sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
44ee3356d938d1889cc81997f2b16ff1af9f44495f04130584496ae5ffbdff2f | R | 2,920 | 65 | #### load packages ####
targetPackages <- c('tidyverse','gtools','arrow')
newPackages <- targetPackages[!(targetPackages %in% installed.packages()[,"Package"])]
if(length(newPackages)) install.packages(newPackages, repos = "http://cran.us.r-project.org")
for(package in targetPackages) library(package, character.only = ... |
eb1e1f3d3f95c94802a4b269fae65dfc7fa5a8fe3ed38ba8b4321240c685feec | R | 2,921 | 112 | ######read files
suppressPackageStartupMessages({
library(MatrixGenerics)
library(Seurat)
library(dplyr)
library(SingleCellExperiment)
library(aricode)
library(mclust)
library(scater)
})
################################ Drop out ####################################
#drop out function
dropout_sampling <... |
246d6cda4ece85bbcedf91a8325c6f1dae633fe530266aa7e2bb977ebae43c67 | R | 2,951 | 63 | library(Seurat)
library(tidyverse)
library(parallel)
library(magrittr)
library(ggtree)
library(ape)
library(patchwork)
library(scrattch.hicat)
setwd("~/cortex/SnRNA/3_mergingDatasets//")
seu <- qs::qread("~/cortex/SnRNA/3_mergingDatasets/SnRNA_seurat.qs")
# seu <- qs::qread("~/cortex/SnRNA/1_SnRNA_preprocessing/SnRNA... |
33257639c2415bfcba6ec342a5bbfe1912d5e1d96b41e5bf918f6ecf69361e90 | R | 2,955 | 101 | ---
title: Data Processing Flow Charts
output:
rmarkdown::html_vignette:
toc_float: true
vignette: >
%\VignetteIndexEntry{Data Processing Flow Charts}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
Sys.setenv(LANGUAGE = "en")
library("sbcdata")
sb... |
e6b75a582d910371efa25584a1ece677243598664821dc0d26bbceceebf96351 | R | 2,958 | 94 | library(SingleCellExperiment)
library(scDHA)
library(aricode)
args<-commandArgs(TRUE)
print(args)
csv_root_path = args[1]
dataset_name = args[2]
save_path = args[3]
print(dataset_name)
#print(save_path)
# csv_root_path = "/home/yanhan/cjy/Single-Cell-Dataset/raw_rds/"
csv_count_path = paste(csv_root_path, dataset_nam... |
67e2382ef4d9923cc398f35c445144d19f36f52279a7ce825ce6844d5d1e1976 | R | 2,968 | 65 | library(tidyverse)
library(Seurat)
library(pvclust)
library(ggtree)
library(magrittr)
library(dendextend)
library(patchwork)
setwd("~/cortex/fig1/")
subclass_color <-c(AST = "#665C47", ENDO = "#604B47", ET = "#CEC823", CHANDELIER = "#E25691",
`L2-L3 IT LINC00507` = "#07D8D8", `L3-L4 IT RORB` = "#09... |
d437899cc72fa241d00b77773f5bf2fa6b289fa6ff2aa5e9507babe6d9af88ec | R | 2,975 | 109 | # Suppress R CMD check notes about NSE variables
utils::globalVariables(c(
"annoy.metric", "bind.feature", "bp_add", "bp_cum",
"dx", "dy", "gene", "label", "max_bp", "med", "name",
"nn.method", "padj", "pos", "qval", "strand", "type",
"x", "xmax", "xmin", "y", "ymax", "ymin"
))
# codes edited from tidyverse/R/... |
ca747a0b24584e50ec53cb07dda642be3a85304a7e827e5e5b8352995303be5c | R | 3,000 | 82 | # heatmaps
library(colorspace)
library(ggpattern)
# TODO, concatenate the data in the pipeline
data = data.frame()
i <- 0
for (experiment in c("ERN", "LRP", "MMN", "N170", "N2pc", "N400", "P3")){
i <- i + 1
tmp <- tar_read(eegnet_HLM_exp_emm_means, branches=i)[[1]]
# normalize to zero for each experiment to have... |
e58a8249a991b59826fbe247eaa43692584d3fa6ff76715fcd06eb33e5d6ae12 | R | 3,014 | 113 |
dnorm.mix = function(x,alpha,xi,tau,p){
n = length(p)
nu = c(0:(n-1))
mu = (1-alpha)*nu/2 + alpha + xi
sdt = sqrt(tau)
z = sapply(1:n,FUN=function(i){return(p[i]*dnorm(x,mean=mu[i],sd=sdt[i]))})
return(sum(z))
}
alpha = 0.322357
xi = -0.226514
tau = c(0.00515209,0.0135272,0.0107974,0.00887801,0.0121268,0.00... |
6f87d3d9ce157c50ac989b9e340df02a3768ca2e46f2a18f0faabd3b918b48e9 | R | 3,023 | 121 | library(purrr)
library(magrittr)
library(tidyverse)
library(Seurat)
library(harmony)
library(ape)
library(uwot)
library(ggtree)
library(treeio)
library(ggtree)
library(treeio)
# library(future)
setwd("~/cortex/SnRNA/3_mergingDatasets/")
qsFiles <- list.files(".", "merge.qs", full.names = T)
x = qsFiles[[1]]
datasets ... |
ed84efacab013d8e00c4c9bd150ae4aafac3ea74efc5d3008725a5cf24f3a76a | R | 3,062 | 95 | # R code to generate Fig3 fig suppl3 of the Platynereis connectome paper
# Gaspar Jekely 2023
# load natverse and other packages, some custom natverse functions and catmaid connectivity info
source("code/libraries_functions_and_CATMAID_conn.R")
# define a list of anatomical annotations to search for
annot_to_search <... |
8a74b2d20b8775e93ae679c86608ebd9e0429ca72d0595d6b912ab95738c6f57 | R | 3,069 | 117 | ---
title: "st_overlay"
output: html_notebook
---
Written by Aunoy Poddar
May 23rd, 2022
# Process the puncta quantified raw data
```{r eval=FALSE}
current_file <- rstudioapi::getActiveDocumentContext()$path
output_file <- stringr::str_replace(current_file, '.Rmd', '.R')
knitr::purl(current_file, output = output_file... |
