"simu_2d_", sprintf("%02d", i.rep),
sep = "")
system(cmd)
# change seed
cmd <- paste("cd simu_2d_", sprintf("%02d", i.rep), "/; ",
"sed -... |
8b255c62d3bbeda2782f7d50fe6e55bc2fd1e4642fd0fba6c484037311a27937 | R | 1,263 | 27 | library(MixfMRI, quietly = TRUE)
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442",
"#0072B2", "#D55E00", "#CC79A7")
myPalette <- cbPalette[c(6, 7, 2, 8, 3, 5, 4, 1)]
par(mar = rep(0.1,4), mfrow = c(2, 2))
image(-shepp0fMRI, axes = FALSE, col = c(myPalette[2:1],
rgb(red = 230 ... |
fe8b82abed596bbce6a9ac18809478c0adc29c6225c2fd2a302b80c17d385ee4 | R | 1,265 | 37 | library(MixfMRI, quietly = TRUE)
library(EMCluster, quietly = TRUE)
.FC.CT$model.X <- "I"
.FC.CT$CONTROL$debug <- 0
K <- 3
### Fit toy1.
set.seed(1234)
X.gbd <- toy1$X.gbd
X.range <- apply(X.gbd, 2, range)
X.gbd <- t((t(X.gbd) - X.range[1,]) / (X.range[2,] - X.range[1,]))
PV.gbd <- toy1$PV.gbd
fcobj <- fclust(X.gbd, P... |
b2e83525f0c104162ae438119278ba10c288ade81aa2f6c2772a10712b962a72 | R | 1,277 | 32 | parser = argparse::ArgumentParser(description="Script to Cluster scATAC data by ArchR")
parser$add_argument('-I','--inputpath', help='input arrow directory')
parser$add_argument('-D','--id', help='tissue ID')
parser$add_argument('-O','--out', help='out directory')
args = parser$parse_args()
library(ArchR)
library(BSge... |
8370c4f08184973d79d559ad19915cd6a50fee64b9c81b8457fbef6e9bd4c664 | R | 1,284 | 45 | library(MixfMRI, quietly = TRUE)
set.seed(1234)
phantom <- shepp2fMRI
### Generate 2d data.
da <- gendataset(phantom = phantom, overlap = 0.01)$pval
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Test 2d data.
i.k <- 4
set.seed(1234 + i.k)
fcobj <- fclust(X.g... |
6796af5e073a96568c6f3695da5e4ac694b777c99da5f36796cf6b13780075a6 | R | 1,287 | 41 | ### Controls
.FC.CT <- list(
algorithm = c("apecma", "em", "ecm"),
optim.method = c("BFGS", "Nelder-Mead"),
model.X = c("I", "V"), # independent or unstructure.
ignore.X = FALSE, # if using location information.
check.X.unit = TRUE, # if checking X in [0, 1].
CONTROL = list(
m... |
fbfdaf7dd63ec057191551aba94da8639a378d0eb8daf31eedb44663c513de0f | R | 1,292 | 38 | #!/usr/bin/env Rscript
library(optparse)
suppressMessages(suppressWarnings(require(VariantAnnotation, quietly=TRUE)))
option_list = list(
make_option(c("-i", "--input"), type = "character", default = NULL, help = "Input bps.txt.gz file"),
make_option(c("-o", "--output"), type = "character", default = "graph... |
49af3309b87e482b8e8137a49641a95c56400f3ce8f91c8e23117e6bad54df0e | R | 1,330 | 29 | # Generated by rstantools. Do not edit by hand.
# names of stan models
stanmodels <- c("bb_glm_fix_conc", "bb_glm", "dm_glm_multi_conc", "dm_glm_robust", "dm_glm")
# load each stan module
Rcpp::loadModule("stan_fit4bb_glm_fix_conc_mod", what = TRUE)
Rcpp::loadModule("stan_fit4bb_glm_mod", what = TRUE)
Rcpp::loadModu... |
fb29f41c1c0ab500f7a0deeca6769fb717b88fced439930b6f10b1ddb3240cf9 | R | 1,331 | 43 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(sctransform)
## read seurat object of QC-filtered cells
data_obj_sub <- readRDS("./seurat_objects/snRNA_cellBender_corrected_QC_filteredCells_S... |
2489b80d4c2070998ff6fe42681a6a2c56418ddbc59931fea8ee761bd1fddd05 | R | 1,367 | 44 | ### This files provides functions for output.
