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|---|---|---|---|---|
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title-block-banner: true
title: "Analysis of Gfap and PDGFRβ reactivity in the ipsilateral hemisphere of KLF4-KO animals"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- na... |
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title-block-banner: true
title: "Point pattern analysis (PPA) for Ki67 expression in the ipsilateral hemisphere"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Unive... |
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title-block-banner: true
title: "Analysis of Ki67/PDGFRβ colocalization in defined ROIs of the ipsilateral hemisphere"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name:... |
fedc12229214f598c465cd5fc9c43658a297690caa8119d43810875379d955c5 | Quarto | 21,828 | 636 | ---
title-block-banner: true
title: "Analysis of PDGFR-β morphology in defined ROIs"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Univerisity Laval
department... |
4dd62ca11a5316c9de7502932e6eeb2ba1518e55241e8f04f8d599af0f300b96 | Quarto | 22,008 | 728 | ---
title-block-banner: true
title: "Analysis of PDGFR-β mRNA (FISH) in KLF4-Flox and PDGFR-β-Cre mice (Control experiment)"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- nam... |
7f1e90c8e7d193fc630e503566a1370df6d526cf0ae474326f798dfbe62d7b20 | Quarto | 22,206 | 662 | ---
title-block-banner: true
title: "Point pattern analysis (PPA) of PDGFR-β and GFAP+ cells (ipsilateral hemisphere)"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Uni... |
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title-block-banner: true
title: "Analysis of PDGFRβ mRNA (FISH) in the injured brain following cerebral ischemia"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Univ... |
12b294862b5cdbbe64b9e976fce050761e19705fc89fe3a4db07d0ea9c54cd34 | Quarto | 26,069 | 736 | ---
title-block-banner: true
title: "Analysis of KLF4 expression in the ipsilateral hemisphere of PDGFRβ^KLF4-KO mice"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Uni... |
278c1dd395bde9512e30ed60ad5cbf043acab1aa6628066253f80f410a95450a | Quarto | 26,099 | 716 | ---
title-block-banner: true
title: "Analysis of KLF4/PDGFRβ colocalization in defined ROIs following ischemia"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Univerisit... |
9f032494243b0f480e1fafedc8313b57023017b7e4297b9c1b518572f83b0cb1 | Quarto | 26,153 | 818 | ---
title-block-banner: true
title: "Analysis of hemispheric shrinkage following cerebral ischemia"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Univerisity Laval
... |
cfe241b23c7f3da3a5858fac0696ceaa68481204ba73c72bae5d8fb11444c2d2 | Quarto | 29,452 | 925 | ---
title-block-banner: true
title: "Analysis of PDGFRβ reactivity (Integrated density) in the ipsilateral hemisphere"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Uni... |
1bc361f232d50cacbacf1698af1ff87976ec15efc2c014a747cb20cc43457d28 | Quarto | 29,814 | 1,015 | ---
title-block-banner: true
title: "Analysis of CD31 and ColIV expression in the cortex of PDGFRβ^KLF4-KO mice"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Univerisi... |
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title-block-banner: true
title: "Analysis of GFAP reactivity (Integrated density) in the ipsilateral hemisphere"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Unive... |
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title-block-banner: true
title: "Analysis of Haralick features from PDGFRβ in defined ROIs"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Univerisity Laval
... |
8d279426f000496d26d750b113d5439224052c0f996fdd1560e3a5798d346629 | Quarto | 32,425 | 1,043 | ---
title-block-banner: true
title: "Structural analysis of KLF4-KO brains in the ipsilateral hemisphere"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Univerisity Lava... |
8822f3f4c7fa177926e4ac42fe5b0bd2cfcf48dafcef084c688f6b400757bef5 | Quarto | 35,271 | 964 | ---
title: "Data handling for Widefield PDGFR-β and GFAP-stained barain sections (Ipsilateral hemisphere)"
subtitle: "Data analysis notebook"
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Univerisity Laval
department: Psychi... |
b00183653c2a4017abcf7965b56668605cddda0e266236b1dc214e762eedf35e | Quarto | 40,685 | 1,260 | ---
title-block-banner: true
title: "Analysis of Klf4 expression in the ipsilateral hemisphere following cerebral ischemia"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name... |
456d71151e51dcfe13e51d85cb592438d9b2bcad4cc35255393ac41396a0f999 | Quarto | 46,039 | 786 | ---
title: "Supplementary tables"
editor: visual
format: html
---
::: {#tbl-panel_shrinkage layout-ncol="2"}
| **Parameter** | **Est.** | **2.5 %** | **97.5 %** |
|-----------------|----------|-----------|------------|
| Intercept | 11.9 | 11.5 | 12.45 |
| sigma_Intercept | 0.2 | -0.25 ... |
25f83a85c51c158b8b34696bef883de129899cbc1b3ad7aad2a019d3d1d92c14 | Quarto | 46,576 | 1,375 | ---
title-block-banner: true
title: "Point pattern analysis (PPA) of PDGFR-β and GFAP+ cells (regions of interest)"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Univer... |
c18496a245570586a01817c3d0c2a92bf51853841bb258ec817e3df6d8b26cf5 | Quarto | 55,544 | 2,000 | ---
title-block-banner: true
title: "Analysis of PDGFRβ attachment to vasculature (CD31+)"
subtitle: "Data analysis notebook"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
degrees:
- PhD
affiliation:
- name: Univerisity Laval
depa... |
015d4e176d8723845b6ae1b76e5b74579173c7b1f2cd0a65bb0a00330875455c | R | 56 | 4 | library(testthat)
library(gGnome)
test_check('gGnome')
|
54413c815fb77d3cc51323da0a66926feb0fc5223e765222147eb006822c7c25 | R | 60 | 4 | library(testthat)
library(transmit)
test_check("transmit")
|
fd4461995dbc4c078b80498f7fa89ac29e8845725aa3a4b080889e7df99bc305 | R | 67 | 5 | library(testthat)
library(fragCounter)
test_check("fragCounter")
|
0c0e32b684045fb8ba2be7b6d9170c30d594f05ef483b2ae3a6b32338a118036 | R | 70 | 4 | library(testthat)
library(JaBbA)
test_check("JaBbA", filter="JaBbA")
|
336336f2ba610a2c4fd95681d74b0fff53886109822c41719aa981b6b8fe03a6 | R | 108 | 3 | ### MixfMRI environment for storing global variables.