bba789a5ac853575b941b0ba4b41766dc56cefa0bdca43c5a81599f71f12d255 | R | 3,076 | 84 |
library(DESeq2)
library(ggplot2)
library(viridis)
library(magrittr)
library(pheatmap)
library(DescTools)
library(pdfCluster)
library(RColorBrewer)
library(SummarizedExperiment)
start_time <- Sys.time()
OUT_DIR <- "/home/burkhart/Software/reticula/data/aim1/output/"
kmeans_maj_vote <- function(expr_matrix,k){
km_... |
9ad4da0a851cc5997712f2a20ad93c888981be9d7df966c60dae43bd2606f1c3 | R | 3,077 | 63 | ## Co-Binding according to scenicplus network ##
library(ggplot2)
library(Seurat)
library(UpSetR)
library(igraph)
library(rcartocolor)
library(ggvenn)
## load data:
eRegulon_md <- read.table("Processed_Objects/eRegulon_metadata_filtered.tsv", h = T, sep = "\t")
cfse_seurat <- readRDS("Processed_Objects/CFSE_sub.rds")... |
22c831d88fc8f477f4c82eb5b3581e4904705a3047ae2cca9d135350fe76492a | R | 3,090 | 81 | ## NFIB CUT&RUN ##
## heatmaps created using "fluff-heatmap" library:
##################################################
## Figure 3c:
#fluff heatmap -f peakCalling/MACS_set2/NFIB_R1R2_pvalue_peaks.narrowPeak -d $project/NFIB_R1R2.bw $project/H3K4me3_R1R2.bw $project2/filtered_E12_sort.bw $project2/filtered_E16_sort... |
33c9aa5730c143445f7c6a017b86221387a4199693525d30064b4b54968c2952 | R | 3,096 | 98 | library(catmaid)
library(tidyverse)
source("~/R/conn.R")
labels <- catmaid_get_label_stats(pid = pid)
gold <- labels %>%
filter(str_detect(labelName, "gold")) %>%
select(labelName, skeletonID)
neuropeptides <- c("ATO", "FMRFa", "FVa", "FVRIa", "leucokinin", "luqin", "MIP", "PDF", "proenkephalin", "RGWa", "RYa"... |
32138472225b054c7d95b887e6e58d0bb255b8fdcafa807febc5aae37b180965 | R | 3,113 | 82 | # READ IN DATA: ----------------------------------------------------------------
# Load CSV file of DESeq2 results, ordered by padj and containing gene symbol and entrez id's.
res_df <- read.csv(csv_deseq2_results)
# MAKE VOLCANO PLOT WITH SPECIFIC GENES LABELED: -------------------------------
# filter the deseq2 res... |
5945d7762b922bdb37531a83eb1ba1cec0f341ce367b21ee09a0eb06dd0ca57a | R | 3,134 | 72 | suppressMessages(library(Seurat))
suppressMessages(library(dplyr))
suppressMessages(library(tidyr))
suppressMessages(library(caTools))
suppressMessages(library(ROGUE))
suppressMessages(library(colorRamps))
suppressMessages(library(tidyverse))
#--------------------------------------------------------------
# Load own m... |
9fbff47ac45055571705dd60926a9476c0bee712a37ab85ec3216e01fbb080f8 | R | 3,134 | 85 | # READ IN DATA: ----------------------------------------------------------------
# Load CSV file of DESeq2 results, ordered by padj and containing gene symbol
# and entrez id's.
res_df <- read.csv(csv_deseq2_results)
# MAKE VOLCANO PLOT WITH SPECIFIC GENES LABELED: -------------------------------
# filter the deseq2... |
8ad55450d1e0c057264971790d2d68955f7181725ac8103caecd2e4e43822375 | R | 3,135 | 85 | # READ IN DATA: ----------------------------------------------------------------
# Load CSV file of DESeq2 results, ordered by padj and containing gene symbol
# and entrez id's.
res_df <- read.csv(csv_deseq2_results)
# MAKE VOLCANO PLOT WITH SPECIFIC GENES LABELED: -------------------------------
# filter the deseq2... |
abc31e47178567fe4958edc74204dd2e8546a293794a853f2586f62dfaaa3fdf | R | 3,151 | 136 | ######read files
suppressPackageStartupMessages({
library(SC3)
library(SingleCellExperiment)
library(scater)
library(aricode)
})
###########
#FACS data#
###########
#dataset <- 'Bladder'
#countspath <- paste0('~/R Scripts/rna_clustering/dataset/',dataset,'_counts.csv')
#labelspath <- paste0('~/R Scripts/rna_c... |
00c250e62d676a6cb74a720b5fd2386f7badd6df223795d5497df4945881159e | R | 3,154 | 122 | require(rphast)
require(ape)
require(dplyr)
require(parallel)
require(Biostrings)
require(ggpubr)
require(seqinr)
require(phangorn)
require(msa)
require(readr)
require(VennDiagram)
source('SCRIPTS/Functions.R')
args = commandArgs(trailingOnly = TRUE)
for (arg in args) {
split_arg <- strsplit(arg, "... |
8680e85a11d769b7e93d500c11d07827e90a920d823782076eb4fa27d0e76b06 | R | 3,170 | 83 | library(DESeq2)
combined.df <- readRDS("~/combined_df.Rds")
tissue.vec <- readRDS("~/tissue_vec.Rds")
datasource.vec <- readRDS("~/datasource_vec.Rds")
study.vec <- readRDS("~/study_vec.Rds")
#minimum shrinkage, leaving max() == integer max
scale.factor <- (.Machine$integer.max - 1) / max(combined.df)
combined.scaled... |
903271095e17a454504543e864b0e88d423b3f278c7318af2d8987d0c3389ce0 | R | 3,170 | 94 | ---
title: "Simple FLIC Output"
author: "Kayla Audette"
date: "2023-11-06"
output: html_document
---
### 1. R Environment
- **Setting Up Environment:**
- The first chunk configures the presentation options for the R code.
- It hides the code and result outputs, such as messages and warnings.
