sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
055c9cf7d112eb85c4200a5a7e62cbeeb6d582d0e6a4b822897292048c7ad83d | R | 2,879 | 114 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getYesNo<-function(DType,CType){
DType=DType
CType=CType
i.yes=c()
j.yes=c()
i.no=c()
j.no=c()
... |
6a56a7a041a782d4462ffab9f2a75310777a61905aa9ef730ba82df47321e3fd | R | 2,892 | 78 | #Extract cells from Ctrl and treatment condition for StemID
library(RaceID)
library(RColorBrewer)
# load preprocessed RData object: sc_aMI_2
sc_aMI_2 = readRDS("sc_AMI.RData")
# subset DMSO treated cells
DMSO_aMI<- sc_aMI_2@expdata[, grep('^aMIDMSO', colnames(sc_aMI_2@expdata))]
DMSO_aMI <- SCseq(DMSO_aMI)
gm <- rown... |
9cfdf66ed9327e6b1f376e0155e4603c9c2747f3b028866d02489d78d08628d2 | R | 2,898 | 59 | library(RCTD)
library(Matrix)
input_dir = '/home/kd/Desktop/porownanie_cable_magisterka/final/preprocessed'
dirs = dir(input_dir) # sciezka do folderu z podfolderami danych
# reference_moje <- dgeToSeurat('/home/kd/Desktop/porownanie/Vignette_SC_wlasne') #SC dowolne, byle sie zgadzamly na... |
a4ccbdd009ca83a437ce21d07f18ca2825755d2e62b99b346f0ac5c76f01413d | R | 2,908 | 130 | ---
title: "Stats. for Figure 10"
output: html_notebook
---
```{r message=FALSE, warning=FALSE, echo=FALSE,include=FALSE}
rm(list = ls())
library(lme4)
library(lmerTest)
library(emmeans)
library(dplyr)
library(ggpubr)
library(ggplot2)
library(rstatix)
library(effsize)
library(magrittr)
library(knitr)... |
7b8eee29061c8522e480f3dd766748ee3d7170d5ceb39aea486bb6fd6e56b8df | R | 2,913 | 55 | # # # # # # # # # # # # # # # # # # # # # # # # #
# SUPREME- Similarity Network Generation Part #
# # # # # # # # # # # # # # # # # # # # # # # # #
require(cluster)
set.seed(404)
topx = 10 # number of edges to keep
# If the similarity metrics are same, then it will include all the edges with that value, t... |
796f5aa4db5cd960c95c0d701ce68b7938e305f8cf690ebc14809b4dc4e0c2fd | R | 2,932 | 72 | library(Seurat)
library(ggplot2)
library(miloR)
library(readxl)
library(gridExtra)
library(cowplot)
setwd("/data/aronow/Kang/single_cell_projects/10X-Pak/integration/patient_derived_engineered/v2_filtered/")
organoids = readRDS("./combined_all_organoids_filtered_v2.rds")
df_cells = read.table("ctype_v6.txt", sep = "\... |
00c9952b31f02586c1a2d3f6d4cdc3096261a702c06c0fde5c9601bdd2554dd1 | R | 2,942 | 69 | shell_call <- function(command, ...) {
result <- system(command, intern = TRUE, ...)
cat(paste0(result, collapse = "\n"))}
shell_call("sudo apt install libgsl-dev")
shell_call("sudo apt-get install libgmp3-dev")
shell_call("pip install igraph")
shell_call("pip install leidenalg")
install.packages('quadprog')
shell_... |
b44145d13ea0100d3360ed6dbba71c26b5d9a63eab7a18647b79ba3a05472b8f | R | 2,960 | 59 | ## Summarizes data, handling within-subjects variables by removing inter-subject variability.
## It will still work if there are no within-S variables.
## Gives count, un-normed mean, normed mean (with same between-group mean),
## standard deviation, standard error of the mean, and confidence interval.
## If there ar... |
e7a74490622cb56748c3da413e2bfa93f06ec52d324194d967ec54c1af4f9532 | R | 3,048 | 80 | # Plot best fitting Omega for different learning models in difficult trials
library(tidyverse)
library(patchwork)
library(ggpubr)
library(rstatix)
load("processed_data/Results/df_LLhood_absolute.Rdata")
# Create subdataframe with difficult trials only
df_Omega <- dfabs_LLhood |>
filter(level == "difficult") |>
... |
3af9568ecbfa01ec541f48868c0383c30368b2eb3f38e71e41837faef77402ba | R | 3,063 | 93 | #From Liam Revell's web page
#http://blog.phytools.org/2012/01/function-to-get-descendant-node-numbers.html
library(phytools)
getDescendants<-function(tree,node,curr=NULL){
#plot(tree,font=0.25); nodelabels(bg="white")
if(is.null(curr)) curr<-vector()
daughters<-tree$edge[which(tree$edge[,1]==node),2]
curr<-c... |
b13ca5a8709ead2dff889229ec777af83064411f852fea7a6f6ac1f197d63713 | R | 3,069 | 78 |
#/home/toolkit/tools/R4.2.0/bin/Rscript
#/home/toolkit/tools/R4.2.0/bin/R
source('https://gitee.com/jumphone/VISA/raw/master/VISA.R')
mat=read.table('mat.txt', sep='\t', row.names=1, header=TRUE,check.names=FALSE)
a=t(mat)[,1]
b=t(mat)[,2]
setwd('/home/disk/database/data/ICS_scHIC/f00_demo')
CEX=1
PCH=15
COL_LAB... |
a01d24f62eddffd7de3585a11164024b0709a34e47f596c091556310a0ec9bb5 | R | 3,097 | 76 | # Load packages
library(DOSE)
library(clusterProfiler)
library(org.Mm.eg.db)
library(enrichplot)
# GO analysis using genes downregulated in TTS compared to BP only in DMSO condition
# Load data file named Decreased_In_C3_comp_IP_DMSO_electroporated.csv for this analysis
symbols_1 <- as.character(read.csv2("Decreased... |
62ca27d1dc2e69128e2e0601560ef742646481dbc4948b7a6f598a9a178f128a | R | 3,121 | 183 |
