sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
81e39a7a184d085a657e0c323fbcb1505dae7ea0d670e29f5940be76b3928844 | R | 1,551 | 58 | fluidPage(
fluidRow(
column(
width = 6, style = "padding:0px;"
),
column(
width = 6,
uiOutput("IwateValueBoxes") %>% withSpinner(proxy.height = "200px")
)
),
fluidRow(
box(
width = 12,
closable = F,
title = tagList(icon("chart-line"), sprintf(i18n$t("%sの発生状況... |
4a228a98ecb0383d5ba79bba60b6462b56de88ee3b795d67a3d61420adfcdab6 | R | 1,552 | 54 | ---
title: "Length of stay in the ICU"
author: "tom pollard"
description: "Length of stay in the ICU for patients in MIMIC-III"
output: pdf_document
date: "10/10/2017"
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
# install.packages("ggplot2")
# install.packages("bigrquery")
library("ggplot2")... |
1d117390b53aaa11411d3c1b6584b09dc0deca7f598f8f9e891496b5a907acfc | R | 1,557 | 56 | #first script to launch before building your network with metacore
#Code to preprocess data so that they are usable for metacore and also the Network pipeline
#From the DEGs in all cell types you are getting a file which is ending woth "List.txt" and can be directly copy paste into metacore
#You are also getting the ... |
485154986ce39210c4e86b96eeeeabdc737d3b2dbc312e45c2c29987053e9ea3 | R | 1,560 | 51 | library("tidyverse")
library("here")
library("sessioninfo")
halo_files <- list.files(here("raw-data", "HALO"), full.names = TRUE)
names(halo_files) <- gsub(".csv", "", basename(halo_files))
halo_anno <- map2(halo_files, names(halo_files), function(fn, samp) {
output <- read_csv(fn) %>%
mutate(
... |
3291511db72718defe4a8686bfd72e360d07fbc206cf56a75bf517587f4286ac | R | 1,562 | 31 | # 20231125 ֮ǰʹÓõÄsnpEFF¶ÔUCRÉÏËùÓеÄSNP½øÐÐÁË×¢ÊÍ£¬ÏÖÔÚɸѡ³öHIGHµÄSNP£¬ÓÃÓÚÏÂÒ»²½ÔÚVEPµ±ÖÐÔ¤²âÄÄЩÓë¼²²¡Ïà¹Ø
rm(list = ls())
setwd(dir = "D:/R_project/UCR_project")
library(tidyverse)
UCRSNPAnno <- read.table(file = "D:/A_projects/UCR/snpEFFAnno/UCRSNPAnno.vcf",
sep = "\t", skip = 7)
c... |
98c7a2d20b25e4de06f2abf93ac8aa54549ca65f2f944bd9bf8fdd0cafafe672 | R | 1,564 | 36 | ## 02_harmony_integration.R
library(Seurat)
library(harmony)
library(scCustomize)
set.seed(1234)
# Process Li2023 dataset
li_obj <- readRDS("li2023_merged.RDS")
li_obj <- NormalizeData(li_obj)
li_obj <- FindVariableFeatures(li_obj, nfeatures = 3000)
li_obj <- CellCycleScoring(li_obj, s.features = cc.genes$s.genes, g2... |
d2fcfaa8d7681dc48134897a16f1a9347c2c6806d4776a3ef24aa75b23128887 | R | 1,564 | 44 | options(max.print = 1000)
options(stringsAsFactors = FALSE)
options(scipen = 999)
library(data.table)
args = commandArgs(trailingOnly=TRUE) # CpG positions
## CpG annotations
# load
a <- fread(args[1], select = c("V1", "V2", "V4"))
names(a) <- c("chrom", "ref_position", "probe_id")
# a
# collapse by strand
a2 <- ... |
6905add707fbaab68754568525a1ccf2c70bf20c6782f60f6841145722585e10 | R | 1,567 | 40 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
library(parallel)
library(MutationalPatterns)
ref_genome="BSgenome.Hsapiens.UCSC.hg19"
chr_orders=c(paste("chr",1:22,sep=""),"chrX","chrY","chrM")
li... |
c2f74d968caeb2c0b1f69a8554d08160c07757f2e8c96feafc905ce9fe859cb3 | R | 1,571 | 46 |
library(RColorBrewer)
library(pheatmap)
currPath <- dirname(rstudioapi::getSourceEditorContext()$path)
setwd(currPath)
setwd('..')
setwd('..')
setwd("Modeling_CRC/Plots")
############ Japanese diet ########################
#### Species correlations #####
taxonomy=read.csv("Taxonomy_JD_Species.csv",head... |
6499b173bd7a52a8d0952900b0eb8b8767373e91de7e6c6da27b2d8c5ff91ecf | R | 1,578 | 29 | region.members <- function(intervals, positions) {
stopifnot(is.data.frame(intervals))
stopifnot(all(c("chr","start","end") %in% colnames(intervals)))
stopifnot(is.data.frame(positions))
stopifnot(all(c("chr","pos") %in% colnames(positions)))
events <- rbind(data.frame(chr=as.character(interval... |
9e0460b458331c133d6ad486d552d40c19be386bb507491b2b3cdb52490615c9 | R | 1,582 | 63 | #!/usr/bin/env R
#
# get the sce comprising just identified marker genes
#
# Notes:
#
# * ran on jhpce using the following, with "NMEM" as either 10G or 100G:
#
# request more mem with qrsh
# qrsh -l mem_free=100G,h_vmem=100G
# module load conda_R/4.1
# R
#
#
# Troubleshooting:
#
# * PROBLEM: when using as.matr... |
0764c82a5e70b2997e5f7ef9548fe9852d8685700f69fea4346e9885307a03ac | R | 1,591 | 48 | ################################################################################
### LIBD pilot 10x-Frankenstein (n=12) snRNA-seq samples
### ** PAN-BRAIN ANALYSIS **
### - R-batch job for detxn of optimal PC space with ~'sce.pilot.n12'~ object
### MNT 07-08Feb2020 (now on 'sce.all.n12') ~29-30Jan202... |
593abf0eb53b6a43ada5e6b58116f13f1c4ea9283f22313409ac7ce1dafef426 | R | 1,593 | 56 | # Siwei 29 Feb 2024
# Make line-dot and circus plots for Alena's PICALM paper
# init ####
library(readxl)
library(ggplot2)
library(RColorBrewer)
## make line-dot plot #####
df_raw <-
read_excel("IPA_pathway_for_graphing.xlsx",
sheet = 2)
df_2_plot <-
df_raw
df_2_plot$Ingenuity_Canonical_Pathways <-
... |
cc61260c8f78f91b0327431ba8f28d8b460343eed6a31ce24f1af02e3fd455d9 | R | 1,604 | 59 | output$renderCalendar <- renderUI({
if (input$linePlot == "confirmed") {
if (is.null(input$regionPicker)) {
tags$p("未選択です。地域を選択してください。")
} else {
tagList(
echarts4rOutput("confirmedCalendar", height = "130px"),
calendarDateRangePicker("confirmCalendarDateRange")
)
}
} e... |
04e9aa419b75f6606df112fe238d87e61c7a2209bdb65ca6e236fecf1daa497e | R | 1,609 | 62 | # =============================================================================
# Seurat标准流程脚本
# =============================================================================
# 功能：合并正常与疾病样本，进行Seurat标准流程分析
print("Hello world!")
