sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7d10ff78f6e11cb25bfaaef89c723974b881ec922356578772c4db93f9cc7612 | R | 3,488 | 118 | ---
title: "MicrogliaDensity_Analysis"
author: "Jenn Kim"
date: "`r Sys.Date()`"
output: html_document
---
# load libraries
```{r}
library(tidyverse)
library(cowplot)
library(ggplot2)
theme_set(theme_cowplot())
library(nlme)
library(lsmeans)
```
# load data
```{r}
data <- read.csv("MicrogliaNumbers-Density.csv") %>% ... |
159fe19020204141d1bcf4ad9806764f89b4bf700e9c2a7009c799d3989eb396 | R | 3,489 | 66 |
options(stringsAsFactors = FALSE)
library(limma)
library(edgeR)
library(plyr)
library(data.table)
library(cit)
library(doParallel)
library(pbapply)
library(WGCNA)
library(parallel)
# Read in Oligo snRNAseq expression, aggreagted to sample level -------------------------------------------------
exprCounts <- fread(in... |
a594af674a32c56c4424ca9c845c5e6638cbbdfb6d3ad4f3c7bcc6cf396d956c | R | 3,503 | 47 | library(data.table)
library(dplyr)
#RBP
for(dir in c("m6A2me", "me2m6A")){
RBP_enrichment <- fread(paste0("~/Downstream_analysis/Regulators/Lung.m6A_peak_RBP.", dir, ".txt"), sep = "\t", header = FALSE, col.names = c("RBP", "signif", "background")) %>%
mutate(prop_signif = signif/sum(signif), prop_background = back... |
bf120f4b980783718e3e02d98a75ea872b2c09ee2784fdb8d21b7b67e994104e | R | 3,503 | 78 | ## ---------- Script -----------------
##
## Purpose: input variables for differential analysis of Iso-Seq targeted mouse transcriptome datasets
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
## ---------- packages -----------------
suppressMessages(library("dplyr"))
suppressMessages(library("stringr"))
suppr... |
fcb36ecb46485431640bf8c076c56c438eae17b9cde2d8bbca5fcd143be4580c | R | 3,506 | 125 | #' @name DEPRECATED-spc.loess
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' LOESS smoothing interpolation for spectra
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility re... |
e03b22de6e306be2327f204960d65c04178a217751c8a313842c9ee315312510 | R | 3,507 | 145 | # libraries
library(readxl)
library(tidyverse)
library(ggpubr)
library(rstatix)
library(emmeans)
library(sjstats)
library(lme4)
library(lmerTest)
library(MuMIn)
library(multcomp)
library(nlme)
library(r2glmm)
library(ggplot2)
library(ggthemes)
library(svglite)
library(sjPlot)
# prepare --------------------------------... |
603770f11cc1824c36198074ea5fa4d0b0e45e0f05b580efadec4255d70d85f0 | R | 3,519 | 91 | require(data.table)
require(mldata.export)
main_dir <- "./data_raw/glass_identification"
write_dir <- main_dir
dataset_name <- "glass_identification"
#### Data preparation ----------------------------------------------------------
data <- data.table::fread(input=file.path(main_dir, "data", "glass.data.csv"))
# Set... |
eab9e05c9799a7334828ea5bb45b998f9f3e4e8e9631d2893256ace8556f4322 | R | 3,523 | 106 | library("tidyverse")
library("clusterProfiler")
library('data.table')
library("BiocParallel")
# prepare from GMT files download from gprofile
ReadGMT_GSEA <- function (gmt.file) {
pathwayLines <- strsplit(readLines(gmt.file), "\t")
pathways <- lapply(pathwayLines, tail, -2)
fetchName <- function(x){
unlist(... |
a4e0c641c08a509c5b83d8645498a372f6ceff350adee5aa6681cd65510a614d | R | 3,530 | 108 | #!/usr/bin/R
# --------------------------------------------------
# Get the GO term enrichments for external datasets:
# Updated 12/01/2023
# --------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))
}
... |
ececbf381129bf2d83f768f443b85420c6056434b72cf1e647e4af28f7f6062c | R | 3,534 | 73 | ## ----------Script-----------------
##
## Purpose: Generate post-sqanti plots for summary of Iso-Seq targeted mouse transcriptome datasets
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
##
## ---------- Source function and config files -----------------
# source all general scripts related to long-read sequen... |
cd954e718796f85dd1f984eda41ee257f4e0dad6658b7d26ef5b57f00e90d66e | R | 3,541 | 89 | #!/usr/bin/R
# ----------------------------------------------------------
# Enrichments on aggregated differential expression results:
# Updated: 11/25/21
# ----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R')... |
4c56b4c422e646bf342f5aa61bffae1911c1a977aa7072661b23e3ac6390d6ad | R | 3,543 | 112 | #!/usr/bin/R
# ------------------------------------------------------
# Calculate metadata statistics for the reviews response
# - stats on amyloid / nft burden
# - stats on staging
# - stats on pathol diagnosis
# - comparison to Montine ABC score
# (all of these on ours vs. full ROSMAP cohort)
# Updated 09/19/2023
# ... |
bb77c130e843622a8a2ae895df1196d91c0f20e7164cbda6777dd15c1a285a8c | R | 3,544 | 102 |
library(here)
suppressPackageStartupMessages({
library(tidyverse)
library(NanoStringQCPro)
require(Biobase)
})
# all RCC files should be in a single folder
rcc_files <- dir(here("data/rcc_files"), full.names = TRUE)
# RLF file is NanoString panel specific and is required to load RCC files
RLF_FILE <- here("ref/Hawki... |
e146ceb8f7a99b833c6be6a1e8e0d73fa2ad780e226a61633c5177aef377c430 | R | 3,554 | 94 | setwd("/media/hansruedi/3b32f153-0651-4d56-b863-9b070ac7dd55/home/hansruedi/Brain_region_final")
library(ggplot2)
library(Seurat)
pbmc <- readRDS(file = "In_raw_mscores_integrated2.rds")
addition=read.table("Inh_Metadata_addition_cell_type_highres.txt",header=TRUE,row.names=1,sep="\t")
pbmc=AddMetaData(pbmc,ad... |
b89e88a67a6afdcf6fa90fa7e9b913ba906120a32d64039b1effc97e14ab3bfd | R | 3,561 | 87 | # helper functions for UI
#' Round and format the mean, min, and max as a single character string
#' @param x the numeric variable to summarize
#' @param digits the number of digits to use in rounding
#' @param prespace An integer indicating the number of spaces to include at the beginning of the string. Default: 0
#'... |
078dad0efbafab71d8efc4469e644967470c83c71d5722b7bb724faa4cab7795 | R | 3,563 | 56 | ####################################################################################################
