sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
e9fbceebfd6edfb347c418e568d74e3f82ecbc9e0dad67663d652a0379f836e8 | R | 2,561 | 70 | # Load necessary libraries
library(data.table)
library(optparse)
# Parse command line options
option_list = list(
make_option(c("--bamdir"), type="character", default=NULL, help="Directory containing BAM files", metavar="directory")
)
opt_parser = OptionParser(option_list=option_list)
opt = parse_args(opt_parser)
... |
e4c1b606ea6a67c962fba0102ed6777c2c8e1852ad5228b482901fb3c7263806 | R | 2,562 | 92 | # Siwei 05 Jul 2023
# plot 1MB proximal region of rs10792832
# chr11:86156833
# init #####
library(Gviz)
library(rtracklayer)
library(BSgenome)
library(BSgenome.Hsapiens.UCSC.hg38)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(ensembldb)
library(org.Hs.eg.db)
library(grDevices)
library(gridExtra)
library(RColo... |
a69a4a210973f5ceb01e52c51e6c005094f17509b1d88c64e25beb9a23b4bfd9 | R | 2,567 | 86 | ---
title: "exp042_neutrophils_trajectory"
output: html_document
date: "2023-03-30"
---
Code for pseudotime analysis
```{r}
setwd("/Users/mary/non_dropbox/exps/exp042_meninges_stress_dropseq/")
library(ggplot2)
library(tidyverse)
library(magrittr)
library(here)
# Load sce object
load(here("res/processed.RData"))
`... |
7a84c2fc587d5528e3fff6b90cd70c29cabc6bf8f551920bfeedd7ab33a99dea | R | 2,570 | 89 |
## Annotate EWAS results with ATAC-seq peaks ##
library(data.table)
# Run either...
# bulk fetal
EWASres <- paste0(resultsPath, "ageReg_fetalBrain_EX3_23pcw_annotAllCols_filtered.rds")
outputFile <- "ageReg_fetalBrain_annot_allTissuePeaks.rds"
celltypes <- NULL
Pcol <- 'P.Age'
# FANS fetal
EWASres <- paste0(resu... |
43930bfd3e191ca2e015a768bf7d4b43ade8592b577fe51a8af13de58a162250 | R | 2,571 | 74 | library(coin)
library(exact2x2)
tables = list()
tables[[1]] = matrix(c(23, 15, 19, 31), ncol=2, byrow=TRUE)
tables[[2]] = matrix(c(144, 33, 84, 126,
2, 4, 14, 29,
0, 2, 6, 25,
0, 0, 1, 5), ncol=4, byrow=TRUE)
tables[[3]] = matrix(c(20, 10, 5,
3, 30, 15,
0, 5, 40),
... |
e4d3232c530a4fde1b7d1833294ed33d3cadb6a3be482ff4c41bb61379e95f3f | R | 2,572 | 75 | ---
title: "Plotting a CONSORT Flow Diagram"
output:
html_document: default
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
CONSORT Flow Diagrams can be used to plot the flow of data selection of a patient cohort.
For more details, see:
[http://www.consort-statement... |
2801bed99e3b7e0b54d30e3c8f6d7cbc3c1e03a2f708a107b1152e58963ab421 | R | 2,576 | 77 | ## Data Preparation WGCNA
# Written by LZ 2021-07-14
# last change: LZ 2021-07-16
library(WGCNA)
library(data.table)
library(dplyr)
library(readr)
library(varhandle)
library(matrixStats)
library(DESeq2)
DF <- data.frame
setwd("/path/to/")
options(stringsAsFactors = FALSE);
get(load("WGCNA/log_counts.Rdata"))
... |
d6fc3c4d44dee2a62aac27e54288b817b635080570951f93b4b5ceda1378ab95 | R | 2,577 | 51 | #radius of gyration linear regression model
radius_regression=read.csv('..data/radius_seperate.csv')
model_radius<-lm(radius_difference~pLDDT+ES_ISO+ES_REF+ADS+ACS+MXE+IR_ISO+IR_REF+AFE+ALE+MXE_AFE+MXE_ALE,data=radius_regression)
summary(model_radius)
effect_size <- coef(model_radius)
p_value <- summary(model_radi... |
4712a4678184b684820eee5d2ec342c53b5d7c3a17dd501ca36043def9d47b9f | R | 2,582 | 89 | #' Install aif360 and its dependencies
#'
#' @inheritParams reticulate::conda_list
#'
#' @param method Installation method. By default, "auto" automatically finds a
#' method that will work in the local environment. Change the default to force
#' a specific installation method. Note that the "virtualenv" method is ... |
fd789a1945c6190c8286d4e50c5223007d5a6fbbe968e9b01b3e59d636fc7b39 | R | 2,584 | 72 | # load packages
require(tidyverse)
require(Seurat)
# Load data --------------------------------------------------------------------
data.dir <- '/path/to/filter_multimap_counts/'
setwd(data.dir)
# get directory names
subdir.names <- list.files(data.dir, pattern = 'filtered',
recursive = T,... |
1598a3882a6b167124c4bc28674cf5974bd2c982b15ad24f0a1550b421f1f4e2 | R | 2,592 | 92 | # Helper functions for working with KFAS objects
cbind.fill <- function(...){
nm <- list(...)
nm <- lapply(nm, as.matrix)
n <- max(sapply(nm, nrow))
do.call(cbind, lapply(nm, function (x)
rbind(x, matrix(, n-nrow(x), ncol(x)))))
}
flatten <- function(m) {
f <- t(apply(m, 3, c))
dm <- dim(m)
df <... |
44975ee2ec8ed5bf4c86067bd89189750486a6776113116b6bd14fa3006e9290 | R | 2,600 | 78 | # =============================================================================
# 细胞类型差异表达分析脚本
# =============================================================================
# 功能：按细胞类型进行差异表达分析和GSEA富集分析
print("Hello world!")
rm(list = ls())
# 加载配置文件
source("../config.R")
# 设置工作目录
setwd(ENV_DIR)
lf <- list.files("./"... |
fd8344cf45d7779ffdf6bc65788695290748d96c3fc856822112fd76ff9e09d0 | R | 2,611 | 95 | #' Use R to upload a file to the UK Biobank RAP
#'
#' @description Use R to upload a file to the UK Biobank RAP (really just a wrapper for `dx upload`)
#'
#' @return NA
#'
#' @author Luke Pilling
#'
#' @name upload_to_rap
#'
#' @param file A string. Filename of the file to be uploaded (character)
#' @param dir A string... |
0b07f8410b12c56bd11bd4ecb445a7749ac17554c824c8d6e94ceb19e999acd0 | R | 2,617 | 57 | #' Small Subset of Tabula Muris FACS scRNA-seq data
#'
#' A subset of 4000 cells from the Tabula Muris FACS scRNA-seq data
#' that has already been normalized using the `scran` package, providing
#' the log-normalized counts. Cluster labels are under a labeled `cluster_name` column.