#/home/toolkit/tools/R4.2.0/bin/R
library(Seurat)
library(Signac)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
library(data.table)
library(stringr)
pbmc=readRDS('./eQTL/scAtlas_pbmc.rds')
SNP_TSS=read.table('./eQTL/SNP_TSS_500k.bed.loop.txt',sep='\t',row.names=NULL,header=FALSE)
SNP_TSS_TAG=SNP_TSS[... |
2810a5b0001a676d5156707016764a506a8a3580c1018c685cdc66022a8f68a7 | R | 3,125 | 120 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getYesNo<-function(DType,CType){
DType=DType
CType=CType
i.yes=c()
j.yes=c()
i.no=c()
j.no=c()
... |
b0fc3092d853d2dae65cee8984805168b250d2ea7f8c965e050b29de810ef100 | R | 3,143 | 82 | context("Test .search_tilt_perc")
test_that(".search_tilt_perc works", {
# load("tests/assets/test_data1.RData")
load("../assets/test_data1.RData")
averaging_mat <- test_data$averaging_mat
score_1 <- test_data$score_1
score_2 <- test_data$score_2
svd_1 <- test_data$svd_1
svd_2 <- test_data$svd_2
target... |
11a200f9f3bdb3e2bb84d0bc00e45a29a31648d71f456065a5cd2b094105322d | R | 3,151 | 87 | # Utilities for PACNet webApp only
#' network influence score
#'
#' Computes network influence score (NIS).
#' @param expDat ranked query expression matrix, output of running logRank()
#' @param stQuery query sample metadata table
#' @param iGenes character vector of gene names found in both training data and query da... |
e9c0a9d1455125f99cbcf94c5299e9441838a3e9e5685ffbea420454f01d3df7 | R | 3,152 | 96 |
#' Check data object
#'
#' We choose to summarize the clustered gene expression data and metadata in a data object of
#' the \code{\link[SingleCellExperiment]{SingleCellExperiment}} class.
#' This function helps to check if the data object is compatible with \code{\link[FRmatch]{FRmatch}}.
#'
#' @param sce.object Data... |
93e85015d006cccd006df08062782212e97c2882a1bc3a6cb72f6bc4e185bc91 | R | 3,213 | 100 | #' Fetch PubMed records by PMID
#'
#' It downloads metadata for a set of PubMed articles identified by their PMID (PubMed Identifier).
#' This is useful for retrieving specific known articles, updating existing datasets,
#' or downloading records identified through other sources.
#'
#' @param pmids is a character or nu... |
95ed0e737cc34a9c9ac6dcb4608ee2a068d492ae3489e124e368888664a28b26 | R | 3,226 | 111 | ---
title: "SiT - Figures"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression isoforms in tissu... |
cbc43e0e5a775c4b6e50c73342c7b4b591c953971f743324f6949a702c1a9eb0 | R | 3,234 | 83 |
#' Normalization function for FR-Match analysis.
#'
#' @param sce.object Data object.
#' @param scale Boolean variable indicating if to scale the PER GENE expression to the range [0,1]. Defualt: \code{TRUE}.
#' @param norm.by Normalization options PER CLUSTER. Choose from \code{"softmax", "mean", "median", "none"}.
#'... |
7672dcf9121ef0b186d18bca2d1099753d6fa8253eda8ec5089ae404c5eb426f | R | 3,236 | 85 |
#This script takes the CNR and Euler number output from
#cnr_euler_number_calculation.sh
#for Freesurfer processing and calculates binary flags for the euler number and cnr subcategories
#adopted from https://github.com/PennBBL/conte/tree/master/freesurfer
## modified by Hyun Joo Yoo, hyunjooy@usc.edu, 08/22/2020
##... |
062233c90dce2be636bff0878ad4a59ae805fcf57310e35194dabe1f97bdd652 | R | 3,245 | 137 | ---
title: "Stats. for Figure S12"
output: html_notebook
---
# Libraries
```{r message=FALSE, warning=FALSE, echo=FALSE,include=FALSE}
rm(list = ls())
library(lme4)
library(lmerTest)
library(emmeans)
library(dplyr)
library(ggpubr)
library(ggplot2)
library(rstatix)
library(effsize)
library(magrittr)
... |
22f806798f87b60b4768ae0182b787fa2530de578a3d583be276c8c9c008c62a | R | 3,260 | 90 | library(lme4)
library(MuMIn)
library(lmerTest)
changeDirection <- function(df,form){
colnumber=which(colnames(df)=='DistanceBin')
for (sub in unique(df$ID)){
for (hemi in unique(df$Hemi)){
for (cond in c('C','I')){
mask=df$ID==sub & df$Cond==cond & df$Hemi==hemi
if (sum(mask)>0){
... |
72f3da2805de02b513e4aedd8d3424fd614178c0b0e4ff0786d2b4dcce28b13f | R | 3,264 | 81 |
# library(cowsay)
#https://onlineasciitools.com/convert-text-to-ascii-art
# writeLines(capture.output(say("Hello"), type = "message"), con = "ascii_art.txt")
# art <- readLines("logo.txt")
# dput(art)
#'
# metnormalizer_logo <-
# c("", " _ __ _ _ ",
# "... |
659faf1d1147b5d4aa6dc58df15a874581da16cf852844082888ab7b71f547ac | R | 3,266 | 61 | # genes
genes_mature = c('STMN2', 'GAP43', 'DCX')
genes_progenitor = c('VIM', 'HES1', 'SOX2')
genes_cycle = c('CCNB2', 'CDK1', 'MKI67')
genes_npc = c('PTTG1', 'PCNA', 'HMGB2', 'MKI67')
genes_choroid = c('TPBG')
genes_mesenchymal = c('DCN')
genes_microglia = c('CD74')
genes_glia = c('HOPX', 'FAM107A', 'TNC', 'AQP4', 'S... |
