sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
25c672eff121a5b69d64260e40ad8484f6f81af15156d2dfc8099455c00478ee | R | 3,746 | 126 | ####
library(readxl)
library(jaffelab)
library(readr)
library(pracma)
## read in reference data
ref = read_csv("raw_data/square_MSN_exp_rnascope.csv")
ref = as.data.frame(ref[,1:5])
ref_mat = as.matrix(ref[,2:5])
rownames(ref_mat) = ref$Population
ref_mat = ref_mat[,c(1,2,4,3)]
## read in long data
dat =read.csv("raw... |
1719aa7bd46923f9ab7be6f41bf74dc8aefb995d91cf7f98f5d4291292ee9dc2 | R | 3,747 | 85 | library(CellChat)
library(patchwork)
options(stringsAsFactors = FALSE)
s_qc.combined_sub <- subset(s_qc.combined,Doublets=='N')
data.input =GetAssayData(object =s_qc.combined_sub,slot = 'data') # normalized data matrix
meta = s_qc.combined_sub@meta.data # a dataframe with rownames containing cell mata data
cell.use_CR... |
87e510a23da326977305397cfa155bb3c96766f5191aeb72beab333b718f33b7 | R | 3,757 | 111 |
rm(list=ls())
set.seed(123)
source('load_libraries.R')
source('aux_functions.R')
library(biomaRt)
# Function to detect delimiter
detect_delimiter = function(filepath) {
# Read the first line of the file
first_line = readLines(filepath, n = 1)
# Check for common delimiters
if (grepl("\t", first_line)) {
... |
61014aca32ddc65e0ee848f0895109668a14923a8c8467f0ba6ef093da243e4a | R | 3,766 | 131 | # Siwei 09 Aug 2023
# Siwei 05 Jul 2023
# plot 1MB proximal region of rs1532278 (CLU)
# chr8:27608798
# init #####
library(Gviz)
library(rtracklayer)
library(BSgenome)
library(BSgenome.Hsapiens.UCSC.hg38)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(ensembldb)
library(org.Hs.eg.db)
library(grDevices)
library(g... |
c95ff38a12b9eddba36b80187012150ecc86d002e595a0a2d4536e54eefe1cfc | R | 3,775 | 98 | #!/usr/bin/env Rscript
#### Fine cell type annotation of B cell subset for MBM_sc
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
library(viridis)
'%notin%' <- Negate('%in%')
path.ct <- 'data/cell_type_DEG/MBM_sc/bcells/'
filename <- 'MBM_sc_bcells'
seu <- readRDS('da... |
62a1672db6180cbe154b612c3f40505b03a3bdefaccf5dce63d76f09e43d4693 | R | 3,783 | 98 | ################################################################################
### LIBD 10x snRNA-seq [pilot] revision (n=10)
### STEP 01.batchJob: Read in SCEs and perform nuclei calling (`emptyDrops()`)
### Initiated: MNT 25Feb2021
################################################################################
li... |
6d6ddd5da4ac902bf730fe118e1b89edafab8d64efa01b157e739b0cd474faea | R | 3,784 | 94 | # FFERREIRA 12/26/2024
# Sanford Consortium - UCSD
# Prepares table to export as SUP table
# Loads LIBs
library(tidyverse)
# Sets WD
wd <- getwd()
setwd(wd)
# OBJs to read INFILE
g1 <- "10" # Treatment
g2 <- "CTRL" # Control
c1 <- "C136" # cell lineage #1
c2 <- "NOVA1-C15" # cell lineage #2
batch <- "batch2" # ... |
accfe53007c2c2d6733d3a5bc64b79b95e3206954df6bc48f6ddbcae2314553a | R | 3,784 | 83 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
message = FALSE,
fig.path = "man/figures/README-",
out.width = "100%",
warning = FALSE
)
```
```{r library, echo=FALSE}
li... |
8a9ad5e9b57e20d41ced9d9fde933613c503ecccef75f6d03f0b989fe67f0fc6 | R | 3,796 | 115 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(ClustAssess)
library(Seurat)
library(ggplot2)
library(dplyr)
library(reshape2)
ca_folder <- file.path(project_folder, "objects", "R", "clustassess")
ca_app_folder <- file.path(pro... |
d7460d6974f5ee5bac6b45e411d2c590a3e225f38aa19af0491bd3499535b76e | R | 3,796 | 72 |
## Principal Component Analysis (PCA) on FANS lifecourse DNAm dataset ##
library(ggplot2)
library(data.table) # for fread
library(RColorBrewer)
library(viridis)
library(scales) # for rescale
library(pscl) # for pR2
celltype_cols <- c(plasma(4)[2],viridis(4)[3]) # plasma(4)[2] = #9C179EFF = purple; viridis(4)[3] = #... |
4b86bdc7c695244c0d0ec82c0ebcb1561b4558a3838681d4d75dd177f74ea68b | R | 3,799 | 99 | setwd("Documents/ZS/NuevasImagenes/")
# Upload ZS Revelen disease/pathway/annotation table
diseaseExp <- read.csv("~/Documents/ZS/NuevasImagenes/exploratory_reshaped_disease.csv")
data <- data.frame(diseaseExp)
library(dplyr)
library(ggplot2)
library(stringr)
library(tidyr)
# Group by annotation and calculate the ... |
6e5f85efdf41f826d9a11897182a8b44a5eabf575a7893f55b11d9d6cd9e05f6 | R | 3,807 | 74 | #' Translate gene ids of a given dgeMatrix of seismic specificity scores from a given organism (e.g., mouse)
#' to another (e.g., human) based on orthology.
#'
#' Currently supported organisms: hsa, mmu (human, mouse, respectively)
#' Currently supported IDs: symbol, ensembl, entrez
#'
#' Unmatched rows are dropped fro... |
0262aad08a416de63e93fc77bef7623d1164372071ddf9365f7998452e9c3744 | R | 3,810 | 87 | library(tidyverse)
#' Prepare Heatmap Dataframe
#'
#' Prepare a dataframe containing sample-wise PAS usage differences for plotting as a heatmap of samples (x) * events (y)
#'
#' @param df A dataframe containing sample-wise differences in PAS usage, (e.g. `ppau_delta_paired_cryp`).
