sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d874cdeb2ffe4912c5f0114e9f551e31ebd6ee0687b096dd9b22f56f1067fa70 | R | 7,535 | 167 | #' Predict genesets associated with survival
#' @param maf an \code{\link{MAF}} object generated by \code{\link{read.maf}}
#' @param top If genes is \code{NULL} by default used top 20 genes
#' @param genes Manual set of genes
#' @param geneSetSize Default 2
#' @param minSamples minimum number of samples to be mutated t... |
c6d661b5d12638f31523b2957082c131060ea1d246b86f41094b739b4b52e10e | R | 7,540 | 233 | # This code is to plot the distribution of metabolites in Sub2
# WANG. 28-April-2023
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Research_projects/BBSC/MRS/Output_v2_4/QuantifyResults")
# install and load the raincloudplots packages
# if (!require(remotes)) {
# ... |
6f36bcbca1207c463c4a2af423e19ae91bba0708374147c8fa41030575c12f74 | R | 7,558 | 233 | # This code is to plot the distribution of metabolites in Sub1
# WANG. 28-April-2023
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Research_projects/BBSC/MRS/Output_v2_4/QuantifyResults")
# install and load the raincloudplots packages
# if (!require(remotes)) {
# ... |
99eb7b6dfc6afbd890e5e805f5d5e064225f8bd1fd97328928a5f0026b03fe17 | R | 7,576 | 171 | #!/usr/bin/env Rscript
#load required packages
library(Seurat)
library(dplyr)
library(ggplot2)
library(gridExtra)
#set all directory paths
basedir <- paste0("/gladstone/bioinformatics/adnetworksppg/Project_1_Huang/NK01/",
"results/HMGB1_snRNA_mm10/06_seurat_analysis_batch1_batch2_merged/data")
indir... |
ea7002d6ea08abac2cbf690f883aa5dd9d7bafbcd6381351a587636054725fd9 | R | 7,587 | 195 | # TODO: Add comment
#
# Author: Necla Kochan and Gokhan Karakulah
###############################################################################
#' @title Stemness Score of Glioma Cells Using scATAC-seq Data
#'
#' @description The \code{stemness.score} function estimates the stemness index of
#' each glioma cell usin... |
51ec431b0e5e245bcd6847d55e9dba43225a36219ae8e929ab85edc13947b8ac | R | 7,588 | 78 | rm(list=ls())
options(stringAsfactors = FALSE)
args <- commandArgs(trailingOnly = TRUE)
print(args)
############################################################################################################
############################################################################################################
#... |
9b90952e155eacd09d32a987e512cc72810103d44b73a1e2e35cfb4a61195ba5 | R | 7,591 | 314 | # ========================== #
# 5. APOE2 stratified: NSHD #
# ========================== #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(data.table)
library(dplyr)
library(magrittr)
library(mice)
library(purrr)
library(tidyr)
## Read in data ----
# Principal components
pcs <- read.ta... |
20bf890f43d8752bae5ccb48e836fee5f45260fc959fac0383f5cd8d14825295 | R | 7,599 | 219 | ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors = F)
## packages for data table processing/plotting
library(tidyverse)
library(tidymodels)
library(rcartocolor)
library(ggsci)
## main Seurat package snRNA-seq pacakges
library(Seurat)
li... |
54ce18bdc71fee2ef2324f6021694b1392a3813c6c603d937e127aef3c063d68 | R | 7,603 | 203 |
#Integrate:
#This script takes all the processed samples and merges them together. We check the name of the file for tags indcating age, separated by '_'
#So 'E16' means embryonic day 16, and should be sorted earlier than 'P1' meaning postnatal day 1. If you have your own files,
# tag them on the end with numbers to ... |
1d98b12de7abf5e055b95c1054094144460b5fa76c9035eb832470780ad692b4 | R | 7,620 | 211 | ### create gene count file for each sample ###
library(Rsubread)
BAMs <- list.files(pattern="*.bam")
# inbuilt has been changed to RefSeqGTF UCSC 2020
for (f in 1:length(BAMs)){
fc_mx <- featureCounts(BAMs[f], annot.inbuilt = "hg38",
isPairedEnd = TRUE, countChimericFragments = FALSE,
... |
44c4f006e9a56176e59d9ca3daf8cfde4ba7e2a03e0eb6b84e5ead62422c6943 | R | 7,621 | 154 | library(tidyverse)
library(here)
library(Seurat)
library(SeuratDisk)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
#############################################
## 1) format the files for GEO submission
fq_fn = list.files(here('data/raw_data/fastq'), full.na... |
4e81321176a5720a96a9d29f296a602569712fd028f58d5bfae7cdbf54a23d84 | R | 7,637 | 253 | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library("argparse"))
# create parser object
parser <- ArgumentParser()
# specify our desired options
# by default ArgumentParser will add an help option
parser$add_argument("-i", "--input",
required = TRUE, help = "list of summit bed")
parser$add_argument("-g",... |
7a64b4a50f27279fbef026379286976603d89922bbae63f55827902f25d99e92 | R | 7,644 | 236 | # library(S4Vectors)
#' @export
initAnnot <- function(cemballMeta) {
cemballMeta$L1Annot <- cemballMeta$L1
cemballMeta$L2Annot <- cemballMeta$L2
cemballMeta$L3Annot <- cemballMeta$L3
return(cemballMeta)
}
#' @param allmeta meta from snap@metaData.