620d6381722eafbda13605644a153fae2ccb75a40502dbd9543702f9fe44a734 | R | 3,279 | 113 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
COL_LIST=list(DType=c('scATAC'='indianred1','scHIC'='royalblue1'),
CType=c('Astro'='red','MG'='gold1','Neuron.... |
d578cb6a24b77314c9b31548f361334c224401015ccc1e2d40520d2dd58e1f45 | R | 3,284 | 67 | make.slouch.simmap.table<-function(simmap.output,num.it){
#,model.type,pred.type,num.sp,prev.output=NULL){
#Code to create or update a table from slouch output
#Format for calling function: make.slouch.table(Slouch output,type of model,type of p... |
3bc506a227906e84e3dce2d2753b9e3d32b5ae6631e0ceb6b2d374e08201cc69 | R | 3,287 | 156 | ---
title: "Figure S13"
output: html_notebook
---
# Libraries
```{r message=FALSE, warning=FALSE, echo=FALSE,include=FALSE}
rm(list = ls())
library(dplyr)
library(ggpubr)
library(ggplot2)
library(rstatix)
library(magrittr)
library(knitr)
library(openxlsx)
```
# Read Data
```{r}
df.all <... |
339b1bdc596e74f0b50ca9f97242f38282882e7833a8ec2af4c3aa46249a37db | R | 3,331 | 116 | # Data preprocessing for GLM Results
# IMPORTANT: It is absolutely necessary to start a new R Session before
# running this script!
# .rs.restartR()
# Purpose: For plotting!
library(fs)
library(tidyverse)
### === LOAD DATA === ###
file_list <- dir_ls("processed_data/GLM")
# Load all data at once
lapply(file_list... |
1bb458e4ec4dadea8b062351e3a2fbf9aefe10a54cec2c149de0c877332d61ec | R | 3,333 | 71 | # RPCA (fast integration using reciprocal PCA) after removal of 9 low quality clusters
library(Seurat)
library(ggplot2)
library(patchwork)
library(dplyr)
library(SeuratWrappers)
library(monocle3)
library(harmony)
options(future.globals.maxSize=(90*1024^3)) # set max memory size as 60G
setwd("/data/aronow/Kang/single_c... |
7972d77e44ae523efc025d54f4e0d70ee9f3260e5d58b948735cabdcd4a53710 | R | 3,337 | 123 | ---
title: "AspectRation"
author: "Gianfranco"
date: "10/23/2021"
geometry: margin=1cm
output:
pdf_document: default
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(lme4)
library(MuMIn)
library(lmerTest)
library(data.table)
setFactors <- function(df){
... |
10156fdc500c5cbbbcdabb595529481f3f2eb70bc640b1fa2b6f36b37b075fe7 | R | 3,342 | 76 | prepare_matrix_B <- function(ST_data, address_candidate, cell_types, lead_genes, tresh_tau, tresh_rho ){
nTypes <- length(cell_types)
list_marker_genes_all_types <- vector("list", length = nTypes)
for(i in 1:nTypes){
print(i)
candidate_markers <- unique(unlist(read.table(paste0(address_candidate, cell_typ... |
48b763045389a7e24fc67945746728cb5efc4a11efdf2ee85be4af6389edee43 | R | 3,361 | 80 | library(Seurat)
library(SeuratWrappers)
library(monocle3)
library(ggplot2)
library(ggridges)
source('../functions/_global.R')
source('../functions/_visualize.R')
# load data for integrated organoid samples
donor = readRDS('../../donor_reintegrate/donor_reintegrated.rds')
donor = donor[, donor$cellclass_draft_v2 %in% ... |
ff65d671e56544a8e2e7209e334ec4ba6ef3bbe85de624335c19071ea03e6a28 | R | 3,366 | 83 | suppressMessages(library(tidyverse))
suppressMessages(library(lightgbm))
suppressMessages(library(optparse))
suppressMessages(library(fs))
#Parse arguments
option_list <- list(
make_option(c("-p", "--path"), type = "character", default = NULL, help = "Location of BoostMEC model objects"),
make_option(c("-d", "--da... |
6845a342d0456436799ab709b581493f27653af4339bb0c36fe6823c5be73710 | R | 3,378 | 103 | ### Create sample distribution plots & linear age regression ###
library("readr")
library("plyr")
library("dplyr")
library("ggplot2")
# Meta data for whole chimpanzee sample #
chimp.meta <- read.csv("Chimp_meta_new_223.csv")
# Meta data and whole brain volumes for #
# GM, WM, & CSF for QC passed chimp... |
8ce5b6fda445b0efbd774caf8370129bdec5b2ceadaa22ce45ac7aa11dd4fd6e | R | 3,387 | 95 | ## Slingshot for pseudotime analysis
# Load packages
library(BiocStyle)
library(readr)
library(data.table)
library(dplyr)
library(tools)
library(SingleCellExperiment)
library(HDF5Array)
library(mvoutlier)
library(scran)
library(scater)
library(SC3)
library(slingshot)
library(stringr)
library(org.Mm.eg.db)
library(m... |
4674c94f353d5ea632e5cc53ae3b18f35e892a5020063be1eee6b195836879a5 | R | 3,389 | 116 | ## given 2 vectors, find the hyperplane 2D representation
.representation_2d <- function(vec1, vec2, tol = 1e-6){
stopifnot(length(vec1) == length(vec2), .l2norm(vec1) > tol, .l2norm(vec2) > tol)
unit1 <- vec1/.l2norm(vec1)
tmp <- .project_vec2vec(vec2, unit1, orthogonal = T)
if(.l2norm(tmp) < tol){
retu... |
18e4e8f4585fbdcf070b5a70b0334e7f4db7599a6c5e9674654b9b7c335cffd1 | R | 3,400 | 126 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getYesNo<-function(DType,CType){
DType=DType
CType=CType
i.yes=c()
j.yes=c()
i.no=c()
j.no=c()
... |