#'
#' @format ## `tmfacs_sce_small`
... |
914d118160a9ed909b944e6de42bb3a05635374f8415ddb6a1af87f678e1538c | R | 2,617 | 72 | # load packages
require(tidyverse)
require(Seurat)
# Load data --------------------------------------------------------------------
data.dir <- '/path/to/mmCortex_snRNA-seq/filter_multimap_counts/'
setwd(data.dir)
# get directory names
subdir.names <- list.files(data.dir, pattern = 'filtered',
... |
786d9ff4ffa7d83feb72244c3609be70864f9dc349a91e5fd79323ace4191b23 | R | 2,619 | 87 | #This script applies a threshold to normCoverage and splits it in several sub files to execute with k fold batch strategy
#coverage=read.csv(file="Y:/Microbiome/NDcollect/HQ/tablesBS/normCoverage.csv")
load("E:/BackupInesLeave/ND/HQ/tablesBS/2018-12-07workspace.RData")
coverage=agspeciesMat
load("E:/BackupInesLeave/ND... |
21c4bbace73c1e548af13a119311b50a88e28213f12e77a02f4c7be454347033 | R | 2,628 | 81 | #script to compute the DEGs inside the PD brain organoids dataset
#06/2024 Sophie Le Bars
#LOAD the R package
library(Seurat)
library(scales)
library(dplyr)
PD_brain=readRDS("combined.all_norm_PCA_INT_ANOT_DB.rds")#READ the preprocessed and annotated Seurat object from Sarah Nickels paper
DimPlot(PD_brain, ... |
a1f2d1e9cb77423bc88ef95203ef85f3e9685894f358ded62f6e011dac5d99ba | R | 2,638 | 74 | library(readr)
library(dplyr)
library(optparse)
option_list <- list(
make_option(c("-s", "--samplesheet"), type = 'character',
help="samplesheet à la CIRIquant, without paths"),
make_option(c("-p", "--path"), type = 'character',
help="source path to get gtf from"),
make_option(c("-o"... |
044254ebdfc878e22d00fb29d68c0bd1def4a0ba31fb85660309635bf8a91d94 | R | 2,639 | 45 | #!/usr/bin/env Rscript
library(rmarkdown)
library(optparse)
option_list = list(
make_option(c("-r", "--report"), type="character", default=NULL, help="report template file", metavar="character"),
make_option(c("-o", "--output"), type="character", default="RNAseq_report.html", help="output file name", metavar="cha... |
157b86046d09bfc8f865a6185e1c268c999a873f47b5c2e91f2e7767a890336b | R | 2,639 | 72 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == "academic" && is.null(GLOBAL_VALUE$Academic[[1]])) {
GLOBAL_VALUE$Academic <- list(
onset_to_confirmed_map = fread(paste0(DATA_PATH, "/Academic/onset2ConfirmedMap.csv"))
)
}
# data <- fread(paste0(DATA_PATH, '/Academic/onset2ConfirmedMap.csv')... |
55565bd3e21d622e668ec77bdd96c6146c2da64c609afd0b567240ed6381fde1 | R | 2,639 | 74 | # Load packages
library(tidyverse)
# Clear global environment
rm(list=ls())
dt.path = "/home/emba/Documents/EMBA/BVET"
df.inc = read_delim("/home/emba/Documents/EMBA/VMM_analysis/FSL_dur/all_use-new", show_col_types = F) %>%
filter(diagnosis != "pilot") %>% select(subID, diagnosis)
# load raw data
# columns of In... |
e872988ca3e06fc9cd25a5480a4b360bebdc209bcd533cec78ab3a1b6550f3e2 | R | 2,643 | 106 | # データの読み込み
loadDataFromFile <- function(fileList, FilePath, fileName, object, index) {
# 実際のファイル名を取得
dataName <- fileList[sapply(fileList, function(x) {grepl(fileName, x)})]
# 保存
object[[index]] <- fread(file = paste0(DATA_PATH, FilePath, dataName))
return(object)
}
convertUnit2Ja <- function(x) {
x <- as.... |
7198f6dae624d55b6d217589872e9837764ce0dd5298a2a943ec7c412794bd5f | R | 2,644 | 65 | setwd("MERFISH_Moffit/")
library(liger)
library(Seurat)
library(ggplot2)
# Moffit RNA
Moffit <- Read10X("data/Moffit_RNA/GSE113576/")
Moffit <- as.matrix(Moffit)
Genes_count = rowSums(Moffit > 0)
Moffit <- Moffit[Genes_count>=10,]
# MERFISH
MERFISH <- read.csv(file = "data/MERFISH/Moffitt_and_Bambah-Muk... |
1de582b1bb5c03b02ff96545e7fb607cac8330a13889fbbf64009fd1915078e5 | R | 2,652 | 59 | predict.multi.MM <- function (M, new_dat, pred_time = 180, alpha = 0.20){
# this function retrieves predictions of Selles MM for multiple patients. Since prediction function for Selles MM
# (IndvPred_lme) only works for individual patients this function loops through patients and stores results.
#
# Arguments:
... |
1cea1ba9bda74b5874a80bcc4c8d8743c8d478fb474818594e54b4ee2433855c | R | 2,655 | 67 | #' Prepare dynamic range data for a single source (LFQ or LBQ)
#'
#' Extracts and standardizes abundance information from input data for a specific source,
#' ranks the values by descending abundance, and tags the data with the source label.