#' @param l1 characters, like "NonN", "GLUT" or "GABA"
#' @para... |
1eb0b6467cede52379d2556d71fdb0a85bb8334a09a3a8b8009f6e4017df22c5 | R | 7,649 | 242 | ##################################
# LC project
# Downstream analyses on TH+ spots
# Lukas Weber, Nov 2021
##################################
# module load conda_R/4.1.x
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
library(SpatialExperiment)
library(here)
library(ggplot... |
af10ad31c12ca01d416d4976f2681dffa9d7cd7120ba7ae374bcc5fce8d817a6 | R | 7,656 | 201 | #' Draw two barplots side by side for cohort comparision.
#' @details Draws two barplots side by side to display difference between two cohorts.
#' @param m1 first \code{\link{MAF}} object
#' @param m2 second \code{\link{MAF}} object
#' @param genes genes to be drawn. Default takes top 5 mutated genes.
#' @param orderB... |
21aaed6b8ff05b5212b4e282726cf9b72992e41826bbe7da54edcc2a1c9d12e3 | R | 7,688 | 252 | ---
title: "tricycle_analysis Neuro subset"
author: "Jared Slosberg"
date: "4/5/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(monocle3)
library(tricycle)
library(tidyverse)
library(ggplot2)
library(here)
library(cowplot)
source(here("scripts/access... |
d0535fcd595ecd54e8b26a8b7bf77eaa5ff6cf9f1598c7e0039cca304c678223 | R | 7,726 | 162 | #######################################
#############Extra analysis#############
#######################################
#Project: Long-term effects of prenatal infection on the human brain: a prospective multimodal neuroimaging study (in the Generation R cohort)
#This is an extra analysis. Given our structural findi... |
69358bc53d5b8f8c2d6a98770e8ff7312b000a4f0513df222810bad943e33af7 | R | 7,736 | 197 |
##############CHECK##############
#Comparison:Control VS LPS : Update your script name and comparison here + Double check the folder you are in to aviod mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDoc... |
61bea4065b59b126aa2ec9f1310432a9f79f4645fd6924cdc584567d85660815 | R | 7,756 | 244 | ################################################
# LC analyses: Grimm et al. (2004) rodent genes
# Lukas Weber, Oct 2022
################################################
# module load conda_R/4.2
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
library(SpatialExperiment)
li... |
3e79d769afa803a3743a62f6c37518d40cc0e1968099e4a85b93c8e3f5c73be7 | R | 7,771 | 203 | #Visualize projections of 6mo patterns for our data, teichman atlas, vanderbilt data
#pattern by dataset grid
library(monocle3)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(here)
library(grid)
library(gridExtra)
library(purrr)
library(viridis)
source(here("scripts/accessory_functions/monocle_mods.R"))
sour... |
2e79022d8d6ebd884568a527be08b72b6d9824fd9d7a7598fa4e02fcf926e12c | R | 7,772 | 197 | #--------------------- based on binaomial distribution, estimate threshhold.
get_threshold = function(gene_muts, gene_length){
th = which(unlist(lapply(X = 2:gene_muts, FUN = function(x) dbinom(x = x, size = gene_muts, prob = 1/gene_length) )) < 0.01)[1]
return(th+1)
}
#-------------------- end of function.
#----... |
9b2cea4ea8f8d3e9e932b9b0cc5cd743bceec9b8d88194ef8b583586fde4b4c6 | R | 7,775 | 179 | # make command list
# then make nii list
if (!require("pacman")) install.packages("pacman")
pacman::p_load(sqldf)
# 1) prep commands for dcm to nii conversion (requiring only a varying input file)
allfiles = list.dirs(path = "/cluster/projects/p33/users/maxk/BBSC/structural", full.names = T)
filesvector = data.frame(al... |
fe23eea96e4efbd8be53f87daa5d569934c15bb54b3ba8de3d1078ae794f1d56 | R | 7,782 | 156 | library(dplyr)
library(purrr)
load("cell_exp_domain.RData")
load("cluster_1_2_3.RData")
load("domain_elegans.RData")
load("celltype.RData")
load("neuronal_non neuronal_cells.RData")
#get neu_list and non_neu_list from "Neurons_non neuronal_cells_heatmap.R"
mean2 = function(x){
mean(x,na.rm = T)
}
sd2 =... |
50d8768509e8a18b890ddd373264749aba7c6768477b53477857c174eccfbbb9 | R | 7,783 | 169 | ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors = F)
## packages for data table processing/plotting
library(tidyverse)
library(rcartocolor)
library(data.table)
library(ggsci)
library(here)
library(readxl)
library(SingleCellExperiment)
l... |
d377afcd2a1b946b8f40fb5976daaaf05638cda7c544222ebbd398c097ab4fc4 | R | 7,785 | 169 | ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors = F)
## packages for data table processing/plotting
library(tidyverse)
library(rcartocolor)
library(data.table)
library(ggsci)
library(here)
library(readxl)
library(SingleCellExperiment)
l... |
e2ce745072e3dc229391da39d67bdffaaa82351b32caa143aa43961f47e45a86 | R | 7,794 | 172 | library(stats)
library(purrr)
library(dplyr)
load("bulk_dev_RNAseq.RData")
load("stress_RNAseq.RData")
load("Cel_dup_N4Cel.RData")
load("Single_copy_genes.RData")
load("cluster_1_2_3.RData")
load("domain_elegans.RData")
load("sc_exp.RData")
load("celltype.RData")
load("L4_exp.RData")
zscore = function(x){... |
d182592355326b58f1785c8ab1632d5730242d09e38dc13ce3a7198960444d7f | R | 7,799 | 220 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(finalfit)
library(mice)
library(purrr)
library(dplyr)
library(lavaan)
library(ggplot2)
library(qwraps2)
```
# Load Data
```{r}
latents = read.csv('data_derived/... |
0f4f50095345983c6694789eee446afbc538443e8bc48c1aee00511405440ce1 | R | 7,821 | 191 | #!/usr/bin/env/Rscript