rm(list = ls())
# 加载配置文件
source("../config.R")
# 设置工作目录
setwd(ENV_DIR)
lf <- list.files(".... |
1957086137c751cfb20b17c89253debb6775167b8a05eff908509bc001727f63 | R | 1,612 | 52 | library(tidyverse)
library(hyprcoloc)
total_result <- data.frame()
inputpath <- "/mnt/data/lijincheng/mGWAS/result/02MRBMA/hyprcoloc/MVinput_res/"
inputfiles <- list.files(inputpath,pattern="_MVinput.RDS$",full.names = T)
full_name <- gsub("_MVinput.RDS$", "", basename(inputfiles))
for(i in 1:length(inputfiles)){
... |
cf18ba6d6a0fe7d5649ad35bb73fe0d762be7d3ec5929596d9a8def7dc5694e3 | R | 1,614 | 55 | # =============================================================================
# 样本差异表达分析脚本
# =============================================================================
# 功能：按样本进行差异表达分析
print("Hello world!")
rm(list = ls())
# 加载配置文件
source("../config.R")
# 设置工作目录
setwd(ENV_DIR)
lf <- list.files("./")
for (file i... |
34e9cde8244b7636caa742d03bc3a67447b0b6f6efa3763731f4b75a39d7c052 | R | 1,615 | 11 | freeze_features <- c("freeze_time_sec", "freeze_behavior_proportion", "freeze_num_bouts", "freeze_avg_bout_lens", "freeze_first_bout") #"freeze_bout_3sec", "freeze_bout_6sec", "freeze_bout_9sec"
circle_features <- c("circle_num_bouts")
of_features <- c("f2_distance_cm", "f2_center_distance_cm", "f2_periphery_distance_c... |
ab1d5b4d8ade6e42a645e649ed36e7491211fd954d14066ec924108dc0f20e28 | R | 1,616 | 40 | fluidRow(
Component.MainValueBox(
mainValue = sum(mhlwSummary[日付 == max(日付)]$検査人数),
mainValueSub = getFileUpdateTime(mhlwSummaryPath),
sparklineName = "pcrSparkLine",
diffNumber = (sum(mhlwSummary[日付 == max(日付)]$検査人数) - sum(mhlwSummary[日付 == max(日付) - 1]$検査人数, na.rm = T)),
text = i18n$t("検査人数"),
... |
a2137023d727e56ab320f8494aceb9dc63e566e8876d5c0f718a7dc8bd7456a4 | R | 1,617 | 47 | data <- read.csv('E:\\path_to_repo\\statsmodels\\datasets\\grunfeld\\grunfeld.csv')
#data <- read.csv('../../../statsmodels/datasets/grunfeld/grunfeld.csv')
data <- data[data$firm %in% c('General Motors','Chrysler','General Electric','Westinghouse','US Steel'),]
attach(data)
library('plm')
library('systemfit')
panel <... |
c1534af4334e74b0d8ee23cef4146646ea880c74d18c2f08cd0742187d573581 | R | 1,618 | 53 | # exonicµÄgo¸»¼¯·ÖÎö½á¹û
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(readxl)
library(stringr)
library(dbplyr)
exonic_UCR <- read_xlsx(path = "01-data/10-GO_enrichment_analysis/exonic_GO_enrichment_analysis.xlsx",
sheet = 2)
exonic_UCR$... |
fbe70f3f1c828a0db8bba9463c47fc7cc1efb48890fdb54b2e3a53f538b6bf16 | R | 1,623 | 44 | suppressPackageStartupMessages(library(optparse))
option_list = list(
make_option(c("-m", "--feature_map"), type='character', default = NULL,
help="Create up/downstream introns by intron map of circRNA to intron mapping (tsv)"),
make_option(c("-i", "--introns"), type='character',
hel... |
fd2985c0b3acefaf20249d92fe69396cd23efc4553b8a99f12c3a40e43c84c15 | R | 1,625 | 48 | as_regulon_network = function(regulons){
regulators = regulons[['regulator']] %>% unique()
regulons = sapply(regulators, function(regulator_oi){
X = regulons %>%
filter(regulator %in% regulator_oi)
X = list(
tfmode = setNames(X[['tfmode']], X[['target']])... |
2a0c455867f51c43f4e4a66e5e14c9a172624ef20a444702f8464b9d298b8b20 | R | 1,627 | 55 | #' Similar to the base sweep function, but can be used for sparse matrices as well.
#'
#' Enables fast computations across columns or rows (margin=1 and 2 respectively)
#' Only matrix, dgeMatrix, dgTMatrix, dgCMatrix, or dgRMatrix types are supported.
