sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1a3bfceee49315276afd4a58fd41e2fd00e710744e006a392adb328755490576 | R | 2,869 | 83 | # 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)
# Setting WD
setwd(... |
35a3bc256194860f0caa7201841e1a687c9dc4f11788d5078332c92ceff2c816 | R | 2,869 | 83 | # 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)
# Setting WD
setwd(... |
b446b7c8ce847b37c13dade91cead5c2e2375a50903821b2a27e08d968d0a2fd | R | 2,870 | 81 | library(GGally)
library(ggplot2)
#setup for figure
wd <- 5
ht <- 5
fig.nrows <- 1
fig.ncols <- 1
pt <- 8
cex.general <- 1
cex.lab <- 1
cex.axis <- 1
cex.main <- 1
cex.legend <- 1
d1<-read.table("Melnikov2012/processed/mtsa_mel12.m1000.t5.e5.8mers.txt", stringsAsFactors=F)
d2<-read.table("Kheradpour2013/processed/mtsa... |
9ed3fd652bb981c73bab45176b509653285612b82ce76aeb7e5b8752dedc1527 | R | 2,874 | 83 | # spatial qc and dotplot
library(Seurat)
library(Signac)
library(here)
library(ggplot2)
library(gridExtra)
library(stringr)
# create a plot dir
plots <- here("ckd","figures")
dir.create(here(plots))
rna <- readRDS(here("ckd","spatial","step1b_spatial.rds"))
rna@meta.data$library_id <- gsub("_"," ", rna@meta.data$li... |
7ea5e22ca512cdda95b956d19873cf9d729953899f5ac46546544ab45503a960 | R | 2,875 | 76 | library(Seurat)
library(SeuratData)
library(dplyr)
# stable cluster
one_bootstrap = function(obj, res, origin_res_label, algorithm, k.param = 20, reduction=reduction, dims=dims){
obj_random = obj[, sample(colnames(obj), length(colnames(obj)) * 0.5)]
obj_random = FindNeighbors(obj_random, k.param = k.param, redu... |
c99426359b1041181b15c7f555e416ada751bb40c9675d12b989d477e8e9f393 | R | 2,876 | 100 | # use neighborhood identified in step1 and degs from step2 to identify neighborhood-specific gsea
# this script will analyze degs for pathway enrichment
# to run locally:
# SCRATCH1=/mnt/g/scratch
# docker run -it --rm \
# --workdir $HOME \
# -v /mnt/g/reference:$HOME/reference \
# -v /mnt/g/cellranger_atac_counts:$HOM... |
cc7de54e4f35343877d61fb63b72a1861a8c50392971a54b94aeb88c98fae118 | R | 2,877 | 76 |
volcano_plot = function(
m = NULL,
group = NULL,
group2 = NULL,
d = NULL,
center.rows = TRUE,
vis.lfc = log2(2.5),
vis.p = 0.01,
... |
f59057fbb7b7e576f3a7edec113e2616347d6467c409f4811a587febe8dfc07e | R | 2,883 | 84 | # 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)
# Setting WD
setwd(... |
4add5037d72050aae6dd68d0f9944a92f9c56fa2af55248bc025a37fa0e1be3f | R | 2,886 | 96 | #05/25/2023 Fengyao Yan
#Make the dot plot for NNLS GTEx sensitivity test
library(tidyverse)
library(ggplot2)
pearson_dots <- data.frame(matrix(ncol=4, nrow=0))
colnames(pearson_dots) <- c('tissue', 'pcc', 'n_gene', 'model')
pcc_nnls <- read.csv('../../data/gtex/pcc_all_gtex_nnls.csv', row.names = 1)
pcc_deep <- rea... |
8fdb66ee09715e891d7fbe170977ced77fe4d0d3a27fa33ccb4f3d5dc74da82f | R | 2,886 | 84 | #Visualize the nonfetal & healthy subset of gut cell atlas.
