sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
265237e9f1ff6cf9452095bcb64d2bb632443d1347c6722dffa2a22c592fb817 | R | 5,146 | 148 | # 13 Jun 2022 Siwei
# Calc Bulk atac-seq of NGN2-Glut
# init
library(readr)
library(plyr)
library(dplyr)
library(stringr)
library(Rfast)
library(ggplot2)
library(RColorBrewer)
### NGN2_Glut_NEFM_pos
ASoC_df_raw <-
read_delim("two_batchs_merge/temp_merge/output/GABA_14_lines_merged_SNP_24May2022_DP20_4_R.txt", ... |
6ec7e0123b5ca9f24d00ce7bc89efe34a27f236fb3af9a6a756ac516643ce98d | R | 5,150 | 155 | # Brain Perfusion Analysis - Simplified Version
# Creates hemisphere comparison figures for Gross, CT, and H&E assessments
# Load required libraries
library(tidyverse)
# Define color palette
cbPalette <- c("L" = "#3182bd", "R" = "#FF7F7F")
# === HELPER FUNCTIONS ===
# Function to create hemisphere compar... |
af582ebfda6f9c530dd386698da6c69c5e644f307473775be6722ba9ef8f9a4c | R | 5,162 | 119 | # **********************************
# Tabula muris dataset analysis
# **********************************
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if (!require("tidyverse")) {
install.packages("tidyverse... |
c35885cbbac3598e5ae14fd1808da46291202a609bd7c83aec6468449e4449eb | R | 5,166 | 148 | # ICC Analysis for Perfusion Rating Data
# Modified for specific CSV structure with Grader 1 and Grader 2 columns
# Load required libraries
if (!require("irr")) install.packages("irr")
library(irr)
# ===== FILE SELECTION =====
cat("\nPlease select your perfusion rating CSV file in the dialog box...\n")
data_... |
0c8ac7e6fc8db9b84c0e49e76ec44aa75e709b3ac8a802badc71b4a1a5c6398e | R | 5,171 | 125 | #' Get UK Biobank participant phenotype data
#'
#' @description Using a Spark node/cluster on the UK Biobank Research Analysis Platform (DNAnexus), use R to extract a provided set of variables. Using code from the UK Biobank DNAnexus team https://github.com/UK-Biobank/UKB-RAP-Notebooks/blob/main/NBs_Prelim/105_export_p... |
146d88b487fcb35910015678cbc04b5c96b85696da93b97611b8a00150fe092d | R | 5,173 | 117 | library(tidyverse)
#' starting from bottom (i.e. the filename) of a UNIX-like filepath, return the directory name at a specified upstream 1-based level
getDirectoryAtLevel <- function(filepath, level) {
# Split the filepath into components
components <- rev(unlist(strsplit(dirname(filepath), "/")))
# Check if... |
9de64d748665eb5fbec07fc564dbdf235766f7b2c238552f66135dba3f212747 | R | 5,179 | 166 | ---
title: "iCLIP Intron enrichments in features"
author: "Michael Rauer"
date: "`r format(Sys.time(), '%d %B, %Y')`"
params:
rmd: "iCLIP.introns_analysis.Rmd"
output:
html_document:
code_folding: hide
df_print: paged
fig_caption: yes
number_sections: yes
tables: yes
toc: yes
toc_float:... |
38bec9c4bfe1524234d0325204b2fd83f729d068d9c1d094ca85c98a2e864718 | R | 5,180 | 132 | #' Run differential rhythmicity analysis using linear model selection
#'
#' This function runs the information criteria-based model selection proposed by
#' Atger et al. (2015) for identifying timeseries with different rhythms in the
#' two datasets.
#'
#' @inheritParams compareRhythms
#' @keywords internal
compareRhy... |
75c65d05bfe32fffb496986124b8deb090bc74ef349a9213c43a6b1d25a399c4 | R | 5,180 | 107 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
library(parallel)
library(MutationalPatterns)
ref_genome="BSgenome.Hsapiens.UCSC.hg19"
chr_orders=c(paste("chr",1:22,sep=""),"chrX","chrY","chrM")
li... |
00915ce28f64ace6211577aed06593c27d80c654ceb18583d3e8cb111d82795b | R | 5,184 | 160 | #' Plot_Zygosity_Sinle
#'
#' Plot each SNP, without any summarization
#' @param Table The LOH table containing the output of the DeletionTable function
#' @param Organism "Human" or "Mouse"
#' @export
#' @return None
Plot_Zygosity_Sinle <-
function(Table, Organism) {
tbl <- Table
if (Organism == "Human") {... |
4ee8131fd5980d35203587f70104cc0c43b09e2cd3877e2927aa33ca1fae0c14 | R | 5,189 | 136 | # group 1 (split): introns, flanking circRNA. Split into upstream and downstream
# group 2: genome-wide. Note, Discard short introns
# group 3: Introns of circRNA genes, not flanking the circRNA exon
# group 4: introns of functional targets (carrasco2020)
# [open] group 5: differential Exons (DEXseq, carrasco2020), ups... |
0c125bfa875676d8e1e03cc2a33bac3c7f0b9d8a476d7450ae05e2525f8ee301 | R | 5,199 | 163 | library("SingleCellExperiment")
library("BisqueRNA")
library("here")
library("tidyverse")
library("sessioninfo")
library("BiocParallel")
n_runs = 1000
marker_label <- 'MeanRatio_top25'
marker_file <- here(
'processed-data', '08_bulk_deconvolution', 'markers_MeanRatio_top25.txt'
)
sce_path = here("processed-data", ... |
60d6ab3afd4a7bb2901cefcd8904719cfc64b92299094e6929ae8f0f7f19da41 | R | 5,201 | 136 | # Finding differentially expressed features (cluster biomarkers) using Seurat (Harmony subclusters)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# August 2022
# Based in the Seurat Vignettes by the Sajita Lab
# activate conda enviro... |
86b2ec53e9e11c2e03d75c7bdd4c6482ca3789414ac82dff26d019c609a04147 | R | 5,204 | 114 | ```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(WVPlots)
