sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
a9e6ada9be309333938256f52f0d6513b312078a9e747ffb747bf9da576f620e | R | 2,206 | 91 | #!/usr/bin/env R
# Author: Sean Maden
#
# Description:
#
# Notes:
#----------
# load data
#----------
bulk.dpath <- file.path("dcs04/lieber/lcolladotor/deconvolution_LIBD4030",
"Human_DLPFC_Deconvolution/processed-data",
"01_SPEAQeasy/")
list.files(bulk.dpath)
# [1] ... |
2a93dbd0d9db6a205f77102913ae6719a8a02b08378b81f3f60d25b8bf20fde9 | R | 2,207 | 57 | # ********************************
# GWAS data processing
# ********************************
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if(!require("tidyverse")) {
install.packages("tidyverse")
library("t... |
aaed9604b03a517b1aad64b6d10e17772d8371f4ea1fd344f2560fcea77c0640 | R | 2,207 | 48 | library(optparse)
op_list <- list(
make_option(c("-l", "--input_loom"), type = "character", default = NULL, action = "store", help = "The input of aucell loom file",metavar="rds"),
make_option(c("-m", "--input_meta"), type = "character", default = NULL, action = "store", help = "The metadata of Seurat object",metava... |
84efb5737d9fbd7ae518cd1e267cc7ccf5a7e994ae343aaf693eaf5e869ae7d3 | R | 2,219 | 58 | library(feather)
# RNA-seq data path
data_loc = '/data/rnaseqanalysis/shiny/facs_seq/Mm_VISp_14236_20180912'
# Project path
project_path <- file.path(getwd(),'..','..','..','assets','aggregated_data')
# Annotation data
anno_feather_path <- file.path(data_loc,'anno.feather')
anno_feather_data <- read_feather(anno_fea... |
f94de235b84768d47055035392b5453499095fcff03e4cf2a745649751f2cf78 | R | 2,219 | 34 | #' SRAtoSNP
#'
#' In case of analyzing SRA file, it is also possible to use the following function that performs step 1-4 in one function
#' @param File The Path to the SRA File
#' @param Library_Type "Paired" or "Single"
#' @param TopHat_Threads Number of threads used for the alignment
#' @param Transcripts_An... |
3b5af3fe0decdb15688ec007d0e9db77d50b02085175f32f746c069a7a407f6a | R | 2,220 | 68 | library(survival)
library(R2nparray)
ixd = list(c(20,1), c(50,1), c(50,2), c(100,5), c(1000,10))
res = list()
for (ix in ixd) {
fname = sprintf("results/survival_data_%d_%d.csv", ix[1], ix[2])
data = read.table(fname)
time = data[,1]
status = data[,2]
entry = data[,3]
exog = data[,4:dim(data... |
2c0ce5e8385a19081b0d42b8b8878dd56823154fb6601f1239a28b31544ffec0 | R | 2,226 | 55 | #' dmrff.stats
#'
#' Calculate statistics for a set of genomic regions.
#'
#' 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 regions Data frame of genomic... |
f8e4da06aaceca52b7dce3c8d468a7419b48b9b14526eb4ffc6952b68d516aa7 | R | 2,231 | 58 |
## EWAS functions for FANS dataset ##
# Cell-type EWAS
Cell <- function(row, pheno){
if(age.group=='Fetal'){
full.model <- lmer(row ~ NewCellType + Age + Sex + Plate + (1|Individual_ID), data=pheno, REML = FALSE)
null.model <- lmer(row ~ Age + Sex + Plate + (1|Individual_ID), data=pheno, REML = FALSE)
}
els... |
a5162bf1c5542111ff64bf59ba17ec1bde67d52f471a3143d401cb9746f970b7 | R | 2,235 | 47 | # classify Sex using cellXY
library(speckle)
library(SingleCellExperiment)
library(CellBench)
library(cellXY)
library(CellBench)
library(BiocStyle)
library(scater)
library(qs)
library(Seurat)
library(rlang) # might have to reinstall
library(caret) # might have to reinstall
# only get samples that have male,... |
5594073a6060948bec431fcf0b51a1b11443554c6d05bed7f92d5d1a0ddf420e | R | 2,240 | 52 | rm(list=ls())
source('load_libraries.R')
source('scripts/aux_functions.R')
# Read autism gene data; repeat for other two
autism_genes = read.csv("data/SFARI-Gene_genes_01-23-2023release_03-02-2023export.csv", sep = ",") %>%
filter(gene.score == 1 | syndromic == 1) %>%
.$gene.symbol %>%
unique()
# read disease g... |
4f50113e5237390d9ed1951156b42fdd06c546c530c94e65a6b2d1eef9531e0a | R | 2,242 | 83 | #!/usr/bin/env Rscript
# Function for importing BAM file and calc accuracy
import_bam_get_accuracy <- function(bamfile) {
bam <- GenomicAlignments::readGAlignments(bamfile,
use.names = TRUE,
param = ScanBamParam(tag = c("NM", "... |
dbea5f55a7e6931d058f113df5b8090dd1756cbe9517ce99c3a96b07640eae8e | R | 2,242 | 68 | # ----------------------------------------------------------------------------
# Libraries and setup ----
library("argparse")
library("ggplot2")
library("tidyverse")
library("Seurat")
# Command line arguments ----
parser <- ArgumentParser(description = "Differential expression analyses")
parser$add_argument('--seura... |
4164174f887a73c268e4868b88411d6477bdc0d0b71701ee2248138c523a3b55 | R | 2,243 | 69 | library(here)
library(tidyverse)
library(SingleCellExperiment)