## pipeline for pairwise alignment of two genomes using lastz, and UCSC's kent tools.
## Assumes lastz is in system path, as well as kent binaries.
##
## Input: Target and Query 2bit files
## Output: Pairwise genome alignment of known chromosomes
## lastz
## kent binaries download instructio... |
6563d2022435cea4d1b4f39bf1e81dd859440fea8563906386e07b49246efbfa | R | 7,830 | 207 |
##############CHECK##############
#Comparison:Control VS TGFB : Update your script name and comparison here + Double check the folder you are in to avoid mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDo... |
32985b7800ea8311e36094bbc6103712b29fcd29d9efc259fdf83408a74fd412 | R | 7,834 | 206 |
##############CHECK##############
#Comparison:Control VS IL10 : Update your script name and comparison here + Double check the folder you are in to aviod mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDo... |
6af6b78104fd5676269e55ff5998c79a3d2731130b80aafbd40e953e7a95208f | R | 7,836 | 245 | # this file stores DE methods operate in R
# load packages
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(stringr)
})
# limma-voom, adapted from https://github.com/csoneson/conquer_comparison/tree/master/scripts
doLimmavoom <- function(sce, cellTypes) {
# input must ... |
8b74fd8a38b84b82aead7499ad2c50fc76efcff761dea682dde6906d44aa5727 | R | 7,837 | 214 | ################################################################################
# Neural substrates of parallel devaluation-sensitive and
# devaluation-insensitive Pavlovian learning in humans
# brain behavior correlations
# last modified on MAY 2023
###################... |
7dce72d6cad4746c669fae5e61093398b3f6196196a0b1b1db45870c10056d7f | R | 7,842 | 192 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
fig.align = 'right',
out.width = "100%"
)
```
# Symphony <img src="man/figures/symphony_lo... |
879e6457e79e848d2a31b9b8e3a92412c121b99c9da1744a7c94d8acba4ab436 | R | 7,845 | 201 | #!/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
main_f <- as.character(args[7])
source(paste0(main_f,'config.R'))
construct.grid = function(interv1,interv2,min_dist,max_dist){
return(ddply(interv1, .(chrom), function(i1) {
i2 = interv2[as.character(interv2$chrom) == as.character(i1$chrom[1]),]
if (nrow(i... |
96338e6d05add2444535778dd9aa289eaf50bb11f9b3b1704ee61a7ffa505979 | R | 7,845 | 195 | #--- Small function to read segments
readSegs = function(seg){
seg = suppressWarnings(data.table::fread(input = seg, sep = '\t', stringsAsFactors = FALSE, header = TRUE))
#seg$Sample = gsub(pattern = '-', replacement = '.', x = seg$Sample)
#Replace chr x and y with numeric value (23 and 24) for better ordering
... |
86b4e3a6c960231526f1002fb8eeb786f0a32f7a0dd735cbc462fd230a01e647 | R | 7,850 | 205 | ##' plotMulPhenGM
##'
##' @title Visualize gene association network graph for activated gene modules under multiple cell phenotypes
##'
##' @description For multiple cell phenotypes, the \code{plotMulPhenGM} function will display the activated gene modules for each phenotype and show the connection and status of ge... |
857c825f39b8c4e5b0fd62f854793a738ecd255a58fb855f99ddf6bca1c9bd62 | R | 7,853 | 207 |
##############CHECK##############
#Comparison:Control VS TGFB : Update your script name and comparison here + Double check the folder you are in to avoid mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDo... |
9a93d3830ce6a139a66f4db3a5b62f4270360df26c276a2b8e4bb600c2ec08ff | R | 7,853 | 208 |
##############CHECK##############
#Comparison:Control VS IL10: Update your script name and comparison here + Double check the folder you are in to avoid mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDoc... |
2996f45f8ffd796bea3fc6bf656c01b68d03e9429aad0bc3569c0ea0f9cd9a30 | R | 7,856 | 223 | # Libraries
snapshot <- "/lab-share/RC-Data-Science-e2/Public/DST_software/pipeline_R_snapshot/r4.1-20220803-bch-dst-v1/"
.libPaths(snapshot)
library(Seurat)
library(dplyr)
library(ggplot2)
library(velocyto.R)
library(velociraptor)
# Setting WD
setwd('/lab-share/RC-Data-Science-e2/Public/Daniel/p147-scRNAseq-Koehler'... |
83e241b68ba8fb33e47b8778049d07bc2964fd3cb8a75341d178eae6f712ae0b | R | 7,891 | 201 | #!/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
main_f <- as.character(args[7])
source(paste0(main_f,'config.R'))
construct.grid = function(interv1,interv2,min_dist,max_dist){
return(ddply(interv1, .(chrom), function(i1) {
i2 = interv2[as.character(interv2$chrom) == as.character(i1$chrom[1]),]
if (nrow(i... |
9a15cb7724fa81dbc1f3625d8c17899b6c7613b0850e37de7f387d979b74139d | R | 7,893 | 207 |
##############CHECK##############
#Comparison:Control VS IL10 : Update your script name and comparison here + Double check the folder you are in to avoid mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDo... |
fa4b28386f29980edd8d7a2850312229c59243918195903b245cf3f80dcb3a66 | R | 7,902 | 208 |
##############CHECK##############
#Comparison:Control VS TGFB : Update your script name and comparison here + Double check the folder you are in to avoid mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDo... |
