sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c8b612c4a5dba4d7bf3a33c4c9cb252f3d4770079afc8121fefbd509d5f205ab | R | 2,846 | 82 | # This script was adapted from a script provided by the Intergalactic Utilities Commission (IUC)
# Load packages -----------------------------------------------------------
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
library(ggrepel)
})
setwd(dirname(rstudioapi::getActiveDocument... |
1e79b615d8fa64155edbe573f4290f9cc264434c8449c231ad90b89639f20e26 | R | 2,875 | 93 | ### This file gives an extend initialization.
initial.em.gbd.extend <- function(PARAM){
N.all <- PARAM$N.all
N.gbd <- PARAM$N.gbd
K <- PARAM$K
p.X <- PARAM$p.X
### For the 1st cluster.
gcenter.X <- colMeans.gbd(.MixfMRIEnv$X.gbd)
gcenter.PV <- 0.5
### For the rests.
id.PV.gbd <- which(.MixfMRIEnv$P... |
500f1a8888eca12bf5330f04144a3edd0c90c06baf3702bdd03f004cc8dc74e2 | R | 2,877 | 99 | library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
# proj <- loadArchRProject(path = "Dracheva_ALS_FTD_addRNA/")
#
# meta_data <- getCellColData(proj) %>%
# as.data.frame() %>%
# rownames_to_column("cell_id") %>%
# as_tibble()
#
# # Exc
# meta_da... |
c9c46d58b3fe3a00f3caf1bca141341d0f43ceb88b94867223aca8678e29598e | R | 2,881 | 49 | library(Seurat)
library(dplyr)
library(cowplot)
library(ArchR)
library(ggplot2)
###Figure2a####
rds = readRDS('Save-ArchR-Project.rds')
cellColData = data.frame(rds@cellColData)
cellColData[,1][cellColData[,1] == 'adult_hindbrain_PL2011260025'] = 'Rho-1'
cellColData[,1][cellColData[,1] == 'adult_hindbrain_... |
33e6248791a702f25d866827df57b323cf3d4fbc98781445b8fb124db6212736 | R | 2,882 | 93 | ### This file gives a qnorm extend initialization.
initial.em.gbd.qnorm.extend <- function(PARAM){
N.all <- PARAM$N.all
N.gbd <- PARAM$N.gbd
K <- PARAM$K
p.X <- PARAM$p.X
### For the 1st cluster.
gcenter.X <- rep(0, p.X)
gcenter.PV <- 0
### For the rests.
id.PV.gbd <- which(.MixfMRIEnv$PV.gbd < PAR... |
f0ee59af14505f1691326e9c07e8b976cc73be778a9794fbd03f6c28e4cd3d17 | R | 2,901 | 49 | library(Seurat)
library(dplyr)
library(cowplot)
library(ArchR)
library(ggplot2)
###Figure2b####
rds = readRDS('Save-ArchR-Project.rds')
cellColData = data.frame(rds@cellColData)
cellColData[,1][cellColData[,1] == 'adult_hindbrain_PL2011260025'] = 'Rho-1'
cellColData[,1][cellColData[,1] == 'adult_hindbrain_... |
08fbb18e64391edc11df1a6a9ba589f0734ab0409814aac5b80e5e9446f1de88 | R | 2,904 | 117 | library("ggplot2")
library("gtools")
library("graphics")
library("pastecs")
args <- commandArgs()
#print(args)
outdir <- args[6]
print(outdir)
baffile <- args[7]
print(baffile)
sample <- unlist(strsplit(baffile,"/"))
print(sample)
sample <- unlist(strsplit(sample[length(sample)],"_"))[1]
print(sample)
f2 <- function(... |
34f56707f14d95501a7efedbea02afb5e5995c5a3f1e6d7058c7ba10112c88f8 | R | 2,929 | 70 | #--------------------------------------
# Compare length and area measurements
# to surface and volume measurements
# 25 Jan 2023
#--------------------------------------
source("./common.R")
# select variables
fw <- data.frame(
`Binomial_name_timetree` = character(),
`English_name` = character(),
`Log10Brain... |
87a9bdee9dc1cff3e5e83362c7185c7ea5218d762fa5725cbeb0cc90a0737b75 | R | 2,931 | 82 | library(faux)
library(lme4)
Ntests<-1000 #tolal N of SNPs tested
NSims <-10000 #make Nsims at least 10 times more than N tests
# We define a df to store the results for the intercept only model and the model with random slopes too
res_slopes<-as.data.frame(matrix(ncol=2, nrow=NSims))
names(res_slopes)<-c("B","P")
... |
41d31b597417cf470f5c72ee6e0a004f4074bead124689f64a8780451b43c813 | R | 2,932 | 111 | # packagelist <- c('remotes','tictoc','utils')
# missingpackages <- packagelist[!packagelist %in% installed.packages()[,1]]
# if (length(missingpackages)>0){install.packages(missingpackages)}
# if (packagelist[!'osfr' %in% installed.packages()[,1]]){remotes::install_github("centerforopenscience/osfr")}
# packagelist <-... |
7b5909649f843ec8db09cc9377e05b67ec34359b1bce265a7e7a8ffae1d6bfdf | R | 2,936 | 98 | library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_addRNA/")
meta_data <- getCellColData(proj) %>%
as.data.frame() %>%
rownames_to_column("cell_id") %>%
as_tibble()
# Neunons
meta_data_neu <- me... |