.MixfMRIEnv <- new.env()
.MixfMRIEnv$MPI.gbd <- FALSE
|
e8a58cc6c8807c2ca355220060de158cbfa9ba5e2148b3a2f3c6eb01ba383f5e | R | 137 | 3 | library(pheatmap)
data<-read.table("S-Fig3f_DAR_reverse_between_DG_CA_ATACseq_Zscore.txt")
pheatmap(f,cluster_cols = F,show_rownames = F) |
fdc1e399d47b8a259dfb4f25bc7fd875dc4c62eeceffe893644b786f4a72dba9 | R | 142 | 5 |
z<-read.table("Fig3b_DAR_associated_gene_Exp.txt",header = T)
ggplot(z, aes(x=Group, y=Exp, fill=Type)) +
geom_boxplot()+
theme_classic() |
310751dd8eed110459addd43a06e14262d97bee8a675b913a9afa6ac90b6f04d | R | 167 | 6 | install.packages("lmerTest")
install.packages("broom")
install.packages("emmeans")
install.packages("ggplot2")
install.packages("gridExtra")
install.packages("sjPlot") |
d414225aaa9c3797f5f5913039ac515945e1a427aae3b42dea85de588ca439c5 | R | 180 | 3 | library(pheatmap)
data<-read.table("Fig1d.Region_sepcific_expressed_Gene_cpm_Zscore.txt",header = T)
pheatmap(data,cluster_rows = F,cluster_cols = F,show_rownames = F,fontsize = 8) |
b0717d8e1d28d93504a72283c17ccfb00ab278df6dee8501ab711ab4e37b9c83 | R | 210 | 6 | test_that("construct simple codebook", {
codebook <- construct_codebook(c('A', 'B', 'C'))
expect_equal(codebook$A$index,1)
expect_equal(codebook$A$size,1)
expect_equal(codebook$A$symbol,'A')
})
|
b16e95bce16190863e0dbb489cb33593a7310f44443ce506d7e2b672b075190f | R | 213 | 10 | #------------------------
# Plot phylogenetic tree
# 25 Jan 2023
#------------------------
source("./common.R")
# <<Figure 5>>
plot.phylo(tree_en, tip.color = cl, font = 1)
save_pdf("../data/derived/fig.5.pdf")
|
df2dc6e70924b05d5de40dde65c12d63e04c9310fb29bda7d485eb4e21ce57c0 | R | 248 | 6 | summarized.overlap <- function(overlap.mat){
x <- eigen(overlap.mat, symmetric = T, only.values = T)
p <- nrow(overlap.mat)
if (p == 1) 1 else
max(min((p * x$values[1] / sum(x$values) - 1) / (p - 1), 1), 0)
} # End of summarized.overlap().
|
9d82b861f4a03af20a7c7706cdfce02723ac58b6999970a8762fbc396b120141 | R | 264 | 10 | #!/usr/bin/env Rscript
args=commandArgs(trailingOnly = T)
numers=read.table(args[1], header=T, row.names=1, stringsAsFactors=F)
#ss=strsplit(colnames(numers),split="_")
#meta=do.call(rbind,ss)
#colnames(meta)=c("sex","tissue","id")
save(numers, file=args[2] )
|
4904387e0a6220ddfd5debae1f3503397d315dc111dd11c8d2a64fb567696a59 | R | 269 | 12 | ### Lastest load into a package.
.onLoad <- function(libname, pkgname){
library.dynam("MixfMRI", pkgname, libname)
invisible()
} # End of .onLoad().
.onUnload <- function(libpath){
library.dynam.unload("MixfMRI", libpath)
invisible()
} # End of .onUnload().
|
68a2155f0ce69e2dbd79e1ac00b3839f340578e4cbdfedd41f92d742058285b4 | R | 311 | 7 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
count_spikes <- function(group_index, num_groups, spike_time, timesteps) {
.Call('_transmit_count_spikes', PACKAGE = 'transmit', group_index, num_groups, spike_time, timesteps)
}
|
5e0cbcfc4d8ba11161193915295624316c0d5b61b13c1b093022152f330cc4c8 | R | 345 | 13 | library(ggplot2)
library(pheatmap)
library("ggplotify")
library(cowplot)
setwd('output path')
data =read.table('Sample_AverageExpression.xls',sep = '\t')
res <- cor(data)
res = round(res, 2)
p1 = pheatmap(res,cluster_cols = T,cluster_rows = T)
plot_list =as.ggplot(p1)
ggsave(plot = plot_list,filename = 'cor_Sample.pd... |
37f77f18f377ad8638150d0a99e9a5e765696fbfcb26bf37e4ff71a263d3b1ab | R | 348 | 14 | 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]
### plot hist
fn.plot <- paste("./plot/", case, "_hist.pdf", sep = "")
pdf(fn.plot, height = 4, width = 6)
hist(PV.gbd, nclass = 100,
xlab = "p-val... |
632b43a287f208a730ab4359e16053db94750dc2156834ed1ce302e61c102fe8 | R | 357 | 9 | ### Do not delete this file and file name!!