mb.print <- function(PARAM, CHECK){
ETA <- PARAM$ETA
BETA <- matrix(do.call("c", PARAM$BETA), ncol = PARAM$K)
if(PARAM$p.X > 0){
MU <- PARAM$MU
# SIGMA <- matrix(do.call("c",
# lapply(PARAM$SIGMA,
# ... |
9719879aecf5654272058d73bf4ce4f4acde34a77b8ea9d8bc79e4914b4a647d | R | 1,374 | 34 | # From Michael Roberts - 2021-10-27
# This file contains a function to generate a Linear Mixed Model (LMM) and p
# value statistics of comparisons of each Fixed Effect to the Control.
#
# Input data format: Excel file with three columns
# Column 1: Response values (e.g. spikes)
# Column 2: Treatment condition ... |
f83900c254f1ba80042f1e9a8f07b96a9c8595760fbe264af32ed1336b4ede14 | R | 1,379 | 29 | d<-read.table("S-Fig5f_DG_Gene_expression.txt")
par(las=1)
boxplot(as.numeric(d["Sox5",5:8]),as.numeric(d["Sox5",9:12]),
as.numeric(d["Sox11",5:8]),as.numeric(d["Sox11",9:12]),
as.numeric(d["Sox18",5:8]),as.numeric(d["Sox18",9:12]),
as.numeric(d["Sox8",5:8]),as.numeric(d["Sox8",9:12]),
a... |
c2b29d469c5a8fcb60e38d9d64442b14bfd1fa5599da7ee14dd90345d22fce3b | R | 1,383 | 50 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_rmLowQualitySample... |
c39e564bde00d9c77772756787c5c61cc0089eb6e9a1b14ff6750b71b9ce43b5 | R | 1,383 | 50 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_rmLowQualitySample... |
66cd166d29d6f70d16ecbe2eaa12a669969db17797bb4d6d25a1221778016e91 | R | 1,384 | 38 | library(gUtils)
library(gTrack)
library(testthat)
## read input
jab = system.file('extdata', 'jabba.simple.rds', package="gGnome")
jab.gg = gG(jabba = jab)
## use just a subgraph for faster walks
sg = jab.gg$copy$subgraph(seed = GRanges(seqnames = "1",
ranges = IRanges(start =... |
b163cfe59adc9e50dd58d1eaf9dacec7d7dd05c3e9664186d07097eb1541dabe | R | 1,386 | 39 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(sctransform)
library(scales)
library(harmony)
## read the sctransform-normalized seurat object
data_obj_sub <- readRDS("./seurat_objects/snRNA_c... |
63877fed38f8f14cbeeb2b3ed01353af6e2d075b1405501421907a9f9d452c40 | R | 1,391 | 56 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
[](https://travis-ci.org/msk... |
4d02fc88953ab8ba5bd6bc2ceba51d9f8f253f507aa50eb55e9a635a8d385ddc | R | 1,400 | 37 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
data_obj_sub <- readRDS("./seurat_objects/snRNA_cellBender_corrected_noQCFilters_scTransformed_clustered_SeuratV4_object.rds")
meta_data <- data_obj_s... |
910995e390df044d64ea699b152bbb5dac7bad04bb439870f9d9f7a002d9155b | R | 1,413 | 42 | ### Copy some functions from "AnalyzeFMRI/R/spatial.mixture.R" since