#' @param le_id_order A vector speci... |
9c4a8c9384e778a815d778e9214dfeb7e3d5118b53d7306b210f7efb078817d9 | R | 3,820 | 74 | ########
#library(SCnorm)
library(lmerTest)
library(SingleCellExperiment)
library(data.table)
library(emmeans)
library(dplyr)
library(reshape2)
args = commandArgs(trailingOnly = TRUE)
path = args[1]
norm.sce = args[2]
meta.data = args[3]
chrom.state = args[4]
#####loading normalized data and cell meta infor
norm.sce... |
5be3e7de766b7492c4cefc122299690fafe38a09e2fa09dd11b36ae11587502c | R | 3,823 | 122 | # ±È½Ï²»Í¬Àà±ðµÄUCRÉϵÄGCº¬Á¿
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(Biostrings)
library(ggplot2)
library(ggpubr)
# get ucr type
load("D:/R_project/UCR_project/02-analysis/12-karyoplote/UCR_type_gene.Rdata")
# get ucr sequence
ucr_sequence <- readDNAStringSet(filepath = ... |
00ec6c93d890c884e04acb2cb84525275e2a731b34259e3648ec231f73ead8bb | R | 3,824 | 132 | # ¸ù¾ÝUCRµÄ»ùÒò×éλÖöÔUCR½øÐзÖÀ࣬²¢Ñ°ÕÒ×î½üµÄ»ùÒòÓÃÓÚÏÂÓεĸ»¼¯·ÖÎö
rm(list = ls())
setwd(dir = "D:/R_project/UCR_project/")
library(tidyverse)
UCR_with_overlap_ratio <- read.table(file = "data/0A-EvolutionAnalysisData/UCR classification/UCR_with_overlap_ratio.bed",
sep = "\t... |
3e5adb93b02f5fbcd7e3080a37c5ae2b1dbbe3b3d84500fb7856e4c606a31755 | R | 3,829 | 107 | # The original version should do some manually working
# With more understandings about dbsnp (https://www.ncbi.nlm.nih.gov/snp/) and ensembl (http://rest.ensembl.org/), I re-wrote the function
# The function provides two options for database, one for dbsnp, another for ensembl. I prefer dbsnp and add additional functi... |
c04e8bff683b9aee196ced49d6be11680243c79b620764f5c80009c9ecf4ed68 | R | 3,829 | 140 | #!/usr/bin/env R
# Author: Sean Maden
#
# Summarize sce data by donor for ROSMAP sce object.
#
#
#----------
# load data
#----------
sce.fpath <- file.path("rosmap_snrnaseq", "sce_all_rosmap-original.rda")
sce <- get(load(sce.fpath))
# get metadata
cd <- colData(sce)
#------------------------------------------------... |
fda1a776db51e0e8d1e8a2b26adc0d72b3735170e1bd4507aeb36267edcb20ee | R | 3,835 | 68 |
context("compareRhythms_limma")
load("test_data_ma.rda")
exp_design_batch <- cbind(exp_design,
batch = ifelse(seq(nrow(exp_design)) %% 2, "a", "b"),
stringsAsFactors=TRUE)
test_that("limma analysis works for default params", {
results <- compareRhythms(expr, exp... |
cddf48107aa642327f3c1b80f55870d03dd6f47926a0a64215da3c2fa339dd5f | R | 3,837 | 134 | # This is a copy of the original code from the standard version of the
# sva package that can be found at
# https://bioconductor.org/packages/release/bioc/html/sva.html
# The original and present code is under the Artistic License 2.0.
# If using this code, make sure you agree and accept this license.
library(matrix... |
20453fd8d8fe3c36574290227e222f36f720f44adddd18a57f328a47934e372c | R | 3,839 | 106 |
## Loading libraries
library(dplyr)
library(data.table)
library(nlme)
## Loading data
data_base.a <- readRDS(paste0(directory,"DLPFC_matched_cross_autosome.rds"))
# cn: 480 rows 17945 columns
# dlpfc: 622 rows, 17105 cols
# pcc: 388 rows, 18402 cols
# name corresponding tissue to whichever data loaded
#tissue <- "D... |
4bea3e9c150579491c9c15143fe6d69ddf35a3a07e1eeabf82ab45e4cee0560c | R | 3,842 | 111 | #prepare files for scDRS/FUMA/MAGMA
##### 1. load packages and data#######
###load packages
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if (!require("tidyverse")) {
install.packages("tidyverse")
library(... |
b56cf22842211ca6fe4ed1b38f67cb06724b38a932419b7a754736a4fe164a32 | R | 3,854 | 86 | # library(gtools)
# library(data.table)
#
# 統合部分 =====
# pcrByRegion <- fread(file = paste0(DATA_PATH, "MHLW/pcrByRegion.csv"))
#
# detailByRegion <- fread(paste0(DATA_PATH, "detailByRegion.csv"))
# detailByRegion[, 都道府県名 := gsub("県|府", "", 都道府県名)]
# detailByRegion[, 都道府県名 := gsub("東京都", "東京", 都道府県名)]
#
# detailByRe... |
9caa921befeb3e37b08608322c800fa00d3de97fc34852d344a8680c892c9a90 | R | 3,856 | 89 | setwd("osmFISH_AllenSSp/")
library(liger)
library(hdf5r)
library(methods)
# allen VISp
allen <- read.table(file = "data/Allen_SSp/SSp_exons_matrix.csv",
row.names = 1, sep = ',', stringsAsFactors = FALSE, header = TRUE)
allen <- as.matrix(x = allen)
Genes_count = rowSums(allen > 0)
alle... |
67f0be03b87474b2d05d533c1e618a4111b9c471e76237590b31af2222035189 | R | 3,859 | 115 | library(sscVis)
library(data.table)
library(grid)
library(cowplot)
library(ggrepel)
library(readr)
library(plyr)
library(ggpubr)
library(ggplot2)
library(tidyverse)
############################################################################################
# Useful function
do.tissueDist <- function(cell... |
69f978e2a6d0f7cc6b882eda155e04c2133fa6b91165bc83661874b1a7a7c053 | R | 3,886 | 111 | #' Population priors
#'
#' This script estimate the template of population priors on DHCP data (2nd release)
#' Should run in parallel by: rel2_estimate-template_term.py
#'
#'
#' @author Diego Derman, FRG - IUB
#' v1: 2021-08-12
#' v2: 2022-02-01
#' v3: 2022-02-16
#' duration: 7 minutes for 35 subjects, using 48 thre... |
3eb95e309bfc93d4810b96d1bf92ba2cfd5912038adb4b5d29aa8f0b4942a655 | R | 3,891 | 117 |
library("slurmjobs")
library("tidyverse")
library("here")
#### slurmjob setup ####
hvg_prop <- as.character(seq(10,100, 10))
# hvg_files <- sprintf("../../processed-data/06_marker_genes/09_HVGs/HVG%d0.txt", seq(1,10))
# all(file.exists(hvg_files))
## 1 01_deconvolution_Bisque
# job_loop(loops = list(HVG=hvg_prop),
... |
2d1e7454e6e18ee128bb15a53fa273bf0515336267cc439c0589c7e3c6441dcf | R | 3,895 | 106 | pred.vizB <- function (cur_pat, act=F, labs=F){