library(sessioninfo)
library(data.table)
library(Matrix)
rse_gene_path = here("processed-data", "rse", "rse_gene.Rdata")
sce_in_path = here(
"processed-data", "13_PEC_deconvolution", "sce_CMC_initial.rds"
)
sce_out_path = here(
"processed-data", "1... |
44b1e6e5ff0f3c18e7780dce5d1b378ad780aec4f3f880ad7a46a50d2d0e36b5 | R | 2,250 | 59 |
## Run Age linear regression on postnatal bulk samples at the bulk fetal dDMPs ##
library(data.table)
#1. Load data ===================================================================================================================
load(paste0(MethylationPath,"EPICBrainLifecourse.rdat"))
betas <- epic.betas
phen... |
f6f23ced75c805bb64d556744f9399199cac9446c6849e67d7c09f03d581b7c8 | R | 2,252 | 78 | #fit a scDesign3 model and save it
#automatically use log Library size and cell type as covariate
#load arguments
#1: sce data set
#2: new coldata
#3: output file name
#4: number of cores to use
args <- commandArgs(trailingOnly = TRUE)
options(warn = -1)
sce_file <- args[1]
new_coldata_file <- args[2]
output_header ... |
e814990169f99448d9e0ab4352cfd7ef03c1cba1290568b30370afd2634297b3 | R | 2,253 | 60 | # load packages
require(tidyverse)
require(Seurat)
# load Seurat object (pre-doublet identification)
sdata.align <- readRDS('sdata_align_snRNA-seq_prelim.rds')
# set Idents (resolution = 0.5)
Idents(sdata.align) <- sdata.align$seurat_clusters <- sdata.align$integrated_snn_res.0.5
# rename Idents in full dataset
sdat... |
849a00e0fad42f2621a7b7d48a1b517ee4b0d768d3109c1c5895bbbe5a8a3c4b | R | 2,255 | 52 | # GNU General Public License v3.0 (https://github.com/IanevskiAleksandr/sc-type/blob/master/LICENSE)
# Written by Aleksandr Ianevski <aleksandr.ianevski@helsinki.fi>, June 2021
#
# Functions on this page:
# auto_detect_tissue_type: automatically detect a tissue type of the dataset
#
# @params: path_to_db_file - DB file... |
e2a476929c699c934eec6398a6f8988c774f3fe042467bdb889cb39e43ef33d9 | R | 2,255 | 54 | library(Seurat)
library(future)
set.seed(1234)
setwd("/ALS_multiome")
# Define sample names and their corresponding metadata
sample_names <- c(paste0("CTRL", 1:6), paste0("C9ALSFTLD", 1:6), paste0("C9ALSnoFTLD", 1:3), paste0("sALSnoFTLD", 1:8))
snRNA_diagnosis <- c(rep("control", 6), rep("C9ALS", 9), rep("sALS", 8))
... |
1a490389ca54b1866a4f08244e1912acdcb66028bf245234757f89488eb88c3a | R | 2,262 | 67 |
## Plot MAGMA results ##
library(ggplot2)
# bulk fetal MAGMA results
bulkRes_ASD <- read.table(paste0(bulkPath, "ASD2019_ageReg_fetalBrain_MAGMA.gsa.out"), header=T)
bulkRes_SCZ <- read.table(paste0(bulkPath, "SCZ_ageReg_fetalBrain_MAGMA.gsa.out"), header=T)
# FANS fetal MAGMA results
fansRes_ASD_N <- read.table(p... |
567b702422e24afa6c57a4ed8c3bd3ac447357db80ac014d069c94dd5c78eae7 | R | 2,262 | 77 | #####
# Script for predicting methylation classes using MARLIN
# as in Steinicke, Benfatto, et al., manuscript in preparation
#
# The script takes in input multiple files (one for each sample) containing the
# methylation calls of CpGs restricted to positions of probes in our reference.
# Values are binarized and missi... |
af9451ccebc9a9bbabc3f39002c7ab6fdc8046df470c4ce229cae8bec11513bf | R | 2,262 | 81 | #!/usr/bin/env R
#
# Get marker genes from snRNAseq data. Uses scran::findMarkers to find marker
# genes for clusters (e.g. cell types).
#
#
library(scran)
library(SingleCellExperiment)
#-----------
# set params
#-----------
celltype.varname <- "cellType_broad_k"
#----------
# set paths
#----------
# sce object pa... |
e136fb9698cb9ddcbdbcc789f776ec13e9a4a9a36cd0f89781ab5d31f2c9379c | R | 2,268 | 68 | #!/usr/bin/env Rscript
# date: 1/9/2023 by Hongjian Jin @ St Jude Children's Research Hospital
library(optparse)
option_list <- list(
make_option(c("-d", "--dataDir"), type="character",default=".",
help="character. fastq_screen output directory ")
,make_option(c("-o", "--output"), type="character", default=NA,... |
66179b57c1de3320f6df03ad1b98c91e4f11b2a059d0ea2a61db0b5a205ea4fd | R | 2,272 | 72 |
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... |
9675091315fc99ccf676e8b987cc67dbff4a3d314c7ac2df504f92afab181dd8 | R | 2,279 | 61 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == "route" && is.null(GLOBAL_VALUE$positiveDetail)) {
# 詳細データけんもねずみ
GLOBAL_VALUE$positiveDetail <- fread(paste0(DATA_PATH, "positiveDetail.csv"))
}
})
output$infectedRouteRegionSelector <- renderUI({
pickerInput(
inputId = "infectedRouteByRegionPi... |
0b81f9fd213ad86e555e10b4c48e57ac65cb1ab295540fddee4c6f3ca6ad5b45 | R | 2,283 | 70 | ---
title: "Label fields"
description: >
Assign categorical UK Biobank fields the labels from the showcase schema.