### This file should be loaded before all other *.r files.
### This is to avoid the false positive messages from R CMD check.
### "no visible binding for global variable"
### Suggested by Prof Brian Ripley
### ?globalVariables
if(getRversion() >= "2.15.1"){
utils::globalVar... |
5997d2aeb5c60060edcff5df92eb16eee4cc4eb48d42ed02826645ce540e6770 | R | 372 | 20 | case <- "ds.pval"
.FC.CT$algorithm <- "apecma"
.FC.CT$model.X <- "V"
.FC.CT$ignore.X <- FALSE
.FC.CT$CONTROL$debug <- 1
.FC.CT$CONTROL$RndEM.iter <- 50
.FC.CT$INIT$min.1st.prop <- 0.99
.FC.CT$INIT$max.PV <- 0.05
K.min <- 2
K.max <- 20
max.try.fclust <- 3
seed <- 1234
if(!file.exists("output")){
dir.create("output")... |
e23b4ec536b5e14b2a16de6508d2d146493ce8513a8c8c9c9232692b0441954a | R | 372 | 20 | case <- "ds.pval"
.FC.CT$algorithm <- "apecma"
.FC.CT$model.X <- "I"
.FC.CT$ignore.X <- FALSE
.FC.CT$CONTROL$debug <- 1
.FC.CT$CONTROL$RndEM.iter <- 50
.FC.CT$INIT$min.1st.prop <- 0.99
.FC.CT$INIT$max.PV <- 0.05
K.min <- 2
K.max <- 20
max.try.fclust <- 3
seed <- 1234
if(!file.exists("output")){
dir.create("output")... |
822f8cb44e87fd1094316db8a22e5408c1a35c33231be35d584570f5e05a5136 | R | 375 | 20 | case <- "jssv44i11"
.FC.CT$algorithm <- "apecma"
.FC.CT$model.X <- "I"
.FC.CT$ignore.X <- FALSE
.FC.CT$CONTROL$debug <- 1
.FC.CT$CONTROL$RndEM.iter <- 50
.FC.CT$INIT$min.1st.prop <- 0.975
.FC.CT$INIT$max.PV <- 0.05
K.min <- 2
K.max <- 20
max.try.fclust <- 3
seed <- 1234
if(!file.exists("output")){
dir.create("outpu... |
4078acdef267f21846fb9670917ca0cdd3ccab32b9ab6e4ffac0eeb528617ba4 | R | 387 | 20 | case <- "pval.olddata.complex"
# case <- "pval.olddata.mag"
.FC.CT$algorithm <- "apecma"
.FC.CT$model.X <- "V"
.FC.CT$ignore.X <- FALSE
.FC.CT$CONTROL$debug <- 1
.FC.CT$CONTROL$RndEM.iter <- 100
.FC.CT$INIT$min.1st.prop <- 0.8
K.min <- 2
K.max <- 15
max.try.fclust <- 3
seed <- 1234
if(!file.exists("output")){
dir.c... |
9b0c3b41791253f0b3edc42a73f92f11d350076f93c79e455d33463d589819bc | R | 387 | 20 | case <- "pval.olddata.complex"
# case <- "pval.olddata.mag"
.FC.CT$algorithm <- "apecma"
.FC.CT$model.X <- "I"
.FC.CT$ignore.X <- FALSE
.FC.CT$CONTROL$debug <- 1
.FC.CT$CONTROL$RndEM.iter <- 100
.FC.CT$INIT$min.1st.prop <- 0.8
K.min <- 2
K.max <- 15
max.try.fclust <- 3
seed <- 1234
if(!file.exists("output")){
dir.c... |
860c7f527d819cbec6e77067b777fc37c86757c4c28150b99064cddaf9b2e16c | R | 395 | 22 | library(shiny)
library(shinyBS)
library(shinyWidgets)
library(tidyverse)
library(stringr)
library(Matrix)
library(Seurat)
library(metacell)
library(scater)
library(Biostrings)
library(igraph)
library(plotly)
library(data.tree)
library(collapsibleTree)
library(phylocanvas)
library(msaR)
library(ape)
library(shinycssloa... |
0f73db3c4f442bcdaa2ee85c5d3ba5a92512691be9b1697f424814fbd2898a80 | R | 407 | 15 | 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]
# PV.gbd[PV.gbd > 0.3] <- runif(sum(PV.gbd > 0.3), 0.3, 1)
### plot hist
fn.plot <- paste("./plot/", case, "_hist.pdf", sep = "")
pdf(fn.plot, height = 4, w... |
c24ac2c2f262b7b2e459dc51219b96dd8f6e62df3f0d3725239566e2a06d3c38 | R | 409 | 19 | countMiceAndLitters <- function(df, filterByVar, groupingVars){
count <- df %>%
filter(
!is.na({{ filterByVar }})
) %>%
group_by(!!! groupingVars)%>%
select(
mouseID,
damID,
!!! groupingVars
) %>%
summarise(
numCells = n(),
numMice = length(unique(mouseID)),... |
21ddce02cefeb84c772f41ce30001b19339a14b2f341b713d8f4618dfcf39fa2 | R | 410 | 21 | case <- "ds.pval"
.FC.CT$algorithm <- "apecma"
# .FC.CT$model.X <- "V"
.FC.CT$ignore.X <- TRUE
.FC.CT$CONTROL$debug <- 1
.FC.CT$CONTROL$RndEM.iter <- 50
.FC.CT$INIT$min.1st.prop <- 0.99
.FC.CT$INIT$max.PV <- 0.05
.FC.CT$INIT$class.method <- "simple"
K.min <- 2
K.max <- 10
max.try.fclust <- 1
seed <- 1234
if(!file.exi... |
53cff1f2380e506cf82aa46011ee6549ca2c1b0b70f1b2129d7be601d66382a8 | R | 425 | 21 | case <- "pval.olddata.complex"
# case <- "pval.olddata.mag"
.FC.CT$algorithm <- "apecma"
# .FC.CT$model.X <- "I"
.FC.CT$ignore.X <- TRUE
.FC.CT$CONTROL$debug <- 1
.FC.CT$CONTROL$RndEM.iter <- 100
.FC.CT$INIT$min.1st.prop <- 0.8
.FC.CT$INIT$class.method <- "simple"
K.min <- 2
K.max <- 10
max.try.fclust <- 3
seed <- 123... |
a8053296e152a6918371a7833f4f8502c484cd35fcfa4d919ead20342639e648 | R | 427 | 9 | data<-read.table("Region_ctrl_DAR_NAc_other_RUVr_k3.txt",header = T)
plot((-1)*data$logFC,-log10(data$FDR),pch=20,col="grey",xlab="log2FC",ylab="-log10(FDR)",main="NAc")
d1<-data[data$logFC<(-1) & data$FDR<0.01,]
points((-1)*d1$logFC,-log10(d1$FDR),pch=20,col="red")
d2<-data[data$logFC>1 & data$FDR<0.01,]
points((-1)*d... |
5ba24b083504179681fecdac2301da9af73d308df57d1b41f9378dd303c531c5 | R | 428 | 17 | #' The 'leafcutter' package.
