sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a47a894b36b4581ddc2b76e1759211ca59fbdcdc175b2fc247807e4ac127d3c6 | R | 5,342 | 116 | ### Nightingale Rose Plots of NHP feature importance by epitope and feature type
dictionary <- read.csv(file = "/Users/kpullen/autoencoder/serology_feature_dictionary.csv",header= TRUE)
scores <- read.csv(file = "/Users/kpullen/autoencoder/importance_Primates2Humans_protection_merged.csv",header= TRUE)
all<-merge(dict... |
f3aa8f6f7cd3f2c49c01a408f5f8793ecf72af465b692198e8b2531119150f01 | R | 5,352 | 150 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(data.table)
library(RColorBrewer)
library(rcartocolor)
library(ggsci)
## stats
library(SeuratDisk)
library(tidymodels)
library(broom)
library(broom.mixed)
library(lme4)
library(lmerTest)
library(swfdr)
ss <- function(... |
979958390b3ec63193bf70dc5cc5b526b65d43b220a32e066264f437fd617250 | R | 5,355 | 113 | #' Function for mapping query cells to a Symphony reference
#'
#' @param exp_query Query gene expression (genes by cells)
#' @param metadata_query Query metadata (cells by attributes)
#' @param ref_obj Reference object as returned by Symphony buildReference()
#' @param vars Query batch variable(s) to integrate over (co... |
a6427eca571c9ec0335029f0d3b29cb57b17e7675d789eb530e4a7fcbd489bde | R | 5,359 | 128 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(future)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors ... |
de6357d3d75c7e342d5c3bac9b84f6ef49a8c3bfb3358ac6f28821921bf5fa24 | R | 5,361 | 170 | # save all the critical meta data into one file
# can be shard with the community
# and used for figures generation
library(tidyverse)
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)
... |
47841ba4d4b370ef85685612c748af81b2696a3fb61ee8da393965fa64d9d819 | R | 5,387 | 123 | #' Plots vaf distribution of genes
#' @description Plots vaf distribution of genes as a boxplot. Each dot in the jitter is a variant.
#'
#' @param maf an \code{\link{MAF}} object generated by \code{\link{read.maf}}
#' @param vafCol manually specify column name for vafs. Default looks for column 't_vaf'
#' @param genes ... |
2a0a6f34b62a708d16cd554cb6f4f2a2e5bdb33f7127b30e2de016998a5091e6 | R | 5,391 | 115 | ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors = F)
## packages for data table processing/plotting
library(data.table)
library(tidyverse)
library(RRHO2)
library(here)
DATADIR='/projects/pfenninggroup/singleCell/McLean_chronic_opioid_m... |
a1b41af6ae52a43c34e898b01a3da6a817d2a7c647bb2eac6e9acbdd7d5819e1 | R | 5,394 | 131 | ###MainClusterWhitfild
library('igraph')
library('biomaRt')
library('neat')
library('Oscope')
load('~/Manchester/Alexis/Oscope++/OscopeRcode/casestudy/WhitfieldStandardOscope/StandardOscope.RData')
#mypath='/home/luisa/Manchester/Alexis/Oscope++/'
mypath='~/Manchester/Alexis/Oscope++/'
source(paste(mypath,'OscopeRcode... |
5f57e381193ea25ccd497e3da5ae9ad2f956a9c98289ccfc2de23b8db7f62ef8 | R | 5,398 | 103 | rm(list=ls())
logOddsdir <- "~/Dropbox (Gladstone)/YH_MN02/seurat_analysis_no_S521G/04_log_odds_histopathology_calculation/"
logOdds_astro_dir <- "~/Dropbox (Gladstone)/YH_MN02/seurat_analysis_no_S521G/10_subclusters_log_odds_histopathology_calculation/"
logOdds_micro_dir <- "~/Dropbox (Gladstone)/YH_MN02/seurat_analys... |
dfcdccc6ffcde1ef7ba97b90e73d000db5598ff6f14930b8f7c4dcb7fc767f69 | R | 5,401 | 129 | source("src/lib/Genome.R")
SampleInfo <- read.csv("data/SampleInfo.csv", as.is = TRUE, check.names = FALSE)
rownames(SampleInfo) <- sampleIDs <- SampleInfo[, "SampleID"]
qualOutInPairs <- c(
"Aread5End", "AreadAndFrag", "AbothFrag",
"Bread5End", "BreadAndFrag", "BbothFrag",
"Cmate5End", "CmateReadAndFra... |
ef07658fa56a174740eb364c5c27260cc9e283feb1c0c3d53c7ab8fadbde3faf | R | 5,405 | 114 | main_f<- '/home/hpc/bonev/projects/ram/results/hg38/hic/'
main_dir<-'/home/hpc/bonev/projects/ram/'
genome <- 'hg38'
chrom_sizes_f <- '/home/hpc/bonev/annotations/hg38/encode/hg38.chrom.sizes'
map3c_f <- "/home/hpc/bonev/software/hicpipe/"
cells <- c('3DRAM_D45_Pax6', '3DRAM_D45_Tbr2')
bowtieInd <- "/home/hpc/bonev/an... |
313a5b37ab843cceea15ea376f656328d7df412d1e0b622e6864bd1a3cac90a3 | R | 5,410 | 199 |
#PrepareSpatial:
#This script loads each well of the spatial samples, alongside the metadata giving spatial position.