# Design B: Plots scaled ystar density distributions on inside of ARAT recovery plot.
# Arguments:
# <cur_pat> an integer specifying the patient number
# Requires:
# <pred_xgb> data frame with bootstrap prediction results (output predict.XGB)
# <dat_p... |
c7248627c3b01719d248649c173b35a4a53d34168408412b245858927c191d58 | R | 3,900 | 137 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(qs)
library(future)
library(foreach)
library(dplyr)
library(EnsDb.Hsapiens.v86)
library(gprofiler2)
objects_folder <- file.path(project_folder, "objects", "R")
so_... |
7c57ab1752b871c5cd2f5fd0d51d8631ba626b18af04b7fe646d20c7bc64be5b | R | 3,905 | 101 | library(DESeq2)
library(ISoLDE)
# Read Data ---------------------------------------------------------------
# DDS object with allele-specific counts at the gene level
dds <- readRDS("results/DESeqDataSet/dds_gene_allele.rds")
# Prepare Data for ISolDE -------------------------------------------------
# ISoLDE has ... |
8b762eb79d22c1fd8ed9983776f462ffc9f42892946b36db73b926904dd35a27 | R | 3,923 | 93 |
library("SingleCellExperiment")
library("BisqueRNA")
library("here")
library("sessioninfo")
## get args
args = commandArgs(trailingOnly=TRUE)
marker_label <- args[1]
marker_file <- NULL
if(marker_label == "FULL"){
message("Using FULL gene-set")
} else {
marker_file <- args[2]
stopifnot(file.exists(marker_file)... |
f0fecf9d9762c00c84e6022b620ad38fc2352ebbe31eda66a385877e798814e4 | R | 3,924 | 101 | # Reference values for PyMARE's permutation test.
#
# Writes pymare/tests/data/metafor_permutest_reference.json, which
# pymare/tests/test_metafor_permutest.py reads. Run it through the harness in
# this directory rather than directly, so the R and metafor versions are the
# pinned ones:
#
# validation/metafor/rege... |
e2a671964b3c5c1ed16070ed91f14a916d6bc97d5e1079ac5540f8db52a51501 | R | 3,926 | 74 | ########
#library(SCnorm)
library(lmerTest)
library(SingleCellExperiment)
library(data.table)
library(emmeans)
library(dplyr)
args = commandArgs(trailingOnly = TRUE)
path = args[1]
norm.sce = args[2]
meta.data = args[3]
chrom.state = args[4]
#####loading normalized data and cell meta infor
norm.sce = readRDS(norm.sc... |
38fea01e90798a6f703e3fb34a80b7a73391f45750dc50e787e85ab598b7fb49 | R | 3,929 | 101 | library(DESeq2)
library(ISoLDE)
# Read Data ---------------------------------------------------------------
# DDS object with allele-specific counts at the gene level
dds <- readRDS("results/DESeqDataSet/dds_gene_allele.rds")
# Prepare Data for ISolDE -------------------------------------------------
# ISoLDE has ... |
8ed9fc4156353f7d1ddede6520f4c9dcc281eef4e09850776465787dcb30d861 | R | 3,940 | 148 | # R code to dump fair test dataset to