- The workspace is ... |
7fe6e53d3b6c61625fcf451467923b8fdd0b183f1e7b70c265e3036fec3d902d | R | 3,174 | 78 | ## GRN description ##
library(ChIPseeker)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
txdb <- TxDb.Mmusculus.UCSC.mm10.knownGene
library(clusterProfiler)
library(EnsDb.Mmusculus.v79)
edb <- EnsDb.Mmusculus.v79
seqlevelsStyle(edb) <- "UCSC"
library(ggvenn)
## SF10 b-d
eRegulon_md <- read.table("Processed_Objects/eRegu... |
63f294d3e1eac87db134853c2665c7c2069e9520e98c060ee110d172bfed719d | R | 3,179 | 93 | ## use the data from Mitchell et al. and generate pseudo-bulks
library(ggplot2)
library(ggpubr)
library(phangorn)
library(RRphylo)
folders <- list.files("./Published_data/Mitchell_et_al/", pattern = "00")
folders <- setdiff(folders, "KX007") # no data available
snvs <- list()
for(i in folders){
mut.file <- li... |
ff2a8aaa899033c7a76f28869862940c76b305d2fa96d60e4d7d0cdb46bf88c8 | R | 3,183 | 98 | library(magrittr)
TCGA_OUT_DIR <- "/home/jgburk/PycharmProjects/reticula/data/tcga/output/"
TCGA_IN_DIR <- "/home/jgburk/PycharmProjects/reticula/data/tcga/input/"
X <- readRDS(paste(TCGA_OUT_DIR, "zsl_tcga_rxn_pca_nls.Rds", sep = ""))
Y <- readRDS(paste(TCGA_OUT_DIR,"zsl_tcga_tissue_vec_train.Rds",sep=""))
tissue_a... |
2f4b5686ec7298495531804ea3b734f93718db210d702d15554fd5bf0a3d3eca | R | 3,189 | 87 | # READ DDS OBJECT FOLLOWING DESEQ2 ANALYSIS FROM RDS FILE: ---------------------
dds <- readRDS(rds_deseq2_results)
# Store results in res
res <- results(dds)
# ANNOTATE DESEQ2 RESULTS WITH GENE SYMBOLS AND ENTREZ IDS: --------------------
ensembl_ids <- rownames(res)
# annotate with gene symobols using org.Mm.eg.db ... |
9d08af9c95e84c6503d117770e83cc94c559c76dfd1bac63421d77717583d8c8 | R | 3,202 | 97 | #' @export
setGeneric("sc3", signature = "object", function(object, ks = NULL,
gene_filter = TRUE, pct_dropout_min = 10, pct_dropout_max = 90,
d_region_min = 0.04, d_region_max = 0.07, svm_num_cells = NULL,
svm_train_inds = NULL, svm_max = 5000, n_cores = NULL, kmeans_nstart = NULL,
... |
1bb833fc958f03c78779f03d47635f50e9b089db763889e9c480a763450422b0 | R | 3,208 | 64 | library(qs)
library(parallel)
library(magrittr)
library(tidyverse)
library(Seurat)
library(org.Hs.eg.db)
library(rrvgo)
setwd("~/data/STEREO/AnalysisPlot/")
seu <- qread("./Seu_merge.qs")
genes <- read.csv("../../ensemble93gtf_rmXY.csv")
seu <- seu[rownames(seu) %in% genes$gene_name,]
colorPallete <- c(ggsci::pal_aaa... |
9f75b07f4a8764ee8793b85f2ead89ae875546aae8e0bd591dad4337fbce814c | R | 3,221 | 82 | # READ DESEQ2 RESULTS RDS FILE: ----------------------------------------
dds <- readRDS(rds_deseq2_results)
# STORE DESEQ2 RESULTS: --------------------------------------------------------
res <- results(dds)
# ANNOTATE RESULTS WITH GENE SYMBOLS AND ENTREZ IDS: ---------------------------
ensembl_ids <- rownames(res)... |
c3e0727df6b43245f18a19b45d53d2e30db0955f04005cc8884391a0c45a7558 | R | 3,221 | 73 | #install.packages
library(Seurat)
library(ggplot2)
library(DoubletFinder)
library(dplyr)
library(ggplot2)
library(cowplot)
library(reshape2)
library(MAST)
setwd("/athena/ganlab/scratch/lif4001/Mouse_pgrn_mertk/integration_with_Axl")
# remove cluster 14 (no significant marker genes), cluster 15 and 16 (doublets)
PGRN <... |
10e06af086c927e7b42820b8b06f22bb7304b13f1bf049e0c1e4cb836f454175 | R | 3,225 | 112 | # single-cell analysis package
library(Seurat)
# plotting and data science packages
library(tidyverse)
library(cowplot)
library(patchwork)
# co-expression network analysis packages:
library(WGCNA)
library(hdWGCNA)
# using the cowplot theme for ggplot
theme_set(theme_cowplot())
# set random seed for reproducibility
... |
d9f35060adc90896dfa98e53cbad2ad09645c772edbd6c28c36a3ab2ce6b94c4 | R | 3,239 | 112 | library(tidyverse)
library(qs)
library(parallel)
library(BPCells)
library(SeuratObject)
library(SeuratDisk)
library(magrittr)
library(Matrix)
library(RANN)
devtools::load_all("~/seurat/")
setwd("~/cortex/STEREO/GEM/")
cortexMeta <- read.delim("../cortex") %>% column_to_rownames("chip")
qsFiles <- list.files("bin200/"... |
3706fc76a1a36bf2f1555ecd792be283f87dcfb0feac6eb22d63677a85f2fc1a | R | 3,255 | 112 |
#load data######
#summarized AUCell score of GO terms in each one of twenty tumor bins
load('~/Axonal-Injury/RData/FigS2h-o/LIST_MEDIAN.RData')
#summarized AUCell score of GO terms in each one of twenty tumor bins (control data, pseudo spot, see Method)
load('~/Axonal-Injury/RData/FigS2h-o/LIST_MEDIAN_CONTROL.RDat... |
15fd7f635ea98d0fe41bb7ab7d678f6c773f35af76159345492a7021456b05b7 | R | 3,256 | 81 | # required external packages for SIMLR
library("Matrix")
library("parallel")
# load the igraph package to compute the NMI
library("igraph")
# load the palettes for the plots
library(grDevices)
# load the SIMLR R package
source("./method/SIMLR/R/SIMLR.R")