library(clue)
library(tidyverse)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggExtra)
library(cowplot)
library(corrplot)
library(visreg)
library(ggcorrplot)
library(ggseg3d)
library(ggseg)
library(ggsegSchaefer)
#library(ggsegYeo... |
77c43f56d7c492f47ddbfe07b29b945f42e85eddcb7572b85791dae70fdb7590 | R | 5,221 | 173 | # Siwei 21 Jun 2024
# Import scRNA-seq data of previous microglia to identify potential samples
# init ####
{
library(Seurat)
library(Signac)
library(edgeR)
library(DESeq2)
library(MAST)
library(future)
library(stringr)
library(harmony)
library(readr)
}
plan("multisession", workers = 6)
set.see... |
4e89be2822c11b34ea1b7d6793704d21943794e15ef8806dd1a0df3e8516e1f0 | R | 5,223 | 135 | #!/usr/bin/env Rscript
# Author: Tim Sterne-Weiler, 2014
# tim.sterne.weiler@utoronto.ca
# Updates: Manuel Irimia, 2015-present
# mirimia@gmail.com
suppressPackageStartupMessages(require(optparse))
option_list <- list(
make_option(c("-p", "--prompt"), action="store_true", default=TRUE, type="logical", help="User ... |
43accd5cf289da834d0d1d2eb5aa58c440a3ec072404f29beda0ef7750addb1d | R | 5,233 | 105 |
##basic GSEA of DESeq2 results
##also the msigdb
library(clusterProfiler)
library(msigdbr)
library(tidyverse)
get_cds_list<-function(lfcs){
df<-lfcs %>% as.data.frame() %>% mutate("stat"= log2FoldChange * -log10(pvalue))
geneList<-df[, "stat" ]
names(geneList)<-sapply(row.names(df), function(... |
832a5e4c0a7484c82f9a9264251e819109f394ea3c6ca244f3c1d666f77400f0 | R | 5,237 | 167 | #5C
library(ComplexHeatmap)
library(ggplot2)
young_kznf <- kznf_infer %>% filter(age=="young")
# preprocess tcx
tcx_res_kznfs <- mayoTEKRABber$tcxDE$gene_res %>%
data.frame() %>%
filter(abs(log2FoldChange) >= 0.5 & pvalue < 0.05) %>%
filter(rownames(.) %in% kznf_infer$external_gene_name)
tcx_exp_kznfs <-... |
cc854d282a5c41b56d6b2e83fce7aca0fd4300f16866c3ce1ac7949100a6b58d | R | 5,239 | 119 | # Script to explore expression of genes from protective rare variants in DLPFC Inh1
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# Jan 2024
# Run the script using my conda environment (conda activate use_seurat_r4), from its director... |
bf49a9f5404a92b5ec44553048d54278c9718da0af70ee34eadeab86308325f6 | R | 5,245 | 129 | suppressPackageStartupMessages(library(optparse))
option_list = list(
make_option(c("-e", "--eej_count"), type='character', default = NULL,
help="count matrix for exons, feature counts junctions files"),
make_option(c("-i", "--eij_count"), type='character',
help="count matrix for int... |
59127aabf4172e9685f56f80538cffdb9d7c166ca2db9051e0d16184fd9eb1a6 | R | 5,255 | 173 | # Siwei 26 Jan 2025
# plot new Ex. Fig 5c
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
library(dplyr)
library(data.table)
library(DescTools)
library(multcomp)
library(gridExtra)
}
rm(list = ls())
... |
862b4a249a5bc12650f967615c52151ca54adfce079f1759ee0bb58a4dcc059e | R | 5,266 | 118 | #Analysis of Tabula muris data set
##### 1. load packages and data#######
###load packages
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if (!require("tidyverse")) {
install.packages("tidyverse")
library("ti... |
35b4c778970e6bc9856887fb00f715ea247b497e36aa7e2262ba941640fd2b90 | R | 5,271 | 150 | # Siwei 24 Apr 2024
# make Upset plots to show peaks between all cell types
# init #####
{
library(readr)
library(UpSetR)
library(stringr)
library(RColorBrewer)
}
# load data ####
df_peaks_file <-
list.files(path = "indiv_peak_names",
pattern = "*.bed",
full.names = T)
df_raw <... |
99a54f45bc26c5354d16e7179c11e2a3fae79558e821e431e86219018478a0eb | R | 5,275 | 137 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(Signac)
library(ClustAssess)
library(qs)
library(rhdf5)
library(dplyr)
library(future)
library(doParallel)
library(ComplexHeatmap)
figures_path <- file.path(projec... |
4512537f28b58e58045161d42a2a5b61c49bb280baa2fbf0ef928b49440af8a5 | R | 5,277 | 137 | library(tidyverse)
library(nullranges)
library(cobalt)
requireNamespace("ks")
set.seed(123)
# Function to add pseudocount and log2 transform using tidyverse
add_pseudocount_and_log2 <- function(data, columns, pseudocount = 1.01) {
data %>%
mutate(across(all_of(columns), ~log2(.x + pseudocount)))
}
# Function t... |
1aa7fef0ad21e47c2950dadf7af17923bc9db26a96d5246f84d3c9308b809712 | R | 5,278 | 100 | #inputFile='feature_infor.txt'
#inputDirectory='/home/yli4/development/JUMPg/JUMPg_v2.3.3/gnm_stage1_test1/intermediate/sum_accepted_PSMs/misc'
#nTotLines=3724
#search_engine='jump'
setwd(inputDirectory);
#suppressPackageStartupMessages(suppressWarnings(library(tcltk)))
library(MASS);
#library(... |
9b979df6d0e3357c7f9c433f2deb738084ed596da01f545d0337392b01c26a9c | R | 5,284 | 100 | #inputFile='feature_infor.txt'
#inputDirectory='/home/yli4/development/JUMPg/JUMPg_v2.3.3/gnm_stage1_test1/intermediate/sum_accepted_PSMs/misc'
#nTotLines=3724
#search_engine='jump'
setwd(inputDirectory);
#suppressPackageStartupMessages(suppressWarnings(library(tcltk)))
library(MASS);
#library(... |