### "AnalyzeFMRI" is archived by CRAN.
cluster.threshold <- function(x, nmat = NULL, level.thr = 0.5, size.thr) {
## thresholds an array at level.thr
## calculates the number of contiguous clusters and their sizes
## answer is an array ... |
5cb5438d870e7f084ab956ca9b66132b67e9b83ea0b0664ac9531044caa4c91f | R | 1,415 | 35 | library(MixfMRI, quietly = TRUE)
library(RColorBrewer, quietly = TRUE)
##
## 3 phantoms with proportion of activated voxels:
## shepp0fMRI: 0.917%
## shepp1fMRI: 2.249%
## shepp2fMRI: 3.968%
##
## plotting the phantom
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442",
"#0072B2", "... |
b06aa8b1ba9eed391a16378b8b8b3d248e5dd243bf0877df989fa08b27024925 | R | 1,442 | 46 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 2d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.new.rda", sep = "")
load(fn.i... |
cfcfef9437704d04c90c8a0c44aead8f855092ba236ef5b3e87e374f5edbbd47 | R | 1,442 | 46 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.new.rda", sep = "")
load(fn.i... |
8029994b72a95f6b23a3118dc9f97097a97cb85d04dd01310e7ee676a63e54a0 | R | 1,443 | 46 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.new2.rda", sep = "")
load(fn.... |
d01f623d1b5b034cffe27412d3a5e8eb0bd269f1247fae1eb2da4b4581baa7c0 | R | 1,443 | 46 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 2d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.new2.rda", sep = "")
load(fn.... |
4b5b3daf9be28e5b57e88642d53547385018d1defdef6c04a796f751c6ad43b9 | R | 1,465 | 82 | getStillImgName = function(
damID,
DOB,
PND,
ZT,
min,
imgFolderPath = damImgsFolder
){
thisDateTime <- getZT_DateTime(DOB, PND, ZT)
thisDate <- getZTDate(thisDateTime)
thisTime <- getZTHour(thisDateTime)
regEx <- paste0(
# "^.*", # looks for the whole file path when matching, not ... |
79d6424cb21db4e957568df0d2f1c741b4629d164ab34628c92f79284558e92a | R | 1,465 | 44 | #fig5d
#Drd
data<-read.table("Fig5d_boxplot_Gene_Drd_expression_inNAc.txt",header = T)
par(las=1)
a<-boxplot(data,at=c(1,2,4,5),axes=F,col=c("darkgreen","purple"))
axis(2)
axis(1,at=c(1.5,4.5),labels = c("Drd1","Drd2"))
legend(3,320,"METH",bty="n",pch=15,col="purple",xjust=0,cex=1)
legend(3,300,"Sal",bty="n",pch=15,col... |
8469c9aa3a209ea24bf61f76f04327a74c1c39a912647fd483eeec54651d4f37 | R | 1,468 | 40 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(sctransform)
library(scales)
library(harmony)
## read the sctransform-normalized seurat object
data_obj_sub <- readRDS("./seurat_objects/snRNA_c... |
c0c5e3a99af98b0af80345ec98002718c87981b1222b118f68cd07d8e2cf23ee | R | 1,468 | 40 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(sctransform)
library(scales)
library(harmony)
## read the sctransform-normalized seurat object
data_obj_sub <- readRDS("./seurat_objects/snRNA_c... |
ff408b04eae71d2c72c3433f69a9a6aaee1095ccfa1640a82a32a1297f2e21cc | R | 1,469 | 53 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "./Dracheva_ALS_FTD_addRNA_Glia_subs... |
1b55f7aad573b7f66c148483874c724712b0740fb0f793649854e134e730cdaa | R | 1,474 | 53 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "./Dracheva_ALS_FTD_addRNA_Neu_Inh_s... |
8141755c514bb711a0df8f5c163091266d3635700b47f5c5275409fd293bb60d | R | 1,474 | 53 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "./Dracheva_ALS_FTD_addRNA_Neu_Exc_s... |
b7d694aef7e8d045d4662cab11860f839412e37f9f0b70fb4164820893c6838b | R | 1,475 | 51 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 2d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
ret <- NULL
ret.PARAM <- NULL
for(i.k in K.min:K.max){
ret.PARAM[[i.k]... |
4c9875f1af683a6c69b23aacfcff3fc437e3a5acca87440658d1004e67c5d862 | R | 1,476 | 40 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(sctransform)