#'
#' @description A DESCRIPTION OF THE PACKAGE
#'
#' @docType package
#' @name leafcutter-package
#' @aliases leafcutter
#' @useDynLib leafcutter, .registration = TRUE
#' @import methods
#' @import Rcpp
#' @import rstantools
#' @importFrom rstan optimizing
#'
#' @references
#' Stan Dev... |
c25a9c36f6343afa769257f51611582449b06b4e82da89705a30c3a41fdf1f27 | R | 436 | 19 | install.packages('ggplot2')
install.packages('SimInf')
install.packages('VSF')
install.packages('devtools')
install.packages('e1071')
install.packages('nleqslv')
install.packages('C:/Users/eric_fortune/Downloads/VFS_1.0.2.tar.gz', repos = NULL,type = 'source')
install.packages('deSolve')
install.packages('RMAWGEN’')
l... |
f50504592f73e0ce87acfe6224e1009bbff44caa29e8b02c2b8a5c0d28a9380d | R | 447 | 18 | #!/usr/bin/Rscript
# Loading data from stding
args = commandArgs(trailingOnly=TRUE)
metal = fread(args[1])
N = as.numeric(args[2])
## Library loading
.libPaths("/mnt/rreal/RDS/acarrasco/R_libs/")
library(data.table)
library(stringr)
library(tidyverse)
# To perfrom the harmonization QC, we used the code from -> https... |
b73095756a993c9d952c621a50425ba2b8f724fe354124cf6e9677accdd17c48 | R | 491 | 11 | library(ggplot2)
NAc<-read.table("DistanceTSS_Region_ctrl_NAc_other_DAR.txt");
DG<-read.table("DistanceTSS_Region_ctrl_DG_other_DAR.txt");
CA<-read.table("DistanceTSS_Region_ctrl_CA_other_DAR.txt");
SVZ<-read.table("DistanceTSS_Region_ctrl_SVZ_other_DAR.txt");
a<-boxplot(as.numeric(NAc$V2/1000),as.numeric(DG$V2/1000),... |
631af022f8ecade27e1068df1e6ae53ef06dd3ae342629bfe5cbfb4dfd041d1e | R | 500 | 11 | require(data.table)
f <- fread("grep -v ^# /broad/broadsv/NA12878/GCAT/gcat_illumina_150x/snowman/v115/v115.snowman.indel.vcf", sep='\t')
hdr <- fread("grep ^# /broad/broadsv/NA12878/GCAT/gcat_illumina_150x/snowman/v115/v115.snowman.indel.vcf", sep="\t", header=FALSE)
## rescale the quality score
f[, SL := gsub(".*?:... |
66f5ce41dc0960d82629def004e1f5d1e0e1471778110522b14828c16ab3a613 | R | 509 | 20 | library(MixfMRI, quietly = TRUE)
set.seed(1234)
da <- gendataset(phantom = shepp2fMRI, overlap = 0.01)$pval
### Check 2d data.
id <- !is.na(da)
PV.gbd <- da[id]
# pdf(file = "maitra_2d_fclust.pdf", width = 6, height = 4)
hist(PV.gbd, nclass = 100, main = "p-value")