439d2ce0917e17edca820116a92e1b27ca51225b152f21fec224b445b661598c | R | 5,291 | 97 | context("Comprehensive Test for Classification Metric")
test_that("running dataset test", {
act <- aif360::binary_label_dataset(
data_path = system.file("extdata", "actual_data.csv", package="aif360"),
favor_label=1,
unfavor_label=0,
unprivileged_protected_attribute=0,
privileged_protected_attr... |
10852d9ddc0b88891c0d496913b4f8a23c40831684bf16997f3953b6772b0821 | R | 5,292 | 138 | library("tidyverse")
library("SingleCellExperiment")
library("here")
library("sessioninfo")
library("here")
library("patchwork")
#### Set-up ####
plot_dir <- here("plots", "03_HALO", "12_HALO_cell_size")
if (!dir.exists(plot_dir)) dir.create(plot_dir)
data_dir <- here("processed-data", "03_HALO", "12_HALO_cell_size")... |
3c6ec93a3a8930eadea4fb376b98a4247106cebe7da3f7f72c0836bcc0fd8f6b | R | 5,292 | 138 | ## create variable y, such that cor(x,y) == r
rcor <- function(x,r) {
e <- rnorm(length(x), mean=0, sd=sqrt(1-r^2))
mx <- mean(x)
sx <- sd(x)
s <- (x-mx)/sx
(r*s + e)*sx + mx
}
## create a variable that corresponds to a true dmr in a dataset
## 1. select a random cluster of CpG sites of minimum siz... |
68bfed78fc005fa2ef922a9d74dc2193dab84eb228bdf682e932749edc851891 | R | 5,292 | 112 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
library(ggbreak)
library(MutationalPatterns)
library(factoextra)
#### PTA excess Indel burden by CTE and AD cases ####
df <- read.table("data/Table... |
ce7f9a847e67b4610fad79f94b362e61a4fe9d14e0081d3a72a49827afca6761 | R | 5,294 | 150 |
library("recount3")
library("SingleCellExperiment")
library("BisqueRNA")
library("tidyverse")
library("here")
library("sessioninfo")
# library(DeconvoBuddies)
data_dir <- here("processed-data", "07_GTEx", "01_GTEx_Bisque")
if (!dir.exists(data_dir)) dir.create(data_dir, recursive = TRUE)
#### Load GTEx data with re... |
f472fe1295a6a56351f2c2f34eeaf85b4a640a3789a3bd2671a0f310042f82d7 | R | 5,295 | 88 | runRegressionAnalyses <- function(fluxAll,AD_risk,Sex_moderation,e4_moderation,e2_moderation, file,type){
#' runRegressionAnalyses - Runs a series of regression analyses on metabolic fluxes or microbiome data.
#' This function performs a series of regression analyses on the provided flux data, considering different
... |
7865d5149da96cb60e9d26de01b6d4bf34b750b593fa1a78779319ab60e3613f | R | 5,312 | 150 |
library("tidyverse")
library("sessioninfo")
# library("DeconvoBuddies")
library("here")
library("slurmjobs")
# library("viridis")
# library(spatialLIBD)
# library(ggrepel)
# #library("GGally")
## prep dirs ##
plot_dir <- here("plots", "08_bulk_deconvolution", "15_method_runtime")
if (!dir.exists(plot_dir)) dir.create... |
41948ee798f59f0f42e9f8a9835e1d77b015cc8f5309f4b51206122bfcfa8967 | R | 5,314 | 126 | #!/usr/bin/env Rscript
#### Estimate CNAs using inferCNV with patient argument provided
#### Author: Jana Biermann, PhD
print(paste('Start:', Sys.time()))
library(dplyr)
library(Seurat)
library(infercnv)
library(stringr)
library(gplots)
library(ggplot2)
library(viridis)
# Get patient argument
pat <- commandArgs()[6... |
38077446924dff44a1a4f821cc3911ad7064f31105f14fbe0e2df4a798a36da1 | R | 5,319 | 140 | library("tidyverse")
library("sessioninfo")
library("here")
## prep dirs ##
data_dir <- here("processed-data", "13_PEC_deconvolution", "10_get_est_prop_donor_subset")
if (!dir.exists(data_dir)) dir.create(data_dir, recursive = TRUE)
#### data details ####
## dataset properties
dataset_lt <- tibble(Dataset = c("2107UN... |
607d03c26867b2f7715f4abae253c4817ba658c90467ffbd39fb595a35a909e3 | R | 5,320 | 187 | # Siwei 20 Jun 2023 #####
# plot a large PCA include MG, Ast, GA, and possibly NGN2
# ATAC-Seq data using the count matrix of Kosoy et al. (syn26207321)
# (microglia regulome)
# init #####
{
library(readr)
library(edgeR)
library(Rfast)
library(factoextra)
library(Rtsne)
library(irlba)
library(stringr)... |
a584b8971ac3b95fa2d1f3a3bd53df830da3440a2552b4af2effe040e7aa1c5f | R | 5,324 | 173 | brew install imagemagick --with-fontconfig --with-librsvg --with-fftw---
title: "Lenia"
output: html.notebook
---
```{r}
SIZE <- 2^8
MID <- SIZE / 2
```
```{r}
kernel.core <- function(r, kernel.type)
{
rm <- pmin(r, 1)
if (kernel.type == 0)
return ( (4 * rm * (1-rm))^4 )
else
return ( exp(4 - 1 / (rm * (... |
8d33440e6ef08fece11da2ed752db1277957cd63686fce3bb96ef7cb8483891b | R | 5,328 | 80 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
#### QC-corrected excess Indels by group ####
high_indel_cases_CTE <- as.character(c(6195, 6489, 7038, 7932,
... |
78d392defde8cd79987391c2095e1496db867c727a6a159ebdb6f8cb41d6a4c0 | R | 5,341 | 154 | library(tidyverse)
library(gprofiler2)
library(data.table)
library(org.Hs.eg.db)
library(GO.db)
library(reactome.db)
set.seed(1234)
process_apa_data <- function(directory = "data/APA", output_dir = "results/APA") {
# Read APA result files
files <- list.files(directory, pattern = "\\.txt$", full.names = TRUE)
... |
3b69f100e8423c995df06a1699dbc4d392c09247438a0d8bd0dc81497546ed70 | R | 5,342 | 146 | #' dmrff
#'
#' Identifying differentially methylated regions efficiently with power and control.
