sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
6acd7b179511f7fd476e254c8ba9bb08388ff363ea12931817999e3daf485691 | R | 1,394 | 37 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import peaks ----
files <- c("db/peaks/ATAC/midbrain_peaks.narrowPeak",
"db/peaks/ATAC/limb_peaks.narrowPeak",
"db/peaks/ATAC/heart_peaks.narrowPeak")
peaks <- lapply(files, importBe... |
a0e8a978f3a0dc06b9894ae846b50a2a9807a0406d3ff56324f1274bccda8f77 | R | 1,400 | 34 | # ==========================================================================
# Script: 03_Marker_Gene_Heatmap_Bulk.R
# Purpose: Visualization of Marker Genes in Bulk RNA-seq
# ==========================================================================
library(pheatmap)
library(dplyr)
library(readr)
# 1. Load ... |
bbd06840cb072f60730a03db11a06be5884bdfdb1ad249f55b0716aa27dcc846 | R | 1,402 | 29 | library(GenomicRanges)
library(GenomicFeatures)
library(dplyr)
library(readr)
library(VariantAnnotation)
library(rtracklayer)
library(stringr)
library(purrr)
clinvar_vcf <- readVcf("data/clinvar_20250421.vcf")
clinvar_vcf_gr <- rowRanges(clinvar_vcf)
seqlevelsStyle(clinvar_vcf_gr) <- "UCSC"
orfanage_txdb <- makeTxDbF... |
f358561c934132f97718e436f44e15d06cc368813342826018b5dd278f90823a | R | 1,406 | 41 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import collapsed test sets ----
bed <- importBed("db/bed/collapsed_test_set_paper.bed")
# Extract sequence ----
bed[, genome:= tstrsplit(name, "__", keep= 2)]
bed[, sequence:= getBSsequence(.SD, genome),... |
19461e4b16897e0bec9429955752cae296488124383c20524ae7804aeb2e6e7a | R | 1,411 | 41 | #' Ligand-receptor interactions in CellChat database for mouse
#'
#' The ligand-receptor interaction database curated in CellChat tool
#'
#' @format A list includes the ligand-receptor interactions
#' @source \url{https://github.com/sqjin/CellChat/}
"CellChatDB.mouse"
#' Ligand-receptor interactions in CellChat databa... |
a2d2c1220b65be88b2615efd0ccdae5b9f682f262da8827a82c9cdda2f81ab8b | R | 1,417 | 32 | # Open a connection to a log file
logfile <- file("tests/test_files/fastq_pipeline_tests/single/outdir/03_featurecounts_single_end/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("Rsubread")
fc <- featureCounts(files = c("tests/t... |
150824004b3f5ed9091487c54a7adead2e33b5df4bc3e4fdb5e1db9ab8bbba44 | R | 1,451 | 38 | # Open a connection to a log file
logfile <- file("tests/test_files/fastq_pipeline_tests/paired/outdir/03_featurecounts_paired_end/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("Rsubread")
fc <- featureCounts(files = c("tests/t... |
9959cddf78632a0d0b17310934560a6b66f982b8839dae7d3f462d7debf45dfe | R | 1,456 | 43 | #!/usr/bin/env Rscript
## 00_run_all.R — execute every methylation notebook in order
##
## Outputs:
## ../results/ per-stratum DMP + gene-level + mCSEA + RNA-vs-meth concordance
## ../figures/ per-stratum volcanos + cross-stratum heatmap
##
## Run:
## cd Methylation/r_notebooks
## Rscript 00_run_all.R
HERE... |
b0dff61f775a4352fae270110ef479bc73d819d7ce35e914059d351c51fdda05 | R | 1,456 | 41 | #' Read a VCF into a tidy tibble
#'
#' @param file Path to a VCF file.
#' @param info Character vector of INFO‑field keys to pull out into columns.
#'
#' @return A tibble with at least
#' chrom, start, end, ref, alt and one column per element of `info`.
#' @examples
#' df <- read_vcf("example.vcf", info = c("... |
0d1f90d44f1092871d783e1ebf5355ecad4c5861ef55e59b77509460c1c9ac60 | R | 1,465 | 50 | Modules <- setClass(
# Set the name for the class
"Modules",
# Define the slots
slots = c(
coverage = "data.frame",
abundance = "data.frame",
annotation = "data.frame",
db="ModuleDB"
),
# Set the default values for the slo... |
55e742687603c2e2e1247670bc06dc27c9ef1d05ea5db97077eb552d9cc2cd41 | R | 1,485 | 65 | #### Multivairable LDSC #####
library(devtools)
require(GenomicSEM)
library(data.table)
library(dplyr)
## Set working directory
setwd("/mnt/lustre/working/lab_esked/damianWo/Chapter2/1_genomicSEM")
## import all sumstat files
traits <- list.files(pattern = "*sumstats.gz$", recursive = TRUE)
## Enter sample prevale... |
df3847b805ae2828675ee3f63285f3d4730594024d5dc8c87d206186441b7e9e | R | 1,486 | 49 | library(dplyr)
library(tidyr)
library(ggplot2)
my_theme <- theme_bw() +
theme(
axis.text.x = element_text(size = 20, vjust = 0.5, angle = 90, hjust = 1, color = "black"),
axis.text.y = element_text(size = 20, color = "black"),
axis.title.y = element_text(size = 25, color = "black"),
... |
d716f6ce6e4f620668cc9a2c9c2bb0eebe7bebaa47268a8d0240387affaca05d | R | 1,488 | 36 | ---
title: "ED Fig 11d-f - EB3 length under CK-666 (DIV-2, raw images)"
author: "Lin et al., Nature 2026 (Bradke lab, DZNE)"
output: html_notebook
params:
div: 2
image_pipeline: "raw"
summary_variable: "growth_speed_median"
pdf_name: "EB3_length.eps"
d_drive_source: "D:\\DVElite\\CK666_LA_EB3\\240225_DIV2_EB3... |
14815ea3089626a3b56815ce31324e731a5b194078fc2c91240bfc8076b782c7 | R | 1,498 | 58 | ---
title: "Stats_coupling"
output: html_document
date: "2024-12-17"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
coupl_contr = read.csv("./derivatives/Group/spindles/SP_coupling_contr.csv")
coupl_pat = read.csv("./derivatives/Group/spindles/SP_coupling.csv")
```
```... |
60bade1991136a6e49d883f1b9cd9006ec5310a341bd5cfb75ba4aec496688af | R | 1,504 | 63 | #!/usr/bin/env Rscript
# Author_and_contribution: Jieran Sun & Mark Robinson; Create the script
suppressPackageStartupMessages(library(optparse))
option_list <- list(
make_option(
c("-i", "--input_file"),
type = "character", default = NULL,
help = "Input containing the aggregated labels."