b42597ca1bd7b6d5e00feb6f75c68d75576e33966ff0f309736bea6ba71e3401 | R | 2,936 | 90 | # init ----------------------------
library(languageserver)
library(dplyr)
library(tibble)
library(readr)
library(tidyr)
library(lme4)
library(lmerTest)
library(broom)
library(emmeans)
library(ggplot2)
library(gridExtra)
library(sjPlot)
setwd('C://Users/micha/Sync/Research/mri-pet/mri_surv/')
########################... |
0ea416439a5448910985a046675ab086363835ec705f5c4c80095f3a8101a7d2 | R | 2,944 | 100 | library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 1)
addArchRGenome("hg38")
# proj <- loadArchRProject(path = "Dracheva_ALS_FTD_addRNA/")
#
# meta_data <- getCellColData(proj) %>%
# as.data.frame() %>%
# rownames_to_column("cell_id") %>%
# as_tibble()
#
# # Inh
# meta_da... |
1c3ba17082bb358d76049f2e5d8b5ba3f3a7e3f5f8335f8b0c483d5871b4a7bc | R | 2,952 | 73 | #------------------------------------
# Compare folial with measures
# to folial width measures from
# Sultan and Braitenberg (although
# they do not denote the same thing)
# 25 Jan 2023
#------------------------------------
source("./common.R")
# select variables
fw <- data.frame(
`Binomial_name_timetree` = chara... |
a34d6cffda28bf1856f15847a61694c1b78e4150dcda79b88af318dd0ad0d4b2 | R | 2,977 | 97 | ### Perform H0: alpha_1/(alpha_1 + beta_1) == alpha_2/(alpha_2 + beta_2) vs
### Ha: alpha_1/(alpha_1 + beta_1) != alpha_2/(alpha_2 + beta_2)
lrt.betamean <- function(PV.gbd, CLASS.gbd, K, proc = c("1", "2")){
K.CLASS <- as.integer(max.gbd(CLASS.gbd))
if(K.CLASS > K){
stop("CLASS.gbd and K are not match... |
c8609b434032c5d0a757b5f6926e5a56c31bcb8682ea8967a42c1d433814a5e4 | R | 2,978 | 102 | #------------------
# Phylogenetic PCA
# 25 Jan 2023
#------------------
source("./common.R")
# All phenotypes
#----------------
# load imputed data
load("../data/derived/fit_OU.RData")
dfOU <- imp_2$estimates
rownames(dfOU) <- df$Binomial_name_timetree
# fit mvgls
variables <- c(
"Log10BodyWeight", "Log10BrainWe... |
781791f45589ccf7b73b5495748ecd2b1b3b82483cfd50b7b046727805b7bccb | R | 2,979 | 98 | ### This file gives a simple initialization.
initial.em.gbd.simple <- function(PARAM){
N.all <- PARAM$N.all
N.gbd <- PARAM$N.gbd
K <- PARAM$K
p.X <- PARAM$p.X
### For the 1st cluster.
gcenter.X <- colMeans.gbd(.MixfMRIEnv$X.gbd)
gcenter.PV <- 0.5
### For the rests.
id.PV.gbd <- which(.MixfMRIEnv$PV... |
ec2adddf12db0810fe1aac087d10a1cde516ff84258f38067bfcb50700924842 | R | 3,009 | 63 | #!/usr/bin/env Rscript
library(optparse)
library(leafcutter)
arguments <- parse_args(OptionParser(usage = "%prog [options] counts_file groups_file cluster_significance_file", description="LeafCutter differential splicing plotting tool. Required inputs:\n <counts_file>: Intron usage counts file. Must be .txt or .txt.gz... |
b2e0e87dfcb06a27375b9418cce059f3b40be96dac524b99635c6828c509f37a | R | 3,011 | 79 | library(Seurat)
library(Matrix)
seed <- 2020
set.seed(seed)
# read data matrix
# after reading, suppose the data matrix is saved in 'data'
# add col and row names for seurat object
colnames(data) <- as.character(1:dim(data)[2]); colnames(data)
rownames(data) <- as.character(1:dim(data)[1]); rownames(data)
... |
3e1f487835f74336294055433a0fe0cce2c6ce8378a124a14f909b48dace2f63 | R | 3,035 | 52 | data<-read.table("S-Fig5d_Drd_Egr_Gene_expression_inNAc.txt")
#Drd1~Egr1
c<-round(cor(as.numeric(data["Egr1",]),as.numeric(data["Drd1",]),method ="pearson"),2)
plot(as.numeric(data["Egr1",1:8]),as.numeric(data["Drd1",1:8]),pch=20,
xlim=c(0,200),ylim=c(0,350),xlab="Egr1",ylab="Drd1",main=paste0("Cor: ",c),
col... |
ace99cc875109c9700c15466264618c652b5eae50636aa34da9e60c3dbe00c6b | R | 3,048 | 110 | ### This file contains files for estimating parameters emperically.
# 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)
# M1 <- mean(x)
# M2 <- mean(x^2)
# (alph... |
40470f66a7bb64fe56d91ce55f7eab74a422ef34ddf6987b111294d99914880a | R | 3,051 | 86 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(scales)
library(viridis)
library(scico)
library(shades)
df <- read_tsv("./GO_webgestalt_results_affinity_rmRedundant_toPlot.tsv") %>%
mutate(
types... |
797642e6d8de1d72e75901536547bf0a46ecc8e183c6f79a5a74924fa6ed4b36 | R | 3,060 | 99 | ### This file gives a qnorm simple initialization.