#'
#' Warning! Ensure that the order of the CpG sites corresponding to the the rows of `methylation`
#' match the order of the CpG sites corresponding to the other variables,
#' e.g. `estimate` and `chr`.
#'
#' @param esti... |
1d9815ec49a990398fb27903020d27a64e5fe98c9677c1e3b3a74d530f3fdb06 | R | 5,349 | 194 | library(here)
library(readr)
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)
library(brms)
library(bayesplot)
library(tidybayes)
# set params ----
color_scheme_set("red")
theme_set(theme_ggdist())
# import data ----
dat <- read_csv(here("out", "degree.csv"), show_col_types = FALSE) |>
drop_na() |>
... |
45f78213bf7004b7a3c8d03f271a93e039085c0bef527ebfe464a81a75151c16 | R | 5,355 | 139 | ---
title: "Ascertain diagnoses"
description: >
Ascertain UK Biobank participant diagnoses from all sources (medical records and self-report data).
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Ascertain diagnoses}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, incl... |
718ab060391394b55718d6dbfbdae953cafbd2d46d35ee69d65486ed943c02f0 | R | 5,360 | 155 | ---
title: "Plot muliplexing results"
author: "C-M Svensson"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
rm(list = ls())
knitr::opts_chunk$set(echo = TRUE, fig.width = 12, fig.height = 12)
library(dplyr)
library(latex2exp)
library(tidyverse)
library(ggplot2)
library(readx... |
0ce87beb0701a1cd81617743214bb33ab4e600d11208b68d4812e3523d1cefa8 | R | 5,363 | 107 | ##### 1. load packages and data#######
###load packages
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if (!require("tidyverse")) {
install.packages("tidyverse")
library("tidyverse")
}
if (!require("scran")) ... |
b77aca83a42a466201568c13a48badad767bc50aabb7e6c3a65fa03d24d4a08f | R | 5,380 | 118 | #!/usr/bin/env Rscript
### title: Analysis of non-immune and non-tumor cell types (UMAPs, DEG)
### author: Jana Biermann, PhD
library(Seurat)
library(dplyr)
library(ggplot2)
library(gplots)
'%notin%' <- Negate('%in%')
colBP <- c('#A80D11', '#008DB8')
colSCSN <- c('#E1AC24', '#288F56')
colCNS <- c('#92D84F', '#7473A6... |
07a75e3fbaac80dad2e755966e5e3e0d8d6bc0f3a93b513175ecbbe97d6eb6bd | R | 5,393 | 140 | #!/usr/bin/env Rscript
# ==============================================================================
# Script: rnaseq_transcript_and_gene_level_differential_analysis.R
# Author: Alireza Ghahramani
# Contact: aghahram@uwo.ca
#
# Purpose:
# - Transcript-level RNA-seq DESeq2 analysis (StringTie quantification)
# - ... |
87f48674cdffc158573a678f4ccd655d9cfcfa38238ccaa4647c6467e57287c6 | R | 5,401 | 156 | #' Plot_Zygosity_Blocks
#'
#' Plot blocks of heterozygous and homozygous SNPs
#' @param Table The deletion table containing the output of the DeletionTable function
#' @param window the block size in bp, usually 1500000
#' @param Max How many Heterozygouse SNP need to be in a block to get the full color, usually 6
#' @... |
1541a78d34951d76121fcf77af7dba21134e4a57288ebc7f8e2806edb25a9138 | R | 5,404 | 94 | #### Figure S18 ####
#### hDEGs near HARs and hCONDELs in SynGO dataset ####
library(stringr)
library(reshape2)
library(ggplot2)
library(here)
# syngo:
syngo_terms <- read.table(here("data/syngo_analysis", "syngo_terms_id.txt"), sep="\t", header=TRUE)
syngo_genes <- read.table(here("data/syngo_analysis", "syngo_onto... |
9fb1039e1e5bd232028b3747e342a6eb2571b584641d593fe6000b1bc2f314bb | R | 5,413 | 160 | # Plot_Zygosity_Blocks_noxy
#
# Plot blocks of heterozygous and homozygous SNPs
# @param Table The deletion table containing the output of the DeletionTable function
# @param window The block size in bp, usually 1500000
# @param Max How many Heterozygous SNPs need to be in a block to get the full color, usually 6
# @pa... |
5e5713167afa8b94e82e7c37a1613b4a41995991ef465c776539ccacddc912f0 | R | 5,438 | 135 | #!/usr/bin/env Rscript
#### Integration using Harmony and LISI score
#### Author: Jana Biermann, PhD
library(Seurat)
library(dplyr)
library(ggplot2)
library(ggrastr)
library(harmony)
library(gplots)
library(lisi)
library(tidyr)
library(magrittr)
library(viridis)
library(scales)
colBP <- c('#A80D11', '#008DB8')
colSC... |
0f6e5b84c9c1b79ac705f0735081a51fdd9102d0e367f50592fcb9c0ae1dc812 | R | 5,441 | 159 | #!/usr/bin/env Rscript