),
make_o... |
b4c7e01124ff9033dcdc2d6fb5475c54887092367ef936affbe242853d496890 | R | 1,507 | 36 | #!/bin/bash
#PBS -N gsamixer
#PBS -l walltime=6:00:00
#PBS -l select=1:ncpus=8:mem=36GB
module load singularity/3.7.1
cd ./MIXER_GSA
FILE=($(awk -v var=$PBS_ARRAY_INDEX 'NR==var {print $1}' sumstats.txt))
# custom path & settings
export THREADS=8
export MIXER_SIF=./singularity/gsa-mixer-2.1.1.sif
export ... |
3568d7ad59a525240087542de73684f4b185362a607a3ffc7f213e911f8ea605 | R | 1,516 | 53 | #!/usr/bin/env Rscript
# Author_and_contribution: Jieran Sun & Mark Robinson; implmented method
# Author_and_contribution: Peiying Cai; created template
# Author_and_contribution: ENTER YOUR NAME AND CONTRIBUTION HERE
suppressPackageStartupMessages(library(optparse))
option_list <- list(
make_option(
c("-i", "... |
99c01a1ec6480e850d8cc66a6cb9e5d674371ee8eea60f5ccb2b2a4ef8f1b6a6 | R | 1,533 | 48 | #!/usr/bin/env Rscript
library(GenomicRanges)
library(GenomicFeatures)
library(rtracklayer)
library(dplyr)
library(tidyr)
library(readr)
args <- commandArgs(trailingOnly=TRUE)
annotation_gtf <- args[1]
predicted_cds_gtf <- args[2]
output <- args[3]
gencode_CDS <- makeTxDbFromGFF(annotation_gtf) %>%
cdsBy(by = "t... |
f4c9842a16efa7cab621b34cbcbfdeaab1dd9a2ccffa6f99092527db5c58843b | R | 1,551 | 51 | library(rtracklayer)
library(GenomicFeatures)
library(dplyr)
library(ggplot2)
library(patchwork)
my_theme <- theme_classic() +
theme(
axis.title.x = element_text(size = 13),
axis.title.y = element_text(size = 13),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(siz... |
e4367e5634c53fb8f9e1f7d8f003967c39fa19faafa1199427b9763dc3e646a5 | R | 1,561 | 51 | # Setup script for the PhysMAP R environment.
#
# Reviewers / first-time users: prefer
#
# R -e 'renv::restore()'
#
# which reads the bundled renv.lock and installs the exact package versions
# used to produce the paper figures (R 4.2.1, Seurat 4.3.0, dplyr 1.1.2, ...).
#
# Run THIS script only when you want to refre... |
4693b35086238170c963c33c67c31a12e51090447ae9d3ecec422fa3651badbb | R | 1,563 | 46 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import metadata
meta <- readRDS("Rdata/paper_metadata_v3.rds")
meta <- meta[dataset=="accessibility" & ID=="model1_bulkATAC_tsx3Aug_2xBal_noW" & set=="chr18"]
# Create accessibility predicted bedgraph fi... |
7efe3ecd792c18152bf87cbc6755e0ce2eb75bd22ab87b338e3971561b18645a | R | 1,566 | 48 | rm(list=ls())
library(tidyverse)
library(mgcv)
library(emmeans)
stress_df_long <- readRDS("C:/stress_df_long.rds")
stress_df_long$ID <- as.factor(stress_df_long$ID)
stress_df_long$Time <- as.factor(stress_df_long$Time)
model1 <- gam(VAS ~ Time + s(ID, bs="re"), data = stress_df_long, method ='REML', family... |
1bf98948fc3a5115a005d6355b7765029ecb724e6a52b8b9d10c898d076d48e0 | R | 1,568 | 48 | #!/usr/bin/env Rscript
## 00_run_all.R — execute every transcriptome notebook in order
##
## Outputs:
## ../results/ per-stratum DE TSVs + cross-stratum summary
## ../figures/ per-stratum volcanos + cross-stratum heatmap
##
## Run:
## cd Transcriptome/r_notebooks
## Rscript 00_run_all.R
##
## Or execute the... |
3ec1067a297df54f4220f116ac044d7bc45a519555b30a12fa4f9ed4df5f401c | R | 1,601 | 31 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Selected sequences object were created in:
# file.edit("git_deepATAC/subscripts/create_clean_list_sequences.R") # Clean list of designed sequences (different EVO/LEDIDI designs...)