d8b8304059726f576795b7eeaaa437450e52a031bc0a39f29d04b72942d28e25 | R | 7,909 | 176 | ---
title: "Tissue and cell type specificity analysis"
author: "Amy Hicks"
date: "29/09/2022"
output:
html_document:
code_folding: hide
highlight: kate
df_print: paged
toc: true
toc_float: true
---
```{r setup, include=FALSE}
library(DT)
library(readxl)
library(ggplot2)
library(ggpubr)
library(t... |
c24dd2af6d7b6c48b70d5f7c0fb50a756c9c18b68cdfdb6a8f46b792625b9143 | R | 7,917 | 208 |
##############CHECK##############
#Comparison:Control VS TGFB : Update your script name and comparison here + Double check the folder you are in to avoid mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDo... |
15df59ca559a23a0ada066c5fea9bdd5344513287b8318e11af4efa5c791ceec | R | 7,922 | 208 |
##############CHECK##############
#Comparison:Control VS IL10 : Update your script name and comparison here + Double check the folder you are in to avoid mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDo... |
7f8d300aacd833e2d5b4ff077d039874ca229c1e0f27627b62fcd8d31a3a27d8 | R | 7,926 | 144 | #' Plot differences between APOBEC enriched and non-APOBEC enriched samples.
#'
#' @description Plots differences between APOBEC enriched and non-APOBEC enriched samples
#'
#' @details Plots differences between APOBEC enriched and non-APOBEC enriched samples (TCW). Plot includes differences in mutations load, tCw motif... |
440c9536056719f0d82aadf60ac0868eb0a43e68eafc4a99a444710643c687e8 | R | 7,927 | 170 | ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors = F)
## packages for data table processing/plotting
library(SingleCellExperiment)
library(ComplexHeatmap)
library(rcartocolor)
library(data.table)
library(tidyverse)
library(ggsci)
library... |
d19ec91385e2e398709b88306e8d1d20334f7e3484d150417e5d569d02ccc633 | R | 7,931 | 193 | # Run check-volume-conduction.R and LME_in_R.R first
library(tidyverse)
library(lme4)
library(car)
library(zoo)
library(ggplot2)
library(qqplotr)
library(patchwork)
library(interactions)
library(effects)
library(MuMIn) # for effect size
library(sjPlot)
library(dplyr)
library(performance)
library(lmerTest)
anova_file ... |
4eb337aaa6184427a2321258ff215fcb38f54cbb85c375c224c840c6a9311f2a | R | 7,949 | 196 | #' @rdname distance
#' @title calculate spatial interlacement distance graph for cell clusters
#' @name spatial_distance
#' @description
#' Function to calculate spatial interlacement distance graph for cell clusters, where
#' we define the interlacement distance as the number of neighboring cells between two cluster
#... |
f45230474ac519e0a3658c3857017034ad1871c7123ecc6f3107813bbb8ce3cf | R | 7,969 | 189 | ##### GeneSet enrichment analyses #####
# DEG enrichment test
#
# 21 aug 2016
# 15 Mar 2017 added ExpTs test
# Kyoko Watanabe
#######################################
DEGtest <- function(genes, allgenes, adjP.method="BH", MHC=TRUE, ensgdir, filedir){
require(data.table)
load(paste(filedir, "/gtex.avg.RPKM.genes.RDa... |
a341ed3fdde48c2ce9183d061dc8a126b12e2c86877bdd6f2f538b08f7503bb2 | R | 7,982 | 154 | #' Performs mutational enrichment analysis for a given clinical feature.
#' @description Performs pairwise and groupwise fisher exact tests to find differentially enriched genes for every factor within a clinical feature.
#'
#' @param maf \code{\link{MAF}} object
#' @param clinicalFeature columns names from `clinical.d... |
7eeb11b838fe4e892911f534edae49c781ef78bd4ca70b23ca4851bdeb7835f0 | R | 7,989 | 161 | # Libraries
library('scales')
library('ggplot2')
library('tidyr')
library('viridis')
library('plotly')
# -----------------------------------------------------
# Variables
id <- "HC10"
ses <- "ses-pre"
out <- "/Users/rcruces/tmp/derivatives"
# Resting state path and name
subject_dir <- paste0(out, "/sub-", id, "/", s... |
ac8378512c2c171c4633e0d0efba4acf4fb6bf7b9c4bc1c188cbd1d1e15929ec | R | 7,991 | 212 |
##############CHECK##############
#Comparison:Control VS IL10 : Update your script name and comparison here + Double check the folder you are in to aviod mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDo... |
9d802417e363e68d69089d0c7e8aab66c0b44720917c2c069af7814f94b8080c | R | 7,999 | 211 |
##############CHECK##############
#Comparison:Control VS LPS : Update your script name and comparison here + Double check the folder you are in to avoid mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDoc... |
162832e253a89ac79344b5741a291532b40b298e2eff5cc49ab6e1c0b23c90d7 | R | 8,027 | 179 |
#' @title Plot model and Outliers
#' @description Plot model and Outliers
#' @param formula PARAM_DESCRIPTION
#' @param data PARAM_DESCRIPTION
#' @param mod PARAM_DESCRIPTION, Default: loess
#' @param n.sd PARAM_DESCRIPTION, Default: 1.5
#' @param ... PARAM_DESCRIPTION
#' @param selected PARAM_DESCRIPTION, Default: ... |
515d0850be099e760c21b053880dcab87dce330b4d170a446f74efbcef759d51 | R | 8,029 | 153 | #' Plot gistic results along linearized chromosome
#' @description A genomic plot with segments highlighting signififcant Amplifications and Deletion regions.