#It compiles everything into a seurat object ready to be integrated togather in a subsequent script.
args <- commandArgs(TRUE)
path<-args[1]
file<-args[2]
#---- Load Libraries --------------------... |
a0c35c3ed9ce62d4043326d52caf5d19b0c0035fcf7cd83a9507b7e4e19c8d6d | R | 5,411 | 178 | ---
title: "NESC_nonPolar_mets_analysis_Final"
author: "Alise"
date: "2020 M03 14"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
setwd("Z:/16-Our Papers/In Preparation/IPD_metabolism _ Alise/Figures/Figure1/originals/Non-Polar_mets")
getwd()
metdata <- read.csv(... |
dd102d6bfc3b7cc2034339bc37e376080bc05ee1959e0256f5cc06d9dc644e27 | R | 5,415 | 119 | #' Plots decomposed mutational signatures
#'
#' @description Takes results from \code{\link{extractSignatures}} and plots decomposed mutational signatures as a barplot.
#'
#' @param nmfRes results from \code{\link{extractSignatures}}
#' @param contributions If TRUE plots contribution of signatures in each sample.
#' @p... |
4570ee30ff56179be5ab4b0ede83b942845a90c324e642d096d9a46a094e45b4 | R | 5,416 | 173 | # add new samples
setwd("/nfs4/chaozhang/proj/Neuron/Aritra/merFISH/script")
source("merFISH_pipeline.R")
source("functions.R")
merFISH_Behavior = merFISH_pipeline("../data/20221001_PFCL1_Results_BEHAVIOR-NOT PAIN/", "Behavior")
merFISH_pain10 = merFISH_pipeline("../data/20221012_PFCL1_Results/", "Pain10")
merFISH_pa... |
e7cdf32fa30c45158a3cfd632264f3f8408b170e88d3f032cb32423d1efea97d | R | 5,423 | 118 | #' Segment and plot log ratio values with DNACopy
#' @description The function takes logR file generated by \code{\link{prepAscat}} or \code{\link{prepAscat_t}} and performs segmentation with \code{\link{DNAcopy}}
#' @param tumor_logR logR.txt file generated by \code{\link{prepAscat}} or \code{\link{prepAscat_t}}
#' @p... |
84096625ede44dfd84a759670c7113b9dc1740c13bce6f78091957cfa0f8ebeb | R | 5,426 | 214 | #####################################
# LC project
# Script for deconvolution of ST data
# Lukas Weber, Apr 2022
#####################################
# module load conda_R/4.1.x
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
library(SpatialExperiment)
library(SingleCellE... |
ea8891d3ab7b93290b8492a8bc11dc2554d65ddd06810ced9a765bdf019a8698 | R | 5,441 | 102 | rm(list=ls())
logOddsdir <- "~/Dropbox (Gladstone)/YH_MN02/seurat_analysis_no_S521G/04_log_odds_histopathology_calculation/"
logOdds_astro_dir <- "~/Dropbox (Gladstone)/YH_MN02/seurat_analysis_no_S521G/10_subclusters_log_odds_histopathology_calculation/"
logOdds_micro_dir <- "~/Dropbox (Gladstone)/YH_MN02/seurat_analys... |
d23b73981315d525fa85e32e01963a0d4b580505145812ccc8e0f364949dce3b | R | 5,456 | 155 | library(phytools)
library(evomap)
library(ape)
library(geiger)
library(ggplot2)
library(nlme)
library(stats)
library(DescTools)
library(GGally)
library(caper)
library(smplot2)
setwd("your directory")
##Import tree and data
mammals<-read.table("final_compilation with percentage values.R1.csv",header=T... |
e7d3c7a68c78f6bd0c4ff4607eee773cce0433ec4c2dc6c4a35001e927464d88 | R | 5,457 | 179 | library(SnapATAC)
library(rjson)
library(optparse)
library(pureRUtils)
op <- list(
make_option(c("--config"), type = "character", default = "config.json"),
make_option(c("--group"), type = "character", default = "NA")
)
args <- parse_args(OptionParser(option_list = op))
pureRUtils::checkArgsExistOrStop(args)
pur... |
2a45654bc55068fd8b9ce0f76cd3cc432d998000823863c5eff177ceacd22256 | R | 5,461 | 196 | #' @title Center a matrix column-wise
#' @description Center a matrix column-wise
#' @param m a matrix or Matrix
#' @param by either "mean", "median" or a numeric vector of length equal to the number of columns of ‘m’. Default: "mean"
#' @return column-centered matrix
#' @rdname colcenter
#' @export
colcenter = func... |
b9593487bd9bd1811e5b0fce4f9c8b9e30b4c0a248098b9db4b95372402f6eb2 | R | 5,466 | 145 | library(data.table)
library(GenomicRanges)
library(rtracklayer)
library(Matrix)
library(foreach)
#library(doParallel)
#registerDoParallel(cl = 4)
PREFIX = commandArgs(T)[1]
RESOL=as.numeric(commandArgs(T)[2])
WINDOW_SIZE= as.numeric(commandArgs(T)[3])
REF = commandArgs(T)[4]
WD = commandArgs(T)[5]
option... |
108028a1c9599e6624acd6c44136f9ccf564a6018a24b007fdf89a952128e0c8 | R | 5,468 | 219 | # This code is to plot the Fig 5
# WANG. 04-April-2024
# Remove all objects created before to prevent clash
rm(list = ls())
library(dplyr)
library(ggseg)
library(ggplot2)
library(tidyr)
library(patchwork)
library(readr)
library(reshape2)
library(stringr)
# Set the working directory to the path where your files are... |
cf918a73292e8724e58ac0ed913c0607f7d47370eadfaf301ef54cda0ab110e0 | R | 5,474 | 127 | source("src/lib/Genome.R")
## Load genomic feature data
EnsFeatures <- readRDS("data/GenomicFeatures/EnsFeatures.RDS")
EnsFlanksByGene <- readRDS("data/GenomicFeatures/EnsFlanksByGene.RDS")
## Load sample grouping data
SampleInfo <- read.csv("data/SampleInfo.csv", as.is = TRUE, check.names = FALSE)
rownames(SampleInf... |
6411a9529d53618beca57058a1c405ad2f47638c0d48c00b06f37ac6fc6e4650 | R | 5,478 | 134 | #' Display protein domains
#' @param gene HGNC symbol for which protein structure to be drawn.