#'
#' @param data A data frame containing abundance values. For LFQ, must incl... |
2c2a91751b364d88b5b65971ec1fb8042462fc07aa084b577e673569f9dbff21 | R | 2,660 | 76 | # First run main.R until definition of Selles et al. Mixed Model (M_mm)
# this scripts retrains a base XGB model and plots the relative feature importance
# through a SHAP beeswarm plot
library(SHAPforxgboost)
# 1) create model formula for shap model
shap_ftrs <- xgb_ftrs[xgb_ftrs != "Number"]
dummy_ftrs <- c("SA.0",... |
3b1d9333ea5a0adaf61cfa217db4f9ff9b72cc078d71bf9bc6d815072966c580 | R | 2,665 | 106 | # Siwei 03 Nov 2022
# make plots for Alena
library(ggplot2)
library(readxl)
library(stringr)
Alena_table <- read_excel("Alena_table.xlsx")
# add a small value to BinomFDRQ for -log10 transformation
Alena_table$BinomFdrQ <-
Alena_table$BinomFdrQ + 1e-301
df_to_plot <-
Alena_table[1:20, ]
df_to_plot$GO_disc <-
... |
ffd5566e3619768c4d762bf16d3628771db19dbb9e092aba583c5053f1dd2db5 | R | 2,670 | 49 |
## Plot first principal component (PC1) vs Age for each WGCNA module ##
plotAllModuleProbes <- function(betas, pheno, module, module_df, returnSubmod=FALSE, colour, toPlot=TRUE, loop=TRUE, i=NULL, axisTextSize=19, axisTitleSize=22, plotTitleSize=23, xAxisCol='Age'){
#1. Extract DNAm data for all probes in module =... |
3eb0dc5b39bc21ce586bc9d70838ef19f2ddd03985127d5472cfa416807b2e48 | R | 2,671 | 46 | #### Running DGE ####
#### Oligodendrocytes analysis ####
### Pre work ####
library(tidyverse)
library(Seurat)
library(SeuratObject)
library(ggplot2)
library(doParallel)
library(future)
library(cowplot)
library(patchwork)
library(monocle3)
library(monocle)
library(SeuratWrappers)
library(Nebulosa)
librar... |
e378b822b059bbf83a2cda353279dc23fe905a4e602287f803c969607edcdaab | R | 2,686 | 47 | # this script serves to create a visualisation of the imputation predictor matrix and methods
library(tidyverse) # version 2.0.0
library(MetBrewer) # version 0.2.0
# define colourblind-frinedly palette
palet <- met.brewer("Hiroshige")
# load predictormatrix with method column as df
predmat <- read.csv("/data/pt_life/R... |
00a46d41a12c52671e969a62033581904afff996c51b68a49d5dc56e154e50bc | R | 2,687 | 66 |
## Comparison of Age linear regression statistics in an independent Illumina EPIC 450K fetal cortex cohort ##
# Fetal 450K cohort: Spiers et al. (2015). DOI:10.1101/gr.180273.114
library(data.table)
library(ggplot2)
library(grid)
library(gridExtra)
#1. Load results ===============================================... |
422a0773ad9be478d28ba6c106fd1439fa738dd6d0c7e98ec6f8fa853d716216 | R | 2,697 | 64 | # utils/plink_utils.R
# -----------------------------------------------------------------------
# Drop-in replacement for plink2R::read_plink().
# Zero external dependencies — uses only base R.
#
# Returns list(bed, bim, fam) with the same structure as plink2R:
# $bed N×P numeric matrix; dosage = copies of A1 (first... |
b839de24b42475e271cc96e1045cf4a0efbaf07f656662ba7dc80365452a5ead | R | 2,698 | 62 | # load packages
require(tidyverse)
require(Seurat)
require(pbapply)
require(tictoc)
# load data and gene names
sdata.align <- readRDS('sdata_align_pseudotime_minmin.rds')
m.downsample <- readRDS('downsampled_counts_matrix.rds')
gene.names <- read_csv('gene_names.csv')
# get metadata, remove cells with NA pseudotime ... |
b85cfc41377ae6c63c5cdaa1d85eb7a270d63ed5ccb6387ed5355c71a6c6b182 | R | 2,699 | 72 |
library("SingleCellExperiment")
library("purrr")
library("here")
library("sessioninfo")
# Tab-delimited tabular input format (.txt) with no double quotations and no missing entries.
data_dir <- here("processed-data" , "08_bulk_deconvolution", "07_deconvolution_CIBERSORTx_prep")
if(!dir.exists(data_dir)) dir.create(da... |
5d750c48b1c8350dbba180c8ad61286fee4086492cce0446578d6e9f3eb77eca | R | 2,700 | 90 | # =============================================================================
# 单细胞代谢通路分析脚本
# =============================================================================
# 功能：使用AUCell方法计算KEGG代谢通路活性
print("Hello world!")
rm(list = ls())
# 加载配置文件
source("../config.R")
# 设置工作目录
setwd(ENV_DIR)
lf <- list.files("./")... |
98e72b98b5a5a6943774584e8ffab78a1ecd272a788b7937067e0b7c73551dd9 | R | 2,708 | 55 |
## Linear regression to test association of DNA methylation with Age and Sex in bulk fetal cortex ##
library(data.table)
library(doParallel)
#1. Load data ===================================================================================================================
load(paste0(PathToBetas, "fetalBulk_EX3_23p... |
34a068244a1363f3faf16b3725adc40973767e9349f23ce708107b60443714b6 | R | 2,712 | 63 | #################################################################################
#################################################################################
### Cell Type Composition Deconvolution Using Dampened Weighted Least Squares ###
##########################################################################... |
01c576808a29d76c60d6aa69e73e86edf840304c895c7f6c9fa656306d7eb4ad | R | 2,714 | 98 |