# =========================================================================
# Title: Identification and Classification of Upregulated LTRs as Full-Length or Solo Using RetroTector
# Script: ltr_classification_solo_full_length.R
# Author: Alireza Ghahramani
# Contact: aghahram@uwo.ca
#
# Descripti... |
125f9c78d5aeac0169107b9055e74f754227b4badda5cab013361f7d0a6bf223 | R | 5,451 | 58 | #-----------------------------------------------------------------------------------------#
# Step0 set options & mkdir directory #
#-----------------------------------------------------------------------------------------#
Sample=sample_list.txt
Rawdata_path=rawdata
... |
d2af29e8deea0e9f8682a7266a389bf0e299acc997467d32aef07090e926b1cc | R | 5,453 | 142 | # libraries
library(tidyverse) # tibble stuff
# total number of flips
n.trl = 240
# file paths
dt.path1 = paste('/home/emba/Documents/EMBA', 'BVET', sep = "/")
dt.path2 = paste('/home/emba/Documents/EMBA', 'BVET-addMRI', sep = "/")
# load the relevant data in long format: only people with separate logs for ru... |
0cf1d2cf8e083d276b1570d76208f6e1dc16d644c5f206094db0c00740b47cab | R | 5,464 | 94 | #Run QC filtering for each sample ############################################################################################################
#Load libraries
library(Seurat)
library(tidyverse)
library(scDblFinder)
#Create directory
dir.create("1_results_QC_filt", showWarnings=T)
#Create function to read counts, cre... |
4da04695f382a74a4c1b9fd3fa63baf755d564e0225afb96b8bf45b9b558bc05 | R | 5,466 | 141 | fluidPage(
fluidRow(
box(
width = 12,
closable = T,
enable_label = T,
label_text = "New",
label_status = "warning",
solidHeader = T,
status = "warning",
title = tagList(icon("bullhorn"), i18n$t("お知らせ")),
collapsible = T,
collapsed = T,
tags$small(
... |
3e56b5a8d3f372fa694cf99b5402e08fe12e22b4c5e946c44e28f273b9f0f83c | R | 5,467 | 160 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(Signac)
library(ClustAssess)
library(qs)
library(rhdf5)
library(dplyr)
library(future)
library(doParallel)
# 50 GB
options(future.globals.maxSize = 50 * 1024^3)
pl... |
e550cfb057d0f83c5015522493f0ac0d42d1637944f5f6d71813a3be326de2d4 | R | 5,470 | 200 | # Siwei 11 May 2023
# Process all Asts (FASTQs trimmed)
# May 2023
# init
library(readr)
library(vcfR)
library(stringr)
library(ggplot2)
library(parallel)
library(MASS)
library(RColorBrewer)
library(grDevices)
# load the table from vcf (the vcf has been prefiltered to include DP >= 20 only)
df_raw <-
read.vcfR(f... |
8c8afeb7e06494d0eb2f7ba6e259195320715ca2d4bc037aac399f316a64ffb7 | R | 5,471 | 134 | library(tidyverse)
# output of get_num_pas.py - used to get le_ids of interest (as a vector)
le_ids <- read_tsv("processed/curation/cryptic_annot_comparison/2024-09-03_le_id_pas_counts.tsv", col_select = le_id) %>%
pull(le_id)
# top level directory storing PAPA outputs for all runs
# i.e. <papa_results_dir>/<dataset... |
43caa053610a3a71183bf8885a18cbe8ad00da8f14f39e724d5b4832b4fc0ec0 | R | 5,486 | 152 | # ----------------------------------------------------------------------------
# Libraries and setup ----
library("argparse")
library("ggplot2")
library("khroma")
library("tidyverse")
library("Seurat")
library("ComplexHeatmap")
library("RColorBrewer")
# Command line arguments ----
parser <- ArgumentParser(description... |
bb79f2d1be4fac8133d3dfb18f6ca6a4c702471ab83d63906731e08b5c53b41b | R | 5,486 | 117 | #prepare files for scDRS/FUMA/MAGMA
##### 1. load packages and data#######
###load packages
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if (!require("tidyverse")) {
install.packages("tidyverse")
library(... |
76025fdafa973de067152631fe427469c118bc8d9d4de9b708d4c0e93481e922 | R | 5,490 | 129 | library(qs)
library(scDblFinder)
library(SingleCellExperiment)
library(Seurat)
library(dplyr)
library(stringr)
library(AnnotationHub)
library(ensembldb)
library(gridExtra)
library(RCurl)
library(ggplot2)
library(cowplot)
# Replace all *...* with respective text
# open merged_seurat file. Check path ---... |
e10b967eca827048d6a376c35e15aa58b3577bbe1a865713d839cc2646c80d59 | R | 5,490 | 140 | #!/usr/bin/env Rscript
#### title: Tumor cell integration, UMAPs and pathway signatures applied to tumor cells
#### author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
'%notin%' <- Negate('%in%')
colBP <- c('#A80D11', '#008DB8')
colSCSN <- c('#E1AC24', '#288F56')
cycling <- c(... |
8fad2f31830346639d7edb437b884b11ad0d43384ca7bcc756bef772f62c12fc | R | 5,493 | 183 | # 25 May 2022 Siwei
# process new NGN2-glut batch data (May 2022)
# note that this batch of NGN2-glut 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(RColorBr... |
7c5f37d84f00883a58693a56afb1cba435d66f214592c3e5672a1b9820c23724 | R | 5,499 | 165 | # UCRÏà¹ØµÄlncRNAµÄÏ໥×÷ÓÃÊýÄ¿