#Facets by age and their annotated cell types
#Expression of stx3, uchl1, slc17a9
library(tidyverse)
library(monocle3)
library(here)
source(here("scripts/accessory_functions/monocle_mods.R"))
nfh <- readRDS(here("teichman_gut_atlas/data/mesenchymal_gut_nfh... |
e6d4eaaa5dda014e8f0fa223487b7d95fc66f5e3810c67955c2be54fb901843c | R | 2,892 | 89 |
#' @title FUNCTION_TITLE
#' @description FUNCTION_DESCRIPTION
#' @param data PARAM_DESCRIPTION
#' @param x PARAM_DESCRIPTION
#' @param xlab PARAM_DESCRIPTION, Default: 'Cell Score'
#' @param mode.names PARAM_DESCRIPTION, Default: c("Mode1", "Mode2")
#' @param ... PARAM_DESCRIPTION
#' @param prob PARAM_DESCRIPTION, Def... |
2b24f06e2b61f7fd7a48fed2a3552890b9ec9c2e9ae0acdf05ceda4458d27308 | R | 2,896 | 67 | #!/usr/bin/R
suppressPackageStartupMessages(library("stringr"))
suppressPackageStartupMessages(library("dplyr"))
suppressPackageStartupMessages(library("tidyverse"))
# * meta functions
cal_cv <- function(x){
cv <- sd(x) / mean(x)
return(cv)
}
meta.TE.sumcount <- function(anno, count, level){
if(identical(anno$... |
1c1c5a946e8d5f53da15f5061884571ce9107484a90ce736ab5640cca5376581 | R | 2,902 | 94 | #
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors = F)
library(tidyverse)
library(broom)
library(here)
library(rcartocolor)
library(lme4)
library(lmeTest)
library(tidymodels)
## main Seurat package snRNA-seq pacakges
library(Seurat)
li... |
45220422d74cc313b942d9b5af797ddb0a50030bdb2d3c1689216069f42ec9dd | R | 2,911 | 64 | ##############################################################################
########### This is the script for extracting within and between connectivity
##############################################################################
#Project: Long-term effects of prenatal infection on the human brain: a prospective... |
650117ae716f7ca492c22e9084ce77168fab2fd01ba83cfe741f000f200880bd | R | 2,912 | 90 | # Fit full model and output the odds ratio
# update on 9.27.21: add E value
# load library
library(plyr)
library(tidyr)
library(dplyr)
library(Hmisc)
library(writexl)
library(ggplot2)
library(rms)
library(kableExtra)
library(EValue)
load("~/Box/CCC19_TS/48 - Breast cancer/Data/48_impute.20.123_Jundat.v3.RData")
load(... |
a1f6787e39ec3dbd77e9424b514f3af2ee6073e4ab9b847903fbb054e141cf79 | R | 2,913 | 85 | # 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... |
2f128980c8218814d0cad1057f60d764ef56b9f3e8c6d83a83a6eab0f883860e | R | 2,914 | 56 | setMethod( "show",
signature = "comorbidity",
definition = function( object ) {
cat( "Object of class 'comorbidity'\n" )
cat( " . Search: ", object@search, "\n" )
cat( " . Only comorbidities between index diseases:", object@intraCode, "\n" )
cat( " . Aggregate t... |
7037d44863bfcd7f81f2389a329a2e985573e25d31d624fecf8a25d4d863f391 | R | 2,914 | 70 | "pwr.2p2n.test" <-
function (h = NULL, n1 = NULL, n2 = NULL, sig.level = 0.05, power = NULL,
alternative = c("two.sided", "less","greater"))
{
if (sum(sapply(list(h, n1, n2, power, sig.level), is.null)) !=
1)
stop("exactly one of h, n1, n2, power, and sig.level must be NULL")
if (!is.null(h)... |
a41139184470ce832083e972f7c453ec36042ee38943d4dd581177f98b3d9b77 | R | 2,920 | 85 | # 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... |
c9f1938f167b6cac4586482232b7d403811daca61404ae05eec0a5ff3625a35b | R | 2,921 | 85 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(data.table)
library(RColorBrewer)
library(rcartocolor)
library(ggupset)
library(svglite)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactor... |
55d0b53b76f10a237bea0b4b1fdb0e915da9bcfa71d95e664df742b9ffee96da | R | 2,927 | 113 |
# For each HLA,
# this code outputs the TCRs that appear at least twice among the subjects
# with known HLA existing or not status
# and their corresponding frequency among the subjects under consideration
# the public TCR and HLA allele information used here are based on all 666
# Emerson HIP subjects
args = com... |
02db06531b83f0f3acdbd49a735fd06fe20322a123f074bbf020f6260e17a9ff | R | 2,930 | 71 | "pwr.t.test" <-
function (n = NULL, d = NULL, sig.level = 0.05, power = NULL,
type = c("two.sample", "one.sample", "paired"), alternative = c("two.sided",
"less","greater"))
{
if (sum(sapply(list(n, d, power, sig.level), is.null)) !=
1)
stop("exactly one of n, d, power, and sig.level mus... |
20169602816bc0d86f677eb7f3b5f81a7eb7404368825e45f0d82b4999b5e48a | R | 2,931 | 75 | "pwr.t2n.test" <-
function (n1 = NULL, n2= NULL, d = NULL, sig.level = 0.05, power = NULL,
alternative = c("two.sided",
"less","greater"))
{
if (sum(sapply(list(n1,n2, d, power, sig.level), is.null)) !=
1)
stop("exactly one of n1, n2, d, power, and sig.level must be NULL")
if (!is.null(... |
b9c0c7e399d13178367a62b18f53f2c3fac16a9746e3d9a2537feda1fe29d24a | R | 2,946 | 59 | ---
title: "Preparing GR peaks & targets"
author: "Pierre-Luc Germain"
date: "3/1/2022"
output: html_document
---
```{r}
suppressPackageStartupMessages({
library(AnnotationHub)
library(epiwraps)
})
download.file("https://www.encodeproject.org/files/ENCFF567KLR/@@download/ENCFF567KLR.bed.gz", "extdata/ENCFF567KLR_G... |
ff800a674ddec096b418b72942586195e6c557599c94cda5014d26ee624fd564 | R | 2,946 | 69 | # Kategis detection algorithm by Moritz Goretzky @ WWU Munster, which exploits the definition of Kategis (six consecutive mutations with an avg. distance of 1000bp ) to idetify hyper mutated genomic loci.