# file.edit("git_deepAT... |
4ebed0fb4dbd47e870fb465e8249e1fa44b107fcf2ce5b4ac30e7f9731262951 | R | 1,610 | 40 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import metadata ----
meta <- readRDS("Rdata/paper_metadata_v3.rds")
meta <- meta[dataset=="accessibility" & ID=="model1_bulkATAC_tsx3Aug_2xBal_noW" & set=="testCenterActBins"]
# Output files ----
meta[, ... |
0e89949b1bf2e00210b62295ef330efd4befb017f876880c4307e8472e653361 | R | 1,611 | 48 | #!/usr/bin/env Rscript
library(GenomicRanges)
library(GenomicFeatures)
library(rtracklayer)
library(dplyr)
library(tidyr)
library(readr)
args <- commandArgs(trailingOnly=TRUE)
annotation_gtf <- args[1]
predicted_cds_gtf <- args[2]
output <- args[3]
gencode_exons <- makeTxDbFromGFF(annotation_gtf) %>%
exonsBy(by ... |
04c086b911fc83a7476efb6f55e51dc8d45afc91e999d500b6d9e86c1f0f54e6 | R | 1,614 | 45 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
require(metap)
require(AUCell)
require(Matrix)
# Import data ----
dat <- readRDS("db/single_cell/subsetted_sc_dataset.rds")
# Import motifs ----
mot <- readRDS("Rdata/motif_clusters_paper_3_tissues_single_... |
4228146d3a7da89370994b91cf52271e1b22cac5edcd573ffc216e94b49aac9e | R | 1,638 | 34 | library(rstanarm) # main package for simple bayesian model implementation
library(bridgesampling) # required for using stan derived MCMC samples in bayes factor computations
library(bayesplot) # provides diagnostic tools like mcmc_traice
library(here)
setwd(here())
options(mc.cores=parallel::detectCores())
... |
01aab9ae10b01434f4b913833dcc5b8b3dd7525072fafbb7745386ff848ea8ad | R | 1,652 | 47 | library(dplyr)
library(ggplot2)
library(readr)
library(stringr)
library(rtracklayer)
percolator_res <- read_tsv("nextflow_results/V47/orfanage/hybrid_percolator.tsv") %>%
# Replace proteinIds: take the first ID (splitting on comma)
mutate(proteinIds = sapply(str_split(proteinIds, ","), `[`, 1)) %>%
# Repla... |
38ef291606ba41cc035e92b01d966e7d7009610388b05956b734ceca4af097c7 | R | 1,656 | 33 | # Open a connection to a log file
logfile <- file("*PLACEHOLDERPATH*/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("limma")
design_matrix <- read.table("tests/test_files/test_design_matrix.csv", header=TRUE, sep= ",")
design_ma... |
9306cf0a83fbb1d092776ba0b77ad8c75db9ca56ceeb4170fbf3b921366c598d | R | 1,657 | 57 | library(Gviz)
library(biomaRt)
library(rtracklayer)
library(GenomicFeatures)
library(readr)
library(dplyr)
library(arrow)
mart <- useEnsembl("genes", dataset = "hsapiens_gene_ensembl")
txdb <- makeTxDbFromGFF("/project/rrg-shreejoy/Genomic_references/GENCODE/gencode.v47.annotation.gtf")
df <- getBM(
attributes = c(... |
6db1d19109a3dc520bb5a6bdaa5cb9753e3d9e0af787df84edcbe2a2063604b2 | R | 1,659 | 50 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import metadata ----
meta <- readRDS("Rdata/paper_metadata_v3.rds")
meta <- meta[dataset=="activity" & ID=="model1_bulkATAC_tsx3Aug_2xBal_noW" & set == "test"]
meta <- meta[tissue %in% c("limb", "heart", ... |
9790da4bf1467b49fd8d7c77ae3e99263570a7282c14aedc20d5dc61e70a0a1d | R | 1,660 | 50 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import enrichment ----
enr <- readRDS("db/motifs/revision_motif_enrich_atac_vista_designed_vs_rdm_genomic.rds")
enr[, sig:= padj<0.05 & log2OR>0 & set_hit>=5]
setorderv(enr, c("sig", "log2OR"), -1)
# Sel... |
42be56fc6959c5870d0b2374968b607775307b82765718a36139f1061e1dad82 | R | 1,671 | 63 | # Testing for segregation script
## Source scripts
source("./R_scripts/segregation.R")
source("./R_scripts/segregation_by_type.R")
load('./test/testanswer.RData')
pass <- list()
#### 1. Test segregation() ####
m <- matrix(NA,4,4) # Create test data
m[1,] <- c(1,4,2,2)
m[2,] <- c(4,1,3,2)
m[3,] <- c(2,3,1,8)
m[4,] ... |
65184f7507741a7592550fe71495054f8d0819808a683b60bc5a696800ddfaa9 | R | 1,700 | 42 | df$seg_asso_proc[1]
df$seg_sensory_proc[1]
sense_i <- which(label$Chan_system_type_label[i_349]==1)
asso_i <- which(label$Chan_system_type_label[i_349]==2)
p349 <- label$Power_label[i_349]
submat <- cube349[,,1]
submat_noneg <- submat
submat_noneg[submat < 0 ] <- 0
w.mat <- submat_noneg[sense_i, sense_i]
mean_sense... |
4c192ae838f5dbe827a03c83af2f14cabab72ff99ba7c53d0885dd5e9617ee53 | R | 1,701 | 37 | ###
###
library(readxl)
library(biomaRt)
## read in genes
geneTab = read_excel("cortical layer marker gene list_1.xlsx")
geneTab = as.data.frame(geneTab)
colnames(geneTab)[1] = "Gene"
## get human/mouse conversion
ensembl=useMart("ensembl",dataset = 'mmusculus_gene_ensembl')
MMtoHG = getBM(attributes = c('ensembl_gen... |
7749e0e293eb3f972ce4110aea2f8883a38ba3181e75de3c81962fbee2decadc | R | 1,704 | 71 | #!/usr/bin/env Rscript
# Author_and_contribution: Niklas Mueller-Boetticher; created template
# Author_and_contribution: ENTER YOUR NAME AND CONTRIBUTION HERE
suppressPackageStartupMessages(library(optparse))