# checking compatibility.
#
# Usage:
# Rscript test_get_R_tweedie_var_weight.R > res_R_var_weight.py
cat("# This file auto-generated by test_get_R_tweedie_var_weight.R\n")
cat(sprintf("# Using: %s\n", R.Version()$version.string))
cat("import numpy as np\n")
cat("import pandas as ... |
1b8e1caa2032008cefc4c4d491080cfdb9aa616258e29371b55f23eae053c44c | R | 3,941 | 153 | # ·ÖÎöһϻ®ºÛʵÑéºÍtranswellʵÑéµÄ½á¹û
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(readxl)
library(ggplot2)
library(reshape2)
library(ggprism)
library(ggview)
library(ggsignif)
# caki1 ---------------------------------------------------------... |
8b22a992ccfd653ea11442e7816eeec7c0fc9ae4060acf2b625cc8e3a9807ef6 | R | 3,941 | 96 | # Author: Lauren Rylaarsdam, PhD
# 2024-2025
############################################################################################################################
#' @title indexChr
#' @description If the whole hdf5 file had to be searched for relevant reads
#' every time gene-specific methylation information wa... |
7e32c5f0ff9040c67b5b669268a763888d305c622421c23dc903df2816126321 | R | 3,945 | 98 | # Load required libraries
library(dplyr)
library(ggplot2)
library(gridExtra)
library(ggdist)
library(RColorBrewer)
# Define color palette globally
my_colors <- brewer.pal(12, "Set3")
# ============================================
# Data Preparation
# ============================================
# Load data
data <- r... |
1013e864644c15000de94a3cd27be9b04530e416001736aad2c0ba08c406324b | R | 3,950 | 159 | # Siwei 16 Jun 2023 #####
# plot a large PCA include MG, Ast, GA, and possibly NGN2
# ATAC-Seq data using the count matrix of Hauberg ME et al. (GSE143666)
# need to add data from Kosoy R et al. (syn26207321)
# init #####
library(readr)
library(edgeR)
library(Rfast)
library(factoextra)
library(Rtsne)
library(irlba)
... |
d21f354214106489e9f9311638713ea28ccdcc88cc075497eedc2363880adeae | R | 3,960 | 118 | ## Load plink before starting R:
# module load plink/1.90b6.6
## Also load twas fusion code
# module load fusion_twas/github
library("SummarizedExperiment")
library("sessioninfo")
library("getopt")
library("BiocParallel")
library("data.table")
## Flags that are supplied with RScript
spec <- matrix(c(
"cores", "c"... |
7a85548da10d5d8eab8926d6f65b93a615be775e5eeeb9beb1800ec340934b62 | R | 3,962 | 123 | library(ComplexHeatmap)
library(ggplot2)
library(ggpubr)
# import brain data
read_rds_files("../../paper_source_code/data/results_rdata/")
c1_kznfs <- unique(HmPtC1$corrRef$geneName) #285
c1_kznfs_res <- HmPtC1$DEobject$gene_res %>%
data.frame() %>%
filter(log2FoldChange <= -1.5 & pvalue < 0.05)
select_kznfs... |
54523d5eb1512c109e88314370d43af3ce1e3c78ffe153e80b8c3a59fdbc11fa | R | 3,964 | 101 | library(DESeq2)
library(ISoLDE)
# Read Data ---------------------------------------------------------------
# DDS object with allele-specific counts at the gene level
dds <- readRDS("results/DESeqDataSet/dds_isoform_allele.rds")
# Prepare Data for ISolDE -------------------------------------------------
# ISoLDE h... |
31c17e4f7953a84e9cab71ff9c769050f9e4cbd56ebdc37aae00edd2bfe90b0a | R | 3,967 | 77 | # FFERREIRA 03/24/2024
# Nina Project - Neanderthal
# Merges the MAP file for PC-Genes only created in the previous R code with TXimport data
# Sets WD
wd <- getwd()
setwd(wd)
# General OBJs
sep <- "\t"
nas <- c("NA", "", NA)
myComps <- c("ASC-10_vs_ASC-30", "ASC-10_vs_ASC-CTRL", "ASC-30_vs_ASC-CTRL",
"... |
63c0098cbc5d50727a86b55e2fa0abdee7d68325ac0c83fe3221d16118a7e713 | R | 3,969 | 105 | #this function takes the total rna tables produced by featureCounts, and gives a reasonable output data frame and
#metadata frame
make_deseq_dfs = function(total_table, grep_pattern = "", leave_out = "", base_grep = "", contrast_grep = ""){
if(grep_pattern == ""){
grep_pattern = glue::glue("{base_grep}|{contras... |
847566beda52c9bb964356a0e27b2c964c2a6fe7b237df885d959d02fff3dca6 | R | 3,971 | 61 |
library("SummarizedExperiment")
library("tidyverse")
library("here")
library("sessioninfo")
#### Set up ####
## dirs
plot_dir <- here("plots", "09_bulk_DE", "01_bulk_data_exploration")
if(!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
## load colors
load(here("processed-data", "00_data_prep", "bulk_c... |
b7de64b3ceacb00e1e311155cc2acf76a278c86a7b22ac6e22b59a2500bf82f8 | R | 3,979 | 124 | library(tidyverse)
library(tidytext)
# read in kmer distribution tables for all experiments
dbrn_paths <- list.files(path = "data/peka_qapa",
pattern = "_6mer_distribution_genome.tsv$",
recursive = T,
full.names = T) %>%
set_names(str_remove(... |
18b20e4025d6d98c2d96c0091fc74bc86584b1dbd052bd70d5938693d76e2a5a | R | 3,994 | 73 | #### Figure S5C #####
#### Expression of genes associated with perisynaptic astrocytic processes ####
library(matrixStats)
library(gplots)
library(stringr)
library(here)
# human astrocyte DEGs:
h_c <- read.table(here("data/astrocytes", "Astro_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
h_c <- h_c[h_c$padj<... |
636e3962976d09068b705cdbf84c8b57022d0edd9dc38b7310125c548c61d7ed | R | 3,999 | 99 | #!/usr/bin/env Rscript
#### Fine cell type annotation of CNS and stromal cell subset for MBM_sn
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
library(viridis)
path.ct <- 'data/cell_type_DEG/MBM_sn/cns/'
filename <- 'MBM_sn_cns'
seu <- readRDS('data/cell_type_DEG/MBM... |
80dae988c90cf21d0e8fcedeaa5f5ecc19206bf1e20c0614b84a87dbd3857b5b | R | 4,001 | 121 | #!/usr/bin/env Rscript
# FFERREIRA 03/24/2024
# Nina Project - Neanderthal
## Aggregates gene-level COUNTS / get TPM values
################
# 0. SETS UP ENV
################
# Loads libraries
cat(paste("Loading libraries...\n", sep = ""))
library(tximport)
library(readr)
#library(icesTAF) # Only necessary to perfo... |
4af823d44cf549896f323f706b6aaa1ab9a838e9108c7b830f89856d73a746ea | R | 4,002 | 127 | # fig 3D
library(ComplexHeatmap)
library(TEKRABber)
library(twice)
library(tidyverse)
load("data/primateBrainData.RData")
data("hmKZNFs337")
data("hg19rmsk_info")
# prepare datasets including raw counts of KRAB-ZNFs and TEs
# convert to TPM
# genes
df_hm_gene <- hmGene[,c(-1)]
rownames(df_hm_gene) <- hmGene$geneID
# ... |
3787e2deadd9f47aa5c5d69d92fa85f36ab7b20159799cfdd83ecd2609bc8281 | R | 4,018 | 112 | # libraries ---------------------------------------------------------------
library(Seurat)
library(tidyverse)
library(GGally)
library(cowplot)
library(ComplexHeatmap)
library(scales)
library(circlize)
library(DESeq2)
# read in the final object ------------------------------------------------
# scobj <- readRDS("../..... |
506c0dcb4797ab83c55afa1f4fdda7101cfff896254eefd4fbab7eb45776c6f1 | R | 4,018 | 129 | library(ggplot2)