# An algorithm which starts with a double-ended queue to which six consecutive mutations are added and their averag... |
d065411f61fadfb83bc6e441830b38ce0196bb46d7d8a42c4345778de2f3c501 | R | 2,950 | 85 | #-----------------------------------------------
# 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 ... |
2eb07ee93e1cd548afdc92e32867774129262351f1fd933700e215f98521993c | R | 2,951 | 99 | # ===================== #
# 6. APOE2 stratified #
# meta-analyses #
# ===================== #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(data.table)
library(dplyr)
library(magrittr)
library(metafor)
library(purrr)
## Read in data ----
nshd <- read.csv("results/nshd_e2_st... |
86b6e1d5dd54b6ef494612b6e211856ae4ee8b153342fe8a31308a0f376f048e | R | 2,955 | 66 | library(Seurat)
library(tidyverse)
library(patchwork)
library(reshape2)
library(cowplot) #for plot_grid
# script to calculate percentage of mitochondrial reads for quality control filtering of cells
# input is digital expression matrix (DGE) generated by the dropseq alignment pipeline
# output is a file with mitochon... |
259f68d293dcbffcdb32da10b6b38c82fd553474a6dc854bf19f556bbf5b3970 | R | 2,956 | 110 | 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(ggplot2)... |
0bb54c1b689d29eb93b28a349f5bc1af7c1f85e66d19b7eece07642e7883557b | R | 2,964 | 65 | # Extract left, right, and summed BNST volumes from the labelled segmentation masks.
args <- commandArgs(FALSE)
file_arg <- args[grepl("^--file=", args)]
this_file <- if (length(file_arg) > 0) sub("^--file=", "", file_arg[1]) else "scripts/01_extract_bnst_volumes.R"
this_file <- gsub("~+~", " ", this_file, fixed = TRUE... |
f1f5e3394aa98d0577f36616d9d0b93a6077a522b85d3b975db0ff5612d0505f | R | 2,967 | 107 | library(igraph)
####GRAPH filename
genedet='../OscopeData/GenesLabels/Mapping/Water123.txt'
netdirList=c('../OscopeData/dataAdjacencyMatrixe02/','../OscopeData/dataAdjacencyMatrixePiDiv9/','../OscopeData/FirstData/')
outdirList=c('../Communities/Networks02/','../Communities/NetworksPiDiv9/','../Communities/FirstData/'... |
06d729aa3e583af4e953f7214e0234e8fc92ed1c79af3380c9a92227c60033ea | R | 2,974 | 74 | ## packages for data table processing
library(tidyverse)
library(stringr)
library(RColorBrewer)
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)
F... |
df6e40f473f685d0a0bc652a47d0c477b4c180df7a80d6a8ea6f275030851192 | R | 2,974 | 87 | ---
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(gplots)
source('functions/mediation.R')
source('functions/med.plot... |
f7b93a0701526190c0d9ecea88c332daacc6d4ac458f1a59c78bafbe3bacf6ea | R | 2,976 | 73 | # compare with MOp
MOp = readRDS("../../merFISH_MOp/MOp_mouse1sample3.RDS")
SpatialPlot(MOp, group.by = "subclass" )
genes = intersect(rownames(MOp), rownames(merFISH_integrated))
# All
merFISH_integrated_L2_1 = merFISH_integrated
merFISH_integrated_L2_1 = NormalizeData(merFISH_integrated_L2_1[genes,])
merFISH_avg... |
7f888d918c4dc5384e66c2e017fae10d2d2082af09a02367598579b249cc94d4 | R | 2,989 | 135 | # 05/23/2023 Fengyao Yan
# apply NNLS deconvolution on the Gtex semi-silico data
library(nnls)
library(data.table)
library(pbapply)
mse_all <- NULL
pcc_all <- NULL
is_sens_test <- FALSE
data_folder <- '../../data/gtex'
data_raw <- '../../data/raw'
f_name <- paste(data_raw, '/selected_gtex_top_1_NT_1_fc_3_data.csv... |
b7f6bbba6d91e191c00688c2476ea9bfa814ca28429e7f6826a4ef94e8aa149a | R | 2,990 | 73 |
#' Write an output makefile for permutation testing
#'
#' Generate a makefile for running a specific analysis
#' @param prefix Prefix for output, to be prepended to outputs
#' @param resultnames Names of results from processvoxel. You can return multiple permutation statistics, with names, and they will be used to wri... |
581ec23be8aac7e4543123fcbeaba236696eab0db5fac115441d96fef51d7207 | R | 2,992 | 73 | #' @title Find cell clusters and retrieve significant expresion programs
#' @description Cell clusters are filtered on the basis of size, their corresponding expression programs are subsequently filtered on the basis of number of genes with sufficiently high fold-change values and sufficiently small p-values, and last... |
90b4cc698c07ff6e84644728960a447980fced83f73c3a37384b0d782c45101d | R | 2,993 | 69 | #' Write an output PBS submit script
#'
#' Generate an PBS submit script for the given workflow
#' @param prefix Prefix for output, to be prepended to outputs
#' @param resultnames List of names for the expected outputs
#' @param modelfile Name of the model file that contains the processVoxel command
#' @param designma... |
5d21dd21b6aa2b82a473f72d6a812d4e95253af97d39e821392c45dfa25e9a24 | R | 3,000 | 68 |
#' @title Plot State Hierarchy (Quadrant plot)
#' @description Plot state hierarchy
#' @param X two-column matrix or dataframe with x and y coordinates.