### This function uses qnorm.X.gbd and qnorm.PV.gbd to compute distance.
initial.em.gbd.qnorm.simple <- function(PARAM){
N.all <- PARAM$N.all
N.gbd <- PARAM$N.gbd
K <- PARAM$K
p.X <- PARAM$p.X
### For the 1st cluster.
gcenter.X <- rep(0, p.X)
gcenter.PV... |
87d3672627a7f1acad26799beb3167d6947971799f10408035dc1e4c03ed1fef | R | 3,070 | 60 | library(pheatmap)
library(ggplot2)
library(stringr)
co <- c('#3361A5','#3164AB','#3067B1','#2F6AB7','#2E6EBE','#2C71C4','#2B74CA','#2A78D1','#297BD7','#287EDD','#2682E4','#2585EA','#2488F0','#238CF3','#218FF4','#2092F5','#1F96F6','#1E99F7','#1C9CF8','#1B9FF9','#1AA3FA','#18A6FB','#17A9FC','#16ADFD','#14B0FE','#16B... |
cbfc6ebfa648c4b3a5eec57a145d89c87c1b3c7e1f504b98523f1fec0938e108 | R | 3,118 | 103 | context("unit testing fragCounter operations")
library(fragCounter)
library(data.table)
library(GenomeInfoDb)
### Making small BAM
# cd /gpfs/commons/groups/imielinski_lab/projects/PurityPloidy/Flow/Remix_Bams/HCC1143_1_5_4 1020
# samtools view -H downsample.bam > header_downsample.sam
# samtools view downsample.bam ... |
4432e557e601469ae84d8ccc6089b497da042cb0daff92f8a683a9e2e1bedc6f | R | 3,149 | 80 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(tictoc)
# read in annotated data
data_obj_sub <- readRDS("./seurat_objects/snRNA_cellBneder_corrected_postQC_scTransformed_clustered_2ndRoundAn... |
f6e2210feeae2e4d4b72e7991fc594f225079b7c73058ef4ead0e53e6682cfc3 | R | 3,151 | 85 | #!/usr/bin/env Rscript
library(optparse)
option_list = list(
make_option(c("-i", "--input"), type = "character", default = NULL, help = "Input asqg file from snowman run --write-asqg or sga"),
make_option(c("-c", "--inputcc"), type = "character", default = NULL, help = "Input cc file from snowman run --wr... |
f79601a9d54fb2764ef9643ee58eae95b30f22134ab1062cf78cff28a54113c1 | R | 3,178 | 116 | ### compute correlation
# dim(da.ts) = 40 x 5 x 10
cba.cor.2d <- function(da.ts, da.m = NULL, adj.dist = TRUE,
fun.sim = stats::cor){
dim.da.ts <- dim(da.ts)
dim.da <- dim.da.ts[-length(dim.da.ts)]
dim(da.ts) <- c(prod(dim.da.ts[-3]), dim.da.ts[3])
### Build inside brain ids
id.inside <- 1:prod(dim.da)
... |
96b4dd90d4cc6af3af7d6f9bab9af18ece87a58d2f1f8d72055f6677dee97be0 | R | 3,180 | 126 | library(tidyverse)
library(BSgenome.Hsapiens.UCSC.hg38)
library(EnsDb.Hsapiens.v86)
library(doParallel)
library(devtools)
library(Signac)
library(Seurat)
library(bayestestR)
library(Pando)
rename <- dplyr::rename
filter <- dplyr::filter
data(motifs)
data(phastConsElements20Mammals.UCSC.hg38)
data(SCREEN.ccRE.UCSC.hg3... |
54805ae2e9964ff043553e37a280478c7bfd49fb202ae46a6bbf16a05a85623b | R | 3,202 | 68 | #' Beta binomial GLM likelihood ratio testing
#'
#' We recommend using \code{\link{dirichlet_multinomial_glm_anova}} instead, but this is here for comparison.
#'
#' @param ys numerator counts
#' @param ns denominator counts
#' @param xFull matrix of covariates for full model. First column must be ones.
#' @param xNul... |
cf87b4861cd47dcf449a6fe587fc76cb0475c1b1f8b82f0be409c8d64cf5fcd9 | R | 3,214 | 102 | ### This file gives a prob extend initialization.
initial.em.gbd.prob.extend <- function(PARAM){
N.all <- PARAM$N.all
N.gbd <- PARAM$N.gbd
K <- PARAM$K
p.X <- PARAM$p.X
### For the 1st cluster.
gcenter.X <- rep(0, p.X)
gcenter.PV <- 0
### For the rests.
id.PV.gbd <- which(.MixfMRIEnv$PV.gbd < PARAM... |
1f8b219b046290641c8bb09f125edf5d0abffd660d33ee185c3b1aa46b17de70 | R | 3,228 | 66 | files<-list.files(path="Fig1f",pattern="overlap")
NAc_list<-read.table(paste0("Fig1f/",files[3]));
DG_list<-read.table(paste0("Fig1f/",files[2]));
CA_list<-read.table(paste0("Fig1f/",files[1]));
SVZ_list<-read.table(paste0("Fig1f/",files[4]));
#ATAC
CA<-read.table("Fig1f/Fig1f.ATAC_Control_CA_vs_other.txt")
DG<-read.ta... |
727dbefeb45c7b01902db21dfd7ed5409b6f24122dd2291f82161cd519d2c32d | R | 3,236 | 65 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
rna <- read_tsv("MAST_res_level2_summary.txt")
plot_FC_cor <- function(selected_cell_type, selected_region, FDR_threshold, FC_threshold){
message(paste0("Processing... |
7964ab9106c943e671a93818737f3d55dc53ba82824f6e5dc1aac4f978b1c12a | R | 3,236 | 118 |
getSymAndString <- function(colVar){
colSym <- ensym(colVar)
colName <- as.character(colSym)
return(
list(
"symbol" = colSym,
"string" = colName
)
)
}
selectIfExists <- function(
df,
colVar
){
var <- enquo(colVar)
info <- getSymAndString(!! var)
info$string
df %>%
select_i... |
f42d1d93d393f1e5b97f5c18de39e1b7fb768cb84e18c509989020ec971dcd4e | R | 3,245 | 109 | # 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_addRNA_cellBender... |
2fb674010d630a3d7b3000eecb70ea49aca419b2f79b21218a8856a02e115a65 | R | 3,282 | 75 | ---
title: "Installation"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Vignette Title}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
The prerequisites for LeafCutter are
* [samtools](http://samtools.sourceforge.net/) should be available on your PATH
* [regtools](https://r... |
02d52a3e840aaf8c7a0b52450d6a1f166e3d5730b329c9918debce66569448de | R | 3,303 | 105 | library(tidyverse)
library(readxl)
library(fs)