971f92b16abc98c51cdf26138b8d134cce0fda5b81f26bc8f54c4afb1e042f85 | R | 3,405 | 61 | #Generate 10% missing values at Random
NeuroIm1.mis <- prodNA(NeuroIm1, noNA = 0.1)
#Check missing values introduced in the data
summary(NeuroIm1.mis)
# mice_plot <- aggr(NeuroIm1.mis, col=c('navyblue','yellow'),
# numbers=TRUE, sortVars=TRUE,
# labels=names(NeuroIm1.mis), cex.axis=.... |
7939b7141fe3daa9c788f2d27ebbcf3b12e60147844ab9686a48b0653088a219 | R | 3,413 | 102 | # Quantify orthogonal predictions between models
# df = data from response-based model
# df_generative = data from stimulus-based model
# Transition Probability
CountRatio_TP <- function(df, df_generative) {
# Compute response-based correct prediction
respcorrect <- tibble(df$response,
... |
5974e929c798e358aadd91b04c157500e7b2e684e012693bcb7e9888e39b69e7 | R | 3,433 | 82 | suppressMessages(library(tidyverse))
suppressMessages(library(lightgbm))
suppressMessages(library(optparse))
suppressMessages(library(fs))
#Parse arguments
option_list <- list(
make_option(c("-p", "--path"), type = "character", default = NULL, help = "Location of BoostMEC model objects"),
make_option(c("-d", "--da... |
881f8c2237aa23b42c46a9f868fb9d7bc0837e146dcb07e43aa460337e5c79f7 | R | 3,496 | 141 | ---
title: "AF22_DEU"
author: "Marika Oksanen"
date: '2022-06-17'
output: html_document
---
```{r}
library(DEXSeq)
library(biomaRt)
library(VennDiagram)
library(tidyverse)
```
## Preparations
Flattened annotation file was created with python script dexseq_prepare_annotation.py
-r was set to no --> shared exons are e... |
9acd9df520a7b7bf5bc11ff00c55f5def13a8c4fccb134dcd149a647452c2865 | R | 3,517 | 78 | #' Plot cell enrichment
#'
#' @param cell_enrichment_res the result of \code{tiltedCCA::postprocess_cell_enrichment}
#' @param col_palette vector of colors (as strings), equal to the number of celltypes in \code{cell_enrichment_res}
#' @param cex_axis positive number (graphical parameter)
#' @param cex_lab positive nu... |
4e3b5dae643e4f357a5cca5769b24d5668f7e89d99f0e28b689bfa7142e379f3 | R | 3,534 | 129 | #' @title peak_plot
#' @description peak_plot
#' @author Xiaotao Shen
#' \email{shenxt@@sioc.ac.cn}
#' @param index index
#' @param sample sample.
#' @param sample.nor sample.nor.
#' @param QC QC
#' @param QC.nor QC.nor
#' @param tags tags
#' @param sample.order sample.order
#' @param QC.order QC.order
#' @param tags t... |
cb042e77ce6ef4228775e0f8a72cc6d1c8d85f804a480c437593a1e22e84a8e6 | R | 3,578 | 88 | library(Seurat)
library(SeuratWrappers)
library(monocle3)
library(ggplot2)
library(ggridges)
library(dplyr)
cds = readRDS("./cds_monocle3.rds")
df_cells = read.table("../ctype_v6.txt", sep = "\t", header = 1, row.names = 1)[colnames(cds),]
for (col in colnames(df_cells)){
if (!col %in% colnames(cds@colData)){cds@col... |
7dd778b064b6c91d982716feefe6f3d9991def62b20c7c95d0fc5ccbf2a95a06 | R | 3,620 | 77 | ## VERY IMPORTANT -- SCRIPT 1 [FOR THE PIPELINE WORKFLOW]
#!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
#print(arg_file)
#j_global=as.numeric(args[2])
dataset_file = as.array(args[2])
#workspace_directory=as.array(args[4])
workspace_directory=as.array(args[3])
#i_... |
91d2e62d88dc378a538a8b68c5605a40d8389cbbf4363885fe9427184177d341 | R | 3,632 | 84 | # Correlate gene expression between clusters
source("./src/seurat_utils.R")
for (cell_type in c( "GABA", "Glut")){
print(paste("Comparing cell type", cell_type))
print("Loading bird data")
bird <- LoadH5Seurat("./data/seurat/bird.h5seurat")
bird # 218342 features across 29497 samples within 1 assay
# Selec... |
036a3c6e27b637bd3e464cbb7f06ac727becf26ebfa0ba964b5e7ce6ca5b764e | R | 3,676 | 112 | context("Test svd")
## .svd_safe is correct
test_that(".svd_safe works", {
set.seed(10)
mat <- MASS::mvrnorm(n = 100, mu = rep(0, 10), Sigma = diag(10))
rownames(mat) <- paste0("c", 1:100)
colnames(mat) <- paste0("g", 1:10)
res <- .svd_safe(mat = mat,
check_stability = T,
... |
460786efbee7308c2fbd45970ab5e504ccf76d099132d5f6113d0e7ff2d7f3b7 | R | 3,681 | 75 | library(ggplot2)
library(Seurat)
# load data
setwd("/data/aronow/Kang/single_cell_projects/10X-Pak/integration/patient_derived_engineered/v2_filtered/visualize/")
organoids = readRDS("../combined_all_organoids_filtered_v2.rds")
df_cells = read.table("../ctype_v6.txt", sep = "\t", header = 1, row.names = 1)
df_cells = ... |
56b9fd5d54af8791320216c7af23639fd3cfbc3cd7ebd0abffae7c09d23211c6 | R | 3,682 | 131 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
COL_LIST=list(DType=c('scATAC'='indianred1','scHIC'='royalblue1'),
CType=c('B'='blue3','Mono'='gold1','T'=... |
154f694b862eb190452cb5657b3725374a67cd164d00626e60e900d9a6366d5a | R | 3,712 | 107 | ---
title: "Heatmaps"
author: "Bismark Appiah"
date: "14 December 2021"
output:
pdf_document: default
html_document: default
---