#' adapted from https://stackoverflow.com/questions/55407656/r-sweep... |
9d1e07d0f2519b3df05f13fadfe186d7be6abf8de523cb20ebda44aff0271c92 | R | 1,628 | 60 | #------------------------------------------------------------ Experiment UI ----
experiment_ui = function(id) {
ns = shiny::NS(id)
bs4Dash::tabsetPanel(
type = "tabs",
shiny::tabPanel(
title = "Metadata",
shiny::uiOutput(
outputId = ns('up_metadata_ui')
)
),
shiny::tabPane... |
e214d9afc39bd3e91cbfce8a6a44b4586e219d0d7545d60aa53dcf52809329fa | R | 1,630 | 44 |
suppressPackageStartupMessages(library(optparse))
option_list = list(
make_option(c("-g", "--gene_set"), type='character', default = NULL,
help="Features for gene set"),
make_option(c("-f", "--format"), type = 'character', default = 'tsv',
help = 'Format of gene_set. Either \'gff\' or... |
2cff8d8305d7c3c9aa2233d13aaaac784bc827c5e385da1b3968994907f5ef62 | R | 1,635 | 45 | library(introdataviz)
library(tidyr)
library(dplyr)
library(ggplot2)
library(ggpubr)
exp <- readRDS("~/Desktop/phd/pBrain/data/primateBrain_kznfTEexp.rds")
# split gene and TE dataframe
kznf_exp <- exp[,c(1:285, 1191, 1192)]
kznf_exp_longer <- kznf_exp %>%
mutate(sample = rownames(.)) %>%
pivot_longer(cols = ... |
6b03861ca12e1df787914735c9f91a4dda806af3b3ebbc953bbf7dcd8ffcf17f | R | 1,636 | 55 | library(EnsDb.Hsapiens.v86)
ensdb <- EnsDb.Hsapiens.v86::EnsDb.Hsapiens.v86
columns(ensdb)
head(keys(ensdb))
head(keytypes(ensdb))
keytypes(ensdb)
head(keys(ensdb,keytype = "GENEBIOTYPE"))
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <-
TxDb.Hsapiens.UCSC.hg38.knownGene::TxDb.Hsapiens.UCSC.hg38.knownGene
keytypes... |
77ef583f880736d38e88ccbd8b7e8c73aecca14bd2df9e5f757bc61aafaeda74 | R | 1,639 | 67 | library(data.table)
source(file = "01_Settings/Path.R", local = T, encoding = "UTF-8")
gMobility <- fread(paste0(DATA_PATH, "Google/Global_Mobility_Report.csv"))
jpDt <- gMobility[country_region_code == "JP"]
nameJa <- list(
"Hokkaido" = "北海道",
"Aomori" = "青森",
"Iwate" = "岩手",
"Miyagi" = "宮城",
"Akita" = "秋田... |
a803e74683b166a9491d2e48189498618f38a10b65081f20beb366fba005bcb8 | R | 1,639 | 56 | library("SingleCellExperiment")
library("rafalib")
library("iSEE")
library("lobstr")
library("here")
library("whisker")
library("usethis")
library("withr")
library("rsconnect")
library("sessioninfo")
load(here("rdas", "revision", "regionSpecific_HPC-n3_cleaned-combined_SCE_MNT2021.rda"), verbose = TRUE)
source(here("... |
c13463002987a06c360aa3720330ba1aaa711ae9351d88a87f32ff542e537338 | R | 1,642 | 46 | shrink.candidates <- function(start.idx, end.idx, FUN, ...) {
stopifnot(length(start.idx) == length(end.idx))
stopifnot(all(start.idx <= end.idx))
do.call(rbind, parallel::mclapply(1:length(start.idx), function(i) {
shrink.candidate(start.idx[i], end.idx[i], FUN, ...)
}))
}
shrink.candidate... |
eb6d5e857fe7900e215433991d301e86d7684e43ed4d731f9580b174fa2949ea | R | 1,642 | 44 | arg = commandArgs(trailingOnly=T)
lst = read.table(arg[1],as.is=T)[,1]
names = gsub(".wgt.RDat","",basename(lst))
N = length(lst)
models = c("top1","blup","enet","bslmm","lasso")
colnames = c( "id" , "nsnps" , "hsq" , "hsq.se" , "hsq.pv" , paste(models,"r2",sep='.') , paste(models,"pv",sep='.') )
mat.snps = matrix(n... |
46e3646cb2670d020af7076c6e43af29f71117942c1fe8884182a0899d9bcdcd | R | 1,644 | 38 |
## Plot bulk fetal Age DMRs ##
#--------------------------------------------------------------------------------------------------------------------------------
# To fix bug with cache associated with AnnotationHub:
# From http://bioconductor.jp/packages/3.13/bioc/vignettes/AnnotationHub/inst/doc/TroubleshootingThe... |
efa1a6a5860291f0b31dd39cd74139d291d9b0634362d6cfa9b9bdded8bad8e3 | R | 1,644 | 64 | library(tidyverse)
library(ggstatsplot)
deg <- "/Volumes/T9/drosophila_nanopore/DRS_compairison/results/Excel/DE/tau_ctrl_results-nanocount_with_TPMs.csv"
polyA <- "/Volumes/T9/drosophila_nanopore/DRS_compairison/results/Excel/polyA_length/polya_diff_per_transcript.csv"
m6a <- "/Volumes/T9/drosophila_nanopore/DRS_com... |
4ca904a2768fba647ab4a55ceb5997b7bb774e15e02beb748d6204e47f930af6 | R | 1,647 | 41 | library(tidyverse)
# find paths to all kmer distrivution tables for different comparisons
dbrn_paths <- list.files(path = "data/peka_papa/2023-12-15_papa_cryptics_kmer6_window_250_distal_window_500_relpos_0",
pattern = "_6mer_distribution_genome.tsv$",
recursive = T,
... |