# Author: Sean Maden
#
# Smooths of DAPI signal by marker signal for each slide. Makes smooths (using
# geom_smooth) of DAPI x Marker signal plots, grids each slide with facet_wrap().
#
#----------
# load data
#----------
dpath <- file.path("HALO", "Deconvolution_HALO_analysis")
fnv <- list.files(dpa... |
b5a75bef807dc1c0aaee7b4f21bab5effd6f58fdb94e2dfa9d498ee495b9a15a | R | 4,028 | 93 | library(SingleCellExperiment)
exprMat <- as.matrix(GetAssayData(object = tmp, slot = "data"))
cellInfo <- colnames(x = tmp)
cellInfo <- data.frame(seuratCluster=Idents(tmp))
library(SCENIC)
db='C:/Users/nelso/Downloads/Ristarget/'
list.files(db)
scenicOptions <- initializeScenic(org="mgi", dbDir= db , nCores=10)
sav... |
a54719e9bd1a417d8abaea6be2bca26c02a1e63d0b9235ef0b8668b3aa03645b | R | 4,032 | 131 | # Siwei 02 Feb 2024
# Plot Jubao's Diseases bar plot
# init ####
library(readxl)
library(reshape2)
library(plyr)
library(ggplot2)
library(scales)
library(RColorBrewer)
# load data #####
df_raw <-
read_excel("Jubao_Diseases_bar_plot_v2.xlsx")
df_list_by_cell_type <-
split(x = df_raw,
f = df_raw$Cell_Ty... |
58069226eaed7a2d740409251337ff78bff7193bc30c1055ce54af867cec9505 | R | 4,039 | 125 | # Script for combing all risk gene TPM counts and performing PCA (Supplementary Figures). Written by: J Gleeson (2023)
# Directory containing IsoLamp output files for each gene ending in '_TPM_values.csv'
setwd("<path to directory here>/PCA_data/")
library(gghalves)
library(ggplot2)
library(ggfortify)
library(data.ta... |
71e1dccf249116399265ca8c2fd368a5b85007ef427a0412810a96ba38e8b7d7 | R | 4,041 | 125 | #' Siwei rewrite 18 Oct 2023
#' CreateVCF
#' Since the bams output from bowtie2 pipeline has been added for readgroups,
#' sorted, and indexed, skip the first three steps
#'
#'
#' Create the VCF (Variant Call Format) file
#' @param Directory The Path of to the BAM Directory
#' @param Genome_Fa Path for whole genome FAS... |
ca365177bfd1c0036dd8dcccae5b7084dfb53dc9d7199006768d9053cf563579 | R | 4,043 | 95 |
library("SingleCellExperiment")
library("BisqueRNA")
library("here")
library("sessioninfo")
## get args
args = commandArgs(trailingOnly=TRUE)
marker_label <- args[1]
marker_file <- NULL
if(marker_label == "FULL"){
message("Using FULL gene-set")
} else {
marker_file <- args[2]
stopifnot(file.exists(marker_file)... |
904b0d19aca1f28b299da387791338ff16dccdcafb1d32e42d202ddb08da15e8 | R | 4,048 | 110 | #!/usr/bin/env Rscript
#### Integration analysis: LISI plots
#### Author: Jana Biermann, PhD
library(Seurat)
library(dplyr)
library(ggplot2)
library(gplots)
library(ggrastr)
library(viridis)
library(scales)
colBP <- c('#A80D11', '#008DB8')
colSCSN <- c('#E1AC24', '#288F56')
### Select one label
label <- 'MBPM_scn'
... |
d85fec9cd0223c4d1143cc5459a85bd530f8742427027baf6ef54115aa22197e | R | 4,049 | 95 | #' Plot influential genes for a given trait and cell type after running find_inf_genes().
#'
#' @param inf_df A data.frame or data.table of influential gene scores output
#' by seismic. Must contain columns that correspond to genes, seismic
#' specificity scores, MAGMA trait z-scores, dfbeta values, and a Boolean
#' co... |
53eb6300ddcf75387e41929474075424383c2268b4f73c859e00e3351a040d16 | R | 4,056 | 108 |
#### ===============================================================
#### monocle2
#### ===============================================================
#### Load Packages
library(monocle)
library(dplyr)
library(Seurat)
library(patchwork)
library(ggsci)
library(harmony)
library(ggplot2)
library(ggsci)
options(mc.cores... |
6acceff68589768f23968cd3a06c8562c4bfa296df22e42560353309202661a0 | R | 4,056 | 153 | # Siwei 13 Sept 2023
# make trace plot for Hanwen
# compensate for background quenching effect
# init ####
library(readxl)
library(baseline)
library(gWidgets2)
library(tidyr)
library(ggplot2)
library(RColorBrewer)
# load data ####
Stimulation_trace_signal <-
read_excel("Stimulation_traces-7hr-013123.xlsx",
... |
d2a6be4266b063329922ad31ebc6ce433859cfc0fcbd95e8d55ce320abd22181 | R | 4,056 | 121 | # ʹÓÃeasyTCGA¶ÔncRNAÏà¹ØµÄlncRNA½øÐзº°©·ÖÎö
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(easyTCGA)
library(stringr)
library(ggplot2)
library(forcats)
library(ggview)
# °²×°easyTCGA --------------------------------------------------------------
BiocManager::install("TCGAbioli... |
2ebac36fe63231cbdcebdcecc15127a973b2bee11278c0966d34955b5ad9a8fc | R | 4,057 | 146 | # ====PCR検査数の推移図データセット====
pcrData <- reactive({
dt <-
rbind(
dailyReport[, .(
日付 = date,
国内 = pcr.d,
チャーター便 = pcr.f,
空港検疫 = pcr.x,
クルーズ船 = pcr.y
)],
cbind(
mhlwSummary[日付 > "2020-05-08" &
分類 == 0][, .(
国内 = sum(検査人数),
... |