# dev.off()
### Test 2d data.
id.loc <- which(id, ar... |
897909db93142f38f2f7c111400bd39e300c8d1ed52060aeb091b6d4b44e834a | R | 516 | 21 | library(MixfMRI, quietly = TRUE)
dim <- c(4, 5, 4, 10)
set.seed(123)
da.ts <- array(rnorm(prod(dim)), dim = dim)
id.class <- cba.cor(da.ts)
table(id.class)
fun.tanh <- function(a, B){
d <- 1 / apply(B, 2, function(b){ dist(rbind(as.vector(a), b)) })
tanh(d)
}
id.class.tanh <- cba.cor(da.ts, fun.sim = fun.tanh)
tab... |
41d7430534b9b46fe24b95f6c68da892dc2f01bb856bceb4d2f2ed59af9626b0 | R | 518 | 28 | ---
title: "Advanced usage"
author: "Jonas Simon Fleck"
date: "5/18/2022"
output: html_document
---
```{r, echo = FALSE, message=FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
```
- Interaction terms
- motifs
- aggregation
- Filtering the graph
```{r, results='hide'}
library(tidyverse)
library(Pando)... |
944f419b8e16f83158a819446a84ed51ebdfee30c458d51bb5f891655efaa476 | R | 521 | 9 | library(pheatmap)
data<-read.table("Fig2d_DEG_of_DG_CA_reverse_w_Zscore_Expression.txt",header = T)
pheatmap(data[,3:ncol(data)],cluster_rows = F,cluster_cols = F,fontsize = 7)
paletteLength <- 50
myColor <- colorRampPalette(c("navy", "white", "firebrick3"))(paletteLength)
myBreaks <- c(seq(-2, 0, length.out=ceiling(p... |
8217be2463b6753f448a456710b29f7a2b87f24e2da710972d91a12cdfd72329 | R | 522 | 25 | case <- "simu.2d"
.FC.CT$algorithm <- "apecma"
.FC.CT$model.X <- "V"
.FC.CT$ignore.X <- FALSE
.FC.CT$CONTROL$debug <- 1
.FC.CT$CONTROL$RndEM.iter <- 50
.FC.CT$INIT$min.1st.prop <- 0.95
.FC.CT$INIT$class.method <- "prob.extend"
K.min <- 2
K.max <- 8
max.try.fclust <- 3
seed <- 1234
smooth <- FALSE
if(!file.exists("out... |
4cfb7364b203d8d40593a77855750c111932bae61ab27190ecea14cd8fdad0f5 | R | 591 | 20 | library(MixfMRI, quietly = TRUE)
set.seed(1234)
### Test maitra's simulation.
da <- gendataset(phantom = shepp1fMRI, overlap = 0.01)$pval
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
### Run noX and 2 clusters.
.FC.CT$ignore.X <- TRUE
.FC.CT$INIT$class.method <... |
b2000fc47e8272f65c16bfacf5de34d1de5f4392c8dbb09c80b2015c75d4f728 | R | 594 | 24 | test_that("testing adding channel noise", {
codebook <- construct_codebook(c('A', 'B', 'C'))
signal <- encode_signal(
codebook=codebook,
symbol = 'A',
signal_power = 3,
duration_in_seconds = 10
)
result <- add_channel_noise(
codebook,
signal... |
0c43fccb9439c2fff687dccc6e873e98a549f4936c3a7f36dffd7be1298b548b | R | 607 | 30 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 8)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_latest_atac_anno_a... |
765e0b48880ed678c4c94859a55abcc2bb43859737b423cd20aa3dacb03b9673 | R | 609 | 15 | library(tidyverse)
df <- read_tsv("count_cell_barcodes_pre_post_multiplets_removal.txt", col_names = c("sample", "original_count", "filtered_count")) %>%
mutate(rate = 100*(1 - (filtered_count/original_count)),
rate = round(rate, 2))
p <- df %>% ggplot(aes(sample, original_count))
p + geom_bar(fill = "#D... |
9a3df76049f4a37aba60a4b3ae5cd6a8dadd157e560e7d40aa3518670e43c46c | R | 636 | 22 | ### This file contains major functions for solving MLE of SIGMA.
mle.SIGMA.I <- function(PARAM, i.k){
N.gbd <- PARAM$N.gbd
p.X <- PARAM$p.X
B <- W.plus.y(.MixfMRIEnv$X.gbd, -PARAM$MU[, i.k], N.gbd, p.X) *
sqrt(.MixfMRIEnv$Z.gbd[, i.k] / .MixfMRIEnv$Z.colSums[i.k])
tmp.SIGMA <- colSums.gbd(B^2)
tmp.SI... |
619741690c61332800d25bf3fe7d1e686957dfd494796f6288497779576c8230 | R | 675 | 25 | library(MixfMRI, quietly = TRUE)
set.seed(1234)
### Test 2d data.
da <- pval.2d.mag
id <- !is.na(da)
PV.gbd <- da[id]
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
ret <- fclust(X.gbd, PV.gbd, K = 3)
print(ret)
### p-values of rest clusters.
ret.lrt <- lrt(PV.gbd, ret$class, K = 3)
print(ret.lrt... |
4d54d9d3b99e0ba5f0773bdc699e707388200c307a3306fff19496ac8fc0ac24 | R | 676 | 19 | library(ggplot2)
#St18
data<-read.table("Fig4f_gene_St18_expression_updata.txt",header = T)
ggplot(data, aes(x=Region, y=St18, color=Drug)) +
geom_boxplot(position=position_dodge(0.5),width=0.4)+
scale_x_discrete(limits=c("NAc","DG","CA","SVZ"))+
labs(title="St18",x="")+
theme_classic()+
theme(plot.title = ... |
386b9bf29eb211a5f49cd3e48a3ea6f4529104ce7cb3a024bd27c4b9a71df4cc | R | 710 | 21 | create_count_matrix <- function(data_input, data_type_input, rep_num_input) {
if (data_type_input == "dna") {
grep_pattern <- "^dna\\d"
} else if (data_type_input == "rna") {
grep_pattern <- "^rna\\d"
}
header_indeces <- colnames(data_input) %>% grep(pattern = grep_pattern)
count_matrix <- cbind(
... |
cbe0dde07cbb8d88d3acc0ed39941cd060626e0ae3026d90fd4df06a87f03320 | R | 727 | 27 | library(MixfMRI, quietly = TRUE)
library(EMCluster, quietly = TRUE)
# .FC.CT$algorithm <- "apecma"
# .FC.CT$model.X <- "V"
# .FC.CT$ignore.X <- TRUE
set.seed(1234)