# 25 May 2022 Siwei
# process new MG batch data (May 2022)
# note that this batch of MG bulk ATAC-seq data has lower quality
# since their original samples had been frozen-thawed.
# init
library(readr)
library(plyr)
library(dplyr)
library(stringr)
library(Rfast)
library(ggplot2)
library(RColorBrewer)
# init
lib... |
b494a850d65d7e4880fcc36a0230018eb4e59bf6508a68810d604949224cd757 | R | 2,715 | 51 | # function
get_mv_exp_data <- function(l_full=l_full,
min_pval = 1e-200,
log_pval = FALSE,
pval_threshold = 5e-08,
clump_r2 = 0.001,
clump_kb = 10000,
harmonise_strictness = 2 )
{
source(... |
1730f7c7d0bbc9ace48ae72adcce27fa0acb54303466d9443627009941ef1e69 | R | 2,723 | 66 |
library(dplyr)
library(Seurat)
library(ggplot2)
library(googleVis)
# Set address
setwd("/project/Campbell_Lab/yl7mfw/Data Analysis/20240811_Analysis for supplementary figures/FigS1/Comparison with Mouse sSC atlas")
# Load TM.integrated and mouse sSC dataset
sSC.integrated <- readRDS("/sfs/gpfs/tardis/project/Campbel... |
32fc418aef88adeb0d50245b709074c41e981c40fc18ec3af83747de27c56133 | R | 2,723 | 83 | fluidPage(
fluidRow(
column(
width = 5,
style = "padding:0px;",
userBox(
title = userDescription(
title = i18n$t("茨城県"),
image = "Pref/ibaraki.png",
subtitle = i18n$t("関東地方"),
type = 2,
),
width = 12,
status = "navy",
... |
0128c5fbb1e3ade91047e68cab883ee12f2b9f4988211cad8cb4e527b3b478ee | R | 2,726 | 72 | library(tidyverse)
library(fgsea)
source("helpers.R")
set.seed(123)
### GSEA on FC of different event types
# i.e. are the subset of ALE event type genes that show consistent directionality significant with respect to random gene sets of the same size?
plot_df_cryp_volc <- read_tsv("processed/2023-09-26_riboseq_vo... |
03d4685eae1aa6bcad98becf76cc68bead0d28a9ce47669b04ca2f4f9a504f65 | R | 2,734 | 55 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
source(file.path(general_scripts_folder, "RNA", "create_seurat_from_cr_h5.R"))
library(ggplot2)
library(Seurat)
library(qs)
library(tidyverse)
counts_folder <- file.path(project_folder, "... |
9c562def6f067f9779166288cf6c55abe0f2a0de82a6262ac892c602fa378f50 | R | 2,734 | 78 |
library("SummarizedExperiment")
library("tidyverse")
library("here")
library("sessioninfo")
library("jaffelab")
#### Load data ####
## marker_stats
load(here("processed-data", "06_marker_genes", "03_find_markers_broad", "marker_stats_broad.Rdata"), verbose = TRUE)
load(here("processed-data","rse", "rse_gene.Rdata"), ... |
bdb50fccca6f01265c4b3a4f1a39132f989deed8f3089f510f8c9209234d90f7 | R | 2,737 | 55 | library(stringr)
library(data.table)
# library(rvest)
url <- "https://covid19.jsicm.org"
# page <- read_html(url)
# page %>% html_nodes("script")
# list.files(url)
source <- readLines("https://covid19.jsicm.org/_nuxt/a61a103ff0db7be8db27.js")
jsonData <- str_extract_all(gsub('\"', "", source), "JSON.parse\\(.+?\\)")[... |
a99016c7eec7882eaa97f9332e78d158c57e86c56ed7c4445a3d74b6665c1ff3 | R | 2,745 | 72 | #!/usr/bin/env Rscript
### title: Diffusion component analysis of dendritic cells (DCs)
### author: Jana Biermann, PhD
print(Sys.time())
library(Seurat)
library(destiny)
library(dplyr)
library(ggplot2)
library(gplots)
library(viridis)
library(scales)
colBP <- c('#A80D11', '#008DB8')
colSCSN <- c('#E1AC24', '#288F56... |
45d1b20e41325fa2e38f24c5f7e42cf67f646da37e3cf8dd64f70c7bb5a8eac2 | R | 2,753 | 62 | library(tidyverse)
library(ggplot2)
library(ComplexHeatmap)
library(circlize)
library(colorspace)
# Setting working directory -----------------------------------------------
this.dir <- dirname(parent.frame(2)$ofile)
setwd(this.dir)
# Data paths --------------------------------------------------------------
proje... |
fd90cfaa4c226e25de5169322bdf1f8dbf0ebdc113881e1c9686e2bec6ada961 | R | 2,755 | 68 | # This tests the recoverDoublets function.
# library(scDblFinder); library(testthat); source("test-recoverDoublets.R")
set.seed(99000077)
ngenes <- 100
mu1 <- 2^rexp(ngenes) * 5
mu2 <- 2^rnorm(ngenes) * 5
counts.1 <- matrix(rpois(ngenes*100, mu1), nrow=ngenes)
counts.2 <- matrix(rpois(ngenes*100, mu2), nrow=ngenes)
c... |
025584d186e902e8a6d7401d61be146e4a82c99fcfaa43c01f062b59aff6c560 | R | 2,756 | 65 | library("SingleCellExperiment")
library("here")
library("sessioninfo")