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Label fields}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
co... |
8bea1f1bc67a60291bd31ebadc949163648154a8193cc23a72eec90f259e041d | R | 2,288 | 78 | ##
## Generate a volcano plot from a data frame of genes, fold changes (log-scale), and p-values.
##
plot_volcano = function(stats_df,
gene_col,
fc_col,
p_col,
fc_cutoff = 0,
p_cutoff = 0.05,
... |
055c1fc4ab6109ab43295e8f332d3e0a8cee0082a87b46a9f20adbc4301b0e6a | R | 2,292 | 47 | # Compare the social perceptual evaluations between GPT4 Vision and humans in the video experiment temp0 pilot data
# Severi Santavirta 15.5.2024
##--------------------------------------------------------------------------------------------------------------------------------------------------------------------------... |
dbfe12574b4a58ec4c091a29309f2a6fde3b37dd6b2b51c26c9645b37008d897 | R | 2,295 | 78 |
```{r}
library(kronos)
library(tidyverse)
library(ggplot2)
```
Import dataset
```{r}
library(readxl)
bigdata_yjl_ht_new <- read_excel("C:/Users/U177202/PhD/Experiments_thesis/Youth_Jet_Lag_Experiment_2/YJL_Exp2_Microarrays/YJL_Exp2_KRONOS/YJL_Exp2_circadian_clock/YJL_Exp2_circadian_clock_ht_extra/bigdata_yj... |
225245dc2c831278caf8a27d74f903175d05798ce27149e143698c00e5812fac | R | 2,299 | 77 | # ´ÓdevCEÀïɸѡºÍ´óÄÔ·¢ÓýÏà¹ØµÄ»ùÒò£¨ÓÃdevCEËùÓеĻùÒò×öGO£¬ÌôÑ¡Éñ¾·¢ÓýpathwayÀïµÄ£©£¬¼ì²âÆäCEÊÇ·ñËæ·¢ÓýPSIϽµ
# ÓÃinterproscan¼ì²âCEÓ°ÏìµÄdomain
# ɸѡdevCEÀï°üº¬GGAµÄ
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(readxl)
library(biomaRt)
library(curl)
# get human brain down ... |
3cb49953c1859751e07949719e8a9efe205020882799afcfa3d96a0117ed4d12 | R | 2,307 | 70 | # library("purrr")
library("here")
library("jaffelab")
## move file to make names compatable w/ SPEAQeasy
## all basenames for fastq files must be unique
file_dir <- here("raw-data", "bulkRNA")
raw_data_files <- list.files(file_dir, recursive = TRUE)
message("All unique basenames: ", !any(duplicated(basename(raw_data... |
9b0ab07d9dcd7107c931c8eb96ad97f193d52d85da433f702fed2929a92684a6 | R | 2,314 | 53 | #' Edit_dbSNP_Files
#'
#' Before LOH analysis, the dbSNP files need to be edited using the Edit_dbSNP_Files function (done only once):
#' @param Directory the directory were the files are, one GTF file per chromosome
#' @param File_Name the files name, without the number of the chromosomes
#' @param Organism "Human" or... |
3f79c9961567b8ded2805993c82611fa00d279c5667f9be0271c07cf1d6680c3 | R | 2,319 | 53 | #' Calculate statistics for the first-tier mapping
#'
#' Calculate statistics for the first-tier mapping
#'
#' @param seu Modified query Seurat object from \code{mapToMB()}.
#' @param group.by Name of one metadata column to group cells by.
#'
#' @return A matrix of 37 rows. Each column corresponds to one group speci... |
1801dc4974fdc8a44250f063d582e5d37b8058b125a32493c1d405b1b0dc07d5 | R | 2,320 | 65 |
data(datafls)
#now do an MC3 sampling over the growth data 'datfls' with 1000 burn-ins,
#9000 iterations (ex burn-ins),
#and retaining the best 100 models (besides overall MC3 frequencies)
invisible(readline("hit <Return> to do estimate a short BMA MC3 sampling chain."))
mfls =bms(X.data=datafls,burn=1000,iter=9000... |
0cee1373123ef954f869451bee688db139dcfb1f935636574187263c2f12f088 | R | 2,325 | 67 | # filter_tcr_genes ----
filter_tcr_genes <- function(markers){
markers %>%
dplyr::filter(! grepl("^TR[ABG][VDJ]", gene))
}
# make_pseudobulk - wrapper with corrections for when a category has no counts
# for any gene ----
make_pseudobulk <- function(obj, idents){
gp_by = unique(c("Sample", "beta_aa",... |
5244823c95099ab28e53d80fce47afc0e1f5d2189784e78c5524523ee62c32d5 | R | 2,329 | 43 | context("compareRhythms_model_select")
load("test_data_ma.rda")
exp_design_batch <- cbind(exp_design,
batch = ifelse(seq(nrow(exp_design)) %% 2, "a", "b"),
stringsAsFactors=TRUE)
test_that("model selection works for default params", {
results <- compareRhythms(ex... |
5e670ebbefa3cd827f076522385d4d1d087e750ca2874656e69cdaf0dda04216 | R | 2,330 | 72 | # plot ASoC peaks
# need to overlay 3 data tracks
# Siwei 1 March 2019
# init
library(Gviz)
# data("cpgIslands")
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)
########... |
0740cd1ff77ac6d4ba341f40f7bd5dbbd02a1551bd29c38298570fbd7829a40f | R | 2,338 | 54 | ################################################################################
# Script to plot from .csv files the regional brain maps of connectivity to the
# disease epicenters after stratifying by cognition and symptoms
################################################################################
# Co... |
71347f9c5ec30f6ba5a0bb902d82c314d556f0be6093f1fc3eadb6c89bd14faa | R | 2,344 | 79 | compute_circ_params <- function(y, t, period) {
inphase <- cos(2 * pi * t / period)
outphase <- sin(2 * pi * t / period)
X <- stats::model.matrix(~inphase + outphase)
fit <- stats::lm.fit(X, t(y))
amps <- 2 * sqrt(base::colSums(fit$coefficients[-1, ]^2))
phases <- (atan2(fit$coefficients[3, ], fit$coef... |
ccb9d60c6531d5950d833025e21336a8c1c73d14e4876f85e27eb5d0d45d863b | R | 2,346 | 76 | #read GRNs, find coreg matrix, find modules using WGCNA