```{r, setup, include=FALSE}
# Set root directory for markdown file
knitr::opts_knit$set(root.dir = 'your_directory_here')
knitr::opts_chunk$set(echo = TRUE)
```
# 1. R... |
3ccd59e896eee798f57b7520b6d787611e80da987fad18e079ffb64fd8a35888 | R | 3,713 | 132 | #' Horizontal Study Effects with Confidence Intervals
#'
#' Builds a custom version of \code{\link[ggstance]{geom_pointrangeh}}.
#'
#' @inheritParams ggplot2::layer
#' @inheritParams ggstance::geom_pointrangeh
#' @author Ilari Scheinin
#' @export
#' @examples
#' library(ggplot2)
#' library(magrittr)
#' df <-
#' # Use... |
7104fd26970a0f70fee5619511748451001998b545e2c0bddea72c183446b1aa | R | 3,716 | 129 | library(Seurat)
library(dplyr)
library(pheatmap)
library(tidyverse)
library(ggplot2)
library(RColorBrewer)
library(knitr)
library(xtable)
library(ggrepel)
library(cowplot)
library(metap)
library(formattable)
library(STutility)
library(Nebulosa)
library(DoubletFinder)
library(slingshot)
library(formattable)
library(Enha... |
309fcf62b1cc3cf55f1775026358fbec908d013caabb7cbe83aadb083fb12bf4 | R | 3,734 | 99 | library(Seurat)
library(SeuratWrappers)
library(monocle3)
library(ggplot2)
source('../../v2_filtered_new/Figures/functions/_global.R')
source('../../v2_filtered_new/Figures/functions/_visualize.R')
combined_organoid = readRDS("../combined_all_organoids_filtered.rds")
cds = as.cell_data_set(combined_organoid, assay = ... |
00c7a7c7e08e8abb74266f83118bed03e9192a7840797a1e79890e107476e658 | R | 3,748 | 144 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getYesNo<-function(DType,CType){
DType=DType
CType=CType
i.yes=c()
j.yes=c()
i.no=c()
j.no=c()
... |
c8b46671acb5c0512f6844527c876c6b9710ad308b7224c5fd95afd188c3aee5 | R | 3,773 | 83 | library(ggplot2)
library(Seurat)
setwd("/data/aronow/Kang/single_cell_projects/10X-Pak/integration/patient_derived_engineered/v2_filtered/")
# load data
df_cells = read.table("ctype_v4.txt", sep = "\t", header = 1, row.names = 1)
df_cells$source = plyr::mapvalues(df_cells$sample_group, from = c("Engineered-Cre", "Eng... |
792e34aaaf991433311cce662b77af59b77746bfd4f54c076e38987fbadb5b7d | R | 3,792 | 115 | ---
title: "DRAFT_rpechor_gex_cell_type_annotation"
author: "Drew Voigt, Nate Mullin"
date: "11/04/2022"
output: html_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
library("Seurat")
library(stringr)
library(tidyverse)
```
This object was run with the following filters:
nFeatur... |
d413c4a6874f753fa683257796326eda4e84399bf5f5d734d893d5bc87f2500e | R | 3,823 | 143 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
#COL_LIST=list(DType=c('scATAC'='indianred1','scRNA'='grey60'),
# CType=c('B'='blue3','Mono'='gold1','T'='r... |
920542732b1f1b6d5e15027abcc55e1919c02aa3ef60bc414b6c8f40da23b9a0 | R | 3,830 | 164 | ---
title: "Figure 10"
output: html_notebook
---
# Libraries
```{r message=FALSE, warning=FALSE, paged.print=FALSE}
rm(list = ls())
library(dplyr)
library(ggpubr)
library(ggplot2)
library(rstatix)
library(magrittr)
library(knitr)
library(kableExtra)
library(openxlsx)
```
# Data
```{r}
df.all ... |
8ba7e8854dd52622d15c5e7931556945a61f858c90c73ecd97159a6a3cf5f83e | R | 3,865 | 145 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
COL_LIST=list(DType=c('scATAC'='indianred1','scRNA'='grey60'),
CType=c('B'='blue3','Mono'='gold1','T'='red... |
ddbf37a2277d859286dcb1a28d4411f7080c6e00778ff767731d3ec232b6d1c4 | R | 3,865 | 112 | library(Seurat)
library(SeuratWrappers)
library(monocle3)
library(ggplot2)
library(ggridges)
library(dplyr)
library(ggridges)
library(cowplot)
# find trajectory of d101 only organoids
organoids = readRDS("../../v2_filtered/combined_all_organoids_filtered_v3.rds")
organoids_eng = organoids[, organoids$source == "Engin... |
303c0272f96e01041c4747ca649590e040945531b273745ac7ac6007b903ef72 | R | 3,876 | 96 | postprocess_distinct_variable_selection <- function(
input_obj,
input_mat = NULL,
logpval_vec, #larger is more significant
cor_threshold = 0.9,
input_assay = 2,
min_subsample_cell = NULL,
num_variables = 10,
seurat_celltype_variable = "celltype",
seurat_obj = NULL,
verbose = 1
){... |
e8a77e0c26fb1e570062555e78ec55c3284125875f843acfb39ded18f0ab65e9 | R | 3,884 | 102 | context("Test .evaluate_radian")
## .evaluate_radian is correct
test_that(".evaluate_radian works", {
# load("tests/assets/test_data1.RData")
load("../assets/test_data1.RData")
score_1 <- test_data$score_1
score_2 <- test_data$score_2
svd_1 <- test_data$svd_1
svd_2 <- test_data$svd_2
basis_list <- test_... |
d0331558943fa32d1f31ad2e010e499c3319e0e1c489808617be32d10bfbdd1d | R | 3,896 | 91 | # Plot GLM (with interaction) absolute values
library(tidyverse)
library(patchwork)
library(ggpubr)
library(rstatix)
# Load data
load("processed_data/Results/df_GLM_absolute.Rdata")