#' @param gistic an object of class \code{GISTIC} generated by \code{readGistic}
#' @param fdrCutOff fdr cutoff to use. Default 0.1
#' @param markBands any cytob... |
b78f93ae3551bc8bffa13a2a07970404b87a4849dbac3785ad2a5fbbfd6659f1 | R | 8,031 | 302 |
---
title: "Add cancer reactive gene signature information for CD4+ T cells"
author: "Wei Sun"
date: "`r Sys.Date()`"
output:
html_document:
theme: journal
highlight: tango
toc: true
toc_depth: 3
toc_float:
collapsed: true
smooth_scroll: false
number_sections: false
df_print: p... |
66f395e1491bbec773d41625dabbd530223827eb23244fe78fb92d2973d7bdc0 | R | 8,036 | 212 |
##############CHECK##############
#Comparison:Control VS LPS : Update your script name and comparison here + Double check the folder you are in to avoid mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDoc... |
6e2f6adadd9019557ab756946dc1476ca360c4793bbda1cb30635b762e666c2c | R | 8,058 | 212 |
##############CHECK##############
#Comparison:Control VS LPS : Update your script name and comparison here + Double check the folder you are in to avoid mixing diff ex results!
rm(list = ls())
dev.off()
cat("\014")
##############CHECK##############
setwd("/Users/adityanatu/Library/Mobile Documents/com~apple~CloudDoc... |
d8e5bb18a9bf15c8a483eebe0fac0f14df2d35775f058dc419a35eaf55f31486 | R | 8,066 | 235 | #' @rdname plotSpatialScatterpie
#' @name plotSpatialScatterpie
#' @title Spatial scatterpie
#'
#' @description This function takes in the coordinates of the spots and the
#' proportions of the cell types within each spot. It returns a plot where
#' each spot is a piechart showing proportions of the cell type compo... |
81b3667ab512a949c69ea0cc189307e0d14e08262299d1abec9015470bf999a0 | R | 8,068 | 128 | source('scripts/config.R')
source('scripts/aux_functions.R')
source('scripts/plot_functions.R')
source(paste0(main_dir,'results/mm10/hic/config.R'))
source(paste0(main_f,'scripts/main_functions.R'))
source(paste0(main_f,'scripts/aux_functions.R'))
source(paste0(main_f,'scripts/plot_functions.R'))
source(paste0(main_f,... |
d5bc03fd24b5f801726f1fbd02f34054a034b67f46837acd4a75b4f7ce498486 | R | 8,078 | 172 | #' Calculates the k-NN correlation, which measures how well the sorted ordering of k nearest reference
#' neighbors in a gold standard embedding correlate with the ordering for the same reference cells in
#' an alternative embedding (i.e. from reference mapping).
#' NOTE: it is very important for the order of referenc... |
9d67119e23a05b7c95faa32f0372af96406f1c840f04263696dd53942ae1316b | R | 8,093 | 303 | ---
title: "iPSC-DA MPP vs basal: "
author: "J Monzón Sandoval"
date: 'Last update: `r date()`'
output:
html_document:
theme: lumen
toc: yes
number_sections: true
toc_float:
collapsed: yes
smooth_scroll: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TR... |
249d3f59457016c7a38b50d21ceb8a1aab05d02b8309d86766dd1cc96f11af68 | R | 8,124 | 203 | library(lme4)
library(car)
library(lsmeans)
sink('../../output/kinematics_stats_out.txt')
df_9m_exe<-read.csv('../../data/9m/behavior_exe.csv')
df_9m_exe$bi<-FALSE
df_9m_exe$Fthand[df_9m_exe$Fthand=='R ']<-'R'
df_9m_exe<-df_9m_exe[df_9m_exe$FTGhand=='L' | df_9m_exe$FTGhand=='R',]
df_9m_exe$Age<-'9m'
df_9m_exe$Subject... |
dcf230a9559c384c6f59811742fdeb70b402756e64362b9b5c5883999aa2580d | R | 8,125 | 177 | ---
title: "Developing Barcode Outlier Removal"
author: "Stephen P Plassmeyer"
date: "4/6/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../code/mpra_tools/mpra_tools.r')
```
```{r}
compiled_counts <- fread('../../data/compiled_counts/asd3utr_compiled_counts_... |
515844f487135f72714cb3a213efe9d8cc62e74cd96e9f3753aab81457f6223a | R | 8,127 | 337 | #This handy script is taken from https://stat.ethz.ch/pipermail/r-help/2010-November/259210.html
##### FUNCTION DEFS #####
listnames.get <- function(
list.obj,
do.basename=TRUE,
do.name.chain=TRUE,
...