a8e183b21e1892b65d378f9c04bbf430b50b190f17263bbc620dc568f1243d8c | R | 4,057 | 106 | # 查看NUCR-ncRNA在神经系统不同脑区的表达
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(pheatmap)
# get GTEx data
load("D:/R_project/UCR_project/02-analysis/22-Tissue_specificity_across_different_organs/GTEx_exp_tau_normalized.Rdata")
# get brain data
brain_expr <- GTEx_exp_ta... |
1d3e94c215033f65ac63247d4079334d6309323a66a7a7395919c7ca1eba7b99 | R | 4,061 | 92 | library(ggplot2)
library(dplyr)
library(ggrepel)
# Read data
data <- read.table("D6_BMP.txt", header = TRUE, row.names = 1)
neuron_associated_genes <- read.table("Dev_genes_associated_list.txt", header = TRUE)
# Calculate the mean FPKM for DKO and WT
data$mean_fpkm_DKO <- rowMeans(data[, c("DKO_F12_B_D6"... |
ac0ae400605102d8480a49d88321d5456883ec7afe3243dbc7e362188809b75f | R | 4,061 | 123 |
## Analysis of Age linear regression in bulk fetal cortex ##
library(data.table)
library(ggplot2)
library(cowplot)
library(gridExtra)
library(viridis)
library(plyr) # for ddply
'%ni%' <- Negate('%in%') # not in
source(paste0(scriptsPath, "fetal_plotFunctions.r"))
#1. Load betas =================================... |
d243f48bc6904097b98ed3d28b5cbd95f84c631b969f3ca2375ee8f9f7e412d2 | R | 4,062 | 123 | setwd("~/Documents/mixOmics/")
##### Load packages #####
library(mixOmics)
library(readxl)
library(readr)
library(dplyr)
library(foreach)
library(doParallel)
# Importing groups of samples
groups <- as.data.frame(read_excel("~/Documents/mixOmics/Tablas/Groups_full.xlsx",
sheet = ... |
9126581587df65719c1f9b2b48b78ee48ee43e8478f5eaa11d407a3705db2f08 | R | 4,066 | 158 | # ¶Ô֮ǰµÄÎïÖֵıȶԽá¹ûÒ²½øÐÐеÄidentity¼ÆËã
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(stringr)
UCR_location <- read.table(file = "01-data/UCR_raw/UCR_location_refseqid.txt", sep = "\t", header = TRUE)
UCR_length <- UCR_location[, c(4:5)]
# human -------------------------... |
1b547c1fc04c260d1bcbafe4f72d47ab066861ce02528754752cea78d9372562 | R | 4,068 | 124 |
# install.packages("hspe_0.1.tar.gz")
library("hspe")
library("SingleCellExperiment")
library("jaffelab")
library("spatialLIBD")
library("here")
library("sessioninfo")
## get args
args = commandArgs(trailingOnly=TRUE)
marker_label <- args[1]
marker_file <- NULL
## not using txt list of marker genes, methods needs n... |
1337c4c11d64f0102663c08b3bda1ead2c31f49e078ebeeafe2f4adae8409418 | R | 4,070 | 97 |
library("SingleCellExperiment")
library("BisqueRNA")
library("here")
library("sessioninfo")
## get args
args = commandArgs(trailingOnly=TRUE)
marker_label <- args[1]
marker_file <- NULL
if(marker_label == "FULL"){
message("Using FULL gene-set")
} else {
marker_file <- args[2]
stopifnot(file.exists(marker_file)... |
49a1be5d3e0c8d80ccad4ec1e2e8e81b145ef286a768924f638ad00915428c3d | R | 4,072 | 157 | # Siwei 01 Feb 2024
# Plot Jubao's Diseases line graph
# init ####
library(readxl)
library(reshape2)
library(ggplot2)
library(scales)
library(plyr)
library(RColorBrewer)
# load data #####
df_raw <-
read_excel("Jubao_Diseases_plot_line_graph.xlsx")
df_melt <-
reshape2::melt(data = df_raw,
id ... |
15ae2374e962f381dc5bef579963a321245bfb4188ffd3ebf2ad22f5383d3c55 | R | 4,075 | 94 | ---
title: "EIB_behavioural_ReactionTimes"
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")
... |
1d7a0e671f00b20d490a9e5a435f1ab3bc78824a7b737be68329304543d3ee10 | R | 4,083 | 120 | #!/usr/bin/env R
#
#
#
#
library(ggplot2)
#-------
# params
#-------
celltype.varname <- "cellType_broad_k"
#----------
# set paths
#----------
load.dpath <- save.dpath <- "Human_DLPFC_Deconvolution/processed-data/004_marker-gene-expr"
# upset plot datasets
# withdrop
upsetdata.withdrop.fpath <- file.path(save.dpat... |
c9d2ac66a08ad9b5dd931d9a4874442fba916542139552129f4c30aad58f7658 | R | 4,083 | 122 | ################################################################################
# Function to plot regional brain maps from .csv files
################################################################################
# Copyright (C) 2024 University of Seville
#
# Written by Natalia García San Martín (ngarcia1... |
b752b2e9ebae16c35bb19c72d82940baefed0ac90ad8e830864da285a140ec30 | R | 4,085 | 134 | # ----------------------------------------------------------------------------
# Libraries and setup ----
library("argparse")
library("ggplot2")
library("tidyverse")
library("scales")
library("Seurat")
# Command line arguments ----
parser <- ArgumentParser(description = "Differential expression analyses")
parser$add... |
989dc7aa30cd2203bae2b3c0049c0c43880eea6e1b1ba3a2d8f21cc202d05870 | R | 4,087 | 98 | setwd("osmFISH_Ziesel/")
library(liger)
library(hdf5r)
library(methods)
# Zeisel SMSC
Zeisel <- read.delim(file = "data/Zeisel/expression_mRNA_17-Aug-2014.txt",header = FALSE)