source("./method/SIMLR/R/compute.multiple.kernel.R")
source("... |
07cb38a5754e693e3e4c7bcf16f8f1f482fec9d0c21e57d21d978bfa92138aa4 | R | 3,257 | 74 | # R script to download selected samples
# Copy code and run on a local machine to initiate download
# Check for dependencies and install if missing
packages <- c("rhdf5")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
print("Install required packages")
source("https://bioconductor.org/bio... |
b022616a6842e57194308aa287e7b2d5a5ba2672017cff793002bc37168a163f | R | 3,259 | 84 | ---
output: html_document
editor_options:
chunk_output_type: console
---
#Libraries & Source Files
```{r load libraries and source files, message=FALSE}
library(tidyverse)
library(Seurat)
library(viridis)
source("~/OHSU Dropbox/Saunders Lab's shared workspace/arpy/manuscripts/2023_Thai2P4M_FeigeYoung/ms_analyses/1... |
9af37e7bd89abb9e3d1cf7a1c390b35acee42597104040322b02cc73ba687b1d | R | 3,287 | 85 | # Run 'analysis/03_deseq2_e17.R' and 'tables/scripts/tableS6.R' if you haven't already
# READ DESEQ2 RESULTS RDS FILE: ------------------------------------------------
dds <- readRDS(rds_deseq2_results)
# STORE DESEQ2 RESULTS: --------------------------------------------------------
res <- results(dds)
# ANNOTATE RE... |
c91346ec1e67d9355429040c389ae6b4bf7b34552a6e226f987ff474a6bfb5b1 | R | 3,298 | 74 | # R script to download selected samples
# Copy code and run on a local machine to initiate download
# Check for dependencies and install if missing
packages <- c("rhdf5")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
print("Install required packages")
source("https://bioconductor.org/bio... |
b7db87e67d690ee595a0a4e1973c018a03e18fc088c313705b1ef7e8a9cc338a | R | 3,314 | 94 |
```{r, echo=FALSE, message=FALSE, include=FALSE}
if (!requireNamespace("pacman")) install.packages("pacman")
packages_cran <- c("here")
pacman::p_load(char = packages_cran)
if (basename(here::here()) == "zoo"){
path_root = here::here("zoo-bids")
} else {
path_root = here::here()
}
```
## Conversion of data to the... |
35ea31deda2b6cf1a459015a7b986256b34f79e8d9e9fa7a90d7fc4933f51bcb | R | 3,315 | 49 | # returns diffTable containing top two most up-/down-regulated genes based on log2FC
getDiffTop <- function (complete, alpha = 0.05)
{
diff.table <- list();
for (name in names(complete)) {
complete.name <- complete[[name]]
sample.ref <- gsub("[^0-9a-zA-Z]","",strsplit(name,'vs')[[1]][2])
sample.treat <... |
44515ddb324776ebc2478b6ac8df35641653c5576d68624171667bf2cde38463 | R | 3,315 | 87 | # READ DESEQ2 RESULTS CSV FILE: ----------------------------------------
dds <- readRDS(rds_deseq2_results_e17)
# STORE DESEQ2 RESULTS: --------------------------------------------------------
res <- results(dds)
# ANNOTATE RESULTS WITH GENE SYMBOLS AND ENTREZ IDS: ---------------------------
ensembl_ids <- rownames(... |
0ac9881528a2c38ffdaba3b925c6020263b2806360737c949b42f765b36b50fd | R | 3,319 | 110 | library(plyr)
library(dplyr)
library(tidyverse)
library(tidyr)
library(Seurat)
library(patchwork)
library(Matrix.utils)
library(ggpubr)
library(reshape2)
library(data.table)
library(rio)
library(scran)
library(scater)
library(SingleCellExperiment)
library(EnsDb.Hsapiens.v86)
library(edgeR)
library(DESeq... |
9c4547fc72ab0c278398d6438393f83c7905b3487ef021b4aa4ff005f832ea1d | R | 3,329 | 80 | library(Seurat)
library(tidyverse)
library(parallel)
devtools::load_all("~/ClusterGVis-main/")
setwd("~/cortex/fig3")
merge_seu <- readRDS("../SnRNA/SnRNA_seurat.RDS")
IT_seu <- merge_seu[,merge_seu$subclass %in% c("L2-L3 IT LINC00507", "L3-L4 IT RORB", "L4-L5 IT RORB", "L6 IT") ]
IT_markers <- mclapply(IT_seu$subcla... |
a08399aee5f278d9df81b61f70a9a1a5b13e2622bc3695adfec61763555396ad | R | 3,329 | 71 | #' Function to transform fdr into scores according to log-likelihood ratio between the true positives and the false positivies and/or after controlling false discovery rate
#'
#' \code{oFDRscore} is supposed to take as input a vector of fdr, which are transformed into scores according to log-likelihood ratio between th... |
f1829869064a6425f9282ba00f2e8bc5972b4ab1e1c12c7b8edfb0848cdd8414 | R | 3,345 | 93 | library(tidyverse)
library(qs)
library(parallel)
library(magrittr)
library(RANN)
library(ggridges)
library(dendextend)
library(ggpubr)
devtools::load_all("~/spacexr-master/")
setwd("~/cortex/STEREO/2_Deconvolution_and_QC/")
chipList <- read.delim("~/cortex/STEREO/cortexMeta.txt") %>% {setNames(nm = .$chip,.$region)}
... |
d22100044822d13d4b8d6611e0c426793ae0eabfec266da4039897be368f0bf4 | R | 3,346 | 100 | #This code was used to generate the full connectivity matrix of the 3 day old Platynereis larva described in Veraszto et al. 2021
#Gaspar Jekely 2021 Feb
rm(list = ls(all.names = TRUE)) #will clear all objects includes hidden objects.
gc() #free up memrory and report the memory usage.