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(BiocManager)
library(ggplot2)
library(stringr)
library(ggbeeswarm)
library(ggsci)
library(ggpubr)
library(Hmisc)
library(ggview)
library(gginnards)
library(scales)
libr... |
96544e5fcad97902157777ece642f4993e41aae314dee1167a89f0f544c4145a | R | 5,514 | 185 | # 25 May 2022 Siwei
# process new GABA batch data (May 2022)
# note that this batch of GABA bulk ATAC-seq data,
# two of the samples 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... |
99f3b38ad086dcf5ba9cdafeda716a3afaa90a906aaaffcd3a74314099283094 | R | 5,523 | 146 | # Neocortical Localization and Thalamocortical Modulation of
# Neuronal Hyperexcitability contribute to Fragile X Syndrome
# Communication Biology
# Demographics
# Table 1, Supplemental Table 1, Figure 2A
# Author: E. Pedapati
# Version: 3/27/2022
pacman::p_load(tidyverse, labelled, compareGroups, lsmeans, B... |
a47e0bb029208e3c16b4797b6e09c15992458b5991fa67684240773c7db4a0cc | R | 5,526 | 143 | library(Signac)
library(Seurat)
library(stringr)
library(dplyr)
library(tidyverse)
oligo_atac <- LoadSeuratRds("ATAC/Oligo_ATAC_object_with_activity_and_motifs.rds")
oligo <- LoadSeuratRds("oligo_sub.rds")
x <- str_split_fixed(Cells(oligo), "_", 4)
x <- data.frame(x)
# x <- x[x$X4 != "",]
x[str_detect(x... |
95ba521d24afe845a59cfa52fa9fc09ccc9c4502724fb9d64e56583eaa570402 | R | 5,533 | 132 |
library("tidyverse")
library("sessioninfo")
library("DeconvoBuddies")
library("here")
## prep dirs ##
plot_dir <- here("plots", "08_bulk_deconvolution", "11_deconvo_plots_subset")
if (!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
## load colors & shapes
load(here("processed-data","00_data_prep","cell... |
9d37efea5e8eb6c1e3f0b125ad8fa7d6930ea732eaa0803d3655e3d92f10dbd2 | R | 5,540 | 190 | # Siwei 21 Jun 2023
# Process all NGN2s (FASTQs trimmed and the original NGN2-20
# + from 2019)
# note the sample names are complex, need processing (NGN2 and R21)
# Jun 2023
# init
library(readr)
library(vcfR)
library(stringr)
library(ggplot2)
library(parallel)
library(MASS)
library(RColorBrewer)
library(grDevices... |
c356c89f9dec54357380b49ef507c8a06611f54cfd34c7d1912cb3661bbfcfd6 | R | 5,542 | 96 | #Run QC filtering for each sample ##############################################################################################
#Load libraries
library(Seurat)
library(tidyverse)
library(scDblFinder)
#Create directory
dir.create("1_results_QC_filt", showWarnings=T)
#Create function to read unfiltered counts, create... |
df7f1982c40c3628fe5595c79bcb61fe677587afe86a7134b435ee10cc2a3f8a | R | 5,551 | 85 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
#### PTA SNV burden after controlling for QC metrics ####
df <- read.table("data/TableS2_PTA_QC.tsv", header=T, sep="\t")
df$Case_ID <- as.characte... |
b3418b78462f68eec1dd1740674f9af64a776433b430c544176291e275d2ecee | R | 5,569 | 126 | library(tidyverse)
# Script to get a table of non-cryptic APAs produced by the pipeline
# Combined differential analysis
dexseq_all <- read_tsv("data/2023-05-24_i3_cortical_zanovello.all_datasets.dexseq_apa.results.processed.cleaned.tsv.gz")
# df containing cleaned cooridnate columns
le_id_coords <- read_tsv("proces... |
aeb91c51809ae95b0e43d3e1d6192134b72924e72c06ca54896db64170a16ca9 | R | 5,575 | 141 | library(tidyverse)
source("scripts/utils_liu_facs.R")
ppau_delta_paired_median_all <- read_tsv("processed/liu_facs/2024-11-20_liu_facs_decoys_delta_ppau.all_samples.all_ales.tsv.gz")
ppau_delta_paired_median_disease <- read_tsv("processed/liu_facs/2024-11-20_liu_facs_decoys_delta_ppau.subtype_split.all_ales.tsv.gz")
p... |
3190370adba9d0e3ec18d37e800d6be88de32547f3ff978144cb0161fecfe274 | R | 5,578 | 96 | # this script produces plots depicting the different partial effects of LSNS by gender
library(tidyverse) # version 2.0.0
library(gratia) # version 0.10.0
library(MetBrewer) # version 0.2.0
# define the path
path = "/data/pt_life/ResearchProjects/LLammer/gamms/Results/by_gender/"
#choose palette
palette <- met.brewe... |
6d1ab39ad4d983101d865d3a503e6062ffb43a20ad1f4de60860a494de5022af | R | 5,578 | 142 | ---
title: "4. Visualization using xQTLbiolinks"
date: "2023-05-01"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{visualization}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
lang: en-US
---
```{r, include = FALSE}
knitr::opts_chunk$set(
echo=TRUE,
progress =FALSE,
commen... |