5a926e2788897c4f4e2392a79f612776f773932fde74b8aad4041d160703c451 | R | 1,647 | 48 | tabPanel(
title = tagList(
"ワクチンの接種",
boxLabel("New", status = "warning")
),
icon = icon("syringe"),
value = "vaccine",
fluidRow(
column(
width = 8,
tags$br(),
echarts4rOutput("vaccine_line_plot") %>% withSpinner()
),
column(
width = 4,
tags$br(),
tags$p... |
011822d97b75cab5f3baed5787a1abea342ca1f186a30985325d57cd5f3af3b4 | R | 1,648 | 47 | ```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(WVPlots)
library(clue)
library(tidyverse)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggExtra)
library(cowplot)
theme_set(theme_cowplot())
library(corrplot)
library(visreg)
library(ggcorrplot)
library(ggseg3d)
library(ggseg)
library(ggseg... |
e172318ac23dea48f9f79ac678d7fd38a501baee468d28fcf220f608ba317936 | R | 1,659 | 36 | library(STRINGdb)
library(org.Hs.eg.db)
library(dplyr)
library(clusterProfiler)
#library(tidyverse)
combine.DE = readRDS("combine.aneuploid-vs-E.GLMM.DEG.result.rds")
combine.DE = combine.DE[which(rowMax(as.matrix(combine.DE[,c("pct1","pct2")]))>0.1 & rowMax(as.matrix(combine.DE[,c("log.mean1","log.mean2")]))>0.1),]
c... |
e5a859bfac2d0ba2f329388681a28f178d53b580fc2a163641a2c63e3ec04c4b | R | 1,672 | 42 | library(tidyverse)
library(stringr)
# load in pooled m6Anet dataset
m6a_inf <- read_csv("results/methylation/m6anet/m6aNet_ctrl_te_inf//data.result.csv.gz")
m6a_inf_pass <- m6a_inf %>% dplyr::filter(probability_modified > 0.9)
# FBti to Transposon name index
unique_table <- read_table("id_validation_table_20582.tx... |
2ccf10763f929471b86663185334d237f6bef3528ecad54979931de05f9f704c | R | 1,675 | 49 | library(R2nparray)
x <- c(-50, 175, 149, 214, 247, 237, 225, 329, 729, 809, 530, 489, 540, 457,
195, 176, 337, 239, 128, 102, 232, 429, 3, 98, 43, -141, -77, -13,
125, 361, -45, 184)
x <- ts(x, start=c(1951, 1), frequency=4)
conv2 <- filter(x, c(.75, .25), method="convolution")
conv1 <- filter(x, ... |
2ebf045e797c94fb9daa8c7bd4397d810cd698d0d5c252e6d8eca4acd46c82e5 | R | 1,675 | 31 | library(Matrix)
plot_BC<-function(dna, rna, c1=1000, c2=1000, title=title){
layout(matrix(c(1,2,1,3),2,2,byrow=TRUE))
m<-merge(dna, rna, by="BC")
plot(m[,2:3],pch=19, cex=0.1, log="xy", xlab="DNA reads", ylab="RNA reads", col="grey", xlim=c(1,100000), ylim=c(1,100000), main=title)
lines(c(c1,c1),c(1,5000000), l... |
dc146c3de691be59eeca99019b7e6c3c0b886e74da917b72f5a16e05fdfacbd6 | R | 1,676 | 34 | # scRNA-seq data processing and clustering
library(Seurat)
sce <- CreateSeuratObject(counts = counts, project = 'ICI', min.cells = 3, min.features = 200)
sce[["percent.mt"]] <- PercentageFeatureSet(sce, pattern = "^MT-")
sce <- subset(sce, subset = percent.mt < 25 & nFeature_RNA > 500 & nFeature_RNA < 7000)
s_qc_list... |
51f780c022098d6ba5a78d1d78cb87ce9a88553e86ad2e54dec4496907ecf8a9 | R | 1,680 | 51 | # remove the expression values that have been imputed during integration with RPCA
# take the positions (genes x cells) of the missing values of the counts layer of the RNA assay
# remove the imputed values of the integrated assay at the same positions
# takes a few minutes
library(Seurat)
dataPath = "data/"
outfile... |
9296b89cd166bfa57054cd4ad81db4ae55c7f859bd8ec91dbf4240216db6db12 | R | 1,682 | 63 | library(GenomicRanges)
library(rtracklayer)
library(VennDiagram)
library(grid)
library(tidyverse)
################################
# --- Load BED files as GRanges ---
bed1 <- import("results/control_m6Anet_genomic_loci.bed", format = "BED")
bed2 <- import("results/meclip_m6A_loci_sort.bed", format = "BED")
bed3 <- i... |
c71644ff5ad48c67c41f9982fe47a9dc69f882187a2f09d1ba09b818a16c13b8 | R | 1,691 | 56 | ---
title: "Length of stay in the ICU"
author: "tom pollard"
description: "Length of stay in the ICU for patients in MIMIC-III"
output: pdf_document
date: "10/10/2017"
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(ggplot2)
library(RPostgreSQL)
```
```{r dbconnect, include=FALSE}
# Loa... |
3a6512abd7172ed40ff733a912a4ced80aa1e46bbc636d539238aea8d7d175c6 | R | 1,692 | 47 | library(tidyverse)
library(broom)
#' Perform fisher's exact test on 'summary count' df output by scripts/iclip_summarise_coverage.R
counts_to_fisher <- function(data) {
# Split data by region_type into a list of tibbles
data_grouped <- data %>%
group_by(region_type)
region_groups <- data_grouped %>%
... |
86ee39132a46eca729ab6d6c7b39f77f8034156080f0ff490cc2c6cd3a3a0943 | R | 1,693 | 66 |
#' Load BED file into memory