rm(list=ls())
set.seed(123)
source('load_libraries.R')
source('aux_functions.R')
detect_delimiter = function(filepath) {
first_line = readLines(filepath, n = 1)
if (grepl("\t", first_line)) {
return("\t")
} else if (grepl(",", first_line)) {
retur... |
dc1b1dc64b05ed523ab7574c98d79672de2552a9394be807f3a93c091f8c89a4 | R | 2,346 | 98 | library(FKF)
library(KFAS)
options(digits=10)
# Observations
df <- read.csv("clark1989.csv", header=FALSE)
lgdp = log(df$V1[5:nrow(df)])
unemp = (df$V2 / 100)[5:nrow(df)]
# True parameters
params <- c(
0.004863, 0.00668, 0.000295, 0.001518, 0.000306, 1.43859, -0.517385,
-0.336789, -0.163511, -0.072012
)
# Dimens... |
6c9ad3285b2c1c7ebacc3166a1946542d552401f3f337811f8c75eda9f4ab9c8 | R | 2,348 | 68 | library(data.table)
source(file = "01_Settings/Path.R", local = T, encoding = "UTF-8")
data <- fread(paste0(DATA_PATH, "SIGNATE COVID-2019 Dataset - 罹患者.csv"))
dt <- data[
!is.na(受診都道府県) & 発症日 != "" & 確定日 != "",
.(受診都道府県, 発症日 = as.Date(発症日), 確定日 = as.Date(確定日))
]
dt[, 発症から診断までの平均日数 := round(as.numeric(mean(確定日 -... |
229513d8a96d52c010260c5c7c35810859f03206ecf791c62f8418361b251412 | R | 2,362 | 87 |
library("SummarizedExperiment")
library("edgeR")
library("limma")
library("purrr")
library("here")
library("jaffelab")
library("sessioninfo")
#### Set up ####
## dirs
plot_dir <- here("plots", "09_bulk_DE", "04_DE_library-type")
if(!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
## dirs
data_dir <- her... |
5914441a9b2c01b65ede4af34026215eea3ce0dfef48076e7a9cea17c353946e | R | 2,367 | 51 | column(
width = 5,
style = "padding:0px;",
userBox(
title = userDescription(
title = i18n$t("新型コロナウイルス"),
subtitle = i18n$t("Coronavirus disease 2019 (COVID-19)"),
image = "ncov.jpeg",
backgroundImage = "ncov_back.jpg"
),
width = 12,
closable = FALSE,
... |
7b1bd7660905169ce850465cac818e6876cf11926fed2ef8476cf7bb288e3e69 | R | 2,372 | 91 | ## UCR length distribution
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(dbplyr)
library(ggplot2)
UCR_location <- read.table(file = "01-data/UCR_raw/UCR_location_refseqid.txt",
sep = "\t", header = TRUE)
lwd_pt <- .pt*72.27/96
p1 <- ggplot(data = UCR_location, mapping... |
e155b345b47651455b68145c78213028d4ea1781b115ed2b64c75ad8f94ef143 | R | 2,377 | 61 | # First run main.R until definition of Selles et al. Mixed Model (M_mm)
# this scripts store prediction performance of the selles mixed model applied at
# different times post-stroke
weeks <- c(1, 6, 13)
names <- c("Week 1", "Week 6", "Week 13")
perf_wks_mm <- data.frame()
baseline_ARAT <- test_xgb %>%
group_by(N... |
deac54a0bdff2bae01aa64cd5027f59cba2d6f767fd69137914fa117f22f73e1 | R | 2,384 | 65 | # load packages
require(tidyverse)
require(Seurat)
require(monocle3)
# load data
sdata.P25 <- readRDS('sdata_align_snRNA-seq_singlet.rds')
sdata.P25 <- DietSeurat(sdata.P25, assays = 'RNA')
# set Idents to cluster labels
Idents(sdata.P25) <- sdata.P25$cluster_label
# load gene names table
gene.names <- read_csv('gen... |
dd838b25fc899c7e1b20979067bbb5d2084bc80371965d769d56b9b9cea11126 | R | 2,386 | 67 | library(tidyverse)
library(fgsea)
# load in function to get ranked gene list
source("../riboseq/helpers.R")
set.seed(123)
slamseq_hl <- read_tsv("processed/2023-08-22_i3cortical_slamseq_grandr_kinetics.tsv")
# i3 cortical cryptic ale containing genes
cryptic_ale_gene_lists <- read_rds("../riboseq/processed/gsea_ribo... |
70198c6b67a68e5b8477973c77824b4da8eedfdf331a1fa95ce8003dbe4e87b3 | R | 2,387 | 50 | library(KFAS)
options(digits=10)
# should run this from the statsmodels/statsmodels directory
setwd('~/projects/statsmodels-0.9/statsmodels/')
dta <- read.csv('datasets/macrodata/macrodata.csv')
source('tsa/statespace/tests/results/kfas_helpers.R')
# We use the following two observation datasets
obs <- (diff(log(data... |
9ffde4176cb989d97986216a0549e0e6af019b0063d2dcf0f86eaccd801cc264 | R | 2,392 | 54 |
## 2. WGCNA - block wise modules ##
library(WGCNA)
library(data.table)
#1. Load data used in GPMethylation =============================================================================================
load(paste0(dataPath, "FetalBrain_Betas_noHiLowConst.RData"))
betas <- betas.dasen
#2. Betas pre-processing ====... |
656b176f10d612a29f314d41b692a289d30b66c2c10d7a452082f7635026b60c | R | 2,393 | 72 | # Siwei 20 May 2021
# Genotyping to find het individuals on rs10792832 site
# init
library(readr)
library(readxl)
library(factoextra)
# load data
## load 60 MGS samples
MGS_60_ID <- read_excel("60_MGS_ID.xlsx")
## load in genotype data (each row is a sample)
## column 1-6 are not part of the genotyping data
## genot... |
a578f30ab1cf72d9575d2ad538fc124f2b1ed73096e915d079656e77ed4b7d33 | R | 2,397 | 96 | # ͳ¼Æintergenic UCRÖ÷ÒªºÍÄÄЩcCREsÖصþ
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(stringr)
library(VennDiagram)
cCREs_overlap_intergenic_UCR <- read.table(
file = "02-analysis/36-Encode_screen_cCREs/cCREs_overlap_intergeni... |
4334dc04609f1fa6f17ae5f6a78ecd8373c32af25e2f50f737eface8edbd8d25 | R | 2,398 | 69 | #' dmrff.pre
#'
#' Construct an object for including this dataset in a DMR meta-analysis.