# TODO adjust description
option_list <- list(
make_option(
c("-l", "--labels"),
type = "character... |
efe32c7e40bd5bf4574bc7f26b20793b4d77dc6f1de0101c5d2075c8b6ba4403 | R | 1,709 | 43 | # Helper functions that are used in tutorials
# probe locations from cross phenotype names - names are in form X[chr]_[start]_[end] (without []) - transform this to 10^9 * chr + probe midpoint
get_probe_locs <- function(cross){
locfun <- function(x) {d=lapply(strsplit(substr(x,2,99),"_")[[1]], as.integer);1e9*d[[... |
698bd883f4d07e8995b71d5efe191e24598fe2b48e421a08563b1c6e7c55cf78 | R | 1,712 | 45 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import metadata ----
meta <- readRDS("Rdata/paper_metadata_v3.rds")
meta <- meta[dataset=="activity" & ID=="model1_bulkATAC_tsx3Aug_2xBal_noW" & set=="test"]
# Output files ----
meta[, mean_contrib_file:... |
5cad5453f0aeb0b164f73c1b7a33900438b46ff84de68363b74d391814302339 | R | 1,724 | 37 | # packages
require(xgboost)
source("xgboost_script.R")
xg_data = readRDS("XgDataMacaqueBC.rds")
# Get matrices and cell ids. Note that the matrices represent log(TPM+1) gene expression values
train_data = xg_data$train_mat
test_data = xg_data$test_mat
train_id = xg_data$train_id
test_id = xg_data$test_id
# We have pr... |
c76f69b7e1e361d08402d3d8d2bc70a2108fa5eae796e8e5d05a468914038a19 | R | 1,732 | 34 | #This is my example with a dataset starting from the seurat object
#included libraries
library(magrittr)
library(tidyverse)
library(Seurat)
library(future)
library(ggplot2)
# Plotting frac of cell populations with boostraped values
load(file = "data/bootstraped_sampling_cell_count_genotype_1e4_endo_gs.rda")
b %>% ... |
1f9caeedee3049832e7ecaebce7716ee0af58bc6b5fd6a956c1ce18b3138a6d3 | R | 1,746 | 36 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
require(vlfunctions)
# Import metadata ----
dat <- readRDS("Rdata/metadata_ATACSeq_models_processed.rds")
# Import mm10 cordinates VISTA ----
coor <- readRDS("db/peaks/vista_tiles_clean.rds")
# Check that there is no overlap between test and training/valid ... |
3746167003e8da46af2a1d59ccc159f8509df94b66569771a639a9487bbd4ffe | R | 1,754 | 73 |
library(jaffelab)
## PDF --> Excel using Adobe Acrobat
## Excel --> TSV using Excel
## scan subsequent text file
x = scan("HBA_ISH_GeneList.txt", what = "character", sep = "\n")
## get rows for each table
ind = c(1, grep("Table", x), nrow(x))
names(ind) = gsub("\"", "", gsub(" ", "", ss(x[ind], "\\.", 1)))
names(in... |
fb5621ab16f7e85ae401b1d314d7b76c738780d7870d01058ae20b0fefafd9b2 | R | 1,758 | 43 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
source("git_deepATAC/function/augmentation_function_tiling_sliding_window.R")
# Import vista tiles ----
vista <- readxl::read_excel("/groups/stark/shenzhi.chen/db/VISTA_enhancer_dataset/VISTA2024_AllTissuesReferenceAlleles.xlsx")
vista <- as.data.table(vista)... |
c4f27aace4c674631d5c3708751578fb3eba3f7fbadfea1b78421a0625558065 | R | 1,776 | 50 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import ATAC peaks ----
ATAC <- readRDS("db/peaks/bulkENCODE_confident_ATAC_peaks.rds")[, 1:5]
# Import VISTA mm10 peaks ----
vista <- readRDS("db/peaks/vista_tiles_clean.rds")
vista$class <- vista$genome... |
7a9f86767048e7242026ea4967da2981c8e5f87dba2ae52a95bac441f4b6d967 | R | 1,780 | 40 | ---
title: "ED Fig 11d-f - EB3 length under CK-666 (DIV-2, deconvolved cropped)"
author: "Lin et al., Nature 2026 (Bradke lab, DZNE)"
output: html_notebook
params:
div: 2
image_pipeline: "cropped"
summary_variable: "growth_lifetime_mean"
pdf_name: "EB3_length.eps"
d_drive_source: "D:\\DVElite\\CK666_LA_EB3\\2... |
3d31f9ed7e8f4e93ca62fbcb5e7ecbb27aa7413a4036d13ab4a4dd3380613031 | R | 1,782 | 51 | #' Create an Index Matrix for an LDSC Sampling Covariance Matrix
#'
#' Creates the matrix of index values used to map entries in an LDSC genetic
#' covariance or correlation matrix to the corresponding rows and columns of
#' its sampling covariance matrix.
#'
#' @param LDSC_OBJECT An optional LDSC output object contain... |
71134d81335d1da17181093993a15a78d3fe12a6f281967a2ca65348fe860cc2 | R | 1,796 | 71 | ##
###
library('SingleCellExperiment')
library('here')
library('jaffelab')
library('scater')
library('scran')
library('pheatmap')
library('readxl')
library('Polychrome')
library('cluster')
library('limma')
library('sessioninfo')
library('reshape2')
library('lmerTest')
library('WGCNA')
## multithread
allowWGCNAThreads(... |
4ada1fc4f10b0f18b6eaef8233e7b3c8f98232e3782049db8ed167c07d2e7afd | R | 1,798 | 45 | #' Generate data from regularized models.
#'
#' Generate data from regularized models. This generates data from the background,
#' i.e. no residuals are added to the simulated data. The cell attributes for the
#' generated cells are sampled from the input with replacement.