load("processed_data/Results/df_GLM_sig_absolute.Rdata")
# Change factor labels
levels(dfabs_GLM$modality) <- list("Auditory" = "audit... |
820f6c8516e25264f13f027d8f368e71c836f6e69537d9878596527369c8a1eb | R | 3,909 | 80 |
#' Plotting function for bi-directional FR-Match results
#'
#' This function combines and plots two sets of reciprocal \code{\link[FRmatch]{FRmatch}} results (i.e. experiment 1 (E1) query
#' to experiment 2 (E2) reference with E2 markers, and E2 query to E1 reference with E1 markers).
#'
#' @param rst.FRmatch.E1toE2,r... |
82933e5e9ec54eab45ff98b5b58a542ac77f5878e2933aabf60d65d2435e0ed1 | R | 3,921 | 127 | #' Alternating Background Colour
#'
#' Add alternating background color along the y-axis. The geom takes default
#' aesthetics \code{odd} and \code{even} that receive color codes. The codes
#' would preferably be in the 8-hex ARGB format to allow for transparency if
#' the geom is meant to be used as visual background.... |
2d2394ceee609cdbf18de3265684e2fce3863b5ca7d2a35b89708f96a97f32c3 | R | 3,925 | 153 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getYesNo<-function(DType,CType){
DType=DType
CType=CType
i.yes=c()
j.yes=c()
i.no=c()
j.no=c()
... |
4c42a75f4c9c2c5c2a83e5438ec62b53f25f8cc087a60c0693bf88393a49bc85 | R | 3,926 | 105 | # CoDa analysis of fractions spent in each of 8 behaviors
require(data.table)
require(compositions)
require(npmv)
library(plyr)
library(rstatix)
# for visualization
require(ggplot2)
library(ggpubr)
# load data set
dat <- read.table("../Output/Mouse_behavior_data_set.csv",header = TRUE, sep = ",")
dat <- within(dat,... |
d31b237028de799b7864f68236b4b77b75f274258708d6aa379d0fdf46f56f0a | R | 3,927 | 153 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getYesNo<-function(DType,CType){
DType=DType
CType=CType
i.yes=c()
j.yes=c()
i.no=c()
j.no=c()
... |
e78ac6f506c6599eae32e87f6fb4f61ffd86de7255cf87aadfb45f861bc795e7 | R | 3,931 | 153 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getYesNo<-function(DType,CType){
DType=DType
CType=CType
i.yes=c()
j.yes=c()
i.no=c()
j.no=c()
... |
53844fc3203b0d6f09a45a13c4dcbee4fee77bd4d093020c79b0ffe270ae8672 | R | 3,954 | 91 | panel <- c("heart", "hspc", "intestine_colon", "liver", "lung", "neuron", "skeletal_muscle")
other <- c("esc", "endothelial_cell", "fibroblast", "kidney", "b_cell", "t_cell", "monocyte_macrophage")
golden_exists <- function(name) file.exists(file.path(repo_root, "tests", "golden", paste0(name, ".rds")))
eq <- example_q... |
3f96a7f894aea22605b2cbe864e9a2939077eb693f75edf3c5b03c41c69b72f1 | R | 3,985 | 140 | ---
title: "SiT - Mouse Olfactory Bulb (MOB) - 02.nanopore"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of ... |
8558ec6e3f9ed1e5d1d915f548e7e860101eaadd592781795bb2cdeafc88f2b9 | R | 3,985 | 120 | ###################################################################################################
##### Processing of WT Data #####
###################################################################################################
{
library(Seurat)
library(Signac)
library(Ge... |
4a94560a17580e2a3907ee47439f9f6421b86ac0f221ab58ecd09fae94166643 | R | 3,991 | 120 | ###################################################################################################
##### Processing of Mutant Data #####
###################################################################################################
{
library(Seurat)
library(Signac)
library(... |
f4da5818745e5629782f7f891e794b638da4b7522f96f12a51e2e8e7b39118df | R | 3,996 | 100 | ###To find markers for individual clusters
DefaultAssay(obj) <- "RNA"
obj <- NormalizeData(obj, verbose=FALSE)
all_markers <- FindAllMarkers(object = obj,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.25)
print(all_m... |
9841f6b8091570ac776890bd73fdd39ca1d6d8ef5dd3194a000c8bbd45961db3 | R | 4,008 | 84 | # Attach the CBDA library
suppressPackageStartupMessages(library(CBDA))
suppressPackageStartupMessages(library(randomForest ))
suppressPackageStartupMessages(library(glmnet ))
suppressPackageStartupMessages(library(SuperLearner))
suppressPackageStartupMessages(library(bartMachine))
library(CBDA, quietly = TRUE,... |
65a84bc2f1538b44befc7cf1fa0eec5de1d3b563accf88572fe15d6e7e414ac5 | R | 4,015 | 103 | ---
title: "dge_plotting_11042022"
author: "Nate Mullin"
date: "11/04/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(Signac)
library(Seurat)
library(patchwork)
library(tidyverse)
library(ggpubr)
library(patchwork)
library(EnhancedVolcano)
library(RColorBrewer)
li... |
90de6a20cd4d65da6902c6354fa65092201907518bf9385a225d34368292609c | R | 4,016 | 134 | ##' A version of the checkLM function from Morpho that plots specimens by module identity
#'
#' @param lm.tibble a tibble of landmark data
#' @return The sum of \code{x} and \code{y}.