#' @param refSeqID RefSeq transcript identifier for \code{gene} if known.
#' @param proteinID RefSeq protein identifier for \code{gene} if known.
#' @param domainAlpha Default 1
#' @param showLegend Default TRUE
#' @param bgB... |
2ba4349a2879b2bc2c6e525d091e19b214d644530681ff096a061632df004c00 | R | 5,490 | 175 | # This code is to plot Fig. S5
# WANG. 03-April-2024
# Remove all objects created before to prevent clash
rm(list = ls())
library(dplyr)
library(ggplot2)
library(reshape2)
library(patchwork)
library(RColorBrewer)
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Resear... |
073afdb2d305318e63c18f50c08eb388a234bbe9e08b388af894fe863c6a95b0 | R | 5,494 | 135 | # Libraries
snapshot <- "/lab-share/RC-Data-Science-e2/Public/DST_software/pipeline_R_snapshot/r4.1-20220803-bch-dst-v1/"
.libPaths(snapshot)
library(velocyto.R)
library(Seurat)
library(dplyr)
library(ggplot2)
# Setting WD
setwd('/lab-share/RC-Data-Science-e2/Public/Daniel/p147-scRNAseq-Koehler')
# Loading Seurat Obj... |
4a88a61cd85b44324519fffa592396f8775017cf5b7fe2929b282614ff1afc85 | R | 5,496 | 115 | #' Estimate Tumor Mutation Burden
#' @description Estimates Tumor Mutation Burden in terms of per megabases
#' @param maf maf \code{\link{MAF}} object
#' @param captureRegions capture regions. Default NULL. If provided sub-sets variants within the capture regions for TMB estimation.
#' Can be a data.frame or a tsv with... |
54fae7fff8fe0f95c9d3e715121e2c0341eecc9026698c2678a0334b416b49b6 | R | 5,498 | 134 | # Libraries
snapshot <- "/lab-share/RC-Data-Science-e2/Public/DST_software/pipeline_R_snapshot/r4.1-20220803-bch-dst-v1/"
.libPaths(snapshot)
library(velocyto.R)
library(Seurat)
library(dplyr)
library(ggplot2)
# Setting WD
setwd('/lab-share/RC-Data-Science-e2/Public/Daniel/p147-scRNAseq-Koehler')
# Loading Seurat Obj... |
967401a79c7bc4d7d7841b2fcb6115f1acbdd0a4a478aa1915b0f39d380ebf31 | R | 5,505 | 130 | summarizeGistic = function(gistic){
nGenes = nrow(gistic[,.N,Hugo_Symbol])
tsb = gistic[,.N, Tumor_Sample_Barcode]
colnames(tsb)[2] = 'CNV'
tsb = tsb[order(tsb$CNV, decreasing = TRUE),]
vc = gistic[,.N, .(Tumor_Sample_Barcode, Variant_Classification )]
vc.cast = data.table::dcast(data = vc, formula = Tum... |
e1fde45ce08ae0d39ea4c88bf97dedc22e118b02ad4bb67862a1f4a4a91c64bd | R | 5,508 | 145 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' Euclidean distance between two points.
#' @param a A point.
#' @param b A point.
#' @return The distance between two points.
#' @noRd
NULL
#' Squared Euclidean distance between two points.