###
### File: 06_coordinates_results_data.r
### Purpose: Loading the results data to obtain the statistics
### and get differences among the work.
### Authors: Gabriel Rodrigues Palma, Rafael Moral
### Date:... |
2cfbccd5d32c028c061dd6809e342cdbfb73a7bf02c528eef75bc3c4cb812643 | R | 3,566 | 103 | test_that("spm_l1_model enforces per-run vs concatenated confound expectations", {
tmp_dir <- tempfile("spm_l1_expect_")
dir.create(tmp_dir, recursive = TRUE)
nifti1 <- file.path(tmp_dir, "run1.nii.gz")
nifti2 <- file.path(tmp_dir, "run2.nii.gz")
file.create(nifti1)
file.create(nifti2)
gpa <- create_mock... |
6afb33d7e472e849fa85f4f36d4cf876b74e558ff39202bd691d6a58acc03a1e | R | 3,572 | 106 |
stage <- c("40","70","115","168")
tissue <- c("Br", "Li", "Mu", "Pl")
POE <- read.csv("F:\\RNA_20221101\\02_ModifiedGenome\\C_POEgeneSelection\\result\\POE.list.csv", header = T, row.names = 1)
library(customLayout)
library(dplyr)
##Set canvas layout
lay <- lay_new(matrix(c(1,2,3,4,5,6,7,8,9,10,11,0,12,13,1... |
bd007aa61c4656a42f84e0713afaa8d13f3a4d5e032fb915dda6bb55abfc0c1e | R | 3,572 | 178 | rm(list=ls())
data_crtscp<-read.delim("/Users/cazaledl-admin/Library/CloudStorage/OneDrive-UniversityofBirmingham/Post_doc/XP/data/courtscape/.txt",h=T,sep="\t",na.strings = "NA")
str(data_crtscp)
attach(data_crtscp)
###### EXTRACTION DATAS ######
{
ET <- subset(data_crtscp,threat=="ET")
LT <- subse... |
6f6b3d039322686d96f385bec2070538f99d08c9ec35f79b7bed38709197f03b | R | 3,581 | 119 | # Function -------------------------------------------------------------------
.Compare_hh <- function(e1, e2) {
validObject(e1)
validObject(e2)
callGeneric(e1[[]], e2[[]])
}
#' Comparison of `hyperSpec` objects
#'
#' @description
#' The comparison operators `>`, `<`, `>=`, `<=`, `==`, and `!=` for `hyperSpec... |
78db2ed47ff4f539ebbd9dd3c637a76445bbb8b095327a196d5772e37678cedc | R | 3,581 | 116 | #!/usr/bin/R
# ---------------------------------------------------------
# Script to load proportion data for plotting + composition
# Updated 11/04/2021
# ---------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(bindir, '... |
12e9e4620d5e2614866b75f7de792a5fb7e59aa943672e01285c4c56ce431d79 | R | 3,588 | 117 |
library(here)
suppressPackageStartupMessages({
library(tidyverse)
library(Biobase)
})
ESET_RDS <- here("results/eset_raw.rds")
eset <- readRDS(ESET_RDS)
fdata <- fData(eset)
pdata <- pData(eset)
edata <- exprs(eset) %>% as_tibble()
row_names <- filter(fdata, CodeClass=="Endogenous")$Hgnc_Symbol
# FUNCTIONS -------... |
fd34d952df5b42b0a2ea515edeb3ec46c211adfde30699bf0e58ee1725587345 | R | 3,592 | 88 | # This script creates a ped file with all families >3 into trios
# to match desired input for de novo SV filtering
# Define args and load libraries
args = commandArgs(trailingOnly=TRUE)
require(plyr)
library(data.table)
library(tidyverse)
input_ped <- args[1]
# Read input ped
ped <- fread(input_ped,header = TRUE)
#... |
cf5d93bea2d7154a9c84f3672dffd0095ddf04629b501667ac177edf50d15ffe | R | 3,603 | 109 | library(dplyr)
celltype_list = c("Exc","Inh","Ast","Oli","OPC","Mic")
celltype_list2 = c("Ex01","Ex02","Ex03","Ex04","Ex06","Ex07","Ex08","Ex10","Ex13")
region_list = c("AG","EC","HC","MT","PFC")
for (i in 1:length(region_list)){
region=region_list[[i]]
Marker_genes_list <- list()
Marker_genes_list2 <- l... |
6a5c913a584e4d1de00089491203ef0acd2b2d257d2a8559c257ae20d99b6f1d | R | 3,605 | 109 | library(dplyr)
celltype_list = c("Exc","Inh","Ast","Oli","OPC","Mic")
celltype_list2 = c("Ex01","Ex02","Ex03","Ex04","Ex06","Ex07","Ex08","Ex10","Ex13")
region_list = c("AG","EC","HC","MT","PFC")
for (i in 1:length(region_list)){
region=region_list[[i]]
Marker_genes_list <- list()
Marker_genes_list2 <- l... |
1bdb11a3faad2e66f65b9270e5bac5d586bc3f9fd4e7a0f5aa2a3ca845956c3b | R | 3,620 | 89 | setwd("D:/Brain_region_project/Brain_region_final")
library(ggplot2)
library(Seurat)
pbmc <- readRDS(file = "In_raw_mscores_integrated2.rds")
addition=read.table("Inh_Metadata_addition_cell_type_highres.txt",header=TRUE,row.names=1,sep="\t")
pbmc=AddMetaData(pbmc,addition)
Idents(pbmc)<-"cell_type_high_resolu... |
a9345d1231040581318ab1101fef9d8a8de6223870cddc2731d3738dd1c6defd | R | 3,637 | 145 | # For the specified k and cognitive variable, find the correlations with