#'
#' @param vst_out A list that provides mode... |
6cf2ba096a8f37be2e65f58ee1730fa66777a2bb0fb0c1b2440c48e46b646ba1 | R | 1,802 | 57 | summaryGLS <- function(OBJECT = NULL, Y = NULL, V_Y = NULL, PREDICTORS, INTERCEPT = T){
#if (length(PREDICTORS[,1]) != length(LDSC_OBJECT$subS)) {
# warning("The length of predictors must be the same length as the parameters to be modeled.")
#}
###Getting the length of SE estimation predictors
if (is.... |
95c5eb398d696445c96603fb9fbdde5c03221bdb3a70537c0b918658e6d121ea | R | 1,812 | 39 | setwd("/Users/zhangyuan/Google Drive/2023_math_reading_neurotransmitter/GitHub")
rm(list=ls())
library(ggplot2)
library(forcats)
# CMI results (shared mode)
fname = "results/neurotransmitter/cmi/shared/individual_neurotransmitter_regression_results_shared_mode2_wFDRp.csv"
postfix = "barplot_adj_r2_shared_cmi"
output... |
45bbf8a76122c1efee76811991cf670c56a946a9bc4a675a52642c3725d140b8 | R | 1,820 | 60 | #Fig. 1e
# Feature plot showing the expression of Lepr in different cell clusters
library(Seurat)
library(patchwork)
library(ggplot2)
##---------
## Load data
##--------
# Use imputed dataset using relative path
data_path <- "data/ganglia_seurat_object.rds"
if (!file.exists(data_path)) {
stop("Seurat object not ... |
2edd043b5592b075880e69d7ca318bf7f377cc7d3fdc2be97a8042eee58489be | R | 1,845 | 46 | library(lme4) # fits LME model usin REML
library(lmerTest) # gets p-values using Satterthwaite corrected df
behav_dat <- read.csv('paingen_behav_dat_sid_14_164.csv')
heat_dat <- behav_dat[behav_dat$heat == 1,]
mdl <- lmerTest::lmer('Yint ~ stimLvl*placebo + (stimLvl*placebo | sid)', data = heat_dat)
summary(mdl)
###... |
2fcdd54b7918b0f44362b04b708c3bfd574d3b4999d0a76f01f494e76bd1f047 | R | 1,852 | 65 | binary_search <- function(
spe,
do_clustering,
extract_nclust,
n_clust_target,
resolution_update = 2,
resolution_init = 1,
resolution_boundaries=NULL,
num_rs = 100,
tolerance = 1e-3,
...) {
# Initialize boundaries
lb <- rb <- NULL
n_clust <- -1
if (!is.null(resolution_b... |
5981d725a6b26510b37068c337c6b12c1b504af7173eff0cbd771862a377a75c | R | 1,852 | 77 |
#Fig. 1b
# UMAP plot from scRNA Data of mouse superior cervical ganglia (SCG) and stellate ganglia
library(magrittr)
library(tidyverse)
library(Seurat)
library(future)
library(ggplot2)
library(patchwork)
##---------
## Load data
##--------
# Use imputed dataset using relative path
data_path <- "data/ganglia_seurat... |
9c32c944454e148190de425fce181dc3d8b836b14793705cd6cc376055d6da43 | R | 1,858 | 46 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
require(orthogene)
# Import cluster metadata ----
jeff.meta <- "/groups/stark/shenzhi.chen/projects/transferLearningMammalianEnhancerDesign202408/db/motif/motif_annotations.xlsx"
meta <- readxl::read_xlsx(j... |
6228b4e3ff9ee750cbeee580e4db49a03e8606d7248472dc41a950de284b9143 | R | 1,860 | 32 | # Open a connection to a log file
logfile <- file("*PLACEHOLDERPATH*/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("DESeq2")
count_data <- read.table("tests/test_files/big_counted.csv", header=TRUE, sep= ",", row.names = 1)
des... |
6c7d4f816de0917b5d9345fe0e1d44862089b9063eb9a4dba21a2dce75fe1c06 | R | 1,865 | 51 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
row_mean_dgcmatrix <- function(matrix) {
.Call('_sctransform_row_mean_dgcmatrix', PACKAGE = 'sctransform', matrix)
}
row_mean_grouped_dgcmatrix <- function(matrix, group, shuffle) {
.C... |
d8cef8fe0dd324082ba5ddd2e6ec1626f37bda3965f67b8d340d1dcb466d346b | R | 1,876 | 38 | # =============================================================================
# install_packages.R
# Install all R / Bioconductor / GitHub dependencies for the NPY-GBM pipeline.
# Run once before executing the analysis scripts: Rscript install_packages.R
# Developed and tested on R >= 4.0 (Methods 4.12).
# =========... |
6f938bc7ff80b1a0a86dc92c049d0bf03ebb3e48dc73a857bf92a323cc9261a1 | R | 1,889 | 41 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
# Make metadata ----
meta <- data.table(bed_file= list.files("db/bed/bulkATAC/", "_test.bed$", recursive = T, full.names = T))
meta[, dataset:= tstrsplit(bed_file, "/", keep= 5)]
meta[dataset=="ATAC", c("tissue", "augmentation", "balancing"):= tstrsplit(bed_f... |
09537d81f361ef291409d08ff3d5ff569bb5e6e8f4bf4e66faa07f91d04f10f4 | R | 1,904 | 46 | ---
title: "ED Fig 2c - pooled-replicate neurite-actin xcorr (SD envelope)"
output: html_notebook
---
<!--
================================================================================
WRAPPER for ED Fig 2c (pooled-replicate CCF with SD envelope).