library(scales)
library(harmony)
## read the sctransform-normalized seurat object
data_obj_sub <- readRDS("./seurat_objects/snRNA_c... |
618fef41bd63eb714064b9166c8092ac67be9ea03c85df71f3e2474a92da60f6 | R | 1,476 | 49 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
ret <- NULL
ret.PARAM <- NULL
for(i.k in K.min:K.max){
fn.in <- paste(... |
d23e68e99b8f01460103e85eb018feff6e87b012b6de20fbcd9030718fb81a23 | R | 1,476 | 52 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
ret <- NULL
ret.PARAM <- NULL
for(i.k in K.min:K.max){
ret.PARAM[[i.k]... |
232a032dbb2d67d26895dd3b0fca933691408c1b0a4425f3ab40e9f0e8c9b78d | R | 1,480 | 49 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
ret <- NULL
ret.PARAM <- NULL
for(i.k in K.min:K.max){
fn.in <- paste(... |
7847832a7073d885a58565cc5fe867af4fc8cb114b58cfeb18b702f976e18b2c | R | 1,504 | 53 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "./Dracheva_ALS_FTD_addRNA_Glia_subs... |
17556add88a0afc81338c4a94636903859c558a959bb2cd9636de85e9f2649a7 | R | 1,509 | 53 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "./Dracheva_ALS_FTD_addRNA_Neu_Exc_s... |
a5c4a63823927f6debf3b8949899bd4bad4c6bb861d7337e1f195c0d4af5f6ca | R | 1,509 | 53 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "./Dracheva_ALS_FTD_addRNA_Neu_Inh_s... |
a57ce037fd9754343a52249d7e65886fd8f57efe902ff6e9db4835870faff10f | R | 1,510 | 50 | library(MixfMRI, quietly = TRUE)
set.seed(1234)
N <- 1000
K <- 3
p.X <- 3
eta <- c(0.60, 0.30, 0.10)
beta <- matrix(c(1, 1, 0.5, 6, 0.8, 4), ncol = K) # 2 * K
mu <- matrix(c(0, 0, 0, -2, -2, -2, 4, 4, 4), ncol = K) # p.X * K
std <- matrix(c(1, 1, 1, 2, 1, 2, 1, 2, 1), ncol = K) # p.X * K
### For toy1... |
461e2aee7f0492b6e4d5df3d0653dd36020237c267e7cb873d8ad7a15a5b4043 | R | 1,512 | 53 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(scales)
library(scico)
library(tictoc)
library(shades)
# read in annotated data
# data_obj_sub <- readRDS("./seurat_objects/snRNA_cellBneder_co... |
5d27879951e0fea8cb6d8267799c629bd22a3e322a4c71885cdb43963904af13 | R | 1,512 | 48 | library(ggplot2)
library(car)
library(nlme)
require(multcomp)
#Read in data
d <- read.csv('sholl_InputData.csv') #enter path of file
#loess smoothening
ggplot(d, aes(x=Radius, y=Intersections, group=condition, fill=condition)) +
stat_smooth(col='black', method='loess') + ylab('# Intersections') +
xlab ('Distanc... |
a8a0efb7c2186a2e4a0c6109a7710b3c47a81cddbab21b5db5bf347746807954 | R | 1,514 | 66 | library(data.table)
library(tidyverse)
library(mpra)
library(magrittr)
library(config)
#######################
# LOADING ENV & FUNCS #
#######################
# loading config and getting wd
config <- config::get(file = "where_the_config_file_is_stored/config_scz.yml")
current_wd <- str_c(config$scz_file_foldername)
... |
e1b7d80576f55cb9a0aa4772bd2e9c6aadd46d394ab48812adc483d566afd09f | R | 1,522 | 47 | #!usr/bin/Rscript
.libPaths("/data/kronos/kronos/acarrasco/R_libs")
library(data.table)
library(dplyr)
library(tidyverse)
ChunkFileCreator <- function(nSamples, ChunkSize) {
numberSamples = nSamples
totalChunks = ceiling(numberSamples/ChunkSize)
Skipnumber = 0
fileNumber = 1
ChunkSize = ChunkSize
columnna... |
3cb1677e5cf903eb812e5ea06a55c55cbf7e42be7bd92a6130afb2e89909a29e | R | 1,542 | 45 | #!usr/bin/Rscript
# Loading libraries
.libPaths("/data/kronos/kronos/acarrasco/R_libs")
library(dplyr)
library(data.table)
library(tidyverse)
# Loading the sample chunks of the genetic file
print("loading sample chunks of the genetic data")
partone <- readRDS("part1.rds")
parttwo <- readRDS("part2.rds")
partthree <- ... |
491d7608d6d5b06b007bc51d41fa379b8fcfe1b20e9f42d49b2d272c703c5980 | R | 1,543 | 50 | ### This file gives initializations.