7aaed33b4ba9121864689153c230b4182e89fdc59ed8461f0d27c7ba12535882 | R | 5,579 | 147 | #!/usr/bin/env Rscript
#### Integration using Conos and LISI score
#### Author: Jana Biermann, PhD
library(Seurat)
library(dplyr)
library(ggplot2)
library(ggrastr)
library(gplots)
library(lisi)
library(tidyr)
library(magrittr)
library(viridis)
library(scales)
library(igraph)
library(leidenAlg)
library(conos)
colBP <... |
ddf073f4cb1b70a82660d7b48da49549262d6938d9ea3c2863b771f6cfb259b8 | R | 5,580 | 132 | rm(list = ls())
setwd(dir = "D:/R_project/UCR_project")
library(tidyverse)
library(ggplot2)
load("02-analysis/04-SNP_Quality_Control/filtered_TOPMed_SNPs/passed_UCR_SNPs.Rdata")
load("02-analysis/04-SNP_Quality_Control/filtered_TOPMed_SNPs/passed_UCR_left_SNPs.Rdata")
load("02-analysis/04-SNP_Quality_Control/filtered... |
76840098538ecdea453be9ab97df4931a024bc67ad1a4011d22ed4759dfd8c52 | R | 5,603 | 165 |
require(ggplot2); require(scales); require(reshape2);
#install.packages("dplyr")
require(dplyr)
#require(Hmisc)
library("readxl")
library(RColorBrewer)
library("ggsci")
#install.packages("ggrepel")
library("ggrepel")
library(ggpubr)
library(stringr)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
#s... |
cb2beae1312d61e3fbbe2211d2adc847687a1210976dae549adfb006c7dfabde | R | 5,604 | 129 | #!/usr/bin/env R
# Author: Sean Maden
#
# Plot cell quantifications by assay type. Reads outputs from script
# "cor-celltype-prop-count...R"
#
library(ggplot2)
library(gridExtra)
library(ggrepel)
#----------
# load data
#----------
dfsn.fpath <- file.path("dlpfc_ro1", "df-snrnaseq-all_cell-prop-abund_dlpfc-ro1.rda"... |
5c29e66a15e0ec1afeb5f071a26e87a1b1e798c46ef43e04dbb1947fefd18a31 | R | 5,605 | 159 | #
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(dbplyr)
library(Biostrings)
# ÕûÀíÐòÁÐ --------------------------------------------------------------------
## UCR left
UCR_left <- readDNAStringSet(filepath = "02-analysis/14-GC_content/ucr_left_homemade_GRCh38p14.fa")
print(UCR_left)
names(UCR_lef... |
834a64da2ee7baeec914de4b9e3028284bf657f7f97b460d2be2ae93f9285600 | R | 5,605 | 209 | ---
title: "CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project - unique DEGs AD vs RES"
author: "Isabel Castanho"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float:
collapsed: false
toc_depth: 4
code_folding: hide
---
---
# Cell-type enr... |
81b90bff360a22f0ec5e27ca226209e215ed88259fa907868127cb3c3f52f78a | R | 5,611 | 122 | library(Seurat)
#library(SeuratData)
library(ggplot2)
library(patchwork)
library(dplyr)
library(SingleR)
library(SingleCellExperiment)
library(scater)
library(pheatmap)
library(rlist)
library(stringr)
library(grid)
library(ggrastr)
library(metR)
library(akima)
homedir = "/data"
#read in table of signature genes, manu... |
49aff82a8643feee0071ab1e36e1e22e302704bcce81364233bd893227b8e90f | R | 5,621 | 141 | # Clear workspace and load necessary libraries
rm(list = ls())
start_time = Sys.time()
source('load_libraries.R') # Assume this loads required libraries like dplyr, tidyr, igraph, etc.
source('functions_for_network_analysis.R')
source('functions_for_drug_repurposing.R')
InSub=c('Lamp5', 'Pvalb', 'Sncg', 'Sst','Sst.C... |
eee6c61b2efff5c7e7beaa2a678b01672bcf04f0a87cdd710d491ea0e919291e | R | 5,623 | 146 | # ¿´ÔÚ²»Í¬Æ÷¹ÙÀïDDGµÄÊýÄ¿
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(readxl)
library(ggplot2)
development_dynamic_organ <- read_xlsx(path = "01-data/18-Development_dynamic_genes/development_dynamic_organ.xlsx",
sheet = 7, skip = 2)
dev... |
1f2b599160bf7e617d8471a66274df2319e058469714c94f82704e92a4238679 | R | 5,627 | 132 | #!/usr/bin/env Rscript
##
## Summarize Salmon transcript abundances on a gene level for individual samples and combine into a single table.
##
## usage: Rscript --vanilla quant-merge-salmon.R genes.gtf quant_sf_dir out_base
##
# increase output width
options(width = 120)
# print warnings as they occur
options(warn ... |
d173ada431f2b92a04634050fade4cfcd0d4989b1ebe1419bd8a02355f08b78c | R | 5,628 | 127 | library(spacexr)
library(Matrix)
library(stringr)
library(Seurat)
### title: Use RCTD pipeline to assign cell type identities to SlideSeq samples
### author: Yiping Wang date: 03/29/2022
datadir = "/data"
#load in single-nuclei reference, filter for only single nuclei data, and filter out low-quality cells
system(pa... |
8498790090ca58c34c2c4203c4cea4667e885dbeaf44e818b74ccb188017c8a1 | R | 5,647 | 140 | # Reference values for PyMARE's effect-size converters.