... |
d76cd694d494a0bb45b13d9354fdaf89b10ecd567f8e5f1f627cd12eed21f137 | R | 5,512 | 139 | ---
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... |
e46ba5eadd2821accecf93718a4479f8a4585278f4c8da11cb0f13bbfab328c3 | R | 5,528 | 116 | ## packages for data table processing
library(tidyverse)
library(data.table)
library(here)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors = F)
options(repr.plot.width=11, repr.plot.height=8.5)
DATADIR='data/tidy_data/compare_to_Alzhe... |
06b9820bebefbe41e81e2b09e64a12bada182bb99e80adc90ffc00f15c33d6e5 | R | 5,531 | 110 | #!/usr/bin/env perl
## Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2022, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2022, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is sub... |
aed5c53a3c1515dfe4f8af6e76146026ab1a407ff56fe8781b1c686e7a1de277 | R | 5,545 | 174 | # This code is to plot Fig.s8
# WANG. 03-April-2024
# Remove all objects created before to prevent clash
rm(list = ls())
library(dplyr)
library(ggplot2)
library(reshape2)
library(patchwork)
library(RColorBrewer)
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Researc... |
d5039b6aa97a1759fed0dc26059738eefdf88a07dd59f6582669d15437ec240b | R | 5,559 | 103 | GO_treeplots = function(GOframe, path){
require(rrvgo)
require(org.Dm.eg.db)
require(treemap)
require(grid)
require(GOSemSim)
require(RColorBrewer)
#create similarity matrices in parent environment if necessary
if(!exists("BPsim")){BPsim <<- GOSemSim::godata(org.Dm.eg.db, ont="BP")}
if(!exists("... |
89ae033a032346755af99994d5cb92eccfb3da52503447fa3467d05d305fb7aa | R | 5,564 | 148 | #' Identify sample swaps and similarities
#' @description Given a list BAM files, the function genotypes known SNPs and identifies potentially related samples. For the source of SNPs, see reference
#' @param bams Input bam files. Required.
#' @param build reference genome build. Default "hg19". Can be hg19 or hg38
#' @... |
0c1e9c7b681fa501a08f004db5459cdbe3dd1ef38feab5f79fd391174109d730 | R | 5,566 | 106 | #'Prepare input files for ASCAT
#' @description Function takes the output from \code{\link{gtMarkers}} and generates `logR` and `BAF` files required for ASCAT analysis.
#' @details The function will filter SNPs with low coverage (default <15), estimate BAF, logR, and generates the input files for ASCAT. Alternatively, ... |
11eda1645d46f38747eac169ec17fe9b0484b48e397fea399affc0ef7ddc2056 | R | 5,566 | 162 | library(Seurat)
options(Seurat.object.assay.version = "v5")
library(SeuratObject)
library(Matrix)
projdir <- here::here()
workdir <- file.path(projdir, "04.cCREgene/sa2.cicero")
packdir <- file.path(here::here(), "package", "R")
import::from(.from = "cembav2env.R", .directory = packdir,
cembav2env, Sa2PeakCalling, ... |
7eecfc60a445064ee63df2a3dabd2468581394b46c834ddddfff141fb4631a41 | R | 5,578 | 140 | #!/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... |
1629912b0e3c3063359afa7265ef85b454cdb28562b23d92d77ef6bb722e2cff | R | 5,585 | 161 | ---
title: "Figure 2 barplot"
author: "Hao Huang"
date: '2022-09-08'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Load
## load packages
```{r}
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(caret)
library(kknn)
lib... |
cf4852f49f4f34d2812af43b51e0ec5ef1f61bbb60b961abf90061c0549043fe | R | 5,588 | 124 | ##setwd to the folder containing the dataset
##composer:HoangAnhLeP
##Institute: Brain Health Lab - International University, Vietnam
##composingdate:19-03-2023
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DESeq2")
library(edgeR)
library(DESeq2)
library... |
7aec3df5f77b2ad1a3c4c993f1cef0035f83486c0c4e4a2f0f7debf53bea420a | R | 5,619 | 187 | #' @name trainNMF
#' @rdname trainNMF
#' @title train NMF model
#'
#' @aliases trainNMF
#'
#' @description This is the training function used by SPOTLight. This function
#' takes in single cell expression data, trains the model and learns topic
#' profiles for each cell type
#'
#' @param x single-cell dataset. Ca... |
3e0c8268a7c3b617825fa8dfb470532db6813a9186f2bd8d3f6c5bcbbc055af9 | R | 5,626 | 165 | ## Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2022, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2022, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to the terms of... |
18990acbfdfd5326d67c57b0a7ac4d2915efe4ad15f70b83f32ba6b18857ccd8 | R | 5,637 | 139 | ################################################################
# Generate Figure 3e, Supplementary Figure 25, and
# Corresponding Supplementary Figures 26-33
################################################################
library(ggplot2)
library(scales)
library(ggpubr)
library(clusterProfiler)
library(openxlsx)
li... |
85cc404d55aa311144fe62c8fb6476ae82eae4daf53a4004e84c9452fbcddd81 | R | 5,637 | 166 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to the... |
c8ddbf4153fe8e8b4f7bee2b21e57c8709b8e3e4f221c730fd14a9e9832cee57 | R | 5,637 | 139 | ################################################################
# Generate Figure 3D, Supplementary Figure 25, and
# Corresponding Supplementary Figures 26-33
################################################################
library(ggplot2)
library(scales)
library(ggpubr)
library(clusterProfiler)
library(openxlsx)
li... |
d93ef6ad40a408696ae7d2d7ce90f69c8953771b0a0b5128763922bcd95bc1e2 | R | 5,639 | 145 | library(ggplot2)
library(data.table)
library(cowplot)
library(scales)
packdir <- file.path(here::here(), "package", "R")
import::from(.from = "cembav2env.R", .directory = packdir,
cembav2env)
import::from(.from = "annot.R", .directory = packdir,
getSubclassMeta.ordered, mapAllenSubclassTomCGSubclass2)
import::from(... |
b9f74f083e9faeec8655896bd7c51872bc4ede933a177b0e1e6bb1b7118cbb44 | R | 5,649 | 145 | setwd(file.path(path.expand(dirname("~")), "Downloads"))
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(lmtest)
library(ast... |
6465fd9b9cb824a5f0b8bf1f4cb6a2483febdabb86ac9bfd9a8fcc947b7614f2 | R | 5,651 | 153 | #-----------------------------------------------