# the idiosyncrasy metrics and the corresponding uncorrected p-values.
# Output:
# olab,'.csv' Correlation, p-value, and p < 0.05 thresholded versions.
#Set libraries.
library(tidyverse)
#Catches arguments.
args = commandArgs(trailingOnly=T)
k <... |
8dab1fdb94ec2a2b5b8037cc8000cb0c2f1b02e94b18ce28cfef399a0af2ba99 | R | 3,639 | 93 | library(tidyr)
library(dplyr)
library(purrr)
exp_name = snakemake@params[['exp_name']]
phenotype_suffix = snakemake@params[['phenotype_suffix']]
linear_model_res_path = snakemake@params[['linear_model_res_path']]
outlier_mode = snakemake@params[['outlier_mode']]
extreme_quantile = as.numeric(snakemake@params[['extre... |
26667d7526e09fb712b02db439e8415bbf38ce7cad0bfb1302fbabda751bd28f | R | 3,645 | 114 | #!/usr/bin/env Rscript
# Helper script to titrate proband variants & de novo rate given a minimum GQ
###Set master parameters
options(stringsAsFactors=F,scipen=1000)
svtypes <- c("DEL","DUP","INS","INV","CPX","BND")
###########################
###GENERAL HELPER FUNCTIONS
###########################
#Read & clean t... |
e44df585918d0d27d9dc7e37a961b0fbd66aa0776bbfd6bd93c3f1c105675215 | R | 3,657 | 58 | library(Seurat)
library(dplyr)
library(tidyverse)
library(magrittr)
library(liana)
setwd("F:/Brain_region_project/Brain_region_final_12TB/Data_for_Connectome")
pbmc=readRDS("HC_merge_major_cell_type.rds")
Idents(pbmc)<-"cell_type_high_resolution"
pbmc=subset(pbmc,idents=c("Ast DPP10","Ast GRM3","Ast DCLK1",... |
17d9bde314fa1ad99b2bb165f0867605c2530a26e40ab538beb6d9a76cdc3809 | R | 3,667 | 99 | ## ---------- Script -----------------
##
## Script name:
##
## Purpose of script:
##
## Author: Szi Kay Leung
##
## Email: S.K.Leung@exeter.ac.uk
##
## ---------- Notes -----------------
##
##
##
##
##
## ---------- Source function and config files ---------------
# Specific analysis related
SC_ROOT <- "/gpfs/... |
f651e89c40adc577694340e9899c0731dddd8464eb5b8a38a4759dea58a6124c | R | 3,670 | 101 | #' @importFrom checkmate assert_data_frame assert_string assert_file_exists assert_character
#' @importFrom oro.nifti readNIfTI writeNIfTI
#' @importFrom dplyr bind_rows filter select
#' @importFrom doParallel registerDoParallel
#' @importFrom parallel makeCluster stopCluster
#' @importFrom iterators iter
#' @importFro... |
47cb18764b91e9ed18a1b344ff72b3405c0a4176687346024389031c7a01e0ef | R | 3,676 | 86 | rm(list=ls())
library(data.table)
load("CPM_exp.RData")
CPM_exp -> PDGC_CPM_exp
############################################################# step 0
mads <- apply(PDGC_CPM_exp,1,mad) ## calculate MAD value
exp_by_mad <- PDGC_CPM_exp[rev(order(mads)),] ## sort MAD from high to low
library(NMF)
nmf(exp_by_mad[1:4500,... |
ce6610df7687abb72eff4312b33c38b256f708757afc005119c453592f5a1428 | R | 3,677 | 146 | # For the specified k and cognitive variable, find the correlations with
# the frequency metrics and the corresponding uncorrected p-values.
# Output:
# olab,'.csv' Correlation, p-value, and p < 0.05 thresholded versions.
#Set libraries.
library(tidyverse)
#Catches arguments.
args = commandArgs(trailingOnly=T)
k <- a... |
5db0f7309ea85633c659b57936e65e5be19da9d75e91c8e348b8b911ee9b1432 | R | 3,678 | 90 | # Doublet Finder registry
source("Analysis/util.r")
library(DoubletFinder)
library(Seurat)
library(ggplot2)
library(magrittr)
library(dplyr)
library(data.table)
x <- readRDS("Data/Processed/alphasyn-triplication-initial-integrated.rds")
x$orig.ident <- factor(x$orig.ident, paste0('BU-SNCA-', 1:24))
x_list <- SplitObje... |
098c672e7c462b20f5a4598587baa655246f5423d130ee5dc39794c0875c602e | R | 3,691 | 78 | library("tidyverse")
library("gprofiler2")
library("data.table")
library("BiocParallel")
# load the DEG datasets
setwd("./Projects/SNCSynuclein")
# load the DEG files
DEGFileCombind <- readRDS("Step6.DEG/DEGs/DEGFileCombind.rds")
Cluser <- names(DEGFileCombind)
## Filter and only show the significant pathways
for (... |
e3eceb25cccdf57b291ba05e6a3dfc4bdf214c57f872ed69dc4e910b2a62a241 | R | 3,694 | 146 | # Function -------------------------------------------------------------------
.cov_h_ <- function(x, y, use, method) {
validObject(x)
cov(x@data$spc, use = use, method = method)
}
#' Covariance matrices for `hyperSpec` objects
#'
#' @rdname cov
#'
#' @param x `hyperSpec` object.