#'
#' 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 estimate Ve... |
4c2d15df3df68b765421688a19d7b7258d5a36392d1638ba53e593191727c49e | R | 2,398 | 72 | #File to identify differentially expressed genes in a type of excitatory neurons.
library(Seurat)
library(stringr)
library(dplyr)
library(limma)
library(doParallel)
library(foreach)
samps = readRDS("samps_CA3.RDS") # Sample names.
prot_df = readRDS("prot_PSD_df_CA3.RDS") # Dataframe of protein detected per sa... |
cf9a9f4f18b94649142e434bcc09f780e8ce097a4f291e2a027baa268db28b19 | R | 2,399 | 47 | options(BioC_mirror="https://mirrors.westlake.edu.cn/bioconductor")
options("repos" = c(CRAN="https://mirrors.ustc.edu.cn/CRAN/"))
if(!require(installr))install.packages('installr')
if(!require(devtools))install.packages('devtools')
if(!require(stringr))install.packages('stringr')
if(!require(BiocManager))install.packa... |
b7a0776408c75df3341f7b09d5c6e15a7e6844f691b051853e175294638d3d9f | R | 2,401 | 69 | #!/usr/bin/env Rscript
#### Seurat CCA integration of MBPM_scn/MBM_sc/MBM_sn/MPM_sn with anchors, dims and cohort provided
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
print(Sys.time())
anchor <- as.numeric(commandArgs()[6]) #2000 anchors
dims <- as.numeric(commandArgs()[7]) #MBPM_scn:50; MBM_sc:... |
1f992f3e1f258076e0cf516b88c97a9b90ad8e77e1af14d0dcba7213a2345e68 | R | 2,402 | 84 | ## Louise Huuki-Myers Jan 2025
## select highly variable genes from snRNA-seq data to test methods
## part of deconvolution benchmark reviews Round 2
library("SingleCellExperiment")
library("tidyverse")
library("scran")
library("here")
library("sessioninfo")
data_dir <- here("processed-data", "06_marker_genes", "09_H... |
33f9e7799d57af90f81ee3b7cdbb19459da16346275c91111c622e372d68c057 | R | 2,404 | 61 | #' EditVCF
#'
#' Edit the VCF (Variant Call Format) file, Creates file with SNPs data at the BAM directory called variantTable.csv
#' @param vcfdir
#' @param base_name The Path of to the BAM Directory, also the VCF file
#' @param Organism "Human" or "Mouse"
#' @param out_dir "temp directory
#' @export
#' @return None
... |
cb1711526d1d30fd259816caa5ecfdde849e70a6d99d5cd816a065fe439782bf | R | 2,409 | 52 |
if(T){
load("report01/02.merge/COGA.merged.list.allPeaks.Rdat")
RNA.list = dd.list
# Assays(RNA.list[[1]])
cat("================ Normalization ===================\n")
# normalize and identify variable features for each dataset independently
RNA.list <- lapply(X=RN... |
b70edcec9c89d100468f4d2674ea3237cd7b5196651b6e936120e22e232570a8 | R | 2,411 | 75 | library(GenomicFeatures)
library(GenomicAlignments)
library(GenomicRanges)
library(Rsamtools)
library(dplyr)
# --- Load annotation and extract TSS/PAS ---
gtf_file <- "data/referance/dmel-all-r6.43.gtf"
txdb <- makeTxDbFromGFF(gtf_file)
tx <- transcripts(txdb, columns=c("tx_name"))
tss <- promoters(tx, upstream=0, do... |
9f4856a32c9c529178047a07e04e79ee2eb388d2966f07f692085e11b66aecd6 | R | 2,413 | 65 | #' dmrff.candidates
#'
#' Identify candidate differentially methylated regions from EWAS summary statistics.
#'
#' @param estimate Vector of EWAS effect estimates
#' (corresponds to rows of \code{methylation}).
#' @param p.value Vector of p-values.
#' @param chr Feature chromosome (corresponds to rows of \code{methylat... |
404f5d2613e432781cafbbaea7b4997408487ea74d335cd1b38cf1d043b7cecd | R | 2,424 | 52 | #!/usr/bin/env Rscript
### title: Global analysis of integrated object
### author: Jana Biermann, PhD
print(Sys.time())
library(Seurat)
library(dplyr)
library(ggplot2)
library(gplots)
'%notin%' <- Negate('%in%')
colBP <- c('#A80D11', '#008DB8')
colSCSN <- c('#E1AC24', '#288F56')
#### UMAPs
seu <- readRDS('data/M... |
e3f6b68f3423330a014eac62cbbaf5c130e9fa1cf7ceb9efe0af9789fbf0a99f | R | 2,425 | 85 | # Siwei 08 Jun 2022
# plot h2 and enrichment for LDSC results
# init
library(readxl)
library(readr)
library(ggplot2)
library(RColorBrewer)
# load data
combined_output <-
read_delim("combined_output.txt",
delim = "\t", escape_double = FALSE,
col_names = FALSE, trim_ws = TRUE)
df_to_p... |
cfba266d44f80c71723a9572258ea90078f04351ed758d85d6ce408cad6a4218 | R | 2,432 | 61 | suppressPackageStartupMessages(library(optparse))
option_list = list(
make_option(c("-b", "--iclip_bedgraph"), type='character',
help="iCLIP bedgraph (iCount)"),
make_option(c("-g", "--intron_set"), type='character',
help='mapped introns'),
make_option(c('-o','--outfile'), type ... |
fbf6f301c5a56e6c0d3e0d509c064334fb46d4d057c93969d4f1f418ab691bd5 | R | 2,433 | 69 | forecast.density <- function (PI_list,alpha){