# LICENSE
#-----------------------------------------------
# Copyright 2019 Novartis Institutes for BioMedical Research Inc.
# Licensed under the GNU General Public License, Version 3 (the "License");
# you may not use this file except in compliance with ... |
dfa80d48e8fffde121afdd58303da8824464be163806571346c1d4e2bcc5b0fa | R | 5,660 | 154 | #' Function to find variable genes using mean variance relationship method
#'
#' @importFrom methods as
#' @importFrom stats loess median na.omit quantile
#' @importFrom rlang .data
#'
#' @param X expression matrix
#' @param groups vector of groups
#' @param min_expr min expression cutoff
#' @param max_expr max expres... |
aa55ae11ad25ead42f6bbc78614a11485eade1abb062d17776dc22727f857a59 | R | 5,672 | 175 | # This code is to plot Fig. S3
# WANG. 03-April-2024
# Remove all objects created before to prevent clash
rm(list = ls())
library(dplyr)
library(ggplot2)
library(reshape2)
library(patchwork)
library(RColorBrewer)
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Resear... |
f79361c48fe4bcde602353f57ae23987d284c1ace37e709b15bbb7501f50b663 | R | 5,672 | 159 | ---
title: "Figure 2 barplot"
author: "Hao Huang"
date: '2022-09-08'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Load
## load packages
```{r}
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(caret)
library(kknn)
lib... |
96d40e293a49593b9eb8bc22275a16c61075e140bdbde19c333455c03b10bbf2 | R | 5,674 | 112 | ---
title: "Common Variants near Functional De Novo Variants"
author: "Stephen P Plassmeyer"
date: "7/28/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(data.table)
#library(Biostrings)
library(BSgenome.Hsapiens.UCSC.hg19)
library(liftOver)
library(stringr)
librar... |
47a625b8bedf1abdf4d3ec50683e42743035bf336849590a00d26ba9a06534a2 | R | 5,684 | 137 | # cellchat with secreted signaling only
# SCRATCH1=/mnt/g/scratch
# docker run -it --rm \
# --workdir $HOME \
# -v /mnt/g/cellranger_atac_counts:$HOME/cellranger_atac_counts \
# -v /mnt/g/cellranger_multi_counts:$HOME/cellranger_multi_counts \
# -v /mnt/g/spaceranger_visium_counts:$HOME/spaceranger_visium_counts \
# -... |
5301b36d0ae9373efd1e1adaf46698fe026b339dbe68d734d12b60b8a86150e2 | R | 5,684 | 134 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(rlang)
library(writexl)
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(future)
## main differential gene expression package
library(glmGamPoi)
library(SingleCellExperiment)
libra... |
922f9e0a380ed454e36774fd79bde13b3f06b4bc70c8f56a81ec6c58d7b8399c | R | 5,693 | 131 | #' Age distribution comparison: patients subset vs all population.
#'
#' Given an object of class \code{comoRbidity} the age distribution of your patients
#' of interest (those that have at least one code of the ones contained in indexDiseaseCodes.txt
#' file) is represent comparing with the age distribution of the y... |
de609e58d2d7fac30667b16da244753cfdc113f45d9897ee875ed05da7479259 | R | 5,696 | 106 | #' Rainfall plot to display hyper mutated genomic regions.
#' @description Plots inter variant distance as a function of genomic locus.
#' @details If `detectChangePoints`` is set to TRUE, this function will identify Kataegis loci.
#' Kategis detection algorithm by Moritz Goretzky at WWU Munster, which exploits the def... |
3230191350510b6c351939d64908884e1666b80206103a223dcdbde026f90ed5 | R | 5,699 | 109 | #' Per-cell Confidence Score:
#' Calculates the weighted Mahalanobis distance for the query cells to reference clusters. Returns a vector
#' of distance scores, one per query cell. Higher distance metric indicates less confidence.