#' @param y Not supported.
#' @p... |
86d2c4ba45095fa9a49364079867123c2b8743125087d5c40d289f4fda06513b | R | 3,696 | 94 | #analyze data
#conda activate /mnt/analysis-psp/users/timchang/programs/miniconda/R3.6.3
#conda install r-ggforce rcolorbrewer
.libPaths('/mnt/analysis-psp/users/timchang/programs/R/3.6.3/')
library(ggforce);library(ggplot2);library(RColorBrewer)
options(stringsAsFactors=F)
geno_path='/mnt/analysis-psp/users/timchang/... |
ec33ab99ebf8121590fae88ca8e6dfad9bf53cb54b62dd8a543b772b559d455d | R | 3,697 | 125 | #!/usr/bin/R
# --------------------------------------------------------
# Gather tables for RELN validation, compare to RELN list:
# Updated: 07/25/2022
# --------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidy... |
efc383842de527979fc72f9f428e5075e2eb2e9600a2b1563e55d8b7f16bc057 | R | 3,697 | 131 | #!/usr/bin/R
# -----------------------------------------------
# Plot module genes on its top KEGG pathways:
# TODO: pathview > SBGNview (updated w/ reactome)
# Updated 12/21/2022
# -----------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(p... |
f835f51ac7f57a1f09b3d594f8695fae74b7cfdffed1e68dd152e54c4698a2bf | R | 3,700 | 120 | ae_encoder <- nn_module(
"Encoder",
initialize = function(vae_dim, n_batch, n_covariate) {
self$n_layers = length(vae_dim)
self$layers <- nn_module_list()
self$layers[[1]] <- nn_linear(vae_dim[1] + n_batch + n_covariate, vae_dim[2])
if (self$n_layers > 3) {
for (i in 2:(self$n_layers - 1)... |
db4ce473c490477bcfa298dd17936b117eb34c800c2cd57a871054f49c6514bf | R | 3,702 | 87 | #' @title Function to obtain LASSO estimates of a regression problem given summary statistics
#' and a reference panel (without PLINK bfile)
#' @details A function to find the minimum of \eqn{\beta} in
#' \deqn{f(\beta)=\beta'R\beta - 2\beta'r + 2\lambda||\beta||_1}
#' where
#' \deqn{R=(1-s)X'X/n + sI}
#' is a shru... |
accd1f0924ddc3deb1cf3f9e15943ac67d29bcfa0be106e877280894e6c2c8f1 | R | 3,706 | 61 | ## ----------Script-----------------
##
## Purpose: Generate post-sqanti plots for summary of ONT targeted mouse transcriptome datasets
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
##
## ---------- Source function and config files -----------------
# source all general scripts related to long-read sequencing... |
e0675418c940c8694cbef2e85a49371e860e35e26a4adf7e5975ee9fe0033acd | R | 3,709 | 131 | #' @name DEPRECATED-read.txt.Horiba
#' @concept moved to hySpc.read.txt
#'
#' @title (DEPRECATED)
#' Import Horiba Labspec exported ASCII files
#'
#' @description
#' These data input functions are **deprecated** and they will be removed in
#' the next release of \pkg{hyperspec} package.
#' Now functions in packa... |
7dc1123b5ba5068b813c7aaf357b4cdecb7e3151ce377d26167b142e61dfec47 | R | 3,716 | 94 | library(dreamlet)
library(muscat)
library(zenith)
library(scater)
library(Seurat)
library(dplyr)
library(ggplot2)
library(limma)
library(purrr)
library(readxl)
setwd("/New12TB_HD1/F/PFC_429_final_Spring2021/Data/Data_Batches/DecontX_data/Grouped_by_cell_class")
pbmc=readRDS("Inh_decontX.rds")
#Add offic... |
4235dc5c7c25ea498de240bd1d1482892954f313f2b807a087f735321f053612 | R | 3,730 | 130 | #!/usr/bin/R
# -------------------------------------------------------------
# Replot the modules representations for extended data figures:
# Updated 03/18/2022 to standardize sizes
# -------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta... |
75fdbd87fe892721aa43862bdc9d6abd99349b55acc3fccdc01be26c829ded66 | R | 3,738 | 124 | ### models the A+T- to (A+)T+ conversion ###