# Plot forecast densities for bootstrap prediction intervals. These plots can be
# interpreted as probability density functions of possible outcomes. Accepts
# list with PIs.
# Arguments:
# <PI_list> a list with percentiles and future data points for 1 to... |
f2e00f91b24750fb52f0a44f14875e46dde0975c07d51407ef1a2eb0cf5f52fd | R | 2,435 | 81 | #generate magma expression data set in podaman container
#parameters:
#1: path of the sce
#2: path of the gene set directory
#3: output file directory
#4: temporary intermediate file path + header (will be removed later)
args <- commandArgs(trailingOnly = TRUE)
options(warn = -1)
#argument
data_path <- args[1]
gs_dir... |
1cf143e499e5fa3f3ed9a37e6a98397f7a026658429c12bc32d3a23291bbf303 | R | 2,438 | 68 | library(KFAS)
options(digits=20)
# should run this from the statsmodels/statsmodels directory
dta <- read.csv('datasets/macrodata/macrodata.csv')
obs <- diff(data.matrix(dta[c('realgdp','realcons','realinv')]))
obs[1:50,1] <- NaN
#obs[20:70,2] <- NaN
#obs[40:90,3] <- NaN
#obs[120:130,1] <- NaN
#obs[120:130,3] <- NaN
... |
239ad861917463bc67f912d66d79ace752770b7d0b972619d98a3b34392f4b81 | R | 2,439 | 92 | # Copyright (c) 2011, Roger Lew BSD [see LICENSE.txt]
# This software is funded in part by NIH Grant P20 RR016454.
# This is a collection of scripts used to generate C-H comparisons
# for qsturng. As you can probably guess, my R's skills are not all that good.
setwd('D:\\USERS\\roger\\programming\\python\\developmen... |
bda107c0d0119b1c48d294a0ccad6cc16c0d36474537454eec3a0a852b13fe7b | R | 2,445 | 60 | suppressMessages(library(ggplot2))
setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
load("enrichment.rdata")
se <- (enrichment$high - enrichment$low) / (2*1.96)
z <- enrichment$estimate/se
p <- exp(-0.717*2 - 0.416*z^2)
enrichment$p <- p
enrichment$lp <- -log10(p)
#save(enrichment, file = "enrichment_p.rdata"... |
972f6eb655bb8739b0996de8f0bbbd4ff52b1215c6d58d72771f1ff4d3a100a3 | R | 2,447 | 76 | ---
title: "EIB_behav_EIB_effect_on_meanRT"
author: "Barbara Cassone"
date: "AUTOMATIC"
output: html_document
---
```{r echo=FALSE, message=FALSE}
if(!require("pacman")) install.packages("pacman")
library(pacman)
p_load("reshape2","ez","dplyr","lme4","lmerTest", "rmarkdown", "lattice", "ggplot2", "emmeans")
... |
3e9e6313473ef43bf5e2a1bb29f39c0652da03a51294f47ea3bae998ee1bda24 | R | 2,451 | 76 | ---
title: "EIB_behav_EIB_effect_on_dprime"
author: "Barbara Cassone"
date: "AUTOMATIC"
output: html_document
---
```{r echo=FALSE, message=FALSE}
if(!require("pacman")) install.packages("pacman")
library(pacman)
p_load("reshape2","ez","dplyr","lme4","lmerTest", "rmarkdown", "lattice", "ggplot2", "emmeans")
... |
448785bc405208541218834251fa8c57a3255cca72135907db238d35604c20d4 | R | 2,451 | 71 | nm <- 1 # number of measurements to included per patient
mmn <- 7 # maximum measurement number to included per patient
# extract measurement(s) from test set to evaluate the XGB and MM on
eval_xgb <- test_xgb %>%
group_by(Number) %>%
slice_min(Days, n=mmn) %>%
slice_max(Days, n=nm) %>%
arrange(Number, Days) %... |
43d8e9eeb05144452c1975b0728cc39c2da8745f49f16975263b4735f42f9f9d | R | 2,467 | 67 | #get_proxy <- function(snp = "rs10001", r2_threshold = 0.8, build = "37", pop = "EUR")
#{
# stopifnot(build %in% c("37","38"))
#
# # Create and get a url
# server <- ifelse(build == "37","http://grch37.rest.ensembl.org","http://rest.ensembl.org")
# ext <- paste0("/ld/human/",snp,"/1000GENOMES:phase_3:", pop)
# u... |
77ce0168517fb5b8bf7fd0c1506a6cd1cab537f53e5c7edc789fcfd7ac8da290 | R | 2,469 | 79 | # GSE173754
# U937 vs PBMC
rm(list = ls())
setwd(dir = "D:/R_project/UCR_project/")
library(tidyverse)
library(ggplot2)
library(ggrepel)
lwd_pt <- .pt*72.27/96
GSE173754 <- read_tsv(file = "02-analysis/08-PBMC_U937_DEG/GSE173754.top.table.tsv")
GSE173754 <- GSE173754 %>%
mutate(Type = as.factor(ifelse(log2FoldCha... |
0c826023dea90f244201659382a0d67bb7423fb9ff5ccaf73b522f9bc70c0a82 | R | 2,472 | 70 | library(KFAS)
options(digits=10)
# should run this from the statsmodels/statsmodels directory
dta <- read.csv('datasets/macrodata/macrodata.csv')
obs <- diff(log(data.matrix(dta[c('realgdp','realcons','realinv')])))
#T <- t(matrix(
# c(-0.1119908792, 0.8441841604, 0.0238725303,
# 0.2629347724, 0.4996718412, -0... |
8a3766f61b0e9e2a503ad9e2abd37f1719cb68b4a58e709a16ee5fd99dff41b7 | R | 2,475 | 81 | # Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting
# ÎÄÕÂÀïʹÓÃLncRNAs2Pathways½øÐи»¼¯·ÖÎö£¬½øÐÐÁËһЩµ÷Õû£¬ÎÒ¿´¿´Îҵĸ»¼¯·ÖÎö½á¹ûÊÇ·ñÐèÒªµ÷Õû
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(ggplot2)
library(plyr)
library(LncPath)
library(stringr)
# Set parame... |
bfc8f7b3db0e0237c554eff781432e46fdd48e4bd90e6f8170b18c1f16ecb6a1 | R | 2,477 | 57 | #' This script installs all required libraries automatically
#' Please install libraries manually if it was not possible to install an library automatically
#' Missing libraries are listed in missing_packages.txt
#' To install an library please use following two command:
#' install.packages("name of the missing library... |
68b46ffaccb031ce56c4e34d66e894a98a167859b09962fbae3a83bd104079eb | R | 2,482 | 64 | #### Filter indel calls using unmerged bam ####