#' @examples
#' color.palette <- c( "#6a3d9a","dimgrey","#fb9a99", "gold", "#009E73", "#D55E00", "#CC79A7", "cyan2", "#e31a1c", "#007... |
771aa3eb8e82de129cad239d0eb866c6ae6b3c3bf7839992efc98af4c0f654eb | R | 4,024 | 97 | ---
title: "Random Forest Variable Importance"
author: "Andrew Soncrant"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = "~/GitHub/CBDA/Pipeline/DISTRIBUTED/BINOMIALdataset/TEST")
library(gridExtra)
library(ggplot2)
```
## Overview
Here we look... |
ee60b61d4a67ed48cc2259e60453f24a39c865cb6c6781b0e413bd778230e983 | R | 4,054 | 140 | ---
title: "SiT - Mouse Coronal Brain Section (CBS1) - 02.nanopore"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial lands... |
d5c52b3e3623c2258eaef9483e3b91fb41c85242d8fc141ad45a67aa734c076c | R | 4,055 | 141 | ---
title: "SiT - Mouse Coronal Brain Section (CBS2) - 02.nanopore"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial lands... |
da1b18b590fbe59ca1e61fa5c97715c438107c568ce61fe622ab3f9b7c951632 | R | 4,064 | 179 | ---
title: "Figure 5 Stats"
output: html_notebook
---
```{r message=FALSE, warning=FALSE, echo=FALSE,include=FALSE}
rm(list = ls())
library(lme4)
library(lmerTest)
library(emmeans)
library(dplyr)
library(ggpubr)
library(ggplot2)
library(rstatix)
library(effsize)
library(magrittr)
library(knitr)
librar... |
3b44cfdf593d270250addb05e345b29090ad0a9d1a3f63de9fa1ba70ee3230b0 | R | 4,105 | 121 | library(Seurat)
library(glmnet)
library(ggplot2)
library(cowplot)
library(foreach)
library(doMC)
#
getPopulationOffset = function(y){
if(!is.factor(y))
y=factor(y)
if(length(levels(y))!=2)
stop("y must be a two-level factor")
off = sum(y==levels(y)[2])/length(y)
off = log(off/(1-off))
return(rep(off,... |
8aabbc428a198efd34701f9ef533028a7a0d0dc837bcd6cdad42afe7d922ca43 | R | 4,144 | 140 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getYesNo<-function(DType,CType){
DType=DType
CType=CType
i.yes=c()
j.yes=c()
i.no=c()
j.no=c()
... |
93b54c47a078c498377464c515de8b87c4cd8d96c2174143a5cf8dc054790bea | R | 4,145 | 146 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
#COL_LIST=list(DType=c('scATAC'='indianred1','scRNA'='grey60'),
# CType=c('B'='blue3','Mono'='gold1','T'='r... |
8b3e426297fb55686143bba1b6a4e6c62488b9d1a3c32e546ce1219d79f05766 | R | 4,163 | 188 | context("forestplot")
library(ggplot2)
library(vdiffr)
library(magrittr)
# Define df linear case
df_linear <-
df_linear_associations %>%
dplyr::arrange(name) %>%
dplyr::filter(dplyr::row_number() < 30)
test_that("forestplot plots linear results", {
vdiffr::expect_doppelganger(
title = "Associations to BM... |
36919c066c2de199d321fee9d797df349c77515a9d6cfef700e8241623aec920 | R | 4,166 | 105 | ######################## Description and Setup #################################
# This script performs statistical analysis of normalized cortical thickness
# Load Libraries
library(sciplot)
library(rstatix)
library(ggthemes)
library(tidyverse)
# load preconfigured custom plot theme
source('code/analysis/paper_plot_... |
729d8a7e3c8be8106a743907b45a0cd8d08dde8dcd7e031124718c0f3b87d1f0 | R | 4,168 | 101 | # Data preprocessing Part 2
library(tidyverse)
# Load data
load("processed_data/allValues/allValuesAuditory_before_DPP_pt2.Rda")
load("processed_data/allValues/allValuesVestibular_before_DPP_pt2.Rda")
load("processed_data/allValues/allValuesVisual_before_DPP_pt2.Rda")
### === AUDITORY === ###
# Convert intensity l... |
9cfc4b463f42557512bd565e8f7960b7d4efa63ddbc4096cf0514fd56d40e3fe | R | 4,171 | 164 | #' @title metNor
#' @description Normalize data using different normalization methods.