# run analysis_clean.R before!
############################################
print_figures <- F
sensitivity <- F
##extract participants with A+T- profile
tmp <- unique(subset(longATN, ATN=="A+T-")$RID)
longApTm <- subset(longATN, is.element(RID, tmp))
#build data for survi... |
8af0614c3de045279f3e45c9ad890d1f12322ce92e4929df997948a7fa76b1bb | R | 3,746 | 100 | # Modified function from SeqNet package:
plot_network_diff <- function (network_1, network_2, as_subgraph = TRUE,
node_color = adjustcolor("orange", 0.5), node_scale = 5, node_weights = NULL,
edge_weights = NULL, edge_scale = 1,
coords = NULL, main = "Untitled", include_vertex_labels = F... |
2940096ed0dc8ffd0c0dbdcbeb2ed07dfd8a9752657746d839c8fd51764bab1d | R | 3,751 | 126 | #!/usr/bin/R
# ------------------------------------------
# Print the GWAS genes in the modules / DEGs
# Updated 12/16/2021
# ------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))
}
source(paste0(sbindir, ... |
b960658576db23e6221fedeab5ccfc6dd929c287b39532b15e70c6f172df1e3a | R | 3,751 | 62 | library(Seurat)
library(dplyr)
library(tidyverse)
library(magrittr)
library(liana)
setwd("F:/Brain_region_project/Brain_region_final_12TB/Data_for_Connectome")
pbmc=readRDS("EC_merge_major_cell_type.rds")
Idents(pbmc)<-"cell_type_high_resolution"
pbmc=subset(pbmc,idents=c("Ast DPP10","Ast GRM3","Ast DCLK1",... |
fd99a97a33dcc6596cb644ff477328ecb98f9cbf48deebe6069f5fa9cb5eda04 | R | 3,756 | 91 |
##the result of tissue-specific gene expression and chromatin accessibility intensity are visualized with heat maps to see if the same cluster can be found
##
setwd("F:\\Manuscript\\ATAC\\figure\\ori_RNA_ATAC_heatmap")
library(dplyr)
GeneOpen <- read.csv("TisSpeGeneGroupbyPeakintensity_tisMean_promoter.CSV", ... |
1123136f4d13e7d6cdb98f64ec70dccea2b5695b1bd037b59fca5f03ce8fb9bd | R | 3,757 | 91 | library(dplyr)
library(Seurat)
library(ggplot2)
options(java.parameters = "-Xmx16000m")
library("xlsx")
library(svDialogs)
ref<- read.xlsx(file="Zebrafish_SC_Cell type marker_database_081022_v4.xlsx", sheetName = "zebrafish_genes")
#getting total number of markers available in the dataset for each celltype
... |
0c5ae80d58ea6bb3e7d8a6943b6662b4233ca025f92e6433de6bbcd7da75641e | R | 3,759 | 83 |
library(coloc)
library(dplyr)
library(pbapply)
library(data.table)
library(liftOver)
library(GenomicRanges)
#system("wget -P . https://ctg.cncr.nl/documents/p1651/PGCALZ2ExcludingUKBand23andME_METALInverseVariance_MetaAnalysis.txt.gz")
AD_dat <-fread(input = "PGCALZ2ExcludingUKBand23andME_METALInverseVariance_MetaAn... |
5f6f96d7456f575192f1034e6be88ef4421ee4893c5deb22b0330a727795671a | R | 3,760 | 102 | # input:
# data/04_DS1.ssce.Rdata
#
# output:
# data/07_DS1_pseudot_umap.Rdata: pseudotime assignment
options(bitmapType = 'cairo')
library(SingleCellExperiment)
library(igraph)
library(scater)
library(slingshot)
bsave <- TRUE
# (1) Loading data ----
(load("data/04_DS1.ssce.Rdata")) #OJO con NB1/NB2/GCimm1
sce <... |
69cd0672b7328db85d07def26bf3a1af258331098759b38414c269d2c4a6d0b5 | R | 3,760 | 133 | #!/usr/bin/R
# ------------------------------------------
# Plot results for Nebula on e4 interaction:
# - Used for Oli-M7 + Opc-M9 test
# Updated: 06/27/23
# ------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
library... |
7ba8de575855765c466cd30300314af481ec218201195fa24c97ead53ee211c5 | R | 3,778 | 158 | library(testthat)
library(dplyr)
library(sccomp)
library(rlang)
test_that("warnings when symbols are provided for key columns", {
skip_cmdstan()
# Create test data
test_data <- tibble(
donor = rep(c("sample1", "sample2", "sample3"), each = 3),
cell_group = rep(c("A", "B", "C"), times = 3),
coun... |
a7308eef37ce3fd144e655a69ba98133552fbf2a55070d2990d9e05213dc171c | R | 3,780 | 117 | # genPresynCSVsFlyWire.r
#
# Script that generates CSV files of presynaptic neurons given starting FlyWire ID
# Will return 2 layers of inputs: direct and their inputs
# Writes CSV files, named by current FlyWire ID, into specified directory
# Set threshold of minimum number of synapses
# make sure fafbseg library... |
e766c71a88793e6121ed48da8343ac3d6beebaf5c77312fb3c36a3ed9a4c0715 | R | 3,780 | 111 | #!/usr/bin/R
# -------------------------------------------------------------------------
# Determine consistent up / down patterns for each covariate
# Determine unique subtypes of DEGs for each pathology / AD measurement
# Plot coeff. + clusters of these DEGs across EC neurons
# Updated: 12/06/2021
# -----------------... |
9e8bc13b132af2bc6907157fcec1ddf9565fba9fa2887a721bb8bea1e26ba6ed | R | 3,781 | 89 | ## ---------- Script -----------------
##
## Purpose: perform differential analysis on mouse rTg4510 ONT and Iso-Seq targeted datasets using linear regression
## Transcript level separate analysis
## Gene level separate analysis
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
# https://hbctraining.github.io/DGE_wo... |
23f9e18980fc1e463e061a88169bd83a745175ddb390f412b6eca263fea005ae | R | 3,782 | 53 | library(spacexr)
library(spASE)
library(Matrix)
myrctd <- readRDS('results/rctd_hippo_1.rds')
writeMM(myrctd@originalSpatialRNA@maternalCounts, 'hippo_1_slideseq_CAST.mtx')
writeMM(myrctd@originalSpatialRNA@paternalCounts, 'hippo_1_slideseq_129.mtx')
write.table(data.frame(gene_id = seq(1,nrow(myrctd@originalSpatialRN... |
61f94d554a796f6ec7e46bff0a6cc7bb168da5906181b0061b6ac65c00403761 | R | 3,782 | 114 | library(tidyverse)
library(tidyseurat)
library(sccomp)
library(job)
library(patchwork)
prior_overdispersion_mean_association = list(intercept = c(5, 5), slope = c(0, 5), standard_deviation = c(5,5))
job({
load("data/counts_obj.rda")
counts_obj |>
mutate(is_benign = type=="benign") |>
rename(cell_t... |
a73f8e826b0af8fb195204f84b11961f766625695dee0268ea3ce4bbc3562b40 | R | 3,782 | 71 | test_that("get analyser object", {
ana = get("Auc_Analyser")$new()
expect_equal(ana$ana_name, "AUC")
expect_equal(length(ana$plots), 0)
})
test_that("two keywords, two stimuli, paired data", {
yaml = get_testyaml_object("dir", analyser = "Auc", changes = "DataSettings$PairedData = TRUE")
ana = get_analyser_o... |
fe47f78a9a67cbba56ae614496a938e5f7101c59c14d78121929f44d1dc150f8 | R | 3,783 | 107 | #!/usr/bin/R