==================================================================... |
130453c3368ea93ce4ca3781f6b9b75802780c4e13c44f8bf144adbf897470bc | R | 1,904 | 81 | ---
title: "R Notebook"
output: html_notebook
---
```{r}
library(tidyverse)
library(magrittr)
```
```{r}
counts <- as.data.frame(read_csv("OBiroi_bulk_antennal_RNAseq.csv"))
rownames(counts) <- counts$GeneID
counts <- counts[-1]
counts
```
```{r}
cpm <- counts / colSums(counts) * 1000000
cpm
```
```{r}
geneli... |
4ee8c47d7c53c3bfb390d2391fc111943038a493aadd5f6f15064210352b2227 | R | 1,905 | 52 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
require(Seurat)
require(Matrix)
# Import data ----
mat <- fread(
"/groups/stark/shenzhi.chen/projects/transferLearningMammalianEnhancerDesign202408/db/MouseAtlas/GSE119945_gene_count.txt.gz",
sel= 1:3,
... |
671efefd1d943da8f187c03b6c110b84773b68d2b3de15d7e4ff9fcfcaa56870 | R | 1,909 | 50 | library(arrow)
library(ggplot2)
library(dplyr)
# FSM transcript gene type percentages
read_parquet("nextflow_results/V47/final_classification.parquet") %>%
filter(
structural_category == "full-splice_match"
) %>%
distinct(associated_transcript) %>%
left_join(
GENCODE_gtf[, c("transcrip... |
8a0d0633ed2ab5af71e5ed91eaefc478525382f08be8b19ffdd04eda8e7b3b36 | R | 1,909 | 44 | # ==========================================================================
# Script: 02_Bulk_vs_PseudoBulk_Correlation.R
# Purpose: Correlation between Bulk RNA-seq and Pseudo-bulk from scRNA-seq
# ==========================================================================
library(ggplot2)
library(dplyr)
libra... |
1e13ea361a97e0246f4a14287e283ef523fbb2a473972a608bf7b3b46140b5f2 | R | 1,910 | 67 | library(ggplot2)
library(scico)
library(patchwork)
library(readxl)
# Load the data
Wt_E8_data <- as.data.frame(read_excel("Data_1.xlsx", sheet = "Wt_E8.5_data", col_names = TRUE))
# Plot settings
point_size <- 0.8
axis_title_size <- 22
axis_tick_text_size <- 20
plot_title_size <- 28
vjust_pos = -6
# Base theme elem... |
dd5b4dea8d0b9b79c35ca4015e9f84045f7db92d96a30c55dbd10315c6e57879 | R | 1,914 | 57 | suppressMessages(library(Seurat))
suppressMessages(library(ggplot2))
suppressMessages(library(patchwork))
suppressMessages(library(cowplot))
library(tidyverse)
set.seed(123)
setwd('~/Desktop/project/Ciona_ST/')
result_dir <- 'result/result1/featurePlot/'
ciona_nc.combined <- readRDS('result/result1/clustering/ciona_n... |
ee7d3f10f75b856b3aa8d6404cd328ea2218d3afa0d2af2184ea69c62ff37587 | R | 1,921 | 42 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
source("git_deepATAC/function/augmentation_function_tiling_sliding_window.R")
require(vlfunctions)
# Import ATAC-Seq peaks, vista tiles, control regions and compute overlaps ----
vista <- readRDS("db/peaks/vista_tiles_clean.rds")
vista[, start:= start-100]# E... |
5b6aa0c5cb7b2abfab781844ec015d3879a78023061691d25e80cfba6c21d14a | R | 1,923 | 75 | #!/usr/bin/env Rscript
library(GenomicRanges)
library(GenomicFeatures)
library(rtracklayer)
library(dplyr)
library(tidyr)
library(readr)
# Read input files
args = commandArgs(trailingOnly=TRUE)
annotation_gtf <- args[1]
predicted_cds_gtf <- args[2]
novel_CDS <- args[3]
# Get novel UTRs
GENCODE_threeUTRs_gr <- makeT... |
53f4fb7cf85744e6a77fe39578de12173948399bb003fe59ca099b83920aec26 | R | 1,959 | 46 | # -------------------------------------------------------------------------
# Compute FDR-corrected p-values for neurotransmitter regression results
# for joint CCA Mode 2 analyses
# Author: Yuan Zhang
# Date: 2026-04-13
# -------------------------------------------------------------------------
setwd("/Users/zhangyua... |
0db6386b7ffcf4aa484de62e6ff8327003d6262b1636b5917169d101a0fa088d | R | 1,966 | 42 | #Extended_data_Fig. 5a
# DOtplot showing the expression of adrenergic receptors
library(magrittr)
library(tidyverse)
library(Seurat)
library(future)
library(ggplot2)
library(patchwork)
##Load integrated data using relative path
data_path <- "data/ganglia_seurat_object.rds"
if (!file.exists(data_path)) {
stop("Seur... |
7e779b1b4589e4c029dfd0c8444529b6ad35501bbdb5853c5aa867e5d01df330 | R | 1,975 | 66 | ##
library('SingleCellExperiment')
library('here')
library('readxl')
library('Polychrome')
library('rafalib')
library('sessioninfo')
library('WGCNA')
library('lmerTest')
## multithread
allowWGCNAThreads(6)
## Functions derived from this script, to make it easier to resume the work
sce_layer_file <-