# For ds calls, keep 1/1 in unmerged bam; for ss calls, keep 1/0 or 0/1 (i.e. filter out 0/0 or ./.)
# - input: indel_calls
# - output: filtered_calls
options(stringsAsFactors = FALSE)
library(stringr)
# Parse arguments
args <- commandArgs(trailingOnly=TRUE)
input_f <- ... |
caa9a2368f06bc34d3baab3ec172235c96ce689b8081ce12659fe1a18c3fc468 | R | 2,482 | 55 |
create_feature_count_table = function(feature_count_folder, suffix = ".Aligned.sorted.out.bam"){
library(data.table)
# feature counts gives you feature coutns and summariues, make sure that all the files end in this pattern
# _featureCounts_results.txt
feature_count_files = grep(list.files(path=feature_count_... |
c9200ba5cdaeacf7d0d43fb496d2d47dad41810da0f37cd13b83a59084e59523 | R | 2,485 | 49 | ## Singh AK et al: Proteins with amino acid repeats constitute a rapidly evolvable and human-specific essentialome
##This file contains all scripts for plotting.
#1. Plotting all ggplot2 plots
##Note: Add hash to the lines which are not required when plotting any specific plot.
df=read.table(pipe("pbpaste"),header=TRU... |
8c61ecf0863936685562ed6439360b8f7f9e65c9646a6199db88743e662e5ac6 | R | 2,486 | 94 |
library("SummarizedExperiment")
library("edgeR")
library("limma")
library("purrr")
library("here")
library("jaffelab")
library("sessioninfo")
#### Set up ####
## plot dir
plot_dir <- here("plots", "09_bulk_DE", "05_DE_library-prep")
if(!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
## data dirs
data_... |
e9245ffb9490931e239bd21a1c48adf45c0fb3ec9652ef1be701de8a271a490f | R | 2,486 | 58 |
## 5. WGCNA - run pathway analysis on WGCNA nonlinear modules ##
library(WGCNA)
library(data.table)
library(missMethyl)
#1. Load data used in GPMethylation ===============================================================================================
load(paste0(dataPath, "FetalBrain_Betas_LOOZ_EX3_noHiLowconst_n... |
4df943d9a0cbb8af01acdc201e15ade439588d1813d281a777f07a79096774c7 | R | 2,488 | 99 | output$todayConfirmed <- renderUI({
if (length(HAS_TODAY_CONFIRMED) > 0) {
elements <- list()
for (i in 1:length(HAS_TODAY_CONFIRMED)) {
elements[[i]] <- suppressWarnings(boxLabel(
paste(
i18n$t(names(HAS_TODAY_CONFIRMED[i])),
"+",
HAS_TODAY_CONFIRMED[i]
),
... |
2b686f821f69a437d2334559987279ddfa943a225ce46f31c175fdf219196be1 | R | 2,492 | 84 | library(twice)
library(dplyr)
library(ggpubr)
library(Rtsne)
data("hg38rmsk_info")
data("hmKZNFs337")
load("data/mayoTEKRABber_balance.RData")
mayo_meta <- read.csv("data/selectSample.csv")
cbeCtrlCorr <- mayoTEKRABber$cbeControlCorr
cbeADCorr <- mayoTEKRABber$cbeADCorr
cbeDE <- mayoTEKRABber$cbeDE
tcxCtrlCorr <- may... |
7300adf297b3e17a81429d9e14d1a916d963fd0b24b296509e5bb38fbec0d05a | R | 2,492 | 109 | #!/usr/bin/env Rscript
print("##################################")
print("# ArchR: Fragments -> Arrow file #")
print("##################################")
################################################################################
library("optparse")
parser <- OptionParser(
prog = "createArrow_unfiltered.R",
... |
5506030213eb58de42d0297c4c7741fb9c1eb30d7c2d76fb34ec12ad01ae9f83 | R | 2,496 | 67 | setwd("osmFISH_AllenVISp/")
library(Seurat)
library(ggplot2)
osmFISH <- readRDS("data/seurat_objects/osmFISH_Cortex.rds")
osmFISH.imputed <- readRDS("data/seurat_objects/osmFISH_Cortex_imputed.rds")
allen <- readRDS("data/seurat_objects/allen_brain.rds")
genes.leaveout <- intersect(rownames(osmFISH),rownames(... |
998993d9625e25334870862e238d01fd914cecb9808514315bb544954c0115a7 | R | 2,496 | 67 | setwd("osmFISH_Ziesel/")
library(Seurat)
library(ggplot2)
osmFISH <- readRDS("data/seurat_objects/osmFISH_Cortex.rds")
osmFISH.imputed <- readRDS("data/seurat_objects/osmFISH_Cortex_imputed.rds")
Zeisel <- readRDS("data/seurat_objects/Zeisel_SMSC.rds")
genes.leaveout <- intersect(rownames(osmFISH),rownames(Ze... |
0c81e090e9d13dafc3dcd8be92281a43266438bfa4531467b8717791e81088eb | R | 2,497 | 64 | # *************************************************
# Preprocessing steps for Tabula muris dataset
# *************************************************
if(!require("Seurat")) {
install.packages("Seurat")
library("Seurat")
}
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magritt... |
c997e82fa892cfec2c4b781236f16210ceb68fbd899ea08aece032f21aa43c30 | R | 2,498 | 86 | # Siwei 15 Mar 2024
# Make Ast plots
# make plots for Alena
library(readr)
library(ggplot2)
library(readxl)
library(stringr)
# Alena_table <- read_excel("Alena_table.xlsx")
Alena_table <-
read_delim("~/backuped_space/Siwei_misc_R_projects/R_MG_17/Ast_GREAT_enrich_100.tsv",
delim = "\t", escape_double ... |
9ff492cb764680010324d01ec361b892cf937a5315af2b6ff1c66b24840855a6 | R | 2,499 | 67 | setwd("osmFISH_AllenSSp/")