# -----------------------------------------------------------
# Aggregate and plot chunked runs of MAST + RE for DGE
# Plot the comparison between regressions on different pathologies.
# Updated: 02/11/2021
# -----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ')
s... |
d67a0eae4e035b1c88973f8fa5004320bfb3c1954f868d7398ae7c6f321b175b | R | 3,786 | 134 | # Set generic ----------------------------------------------------------------
#' @importFrom lattice latticeParseFormula
setGeneric("levelplot", package = "lattice")
### --------------------------------------------------------------------------~
### levelplot.R - everything that has to do with levelplot-like plotti... |
94b0b4058bf875ba030035d7ea31733345ce756388e2289c2444542b1cb2b354 | R | 3,791 | 119 | # For the specified k, find the correlations between reconfiguration variables
# and plot.
# Output:
# predcorr.csv Correlations between reconfiguration variables.
# predcorr_round.csv Correlations between reconfiguration variables, rounded.
# predcorr.jpg Correlations between reconfiguration variables, visualized.
#S... |
90ee5ec470dd1b53f2ef6eec65d1145d8a901e467a9c0c51d9c0a45d0557ef62 | R | 3,794 | 122 | #!/usr/bin/R
# ------------------------------------------------------
# Compare OPC fraction to OPC genes at pseudobulk level:
# Updated 02/17/2021
# ------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
libr... |
e0befd48076ba7dfc32dae9cf64eebcbb184becd7809467ca23c1b6e127611b3 | R | 3,799 | 134 | #!/usr/bin/R
# ---------------------------------------
# Quantify the cross-module correlations:
# Updated 04/02/2022
# ---------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))
}
library(tidyr)
library(ggpubr)
l... |
88d90051fb549866e298075eb381f0f17014012676363e80eaead42d7efe5884 | R | 3,801 | 147 |
#### Setup options and resources ####
## ----------------------------------
CardinalResources <- list2env(list(logger=simple_logger()))
CardinalNamespace <- environment(NULL)
CardinalEnv <- function() CardinalNamespace
# set up Cardinal defaults
.onLoad <- function(libname, pkgname) {
setCardinalBPPARAM()
setCar... |
b99933a31557d5d8c317c58a742947a24d1b50a85e24570e3072f5f9337d265f | R | 3,801 | 104 | ##fixing plotting of Schoenfeld residuals
#instructions here: https://stats.stackexchange.com/questions/560975/how-to-interpret-schoenfield-residual-plot
#
ggcoxzph_fixed <- function (fit, resid = TRUE, se = TRUE, df = 4, nsmo = 40, var,
point.col = "red", point.size = 1, point.shape = 19, point.alpha = 1,
cap... |
de4b8153024a499e68d954ed20e3ae640e13a7f634cadaf64b968d5c2dd913db | R | 3,803 | 110 | library("tidyverse")
library("batchtools")
library("Seurat")
library("data.table")
library("future")
library("MAST")
library("SingleCellExperiment")
## use the bath tools for RNA contamination detection and doublet removal
setwd("./Step5.FinalClustering")
# load the seurat object
reg <- makeExperimentRegistry("FindMa... |
89a2d502029d8a5f4109fd64395c2006de17d825974e00d1ac9e3cff39af245f | R | 3,808 | 154 |
#' Sequence generation along spectra or wavelengths
#'
#' This function generates sequences along the spectra (rows) or wavelengths of
#' `hyperSpec` objects.
#'
#' Note that [wl2i()] generates sequences of indices along the wavelength axis.
#'
#' [seq()] had to be implemented as S3 method as the generic has only \dot... |
287e4e786e7075727a9d5a46721b16e526b98c548be669ae62f147b9de714b4a | R | 3,811 | 67 |
#' archstabms__plot_growthrate_violins
#'
#' Plot growth rate violins.
#'
#' @param input_dt data.table with fitness estimates (required)
#' @param report_outpath output path for scatterplots (required)
#' @param dataset_name dataset name and suffix for output files (required)
#' @param trait trait name and suffix for... |
a33c8a6ca85be3e6be392254f5eef019b207ade20c2c715d7bfa8bafa8a08781 | R | 3,811 | 145 |
#### Summarize features ####
## --------------------------
summarizeFeatures <- function(x, stat = "mean", groups = NULL,
verbose = getCardinalVerbose(), chunkopts = list(),
BPPARAM = getCardinalBPPARAM(), ...)
{
if ( "FUN" %in% ...names() ) {
.Deprecated(old="FUN", new="stat")
stat <- list(...)$FUN
}
if ( !... |
86b75d08d459e7e16cb0bc7d898b71e5653bb639b75399f6b3e539da86d9c6d3 | R | 3,813 | 97 | #' Create enhanced Volcano plot from Seurat DE
#'
#' @param seurat_object integrated Seurat object. Containing ref_ident and vs_ident in Idents(seurat_object)
#' @param seurat_DE Seurat DE result, from a FindMarkers() function
#' @param ref_ident reference sample name, pct.1 in Seurat DE result
#' @param vs_ident versu... |
f2f856a7119f1ea93698d7a54319a25a99df575ce03779243323bdaa2b2e7629 | R | 3,814 | 94 | #!/usr/bin/R
# ------------------------------------------
# Load metadata for the multiregion project:
# ------------------------------------------
library(cbrbase)
library(RColorBrewer)
library(tidyr)
set_proj('DEVTRAJ')
# Arguments:
args = commandArgs()
if (length(args) > 0){
project = args[1]
} else {
proj... |
c0ec54aed09e49482a4b7de47be43d33682435178242a0715cceca4656235754 | R | 3,817 | 115 | adni_ct_dataset <- dataset(
name = "adni_ct_dataset",
initialize = function(input_list,
data_type = c("train", "test", "all"),
insert_new_batch = FALSE,
new_batch = NULL) {
self$insert_new_batch = insert_new_batch
if (!data_type %... |
23d868fd7298a8a8c64acd307715ecdf1b9e2e8513616171de77b256474c7839 | R | 3,820 | 129 | #!/usr/bin/R