#' @param quadrant.names character vector to label quadrants. Default: c("bl", "br", "tl", "tr")
#' @param main plot title. Default: NULL
#' @param xlab x-axis title.... |
d552d5056e782f053ecd64773c83603f12be3b48cdcd90421820c241f9bff961 | R | 3,003 | 98 | #dir('../OscopeData/dataAdjacencyMatrixe02/',pattern='.csv')
rm(list=ls())
inputdir="../Communities/Networks02"
outputdir="../Communities/Networks02/Overlapping"
# namef1="W45fastgreedy.txt"
# namef2="W123fastgreedy.txt"
# nameOutAll="W123_W45_fastgreedyI.txt"
# nameOutSig="W123_W45_fastgreedySigI.txt"
# namef1="W45w... |
a51b3fdcea7a9bb23c38c36eba1fb7e15060d4d5977485cd577c04d7f440f644 | R | 3,007 | 77 | 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(ggsci)
library(cowplot)
library(here)
DATADIR='data/raw_data'
## make for this subdirs... |
e0eab95713a188e2b6add748d00067dcced07ad8ac04bc1d5dfddc5d2105ab08 | R | 3,014 | 103 | library(argparse)
library(Signac)
library(Seurat)
library(JASPAR2020)
library(TFBSTools)
library(BSgenome.Rnorvegicus.UCSC.rn6)
parser <- ArgumentParser(description = "process input arguments")
parser$add_argument("--rds", action = "store", type = "character",
help = "path to labeled rds file for integrated object")
... |
2dd5ed4a88079e2841bc206459dd0909de303721b88dec67f9da12aa20973a0b | R | 3,017 | 79 | ---
title: "R Notebook"
output: html_notebook
---
---
```{r}
library(ggplot2)
library(RColorBrewer)
library(tidyverse)
library(ggpubr)
library(ggsignif)
library(openxlsx)
library(viridis)
library(readxl)
```
```{r}
setwd("//atlas.uni.lux/users/isabel.rosety/GBA/8-OH-dG ELISA/")
#data <- read.csv("WB data for Rstudio ... |
a7f5f0a89bfb958b0692a30c661fbfdd3a82f3c98b7f92d054a2ad3b8912be62 | R | 3,017 | 84 | ---
title: "StemnesScoRe: An R package to estimate the stemness of glioma cancer cells at single-cell resolution"
author: "Necla Kochan and Gokhan Karakulah"
date: "2023-08-21"
output:
pdf_document: default
html_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Cancer stem c... |
8aa3afbab5e5a4a7dd2d3fd7e93d3b185d6fa2b333d4efc6b39e8c6234e7b3e3 | R | 3,022 | 80 | ---
title: "compile_library_design_files_05052020"
author: "Stephen P Plassmeyer"
date: "5/5/2020"
output: html_document
---
Takes the "bc_and_arrayNames.tab" and the "bc_and_arraySeqs.tab" files from Mike Reiger's original library design and concatenates them into a single .csv file with expected column names for oth... |
eb69fb33b8c624c3fbe8aae4b2b46aa928cdfe3adfddf914c7c75a97c48644a9 | R | 3,028 | 101 | # inspect MPRA data
suppressMessages(library('tidyverse'))
args<-commandArgs(trailingOnly=T)
dna_rna_cnt_fn<-args[1]
out_fn<-args[2]
wd <- 2
ht <- 1.5
fig.nrows <- 1
fig.ncols <- 3
pt <- 8
cex.general <- 1
cex.lab <- 1
cex.axis <- 1
cex.main <- 1
cex.legend <- 1
lwd<-0.5
plot_plasmid_expr<-function(dna_rna_cnt_fn,... |
72fcfce2ebf4f16e6c5b0cefdd68e5b731afa59866a9e39e87c09b9cb11595f6 | R | 3,034 | 114 | set.seed(321)
# Coordinates
x <- matrix(nrow = 10, data = c(seq_len(10), 10:1))
rownames(x) <- paste0("spot", seq_len(nrow(x)))
colnames(x) <- c("coord_x", "coord_y")
# Proportions
y <- replicate(m <- 5, runif(10, 0, 1))
y <- y / rowSums(y)
rownames(y) <- paste0("spot", seq_len(nrow(y)))
colnames(y) <- paste0("type", s... |
0d088407b2b0088f4ebd3461f6a407e192cc5b21f3cdd6c5dac4cad35697f561 | R | 3,042 | 120 | ####################################################
# LC project
# Script for downstream analyses: thresholding spots
# Lukas Weber, May 2022
####################################################
# module load conda_R/4.1.x
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
l... |
6e2c036e2230408371e09f8d6b5c938118a7a1b6155ef467c5dcc22261400108 | R | 3,046 | 108 | #!/usr/bin/env R
# Author: Sean Maden
#
# Plots of tissue by deconvolution reference.
#
library(ggplot2)
library(dplyr)
library(scales)
#----------
# load data
#----------
csv.name <- "table-s1_tissue-references.csv"
csv.path <- file.path("deconvo_commentary-paper", "data", csv.name)
csv <- read.csv(csv.path)
#----... |
5e9c9b41d37639c1a3b73c8971b96187829ec194993151590763dbcf20fb2740 | R | 3,048 | 86 | #-----------------------------------------------
# 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 ... |
189cbae57bd4f3ad11487617058f1461cc47521df3f3b2cb9b1ba2f9393994a1 | R | 3,051 | 79 | ## packages for data table processing
library(tidyverse)
library(stringr)
library(RColorBrewer)
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)
F... |
a6840a125d3b916e881c420fdc4efaf1ab16abd837e29b02a012969102d20824 | R | 3,058 | 91 | #KEGG Enrichment Analysis of the enriched genes
#this script uses clusterProfiler::enrichKEGG() function
#run locally on Ayushi's laptop
#load the required packages
library(org.Mm.eg.db)
library(clusterProfiler)
library(tidyr)
#set the working directories
indir <- paste0("~/Dropbox (Gladstone)/YH_NK01-Results/",
... |
112ee17ab76385e74f098b4c7ed4b294f93606d2487f7e4fc48e305a8e2dea54 | R | 3,060 | 80 | # Core function ----------------------------------------------------------
#' The core function, which converts the DICOM to NII, extracts the JSON information, opens the sequence mapper and converts the data to BIDS.