# Manolis ALS single cell DE genes list, June 2021 submission ver.
file_1 <- "./Manolis_ALS_single_cell_DE_C9_ALS_June2021_submission.xlsx"
file_1_sheets <- excel_sheets(file_1) %>% set_names()
file_1_DE <- map_dfr(
file_1_sheets,
~ read_excel(path = file_1, sheet =... |
1e1f5b181be30e893498a2a1cc3afe02806fce2617e1a98b1bab6ae7122df29b | R | 3,317 | 112 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
library(BSgenome.Hsapiens.UCSC.hg38)
library(ComplexHeatmap)
library(viridis)
library(circlize)
set.seed(666)
addArchRThreads(threads = ... |
6b43e40946a38ce2a5924d8cbbef25d3760cad19456fbc66f7545bed0df15af4 | R | 3,328 | 96 | library(JaBbA)
library(gUtils)
library(gGnome)
library(testthat)
jj = system.file("extdata", "junctions.rds", package = "JaBbA")
sg = system.file("extdata", "segs.rds", package = "JaBbA")
cf = system.file("extdata", "coverage.txt", package = "JaBbA")
## save stuff as bw
cf.bw = system.file("extdata", "coverage.bw", p... |
83e7b5ca9bcf3a77834c75bba9aa0e9f94a13235faf9973e479a3bfa64b4deab | R | 3,336 | 94 | library(JaBbA)
library(gUtils)
library(gGnome)
library(testthat)
jj = system.file("extdata", "junctions.rds", package = "JaBbA")
sg = system.file("extdata", "segs.rds", package = "JaBbA")
cf = system.file("extdata", "coverage.txt", package = "JaBbA")
test_that(desc = "Testing LP JaBbA with .bw coverage input",
... |
d3e004517aed87bad9626a13633aa09252674e16bf1d40d45db50f599afb3ac8 | R | 3,360 | 93 | #!/bin/Rscript
# Loading packages
.libPaths("/mnt/rreal/RDS/RDS/acarrasco/R_libs")
library(MASS)
library(nlme)
library(Matrix)
library(lme4)
library(memoise)
library(dplyr)
library(tidyr)
library(stringr)
library(here)
library(tidyverse)
library(data.table)
# Loading data - #Modify this so that I can take data from s... |
ccf015819d8260edc3e652fef39ffc0a7d270d14c5619f69eda98ba24c78b3b8 | R | 3,374 | 113 | # load things -----
library(dplyr)
library(tibble)
library(readr)
library(tidyr)
library(reshape2)
library(stringr)
if (Sys.info()["sysname"] == "Linux") {
setwd("~/Research/mri-surv-dev/mri_surv/")
} else {
setwd("C://Users/micha/Sync/Research/mri-pet/mri_surv/")
}
tb <- read_csv("./metadata/data_raw/MCIADSub... |
32d9c9b8d0b08b1d2b1096bf77bece16530ec1561eab02b3e4442e2634ee8c42 | R | 3,389 | 84 | ### Get the parameters
parser = argparse::ArgumentParser(description="Script to QC scATAC data by ArchR")
parser$add_argument('-I','--inputFiles', help='input fragment file')
parser$add_argument('-D','--id', help='sample ID')
parser$add_argument('-T','--filtertss', help='tss threhold')
parser$add_argument('-F','--filte... |
2c71cc5303402c35c82b986d728faf02bf815bf4ba0d8069ba25a6fe37bf894a | R | 3,397 | 69 | #!/usr/bin/env Rscript
## source all the required packages
source.all <- function() {
githome <- Sys.getenv('GIT_HOME')
if (!nchar(githome))
githome <- '/xchip/gistic/Jeremiah/GIT'
suppressMessages(suppressWarnings(require(ff, quietly=TRUE)))
suppressMessages(suppressWarnings(require(VariantAnnotation, q... |
11e76cba247ab8a428adc8c2826b6b861de58074360a0f765fc66e77f76bd01c | R | 3,402 | 124 | # init ----------------------------
library(dplyr)
library(tibble)
library(readr)
library(tidyr)
library(broom)
library(ggplot2)
library(gridExtra)
library(sjPlot)
library(FSA)
setwd('C://Users/micha/Sync/Research/mri-pet/mri_surv/')
##########################################
########Now by lobe
tb2 <- read_csv('./me... |
1377ef8321f4e4c5a8410a688f355ea517dbb5ab65e4b169027d7b356ceb4cab | R | 3,406 | 72 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(sctransform)
library(scales)
# read in clustered data, leiden clustering r = 1
data_obj_sub <- readRDS("./seurat_objects/snRNA_cellBender_corre... |
a3ba8aef82b7759976d6633e9d7722d5b8e9590eaaf974fe4327e4b3bafce73f | R | 3,436 | 98 | library(tidyverse)
library(readxl)
library(writexl)
df <- read_tsv("./MAST_res_level2_summary.txt")
df_ALS_motor <- df %>%
filter(cond_1 == "ALS", cond_2 == "Control", region == "MCX", cell_type != "Exc_unknown") %>%
select(-cond_1, -cond_2, -region, -num_cells_cond_1, -num_cells_cond_2) %>%
mutate(
... |
68cbd4b5e3fe4574cccd6522c51a224f5abd03cb5a0c7fa1971919ca96f10ac3 | R | 3,449 | 78 | #--------------------------------------
# Compare length and area measurements
# to surface and volume measurements from Ashwell
# 31 Jan 2023
#--------------------------------------
source("./common.R")
# select variables
fw <- data.frame(
`Binomial_name_timetree` = character(),
`English_name` = character(),
... |
851fa6e5f3ca76cfdf7cde743cfd71e3eadc28fe61e3fd16b7cc48da76d692fd | R | 3,460 | 109 | ### This file gives a prob simple initialization.