## Access Tran et al.
# Download and save a local cache of the data available at:
# https://github.com/LieberInstitute/10xPilot_snRNAseq-human#processed-data
bfc <- BiocFileCache::BiocFileCache()
url <- paste0(
"https://libd-snrnaseq-pilot.s3.us... |
e7a097496509f8d872b175f9cd68547abe86c80503b629f5a48f67bf7ad0d25c | R | 2,756 | 99 | ConfirmedPyramidData <- function(data) {
# [todo] data.tableの次期バージョンがCRANに登録されたらfifelse()での処理を削除
# data.table (remote::install_github('Rdatatable/data.table@b1b1832'))
if (packageVersion("data.table") > "1.12.8") {
data[, `:=`
(
年齢 = data.table::fcase(
年齢 == "00代", "10歳未満",
年齢 == "10... |
2586e7325f811337dbca20a5cab039d83b3e38b8d1854fd77788f8c23ae86164 | R | 2,758 | 84 | fluidPage(
fluidRow(
column(
width = 5,
style = "padding:0px;",
userBox(
width = 12,
title = userDescription(
title = i18n$t("北海道"),
subtitle = i18n$t("北海道"),
type = 2,
image = "Pref/hokkaido.png"
),
status = "navy",
... |
95f52645709ca690b815748c81f74a7b7242ecf6ae0c52bb0cc3e7d8465d4438 | R | 2,764 | 77 | library(DESeq2)
library(tidyverse)
# Read Data ---------------------------------------------------------------
# DDS object with allele-specific counts at the gene level
dds <- readRDS("results/DESeqDataSet/dds_gene_allele.rds")
# filtering out lowly-expressed genes and one sample with very few reads
dds <- dds[row... |
ad56dbfbed4259dbbe2e13f7d4bb98b750dd0d0e4aeaf5a1fd95c0dd71430eb0 | R | 2,768 | 66 | source("src/libraries.R")
load("report01/02.merge/COGA.merged.list.allPeaks.Rdat")
if(T){
ATAC.list = dd.list
# Assays(ATAC.list[[1]])
cat("================ Normalization ===================")
ATAC.list <- lapply(X=ATAC.list, FUN=function(x) {
DefaultAssay(x) = "ATAC_Allcomm... |
0ca644b0c9f541a9db6e0c4ada417382f374a9b1206f7765c7edc7d6d25f2eba | R | 2,779 | 77 | # 03 Batch Correction De.R
# 03 Batch Correction De.R
#######################################################################
#######################################################################
# Create a DE matrix
#######################################################################
###########################... |
6f8df13a3cb30668f3339709bc3d7e4626e294ad3c5b73edf30ea791cd97db96 | R | 2,782 | 65 | split_violin_pbd <- function(brain_region){
kznf_cortex <- kznf_exp_longer %>% filter(region==brain_region)
kznf_cortex <- kznf_cortex %>% filter(value>0)
kznf_cortex$species <-
factor(kznf_cortex$species,
levels = c("Human", "Chimpanzee", "Bonobo", "Macaque"))
kznf_cortex_v <- ... |
651a9c71e1fb2f7ec88deb71e01a0fc335508afc6657f2a082af4983f345c7ca | R | 2,786 | 59 | library(Matrix)
library(ggplot2)
library(Seurat)
library(dplyr)
library(stringr)
library(qs)
library(SoupX)
# replace any *...* with respective text
# Opening files and removing background mRNA using SoupX ----
samples <- paste0("Syn", c(1:17)) # lucicompen
for (sample in samples) {
print(paste0("Proce... |
2b9483851eb78399d57bc69bf8531c9990200ff14f6e718015efe69040564f21 | R | 2,793 | 76 | # GPT social perception: Preprocess the GPT4.1 data for video experiment
# 1. Read data for each batch and add the video names to the dataframes
# 4. Calculate mean datasets of all possible combinations of the datasets
# 5. Store results for analyses.
# Severi Santavirta 14.05.2025
library(psych)
library(string... |
b5ba4f5eae000bf0929a2b6203f69089474490275435ef2c9a5b4d0c45e6eab6 | R | 2,798 | 93 |
run_DE <- function(rse, model, run_voom = TRUE, save_eBayes = FALSE, coef, plot_name = NULL){
print_plots <- !is.null(plot_name)
if(print_plots) pdf(plot_name)
## limma
eBayes_out = get_eBayes(rse = rse, model = model, run_voom = run_voom, print_plots)
#### because of more than 1 component, compu... |
1cfcaf52ae62267c2bc0732a5ebb30bb9399f27b88c74e1a5a5a1248f59f692c | R | 2,801 | 87 | # Warning! R 3.4 and Bioconductor 3.5 are required for splatter!
# library(BiocInstaller)
# biocLite('splatter')
library(splatter) # requires splatter >= 1.2.0
save.sim <- function(sim, dir) {
counts <- counts(sim)
truecounts <- assays(sim)$TrueCounts
drp <- 'Dropout' %in% names(assays(sim))
if (drp) {
... |
1f8d6ba155181d4d60dc7cedcda16496dda4f0437d34558bfe3353a489627e56 | R | 2,803 | 83 | #!/usr/bin/env R
# Author: Sean Maden
#
# Read in HALO settings files.
#
# Notes:
# * Returned table has the following columns:
# - "parameter_name" : value from "Parameter Name" tag, where available.
# - "value": value from "Value" tag, where available.
# - "priority": value from "Priority" ... |
47e73fe6878a655ed73cd60b840270f14a748082226fee5525b45c5693b33675 | R | 2,805 | 69 | #Run GO-BP enrichment analyses ###################################################################
#Load libraries
library(clusterProfiler)
library(org.Hs.eg.db)
library(tidyverse)
#List files
files = list.files(path=".", pattern="exclusive_")
#Create empty dataframe to store results
final = data.frame(GO.id=charact... |
f74b0df2719e99fe5c38b7424f57a4689b3edcf2a92e91586a24753d39ccae64 | R | 2,809 | 74 | library(tidyverse)
bed_dir <- "data/dapars_comparison/apaeval_dapars2_snakemake/seddighi_i3_cortical/exts_elk1_six3_tlx1/rel_quant_beds"
bed_regex <- "_04\\.bed$"
ctrl_regex <- "^NT_"
kd_regex <- "^TDP43_"
outdir <- "processed/dapars_comparison/"
# list full paths to files in a directory matching a regex (and name v... |
db221e9638ed780be2106afd8be1e0199e10c0798f2c8b40ee7295ee6419cd50 | R | 2,825 | 63 |
## Filter probes on IQR ##
# path.betas = full path of DNAm matrix (betas), including filename. Can take .rdat or .rds. If .rdat, function assumes DNAm matrix object contains 'betas' in name.