### Test toy1.
X.gbd <- toy1$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$PV.g... |
0091fad3987710f243606724ceac41623f4caa8a27a3a30cdd477d2558f1969b | R | 735 | 27 | library(MixfMRI, quietly = TRUE)
library(EMCluster, quietly = TRUE)
# .FC.CT$algorithm <- "em"
# .FC.CT$model.X <- "V"
# .FC.CT$ignore.X <- TRUE
set.seed(1234)
### Test toy1.
X.gbd <- toy1$X.gbd[, -3]
X.range <- apply(X.gbd, 2, range)
X.gbd <- t((t(X.gbd) - X.range[1,]) / (X.range[2,] - X.range[1,]))
PV.gbd <- toy1$PV... |
9bac6f973aff694a8b478bfc880fd5bab160e59fb82470ad31767bbbc097041f | R | 747 | 26 | ### compute correlation
cba.cor <- function(da.ts, da.m = NULL, adj.dist = TRUE,
fun.sim = stats::cor){
dim.da.ts <- dim(da.ts)
if(!is.null(da.m)){
dim.da.m <- dim(da.m)
if(length(dim.da.ts) != (length(dim.da.m) + 1)){
stop("Dimension lengths of da.ts and da.m do not match.")
}
if(any(d... |
647ba8a11b8c3e74382f65959cdbe42e1378d3857b5902e65118da529a551520 | R | 758 | 29 | library(MixfMRI, quietly = TRUE)
library(EMCluster, quietly = TRUE)
# .FC.CT$algorithm <- "apecma"
# .FC.CT$model.X <- "V"
# .FC.CT$ignore.X <- TRUE
set.seed(1234)
### Test pstats
id <- which(!is.na(pstats))
PV.gbd <- pstats[id]
X.gbd <- which(!is.na(pstats), arr.ind = TRUE)
X.range <- apply(X.gbd, 2, range)
X.gbd <-... |
c2de589be3c3eba85e14d6c2e2947779343424b7656441c379d12d9421c52ea4 | R | 771 | 15 | library(ggplot2)
data<-read.csv("Fig5a_Motif_enriched_in_DAR.txt",header = T,sep="\t")
ggplot(data,aes(x=Type, y=GENE, size = -log10(LogPvalue), col =Percent ))+
geom_point(alpha=1) +
scale_size(range = c(0.01, 3))+
scale_color_gradient2( low="grey", mid="orange", high="red" ) +
theme(axis.text.x = element_te... |
ff339c16d329136fea0ce64057020d22ed9564a8875427180af9f7b517f3e96d | R | 778 | 18 | library(ggplot2)
require("ggrepel")
data<-read.table("Fig4e_DAR_enhancer_Gene_Exp.txt",header = T)
f<-data
f$Significant <- ifelse(f$Exp_FDR < 0.05 & (f$DAR_logFC*f$Exp_logFC > 0), "FDR < 0.05", "Not Sig")
ggplot(f, aes(x = (-1)*DAR_logFC, y = (-1)*Exp_logFC, shape = Region,size=-log10(Exp_FDR)))+
geom_hline(yinterc... |
19fbf127e1cb9f5f61dbc4fc3c297254c1baaaa1cac40291103ac851d1f892df | R | 793 | 19 | library(MixfMRI, quietly = TRUE)
set.seed(1234)
da <- gendataset(phantom = shepp1fMRI, overlap = 0.01)$pval
set.seed(1234)
da2 <- gendataset(phantom = shepp2fMRI, overlap = 0.01)$pval
xlim <- ylim <- c(0.19, 0.82)
# pdf(file = "maitra_2d.pdf", width = 4, height = 4)
# par(mfrow = c(2, 2), mar = c(0, 0, 1, 0))
par(mfro... |
22466140aa5c87d11083b8b59de571f0288d9e8c282b47e655dec350c00be449 | R | 798 | 18 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
library(sctransform)
library(scales)
# read in full dataset with 1st round annotation
data_obj_sub <- readRDS("./seurat_objects/snRNA_cellBneder_correc... |
c742c05ec680b1d60fa60e1c1dd522662f554009fbaf326b05d0e23487669dfb | R | 798 | 28 | ### Get the Cov(logit eta_k) and 95% CE.
### Partial logL. Return a matrix with dimension M * N.
# partial.logL.I <- function(x, ETA, BETA, MU, SIGMA, post.z)
### Paritial all logit(eta_k). Return a K * (K - 1)
partial.logit.p <- function(ETA){
K <- length(ETA)
d.dETA <- 1 / ETA + 1 / (1 - ETA)
ret <- diag(d.dE... |
d4944a3a74b41f89d57c97bc7ae76e61bd8b291d146465d407fdfee8f5e562e7 | R | 799 | 44 | # time series
step = 7
#in weeks
year = 365
num_year = 15
n = 52 * num_year
t = seq(0, year*num_year , by = step)
a <- 25
b <- 1/58
c <- 10
d <- 1/24
c.unif <- runif(n)
c.norm <- rnorm(n)
amp <- 5
amp2 <- 5
offset <- 13
#temp data
set.seed(1)
y1 <- a*sin(b*t-4)+c.norm*amp
#precipitation
set.... |
c99e5f99edc2e407f0441900fd362bd07a991fd434ed1f6582c86e6c3e85cb39 | R | 808 | 29 | library(MixfMRI, quietly = TRUE)
.FC.CT$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, K = K, min.1st.prop = 0.5)
... |
306588a86f29fd82881060ff2ce04e96592325c10f82b75453617553d870d74e | R | 816 | 28 | library(MixfMRI, quietly = TRUE)
library(EMCluster, quietly = TRUE)
.FC.CT$model.X <- "I"