### This function uses qnorm.X.gbd and qnorm.PV.gbd to compute distance,
### but selects new rest centers proportional to qnorm.PV.gbd.
initial.em.gbd.prob.simple <- function(PARAM){
N.all <- PARAM$N.all
N.gbd <- PARAM$N.gbd
K <- PARAM$K
p.X <- PARAM$p.X
###... |
e05cd04e241394bdf5794b262a06b55103d6b315e2d95768a525050aa8b3fd5c | R | 3,477 | 90 | library(Seurat)
library(readxl)
library(ggplot2)
library(SpaGene)
library(dplyr)
library(tidyverse)
library(RColorBrewer)
def_plotLR<-function(expr,location,normalize=TRUE,topn=floor(0.2*dim(location)[1]),knn=8,LRpair=c("Ptn","Ptprz1"),pt.size=2,alpha.min=0.1,max.cut=0.95){
if (sum(rownames(expr) %in% LRpai... |
fb52a2c8765beea1a618982c3b86c3393e5a742fe79a5dd25c39a63e2d5235ad | R | 3,510 | 100 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(sctransform)
library(scales)
library(harmony)
library(ggrastr)
## read the sctransform-normalized seurat object
data_obj_sub <- readRDS("./seur... |
5f777170704bdb3300d9dd6806d082083f96733c64bcb433ca3e3db8b71fd9a6 | R | 3,536 | 121 | ### compute correlation
# dim(da.ts) = 46 x 55 x 43 x 105
cba.cor.3d <- function(da.ts, da.m = NULL, adj.dist = TRUE,
fun.sim = stats::cor){
dim.da.ts <- dim(da.ts)
dim.da <- dim.da.ts[-length(dim.da.ts)]
dim(da.ts) <- c(prod(dim.da.ts[-4]), dim.da.ts[4])
### Build inside brain ids
id.inside <- 1:prod(d... |
80edbcd2c804ba13ad48160e93096b987aaa98db2970d29d3aebaf7d998f65b4 | R | 3,550 | 90 | library(Seurat)
library(Matrix)
library(scDesign)
# read data matrix
# after reading, suppose the data matrix is saved in 'data'
# add col and row names for seurat object
colnames(data) <- as.character(1:dim(data)[2])
rownames(data) <- as.character(1:dim(data)[1])
# Seurat preprocessing
data.seurat <- Crea... |
e2e4b2d1e1551252887547c940b992ec58323ce68a8ba55f428f7c6f41ee0988 | R | 3,558 | 107 | ### For general methods.
fclust <- function(X.gbd, PV.gbd, K = 2,
PARAM.init = NULL,
min.1st.prop = .FC.CT$INIT$min.1st.prop,
max.PV = .FC.CT$INIT$max.PV,
class.method = .FC.CT$INIT$class.method[1],
RndEM.iter = .FC.CT$CONTROL$RndEM.iter,
algorithm = .FC.CT$algorithm[1],
model.X = .FC.CT$mo... |
80cdffb9740e9a7f0bc8cf185bd4ee4a143d88ba4d893cd7fa916dd5e8380665 | R | 3,560 | 122 | library(tidyverse)
library(ArchR)
set.seed(666)
addArchRThreads(threads = 16)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_rmLowQualitySample/")
getAvailableMatrices(proj)
proj <- filterDoublets(ArchRProj = proj)
proj <- addIterativeLSI(ArchRProj = proj,
useMatr... |
2e513289d389e787ff6860192127da3fd2a4891f1b3fadc40b60607adf00188f | R | 3,610 | 108 | #Sys.setlocale("LC_ALL","Chinese")
setwd("E:/cTBS_HDDM_RTanalyses")
install.packages("lme4")
install.packages("lmerTest")
library(lme4)
library(lmerTest)
## Exp 2 iTBS
# Left iTBS, consonant, quiet
D <- read.csv("Exp2_iTBS_D_L_C_Q_rawdata.csv")
D$turn_type<-factor(D$turn_type,levels=c("Sham","Left iTBS... |
0fb54ed61d7edc92061173556d42ebe79d2f8c563e51de26cb02bca00dbe8ea8 | R | 3,623 | 144 | library(data.table)
library(tidyverse)
library(mpra)
library(magrittr)
library(config)
#######################
# LOADING ENV & FUNCS #
#######################
# loading config and getting wd
config <- config::get(file = "where_the_config_file_is_stored/config_scz.yml")
current_wd <- str_c(config$scz_file_foldername)
... |
4cb3ac187d3a22c966edff421b98845de531fd131e1e390c81042ce9dbcf50cc | R | 3,627 | 87 | #### Thank you for using our code, wish you all the best! ####
#### Key parameters to generate different scenario of Homo-Area can be found in line 36——42 ####
#### Clear the work space
rm(list=ls())
library(Matrix)
library(CMP)
options(warn =-1)
set.seed(110)
#### Prepare the material for Simulation
# M... |
4ebc531e57b9d73e0da78ddd23a6777faeee8ff5880c4941c771e8a9f137898f | R | 3,634 | 105 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ggrastr)
library(ggrepel)
snRNA <- read_tsv("MAST_res_level2_summary.txt")
FDR_threshold <- 0.05
FC_threshold <- 1.2
# FC_threshold <- 2
snRNA_cell_types <- ... |
ebe10e77e6e53958659b6db6bf948e2c615735b3aa47763cb3a36d8db5a0c2f5 | R | 3,698 | 76 | # 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 full dataset with 1st round annotation
data_obj_sub <- readRDS("./seurat_objects/snRNA_cellBneder_corre... |
82cc07e8545729bc70404aba1101f9ef8757fe18b9f0fb9cbf0885f13b32bc18 | R | 3,714 | 169 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
library(BSgenome.Hsapiens.UCSC.hg38)