# path.output = directory path of output file
# name.output = filename of output file. Will be appended to path.output
# npro... |
6f89268a157f3269b9187202ef3038e701fb53c9f0a1dc0ab275ef7f204d26e3 | R | 2,830 | 90 | #!/usr/bin/env R
# Author: Sean Maden
#
# Read in HALO settings files.
#
# Notes:
# * Returned table has the following columns:
# - "parameter_name" : value from "Parameter Name" tag, where available.
# - "value": value from "Value" tag, where available.
# - "priority": value from "Priority" tag, where ava... |
a9f65231ac9a6d98dc1e675b48a50752ef03561f050f2866cd810bb4d885c932 | R | 2,836 | 94 | #!/usr/bin/env Rscript
# in IsoLamp main, if grouping_variable is NULL, don't run Rscript
suppressPackageStartupMessages({
library(reshape)
library(dplyr)
library(rstatix)
library(purrr)
library(optparse)
})
# importing
option_list = list(
make_option(c("-i", "--input"), type="character", default=NULL,
... |
b6f615a5be38ba2e88031b0e645714890c02e54b56f544c73a5bd86a670d2108 | R | 2,837 | 67 | sce <- mockDoubletSCE(ncells=c(100,200,150,100), ngenes=250)
sce$fastcluster <- fastcluster(sce, nfeatures=100, verbose=FALSE)
sce$sample <- sample(LETTERS[1:2], ncol(sce), replace=TRUE)
test_that("fastcluster works as expected",{
expect_equal(sum(is.na(sce$fastcluster)),0)
expect_gt(sum(apply(table(sce$cluster, s... |
fdf28e06de733c0b4a6ded42a5ac6efbd75d49f78942b5aa15aa27a2f8876e55 | R | 2,839 | 56 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
library(MutationalPatterns)
#### COSMIC decomposition by clinical groups ####
# load COSMIC signatures
cosmic_signatures <- as.matrix(read.table("d... |
8390d9b08a77faa2fb2c90c4905fc8c7cc6aee0644b05bb1578f27778cac156b | R | 2,842 | 69 |
## Apply epigenetic clock functions to late fetal, child and adult samples aged between 26 pcw - 104 years.
# Hovath Clock function (https://www.rdocumentation.org/packages/wateRmelon/versions/1.16.0/topics/agep)
# Cortical Clock function (https://github.com/gemmashireby/CorticalClock)
library(ggplot2)
library(wateR... |
95b4ef7c24e8c0b38b5eab81695324c5507947ce2b83575c3ff9beb5c1df5115 | R | 2,844 | 95 | ---
title: "Investigative needle core biopsies for multi-omics in Glioblastoma"
author: "Gerard Baquer"
date: "7/31/2023"
output: html_document
---
```{r}
#Libraries
library("ggplot2")
library("ggrepel")
```
```{r}
#Read
pks<-GBMSpatialOmics:::load("PATH/TO/MSI_DATA")
#Load annotations
pks<-GBMSpatialOmics:::exp.loadA... |
5c8f3090e2c527327ba18a32eababd1bbf9b821c681d73d313ccbcb353ebeb6b | R | 2,846 | 112 |
library(ggplot2)
library(DESeq2)
raw_counts <- read.table("rc_WTd3_WTBMP_D6.txt", header = TRUE)
row.names(raw_counts) <- raw_counts[,1]
raw_counts <- raw_counts[,-1]
raw_counts <- as.matrix(raw_counts)
data <- raw_counts[rowSums(raw_counts) >= 0, ]
meta <- read.table("info_WTd3_WTBMP_D6.txt", header = T... |
6e25f3f24ded3b7a7247d46234be87e2bdec6328a0d25dacf78b956791b51c6e | R | 2,847 | 96 |
library("SummarizedExperiment")
library("edgeR")
library("variancePartition")
library("purrr")
library("here")
library("jaffelab")
library("sessioninfo")
#### Set up ####
## dirs
# plot_dir <- here("plots", "09_bulk_DE", "08_DREAM_library-type")
# if(!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
## d... |
f9740c98f55e00a9e7522a95aa451e99dca8b5acecdbfe135ad998574116e345 | R | 2,848 | 59 | library(topr)
library(tidyverse)
library(data.table)
library(dplyr)
results_log <- fread("geno_assoc_tfLFall_ukb.fastGWA", head=TRUE)#read in the dataset with merged latent factor sumstats (minimum p-value for each SNP)
reg <- fread('LF_final_GP3.csv', head=TRUE, sep=',')#read in the dataset with the latent factor ... |
c97a5e5f077779acce94f141e9af91d7d6f8ac0bc75e0b8351d1c9de88e2e6bb | R | 2,849 | 77 | # Reference values for PyMARE's correlated-effects implementation.
#
# Writes pymare/tests/data/robumeta_reference.json, which
# pymare/tests/test_robumeta_alignment.py reads. Run it through the harness in
# this directory rather than directly, so the R and robumeta versions are the
# pinned ones:
#
# validation/ro... |
9b7870cabe261a5f4a807e9e99785d21b8ebbdfe1b3c79bffa3d4b7d814628c5 | R | 2,850 | 87 | # =============================================================================
# 巨噬细胞代谢通路VISION分析脚本
# =============================================================================
# 功能：巨噬细胞亚群代谢通路分析，使用VISION方法计算KEGG代谢通路活性
print("Hello world!")