)
{
# VALIDATE
if(!is.list(list.obj)) stop("Argument 'list.obj' must be a list.")
# /
#------... |
a373d9a63bad3c8eb5912c83532b6f2a118f93a6cc188f20a8826d182c023055 | R | 8,142 | 263 |
#install.packages("rbioapi")
library(rbioapi)
library(utils)
#READ REFERENCE GENES - full list of guppy vs medaka orthologous genes
dat<- read.delim('guppy_medaka_ortho_fulllist.txt',header=FALSE)
ref_genes <- unlist(dat$V1)
#Read list of signif genes from Pool-seq analyses with merged reps - orthol... |
832c2f4f148f496135b64780e7c3048e1873368fb9a3a72785fa498322fd62af | R | 8,147 | 230 | ## LOAD PACKAGES ----------
library(Biobase)
library(edgeR)
library(qvalue)
library(ssizeRNA)
library(viridis)
library(tidyverse)
library(tictoc)
## BEFORE USING THIS SCRIPT ----------
# User should alter these variables as necessary
# Get list of all objects for each dataset
work_dir <- "G:/Annika/GitHub Repositorie... |
63d7475a58cc5cd3a11490f61017748784035088ba87cf134fb84ffe7b2d8d15 | R | 8,152 | 254 | library(data.table)
library(purrr)
library(ggplot2)
library(stringr)
projdir <- here::here()
rdir <- file.path(projdir, "package/R")
import::from(
.from = "rknn.R", .directory = rdir,
SingleCellGroup,
GroupAnchor,
initSingleCellGroup,
getNeighborMat,
getReciprocalPairs,
sumGroupAnchor,
plotSimMatLimited... |
2a17f7d502219d44feeed9d6251b90b286447205bf39729a37436dbfc2b34b43 | R | 8,167 | 187 | #' Clusters variants based on Variant Allele Frequencies (VAF).
#' @description takes output generated by read.maf and clusters variants to infer tumor heterogeneity. This function requires VAF for clustering and density estimation.
#' VAF can be on the scale 0-1 or 0-100. Optionally if copy number information is avail... |
bcb99b37c2fa8e23412bd6c9f663f4b7069be292f822ef8a2434238a3951f941 | R | 8,197 | 213 | get_lp_data = function(maf, geneID = NULL, AACol = NULL, refSeqID = NULL, proteinID = NULL, defaultYaxis = FALSE, verbose = TRUE){
if(is.null(geneID)){
stop('Please provide a gene name.')
}
#Protein domain source.
gff = system.file('extdata', 'protein_domains.RDs', package = 'maftools')
gff = readRDS(fi... |
22d024922fe71d496ae375c65672ab2a290cee1f76ce73069fa6a600d72c3298 | R | 8,201 | 164 | #' Transform metagenomic classification results of 'mpa' format to 'microtable' object.
#'
#' @description
#' Transform the classification results of mpa (MetaPhlAn) format to microtable object,
#' such as MetaPhlAn and Kraken2/Bracken results. Kraken2/Bracken results can be obtained by merge_metaphlan_tables.py from M... |
df2ea238ebfd4fcf41fb31c6c86c794d234532e34bdc5b167d58b37ee814966c | R | 8,241 | 233 | ---
title: "ChIPseq_datsets"
author: "Amy Hicks"
date: "27/04/2023"
output:
html_document:
code_folding: hide
toc: true
toc_float: true
df_print: paged
---
```{r setup, include=FALSE}
library(biomaRt)
library(tidyverse)
library(ggplot2)
library(GenomicRanges)
library(rtracklayer)
library(IRanges)
lib... |
90c1b922d2e0b35cf3bef69282467b12327d929bc9c3eeb05fd5ca307e160ae8 | R | 8,259 | 272 | source("/local/projects-t3/idea/bherb/software/chooseR/pipeline.R")
library(Seurat)
library(ggplot2)
`%>%` <- magrittr::`%>%`
# Load Seurat object
# Make sure this has already been normalised and that PCA has been performed
setwd('/local/projects-t3/idea/bherb/Hypothalamus/PubRes/Rewrite')
obj = readRDS('./SeuratObj... |
c9e602748a277dc440d8388b0f030731f95d676634a3d65434a99707a22cd0da | R | 8,267 | 161 | #' Performs survival analysis
#' @description Performs survival analysis by grouping samples from maf based on mutation status of given gene(s) or manual grouping of samples.
#' @details This function takes MAF file and groups them based on mutation status associated with given gene(s) and performs survival analysis. R... |
991be83df38252a2f0710331917901dc8ecac88419c482029f07c538e6918bd9 | R | 8,268 | 235 |
###############################################################################
## Load RDS sub-type
###############################################################################
pht <- "m_m" # select the desired sub-type
# pht <- "p_p"
# pht <- "m_p"
# pht <- "p_m"
alldata.int <- readRDS(paste0("./data/sce_",pht,"... |
1ab4339ffba2716ea1310e58178cd87dbfd4d10f603ba1a2dec64c5f855ca37d | R | 8,276 | 172 | #' pfam domain annotation and summarization.
#' @description Summarizes amino acid positions and annotates them with pfam domain information.