library(viridis)
library(scico)
set.seed(666)
addArchRThreads(threads = 16)
addArchRGenome("hg38")
... |
7756eba67d6228120e6070323519a8f9cacaf894f37aa745c2f6a54d40987dbb | R | 3,760 | 106 | require(tidyverse)
setwd('~/Dropbox/Apps/ShareLaTeX/CogSci 2019/code/')
source(file='transmit/R/transmit.R')
alphabet_length <- 4
symbols <- c('A','B','C','D')
codebook <- construct_codebook(symbols)
timesteps <- 1000
#Basic idea here is to pick a distribution, then send INDIVIDUAL messages using that distribution, a... |
973139f1a5596a881ca303a2fd764f64d75d31261dd78413566eb3b700a36b25 | R | 3,783 | 111 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(pheatmap)
library(viridis)
library(scico)
df <- read_tsv("./snRNA_vs_bulkRNA_FC_correlation_stat_summary.txt")
region_relabel <- c(
"MCX" = "motor cortex",... |
82793b173c50b1c4c04d4dbc2454e4ee5676d1a61813daa36a85b8d1529b35bb | R | 3,797 | 111 | ---
title: "Models"
author: "Jonas Simon Fleck"
date: "5/17/2022"
output: html_document
---
```{r, echo = FALSE, message=FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
```
Pando provides various regression models and modeling options that can be used for GRN inference. This vignette gives an overview ... |
bcab0af6af230aa285ddff468ce3107515e14354ec172888c133bdcd039c052f | R | 3,797 | 124 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(DESeq2)
library(variancePartition)
# read in pseudobulk grouping metadata
sample_info <- read_tsv("./pseudobulk_group_metadata.tsv")
# read the pseudobulk cou... |
243ae4d9d5af30b8d324372aefb38c397b95897e49f1c7c02f6b9a404e564b78 | R | 3,800 | 110 | # plot-baseline-v2.R
#
# Successor to plot_baseline(). Uses helpers from plot-slices-core.R to
# remove duplication with before_after_v7. Adds optional per-slice color
# and shape mapping for stable visual identity across run_id and mode
# changes.
#
# Backward-compatible signature: every original argument is preserved... |
7dfb5da5d8d38cfb4e039ccab12fbcfd7bf1ee2cefb7f5436c2c6e3258b333fb | R | 3,810 | 102 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(plotly)
library(htmlwidgets)
rna <- read_tsv("MAST_res_level2_summary.txt")
cell_types <- rna %>%
distinct(cell_type) %>%
filter(!cell_type %in% c("Exc_un... |
42e84a7f52c8f9d0791fd0a1bb89de6519affaf23d69b561f6500ef4a79fc9bd | R | 3,832 | 158 | #' @rdname initiate_grn
#' @export initiate_grn
initiate_grn <- function(object, ...){
UseMethod(generic = 'initiate_grn', object = object)
}
#' @rdname find_motifs
#' @export find_motifs
find_motifs <- function(object, ...){
UseMethod(generic = 'find_motifs', object = object)
}
#' @rdname infer_grn
#' @expor... |
ddbc6a73b27ba47f907c147a77c8f8d750974bf18e4c9ac19ae251d5dde1259b | R | 3,834 | 107 | library(tidyverse)
library(forcats)
library(pheatmap)
library(viridis)
library(scico)
library(RColorBrewer)
library(BuenColors)
library(qvalue)
library(dendsort)
res <- read_tsv("./ldsc_results/ldsc_res_ALS_FTD_latest_snATAC_anno_withOverlapAnnot_summary.txt")
group_order <- c(
"Exc_superficial_MCX_ALS", "Exc_super... |
3bdf68b8dbb4fa15a248040c77cac78bc2e79e42fa70785f390d7bf91a753a9d | R | 3,850 | 141 | library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 8)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_addRNA_addPseudobulkRep/")
# pairwise test
# sanity checks to compare between cell types
marker_oligo_vs_astro <- getMarkerFeatures(
ArchRProj = pr... |
05fda2b0f14b10c8285b1125a93327f47f3e3877781cbeab65d95a8c498c9c68 | R | 3,875 | 105 | "%&%" <- function(a,b) paste(a,b, sep = "")
library(tidyverse)
library(data.table)
library(phyloseq)
library("microbiome")
library(ggplot2)
library(cowplot)
library(vegan)
"%ni%" <- Negate("%in%")
library(microbiomeutilities)
library(ggdendro)
library(RColorBrewer)
library(metagenomeSeq)
#FHS
#Bring in the Meta Data... |
999822ac6546ad6aea4e39dbcf0b31f38fede418a77bf8de36f2c77c789ff2b3 | R | 3,907 | 91 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(miloR)
library(SingleCellExperiment)
library(scater)
library(Seurat)
library(tidyverse)
library(patchwork)
data_obj_sub <- readRDS("./seurat_objects/snRNA_postQC_scTransformed_cl... |
556bdef154959402163d434596efdff6b37e07905a54560da55f579f0506e54c | R | 3,925 | 146 | runif.bcast <- function(n, min = 0, max = 1){
ret <- as.double(runif(n, min, max))
if(.MixfMRIEnv$MPI.gbd){
ret <- pbdMPI::spmd.bcast.double(ret, double(1))
}
ret
} # End of runif.bcast().