#' @author Xiaotao Shen
#' \email{shenxt@@sioc.ac.cn}
#' @param ms1.data.name MS1 peak table name. Default is "data.csv".
#' @param sample.info.name sample.info name. Default is "sample.info.csv"
#' @param minfrac.qc Default is 0.
#'... |
ee0da013c8a3d856526287991a2bbd49d1f5b8587bd095f40fb23b0569cdeee4 | R | 4,185 | 116 | #' Gather bibliographic content from PubMed database using NCBI entrez APIs
#'
#' It gathers metadata about publications from the NCBI PubMed database.
#' The use of NCBI PubMed APIs is entirely free, and doesn't necessarily require an API key.
#' The function \code{pmApiRequest} queries NCBI PubMed using an entrez que... |
6bdf1463a44362382427ea8f346a6376582a4d7145e17d7a9b1fa79484c931d6 | R | 4,186 | 129 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
message = FALSE
)
library(magrittr)
library(ggforestplot)
```
<br>
<div style="text-align:lef... |
6c9825b4eb4f1a60014dc930c63a32234c3e5d41a6286a23827f6f647cfaec71 | R | 4,192 | 103 |
## VERY IMPORTANT -- SCRIPT 1 [FOR THE PIPELINE WORKFLOW]
#!/usr/bin/env Rscript
#args = commandArgs(trailingOnly=TRUE)
#print(args)
#arg_file = as.array(args[1])
#print(arg_file)
#j_global=as.numeric(args[2])
#dataset_file = as.array(args[2])
#workspace_directory=as.array(args[4])
#workspace_directory=as.array(args[... |
37bd4dc16725f2a45112a968cb6b787c8ab91e8ef342b6e27df5d58749368ecf | R | 4,203 | 123 | # Analyze Repetition Preference
# For comparison with analysis_GLM.R and analysis_GLM_LearningModels.R, include
# the interaction
library(tidyverse)
library(DescTools)
source("functions/CountRepetitionPreference.R")
# Auditory | Vestibular | Visual
df_auditory <- read.csv(file = "raw_data/auditoryData.csv")
df_vest... |
e3f7f6104c20881e187bd23ca00a234601770b7f61489dfc32c6920516504cfd | R | 4,228 | 125 | #!/usr/bin/env Rscript
# library
suppressPackageStartupMessages(library("Nonpareil"))
# args
args = commandArgs(trailingOnly = TRUE)
if(length(args) < 3){
cat('Usage: nonpareil_summary.R <seq-depth> <out-base> <npo_file1> [.npo_file...]\n')
cat(' seq-depth = target sequencing depth (eg., 1e9)\n')
cat(... |
d0f08cf96d47c82ec4e18bb4516521a97647b31f4f29b5f696e94feb873210ce | R | 4,236 | 145 | ---
title: "Analyse Network Simulation Data"
author: "Andreas"
date: "10/5/2020"
params:
Test: 5
output:
html_document:
toc: true
toc_float: true
theme: cerulean
---
<!--******************************************************************-->
<!--**********************************************************... |
1392422d4033a1bbb0ede9ff8bd9ca2d581e116f8b32142c85b99f7e4432b6c5 | R | 4,248 | 94 | ## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
catch = TRUE,
collapse = TRUE,
comment = "#>"
)
## ----install, eval=FALSE------------------------------------------------------
# install.packages("devtools")
# devtools::install_github("JCVenterInstitute/F... |
fa0c3a4cc58da03c9e06ec9db2c74c2d2cc49fc3bf6d4845ebe20ec98a56ac2c | R | 4,253 | 139 | #!/usr/bin/Rscript
library(caret)
library(ROCR)
library(xgboost)
library(doMC)
registerDoMC(48)
args <- commandArgs(trailingOnly=TRUE)
# datasets
positive <- rownames(read.csv("./data/Dang.tsv", sep="\t", head=TRUE, row.names=1))
negative <- as.character(read.csv("./data/1kg_LoFT.tsv", head=FALSE)$... |
910fca8d9fed2576646919f72ee481b5398bfedf7e062827cb555a9450d3c3f6 | R | 4,322 | 134 | # read 'KPR all sighting' data
# score # of KPR males seen in first 5min
# determine 2' and 3' association surveys
# summarise activity context
library(tidyverse)
library(here)
library(janitor)
library(skim)
library(readxl)
fn_path <- "/Volumes/WorkDrive/DAP/GoogleDriveSync/DAP_sync/Aerial/DataAnalysis/"
# ids
kpr_i... |
aaec1becc688f406e7320e175a469cd3211a7e00683eee0e3b21a1f98cae61f7 | R | 4,333 | 100 |
#' Make data object
#'
#' This function creates the data object used in \code{\link[FRmatch]{FRmatch}}.
#' See an example of the data object in \code{\link[FRmatch]{sce.example}}.
#'
#' @param dat Cell-by-gene expression data in a data frame or equivalent format. The first column is cell names, followed by columns of ... |
f70bcaa7e21a71447e5ab03d367284eb2c386e6b40b7b7c5ae112dca94e05d19 | R | 4,376 | 102 | library(Seurat)
library(ggplot2)
library(cowplot)
### visualize markers (stacked violin) ###
visualize_markers_violin = function(seu_obj, cell_type_key, genes)
{
DefaultAssay(seu_obj) = 'RNA'
Idents(seu_obj) = seu_obj@meta.data[[cell_type_key]]
VlnPlot(seu_obj, features = genes, pt.size = 0, stack = T, flip = T)... |
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