Sys.setenv('R_MAX_VSIZE'=80000000... |
09664b07dc2d0f65481063748787aba3ce67c33569808d182c719c4b49e155b8 | R | 3,356 | 87 | # modified 2021/02/02 by CT to determine nsub and avoid error in vst
PCAPlot <- function (object, group=NULL,counts.trans,varInt,typeTrans, ntop = min(500, nrow(counts.trans)),
col, batch=NULL,outfile = TRUE,batchRem=FALSE)
{
if (typeTrans == "VST") {
# calculate the number of rows with counts... |
9868d2eb21226a83bea19bc3a6c56e050769ffc1a3513e00a3d237766beaf045 | R | 3,368 | 77 | library(qs)
library(tidyverse)
library(Seurat)
library(magrittr)
setwd("~/cortex/fig4/")
sst <- qread("sstRNA.qs")
Idents(sst) <- "depth_cluster"
geneID_name <- read_csv("../SnRNA/1_SnRNA_preprocessing/gene_kept.csv") %>% {setNames(object = .$gene_uni,nm = .$gene_id)}
clusterMarkers <- FindAllMarkers(sst,only.pos = T)... |
57efdf717adf18e8ce4ab6b6c6689678c121f4af98d28a79090d5df8d600c980 | R | 3,381 | 88 | library(org.Hs.eg.db)
library(Seurat)
library(magrittr)
library(tidyverse)
library(clusterProfiler)
library(enrichR)
setwd("~/cortex/figS1-6/")
devtools::load_all("~/ClusterGVis-main/")
# add cell type
geneid_name <- read.csv("../SnRNA/1_SnRNA_preprocessing/gene_kept.csv") %>% {setNames(.$gene_name,.$gene_id)}
region... |
63cb224560a952f8af38737bbb4af340d3574b31cd38f0e60303da7a61636887 | R | 3,400 | 77 | IN_DIR <- "/home/burkhart/Software/reticula/data/aim2/input/"
PWAY_EDGE_DIR <- paste(IN_DIR,"PathwayHierarchyEdgeWeights/",sep="")
ALPHA <- 0.05
tissue2idx.df <- data.frame(read.table(paste(IN_DIR,"pathway_hierarchy_inverted_targets.txt",sep=""),
stringsAsFactors = FALSE),
... |
bf216c42f422b2340130eb0ca55f0997a2fdedfbd34fefd45d34e69bb91d4233 | R | 3,402 | 65 | tcrGroupsProj <- function(pseud.coord.lst, obj.sc, obj.sp.lst) {
obj <- obj.sc
obj.tcr <- tcrSubgroup(obj)
tcr.subtypes <- c("TRA-TRB+TRD+", "TRA+TRB+TRD+", "TRA-TRB+TRD-", "TRA+TRB+TRD-")
res.lst <- lapply(pseud.coord.lst, function(xx) {
yy.names <- gsub("SC_", "", xx$sc)
lapply(tcr.su... |
ae3a36dfe3fb4c2033edb4397f3848d48e02da5656c8f3353291b7c104fbcd51 | R | 3,417 | 102 | ---
title: "Seurat Microglia Basics Seurat Workflow"
author: "Arpy"
date: '2024-11-12'
output: html_document
---
# Adapted code from K. Young's 7_astrocytes.rmd for the 2P4M project
G.Chin 06/21/24
```{r}
library(tidyverse)
library(Seurat)
library(Libra)
```
#0. Data Load
```{r load data, echo = F}
main.path <- "/Us... |
55c334116299bff3bd30b459dc4ce2737ef2116472572fdcc861b016ff965699 | R | 3,419 | 103 | # archive functions
luckfps <- data.frame(
experiment = c('ERN', 'LRP', 'MMN', 'N170', 'N2pc', 'N400', 'P3'),
emc = c('ica', 'ica', 'ica', 'ica', 'ica', 'ica', 'ica'),
mac = c('ica', 'ica', 'ica', 'ica', 'ica', 'ica', 'ica'),
lpf = c('None', 'None', 'None', 'None', 'None', 'None', 'None'),
hpf = c('0.1', '0.... |
ac877d1a34b12025ea009643e02e87beddc1d56e8f3da93734a84adc56afd264 | R | 3,423 | 104 | # READ DDS OBJECT FOLLOWING DESEQ2 ANALYSIS FROM RDS FILE: ---------------------
dds <- readRDS(rds_deseq2_results)
res <- results(dds)
# ANNOTATE RESULTS WITH GENE SYMBOLS AND ENTREZ IDS: ---------------------------
ensembl_ids <- rownames(res)
# annotate with gene symobols using org.Mm.eg.db package
res$symbol <- m... |
29944c8dcba1e9a3d17725662d9f0dd89c102fdb68781ed9df7629fe5a77dabd | R | 3,433 | 136 | ###############################################################################
## Please source the `2-ukg.R` first (needed for labdesc etc.) if the dataset
## should be regenerated.
###############################################################################
library("data.table")
devtools::load_all()
## read out... |
1e50dd795afee4aca66f29ff34a78dfad7a3004225b00419d10521a1d6cb5e28 | R | 3,441 | 110 | library(plyr)
library(dplyr)
library(tidyverse)
library(tidyr)
library(Seurat)
library(patchwork)
library(Matrix.utils)
library(ggpubr)
library(reshape2)
library(data.table)
library(rio)
library(scran)
library(scater)
library(SingleCellExperiment)
library(EnsDb.Hsapiens.v86)
library(edgeR)
library(DESeq... |
f8b519a2f575c327b83b4c0425af579f8243726075474d829fc619004cf1a452 | R | 3,460 | 90 | #' Function to create a sparse matrix for an input file with three columns
#'
#' \code{oSparseMatrix} is supposed to create a sparse matrix for an input file with three columns.