library(Seurat)
library(ggplot2)
osmFISH <- readRDS("data/seurat_objects/osmFISH_Cortex.rds")
osmFISH.imputed <- readRDS("data/seurat_objects/osmFISH_Cortex_imputed.rds")
allen <- readRDS("data/seurat_objects/allen_brain_SSp.rds")
genes.leaveout <- intersect(rownames(osmFISH),rownam... |
b64b38b4aa8fe47172bfc991b3adc65a9924de75ac9f7aa597a7940c31c2f7fb | R | 2,499 | 78 | ---
title: "EIB_behav_EIB_effect_on_accuracy"
author: "Barbara Cassone"
date: "AUTOMATIC"
output: html_document
---
```{r echo=FALSE, message=FALSE}
if(!require("pacman")) install.packages("pacman")
library(pacman)
p_load("reshape2","ez","dplyr","lme4","lmerTest", "rmarkdown", "lattice", "ggplot2", "emmeans"... |
432f719c950ea8faf8007cae7b221d84d18728cde337331fcb60b0f42cab91e5 | R | 2,507 | 74 | # ******************************************
# Analysis of the Tabula sapiens dataset
# ******************************************
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if (!require("tidyverse")) {
i... |
5758ae1698cd7bfcb04e7d8d2f59f4e77817cc184dc7b889a103a7d64716e576 | R | 2,515 | 75 |
#' Batch convert SLEAP H5 files into DeepLabCut-like CSVs
#'
#' This function loads a directory containing H5 files generated from SLEAP.
#' It processes the first track in the CSV (assuming single-animal) and rearranges
#' the tracked points to match the style of that in DeepLabCut. Generated CSVs
#' are all saved to... |
374b0b4362f0414390d0db009bcb9a6ea572e03cd4833abed852af931e91ba17 | R | 2,518 | 76 | #
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
human_psi <- read.table(file = "01-data/19-Development_alternative_splicing/human.psi",
sep = ",", header = TRUE)
print(colnames(human_psi))
## brain
brain_human_psi <- human_psi[, c(grep(pattern = "Brain", colnam... |
5685f449755f8921c61c0ec24a129c10ee7184b82853e7777ac4c4e84b003555 | R | 2,528 | 84 | #' Get cancer registry data for specific codes
#'
#' @author Luke Pilling
#'
#' @name get_cancer_registry
#'
#' @noRd
get_cancer_registry <- function(
codes,
ukb_dat,
verbose = FALSE
) {
start_time <- Sys.time()
# Check input
if (verbose) cli::cli_alert_info("Searching cancer registry data for {length(unique... |
e835f455f957fb83988f20a7dc9156680fef594645accaa02e494cbf094f6b5c | R | 2,530 | 46 | library(tidyverse)
library(DESeq2)
dir <- "/GPFS/Magda_lab_temp/maitenat/illumina_circ/ciriquant/bsj_files"
KO_filenames <- list.files(dir, pattern = "Adar2KO", full.names = TRUE)
WT_filenames <- list.files(dir, pattern = "WT", full.names = TRUE)
filenames <- c(KO_filenames, WT_filenames)
counts <- map(filenames, fu... |
7cc32843c822e2ad0219ce7d086a28d1f8771727414cd6fbc98bfff1f3ceb2a2 | R | 2,535 | 58 | #Make plots (part 1) ###############################################################################################
#Load libraries
library(Seurat)
library(tidyverse)
#Create directory to store plots
dir.create("4_plots", showWarnings=T)
#Load mapped Seurat data with reductions
df = LoadSeuratRds("3_integrated_samp... |
73d43a5e7c26b7b385b36f69f918eeb70b48f352a48832de75a64bc150af44f7 | R | 2,543 | 97 | # Siwei 04 Mar 2024
# Plot Fig.5B
# init ####
{
library(readxl)
# library(edgeR)
library(stringr)
library(ggplot2)
# library(data.table)
library(reshape2)
library(RColorBrewer)
}
# func ####
# GET EQUATION AND R-SQUARED AS STRING
# SOURCE: https://groups.google.com/forum/#!topic/ggplot2/1TgH-kG5XMA
... |
06a7e52bf59e4b0b0f729d9691f9dd9a18b2296d2860d1f6f1dbbf9f12230d9d | R | 2,544 | 63 |
# Load necessary library
library(igraph)
# Define the full set of nodes
all_nodes <- paste0("G", 1:100)
# Define the file names
input_files <- paste0("DATA/SERGIO-Data/Interaction1_100_", 1:4, ".txt")
output_files <- paste0("RESULTS/SimulationDataPreprocess/adj_matrix_G100_", 1:4, ".txt")
# Process each file
for (i... |
c1a58d4c73043a081b026c42ea92dd85591780e713ef04a9e9469afa6c29bddc | R | 2,546 | 74 | library(bulkAnalyseR)
library(ggplot2)
library(dplyr)
source_folder <-
expr_matrix <- file.path(source_folder, "07.Expr_matrix/expr_matrix.csv")
metadata <- file.path(source_folder ,"metadata.csv")
output_dir <- file.path(source_folder, "08.Bulkanalyser")
gtf_path <- "GRch38_p113.gtf"
gtf <- read.table(gtf_path, he... |
c35a6d4680898244e3d493d6647dbf4a171ec958e7a469fd2f48817e40a8a24a | R | 2,550 | 68 | library(tidyverse)
library(DESeq2)
# isoform-level counts for fracseq data
fracseq_counts <- read_tsv("processed/fracseq/2024-04-30_summarised_pas.counts.tsv")
# At least for part of the analysis, want to try to compare the proportion of total isoform expression in each fraction
# As comparing across samples/fraction... |
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