# x <- rbeta(1000, 0.5, 2)
# (m <- mean(x))
# (v <- var(x))
# (alpha <- 1/v * (m^2 - m^3 - m * v))
# (beta <- (1 - m) / m * alpha)
# alpha / (alpha + beta)
# alpha * beta / (alpha + beta)^2 / (alpha + beta + 1)
initial.em.gbd <- function(PARAM){
if(PARAM$class.method == "prob.ex... |
69a32c5d1db73388c7b0c5c81de768f486fbc4f05d3e7fa4f9e0b2d54917780e | R | 1,544 | 58 | library(data.table)
options(scipen = 999)
# Load hg38 chromosome sizes from UCSC
chr_sizes <- fread("https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes", col.names = c("chrom", "size"))
chr_sizes <- chr_sizes[chrom %in% paste0("chr", c(1:22, "X"))]
# Centromere positions (approximate from UCSC T... |
b5b2c168029737e80a6a651f660d84d2ddc218eed03a59910db92a154e6fadb9 | R | 1,553 | 71 | flexSave <- function(
baseName,
thisFilePrefix = filePrefix,
plot = last_plot(),
fileType = "pdf",
# fileType = "png",
filePath = plotOutputFolder,
width = 7,
height = 7,
units = "in",
compType = currentCompType,
shinySettings = FALSE,
...
){
if(shinySettings){
thisFileName <- baseName
... |
1bb87416a08a7619e93c530d90bf52f6ba498a260f0751681538837916711a6b | R | 1,577 | 45 | library(ggplot2)
acell<-read.csv('st_cell2location_res.csv',header = T,check.names = F,row.names = 1)
colnames(acell)<-gsub('q05cell_abundance_w_sf_','',colnames(acell))
x=0.1
# x=0.5
stat<-apply(acell,1,function(y){
sum(y>=x)
})
table(stat>0)
stat<-ifelse(stat<1,1,stat)
stat1<-data.frame(num=c(... |
82d3c447406a026dacac9fb76eb9413d9e9baa7476efb3bc1d6d1c6e61500fe5 | R | 1,587 | 57 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(data.table)
library(ggplot2)
library(ggrepel)
})
# Parse arguments
args <- commandArgs(trailingOnly = TRUE)
if (length(args) < 2) {
stop("Usage: ./runtime_plot.R input.tsv output.pdf")
}
input_file <- args[1]
output_file <- args[2]
# Read data... |
261513f7d325a02ad2f6e4042a05a6c97db3b389cea90842e1061a999b90985e | R | 1,597 | 47 | library(Seurat)
library(dplyr)
library(cowplot)
library(ArchR)
library(ggplot2)
library(viridis)
parser = argparse::ArgumentParser(description="plot for marker genes")
parser$add_argument('-I','--inputfile', help='input file')
parser$add_argument('-D','--id', help='tissue id')
parser$add_argument('-O','--outputfile', ... |
7cd4c577f954a835ffef6930fc03c67e6fd321aedc3ebd154704aa7d3752c50e | R | 1,597 | 48 | # Checks if packages are installed, and if not installs them
if(!require(fastmatch)) install.packages("fastmatch")
if(!require(tidyverse)) install.packages("tidyverse")
# if(!require(viridis)) install.packages("viridis")
if(!require(Cairo)) install.packages("Cairo")
if (!require(officer)) install.packages('officer')
if... |
92acc7f5a4fbf0d5ab7dbfc6142bef8ae7e2f4dbd1e492f82cfcde564a128e1d | R | 1,605 | 44 | library(tidyverse)
library(ArchR)
set.seed(666)
addArchRThreads(threads = 20)
addArchRGenome("hg38")
# setup input files
inputFiles <- list.files("./atac_fragments", pattern = ".gz$", full.names = TRUE)
names(inputFiles) <- inputFiles %>% str_replace("_atac_fragments.tsv.gz", "") %>% str_replace("./atac_fragments/", ... |
152e103cc07a929f70edfcdba81dcc60aceb0c94c6b00c65f00b8fcaf7a995da | R | 1,607 | 47 | #-----------------------
# Correlation structure
# 25 Jan 2023
#-----------------------
source("./common.R")
# load imputed data
load("../data/derived/fit_OU.RData")
dfOU <- imp_2$estimates
rownames(dfOU) <- df$Binomial_name_timetree
# fit OU
Y <- as.matrix(dfOU[, c("Log10Area.cb", "Log10Length.cb",
"Log10Thickn... |
1cc7195cb76acf065badd4cb8c09a874e64c97b295b748fe659394277d50c8cc | R | 1,622 | 60 |
# read output filename and list of input filenames containing
# CHT results from command line
args = commandArgs(trailingOnly=TRUE)