meta.data <- Zeisel[1:10,]
rownames(meta.data) <- meta.data[,2]
meta.data <- meta.data[,-c(1,2)]
meta.data <- as.data.frame(t(meta.da... |
8323a3a754dc46a628c1121cc92d294933081a674be4dfc0275db6f50baab154 | R | 4,091 | 83 | #----02_variables_and_exp_info_v01----------------------------------------------
#-------------------------------------------------------------------------------
# Locomotor activity analysis for Reinhard et al. 2025 (10.1073/pnas.2506164122)
#
# Requirements:
# 1)scripts:
# 01_setup_v01
# 2)variables:
... |
28ed1a0bb89e7cc8e51f1195d7a5642a78bbf96f4c29b0fd3fd4af19ad344947 | R | 4,095 | 121 |
#
# file paths ################################################
#
ukbrapr_paths = data.frame(
object=c(
"hesin",
"hesin_diag",
"hesin_oper",
"gp_clinical",
"gp_scripts",
"death",
"death_cause",
"selfrep_illness",
"cancer_registry",
"baseline_dates"),
path=c(
"ukbrapr_data/hesin.tsv",
"ukbrap... |
f84f3b1a002bb87fe28e179dec2ea9abe012b2699e156ff9b42bdd4ba67eb82c | R | 4,099 | 105 | ################################################################################
### LIBD 10x snRNA-seq pilot (n=14) re-processing (Bioc v3.12)
### STEP 01.batchJob: Read in SCEs and perform nuclei calling (`emptyDrops()`)
### Initiated: MNT 03Mar2021
####################################################################... |
896fba0f8df3fb864a966a709a0217f290493cae3b809c6627cfe0cbbb06be6d | R | 4,100 | 125 | #' Siwei rewrite 18 Oct 2023
#' SplitNCigarBam
#' Since the bams output from bowtie2 pipeline has been added for readgroups,
#' sorted, and indexed, skip the first three steps
#'
#'
#' Create the VCF (Variant Call Format) file
#' @param Directory The Path of to the BAM Directory
#' @param Genome_Fa Path for whole genom... |
0b2b39d030921648962a94c29fa035172ffe35f772677668c981c2ce279f231c | R | 4,106 | 106 | # load packages
require(tidyverse)
require(Seurat)
require(phateR) # version 1.0.7
require(princurve)
require(scales)
# load Seurat object
sdata.align <- readRDS('sdata_align_RefF_label.rds')
# get PCA
cells.use <- filter(sdata.align@meta.data, compute_phate) %>% rownames()
dims.use <- 1:50
pca.embedding <- Embeddin... |
9278f771e9a722e13bbd9bdb88b3be228eba621f4ae54fe72a499e12bbc9bf0e | R | 4,108 | 102 | # AIM ---------------------------------------------------------------------
# this is the initial step for the counting of senescent cells. In this step I generate the signatures score.
# libraries ---------------------------------------------------------------
library(tidyverse)
library(ggrepel)
library(lemon)
# rea... |
2a62695cd1dd563f8ccbf05818ccaf2acb23fc53ad6db4a1184b76f0c1d2ab4e | R | 4,110 | 139 | ---
title: "R Notebook of rV2 manuscript figure 2C"
output: html_notebook
---
```{r Packages, echo=FALSE}
library(tidyverse)
library(Seurat)
library(Signac)
library(qs)
library(rtracklayer)
library(gUtils)
source("AuxFunctions.R")
```
```{r Set parameters}
cores <- 6
```
```{r Load qs object}
rV2.data <- qread("../s... |
9c01fd2d4e9077c9ba20e64f53271c280c314d8d8c2779e92a9c397ed9dd17c7 | R | 4,110 | 119 | suppressMessages(library(Seurat, quietly = T))
suppressMessages(library(ggplot2, quietly = T))
suppressMessages(library(Rtsne, quietly = T))
normalize <- function(x) {
sf <- rowSums(x)
sf <- sf / median(sf)
x <- x / sf
x <- log(x+1)
scale(x, center = T, scale = T)
}
`%+%` <- paste0
args <- commandArgs(trai... |
0ce770d6a85d24c3c5d27aa5f5ee41f66562a64120ca071eac5e652e7a911616 | R | 4,114 | 139 | ---
title: "R Notebook of rV2 manuscript figure S3A"
output: html_notebook
---
```{r Packages, echo=FALSE}
library(tidyverse)
library(Seurat)
library(Signac)
library(qs)
library(rtracklayer)
library(gUtils)
source("AuxFunctions.R")
```
```{r Set parameters}
cores <- 6
```
```{r Load qs object}
rV2.data <- qread("../... |
a491d1c69b7e543aaebb49357e02a3e51a81f79f404289c04b656f26da65f296 | R | 4,114 | 139 | ---
title: "R Notebook of rV2 manuscript figure S3A"
output: html_notebook
---
```{r Packages, echo=FALSE}
library(tidyverse)
library(Seurat)
library(Signac)
library(qs)
library(rtracklayer)
library(gUtils)
source("AuxFunctions.R")
```
```{r Set parameters}
cores <- 6
```
```{r Load qs object}
rV2.data <- qread("../... |
bc757db4318cf96bb92fe5ce25981a0c63de10567d161542bc345819ded22f9d | R | 4,119 | 127 | # ¿´human intergenic ucrÔÚmouseºÍratÀïÊDz»ÊÇÒ²ÊÇintergenic£¬closest protein coding geneÊDz»ÊÇÒ²¸»¼¯ÔÚbrain development
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggplot2)
# get intergenic ucr
ucr_mouse <- read.table(
file = "01-data/31-Intergenic_is_intergenic_in_mouse_rat/... |
0385e1eeffdfe2cd489d06b681fb69bd1d84e12fb563ee4502fc8fc1eede9044 | R | 4,122 | 126 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(dplyr)