#'
#' @param user_settings_file The path to the user settings file from the "create_user_settungs()" ... |
90cc3333ee913c89cd5d4729a859cbd459e4b5aec3a5634ecdbb790f0edb2138 | R | 3,064 | 90 | suppressMessages(library(dplyr))
library(ggplot2)
library(argparse)
library(patchwork)
library(argparse)
parser <- ArgumentParser(description = "process input arguments")
parser$add_argument("--obs", action = "store", type = "character",
help = "path to file with observed results")
parser$add_argument("--perm", ac... |
072e9a61ac58012f06a30668542ab41621278ff03598e87bf8c8b963d957627c | R | 3,073 | 103 | #dir('../OscopeData/dataAdjacencyMatrixe02/',pattern='.csv')
library(igraph)
####GRAPH filename
genedet='../OscopeData/GenesLabels/Mapping/Water45.txt'
netdirList=c('../OscopeData/dataAdjacencyMatrixe02/','../OscopeData/dataAdjacencyMatrixePiDiv9/','../OscopeData/FirstData/')
outdirList=c('../Communities/Networks02/',... |
04e7d6aa2e39e1bf727be79476c431669e5d02d2cf4bd83892773eb9ae7be83e | R | 3,074 | 63 | #############################################################
# Generate Figure 2A
#############################################################
library(ggplot2)
library(scales)
library(ggpubr)
Input_data <- read.csv(".\\..\\..\\Data\\Manuscript_2D_Simulation_Pattern\\sim_MOB_pattern2_fc5_tau50_count_power1.csv... |
44a2720709b4062344e38ff17424f4fa739c539279210ef339faa6f8e9a62104 | R | 3,090 | 58 | library(tidyverse)
library(ggplot2)
library(ggpubr)
library(ggridges)
library(rstatix)
# Load results--------
results_intermediate_1dec <- data.table::fread('../results/MI_results/landmarks_10foldvalidation_withIntermediate_1000ep512bs_a375_ht29.csv')
colnames(results_intermediate_1dec)[1] <- 'fold'
results_intermedia... |
c5b0e0e57421f986e6350f2726d383b2cc03c4a04e931c2be2f44d921956c386 | R | 3,097 | 143 | ---
title: "WeberDivechaLCdata package"
author:
- name: Lukas M. Weber
affiliation: "Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"
- name: Heena R. Divecha
affiliation: "Lieber Institute for Brain Development, Baltimore, MD, USA"
package: WeberDivechaLCdata
output:
BiocStyle::html... |
27d5fd2f19dd9be5d1c18e858a8f8b991b7a3e84eb82a87038d1812ee022cc49 | R | 3,100 | 94 | sort_hclust <- function(...) {
as.hclust(
dendsort::dendsort(as.dendrogram(...))
)
}
plot_hc <- function(hc, cex = 0.8, mar = c(8,8,4,1)) {
withr::with_par(new = list(
cex = cex, mar=mar),
code = {
plot(as.dendrogram(hc))
})
}
#' hiearchical clustering for subclass
#' cpm: cell by features... |
2fae8ee4e7fe3c20cbf660e9a47bf8fdab8c0d2e1aae72b6e4251938aefc997e | R | 3,112 | 80 | library(boot)
## A function to form completed data by combining imputed data and non-missing data
fill_data <- function(impute = impute.dat0 , data = data.sub, im) {
cbind.data.frame(impute.transcan(x = impute,
imputation = im,
data = data,
... |
a352381f12c371fc9294062d9b4d28f86f066d2a94b3989090a04abcede1fa84 | R | 3,112 | 110 | # from 100 models trained using different sets of random seeds
# each time,
# do permutation
# for n = 1, ..., 100, get the average validation AUC from the first n models
library(ggplot2)
theme_set(theme_classic())
library(ggpubr)
library(pROC)
df_seeds = read.table("../cv_train_predict/ensemble_seeds.t... |
2920ff24c0bdba8330f429021857131002faa5eb75e38fd7236600e37d6ff005 | R | 3,113 | 115 | # 05/23/2023 Fengyao Yan
# Make the distribution plot for MSE change on CMP dataset comparing deep learning vs NNLS
library(ggplot2)
library(tidyverse)
library(data.table)
library(Metrics)
dat_pred <- fread('../../data/cmp/pred_cmp_nnls.csv', data.table = FALSE)
dat_true <- fread('../../data/cmp/true_cmp_nnls.csv',... |
37e4086a106e8f19e0fcc47c29db881609f7981535e6e9572711a07accb22942 | R | 3,124 | 82 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(rlang)
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(future)
## main differential gene expression package
library(glmGamPoi)
library(SingleCellExperiment)
library(DelayedArray)
... |
6ba0d57b6a6070f620e0988884c12f1364e566b253f24c67a432076a51ed94db | R | 3,129 | 74 | library(readr)
########################################
# Below are the variables that will be used in the model
########################################
######## HD ISS #############
# Create some logic to calculate and display HDISS stages based
# Read in phenotypic data from EnrollHD
TotalFunctionalCapacity_Score ... |
688e2564f8e99f5f472a7418b6e4a513a0d4ace4c5bd2b407414e83876cd69d4 | R | 3,132 | 82 | #' Write information that describes how npointillist was called
#'
#' Write information that describes how npointillist was called
#' @param args Arguments provided to npointillist
#' @export
npointWriteCallingInfo <- function(args) {
dir <- dirname(args$output)
if (!dir.exists(dir)) {
dir.create(dir, r... |
d006309594605b840467d538977097c4748d3bcb7b50fe9841e6640dc8b48f80 | R | 3,140 | 80 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(future)
## gene expression heatmap plots
library(Nebulosa)
ss <- function(x, pattern, slot = 1,... |
f6ad5da70047c8524991de6ddf1724f8067c6b6b5ca17c75d80ca8409dc4c596 | R | 3,140 | 80 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(future)
## gene expression heatmap plots
library(Nebulosa)
ss <- function(x, pattern, slot = 1,... |
7b5597683c13249f6c2e8e21531d872d276f737a69180bb493dc3d4a6e671ab4 | R | 3,148 | 62 | #' @rdname color
#' @title Core color mapping function for SpacoR.