#'
#' @param reference Reference object as returned by Symphony buildReference()
#' @para... |
c6c16e3464227c2b6e5d42b17216e11e62ea5a9611ea839e1f414341aa4ffc76 | R | 5,704 | 216 | #Accessory codes for FCSTrans Transformation of FCS
#' Set output to 0 when input is less than cutoff value
#'
#' @param x input array
#' @param cutoff cutoff number
#' @param target target number
#'
ipfloor <- function (x, cutoff = 0, target = 0) {
y = x
if (x <= cutoff)
y = target
y
}
#' Set output to 0... |
d33d85c54a2b0a7a304c7c1d3ee31d849dbb79865ae186c64632f637859f2637 | R | 5,710 | 141 | ## ---------------------------
##
## Script Purpose: Figure demonstrating Brain IDP and CMR IDP indirect efffect sizes
## Boxplot demonstrating how different features mediate the VRF-CF association
##
## (1) Combine both brain IDP SEM analyses and CMR IDP mediation results
## ... |
d6f76e195121c8048f2183d0db6ee0b77402d769ee306e3b68f05c3d6e9dbba7 | R | 5,724 | 176 | ## ---------------------------
##
## Script Purpose: Figure 3 demonstrating single mediation modelling
## Geom_point plot comparing the estimated indirect effects of the mediators
##
##
## (1) Assign new column describing which latents are in which group (Heart v Brain)... |
21b7f97ff0111f8ced8f8d8a0e12ab770f05d46dcaef596e3623ac164de5e761 | R | 5,727 | 120 | #=========================================================================
#
# Copyright NumFOCUS
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/license... |
db894b7c09c30b8bd335d67e5765918524088d7d7bafecc87afd28ff5ac62012 | R | 5,727 | 189 | library(optparse)
library(Matrix)
library(fitdistrplus)
library(ggplot2)
projdir <- here::here()
sa2_subclasspeak_dir <- file.path(projdir,
"supple.07.peakcalling.allinone",
"sa2.subclassv3.final.peak.srt")
pack_dir <- file.path(projdir, "package/R")
import::from(.from = "utils.R", .directory = pack_dir,
checkAr... |
59f7543e7888958aacd22060eb585d2b588817424a8c590751eda81748dbf2dd | R | 5,731 | 136 | library(rsvd)
normalize_data <- function (A) {
# Simple convenience function to library and log normalize a matrix
totalUMIPerCell <- rowSums(A);
if (any(totalUMIPerCell == 0)) {
toRemove <- which(totalUMIPerCell == 0)
A <- A[-toRemove,]
totalUMIPerCell <- totalUMIPerCell[-toRemov... |
3e6e69d8ff71554cf462b205f1aaf9ef9c718a012d5f129944642964f080fd3a | R | 5,745 | 195 | # This code is to
# 1. load the subcortex data
# 2. calculate and plot the CV of the subcortex
# 2. plot the mean, ordered mean; cv and percentage change
# WANG. 04-April-2024
# Remove all objects created before to prevent clash
rm(list = ls())
library(dplyr)
library(ggseg)
library(ggplot2)
library(tidyr)
library(p... |
bc7812b1e34547a744e717f593ac99301b60c7db8752913ae7a727d8fe51e4fe | R | 5,772 | 143 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
library(ggpubr)
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(future)
## AUCell object for DNA damage estimates
library(AUCell)
## for plots
... |
1a17c3d0b4a92d0fb5307d7f3bc6410b7281b7d4b69c23af7f86b56e194acbb4 | R | 5,775 | 124 | ## packages for data table processing
library(tidyverse)
library(igraph)
library(network)
library(sna)
library(stringr)
library(RColorBrewer)
library(data.table)
library(here)
library(leiden)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFac... |
b1629bfb73c5258213bc347f6520f9538fb330c8fd450937d15c68371335bcab | R | 5,781 | 146 | #install.packages("ggsignif")
library(ARTool)
library(lawstat)
library(ggplot2)
library(tidyverse)
library(ggsignif)
library(RVAideMemoire)
library(emmeans)
library(rcompanion)
library(psych)
library(car)
###Setting of the Graph###
#label
lab_homo = expression(paste(italic({piezo ^ {"KO"}})))
print(l... |
6a2ac08282149def705df6ea8709cda3a6735d89b8ec1f8863bb41d7e238cfa0 | R | 5,795 | 180 | suppressMessages(library(Signac))
suppressMessages(library(Seurat))
library(argparse)
suppressMessages(library(GenomeInfoDb))
suppressMessages(library(AnnotationHub))
suppressMessages(library(BSgenome.Rnorvegicus.UCSC.rn6))
library(rtracklayer)
library(ggplot2)
parser <- ArgumentParser(description = "process input arg... |
55c598b2f6426edab28dba3175ea91236c1f1fbae3f909925ef1cccdbade110c | R | 5,811 | 116 | ---
title: "Gene list summary"
author: "Amy Hicks"
date: "03/02/21"
output:
html_document:
code_folding: hide
toc: true
toc_float: true
df_print: paged
---
```{r setup, include=FALSE}
library(tidyverse)
library(biomaRt)
library(readxl)
library(knitr)
library(here)
knitr::opts_chunk$set(echo = TRUE)
``... |
f01eb548467be1c7126e2049d5b88417d0ca48a2fa6b066424b9a2b886bf3cbf | R | 5,812 | 134 | #' compare two cohorts (MAF).
#' @details Performs fisher test on 2x2 contigency table generated from two cohorts to find differentially mutated genes.
#'
#' @param m1 first \code{\link{MAF}} object
#' @param m2 second \code{\link{MAF}} object
#' @param m1Name optional name for first cohort
#' @param m2Name optional na... |
b801f14dac7bd1711a7fe87bee0d290fdf2404e078fc45a1e74cc78d00ee5b28 | R | 5,821 | 156 | #' @name SPOTlight
#' @title Deconvolution of mixture using single-cell data
#'
#' @description This is the backbone function which takes in single cell
#' expression data to deconvolute spatial transcriptomics spots.