rm(list = ls())
# 加载配置文件
source("../config.R")
# 设置工作目录
setwd(ENV_DIR)
lf... |
794a1d53931a0ec36451398154ce99bdff951ac8c16ed22acf8424088db91e85 | R | 2,852 | 99 | library(tidyverse)
library(scattermore)
library(Seurat)
library(here)
#load and wrangle data
meta <- readRDS(here("data", "human_meta.RDS"))
reductions <- readRDS("data", "human_all_umap_coords_2d.RDS"))
to_plot <- reductions %>%
as_tibble(rownames = "sample_id") %>%
filter(sample_id %in% meta$sample_id) %>%
... |
9cd14b348471012401f99adf65178bf382a4a4dec152818f97ca59cdf3a866c9 | R | 2,852 | 67 | ---
title: "R Notebook of rV2 manuscript figure S1 plots"
output: html_notebook
---
```{r Packagies}
library(tidyverse)
library(Seurat)
library(Signac)
library(qs)
source("local_settings.R")
```
```{r Set parameters}
cores <- 6
```
```{r Load qs objects}
e.e12.r1 <- qread("../scRNA_data/e.e12_r1_200623.qs", nthreads... |
b72114be2c3d3bbb3c3b07b63129b5e6f2dfcc9b5bb5fb339a1cd2f59182fa03 | R | 2,858 | 92 | suppressPackageStartupMessages(library(GEOquery))
library(magrittr)
library(oligo)
library(pd.mogene.1.0.st.v1)
library(mogene10sttranscriptcluster.db)
library(dplyr)
GEO <- "GSE52333"
chip <- "mogene10st"
org <- "mm"
annot <- "ensg"
gse <- getGEO(GEO)[[1]]
base_dir <- paste(base::getwd(), "data-raw", sep="/")
if (... |
656ed29c8d214bce08a7286df17bc7a6a3c0f2d3ec073e7dcad610df889aa9b3 | R | 2,859 | 75 | library(mapgen)
setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
bigSNP <- bigsnpr::snp_attach(rdsfile = '/project2/xinhe/1kg/bigsnpr/EUR_variable_1kg.rds')
data('Euro_LD_Chunks', package='mapgen')
folder_gwas_mapgen <- "/project2/xinhe/xsun/psych_analysis/1.torus/data/gwas/"
file_gwas <- list.files(folder_gw... |
b00b21b1bff79733c8ed514216faeba3a7d71534486503540ac6f7bb5f6e8450 | R | 2,859 | 73 | # load packages
require(tidyverse)
require(seriation)
# load data
bin.paa.sax <- readRDS('bin_paa8_alpha9.rds')
# get SAX
sax.tb <- bin.paa.sax %>%
filter(!na.expression) %>% # remove empty genes
transmute(time_point, genotype, mouse_id, mouse_name, sax9) %>%
unnest_wider(col = sax9, names_sep = '_')
# subse... |
d84daa4b1a1b9aeb464593749c2af272b17f451f3ad5c2049be4f800ecc91511 | R | 2,865 | 75 | library(mapgen)
setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
bigSNP <- bigsnpr::snp_attach(rdsfile = '/project2/xinhe/1kg/bigsnpr/EUR_variable_1kg.rds')
data('Euro_LD_Chunks', package='mapgen')
folder_gwas_mapgen <- "/project2/xinhe/xsun/psych_analysis/1.torus/data/gwas/"
file_gwas <- list.files(folder_gw... |
be97ee1ae27676264092d42a057fec6dd19d914eb4928cfe8a98014ff12ff646 | R | 2,869 | 76 | library(mapgen)
setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
bigSNP <- bigsnpr::snp_attach(rdsfile = '/project2/xinhe/1kg/bigsnpr/EUR_variable_1kg.rds')
data('Euro_LD_Chunks', package='mapgen')
folder_gwas_mapgen <- "/project2/xinhe/xsun/psych_analysis/1.torus/data/gwas/"
file_gwas <- list.files(folder_gw... |
9b3c4e8b991d07b2854830cfcb8cbe8b81ab646524481c3590bb2d3f800a19ed | R | 2,872 | 90 | dev <- FALSE
if(dev) {
library(openxlsx2)
meta_data <- read_xlsx(file = "./data/Database/SampleMasterfile.xlsx",
sheet = 1,
skip_empty_rows = TRUE)
experiments <- sort(unique(meta_data$experimentId))
# remove all NLA stuff
experiments <- experiments[!grep... |
34ba54e848636ca8698643c2f4ba1edf87ca5c42e357416341e6907afb9271b3 | R | 2,873 | 75 | library(mapgen)
setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
bigSNP <- bigsnpr::snp_attach(rdsfile = '/project2/xinhe/1kg/bigsnpr/EUR_variable_1kg.rds')
data('Euro_LD_Chunks', package='mapgen')
folder_gwas_mapgen <- "/project2/xinhe/xsun/psych_analysis/1.torus/data/gwas/"
file_gwas <- list.files(folder_gw... |
4c7f934df2e29e8a366385a5ac3f6f2e7fa9cc83535fabb1dbdbea79b5e59547 | R | 2,876 | 84 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(qs)
library(foreach)
library(ggpubr)
library(Signac)
objects_folder <- file.path(project_folder, "objects", "R", "seurat")
qc_folder <- file.path(project_folder, "... |
b277575a61ad7788003c969adf86028bea39b1fbeec7f7ed0b4babe72e94b353 | R | 2,876 | 89 | library(Seurat)
library(BPCells)
blacklist <- c("BTRC_17_BTRC_17",
"BTRC_26_BTRC_26",
"BTRC_15_BTRC_15",
"BTRC_34_BTRC_34",
"BTRC_44_BTRC_44",
"BTRC_39_BTRC_39",
"BTRC_87_BTRC_87",
"BTRC_41_BTRC_41",
... |
995ca02946e2c4e06ceead70660c37a56e3aef65b56094563bdf5f01074e01f1 | R | 2,878 | 76 | library(mapgen)
setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
bigSNP <- bigsnpr::snp_attach(rdsfile = '/project2/xinhe/1kg/bigsnpr/EUR_variable_1kg.rds')
data('Euro_LD_Chunks', package='mapgen')
folder_gwas_mapgen <- "/project2/xinhe/xsun/psych_analysis/1.torus/data/gwas/"
file_gwas <- list.files(folder_gw... |
9c2c9f3dd591795dafe39de251735057c4332e55b8230e3dda571cb6f5ec8fb4 | R | 2,878 | 76 | library(mapgen)
setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
bigSNP <- bigsnpr::snp_attach(rdsfile = '/project2/xinhe/1kg/bigsnpr/EUR_variable_1kg.rds')
data('Euro_LD_Chunks', package='mapgen')
folder_gwas_mapgen <- "/project2/xinhe/xsun/psych_analysis/1.torus/data/gwas/"