library(ggplot2)
library(ClustAssess)
library(qs)
objects_folder <- file.path(project_folder, "objects", "R")
ca_folder <- file.path(objects_folder, "clusta... |
ba2ad6d870bc375d62e5d4703218fb52a5a711e4813d98db972f4286124c44e9 | R | 4,127 | 84 | pred.viz.mm <- function (cur_pat){
# Plots mm recovery pathway for grid plotting
# Arguments:
# <cur_pat> an integer specifying the patient number
# Requires:
# <M_mm> a model object containing the trained mixed effects model
# <eval_mm> a dataframe with the available measurement at the m... |
e28c3dd3c2c8d9bb05020b6504461e4ad51658765292348550d8b5ffe49e1ae6 | R | 4,134 | 100 | library(ggplot2)
library(data.table)
library(Seurat)
library(patchwork)
library(scater)
# QC replicates from each pool and merge
for(i in 17:17) {
print(i)
data <- Read10X(data.dir = paste0("cellranger-count/Pool-", i, "-1/"))
seurat_object_1 <- CreateSeuratObject(counts = data, project = paste0("Pool... |
f89f798c9320473c6661b596049cb57a17927f7056f151d8032ada1752fd8e41 | R | 4,135 | 82 | loadMicrobiomeDataForPipeline <- function(fluxAll) {
#' This function loads, processes, and filters microbiome data for use in a pipeline,
#' ensuring compatibility with other datasets such as fluxes and metadata. The
#' microbiome data includes species abundance, taxonomic classifications (Phylum, Class,
#' Order, ... |
17fb2d3ecac1830d9d6687d969513ad17490f40b5cb3b94463530d01aefe5e92 | R | 4,136 | 92 | # snpEFF¶ÔUCRÉϵÄSNPµÄ×¢Êͽá¹ûÖ»ÓÐ138¸öHIGHÌ«µÍÁË£¬ÏÖÔÚÓÃAlphaMissenseÀ´ÊÔÊÔ£¬¿´¿´»á²»»á¶àһЩ
rm(list = ls())
setwd(dir = "D:/R_project/UCR_project/")
library(tidyverse)
library(data.table)
# ×¼±¸ÓÃÓÚVEP×¢Ê͵ÄvcfÎļþ
UCRSNPPathogenic <- read.table(file = "02-analysis/06-UCR_Pathogenic_SNP/UCRSNPPathogenic.bed",
... |
14baef5a2e41e8c4a6f550a462da39b777035bb490854a6650db6874bd23f926 | R | 4,138 | 96 | 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... |
3b870a060b3f46e0c0034bab21db23561032d2801b533f073ec08ac3373fadcc | R | 4,138 | 101 | #!/usr/bin/env Rscript
#### Fine cell type annotation of T cell subset for MBM_sc
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
library(viridis)
path.ct <- 'data/cell_type_DEG/MBM_sc/tcells/'
filename <- 'MBM_sc_tcells'
seu <- readRDS('data/cell_type_DEG/MBM_sc/main... |
8ecd5e628b28b9d9f309d7c26dbaf20150d61248d1516f2ff542b800c0de6355 | R | 4,138 | 97 | output$genderBar <- renderEcharts4r({
if (!is.null(input$ageGenderOptionRegion) & !is.null(input$ageGenderOptionDateRange)) {
dt <- GLOBAL_VALUE$signateDetail.ageGenderData
updateDay <- max(dt$公表日, na.rm = T)
dt[年代 == "", 年代 := "不明"] # TODO データ作成時処理すべき
# 性別・年代マスター作成
genderAgeMaster <- data.table(
... |
3e9abfa3a016ff60df89b00ed626f022626aa5a292963c2715cba77fb0ddcd9e | R | 4,143 | 130 | tarbase.prepare <- function(TarBaseFile = NULL){
tarbase <- read.csv(Tar.dir, sep = "\t")
mir.sets <- tarbase %>% filter(.,species == "Homo sapiens",
positive_negative == "POSITIVE" )
mir.sets <- mir.sets %>% dplyr::select(.,mirna,geneId) %>% group_split(.,mirna... |
653f34a948c3b88a28172306759ef9b43533a74f32e97d9d848c9405d4d3ab8f | R | 4,144 | 128 | # Siwei 03 Jul 2023
# plot Xiaotong's data in dot plot format
# init
library(ggplot2)
library(ggnewscale)
library(scales)
library(readxl)
library(RColorBrewer)
library(stringr)
# plot the 3 cell types #####
Xiaotong_3_cell_types <-
read_excel("plot_Xiaotong/Xiaotong_all_cell_types.xlsx")
df_to_plot <- Xiaotong_3_... |
ecedfd9bc111bb605a765e94dd96d0d9438654169df93befbae2fe80f2c8a208 | R | 4,144 | 100 | #' fastcluster
#'
#' Performs a fast two-step clustering: first clusters using k-means with a very
#' large k, then uses louvain clustering of the k cluster averages and reports
#' back the cluster labels.
#'
#' @param x An object of class SCE
#' @param k The number of k-means clusters to use in the primary step (shoul... |
07c66ecc88e5b9d9d1322a7f4967bdb8587f3d06f19592a3696601ecf3f3a0b3 | R | 4,146 | 99 | library(Rtsne)
library(dbscan)
library(ggplot2)
library(DescTools)
cleanMatrixForClusterW <- function(mtx, f_row = 0.5, f_col = 0.5) {
cat(sprintf("Filter rows with >%1.2f missingness and columns with >%1.2f missingness.\n",
f_row, f_col))
cat("Before: ", nrow(mtx), "rows and ", ncol(mtx),"columns.\n... |
07c0c61c7e78fc3b950ac726c94ff72a2d96aaf304baebde7c5e696dd9af1f8f | R | 4,151 | 100 |
## Functions to identify and plot DMRs from EWAS results ##
# run.dmrff() - applies dmrff() to EWAS results
# plotDMR() - calls miniman() to plot DMR results from run.dmrff()
# plotDMR_wrap() - a wrapper for plotDMR()
library(dmrff)
library(data.table)
source("DMR_miniman.r") # contains miniman function
epicMan... |
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