#'
#' @param x,y single-cell and mixture dataset, respectively. Can be a
#' numeric matrix or \cod... |
3e4ea34dedcc3307241227e769bca5b8b9abbd8d00f6c2c33b5db3fd1a7f8d3d | R | 5,832 | 137 | 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(ggvenn) # devtools::install_github("ya... |
13a2f92dc825e2cfb615a494ad882d8673eff960de5dbe149c8479d877572d6b | R | 5,849 | 219 |
# test association with survival outcome, on Chowell 2018 subjects
# -- DePTH on Zheng 2021 pos tcrs, cor (dist_DePTH_cor)
# -- Glazer 2022 on Zheng 2021 pos tcrs, cor (dist_CLAIRE_cor)
# -- aa blosum62 (dist_AA)
# -- DePTH on Zheng 2021, set (dist_DePTH_breadth)
# -- Glazer 2022 on Zheng 2021, set (dist_CLAIRE_... |
9024b924eb59946352ef898e4de33bc26b0ec95d5e06b51e0ee3459d140efd4b | R | 5,855 | 128 | #' A graphical summary of the database content.
#'
#' Given an object of class \code{comorbidity}, and the characters used to
#' specify the sex, a figure containing 3 plots, one of the age distribution,
#' another one of the age distribution and the third one with the relation
#' between age and sex distribution. ... |
777e86463e83191b6a3d506923b408b123e00497cf7c7895fba4b6d9cef21c60 | R | 5,860 | 116 | library(tidyverse)
library(cmapR)
library(doFuture)
# parallel: set number of workers
cores <- 16
registerDoFuture()
plan(multisession,workers = cores)
library(doRNG)
library(caret)
library(ggplot2)
library(corrplot)
library(reshape2)
########## The whole pre-processing analysis is in the L1000 folder of the new data ... |
a26e0e39095da8b48e034cdde1ddace2372895f6a9a281f827f193fcf04f260c | R | 5,871 | 136 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
library(ggpubr)
## statistical analyses
library(tidymodels)
library(lme4)
library(broom.mixed)
library(lmerTest)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x,... |
2ba206733ac3b07b8043f8ce579b61cddd46ff24ffe403d4af8035a3760fa64b | R | 5,874 | 186 | # dpcr validation and analysis
library(ggplot2)
library(dplyr)
library(ggplot2)
library(dplyr)
library(here)
df <- read.csv(here("ckd","digital_pcr","Validation run RPTEC & HEK 293T (238,110)-Quantification.csv")) %>%
dplyr::filter(Well != "Average")
clinical_meta <- read.xlsx(here("ckd","clinical_meta.xlsx"))
sam... |
8875425abef2a6665b36b389f4180c9adb8fd0200a928e19c43ff4dde4008472 | R | 5,880 | 122 | library(here)
library(ggplot2)
#################
# genes from https://www.mdanderson.org/documents/Labs/Wood-Laboratory/human-dna-repair-genes.html
dna_repair_genes <- c("AAAS","ADA","ADCY6","ADRM1","AK1","AK3","APRT",
"ARL6IP1","BCAM","BCAP31","BOLA2","BRF2","BRP44",
"CANT1... |
7810cf6750b53cfd00aa0411ab4f54ca14657091784134d7b15b356366d50ae5 | R | 5,881 | 143 | library(tidyverse)
library(ggplot2)
library(ggpubr)
library(ggridges)
library(rstatix)
library(patchwork)
# Load results--------
cpa_intermediate <- data.table::fread('../serology/results_intermediate_encoders/10foldvalidation_wholeModel_32dim2000ep_serology.csv')
colnames(cpa_intermediate)[1] <- 'fold'
# cpa_intermed... |
ae599faf83bfab28a69689ea576ace3a8ddff657faceea6f26ad4f565576e04d | R | 5,886 | 119 | ---
title: "Gene list summary"
author: "Amy Hicks"
date: "03/02/21"
output:
html_document:
code_folding: hide
toc: true
toc_float: true
df_print: paged
---
```{r setup, include=FALSE}
library(tidyverse)
library(biomaRt)
library(readxl)
library(knitr)
library(here)
knitr::opts_chunk$set(echo = TRUE)
``... |
102c88b2e4af1af6a5fcbae25c22d34abfbf499778674894339fdc2e6903ac4d | R | 5,901 | 163 | ---
title: "Plot evaluation"
author: "Hao Huang"
date: "07/07/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Load
```{r}
suppressPackageStartupMessages({
library(ggplot2)
library(glue)
library(reshape2)
library(ggpubr)
})
```
```{r}
topN = 10
```
# Set... |
ae17ab02b83e5dbee1f6065659aeda38ad1927baf73d892ffc5274158c59c131 | R | 5,904 | 180 | ## ---------------------------
##
## Script Purpose: Figure 3 demonstrating single mediation modelling
## Geom_point plot comparing the estimated indirect effects of the mediators
##
##
## (1) Assign new column describing which latents are in which group (Heart v Brain)... |
e0479fd3693a69ebcccf3ea5c137a71004228f424b49a59aee7a79971bff913f | R | 5,905 | 253 | #This script will load any/all packages required throughout the pipeline.