# -----------------------------------------------
# Auxiliary functions for contour plots on UMAPs.
# Updated 01/23/2023
# -----------------------------------------------
library(cbrbase)
library(tidyr)
library(soundgen)
options(width=170)
source(paste0(sbindir, 'auxiliary_plotting_settings.R'))
# Calc... |
bcd4da85891ffcbe23cb87402c96e428148dd29d3a0797e2aaa78a012d8d3ad1 | R | 3,824 | 103 | # ********
# 2c-reint-markers.r
# Code for computing marker genes for initial integration results.
source('Analysis/util.r')
library(Seurat)
library(presto)
library(data.table)
library(magrittr)
library(dplyr)
library(ggplot2)
library(MAST)
# Compute marker genes for the initial integration (SCT.seurat_snn_res.0.1 clu... |
d2833a2f1354586698cea67d49ebce2ce5276fe22bdbd59f1a5eaf7e7dbe4fe7 | R | 3,824 | 116 |
# Read in the cds holding all cells
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjectCode/utility/singleCellUtilFuncs.R")
library(monocle3)
# print(modStatus)
library("optparse")
library(dplyr)
library(ggplot2)
library(RColorBrewer)
monocle_theme_opts <- function()
{
theme(strip.back... |
c0d4faf2f443949ed8fa1578e23c340733f9448396fdae29323900cb9ac23994 | R | 3,828 | 117 | scheduler_safe_token <- function(x, fallback = "job", max_chars = 120L) {
if (is.null(x) || length(x) == 0L || is.na(x[1L]) || !nzchar(x[1L])) {
x <- fallback
}
x <- as.character(x[1L])
x <- gsub("[^A-Za-z0-9_.-]+", "_", x)
x <- gsub("_+", "_", x)
x <- gsub("^_+|_+$", "", x)
if (!nzchar(x)) x <- fallb... |
0d7f039608be6aff9a67022c5fbca6f48cb54a04b930336f976842c6d8d7abf4 | R | 3,832 | 61 | library(Seurat)
library(dplyr)
library(tidyverse)
library(magrittr)
library(liana)
setwd("F:/Brain_region_project/Brain_region_final_12TB/Data_for_Connectome")
pbmc=readRDS("MT_merge_major_cell_type.rds")
Idents(pbmc)<-"cell_type_high_resolution"
pbmc=subset(pbmc,idents=c("Ast DPP10","Ast GRM3","Exc L2-3 CB... |
80265fe113e03f3e3bdbd408027b4e109bce24b1eac45f3bf26ccc94a1af6728 | R | 3,834 | 153 | # For the specified k and cognitive variable, find the correlations with
# the transition distance metrics and the corresponding uncorrected p-values.
# Output:
# olab,'.csv' Correlation, p-value, and p < 0.05 thresholded versions.
#Set libraries.
library(tidyverse)
#Catches arguments.
args = commandArgs(trailingOnly... |
6102d9f84ae3cd5c9a944e4128ed4d7c3272d1fd152343120fb741ffc9b5b7bb | R | 3,835 | 127 | ---
title: "Using PMLB Python interface"
---
```{r message=FALSE}
message("Current working directory: ", getwd())
message("Contents of working directory: ", paste(dir(getwd()), collapse = ", "))
```
```{r setup, include=FALSE}
library(reticulate)
knitr::knit_engines$set(python = reticulate::eng_python)
knitr::opts_... |
60e759fba60b26a7dd400beed92a41960f6de9419ca21247670c73e54c22ff30 | R | 3,839 | 140 | library(testthat)
test_that("get_l1_confounds fails clearly when requested confound columns are missing", {
tdir <- tempfile("confound_validation_")
dir.create(tdir, recursive = TRUE)
sqlite_db <- file.path(tdir, "cache.sqlite")
run_nifti <- file.path(tdir, "run.nii.gz")
confounds_tsv <- file.path(tdir, "co... |
1964df06f0f1d6398195ec57a311f99260711a21c27ea13dd419b095555e3cfe | R | 3,842 | 64 | library(Seurat)
library(dplyr)
library(tidyverse)
library(magrittr)
library(liana)
setwd("F:/Brain_region_project/Brain_region_final_12TB/Data_for_Connectome")
pbmc=readRDS("AG_merge_major_cell_type.rds")
Idents(pbmc)<-"cell_type_high_resolution"
pbmc=subset(pbmc,idents=c("Ast DPP10","Ast GRM3","Ast DCLK1",... |
e6ebc9aadcf95db5fc71e473b84884e20c545837ad774b3a1ea077f4fed53579 | R | 3,843 | 108 | #!/usr/bin/R
# ------------------------------
# Estimate depletion quantities:
# Updated: 12/06/2021
# ------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
library(viridis)
library(ggplot2)
library(ggrepel)
library(ggpubr)
library(... |
227992d4a691c6d14a241e08aa51e725294c2cb5561c754fb301ad2997d96cd7 | R | 3,845 | 61 | library(Seurat)
library(dplyr)
library(tidyverse)
library(magrittr)
library(liana)
setwd("F:/Brain_region_project/Brain_region_final_12TB/Data_for_Connectome")
pbmc=readRDS("PFC_merge_major_cell_type.rds")
Idents(pbmc)<-"cell_type_high_resolution"
pbmc=subset(pbmc,idents=c("Ast DPP10","Ast GRM3","Ast DCLK1"... |
7cc3a353fc483028dd0d0cc56b6abc286fa4b4c8741fcc6e6fa117f44032c015 | R | 3,854 | 116 | #!/usr/bin/R
# ------------------------------------------------------------------------
# Plot the base region proportions figures for the atlas part of the paper
# Updated 11/04/2021
# -----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir... |
954417fe58d993c6361bba76fd065004dd5a77bf22ef868a6c11162561cde6da | R | 3,861 | 104 |