#' @param maf an \code{\link{MAF}} object generated by \code{\link{read.maf}}
#' @param AACol manually specify column name for amino acid changes. Default looks for field 'AAChan... |
03b6d3df90111b761d2cb92f0d9c19034bb6a1f63d73f2eedf0c1e3a8c668d6a | R | 8,286 | 283 | # ========================= #
# 5. APOE2 stratified: UKB #
# ========================= #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(data.table)
library(dplyr)
library(magrittr)
library(mice)
library(purrr)
library(tidyr)
## Read in data ----
# Principal components
serology_dat_genq... |
09ac989635a34185770e24d9fb20a136388922a9fe944530b76b1fa98f9f24c6 | R | 8,297 | 175 | ---
title: "Vgat Comparison"
author: "Stephen P. Plassmeyer"
date: "2/15/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../../../code/mpra_tools/mpra_tools.r')
library(ggpubr)
library(MASS)
theme_set(theme_bw(base_size = 6))
library(lawstat)
li... |
c4d53a4abd2702cbbcefd599211efa10eeb1d7ebb9eee204f506791e1256a5cd | R | 8,316 | 162 | #' Detect cancer driver genes based on positional clustering of variants.
#'
#' @description Clusters variants based on their position to detect disease causing genes.
#' @details This is the re-implimentation of algorithm defined in OncodriveCLUST article. Concept is based on the fact that most of the variants in canc... |
c276d384718264b755ef087f62c6c2d8d39d905a457e5f65f427568a926f6072 | R | 8,346 | 180 | ### LC snRNA-seq analysis
### Build SCE from raw (HDF5) matrices; perform nuclei calling
### qrsh -l bluejay (default mf=2G,h_vmem=2G,h_fsize=10G)
### Adopted from https://github.com/LieberInstitute/DLPFC_snRNAseq/blob/main/code/03_build_sce/build_basic_sce.R
### Initiated MNT 13Dec2021
library(SingleCellExperiment)
l... |
baed35e49b2e245e83403775f03ad5da6359ad17ca42450b2fc0112b34ae73e1 | R | 8,350 | 225 | ## packages for data table processing
library(tidyverse)
library(igraph)
library(network)
library(sna)
library(stringr)
library(data.table)
library(leiden)
## this script was adapted from
## https://github.com/kowaae22/ClarkLabDocumentation/blob/main/MakeigraphFigures.R
## http://kateto.net/network-visualization
ss... |
9564e3e3d62b94b4e3d9117623cfdd99cf33781e05f87d3a14dabaaca56e8f71 | R | 8,370 | 238 | #---Load Libraries------------------------------------------------------------------------------------------------------------------------####
library(Matrix)
library(tidyverse)
library(stringr)
library(EWCE)
library(limma)
library(data.table)
# 2020/03/31 - Files used in this script can be downloaded from: https://ww... |
37b470b1f897eb0e31d940a36610153610419d079ee4afe21bf96d375c302c5d | R | 8,378 | 223 | # only run once for initial installation.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DESeq2")
BiocManager::install("biomaRt")
library(DESeq2)
library(UpSetR)
library(tidyr)
library(RColorBrewer)
library(pheatmap)
library(biomaRt)
libr... |
45ffa95231da3a3853154f75ce546f17514ae66409f3dba03f1f17dd97657bfa | R | 8,385 | 312 | # install.packages("ggplot2")
# install.packages("tidyverse")
# install.packages("ggsignif")
# install.packages("RVAideMemoire")
# install.packages("lawstat")
# install.packages("ARTool")
# install.packages("emmeans")
# install.packages("rcompanion")
# install.packages("psych")
library(ARTool)
library(lawst... |
29718a1096f7c1ca888a18d0e65b68369b8c062551fd91b710e7292e11be89f6 | R | 8,396 | 171 | #' Plot Transition and Trasnversion ratios.
#' @description Takes results generated from \code{titv} and plots the Ti/Tv ratios and contributions of 6 mutational conversion classes in each sample.
#'
#' @details
#' The plot shows 6 canonical substitution classes that combine complementary strand mutations.