if (length(args) < 2) {
usage <- "Usage:\n RScript --vanilla OUTPUT.png CHT_OUTPUT_1.txt [... CHT_OUTPUT_N.txt]"
stop(paste("At least two arguments must be supplied.\n", usage), cal... |
8c40ebdcb2fa2aaaa04bb88b28f6481b89e76d76141bd3943da7504ee8bce639 | R | 1,623 | 47 | library(tidyverse)
DE <- read_tsv("../MAST_res_level2_summary.txt")
FDR_threshold <- 0.05
FC_threshold <- 1.2
genes_in_GO <- read_tsv("Astr.actin.overlap_genes.txt", col_names = "gene")
# genes_in_GO <- read_tsv("Astr.potassium_ion_transport.overlap_genes.txt", col_names = "gene")
DE_sub <- DE %>%
filter(cond_1 =... |
111dee17197ba79d32421e4cae26c1b41b73bfdd440459f80f6298e1840686fc | R | 1,627 | 53 | #!/bin/Rscript
args<-commandArgs(trailingOnly=TRUE)
library.path <- .libPaths()
library("tidyverse", lib.loc = library.path)
#Read in sample heterozygosity table
sample_het <- read.table(args[1], header = TRUE)
#Read in sample call rate table
sample_callrate <- read.table(args[2], header = TRUE)
#Calculate proporti... |
90b795496b98faedaece1ed62f178f45e21c3ae1b553b0d658ba93d9f2efa6c7 | R | 1,629 | 71 | # time series
step = 7
#in weeks
year = 365
num_year = 15
t = seq(0, year*num_year , by = step)
# parameters
#average infection rate
beta0 = 0.09
#amplitude of sinosuoidal wave
beta1_set = 0.25
#controls sinosuoidal infection phase
omega = (2*pi)/365
#recovery rate
gamma = 0.075
#rate of return to susc... |
40b49fd7cce8ccd6d5a38afbffd9609f906dc62443c5698e7d2442e263e25a6e | R | 1,633 | 55 | library(MixfMRI, quietly = TRUE)
source("00-set_condition.r")
### Test 3d data
da <- eval(parse(text = paste(case, sep = "")))
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Load results
fn.in <- paste("./output/ret.", case, ".tp.rda", sep = "")
load(fn.in)
... |
cb0bb037c4ab972d81d7e76c0f5d607ecaaca677a50a5ffdc4840f4dfbb6763b | R | 1,638 | 55 | getZT_DateTime <- function (
DOB,
PND = 5, # for start of lights on cycle; so the early AM during lights out will be subsequent day
ZT = 9, # 0-23; 0 is lights on, either 3a or 4a depending on DST
PND_byZT0 = TRUE # Just add the ZT time from the time of lights on on the indicated PND
# so ZT23, for example, w... |
f45b4b39c416fb6b70a47dcc594993ede3c2bea7287329863f9b3a762b2893db | R | 1,642 | 51 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 16)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_latest_atac_anno_... |
f7740085ad335a351132a68b524277460744eceb11aabcbf8d2e1eab7b1597be | R | 1,649 | 27 | #' JaBbA: Junction Balance Analysis
#'
#' @importFrom gUtils streduce si2gr seg2gr rrbind ra.overlaps ra.duplicated parse.gr hg_seqlengths grl.unlist grl.pivot grl.in grl.eval grl.bind grbind gr2dt gr.val gr.tile.map gr.tile
#' @importFrom gUtils gr.stripstrand gr.sum gr.string gr.start gr.end gr.simplify gr.setdiff g... |
25fddbfa760e6e0b66c32c187c52dce3e881f4d74706e085d61b3e848f5b5c01 | R | 1,651 | 57 | ### Perform H0: alpha = 1 & beta = 1 v.s. Ha: alpha < 1 & beta > 1.
lrt <- function(PV.gbd, CLASS.gbd, K, H0.alpha = .FC.CT$LRT$H0.alpha,
H0.beta = .FC.CT$LRT$H0.beta){
if((H0.alpha <= 0) || (H0.alpha > H0.beta)){
stop("It should be 0 < H0.alpha <= H0.beta.")
}
K.CLASS <- as.integer(max.gbd(CLASS.gbd))
... |
df7445ba4188f5f02ed8b17e21cb7549106ac8c47b9ad3a77b75feb526868a46 | R | 1,684 | 50 | #!/usr/bin/env Rscript
suppressMessages(suppressWarnings(require(optparse, quietly=TRUE)))
option_list = list(
make_option(c("-i", "--input"), type = "character", default = NULL, help = "Input bps.txt.gz file"),
make_option(c("-o", "--output"), type = "character", default = "vars.lite.maf", help = "Output ... |
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