.FC.CT$CONTROL$debug <- 0
.FC.CT$check.X.unit <- FALSE
### Fit toy1.
set.seed(1234)
X.gbd <- toy1$X.gbd
PV.gbd <- toy1$PV.gbd
ret.2 <- fclust(X.gbd, PV.gbd, K = 2)
ret.3 <- fclust(X.gbd, PV.gbd, K = 3)
ret.4 <- fclust(X.gbd, PV.g... |
c1af1be8dba9a5a7272eb9f5c228fc2b755fd6299b38173ed2732c3e70320286 | R | 823 | 17 | library(ggplot2)
data<-read.table("S-Fig5c_DEG_TF_logFC_FDR.txt")
mid<-0
ggplot(data,aes(x=Region, y=Gene, size = -log10(FDR), col =LogFC ))+
geom_point(alpha=1) +
scale_color_gradient2(midpoint=mid, low="navy", mid="white", high="firebrick3" ) +
scale_size(range = c(0.01, 3))+
scale_x_discrete(limits=c("NAc"... |
5985fba19bb6411c21d90d627d77dc03955593288dcc9e7e6b0cefa0a73afe22 | R | 831 | 25 | ### For work flows only.
get.workflow <- function(dir.name = "workflow/",
parallel = c("spmd"), pkg = "MixfMRI"){
dir.name <- paste(dir.name, parallel[1], "/", sep = "")
file.path <- tools::file_path_as_absolute(
system.file(dir.name, package = "MixfMRI"))
file.path
} # End of get.workflow()... |
3b62fc0ab81b1601a5add1e31b178a094e1797a8a1dd21549bb623687fbf3d3e | R | 832 | 42 | ### Density of mulitvariate normal distribution.
# X: N.gbd by p.X
# MU: length p.X
# full: p.X by p.X
dmvn <- function(X, MU, full, log = FALSE){
N <- nrow(X)
p <- ncol(X)
tmp.S <- full
logdet <- log(abs(det(tmp.S)))
B <- W.plus.y(X, -MU, N, p)
tmp.S <- solve(tmp.S)
distval <- rowSums((B %*% tmp.S) * ... |
d82fc57418a747537880563fd1a87e3f9ce7d9d2242fb8217cffe5c3431a7ca9 | R | 838 | 35 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 16)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_latest_atac_anno_... |
1625732fd10d755341cd02d297b499375af2bcedbc692d61cb6a9c2ae8f268da | R | 841 | 32 | printTtestText <- function(t_test){
pval <- t_test$p.value
pText <- case_when(
pval < 0.001 ~ "<0.001",
pval > 0.999 ~ ">0.999",
TRUE ~ as.character(paste0("==", round(pval, 3)))
)
text <- paste0( ## Uses plotmath to format
"list(",
"italic(t)", # italic t
"[", round(t_test$parameter, 2... |
bfc26a544190859f3bed5baa4205fcac771f1c74ae35feacee811fe3a53ced1e | R | 848 | 30 | library(MixfMRI, quietly = TRUE)
set.seed(1234)
### Check 2d data.
da <- pval.2d.complex
id <- !is.na(da)
PV.gbd <- da[id]
hist(PV.gbd, nclass = 100, main = "p-value")
### Test 2d data.
id.loc <- which(id, arr.ind = TRUE)
X.gbd <- t(t(id.loc) / dim(da))
ret <- fclust(X.gbd, PV.gbd, K = 3)
print(ret)
### p-values of ... |
d0033056d6a4b12f77ff38814d3aefe101745b4aebbb106e9bb0246f14e42281 | R | 854 | 25 | toCPXMatrix <- function(Amat){
if (is.null(Amat)) {
return(NULL)
} else if (is(Amat, "sparseMatrix")) {
Amat <- as(Amat, "dgCMatrix")
matbeg <- Amat@p
matcnt <- diff(c(Amat@p, length(Amat@x)))
matind <- Amat@i
matval <- Amat@x
} else if (is(Amat, "simple_trip... |
9965273bdb7afe82f20185d5987024e28365c307a28dc8f1bd8d2d77d2ee49c3 | R | 860 | 22 | test_that("example transmission", {
codebook <- construct_codebook(c('A', 'B', 'C'))
decoded_signal <- transmit_signal(
codebook=codebook,
symbol = 'A',
signal_power = 10,
noise_power = 10,
duration_in_seconds = 10,
time_interval = 0.1,
entropy_threshold =... |
35cfb62d23b7b08eb0fa4274c3cf8d794afa58d68c6cf7be01f6592d0dd459c3 | R | 874 | 27 | ret.summary <- list()
ret.jaccard.summary <- list()
ret.alternative <- list()
ret.jaccard.alternative <- list()
for(i.rep in 1:25){
prefix <- paste("simu_2d_", sprintf("%02d", i.rep), "/new/",
sep = "")
fn <- paste(prefix, "summary_table/summary_table.rda", sep = "")
load(fn)
ret.summary[[i.r... |
47a39f7ec2a17b9803f31ffc69662633d2cea63167a1a52162ed5adf7a14489e | R | 879 | 36 | # Compute AIC and BIC for given PARAM.
aic <- function(PARAM){
d <- compute.d(PARAM)
-2 * PARAM$logL + 2 * d
} # End of aic().
bic <- function(PARAM){
d <- compute.d(PARAM)
-2 * PARAM$logL + d * log(PARAM$N)
} # End of bic().