#'
#' @param input.file an input file containing three columns: 1st column for rows, 2nd for columns, and 3rd for numeric values. Alternativel... |
cfe3dea69ff24076c4e5d4b12a7dda3ccf0d777bc3cb66ab45f3a79617e0d5fa | R | 3,477 | 111 | # code to generate synapses Fig supplement of the Platynereis 3d connectome paper
# Sanja Jasek & Gaspar Jekely 2024
source("code/Natverse_functions_and_conn.R")
dir.create("synapse_tiff_stacks")
# get all synapses from CATMAID
all_syn_connectors <- catmaid_fetch(
path = paste(pid, "/connectors/", sep = ""),
bod... |
145f417d2220c679269b4673725febfe79d810f2a1532343ecaf3a86b074f7ef | R | 3,490 | 104 | ## Standard import & preliminary analysis script for multiple results files from ImageJ
##
##
## Results in .csv-format, additional Group-identifying txt-file (Groups.txt)
##
## best used in an RStudio Project in the results-folder
##
##
## requires tidyverse, beeswarm and vroom packages
##
## example: Filopodia densit... |
ce53feadefdde4cb41c3d200eb96548ba3ac46dfe47f42e05ba527b36e447ce3 | R | 3,507 | 99 | library(parallel)
library(magrittr)
library(tidyverse)
library(rtracklayer)
library(anndataR)
library(Seurat)
setwd("~/cortex/SnRNA/2_codePreprocessingExternalData")
gtf <- readRDS("../1_SnRNA_preprocessing/geneSym_to_geneID.RDS")
files <- list.files(".","_subclass.h5ad")
subclasses <- files %>% str_remove("_subcl... |
d8bd7ae9d4f0e6cac8cd58729adc10cc4d2c2b3bf47ec45e41da0a62e993bfe5 | R | 3,529 | 103 | ## use the data from Fabre et al. and generate pseudo-bulks
library(ggplot2)
library(ggpubr)
library(phangorn)
library(RRphylo)
library(cgwtools)
library(phytools)
source("Simulated_data/Tree_post_processing.R") ## source modalities to extract information from trees
folders <- list.files("./Published_data/Fabre_et_al... |
cf02e37e234a577461691a6f13681710fcadc767a2e6070b4d8882f4535d62d8 | R | 3,581 | 99 |
# List of required libraries
required_libraries <- c("ggplot2", "tidyr", "forcats", "tidytext", "dplyr")
# Check if each library is installed; if not, install it
for (lib in required_libraries) {
if (!requireNamespace(lib, quietly = TRUE)) {
install.packages(lib)
}
}
# Load the libraries
lapply(required_libr... |
fee8dc8e2d1de66c61f904ae3d0d9a6f6ce869d37a4ca88e1ee3a4f0186c3b2f | R | 3,585 | 56 | #' Test for Multiplicative Batch Effects
#'
#' \code{multTest} function will test for multiplicative batch effects in the residuals for each feature after fitting a linear mixed effects model. Uses Fligner-Killeen method for significance testing. Data should be in "long" format. Depends on \code{lme4} package.
#' @par... |
d8b3e8bb241560cfdf90c93eeed82d921eb4f138964cf484ea9aa445aaf2c0f4 | R | 3,616 | 89 | library(qs)
library(tidyverse)
library(vegan)
library(ggrepel)
library(cowplot)
library(ggh4x)
library(magrittr)
setwd("~/data/STEREO//AnalysisPlot/")
output_n = "."
region_color <- c(FPPFC = "#3F4587", DLPFC = "#8562AA", VLPFC = "#EC8561", M1 = "#B97CB5",
S1 = "#D43046", S1E = "#F0592B", PoCG = "... |
29626cec021c8c7d660a817987e25e13d4c5e210a5dfdd0f100de107f2c15110 | R | 3,701 | 131 | ###### load observed data
## specify the VAFs at which model and data are compared; min.vaf must be given in the Run_model.script or defaults to 0.05
if(!exists("min.vaf")){
min.vaf <- 0.05
}
## should the sensitivity model be used?
if(!exists("use.sensitivity")){
use.sensitivity <- T
}
## what lower limit for the ... |
9a2809b9b5d26f5ac65a86fc43e97297a68c0e61803b03b5a9d38ce866e12e18 | R | 3,701 | 76 | ---
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-"
)
# Please put your title here to include it in the file below.
Title <- "Whole-body connectome of a segmented ... |
58748b24d9532f85ede8307ad88831d7e3fe535a28613830dae3b2993bc2340a | R | 3,707 | 99 | require(dplyr)
require(reticulate)
#-----------------------------------------------------------------
# Load own modules
source("modules/utils.R")
source("modules/global_params.R")
source("modules/seurat_methods.R")
source("modules/visualization.R")
source("modules/findMedullaClusters.R")
source("modules/trajectory_m... |
7d0cc1c8ee5ca5112df94e9a562c38965ca1a97aae890ec50ea456fea05e4e0e | R | 3,707 | 164 | ---
title: "prestate_LMM"
output: html_document
date: '2023-10-10'
---
#load libraries
```{r}
library(dplyr)
library('lme4')
library('lmerTest')
library('arrow')
library('gtsummary')
library('emmeans')
library('effectsize')
library('olsrr')
library('ARTool')
```
#load data from cardiac
```{r}
#df<-read.csv('/Volumes/... |
52b8b0817702f76bf3443755ecc2bd2d22b6be6823d3f5831430f16aa1943d76 | R | 3,709 | 101 |
library(patchwork)
# sandbox r2/aic new plot version
#
test <- tar_read(r2aic_table)
test2 <- test %>% filter(metric %in% c("R2", "AIC")) %>%
# capitalize interactions
mutate(interactions = ifelse(interactions == "false", "Absent", interactions)) %>%
mutate(interactions = ifelse(interactions == "true", "Pre... |
2d26158c768fca4860a514f5bb5da194d614404fb041a5e361e4f530e56e3557 | R | 3,729 | 129 | ---
title: "Prepare GWAS summstats"
author: X Shen
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: github_document
---
## **Annotate CHR:BP format to RS format**
------------------------------------------------------------------------
### **Download annotation file**
Check [this requiry](https://... |
7e827f17c08d1972b8778efb9ebe48e68bde7e6ba26d682ab63f6bf8ed4a1af5 | R | 3,744 | 103 | library(DESeq2)
library(ggplot2)
library(magrittr)
library(ggfortify)
library(SummarizedExperiment)
start_time <- Sys.time()
IN_DIR <- "/home/burkhart/Software/reticula/data/aim1/input/"
OUT_DIR <- "/home/burkhart/Software/reticula/data/aim1/output/"
gtex_tissue_detail.vec <- readRDS(paste(OUT_DIR,"gtex_tissue_detai... |
0172a6973527698a540fba982790e2464d874f96089e5533d639fa1e928c1ec5 | R | 3,747 | 76 | #' Normalize input data matrix
#'
#' Mean centers each column of an input data matrix so that it has a mean of zero.
#' Scales the entire matrix so that the largest absolute of the centered matrix is equal to unity.
#'
#' @param X matrix; Input data matrix with rows as observations and columns as variables/dimensions.