#'
#' @description Use Plink to convert BED to RAW then easily load it
#'
#' @return A data frame
#'
#' @author Luke Pilling
#'
#' @name load_bed
#'
#' @param in_bed A string. BED prefix
#' @param verbose Logical. Be verbose (show individual steps),
#' \code{default=FALSE}
#' @param... |
dedb76047650de665a2a44d894e7c20b4b77b9b9456c0f8ef4893d1b244c550b | R | 1,696 | 52 | args = commandArgs(trailingOnly=TRUE)
source('Requirements/function.R')
library(data.table)
if (length(args)!=2){
print("Usage:file_path and norwayname must be provided")
}else{
file_path <- args[1]
norwayname <- args[2]
rnaseq <- read.table(file_path,header = T,row.names = 1)
selected_gene_lengt... |
077c812f39fdcb97f9c34fa677d175c21bf3e7a97b234ef38e5ecbda52c0b6c9 | R | 1,698 | 49 |
source("M:\\josef_new\\eclipse_ws\\statsmodels\\statsmodels_py34_pr\\tools\\R2nparray\\R\\R2nparray.R")
library('mgcv')
library('gamair')
d = data(mpg)
#gam_a = gam(city.mpg ~ fuel + drive + s(weight,bs="cc",k=7) + s(hp,bs="cc",k=6), data = mpg,
# sp = c(6.46225497484073, 0.81532465890585))
#gam_a = gam(... |
924e90af6746076769d72427a46a8ecc4874dc7c5e5a432519eb111052b66618 | R | 1,709 | 51 | # »ñµÃËùÓÐpatternΪdownºÍupµÄdevCE£¬²é¿´exonÉÏÊÇ·ñ¸»¼¯SRSF1µÄ½áºÏmotif£¨hexamer£©
# human_brain_down_devCE
# human_brain_up_devCE
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
human_devAS <- read.table(
file = "01-data/19-Development_alternative_splicing/human.devAS",
sep = ",",
... |
4b25111902e705fda6b7c8534bfaa9b910e48ae4e61df3dbd595c0ca30af52de | R | 1,710 | 42 | # load packages
require(Seurat)
# load Seurat objects (list)
sdata.batch <- readRDS('sdata_snRNA-seq_batch_SCT.rds') # output from 'SCTransform.R'
# select integration features and prepare object
features <- SelectIntegrationFeatures(sdata.batch) # default, nfeatures = 2000
sdata.batch <- PrepSCTIntegration(sdata.... |
926f50cd0bca4c149d051701bfe6bf476d6d422e12fd1d7e6ec55996eaa076d7 | R | 1,710 | 47 | library(tidyverse)
library(vroom)
library(slider)
library(valr)
# import variants
variants <- vroom("data/external/strains/B6xCAST.snps_and_indels.merged.bed.gz")
# get gene annotation - last GRCm38 version
mart <- useEnsembl(biomart = "genes", dataset = "mmusculus_gene_ensembl",
host="nov2020.arch... |
aa192687ef0fdd5ba8ec3103c265e2accad29b438078fb9e4e22c967ec70d677 | R | 1,710 | 38 | library(rtracklayer)
library(GenomicRanges)
setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
df_snp <- readxl::read_xlsx("data/Bulk_NGN2_GABA_ASoC_for Torus.xlsx",sheet = 1)
gr_hg38 <- GRanges(seqnames = paste0("chr",df_snp$CHR),
ranges = IRanges(names = df_snp$ID, start = as.numeric(df_sn... |
f722daabf32612c73c0d6d7ef911e08b241371413ebe94a7957c8b8c1748da9e | R | 1,712 | 59 | fluidRow(
box(
width = 12,
closable = F,
title = tagList(icon("globe"), "World Map"),
fluidRow(
column(
width = 8,
fluidRow(
column(
width = 9,
radioGroupButtons(
inputId = "switchWorldMap",
label = NULL,
... |
f98c13ee5e57fcfeaa29a77ace8eb4f65b3a8ce7cf06ac1535d356884673ac32 | R | 1,712 | 49 | setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(ggsci)
LCOR_pathogeicity <- read.table(file = "01-data/snp_pathogenicity_scatter_plot/LCOR_pathogenicity.bed")
LCOR_pathogeicity <- LCOR_pathogeicity[, c(5, 13:14)]
LCOR_pathogeicity <- LCOR_pathogeicity[!duplicated(L... |
de1d23235a3ffd54aff8859c37b1b9e2a3f73d79dc44b917685b7665de4823d6 | R | 1,713 | 53 | library(KFAS)
options(digits=10)
# should run this from the statsmodels/statsmodels directory
dta <- read.csv('datasets/macrodata/macrodata.csv')
obs <- diff(data.matrix(dta[c('realgdp','realcons','realinv')]))
obs[1:50,1] <- NaN
obs[20:70,2] <- NaN
obs[40:90,3] <- NaN
obs[120:130,1] <- NaN
obs[120:130,3] <- NaN
mod... |
31efef89ec8bad409d7bbb251e05cb8aecd667ccd2ec9f807cd02908712a43bf | R | 1,714 | 46 | library(twice)
library(dplyr)
library(ComplexHeatmap)
data("hg19rmsk_info")
load("data/mayoTEKRABber_balance.RData") # load mayo RNA seq result
c1_confirm <- read.csv("tables/hmc1_sig_forNetwork.csv")
c2_confirm <- read.csv("tables/hmc2_sig_forNetwork.csv")
confirm_mix <- c(c1_confirm$pair, c2_confirm$pair)
confirm_m... |
c1f63aea94c0bed96251b931125ec08bc86c39bd0d66bf6ca2820c01f9472d36 | R | 1,719 | 54 | # =============================================================================
# 配置文件：路径设置
# =============================================================================
# 注意：请根据您的实际环境修改以下路径
# 项目根目录
PROJECT_ROOT <- "."