here('Analysis... |
5d4504d24b1b7bae93ee4453a22b7d026a63266f8ac7a7b4d926c022cababed9 | R | 1,978 | 69 | ############
library(SummarizedExperiment)
library(jaffelab)
library(readxl)
library(VariantAnnotation)
library(rtracklayer)
library(janitor)
# read in sra and supp table info
pd = read.csv("he_SraRunTable.txt",as.is=TRUE,row.names=1)
pheno = read_excel("41593_2017_BFnn4548_MOESM254_ESM.xlsx", sheet=1)
pheno = as.data... |
9a7129213b542a5681429f440ab4f035fb02fd5f324aef8aa5c9aaecfaf71ad0 | R | 1,993 | 50 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
require(AUCell)
require(Matrix)
# Import data ----
dat <- readRDS("db/single_cell/subsetted_sc_dataset.rds")
# Select cell clusters of interest ----
# dat$counts <- dat$counts[,dat$cells$cluster!="Other"]
... |
4029755723fd7d544b6b92c213bfc8ce34468b4601a80ccfeddeb9bada70d758 | R | 2,020 | 73 |
#' Run association test using mix model (GENESIS)
#'
#' @param pheno : data frame of the phenotype
#' @param outcome : as.numeric , outcome to test
#' @param covars_prs : covariates to adjust
#' @param col_id_name : column ID name that match with rownames and colnames kinship matrix
#' @param group.var :
#' @param ... |
379ef8ec84981195ae5ca27e205c4abb22a8397a5aa174f8174b42c740da3805 | R | 2,029 | 44 | # Open a connection to a log file
logfile <- file("*PLACEHOLDERPATH*/logfile.log", open = "a")
# Redirect both output and messages to the file and console
sink(logfile, append = TRUE, split = TRUE)
require("limma")
design_matrix <- read.table("tests/test_files/test_design_matrix.csv", header=TRUE, sep= ",")
design_ma... |
13541c4361cc0c124d2a695589b65db73b87a5654298fc0e412e8e4377b1f316 | R | 2,035 | 69 | #####################################
# Example of meta d calculation for individual subject and
# exemple of trace plots and posterior distribution plots
# using the Function_metad_indiv.R
# AM 2018
#####################################
## Packages ----------------------------------------------------------------
li... |
09da1bc6f9158f5c42143dd1c7896a03ab15a06e0a44895905ae468b05449082 | R | 2,037 | 110 | ### Genomic SEM Models for MDD and Disease Groups ###
library(devtools)
require(GenomicSEM)
## Load in LDSC results
load("LDSCoutput_CVD.RData")
## CVD-MDD Single Factor Model ##
model_CVD <- '
F_CVD=~ NA*HF + CAD + AF + STK
F_CVD ~~ 1*F_CVD
MDD ~ F_CVD
'
model_CVD <-usermodel(LDSCoutput, estimation = "DWLS",... |
b5cd13aaa73b3f65f0163b5becd2527ec7863cf5ee6c71cf1d233b29e6b3ec8d | R | 2,043 | 81 | ModuleDB <- setClass(
# Set the name for the class
"ModuleDB",
# Define the slots
slots = c(
directory = "character",
modules = "character",
hierarchy = "data.frame",
module.names = "data.frame",
hierarchy.file = "character",
module.names.... |
b47e218bcc85e2aada8f122da3ebc6a0d4cee3a83d3117863aa1ca8e3115d293 | R | 2,045 | 69 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import metadata ----
meta <- readRDS("Rdata/paper_metadata_v3.rds")
meta <- meta[dataset=="accessibility" & ID=="model1_bulkATAC_tsx3Aug_2xBal_noW" & set=="test"]
# Plotting parameters ----
Cc <- c("grey... |
9a30f9d3f72c5f0493939c2b1aa47f640d1a43a39a6067c86c6d595877dbcf4c | R | 2,063 | 58 | # brain-maintenance-lgcm: trivariate latent growth curve model and brain
# maintenance index, companion code for Menze et al. (2026).
#
# Copyright (C) 2026 The authors of Menze et al. (2026).
#
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License... |
618c847da7c526b850c2ea14017d8d5844e444434533de8e3ee6df2822cf956e | R | 2,066 | 82 | ---
title: "Or mutants antennal RNAseq"
output: html_notebook
---
```{r}
library(tidyverse)
library(magrittr)
library(ggrepel)
library(readxl)
```
```{r}
anOrMut <- read_excel("Table S1_count-rpkm-DESeq_OR mutant antenna.xlsx",
sheet = 1,
col_types = c(rep("text", 2), re... |
180f8f119bd9de4e8efe34d6617763856a40ea560244cd7712291d6ef2c19657 | R | 2,073 | 83 | #####################################
# Estimate metacognitive sensibility (meta d') for individual subject
#
# Adaptation in R of matlab function 'fit_meta_d_mcmc.m'
# by Steve Fleming
# for more details see Fleming (2017). HMeta-d: hierarchical Bayesian
# estimation of metacognitive efficiency from confidence rati... |
024352421a486b83510bfb6eee7959c177d8e6ecf6ffd16251c72266621f7e02 | R | 2,087 | 49 | # brain-maintenance-lgcm: trivariate latent growth curve model and brain