... |
1fd7570325658881cb5cfe6efddd95bc4985a659fc610a97b5d79c798793af63 | R | 3,774 | 81 | set.seed(88888888)
library(magrittr)
library(ggplot2)
OUT_DIR <- "/home/burkhart/Software/reticula/data/aim1/output/"
toi_summary.df <- readRDS(file=paste(OUT_DIR,"toi_summary_df.Rds",sep="")) # reaction accuracy X tissue
vst_count.mtx <- as.matrix(readRDS(file=paste(OUT_DIR,"vst_count_mtx_train.Rds",sep=""))) # tran... |
e0aa6532ffaf2b031f066e812c75ed628a9e4793d4c7b5d432f240d2d1da90d7 | R | 3,784 | 130 | # required external packages for SIMLR
library("Matrix")
library("parallel")
# load the igraph package to compute the NMI
library("igraph")
# load the palettes for the plots
library(grDevices)
library(aricode)
# load the SIMLR R package
source("./method/SIMLR/R/SIMLR.R")
source("./method/SIMLR/R/compute.multiple.ke... |
9c96a780f5f69f911e33bb65d99615494cd82000e57c89a6f5ac3b0978ea2710 | R | 3,794 | 132 | #load deconvolution resultst
LIST_DECON<-readRDS('/home/clustor2/ma/w/wt215/Axonal-Injury/RData/LIST_DECON_GITHUB.rds')
#load myelin labels#####
load('/home/clustor2/ma/w/wt215/PROJECT_ST/R/LABEL_MYELIN.RData')
spots_myelinhigh<-names(LABEL_MYELIN)[which(LABEL_MYELIN=='Myelin high')]
spots_myelinlow<-names(LABEL_MYE... |
542f53617837631d45d6e08fe39899543880fae0a7f4ac1ed1c132ee88abac98 | R | 3,801 | 92 | args = commandArgs(TRUE)
TUMOURNAME = toString(args[1])
RUN_DIR = toString(args[2])
PRESET_RHO = as.numeric(args[3])
PRESET_PSI = as.numeric(args[4])
library(Battenberg)
###############################################################################
# 2015-05-01
# A pure R Battenberg v2.0.0 SNP6 refitting pipeline im... |
997aecc0a0f55997f055cf06dd072b0131d7a7f0a06b7007efcea91b99c28f92 | R | 3,813 | 49 | # R script to download selected samples
# Copy code and run on a local machine to initiate download
# Check for dependencies and install if missing
packages <- c("rhdf5")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
print("Install required packages")
source("https://bioconductor.org/bio... |
3d5a75209525c5611c4b567a12ba00e66bd66d5ea4bf2574d1da13e27da3a279 | R | 3,816 | 85 | library(qs)
library(parallel)
library(magrittr)
library(tidyverse)
library(org.Hs.eg.db)
setwd("~/cortex/figS1-6/")
devtools::load_all("~/ClusterGVis-main/")
devtools::load_all("~/seurat/")
seu <- qread("../STEREO/st_domain_seu_44slides.qs")
genes <- read.csv("../STEREO/ensemble93gtf_rmXY.csv")
colorPallete <- c(g... |
ab1bada40d2af21bc065e85a29f90ae9932eb8b39e4815dc23cf48f442019e26 | R | 3,823 | 57 | #' Test for Additive Batch Effects
#'
#' \code{addTest} function will test for additive batch effects in the residuals for each feature after fitting a linear mixed effects model. Uses Kenward-Roger method for significance testing. Data should be in "long" format. Depends on \code{lme4} and \code{pbkrtest} packages.
#... |
c0e0babddafc70b414dc1f3da14198112aee2ccbcf658dfa7ceea4ca28d85aed | R | 3,846 | 105 | # Script to generate reference models. The reference models are used to test backward compatibility
# of saved model files from XGBoost version 0.90 and 1.0.x.
library(xgboost)
library(Matrix)
set.seed(0)
metadata <- list(
kRounds = 2,
kRows = 1000,
kCols = 4,
kForests = 2,
kMaxDepth = 2,
kClasses = 3
)
X ... |
4bb76ac1c4fadac09c7ab4637db91ca10e41c06d0dedcd25905f75581ce76f63 | R | 3,848 | 135 | # required external packages for SIMLR large scale
library("Rcpp")
library("Matrix")
library("pracma")
library("RcppAnnoy")
library("RSpectra")
# load the igraph package to compute the NMI
library("igraph")
# load the palettes for the plots
library(grDevices)
library(aricode)
# load the SIMLR R package
source('./m... |
953659a5b502cf82b8bbdb82039832203fa9e09d450bb11f11f40a56023e7f27 | R | 3,863 | 87 | # Fig4_cellchat_analysis.R
# Load required libraries
suppressPackageStartupMessages({
library(CellChat)
library(patchwork)
library(Seurat)
library(SeuratObject)
})
options(stringsAsFactors = FALSE)
future::plan("multisession", workers = 4)
# Define helper function
run_cellchat_pipeline <- function(seurat_obj... |
ba068058d658f4a6173bcde65316eb2d68d78993ffa40a3ac1e59a218b4457de | R | 3,873 | 127 | ---
title: Analysis Script for Study 3 of 'Perceived community alignment increases information
sharing'
author: "Elisa Baek"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```
This is the custom code that was used for the main a... |
947e0f3572e986a02d0ca22fae142a45fd9c51c7bd6af84df83e067bb9b7fc19 | R | 3,918 | 92 | ############################################################################################################################################
### load libraries
library(openxlsx)
library(wesanderson)
library(bedr)
library(ggplot2); theme_set(theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), t... |
70d939e60cea89e44f802cc7ec3ac22f06e05151a689706e2394d06a2432761b | R | 3,925 | 83 | suppressMessages(library(Seurat))
suppressMessages(library(dplyr))
suppressMessages(library(tidyr))
suppressMessages(library(caTools))
suppressMessages(library(future))
suppressMessages(library(DoubletFinder))
#--------------------------------------------------------------
# Load own modules
source('modules/utils.R')... |
5a2bd0aa3b572660ca701075ddf4cd781e2d593346ea925a536bcc44cc964da4 | R | 3,963 | 93 |
# setting directory
local_dir = "~/Downloads"
wd = "/RNA_seq_bioinfo_analysis"
source(paste0(local_dir, wd, "/wd_and_libraries.R"))