#
# Writes pymare/tests/data/metafor_escalc_reference.json, which
# pymare/tests/test_metafor_escalc.py reads. Run it through the harness in this
# directory rather than directly, so the R and metafor versions are the pinned
# ones:
#
# validation/metafor/rege... |
ecac49d8675338300ecb21113f008afa801c87cab9c752d9214c7690a25306cb | R | 5,654 | 106 | ###ÕûÀí½á¹û
library(tidyr)
library(tidyverse)
library(openxlsx)
out_all_res <- data.frame()
out_null_res <- data.frame()
inputpath <- "/mnt/data/lijincheng/mGWAS/result/04replication/Kunkle/FR/"
outlist <- list.files(inputpath)
###
for(i in 1:length(outlist)){
inputpath <- "/mnt/data/lijincheng/mGWAS/result/04repli... |
2cef16b8c5f14434d04d926f9ae29ad7edc04faf531f18114a196721a80e10d7 | R | 5,656 | 144 |
require(ggplot2); require(scales); require(reshape2);
#install.packages("dplyr")
require(dplyr)
#require(Hmisc)
library("readxl")
library(RColorBrewer)
library("ggsci")
#install.packages("ggrepel")
library("ggrepel")
library(ggpubr)
library(stringr)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
#... |
5ad0ade348a2a35763b9fdba8174ba881b62aa4912aa1d4338f97c9b916ed4df | R | 5,659 | 136 | #' Run differential rhythmicity analysis defined in Thaben & Westermark
#'
#' @param expr A matrix of expression values with gene in the rows and samples in columns
#' @inheritParams compareRhythms
#' @keywords internal
compareRhythms_dodr <- function(expr, exp_design, period=24, rhythm_fdr = 0.05,
... |
32d531596603fe5e97cfea31f7f68d470a62b9714745a61f3628210295dd5f10 | R | 5,660 | 129 | # load libraries
library(tidyverse)
library(data.table)
library(Matrix)
library(Rfast)
library(matrixStats)
library(ggridges)
library(reticulate)
library(anndata)
library(scales)
library(ComplexHeatmap)
library(forcats)
library(igraph)
library(mclust)
library(future.apply)
library(UpSetR)
library(gtools)
library(patchw... |
bdbdf4f30b5643ef70667a7c2928ecaf17da9cfb357d34766957d4ef9c4ede4f | R | 5,664 | 164 | library(tidyverse)
library(fgsea)
library(dorothea)
library(decoupleR)
library(janitor)
source("scripts/helpers.R")
# output of dl_collectri.R
collectri_hs <- read_tsv("data/2023-11-15_collectri_homosapiens.tsv")
collectri_hs <- rename(collectri_hs, tf = source)
dorothea_hs_abc <- filter(dorothea_hs, confidence %in% ... |
f8150f1faa24c91ace247be8e63326941bab474e06d45c6df5c5d4362b9ee7ea | R | 5,665 | 120 | get_local_exposures_forMRBMA<- function(inputdf=inputdf,
exposure=""
)
{
message("exposure='Mibio','FR02','pathways'")
message("outcome='LOAD','ADproxy','abeta42','ptau' ")
input <- inputdf %>% dplyr::filter(exposures == ... |
5d574f31f41871e76094709570567496178f34b7c41f307b44eb6dd98bb90cad | R | 5,667 | 141 | # UCR原始数据(hg16)下载，转换成GRCh38坐标，准备getfasta需要的bed文件
setwd("D:/R_project/UCR_project")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(stringr)
# 导入从网站下载的UCR原始数据，转换成方便使用的格式 ----------------------------------------------
# 导入从网站下载的UCR原始数据
UCR_rawdata <- read.table(file = "D:/A_projects/UCR/U... |
0c824dfc949268196007931be868da27bf96485b102251d4492ea4c91aafbc93 | R | 5,672 | 106 | ###ÕûÀí½á¹û
library(tidyr)
library(tidyverse)
library(openxlsx)
out_all_res <- data.frame()
out_null_res <- data.frame()
inputpath <- "/mnt/data/lijincheng/mGWAS/result/04replication/Kunkle/mibiogen/"
outlist <- list.files(inputpath)
###
for(i in 1:length(outlist)){
inputpath <- "/mnt/data/lijincheng/mGWAS/result/0... |
c637f26e7aebc6e406ade8868fb764d9ade052a7eb189b0a976b8f5410065f43 | R | 5,675 | 174 | # Figure 1: Multi-panel visualization of metabolic analysis
# This script creates a comprehensive figure with four panels:
# a) Volcano plot of age-related flux changes
# b) Bar plot showing microbial correlations with metabolites
# c) Forest plot of species abundance vs age
# d) Regression plot of serum arginine vs ag... |
4565a16e9c6d7153f540c97df0228099dcd9eed38d82e93ceaf56cb36f0714bf | R | 5,688 | 183 | # lirbaries ---------------------------------------------------------------
library(Seurat)
library(tidyverse)
library(ks)
# function definition -----------------------------------------------------
.extract_feature_data <- function(exp_data, features) {
# Extract data for input features
i <- colnames(exp_data) %i... |
5edabc7cae10775998a45aaa493707111414f7d2f819f9c06a84e5e49999fd67 | R | 5,701 | 93 | overlap_coefficient<-function(mat){
mat.return<-mat
for(m in 1:dim(mat)[1]){
for(n in 1:dim(mat)[2]){
r<-mat[m,n]/min(sum(mat[m,]),sum(mat[,n]))
mat.return[m,n]<-r
}
}
return(mat.return)
}
setwd("/projects/ren-transposon/home/chz272/transposon/05.Paired-ChIP/20.NovaSeq/04.Single_cell_All... |
8bb8c0b9024792176ee4a6851d62f7c9a7ffd0c51576ee41c833e37b27bb0a0f | R | 5,704 | 134 | # Reference values for PyMARE's cluster-robust covariance and its degrees of freedom.
#
# Writes pymare/tests/data/clubsandwich_reference.json, which
# pymare/tests/test_clubsandwich_alignment.py reads. Run it through the harness
# in this directory rather than directly, so the R, metafor and clubSandwich
# versions ar... |
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