#' @name assign_color
#' @description
#' Assign Colors to Clusters Based on Distance Matrix.
#' This function assigns colors to clusters based on a given distance matrix.
#' It supports colorblind-friendly options and can automatically generate a color ... |
75c74bd609fe74caf60b00776680d7fc44ba62b19d246f47cef7807aa70e6375 | R | 3,154 | 84 | 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(ggsci)
library(grid)
library(gridExtra)
## main Seurat package snRNA-seq pacakges
libr... |
ca15c6e9afe4911f2812f171551f6d5f904d49ebf56c146b5764bd449f96c4a0 | R | 3,157 | 80 | #' Converts gene names of query to match type/species of reference names (human
#' or mouse).
#'
#'Modified from Azimuth::ConvertGeneNames
#'
library(stringr)
convertHomologs <- function(gene.names, reference.names, homolog.table) {
query.names <- gene.names
linked <- homolog.table
# remove version numbers
... |
f7ff612ac49a031fc530ae46f82fa3ed11d85d146deb23676c39f6b9b30d4d4a | R | 3,157 | 121 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(lavaan)
library(semPlot)
source('functions/explained_variance.R')
source('functions/heatmaps.custom.R')
```
# Load Data
```{r}
vrf.cog.heart.brain.trad = read.c... |
76c0c1758c1934c4c90fd74b69260f23312edf5874936ae523aa2e62f8d4dbee | R | 3,159 | 74 | ---
title: "Pathways_normalized_Similarity"
output: html_document
---
```{r}
library(tidyverse)
#library(ComplexHeatmap)
library(pheatmap)
library(RColorBrewer)
#library(reshape2)
#library(janitor)
library(gapminder)
library(viridis)
```
```{r}
setwd("C:/Users/alise.zagare/Desktop/Modeling_final_2CTRLvs2MUT/Model_com... |
a6a46f736fd6ecbab3e51a43d3f6f9157ccf616166047a04d13f78ad21aab9b8 | R | 3,163 | 99 | ---
title: "Biolog_IPD"
output: html_document
---
```{r}
library(tidyverse)
library(ggpubr)
library(ggrepel)
library(viridis)
```
```{r}
setwd("P:\\IPD\\Biolog")
data <- read.csv("MaxRate_norm_forR.csv", header=T, sep=",")
```
```{r}
data %>%
group_by(Condition, Chemical) %>%
summarise(Value = median(Converted)... |
623ddae011f268da8408c3062074a55a9d15d84150c355f92c4210eca9ebd289 | R | 3,164 | 81 | ---
title: "MPRA Tools Expression Calculation Function Unit Test"
author: "Stephen P Plassmeyer"
date: "5/7/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../mpra_tools/mpra_tools.R')
```
# Initial Generation of Benchmark Data
Using the implementation of t... |
cae44b9672d50f9f408f9a328e1e6b517006cfb612cb61a8b2363f822ac78c8c | R | 3,182 | 96 | # 05/19/2023 Fengyao Yan
# This file is written to select TSG (tissue specific genes)
library(matrixStats)
library(tidyverse)
library(data.table)
data_folder <- '../../data/raw'
dat <- fread(paste(data_folder,'/mean_df.csv',sep=''), data.table=FALSE)
rownames(dat) <- dat[,1]
dat <- dat[, -1]
dat <- as.data.frame(t(... |
6a993e8943ab33c80562083ea7ba1bbce67b1436fca69b0ee06fcc4062dfa938 | R | 3,186 | 101 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(finalfit)
library(mice)
library(purrr)
library(dplyr)
library(lavaan)
library(ggplot2)
library(gplots)
source('functions/mediation.R')
source('functions/med.plot... |
4797ad8d7948bf6c3e3518fc272bfee9e9e9497889773d0f402bc333571cbe59 | R | 3,188 | 110 | # -----------------------------------------------------
# Slices a connectome matrix based on lookup tables
# -----------------------------------------------------
## Collect arguments
args <- commandArgs(TRUE)
## Default setting when no arguments passed
if(length(args) < 1) {
args <- c("--help")
}
## Help section
... |
6c575b46dae470f1d49624ea050e45d3cd7c997e0c3a195cb748cb00eaf4f35c | R | 3,189 | 97 | ---
title: "Ethoscope Analysis Pipeline (Yoked Controls) - Abhilash"
output:
html_notebook: default
html_document:
df_print: paged
pdf_document: default
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
Try ex... |
e960645609e224a5b4a866be2a6926aa626797b56cdba3bae9505be23ac79f6a | R | 3,194 | 77 | require(misha)
require(tidyr)
require(plyr)
require(dplyr)
gsetroot('/home/hpc/bonev/trackdb/mm10')
construct.grid = function(interv1,interv2,min_dist,max_dist){