# 环境文件目录
ENV_DIR <- file.path(PROJECT_ROOT, "envs")
# 数据目录
DATA_DIR <- file.path(PROJECT_ROOT, "... |
853c5bf97d5f4021e43ec9369f1bb3ed3279172590ed80f3f53ab662c40bd30d | R | 1,725 | 64 | # �鿴other UCR��other RF��gwRVIS��û����������
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(ggview)
library(ggpubr)
library(ggsci)
library(ggbeeswarm)
library(Hmisc)
library(car)
# other UCR gwRVIS --------------------------------------------------------
other_U... |
550c86ae0c4a575413149b10b25e200694656f4c074a93cf9fabd8090ff78c55 | R | 1,733 | 23 | # read in individual raw Seurat objects
LV_5FBS <- readRDS(here::here("objects", "raw_seurat_objects", "lv_5FBS.rds"))
LV_10FBS <- readRDS(here::here("objects", "raw_seurat_objects", "lv_10FBS.rds"))
LV_15FBS <- readRDS(here::here("objects", "raw_seurat_objects", "lv_15FBS.rds"))
Lv_2hpf <- readRDS(here::here("objec... |
7dc923afd622e08ab1bcf0734cdd996993df7528f1e0793dcf6d705616f2e3e7 | R | 1,742 | 47 | library(feather)
# RNA-seq data path
data_loc = '/data/rnaseqanalysis/shiny/facs_seq/Mm_VISp_14236_20180912'
# Annotation data
anno_feather_path <- file.path(data_loc,'anno.feather')
anno_feather_data <- read_feather(anno_feather_path)
anno_feather_cols <- names(anno_feather_data)
select_subclass_labels <- c("Vip", ... |
967d27d7195560ac234e5961220f918e004bd4815d919e8dbb5922503af0c798 | R | 1,746 | 46 | # Siwei 11 Mar 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)
##########
options(ucscChromosomeNames = F)
plot_AsoC_fi... |
ce5931bd6a4494154d16f870240f6582d627c422a4d236a11fe85366f221978c | R | 1,748 | 43 | library(tidyverse)
## Script to output selected target gene lists to file (supplementary data)
load("processed/ferguson_hela/2023-11-29_hela_ko_tf_activity_gsea.Rdata")
names(elk1_target_list)
# All ELK1, unique ELk1, All ELK4, unique ELK4
lists_to_keep <- c("chipseq_hela_both_elk1", "chipseq_hela_unique_elk1", "ch... |
ef3bf3e32cad66b446e2f5ab6b5e74659eed8016c05edb91a4d6478acd851407 | R | 1,750 | 39 | # Load pre-calculated Seurat object ####
library(Seurat)
load("../data/stoeckius/CBMC.seurat.RData")
# Load DCA denoised data ####
dca <- read.csv("../data/stoeckius/stoeckius_dca.csv", row.names = 1)
# Add imputed data to Seurat object ####
tmp <- dca
rownames(tmp) <- gsub("HUMAN", "IMPUTED", rownames(tmp))... |
59c41ff93966b008a30ce6fc31c787109e1f6ca7cc55deb79c3bda4a4f877e48 | R | 1,751 | 65 | #function for analysis
sweep_sparse = function(x, margin, stats, fun = "*") {
#handle error
if (!class(x)[1] %in% c("dgTMatrix","dgCMatrix","dgRMatrix","dgeMatrix","matrix")) {
stop("Only several saprse matrix types are supported: dgTMatrix,dgCMatrix,dgRMatrix,dgeMatrix,matrix")
}
if((length(stats)!=1) &... |
d377b991a315075ae2610742b37cc55633237e0cd9fb32ea6618220f1f0bb657 | R | 1,752 | 69 | library(mice)
library(vegan)
library(VIM)
library(reshape)
library(ggplot2)
library("GUniFrac", lib="/usr/local/lib/R/site-library")
library(rptR)
mri <- read.csv("data/BIBAGO_MRI_dataset.csv")
head(mri)
# remove NA's
mri <- mri[!is.na(mri$Subject),]
mri <- mri[!is.na(mri$Pen),]
pMiss <- function(x){sum(is.na(x))/l... |
f98b9ad9b6e9c963a191c4e193a4b56a8b6087551e1dbbb5395b43914a0ef914 | R | 1,752 | 46 | # load packages
require(tidyverse)
require(Seurat)
require(monocle3)
# load data
sdata.align <- readRDS('sdata_align_pseudotime_minmin.rds')
downsampled_matrix <- readRDS('downsampled_counts_matrix.rds')
# load gene names table
gene.names <- read_csv('gene_names.csv')
# add downsampled counts, set default assay
sdat... |
9cfc95e6a9c574928db71d428b539fea3919a0576671851c8f30176e82f03770 | R | 1,753 | 58 | ---
title: "SpliceFlow"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{SpliceFlow}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
require(dplyr)
```
```{r setup}
library(SpliceFlow)
library(... |
2b46946d354c2f51a210d6e30e29ac92d936aad3055a4871b2e6c2dc2d1d3de7 | R | 1,754 | 47 | #-------- Download to local directory OR directly source PLACO+ from github
# source("PLACO_v0.2.0.R")
require(devtools)
source_url("https://github.com/RayDebashree/PLACO/blob/master/PLACO_v0.2.0.R?raw=TRUE")
#--------
set.seed(1)
## For an example, let's first simulate a toy set of GWAS summary
## statistics on 2 co... |
1565644667266cf2f6d21009583c0a3fd43fbb118a02437c19204e9fb671d3f8 | R | 1,755 | 59 | # setwd("/Users/yueliu/temp/temp1/Project_Diversity_pQTL/finemapping/MESuSIE/ENSG00000172803")
args <- commandArgs(trailingOnly = T)
library(tidyverse)
library(MESuSiE)
# args[1]="plink.race.list"
race <- read.table(args[1], as.is = T)$V1
if (length(race) < 2) {
cat("fewer than two races, quit\n")
q()
}
if (lengt... |
6877e8811fd9dbcd070ea949d84c15271a32873d7c2a79e3c6a921366f3283af | R | 1,756 | 49 | ################################################################################
### LIBD pilot 10x snRNA-seq: Nucleus accumbens samples - 8 samples (as of revision)
### **Region-specific analyses**
### - R-batch job for detxn of optimal PC space with 'sce.nac' object
### -> see '10x_NAc-n8_step02_clust-ann... |
3591509f505b663291bcfeedcc69a6cba50bb269825323e16db1243f2cd43e57 | R | 1,758 | 43 | # load packages
require(Seurat)
# load Seurat objects (list)
sdata.batch <- readRDS('sdata_batch_SCT.rds') # output from 'SCTransform.R'
# select integration features and prepare object
features <- SelectIntegrationFeatures(sdata.batch) # default, nfeatures = 2000
sdata.batch <- PrepSCTIntegration(sdata.batch, anc... |
016eefd88f8eb5b73f555c6c7da3bf0605057ee2dc1ad1e2368a597b3c85fed7 | R | 1,759 | 41 | library(kronos)
library(ggplot2)
library(gridExtra)
library(tidyverse)
library(corrplot)
library(gprofiler2)
library(outliers)
library(dplyr)
library(readxl)
setwd("/Users/mariareinacampos/Documents/00_MASTER/TFM/Resultats/Results_PD35_hpc")
bigdata_pd35_hpc <- read_excel("/Users/mariareinacampos/Documents/00_MASTER/T... |
9272fbf7ca51144090230c2950e1814cb7ad740a6a5150f59f55ce7614294258 | R | 1,765 | 54 | # This script was run to produce a lightweight SCE object,