rexp.bcast <- function(n, rate = 1){
ret <- as.double(rexp(n, rate))
if(.MixfMRIEnv$MPI.gbd){
ret <- pbdMPI::s... |
731ad26e63d510ac9007b313937232eb051bed82a3cf5055187ce29628d27a6b | R | 3,933 | 121 | #!/nfs/sw/R/R-3.6.1/bin/Rscript
################################################################################
### COPYRIGHT ##################################################################
# New York Genome Center
# SOFTWARE COPYRIGHT NOTICE AGREEMENT
# This software and its documentation are copyright (2023) by... |
7a6cb11e77fbe30f19e1c324d35d3f58a645cf48bc54876720224c34ef598549 | R | 3,941 | 87 | # icc's D ---------------------------------------------------------------
library("vcd")
library("irr")
library("tidyr")
library("readr")
library("dplyr")
library("reshape2")
# load table with individual ratings
tb.handed <- read_csv('/Users/mromano/research/mri-surv-dev/mri_surv/metadata/data_raw/MCIADSubtypeAssessme... |
d8a133d1e5bc4f746b4e8dd100da21bd7c69192ebea9e73d19664a6473146034 | R | 3,949 | 98 | #--------------------------------------
# Load data, reformat it and configure
# globally used variables
# 25 January 2023
#--------------------------------------
# read phenotypes
cb_area_length <- read.csv("../data/derived/csv/01_cb_area_length.csv",
row.names = 1)
ctx_area_length <- read.csv("../data/derived/csv/... |
49597b41c8e3fe657a151f192f3a59345dc5395b412566145e3b30432d841bfb | R | 3,976 | 132 | test_that("decoding a signal", {
codebook <- construct_codebook(c('A', 'B', 'C'))
signal <- encode_signal(
codebook=codebook,
symbol = 'A',
signal_power = 10,
duration_in_seconds = 10
)
signal_plus_noise <- add_channel_noise(
codebook = codebook,
... |
9402ab4ba3c98398fb665765584efbb55e0539edb825bac3a9bbc3657670a2ea | R | 4,008 | 142 | ### DEPRECATED FOR NOW, but keep this file
library(ggplot2)
library(SurvMetrics)
library(caret)
library(survival)
library(pec)
library(tidyverse)
clean_col <- function(data, dtcol) {
data[, dtcol] <- str_remove_all(data[, dtcol], "\\[")
data[, dtcol] <- str_remove_all(data[, dtcol], "\\]")
split_str <- i... |
c0b17ad493673d48cd5e3c0d77b046599aea58e954b0c11d56ee038906d8efd7 | R | 4,031 | 114 | #!/bin/Rscript
## DESCRIPTION ##
# This is the workflow to automate the finemapping from echocolocatoR
# This script takes a txt file with the following informations
# RS_PATH
# SS_path
## Example usage
#Rscript FINEMAPPING.R [path_to_metadata_file.txt]
# Activate echoR conda env
#system("conda activate echoR")
#... |
e8e80098d44daed439790a9256fb1f6837c2a40d736e29f8a4d5756d4d57839e | R | 4,032 | 142 | ###########################################################################
## The following function solves a linear or quadratic program (LP|QP).
#' @name Rcplex2
#' @rdname internal
#' @title Rcplex2
#' @description
#'
#' Modification of Rcplex which takes in mipcontrol parameters.
#'
#'
#' @export
Rcplex2 <- func... |
36950e4025bd35fd2599f02305232b3b7652e04b3c5bde48912b5d234b598607 | R | 4,041 | 108 | options("width"=240)
phantom <- c("shepp0fMRI", "shepp1fMRI", "shepp2fMRI")
overlap <- c("0.01", "0.1", "0.25", "0.50", "0.75", "0.95")
min.1st.prop <- c("0.975")
max.PV <- c("0.05")
model <- c("I")
prefix <- "simu"
ret.summary <- NULL
ret.summary.org <- NULL
K.min <- 2
K.max <- 8
for(i.phantom in 1:length(phantom)){... |
dec1c5e8707cecdf930b14439a5ed8a5d87d9ddb37c55d78b1616300dc2ac495 | R | 4,041 | 119 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(pheatmap)
library(viridis)
library(scico)
library(pdfCluster)
library(ggrastr)
# data_obj <- readRDS("./seurat_objects/snRNA_cellBneder_correct... |
46b03e95dba5f70273c54e11a45c027976f69afd2a073e3e02aab3c27602102a | R | 4,049 | 132 | ### This file contains major functions for ECM iterations.
### CM-step.
cm.step.gbd.ETA.1st <- function(PARAM){
### MLE For ETA[c(1, K)].
w <- 1 - sum(PARAM$ETA[-c(1, PARAM$K)])
tmp <- w * .MixfMRIEnv$Z.colSums[c(1, PARAM$K)] /
sum(.MixfMRIEnv$Z.colSums[c(1, PARAM$K)])
if(tmp[1] <= PARAM$min.1st.prop)... |
ee9d50de5e496a223a3bb0528336eb195f4e86e74b55287fb4cf2b650ca2c3b9 | R | 4,080 | 142 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(scDist)
data_obj_sub <- readRDS("../seurat_objects/snRNA_cellBneder_corrected_postQC_scTransformed_clustered_2ndRoundAnnotated_SeuratV4_object.... |
969226fb714b37066d559501ffab389f35609c7d1397ada583ce4e24daf17168 | R | 4,144 | 152 | context('Tests for utility functions')
library(testthat)
library(GenomicRanges)
library(matrixStats)
library(doParallel)
library(Pando)
#### find_peaks_near_genes ####
test_peaks <- GRanges(
ranges = IRanges(
start = c(10, 20, 30, 40, 45),
end = c(15, 22, 33, 45, 46)),
seqnames = 'chr1'
)
... |
5d92725972d187673c58a2cb25abe872ab79a57649a1606570c914350e1f95f9 | R | 4,155 | 119 | #!/nfs/sw/R/R-3.6.1/bin/Rscript
################################################################################
### COPYRIGHT ##################################################################
# New York Genome Center
# SOFTWARE COPYRIGHT NOTICE AGREEMENT
# This software and its documentation are copyright (2023) by... |
75ac19ba29a3b7de4ab1117d836f7ced5162da7146e8b920ceca2c1bf3057bcf | R | 4,168 | 146 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(scDist)
data_obj_sub <- readRDS("../seurat_objects/snRNA_cellBneder_corrected_postQC_scTransformed_clustered_2ndRoundAnnotated_SeuratV4_object.... |
6f7a7ff590d84f0490984056d396bb240e30149c34fddda4514eb2a972537345 | R | 4,171 | 155 | ---
title: "9. ForestPlots"
author: "AMCalejandro"
date: "31/3/2022"
output: html_document
---
```{r}
library(forestplot)
library(dplyr)
library(tidyverse)
# To load in memory the function to get the forestplots
source("utils/utils.R")
```
# rs36082764
```{r}
SNP_cohort = tibble(
mean = c(-0.66, -0.66, -0.26, ... |
103db6325381b7b05e584fad3466a1f56c2b4bd8822cfa0394ae6e3b6fdea6a1 | R | 4,175 | 118 |
#' Benjamini-Hochberg FDR correction
#' @param p P-values
#' @return Adjusted p-values (q-values)
#' @export
bh=function(p) { q=p; q[!is.na(p)]=p.adjust(pmin(p[!is.na(p)],1),method="fdr"); q }
bfc=function(p) min(p,na.rm=T)*sum(!is.na(p))
#' logistic (sigmoid) function
#' @param g The log odds
#' @return 1/(1+e^-g)
... |
b8e580e97099c49c094269acca334d13c159f9afac57c092c57cb89fe029e9b5 | R | 4,205 | 146 | ###########################################################################