icl.bic <- function(PARAM){
d <- compute.d(PARAM)
-2 * PARAM$logL + d * log(PAR... |
b1013c49013f8d539485ff0d58a8163466c9839409461fd8a5b7c688979b6a24 | R | 884 | 27 | ret.summary <- list()
ret.jaccard.summary <- list()
ret.alternative <- list()
ret.jaccard.alternative <- list()
for(i.rep in 1:25){
prefix <- paste("simu_2d_", sprintf("%02d", i.rep), "/new/",
sep = "")
fn <- paste(prefix, "summary_table/summary_table_new4.rda", sep = "")
load(fn)
ret.summary... |
cb58a2a10a687525794e4a57ae36bcb578fbf08a6405a15810196f6cdb94f3a1 | R | 884 | 27 | ret.summary <- list()
ret.jaccard.summary <- list()
ret.alternative <- list()
ret.jaccard.alternative <- list()
for(i.rep in 1:25){
prefix <- paste("simu_2d_", sprintf("%02d", i.rep), "/new/",
sep = "")
fn <- paste(prefix, "summary_table/summary_table_new5.rda", sep = "")
load(fn)
ret.summary... |
256028e6907cf09e2dfd4779361c4f0a7ffc316229a5ab067e189292beebfaa2 | R | 887 | 41 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(ArchR)
library(Seurat)
set.seed(666)
addArchRThreads(threads = 8)
addArchRGenome("hg38")
proj <- loadArchRProject(path = "Dracheva_ALS_FTD_latest_atac_anno_a... |
ec8890865f4131878dd62cb6db6b2d48049f390bd694d7ce5cbbc4c17f0ba895 | R | 894 | 32 | library(MixfMRI, quietly = TRUE)
library(EMCluster, quietly = TRUE)
# .FC.CT$algorithm <- "em"
# .FC.CT$model.X <- "V"
# .FC.CT$ignore.X <- TRUE
### Test toy1.
set.seed(1234)
X.gbd <- toy1$X.gbd
X.range <- apply(X.gbd, 2, range)
X.gbd <- t((t(X.gbd) - X.range[1,]) / (X.range[2,] - X.range[1,]))
PV.gbd <- toy1$PV.gbd
P... |
0242b33352310219c40486b06eec13cccf35705adee6cbc4a00c9566265406cf | R | 896 | 25 | toCPXMatrix <- function(Amat){
## if (is.null(Amat)) {
## return(NULL)
## } else if (is(Amat, "sparseMatrix")) {
Amat <- as(Amat, "dgCMatrix")
matbeg <- Amat@p
matcnt <- diff(c(Amat@p, length(Amat@x)))
matind <- Amat@i
matval <- Amat@x
## } else if (is(Amat, ... |
345d7475595fea6558c9765bd920e01b6ddee8a4aa8d11ba2eb244f9aa4541cc | R | 904 | 26 | files<-list.files(pattern = "txt")
#NAc
f<-read.table(files[5]);
d<-read.table(files[6])
plot((-1)*f$logFC,(-1)*d$logFC,xlab = "DEG:logFC", ylab="DEG's promoter: logFC",
pch=20,main = "NAc")
abline(h=0,lty=2,col="red"); abline(v=0,lty=2,col="red")
#DG
f<-read.table(files[3]);
d<-read.table(files[4])
plot((-1)*f$lo... |
31f59157c608bf8cd07f7329957ce2e9469ad5b0267634c823ecf42ada20ef23 | R | 907 | 34 | library(ArchR)
library(stringr)
marker = read.table('marker.txt',sep = '\t',header =T) ### marker list
proj = loadArchRProject('adult_Save-ProjAxolotl2') ## data
cor = read.csv('color.txt',sep = '\t',header =F) ## color list
library(dplyr)
anno = read.csv('annonation.csv',header =T) ## annotation list
anno = anno[,... |
2db5e76742104af8d89ad37d356cf3aee661b7c575c59aa28ed3c8e0e58a2b16 | R | 917 | 33 | ### SHELL> mpiexec -np 4 Rscript mpi_fclust2d.r
library(MixfMRI, quietly = TRUE)
library(EMCluster, quietly = TRUE)
library(pbdMPI, quietly = TRUE)
init()
.FC.CT$MPI.gbd <- TRUE
# .FC.CT$algorithm <- "em"
# .FC.CT$model.X <- "V"
# .FC.CT$ignore.X <- TRUE
set.seed(1234)
### Test toy1.
X.gbd <- toy1$X.gbd[, -3]
X.range... |
26d13b4d9b60aba127537e1a45ac5eb8b73141c8922ccc3731f0894ea70c2165 | R | 922 | 25 | # 00-paths.R
#
# Resolves dataFolder and outputFolder from environment variables, with
# platform-aware WSL/Windows switching and an in-repo fallback.
# Sourced by 01-set-up.R; also sourceable standalone by pipeline scripts
# that do not need the full setup (packages, functions, etc.).
#
# Sets: dataFolder, outputFolde... |
3c6a34e933f93f3c60ff7cae9b688c8297a633e927d29f9fd337a1bad8da5272 | R | 924 | 26 | # 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 post-QC seurat object
data_obj_sub <- readRDS("./seurat_objects/snRNA_cellBender_corrected_postQC_SeuratV4_object.rds"... |
8647e443b1fca339d957c1c090999c2d9d0dbd3909aa63b6724d38c598c3bf21 | R | 931 | 24 | # logging which server was used to run this script
system("echo \"Running this on ### $(hostname) ### \\(* w *)/\"")
# load library
library(tidyverse)
library(Seurat)
filtered_cells_obj <- readRDS("./seurat_objects/snRNA_cellBender_corrected_QC_filteredCells_scTransformed_SeuratV4_object.rds")
aibs_obj <- readRDS("/c... |
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