# The following code is performing a DE analysis using the DESeq2 package in R.
# The analysis is based on the Negative Binomial distribution,
# which is a common choice for modeling ... |
49f20fd5460cb609390f564de52002042b5fbb5bbdf6cdb7e9b8193933e46e14 | R | 3,971 | 118 |
#### merge the neigboring bins whose log2 copy ratio are very close
bin.merge.chr = function(segs,min_diff=0.1,adjust=0)
{
copy.diff = diff(segs$log2.copyRatio)
indx = which.min(abs(copy.diff))
seg.tmp = segs
min.diff.tmp = c()
k = 0
while(nrow(seg.t... |
6c0fe5c638dadeadeed8239de3b39d8b3d9ad32e9e84f4d7cf75a8a22cf5432b | R | 4,007 | 96 | context("Models from previous versions of XGBoost can be loaded")
metadata <- list(
kRounds = 2,
kRows = 1000,
kCols = 4,
kForests = 2,
kMaxDepth = 2,
kClasses = 3
)
run_model_param_check <- function(config) {
testthat::expect_equal(config$learner$learner_model_param$num_feature, '4')
testthat::expect... |
e9222014120e9fd51e135d470ee64e268cd9bf9e05445e59b63b1206d62b2158 | R | 4,073 | 119 | #' Function to combine networks from a list of igraph objects
#'
#' \code{oCombineNet} is supposed to combine networks from a list of igraph objects.
#'
#' @param list_ig a list of "igraph" objects or a "igraph" object
#' @param combineBy how to resolve edges from a list of "igraph" objects. It can be "intersect" for i... |
9d219e3b8504de8d6be7df911665860f9b2417297fb1e524fe692b51ca215c14 | R | 4,080 | 104 | library(Seurat)
library(CellTrek)
library(CARD)
require(future)
options(future.globals.maxSize = 500000 * 1024^2)
plan("multiprocess", workers = 20)
#' --------------------------------------------------------------
#' By Seurat
projBySeurat <- function(obj.sc, obj.st.lst) {
ovp.genes <- intersect(rownames(obj.sc... |
6c87d10e690e43d41515f4ef44cba3d8547cbdf1851fdb9e60e54d5c7a18c806 | R | 4,101 | 171 |
## try plot with significances
data <- tar_read(marginal_means)
sign <- tar_read(stats_all)
library(ggplot2)
library(ggdist)
library(ggsignif)
#mean_accuracy <- aggregate(accuracy ~ experiment, data, mean)
# https://rpubs.com/rana2hin/raincloud
ggplot(data, aes(x = factor, y = accuracy)) +
# add half-violin f... |
a6d889a903390f740e20c0c02395f711e5d8e3d26ad6a3ec2cb22f944a3c2b10 | R | 4,110 | 97 | suppressMessages(library(Seurat))
suppressMessages(library(dplyr))
suppressMessages(library(tidyr))
suppressMessages(library(caTools))
suppressMessages(library(colorRamps))
suppressMessages(library(tidyverse))
suppressMessages(library(writexl))
suppressMessages(library(clusterProfiler))
suppressMessages(library(reshape... |
5ce4880f38db74819b7fe2ef522857453f33c4510512b86f855e44d18c852212 | R | 4,121 | 64 | ---
title: "Introduction to Tocky and Data Preprocessing Methods"
author: "Dr. Masahiro Ono"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
bibliography: TockyPrep.bib
link-citations: TRUE
vignette: >
%\VignetteIndexEntry{Introduction to Tocky and Data Preprocessing Methods}
%\VignetteEngine{knitr::rmarkdo... |
cb0a8b82261bb054e121843b24a4c3371926fa51869af976a646e7786c2e161d | R | 4,135 | 119 | #' Function to obtain a projected graph from a bipartitle graph
#'
#' \code{oBiproject} is supposed to obtain a projected graph from a bipartitle graph.
#'
#' @param g an object of class "igraph" (or "graphNEL") for a bipartitel graph with a 'type' node attribute
#' @param verbose logical to indicate whether the messag... |
9935fcd6ed844a8198ef2233d1a60e16cab77bc6142ad6612dab01b8139fa46b | R | 4,149 | 140 | load('~/RData/PSEDUOBULK_MYELINHIGH.RData')
load('~/RData/MYELIN_PDX_LATE.RData')
load('~/RData/MYELIN_PDX_EARLY.RData')
#all genes
allgenes<-Reduce(intersect,list(
results_NSG$gene,results_PDX$gene,results_PDX_E$gene
))
#Select DE genes
#NSG
de_nsg<-results_NSG$gene[which(abs(results_NSG$log2FoldChange)>=0.5 & re... |
8ff59eb55f15e8eec574d28cf739ff848b1fcdcaef245bc260b26c145d23ed55 | R | 4,175 | 174 | #' Convenience function that orders edges or squares
#' @author dw9, kd7
#' @noRd
orderEdges = function(levels, l, ntot,x,y) {
nMaj1 = NULL
nMin1 = NULL
nMaj2 = NULL
nMin2 = NULL
# case 1 or 2a:
if(l>levels[3]) {
#LogR criterion: ntot < x+y+1
if(ntot < x+y+1) {
# take the six options, sorte... |
dfedc385e15ac6d6f862371ac6ba82b4fa1adc0f6e84f693667592baf1c42f52 | R | 4,200 | 60 | library(tidyverse)
library(qs)
library(parallel)
library(magrittr)
library(RANN)
library(ggridges)
library(dendextend)
library(ggpubr)
# fig i
ast_distribution <- spatialCellMeta %>% filter(subclass == "AST")
countByRegion <- ast_distribution %>% filter(str_detect(cluster,"5|4") )%>% group_by(chip,region,cluster) %>... |
75a12fa37d92055caf6dbe2c3463887b273e0a20bd56defac0769d764f086001 | R | 4,201 | 156 | ```{r setup}
library(ggplot2)
```
```{r}
se = readRDS('../data/transcriptome_analysis/STACAS_integrated.rds')
se = add.spacet(se)
se
```
```{r}
stacked_bar_plot = function(
obs_ann,
var_ann,
var_name,
reduction_df,
output_folder,
custom_pal=c()
){
reduction_df = reduction_df[, var_ann]
... |
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