#' See \code... |
0d969dbe9ddcd8b3a2b77718ae1601da91dc67b5c68161e4b4af4d9f1fbd0414 | R | 8,401 | 224 | data_001e<-read.csv("C:\\Users\\ASUS\\Desktop\\Useful\\graduation\\data_analysis\\q_dataenglish.csv",
head=TRUE,encoding = "UTF-8")
##十进制转二进制
library(R.utils)
data_001e$env_type=intToBin(data_001e$env_type)
#处理缺失值
library(randomForest)
data_001e[3:32] <- lapply(data_001e[3:32], as.numer... |
c56e2588e322826256fa4e31c575c046cc5f29e3e1a598e39dc898d4c8bfd654 | R | 8,402 | 183 | library(data.table)
library(kimisc)
args <- commandArgs(TRUE)
##### DEG function #####
DEGtest <- function(genes, allgenes, adjP.method="BH", file){
data <- fread(file, head=F)
colnames(data) <- c("GeneSet", "n", "genes")
allgenes <- allgenes[allgenes %in% unlist(strsplit(data$genes[data$GeneSet=="all"], ":"))]
re... |
bac15ae6ad7d04f2baeac699e3f0d1c603d14e5e208f6c68a1ff9e54471c1e90 | R | 8,409 | 273 | ---
title: "iPSC-DA time course: RNA velocity"
author: "J Monzón Sandoval"
date: 'Last update: `r date()`'
output:
html_document:
theme: lumen
toc: yes
number_sections: true
toc_float:
collapsed: yes
smooth_scroll: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$... |
c227b622c17ce07141ddde256438b2ca0526bfd7cfb3fa495e92fcf5b62da018 | R | 8,420 | 259 |
#THis script annotates clusters by comparing DE genes to a list of cell type markers and computing DEscore
## ----receive input from nextflow----------------------------------------------
args <- commandArgs(TRUE)
path<-args[1]
file<-args[2]
markers<-args[3]
print(path)
set.seed(123)
## ---- Load standard packa... |
ddedbe2da555b45159ed4859ae8cad9515fdb515194aac7f574f00ea06e6e231 | R | 8,421 | 239 |
library(tidyverse)
library(clusterProfiler)
library(msigdbr)
# Read the Differential Expression results
DE.results <- read.table("DE_bulk_RNAseq_ribozero_rpe_choroid_protein_coding_genes_filtered_covariate_Linear_age.tsv",
sep="\t",header=T)
DE.results <- DE.results[!is.na(DE.results$F... |
daf769ed6385fcb1bcbf7122a26ccb031a20e80c3a678a1768b2b9a8744810bc | R | 8,439 | 242 | # Libraries
snapshot <- "/lab-share/RC-Data-Science-e2/Public/DST_software/pipeline_R_snapshot/r4.1-20220803-bch-dst-v1/"
.libPaths(snapshot)
source('/lab-share/RC-Data-Science-e2/Public/Daniel/rFunctions.R')
library(Seurat)
library(dplyr)
library(ggplot2)
library(velocyto.R)
library(velociraptor)
library(ggrepel)
# ... |
34ec053d3828b6d14ec41a0cc14e596016c527fbc57fbd52ca4b52ef1c366cfd | R | 8,480 | 208 | library(plotly)
library(htmlwidgets)
library("rmarkdown")
library(Seurat)
library(monocle3)
library(scales)
library(igraph)
library(ggraph)
library(graphlayouts)
library(qgraph)
library(AUCell)
library(MetaNeighbor)
library(Matrix)
library(dendextend)
flexsplit <- function(dat,char){
test=strsplit(as.character(dat),ch... |
92671305e6ebfe9facad5da4a281e0d7e98ee950a8bb98365d0f656f32a1ca97 | R | 8,497 | 242 | ####╔═════ ═════╗####
####💠Loading Data💠####
####╚═════ ═════╝####
cli_h2("┗ [SCRIPTS] Loading data ingestion functions")
#-------------------------#
####🔺Helper functions ####
#-------------------------#
## Turn a variable into a factor, ordered based on the level order defined within the relevant sheet of t... |
cee5c2d54f9dd6d6cc2a4d4343a23014d20ef1a3f95299d2c3bc6cd19b3722db | R | 8,506 | 221 | #####
# You can use this script to delineate super-enhancers from a .bed, *only* after it has been
# pre-processed by bamliquidator. Conveniently this can be done by riesling.py, which also
# allows for alternative stratifications of super-enhancers.
#
#
# Run as: Rscript get-SuperEnhancers.R input.bed output_directory... |
6c83ec7d2055cc93b21ce38d79d5c49cad47cc51c1649f97a8cbe5098d1905ca | R | 8,519 | 217 | ##' plotCCNetwork
##'
##' @title Visualize cell cluster association network graph
##'
##' @description
##' The \code{plotCCNetwork} function takes cells belonging to the same phenotype as a cluster.
##' When cell phenotypes are not provided, the \code{plotCCNetwork} functions identify cell clusters based on edge ... |
423d5192b93444ed170afbddb46d10e6c5d14ee34d2a0f7013ca4b21715e0706 | R | 8,525 | 134 | # Script to make new ctd expression data from Zeisel 2018 -------------------------------------------------------------------------------####
# 1. Need to make expression x cell type table for input into make CTD for new version of EWCE ---#
# 2. Convert expression data to ctd under new EWCE version functions
#---... |
8e918549318ff36f3269e02c94b61c6d85d9b95c33c79ee1529bbfc29d6a1e1d | R | 8,542 | 191 | library(tidyr)
library(dplyr)
load("L4_exp.RData")
load("domain_elegans.RData")
load("cluster_1_2_3.RData")
load("Cel_dup_N4Cel.RData")
load("Single_copy_genes.RData")
load("gene_count_orthologs.RData")
L4_exp2 = data.frame(t(L4_exp))
for(i in 1:ncol(L4_exp2)){
L4_exp2[165,i] = nrow(filter(L4_exp2[1:164,]... |
528bc986e41813fec1e633a72d9c574b899b116ea268199cd76e3f35321ee25a | R | 8,555 | 243 | # Methods from singlecellmethods ---------------------------------
normalizeData <- function(A, scaling_factor = 1e4, method) {
if(!'dgCMatrix' %in% class(A)) A <- as(A, "dgCMatrix")
if (method == "log") {
A@x <- A@x / rep.int(Matrix::colSums(A), diff(A@p))
A@x <- scaling_factor * A@x
... |
6387c61cfac71f62c666dcfa4b6294c8155c25a7b2a38cc9db84abf33775726b | R | 8,555 | 284 | library(Seurat)
options(Seurat.object.assay.version = "v5")
library(SeuratObject)
library(stringr)
library(purrr)
# support R new feature in 4.2
Sys.setenv("_R_USE_PIPEBIND_" = TRUE)
library(R6)
library(future)
options(future.globals.maxSize = 10e9)
library(future.apply)
plan(multicore, workers = 4)
library(Matrix)
... |
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