#' archstabms__plot_additive_trait_binding
#'
#' Plot folding additive trait.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param input_dt data.table with model free energy estimates (required)
#' @param RT constant (default:0.001987*(273+24))
#' @param report_outpath output path for... |
e551bd2f6405cc403a4668284c63a7f94e97b65d021eb21253151ef3fbe14025 | R | 3,868 | 117 | ---
title: "Python interface"
output:
html_document:
toc: true
toc_depth: 3
toc_float:
collapsed: false
css: custom.css
---
```{r message=FALSE}
message("Current working directory: ", getwd())
message("Contents of working directory: ", paste(dir(getwd()), collapse = ", "))
```
```{r setup, inc... |
e6c70b73846dad68bcc0e78dc5bc41a4a828f3acbf2f790b58baad391a37ab37 | R | 3,868 | 139 | #!/usr/bin/R
# ------------------------------------------------------
# Compare our microglia definitions to Tuddenham markers
# Updated 11/17/2023
# ------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
libr... |
68238bd55ef5d59b12b64774a82716c24be73447a5e7b29b33f2b2511e3a164c | R | 3,879 | 106 | library("batchtools")
## use the bath tools for RNA contamination detection and doublet removal
setwd("./Step8a.SubClusterAnalysis")
reg <- makeExperimentRegistry("Reclustering.Registry",
conf.file = "./Projects/snRNAseq/Step12.SubclusterAnalysis/batchtools.conf.R",
... |
39287855f1664b4c1d4d04a08877a833319a9ada389bba02bd11c997494dee58 | R | 3,881 | 115 | #!/usr/bin/R
# ----------------------------------------------------------
# Score all samples (psbulk) based on shared degs (01e)
# Updated: 01/09/23
# --------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
... |
cc44338ab98a717928877ec369c7ecd7c518d9baea5960402ac85d33a474071d | R | 3,883 | 90 | setwd("F:\\RNA_20221101\\02_ModifiedGenome\\C_POEgeneSelection\\figures_tables\\03_POE基因热图展示")
library(dplyr)
library(edgeR)
library(ggplot2)
library(stringr)
library(magrittr)
library(pheatmap)
### generate the bias score matrix grouping by stage
data_filter <- read.csv("F:\\RNA_20221101\\02_ModifiedGenome\\A_... |
36df9daeaa4d2c009c38d6b70275b20877c4df5faefeb31e73f65fb8fb3a1cea | R | 3,890 | 122 | # genPostsynCSVsFANC.r
#
# Script that generates CSV files of postsynaptic neurons given starting FANC ID
# Will return 3 layers of targets: direct, their targets, and their targets
# Writes CSV files, named by current FANC ID, into specified directory
# Set threshold of minimum number of synapses
# make sure fanc... |
053bab49c33692ceb852f2e7cfd3591bfaca9f0a8272bad71eec6f4ff165a60d | R | 3,892 | 67 | library(Seurat)
library(dplyr)
library(tidyverse)
library(magrittr)
library(liana)
setwd("F:/Brain_region_project/Brain_region_final_12TB/Data_for_Connectome")
pbmc=readRDS("HC_merge_major_cell_type.rds")
Idents(pbmc)<-"cell_type_high_resolution"
pbmc=subset(pbmc,idents=c("Ast DPP10","Ast GRM3","Ast DCLK1",... |
ee1f6f9c6a26f4ea854e0608be0f56eea1f8ca994ab9bbadb5a96790f7729036 | R | 3,900 | 69 | library(data.table)
library(dplyr)
library(coloc)
library(foreach)
library(doParallel)
coloc_abf <- function(i){
Phe1_coloc_dat <- Phe1_dat %>% filter(Phenotype == coloc_pairs[i,exposure])
Phe2_coloc_dat <- Phe2_dat %>% filter(Phenotype == coloc_pairs[i,outcome])
coloc_dat <- inner_join(Phe1_coloc_dat, Phe2_colo... |
ff52e050166c54977da8684273aaaf909268e71712f132be664ecd7355512259 | R | 3,905 | 120 | #!/usr/bin/R
# -------------------------------------------------------------------------
# Plot pseudobulk matrices vs. DE genes from subtypes in entorhinal cortex:
# Updated: 12/06/2021
# -------------------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
sourc... |
17d177eb30313db3fac2f30a9bb710282258ba6a41388f7551aa47206ed48162 | R | 3,910 | 90 | # ********
# 3-pseudode.r
# Code for running pseudobulk DEG analysis.
# In addition, we filter DEG's with the results of 3-de.r, MAST model DEG results.
source("Analysis/util.r")
library(scuttle)
library(edgeR)
library(Seurat)
library(dplyr)
library(magrittr)
library(ggplot2)
#x <- load_integrated()
DefaultAssay(x) <-... |
c7eace38fcb291fe1030668751eb05e8a08752b77643816a362165d0f51aeae1 | R | 3,920 | 98 |
### Ref links
# http://tuxette.nathalievilla.org/?p=1696
# https://swcarpentry.github.io/r-novice-inflammation/05-cmdline/
## -----------------------------------------------------------------------------
# get the input passed from the shell script.
args <- commandArgs(TRUE)
## -----------------------------... |
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