return(ddply(interv1, .(chrom), function(i1) {
i2 = interv2[as.character(interv2$chrom) == as.character(i1$chrom[1]),]
if (nrow(i2) ==0) {
r... |
186fa4131d6b2e89cf4326a0a9eae675461c273e00d2d9f433efe99cfbdf4d33 | R | 3,199 | 85 | ===---
title: "R Notebook"
output: html_notebook
---
---
```{r}
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(tidyverse)
library(ggpubr)
library(ggsignif)
library(openxlsx)
library(viridis)
library(jcolors)
library(readxl)
```
```{r}
#setwd("//atlas.uni.lux/users/isabel.rosety/GBA/OrganoidSize/Plo... |
a4819e0f9a9794fff558e68531480a6b39715d8566a5c3b1292fccb5442d43eb | R | 3,200 | 60 | library(brms)
region.logINR.ROPE<-data.frame(matrix(NA,nrow = 360,ncol = 11))
colnames(region.logINR.ROPE)[1]<-"region"
colnames(region.logINR.ROPE)[2:11]<-paste("pbeta",substr(seq(.01,.1,by=.01),2,4),sep="")
region.p_grade.ROPE<-region.logINR.ROPE
a<-commandArgs(trailingOnly=TRUE)
hcpd_data <- read.csv("n925_hcpd_myel... |
d26931a5a68a80c7069a2b6c5abcd7fe7d2625c895acdfb41b251bf1bd09a8da | R | 3,200 | 103 | # rocprcurve.R: plot ROC and Precision-Recall curve using ROCR library
#
# Copyright (C) 2014 Dongwon Lee
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the Lice... |
67ef3763b0d4275a26885ecdad480701eb45444343c5a20ff58d83b936e3ecb1 | R | 3,204 | 99 | library(tidyverse)
library(glmnet)
library(purrr)
library(ggplot2)
library(ROCR)
library(PRROC)
models <- readRDS("data/models_all_bootstrap.rds")
colnames(models)[1] <- "Celltype"
models$lognormalized <- NULL
#==================================================================================================
# Load hy... |
d1a303a3a1c8ae75bfd16004be426992afeec4a4193117d9b14f4d00008475dc | R | 3,205 | 77 | #' Plot gistic results as a bubble plot
#' @description Plots significantly altered cytobands as a function of number samples in which it is altered and number genes it contains. Size of each bubble is according to -log10 transformed q values.
#' @param gistic an object of class \code{GISTIC} generated by \code{readGis... |
dca7d5897e6f439196c48b70e7605b85789b76da10b72c30886050ab59727901 | R | 3,208 | 137 |
#' @keywords internal
x_in_y_overA = function(A = 0.1) {
function(x, y) {
x = sort(x, decreasing = T)
y = sort(y, decreasing = F)[1:length(x)]
sum( x >= y ) >= A
}
}
#' @keywords internal
kOverA <- function(k, A=100, na.rm = TRUE) {
function(x) {
if(na.rm)
x <- x[!is.na(x)]
... |
fe45ce0aef8b64a7502407681fef99da3b0c3d526332813d1ed8bec615648f48 | R | 3,208 | 96 | library(lavaan)
library(tidyr)
processVoxel <- function(v) {
Y <- voxeldat[,v]
adf <- data.frame(idnum,sex,pds,age,time,target,domain,Y)
data <- aggregate(adf, by=list(adf$idnum, adf$time),FUN=mean)
data$idnum <- data$Group.1
adf <- data
adf <- adf[,c("idnum", "sex", "pds", "age", "time", "Y")]... |
a5cea25c7eb1ce7c830441f8f5440721f73e418331f98d8d65e0e6e38e02e232 | R | 3,210 | 122 | #######################################
# LC analyses: number of cells per spot
# Lukas Weber, Aug 2023
#######################################
# module load conda_R/4.2
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
library(SpatialExperiment)
library(here)
library(dplyr)... |
df39a2dff47d7f97ac7a2e872d532750ef2acbba59aab30111aa44435704afa7 | R | 3,210 | 123 | ##################################################################
# LC snRNA-seq analysis pipeline: testing some alternative methods
# adapting code from Matt N Tran, June 2022
# Lukas Weber, June 2022
##################################################################
# run in interactive session on JHPCE
# screen -... |
59255db98b8613c18b153b6bc9b4181adbc6e9fb685aa887a71ab4a8a2884e75 | R | 3,213 | 85 | # This type of error handling will not always work if run using the parallel library
# It should be run using the --sgeN flag
# We need nlme
library(nlme)
processVoxel <-function(v) {
Y <- voxeldat[,v]
# Time as a fixed effect
e <- try(mod.fixedtime <- lme(Y ~ age + domain + target, random=~1|idnum, method=c("ML"), ... |
131fb3896e6317103f031fcd71066275d4dd88d5c2769f3cf951044cd1c8c3fc | R | 3,215 | 129 | ---