file_gwas <- list.files(folder_gw... |
dca60e79500f1890538c4e5064236aaa28331a2bdfb05a01c716164d90e0c4fc | R | 2,878 | 76 | library(mapgen)
setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
bigSNP <- bigsnpr::snp_attach(rdsfile = '/project2/xinhe/1kg/bigsnpr/EUR_variable_1kg.rds')
data('Euro_LD_Chunks', package='mapgen')
folder_gwas_mapgen <- "/project2/xinhe/xsun/psych_analysis/1.torus/data/gwas/"
file_gwas <- list.files(folder_gw... |
b7666149e7ed940ee9a0cbc799b6a7fa1c095f7a9f4aa9c0185d983101725fb3 | R | 2,896 | 59 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
#### Correlation btw high Indels and high SNVs ####
df <- read.table("data/TableS3_PTA_burden.tsv", header=T, sep="\t")
df$Case_ID <- as.character(... |
67153da5a007b233a84435c34cd45e8192405a7af91d2346f3e6fc4aa0e0093e | R | 2,903 | 90 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(FactoMineR)
library(factoextra)
library(ggplot2)
library(ggrepel)
library(optparse)
})
option_list = list(
make_option(c("-i", "--input"), type="character", default=NULL,
help="input count_data"),
make_option(c("-o", "--ou... |
9ed182d7a91eb81b95e1121aa561e55758f58afcfe9f763d36431d6cf06aea96 | R | 2,904 | 72 | #' Check magma object / file is usable and returns it as a data.table
#'
#' @param magma input magma object / file
#' @param magma_gene_col character string corresponding to the column name
#' in the MAGMA data containing gene identifiers
#' @param magma_z_col character string corresponding to the column name
#' in the... |
26410b434bbb96305f6a69d0e506f4aa95a1b5fb9eac91cdf43622aaf0916189 | R | 2,907 | 98 | #------------------------Postprocessing------------------------
# Takes APAlyzer output and generates differential challenge
# output tsv file
# load libraries
if ( suppressWarnings(suppressPackageStartupMessages(require("optparse"))) == FALSE ) { stop("[ERROR] Package 'optparse' required! Aborted.") }
if ( suppressWa... |
9745ccea72267fda3fa7fd105deffc4bf503030b58bb166166e512a1c1ce4d9e | R | 2,907 | 112 | output$currentActive <- renderEcharts4r({
dt <- simpleMapDataset()
# 本日増加分
todayTotalIncreaseNumber <- sum(dt$diff, na.rm = T)
subText <- i18n$t("各都道府県からの新規報告なし")
if (todayTotalIncreaseNumber > 0) {
subText <- paste0(
sprintf(
i18n$t("発表がある%s都道府県合計新規%s人, 合計%s人\n\n"),
sum(dt$diff > 0)... |
1431638061ab075fe23695a92a76f9a642aa620911811730b3173bfeeba377ca | R | 2,912 | 66 | seven_mr_res <- function(dat){
###MR
library(TwoSampleMR)
res <- TwoSampleMR::mr(dat)
# check the least SNP counts
#print(paste0(id,"_SNPÊý_",res$nsnp[1]))
##Ôö¼ÓContamination mixtureºÍcML-MA-BIC·½·¨
##Contamination mixture£º£¨1£©robust£»£¨2£©Òì¹¹... |
a807903fb8a03dc82b158936632dc49346754e74d3dc6b43b2efeaee975165f8 | R | 2,916 | 99 | #!/usr/bin/env R
#
# Get marker genes for cell types using mean ratio expression (log counts).
# Uses DeconvoBuddies::get_mean_ratio2() function.
#
# devtools::install_github("https://github.com/LieberInstitute/DeconvoBuddies")
library(DeconvoBuddies) # contains get_mean_ratio2() to get marker genes
library(SingleCel... |
18d2f6c5d25b90d0b173640824b6f311e18fcc0ab563bdb27644dd80c294a1c5 | R | 2,917 | 71 | prepareFluxforRegressions <- function(fluxesPruned, SCALE) {
# Prepare flux data for regression analyses.
#
# INPUTS:
# fluxesPruned: A data frame of pruned flux data. Expected to include 'ID' and 'Sex' columns.
# SCALE: A logical flag indicating whether flux data should be log-transformed and scaled.
#
# OUTPUT:
#... |
bd301dc6532a8bff625793a7400eb728a0d020e435c84863d73196a77b7f5d97 | R | 2,919 | 71 | # NGÒѾ¶¨ÒåÁËËÄÀàdevAS£¬Í³¼ÆÒ»ÏÂÿÀàdevASµÄÊýÄ¿
# »ñµÃËùÓÐpatternΪ¡°d¡±µÄdevAS£¬²é¿´ÕâЩASÉÏÊÇ·ñ¸»¼¯SRSF1µÈµÄ½áºÏmotif
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
human_devAS <- read.table(file = "01-data/19-Development_alternative_splicing/human.devAS",
s... |
7115658897bc45e531b441c5788167d661788ffb17d608c57677ea802b6ca622 | R | 2,922 | 44 | ---
title: "Power Simulation results"
author: "LL"
date: "2024-05-31"
output:
html_document: default
pdf_document: default
word_document: default
---
```{r, echo=FALSE, message=FALSE, warning=FALSE}
library(flextable)
library(tidyverse)
setwd("/data/pt_life/ResearchProjects/LLammer/gamms/Analysis/Simulations/")
d... |
97f09a061cabd3bb1b0197a54fcfbd9e533bed823aaf0df2879f32aba25c3950 | R | 2,932 | 94 | rm(list=ls())
set.seed(123)
source('load_libraries.R')
source('aux_functions.R')
detect_delimiter = function(filepath) {
first_line = readLines(filepath, n = 1)
# Check for common delimiters
if (grepl("\t", first_line)) {
return("\t")
} else if (grepl(",", first_line)) {
return(",")
} else {
sto... |
ef5c7c4f57bf2670411896f9479688880e91c8b9a5aad791e369812f403cb6b4 | R | 2,933 | 60 | library(Seurat)
library(SeuratObject)
library(SeuratDisk)
### USED TO CONVERT RDS TO H5AD FOR USE IN SCANPY ####
s <- readRDS("Human_M1_10xV3_Matrix.RDS")
s <- CreateSeuratObject(s)
SaveH5Seurat(s, "Human_M1_10xV3_Matrix.h5seurat")
Convert("Human_M1_10xV3_Matrix.h5seurat", dest="Human_M1_10xV3_Matrix.h5ad")
... |
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