# maintenance index, companion code for Menze et al. (2026).
#
# Copyright (C) 2026 The authors of Menze et al. (2026).
#
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License... |
c921a598ed9793d1b5dc1dfef16a038828d16cb8bb238dd36c6b6f42e6259656 | R | 2,087 | 83 | ---
title: "Atonal mutants antennal RNAseq"
output: html_notebook
---
```{r}
library(tidyverse)
library(magrittr)
library(ggrepel)
```
```{r}
ato_DEG <- read_csv("ato mutants.csv") %>% as.data.frame()
genes <- read_tsv("genelist_beat-side.txt") %>% as.data.frame() %$% gene
```
```{r}
ato_DEG[ato_DEG$Symbol %in% gen... |
a260ec418913130e24f25cf3621070025507846d1a0e066763d6600cbe799bd0 | R | 2,091 | 56 | #!/usr/bin/env Rscript
## 03_bcells_de.R — generated from notebook spec
## Run: Rscript 03_bcells_de.R
## ============================================================
## # 03 — B cells stratum DE (R/limma)
##
## R/limma rerun of stratum `cell_tissue_case_control_b_cells` from
## `Stratified_Analyses/Expression/`. ... |
4c9132cd109a5af056a0bac14a38619dc4a1202fe7ecbbe3fbd83f0cb1c2134f | R | 2,113 | 64 | library(biomaRt)
library(dplyr)
## set some parameter
# DataDir = '/Users/guofanhua/Desktop/gfh/work/StandardBrainTemplateAndAtlas/AllenBrain/'
# FileName = 'microarray/'
DataDir = '/Users/guofanhua/Desktop/gfh/work/experiment/ASL_Mesoscopic2025/reference/2024NNcode_AHBA_gradients-master/outputs/'
FileName = 'expressi... |
1f6cb5e2b0c7cbb99003fe25e1a5ed1becc433ad5f5fe943c93f83eb51cd2838 | R | 2,118 | 40 | # MIT License
#
# Copyright 2018 Broad Institute
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, ... |
7adeae6262cb7e0c0d6decacfbd9fe19943215220422c1f4e677222bb19fbfaa | R | 2,126 | 63 | # =======================
# Setup: Paths and Config
# =======================
# Setup
distance_metric <- 'manhattan'
number_permutations <- 10000
ncores <- 4 # Number of CPU cores for MDMR
# Variables to include in X (must be column names present in the CSV) and output naming from chosen variables
chosen_cols ... |
c20478d24884e3c6b0c47baab5c7b45eb1e530f97fe3350088b70d2b2dc34422 | R | 2,127 | 57 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
devtools::load_all("/groups/stark/vloubiere/vlite/")
# Import data ----
dat <- readRDS("db/contributions/mean_contrib_per_motif_instance.rds")
dat <- dat[tissue %in% c("midbrain", "limb", "heart"), .(contrib= mean(contrib.mot)), .(motif, dataset, tissue)]
dat... |
9147cb0018d9dfd46aca7a8603f3120b389ab8b9e49a8c5eb423662eea073246 | R | 2,132 | 51 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
require(vlfunctions)
# Bin the whole genome ----
bins <- vl_binBSgenome(BSgenome.Mmusculus.UCSC.mm10::BSgenome.Mmusculus.UCSC.mm10,
bins.width = 1001,
steps.width = 1000)
# Resize to account for later augmentati... |
627bf76abdcc729e88919daedbb469cdc7339293622e714d6c9f9f181b1bff65 | R | 2,133 | 94 | ---
title: "Outlier analysis"
output: html_notebook
---
Winsorizing seems like a better approach to deal with outliers compared to mean imputation. This script will focus on winsorizing all variables before checking for multivariate outliers.
# Reading in the data
```{r, message=FALSE, warning=FALSE}
library(tidyver... |
0c36c42b90b630998b7bb710ca404e210c4973017ed788c70ac9ee322e2ae2df | R | 2,135 | 32 | suppressPackageStartupMessages({library(data.table);library(limma)})
S <- "__MS_GEO_ROOT__/Methylation_Data"
E <- "__MS_GEO_ROOT__/Expression_Data"
CAND <- c("ITGB2","IKZF1","CD79B","LXN","SH3BP4","RUNX3","CASP6","CASP8","DGKQ",
"MX1","IFIT1","NUP210","CTSZ","CHL1","ICAM1","HLA-E")
dt <- fread(file.path(E,"Co... |
8f06d8df2b4dd64880e719d3c214975c426ec9ea744aaf4844e1293b70cfcb64 | R | 2,135 | 56 | #!/usr/bin/env Rscript
## 05_whole_blood_de.R — generated from notebook spec
## Run: Rscript 05_whole_blood_de.R
## ============================================================
## # 05 — Whole blood stratum DE (R/limma)
##
## R/limma rerun of stratum `cell_tissue_case_control_whole_blood` from
## `Stratified_Analy... |
b035a4c50413d77d7fa5d9a34fa6795022815666407df5645396ab7b7b715c48 | R | 2,136 | 57 | #!/usr/bin/env Rscript
## 02_tcells_de.R — generated from notebook spec
## Run: Rscript 02_tcells_de.R
## ============================================================
## # 02 — T cells stratum DE (R/limma)
##
## R/limma rerun of stratum `cell_tissue_case_control_t_cells` from
## `Stratified_Analyses/Expression/`. ... |
2dd1e3861cf0a5af6d4fdf38b5c82402d8a344b37bb04e25ef8f6bab1477cfb2 | R | 2,143 | 57 | #!/usr/bin/env Rscript
## 01_pbmc_de.R — generated from notebook spec
## Run: Rscript 01_pbmc_de.R
## ============================================================
## # 01 — PBMC stratum DE (R/limma)
##
## R/limma rerun of stratum `cell_tissue_case_control_pbmc` from
## `Stratified_Analyses/Expression/`. Uses the a... |
845a6939872ab9fe36ca8f05ea8f72f6451ccf168d05448fe399bc11e0f0ea33 | R | 2,172 | 79 | setwd("/groups/stark/vloubiere/projects/DeepATAC_shenzhi/")
# devtools::load_all("/groups/stark/vloubiere/vlite/")
devtools::load_all("/groups/stark/vloubiere/vlite-dev/")
# Import metadata ----
meta <- readRDS("Rdata/paper_metadata_v3.rds")
meta <- meta[dataset=="activity" & ID=="model1_bulkATAC_tsx3Aug_2xBal_noW" & ... |
4db976c739a1a85e1e4e49fb7901c7962f74da7da4af73425b6a1560074700f3 | R | 2,173 | 74 | ---
title: "R Notebook"
output: html_notebook
---
<!--
================================================================================
parac1_file_sorting.Rmd
================================================================================
# Helper utility: PA-Rac1-specific file sorting variant.
# Same body shape as... |
e4500a0c79ed898c91002cbfe6dce32a0a89e0601e58fe5c3e47a0af6d6024a7 | R | 2,174 | 57 | #!/usr/bin/env Rscript
## 04_brainwm_de.R — generated from notebook spec
## Run: Rscript 04_brainwm_de.R
## ============================================================
## # 04 — Brain WM stratum DE (R/limma)
##
## R/limma rerun of stratum `cell_tissue_case_control_brain_wm` from
## `Stratified_Analyses/Expression... |
e562c37292b5baf9250bae754a3af7af3519512190cb213b519404d51efd0ef4 | R | 2,176 | 67 |
# Sanity check functions
.check_equal_length <- function(left, right) {
name_left <- deparse(substitute(left))
name_right <- deparse(substitute(right))
if (!(is.null(left)) & !(is.null(right))) {
if (length(left) != length(right)) {
stop(paste("Length of", name_left,"and",name_right,"should be equal"),... |
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