title: "Getting started with ggrepel"
author: "Kamil Slowikowski"
date: "`r Sys.Date()`"
output:
prettydoc::html_pretty:
theme: hpstr
highlight: github
toc: true
mathjax: null
self_contained: true
vignette: >
%\VignetteIndexEntry{ggrepel examples}
%\VignetteEncoding{UTF-8}
%\VignetteEngi... |
bad34b4cbc61236e0f7c03b6163e1e228ee4e2e3c037983b5a82b84b90bbafe9 | R | 3,223 | 93 | # REQUIRES COMPUTE CLUSTER TO RUN
library(tidyr)
library(Seurat)
library(Matrix)
#==================================================================================================
print("Load Exercise 10x Data (v3 and v2 Combined")
pb <- readRDS("./data/hypo.integrated.final.20210719.RDS") # 8 samples, 40064 cells
p... |
259627a3d5496deea9ee3b5c8fa82dbfaf40f1007a630a8a6f0a17fd2112c618 | R | 3,227 | 70 | ---
title: "Mapping BC Info to Sequence"
date: "May 13, 2021"
author: "Stephen P Plassmeyer"
output: html_notebook
---
```{r}
library(tidyverse)
library(data.table)
```
```{r}
apply.values <- function(d, # data.table, table of MPRA records
values,# data.table, table with value l... |
7444fae9559a7d99e635b71bb0ded816db7e0f78fd9d846e6351adaf3c937a77 | R | 3,240 | 98 | # inspect MPRA data
args<-commandArgs(trailingOnly=T)
plasmid_cnt_fn<-args[1]
mrna_cnt_fn<-args[2]
out_fn<-args[3]
wd <- 2
ht <- 1.5
fig.nrows <- 1
fig.ncols <- 3
pt <- 8
cex.general <- 1
cex.lab <- 1
cex.axis <- 1
cex.main <- 1
cex.legend <- 1
lwd<-0.5
plot_plasmid_expr<-function(plasmid_fn, mrna_fn, titletxt, exp... |
fcf74dca3bcebe3f15c272d9b41b75612c98565a779363d821c7139ae8f010f6 | R | 3,240 | 73 | #!/usr/bin/env Rscript
#You can install the permute package by running:
# install.packages('permute')
library(permute)
message('Loading data...')
#You must have already set up the target model so we can work with the debug
#data (as specified in the readargs.R file)
#The next line presumes you've set your working di... |
3b58ea6ef2c043051de792134a61dffdd704bee30f748fe5e46676b624374d96 | R | 3,241 | 101 | #' @export
setTrackNode <- function(attrkey,
name,
attrPath,
altColor = "0,0,178",
auto.scale = "false",
fontSize = 15,
height = 20,
maximum = 10... |
ed4b8a6537268350f14d258080e3e9fb1b6fd10addad03e7c89433c8cf852ad0 | R | 3,241 | 120 | #X = number of successes
#n = number of trials
#alpha = pvalues threshold
#Details: https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Agresti-Coull_interval
estimate_binconf = function(X, n, alpha = 0.05){
z = 1-(alpha/2)
zsqrd = z^2
nt = n + zsqrd
#pr = X/n
pr = (1/nt) * (X + (zsqrd/2)... |
97018dc2277310b0ff83a84ce745c03a1f65b0379d83660565b6280de1743b1a | R | 3,251 | 91 | library(survcomp)
compute_cindex = function(data, pred_col, time_col, event_col){
x = data[[pred_col]]
time = data[[time_col]]
event = data[[event_col]]
weights = rep(1, dim(data)[1])
#strat = rep(1, dim(data)[1])
cindex = concordance.index(x, surv.time=time, surv.event=event,
... |
40ac2e22cb91190c393c2407c4c208471c76dbb909146f63074327ad3b4f51c7 | R | 3,255 | 82 | topGO_wrapper = function(geneScores,
geneScoresDE = FALSE,
geneScoresDirection = c("Up","Down",NA),
GOmapping,
algorithm = c("classic", "elim", "weight", "weight01", "lea", "parentchild"),
statis... |
d39863eca4bef0b628767d115345d1c709f856ec59e72265d0e635431e130e3b | R | 3,262 | 84 | #' Drug-Gene Interactions
#' @description Checks for drug-gene interactions and druggable categories
#' @references Griffith, M., Griffith, O. L., Coffman, A. C., Weible, J. V., McMichael, J. F., Spies, N. C., et. al,. 2013. DGIdb - Mining the druggable genome. Nature Methods.
#' @details This function takes a list of ... |
3da076c73fb0081555404e6433b09b548bac91e846f842ae6d1716581457c749 | R | 3,264 | 123 | ####╔═══════ ═══════╗####
####💠 Package Manager 💠####
####╚═══════ ═══════╝####
cli_h2("┗ [PACKAGES] Loading external {.file _dependencies.yml} file")
dependencies <- load_yml(here("_dependencies.yml"))
options(
renv.config.snapshot.inference = FALSE,
renv.config.snapshot.validate = FALSE,
renv.confi... |
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