#Not every package is used in every process, but for simplicity they are all loaded together
#This is needed to stop some packages complaining
Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS="true")
options(needs.promptUser = FALSE)
#-----------... |
7eefc93e4a9726e1f3d824054975f81d7c02017636ad247b128f4ca38b6dd67e | R | 5,914 | 227 | ---
title: "LC analyses: additional marker genes"
author: "Lukas Weber"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
toc_depth: 3
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
Analyses for LC... |
9104564705d895b584000619793700810e25b56a2e8f54e89ae13bf9eea2cb76 | R | 5,917 | 138 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(rlang)
library(writexl)
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(future)
## main differential gene expression package
library(glmGamPoi)
library(SingleCellExperiment)
libra... |
72bbd654cd2ffc4069b96869e19e3c587117e97757a99cdfde817f85c6ee5c10 | R | 5,925 | 176 | library(Seurat)
library(symphony)
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(ggplot2)
library(ggrepel)
tpColors <- tenColors <- c('#a9d6e5', '#89c2d9', '#61a... |
a801ad00dbb2fd3dba91a553f61a2479c0b42610decf558086acc5d1a7b64eee | R | 5,926 | 114 | #######################################
####fMRI metrics extraction script####
######################################
#Project: Long-term effects of prenatal infection on the human brain: a prospective multimodal neuroimaging study (in the Generation R cohort)
#Author: Anna Suleri
rm(list=ls()) #clear environment
... |
7d3e3316af0e966e23ab9a1130221581af16279b1d8b290fe5efd0fcd4322a77 | R | 5,936 | 172 | setwd(file.path(path.expand(dirname("~")), "Downloads"))
dataset.raw <- read.delim("results___2023-06-13T171710.txt", sep=",")
#dataset.realtime.aug <- read.delim("realtime-cPLV.csv", sep=",")
dataset <- data.frame(dataset.raw)
dataset$MuPowerR <- log(dataset.raw$MuPowerR)
dataset$MuPowerL <- log(dataset.raw$MuPowe... |
4616ebbde1007c2fad950d33166852569939bd0e28cdf077db4b49708923869e | R | 5,939 | 192 | ---
title: "Vanderbilt MENS projection - old patterns"
output: html_notebook
---
```{r check_interactive}
if(interactive()){
print(date())
print("This was run interactively. Please run with rmarkdown::render")
}else{
print(paste0("This notebook was compiled on ", date()))
}
```
```{r load, message = F, results... |
12f85e05292f4d9e23807e1840d2b0bceb4610d844dd5354b38c303b0c881e15 | R | 5,952 | 105 | library(tidyverse)
library(mmtable2)
library(gg3D)
library(ggpubr)
library(gt)
#library(gridExtra)
style_list <- list(cell_borders(sides = "top",color = "black"),
cell_borders(sides = "bottom",color = "black"),
cell_text(align = "center",size = px(20)))
# Supplementary table 1-4-... |
91608c7f7a5d092c7133684cdd1e81445c23a0b3ab0ce434eb4d317b8135feff | R | 5,958 | 208 | ################################
# LC project
# Script for downstream analyses
# Lukas Weber, Apr 2022
################################
# module load conda_R/4.1.x
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
library(SpatialExperiment)
library(here)
library(scater)
libr... |
db4b14711bbda56437728ff2ea5d0bb6047b011471e82962c0c0c36b30e567d7 | R | 5,964 | 135 |
#' @title One-bar annotation ggplot
#' @description one-bar annotation ggplot
#' @param X named character vector of observations and groups (names); or a dataframe with first two columns as observations and groups.
#' @param title annomap title. Default: NULL
#' @param x.title x axis (observations) title (e.g. 'cells'... |
063e5fe87df30b681d56137537572f1d552160db5bdd58bdceac9d9e3a50c539 | R | 5,972 | 131 | ## packages for data table processing
library(tidyverse)
library(igraph)
library(network)
library(sna)
library(stringr)
library(RColorBrewer)
library(data.table)
library(here)
library(leiden)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFac... |
d339b6b7f68e495df0c61349a56e2a936511b9856bdc75bdc04c6fe5484bd24b | R | 5,973 | 148 | #' Extract read counts from genetic markers for ASCAT analysis
#' @description The function will generate tsv files `<tumor/normal>_nucleotide_counts.tsv` that can be used for downstream analysis. Note that the function will process ~900K loci from Affymetrix Genome-Wide Human SNP 6.0 Array. The process can be sped up ... |
29458b713dc6a995791a7f6ade509142371fefd83f5bcf85523d77fa9173500b | R | 5,976 | 107 |
#' @title Summary Statistics for Bootstrapped State Fractions
#' @description Compute summary statistics for bootstrapped data
#' @param bootstraps a matrix or dataframe of data values in rows by bootstrap repeats in columns
#' @return dataframe with the mean and SEM across all bootstrap repeats (columns) for each row... |
9f2fc640e282e9390a61ed9c2da06c8835f9b3de6569a5e89b89181f4f6fc36c | R | 5,981 | 219 | set.seed(321)
# mock up some single-cell, mixture & marker data
sce <- mockSC(ng = 200, nc = 10, nt = 3)
spe <- mockSP(sce)
mgs <- getMGS(sce)
.checks <- function(res, sce) {
mod <- res[[1]]
mtr <- res[[2]]
expect_is(res, "list")
expect_is(mtr, "matrix")
expect_is(mod, "list")
expect_identical(... |
1021c76320f3850c67d543a72a656d1ab493758f30d5686ee5365d071cc66855 | R | 5,982 | 141 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(rlang)
library(writexl)
## main differential gene expression package
library(SingleCellExperiment)
library(limma)
library(edgeR)
library(swfdr)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, spl... |
15d12ab2dfadbbbf9b9384c921078bd16c0a74dbb2a042c5a4c51fb7c3c510cf | R | 5,991 | 146 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
library(ggpubr)
library(data.table)
library(fgsea)
library(swfdr)
library(msigdbr)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', s... |
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