# subset to the top-25 mean-ratio markers and using a dense counts assay. The
# goal is to make randomly subsetting and pseudobulking as fast as possible in
# the following hspe and bisque scripts, which perform these operations up
# to 1000 times
li... |
79ba97ae3c0cbc8733522b3b23104247c993b55ba8fdd6ccba98ebbfc6051e03 | R | 1,768 | 92 | # ²é¿´ncRNA UCRÔÚlncRNAÉϵÄλÖã¬ÊÇexon»¹ÊÇintron
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(ggprism)
# 66¸öncRNA UCR£¬65¸öncRNA UCRºÍ56¸ölncRNAÖصþ£¬uc.15ºÍmiRNA ENSG00000224592Öصþ
ncRNA_UCR <- read.table(file = "02-analysis/16-New_classifi... |
406c1c4e5e1e287d21986a8359ebd455f2a3dd98e4b1f47088fcfe56e0fe181d | R | 1,774 | 40 | library(rtracklayer)
library(GenomicRanges)
setwd("/project2/xinhe/xsun/neuron_simulation/2.torus")
df_snp <- readxl::read_xlsx("data/SNPs_for_Torus.xlsx",sheet = 1)
gr_hg38 <- GRanges(seqnames = df_snp$CHROM,
ranges = IRanges(names = df_snp$ID, start = as.numeric(df_snp$POS), end = as.numeric(df... |
a85ada6de72bf8fc91a4e7e045fe9bf3f19fb8727fb01a09306c2d5db99ab880 | R | 1,775 | 50 | # Source: View(autoEstCont)
PlotContaminationFraction <- function(object, contaminationRange = c(0.01, 0.8), title, verbose = TRUE) {
dd <- object$fit$dd
priorRho <- object$fit$priorRho
priorRhoStdDev <- object$fit$priorRhoStdDev
p.L = function(x, alpha) {
if (x == 0) {
0
}
else {
qgamma... |
fe04a22544ed2cc6074e7222fc91b5410c837aae583b8f7a5af08190ad857213 | R | 1,779 | 61 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(GenomicAlignments)
library(dplyr, warn.conflicts = FALSE)
library(tibble)
library(tidyr)
library(data.table)
library(dplyr)
library(optparse)
})
# importing
option_list = list(
make_option(c("-i", "--input"), type="character", default=NUL... |
d330bf40f5add745ace701381bdfa1702b30548f3972cdc9d59dae6dc126cec2 | R | 1,783 | 54 | #!/usr/bin/env Rscript
suppressWarnings({
suppressPackageStartupMessages({
library(optparse)
library(dplyr)
library(bambu)
})
options(dplyr.summarise.inform = FALSE)
option_list = list(
make_option(c("-b", "--bam"), type="character", default=NULL,
help="merged bam file"),
... |
317df987deed4b2818ace384df145b67af0780ce03e4af6e9e6fdf321ffa936b | R | 1,785 | 44 | # This script defines the create_binaryMtx function for gene-region analysis.
# Function: create_binaryMtx
# This function processes a data frame containing gene and region information
# and generates a binary matrix where each row represents a gene and each column
# represents a region. The matrix indicates the pre... |
b1019c15751742bf2305bcce95d44c88ada6fd061a1fde41fa2a99fec6a60e8b | R | 1,785 | 36 | # this script checks VIFs for the linear terms in the models
library(car) # version 3.1-3
# prepare df to store VIF results
vifs <- data.frame(matrix(nrow = 5*7, ncol = 3)) # nrow = n_imputations * n_outcomes
colnames(vifs) <- c("imp", "outcome", "max_VIF")
vifs$imp <- rep(1:5, each = 7, length.out = 5*7)
vifs$outcom... |
affbada6a4e404514a24e78f6ef96fba66e04f0713e0232e45f1e8ffc3efff98 | R | 1,787 | 48 | setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggplot2)
PBX3_exon_pathogenicity <- read.table(file = "01-data/snp_pathogenicity_scatter_plot/PBX3_pathogenicity.bed")
PBX3_exon_pathogenicity <- PBX3_exon_pathogenicity[, c(8, 15:16)]
PBX3_exon_pathogenicity <- PBX3_exon_pathogenicit... |
dd24eaf9276ac8dbd2e9000fab545445c44ae381b8ac3d07cdccb0346d41d277 | R | 1,799 | 38 |
context("compareRhythms_dodr")
load("test_data_ma.rda")
test_that("DODR analysis works for default params", {
results <- compareRhythms(expr, exp_design, method = "dodr")
expect_s3_class(results, "data.frame")
expect_named(results,
c("id", "category", "rhythmic_in_CC", "rhythmic_in_KD", "diff_rh... |
79ff0f436dda60d14a0748449ec119c980e6430b6ba7c668d33fc09e9eabc4d2 | R | 1,813 | 44 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
#### SNV burden gyri vs. sulci ####
df <- read.table("data/TableS3_PTA_burden.tsv", header=T, sep="\t")
df$Case_ID <- as.character(df$Case_ID)
sele... |
aca0dd6c25a7b1866275e380b218650151a060454bb42428cd4b13d9d0203594 | R | 1,815 | 68 | install.packages("ggseqlogo")
library(ggseqlogo)
library(ggplot2)
#A C G T
data(ggseqlogo_sample)
pfm <- matrix(c(
0.01 , 0.01 , 0.97 , 0.01
, 0.01 , 0.01 , 0.97 , 0.01
, 0.01 , 0.97 , 0.01 , 0.01
, 0.01 , 0.48 , 0.48 , 0.01
, 0.01 , 0.48 , 0.48 , 0.01
, 0.25 , 0.25 , 0.25 , 0.25
), ncol = 4... |
f4f9b0e4bacb1efee57e638eeb1b629300444d6a86dd6080809bd39cc32ce47e | R | 1,820 | 49 | ################################################################################
### LIBD pilot 10x snRNA-seq: HPC samples
### **Region-specific analyses**
### - R-batch job for detxn of optimal PC space with 'sce.hpc' object
### -> see '10x_HPC-n3_step02_clust-annot_MNT.R'
### for setup of the S... |
9c672687019d071c03c446a5c158e05a1ca4d5ca2aa7de49f6c630c2ee89f703 | R | 1,821 | 61 | ## estimate effect sizes from association tests of features
## se standard errors of the coefficients
## mat input data matrix (features x samples)
ivwfe.stats <- function(estimate, se, mat=NULL, rho=NULL) {
## http://onlinelibrary.wiley.com/doi/10.1002/sim.6835/full
## calculate rho
if (is.null(rho)) {
... |
ae84360586003022c0d739574bde289b71c8665040f4675ac3ad6f723476e07a | R | 1,824 | 42 | library(stringi)
library(tidyverse)
### annoate flair diff module 5 output
deGeneRes <- read_tsv("/Volumes/LaCie/DRS_compairison/results/flair/5_diff_3/isoforms_deseq2_tau_v_ctrl.tsv")
deGeneRes <- read_tsv("/Volumes/LaCie/DRS_compairison/results/flair/5_diff_3/workdir/isoforms_deseq2_tau_v_ctrl_results.tsv")
#split... |
83d7fdac0c5c6c1f0829241781fb48a66c1976b9eb7c75c59113f71a7f978efc | R | 1,827 | 41 | # rfÏà¹ØµÄ·ÖÀà
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(dbplyr)
library(tidyverse)
coding_rf <- read.table(file = "02-analysis/16-New_classification/01-rf_form_protein_coding_gene.bed",
sep = "\t")
ncRNA_rf <- read.table(file = "02-analysis/16-New_classification/02-rf... |
054163df9d51feafa765aa799164323bb914ad52d1ed22f9edccbaec6f24c437 | R | 1,831 | 48 | #' Tophat
#'
#' Align reads to genome using Tophat2. The Script creates new directory called BAM in the same path were the FASTQ dir is located.