## The following function solves a linear or quadratic program (LP|QP).
#' @name Rcplex2
#' @rdname internal
#' @title Rcplex2
#' @description
#'
#' Modification of Rcplex which takes in mipcontrol parameters.
#'
#'
#' @export
Rcplex2 <- func... |
3f6d89e6c58d551a55c8b3642fe707b6690c53e016e1a9fdd8c93298d206e5be | R | 4,251 | 162 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(viridis)
library(scico)
library(ggrepel)
library(shades)
library(ggrastr)
library(ggbeeswarm)
library(circlize)
library(cowplot)
set.seed(666)
a... |
6b824865fd6ce6a2bfa1d75e5d88ceab2160e92ca99ac30a38a46fea33661cf0 | R | 4,267 | 72 | library(ArchR)
library(ggplot2)
library(gridExtra)
library(Rtsne)
library(clusterProfiler)
setwd('output path')
###Figure3e####
proj = readRDS('Save-ArchR-Project.rds')
cellColData=as.data.frame(proj@cellColData)
cellColData[,1][cellColData[,1] == 'adult_hindbrain_PL2011260025'] = 'Rho-1'
cellColData[,1... |
c2bf80a16b7ebb8e6d22c96593e24dee203a1f8f0a7f27bd4bffa1a06bd88e69 | R | 4,270 | 131 |
library(igraph)
library(Matrix)
library(IRanges)
library(ggplot2)
# read OBO format and return an igraph with all "is_a" relations
read.obo <- function(obo.file) {
pat <- "^([^:]+) *: *(.*)$"
obo <- readLines(obo.file)
obo <- obo[grepl("^\\[",obo) | grepl(pat,obo)]
obo <- DataFrame(
section = cumsum(grep... |
a8263a4891af5bcaccde14df9f681be034e7f802c152e01887f8c70a2d7d5db0 | R | 4,289 | 111 | library(dplyr)
library(lme4)
library(lmerTest)
library(sjPlot)
library(ggplot2)
#read data, transform and recode variables
dat <- read.delim("Data_Eyesize.txt")
dat <- dat %>%
mutate_at(vars(fishid,line,rep), factor)%>%
mutate(line = recode(line, sc = 'Control', ss = 'Selection'))
#calculate eye size in relation... |
5a0cd38f4ce145ac3e5a36b357add23c9bcb35e80c4a58313859d455830cbc27 | R | 4,379 | 108 | plot_baseline <- function(df, slice_summary, group_summary, yresult, ylabel,
slice_colors,
plotType, scaleYtype,
hide_xlabels = FALSE,
plot_y_min, plot_y_max,
forceYticks = NULL,
... |
32b4c2f8f46995af58638ddb7b8996e896fe2864263fcc5c2d3110c94274da0c | R | 4,400 | 163 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ggpubr)
library(ggbeeswarm)
# read in data
df <- read_tsv("./MCX_ALS_vs_Control_JessWesternBlot_protein_quantification_batch_May2023.txt") %>%
mutate(diagnos... |
59179a90e95a09b3cd5deb32ca59a989c53eda3b0a9ea121022b10618602b207 | R | 4,409 | 196 | getLMM_model <- function(df, formula){
mod <- lmer(
formula
, df
)
return(mod)
}
getLMM_ANOVA <- function(mod, asDF = TRUE){
ANOVA <- anova(mod)
if(asDF){
ANOVA <- ANOVA %>%
as.data.frame() %>%
rownames_to_column("effect")
}
return(ANOVA)
}
printLMM_summary <- function(mod){
pr... |
73a0e83534714114a49694fbeab860f38824f370831ab2bf912b72a5ecdbeb29 | R | 4,425 | 95 | #' @title Deviance Residual Transformation on Gene Count Matrix
#' @description Performs deviance residual transformation (proposed in Townes et al., 2019)
#' and converts given raw gene count matrix into a continuous matrix with quantities
#' analogous to z-scores and approximately follow a normal distribution.
#' @pa... |
d060b429651e96d38f2fee2634aea4a3753d5b5f2fa9fcda904e766b461f233f | R | 4,482 | 149 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
library(BSgenome.Hsapiens.UCSC.hg38)
set.seed(666)
addArchRThreads(threads = 16)
addArchRGenome("hg38")
proj <- loadArchRProject(path ... |
e8518b024cc211643e9cda7bb4e03cb78b612e8e8dd8490639749a19337625d1 | R | 4,499 | 105 | # edited from geom_errorbar
# https://github.com/tidyverse/ggplot2/blob/main/R/geom-errorbar.R
# https://stackoverflow.com/questions/27585776/error-bars-for-barplot-only-in-one-direction
#' @export
#' @rdname geom_linerange
geom_meanbar <- function(mapping = NULL, data = NULL,
stat = "identit... |
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