#' @param Directory The Path of the directory were the FASTQ files are
#' @param Library_Type "Paired" or "Single"
#' @param Threads Number of threads used for the alignment
#... |
8c54df97349a78f8f1335ecb9da76c26dca08e60959dbe412561f781a96b952f | R | 1,831 | 60 | #' Add gene_id and gene_name
#'
#' @param annotation ENSEMBL annotation in granges
#'
#' @return trimmed exon
#' @export
#' @import dplyr
#' @importFrom magrittr %>%
#' @importFrom dplyr left_join
#' @examples
addGeneName <- function(annotation, intron_database) {
gene_to_gene_name_map <- annotation[annotation$type =... |
0bc10ceeafe584b39197e2877879a0c47fd72ba691e3a343aedad752be6617c3 | R | 1,833 | 42 | #' Dataset Metric
#' @description
#' Class to provide access to functions for computing metrics on an aif360 compatible dataset
#'
#' @param data A aif360 compatible dataset
#' @param privileged_groups Privileged groups. List containing privileged protected attribute name and value of the privileged protected attribut... |
2ba7d31c49d03437b55c524bc1bb5ddf3508e40c846c1aa291556f4e1f9854c2 | R | 1,833 | 41 | #Make plots ###############################################################################################
#Load libraries
library(Seurat)
library(tidyverse)
#Create directory to store plots
dir.create("4_plots_mixed_1.5_2M", showWarnings=T)
#Load mapped Seurat data with reductions
df = LoadSeuratRds("3_integrated_... |
c9b145704a8692d330c14eb98a99ab79f104fead331a116549a2b2e756baec75 | R | 1,833 | 57 | #' @title coxvolcano
#' perform volcanoplot between beta coefficient and p-values from Cox
#' @param df results coxbycol function
#' @param font.size size of font in the graph
#' @usage library(loopcolcox)
#' @usage data(cancer)
#' @usage library(dplyr)
#' @usage cancer%>%select(3:7)->data
#' @usage df<-coxby... |
fd3e41ce3edae909a1140ee113d6fdd11072e0e71f8ca41d9f0e566f79c4972c | R | 1,835 | 58 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(qs)
library(ShinyCell)
objects_folder <- file.path(project_folder, "objects", "R", "seurat")
ca_folder <- file.path(project_folder, "objects", "R", "clustassess")
... |
a0a9d64d0401269c211bbded9adf1e04a1d511c57850ef00f8344ae50c6d6879 | R | 1,837 | 57 | library(plotly)
# Author: Sean Maden
#
# Make 3d density plots from HALO coordinates, DAPI signal intensities from an
# example slide.
# load data
dpath <- file.path("HALO", "Deconvolution_HALO_analysis")
fnv <- list.files(dpath, recursive = T)
fnv <- fnv[grepl("\\.csv$", fnv)]
csvi <- read.csv(file.path(dpath, fnv[1... |
11b64cf5b6de56d11fca4ee047062270f724ccbed2806c4fab2b47a060e465b8 | R | 1,838 | 40 | #Once you build your network with metacore see "Build Network using Metacore pdf" (building algorithm: direct interaction) you can run this script
#get the xls file from Metacore for mapepd and interaction convert them to csv file and proceed with the script
library(tidyverse)
cells=c("Astrocyte","Neurons")#First... |
a049b671b09e50e4e68ec71fdca6388f57e77b53c2fcfb308e6ad7b133193be6 | R | 1,842 | 55 | library(scrattch.vis)
library(feather)
library(tidyverse)
options(stringsAsFactors = F)
# Setting working directory -----------------------------------------------
this.dir <- dirname(parent.frame(2)$ofile)
setwd(this.dir)
# Data paths --------------------------------------------------------------
project_path <- ... |
d1ef9be335bb7f1a83391e2c865577cc4b05f2a3899213d572c8c5afabf1a0e5 | R | 1,851 | 40 |
## 1. WGCNA - soft thresholding ##
library(WGCNA)
library(data.table)
library(cluster)
library(data.table)
#1. Load data used in GPMethylation =============================================================================================
load(paste0(dataPath, "FetalBrain_Betas_noHiLowConst.RData"))
betas <- betas.d... |
89ef2fdcac13f715fa03bf4d9d6dfdaa3210c9a5c87b9f67052cc36ece5bc0ce | R | 1,861 | 57 | 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... |
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