sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d18e0df2765508e3a8df01fd8e2600eac25c01f1dbdeef5f63deefa2bdf13e66 | R | 2,525 | 72 | #' Merge multiple mafs into single MAF
#' @description Merges multiple maf files/objects/data.frames into a single MAF.
#' @param mafs a list of \code{\link{MAF}} objects or data.frames or paths to MAF files.
#' @param verbose Default TRUE
#' @param ... additional arguments passed \code{\link{read.maf}}
#' @return \cod... |
8f8efe832c0e7f6c012d27fa19560faf2bca8f7bfbacc0ba7e1decebd25d57d8 | R | 2,533 | 115 | ---
title: "LC analyses: cells per spot"
author: "Lukas Weber"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
toc_depth: 2
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
```
# Introduction
Investigat... |
a72fea9a696360d235edeb23c9d16c19149c4288e088166cc581f056c3647e77 | R | 2,539 | 67 | 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)
library(msigdbr)
DATADIR='data/tidy_data/compare_to_Alzheimers_stud... |
b85ae05c7d64c8076af363d69edb650685fe7dec8ff316f2f6762ddf110c5d63 | R | 2,541 | 118 |
plot_log2_fpkm_dist = function (fpkm_files) {
num_files = length(fpkm_files);
data_list = list();
max_y = 0
xlim = c(0,0)
for (i in 1:num_files) {
file = fpkm_files[i]
data = read.table(file, header=F)
data = data[,6];
data = log2(data+1)
den = density(data);
data_list[[i]] = den
... |
420d519ea9888d7f9ebb1676bca577421fbbfe245cf115a7b0f2342ae8650da4 | R | 2,542 | 64 | #' Filter MAF objects
#'
#' @description Filter MAF by genes or samples
#' @param maf an MAF object generated by \code{\link{read.maf}}
#' @param tsb remove these samples (Tumor Sample Barcodes)
#' @param genes remove these genes
#' @param isTCGA FALSE
#' @return Filtered object of class \code{\link{MAF-class}}
#' @see... |
9f726138fbc73e89d462e700f0c8e9bddb60431954acbffb6e34defaff51c1d2 | R | 2,545 | 64 | "pwr.2p.test" <-
function (h = NULL, n = NULL, sig.level = 0.05, power = NULL,
alternative = c("two.sided","less","greater"))
{
if (sum(sapply(list(h, n, power, sig.level), is.null)) !=
1)
stop("exactly one of h, n, power, and sig.level must be NULL")
if (!is.null(h) && is.character(h))
... |
45ca4d26a6a53398974b5348a7819e657acc5c788b3721ac3a5d80114d0d2477 | R | 2,548 | 73 | ##Create tables for info by cluster for lmmp
##Create tables for NMF pattern definitions and usage
##Create tables for info by cluster for gut cell atlas (nfh subset)
library(tidyverse)
library(monocle3)
library(here)
### lmmp subset metadata----
lmmp <- readRDS(here("TC_LMMP.rds"))
# pData(lmmp) <- pData(lmmp) %>... |
c03799a91b1f285c763d95ac155c54eecc1a91b13dfa4d02f204336231c7039a | R | 2,555 | 73 | #' Transform the 'phyloseq' object of 'phyloseq' package to 'microtable' object of 'microeco' package.
#'
#' @param physeq a phyloseq object <doi:10.1371/journal.pone.0061217>.
#' @param ... parameter passed to microtable$new function of microeco package, such as auto_tidy parameter.
#' @return microtable object.
#' @e... |
8036594a3ce4cb895e7b6b7e18c57a026852adc9aa870d07882a1e66371ab7d0 | R | 2,556 | 54 | #install and load packagaes
install.packages("ggplot2")
library(ggplot2)
install.packages("ggrepel")
library(ggrepel)
install.packages("dplyr")
library(dplyr)
#read in input data - DEG by DESeq 2 output between 2 contrasts (ex. med vs low liver PAM)
de_genes <- read.csv("DE_results_liver_MPvsLP.csv")
#check ... |
f6f4158003625b0938404ce7ec5250097a36d1447bb8696ee5632548d60038a5 | R | 2,560 | 78 |
invnorm <- function (x) {
y = (rank(x,na.last="keep")-0.5)/sum(!is.na(x))
return (qnorm(y))
}
dg = read.delim ("Exc-DG.combined.modalities.230623.txt")
threshold = 40
# select out the transcripts we are interested in
x = dg[dg$rna.logp > 1.3 & abs(dg$rna.log2fold) <= 2 & !is.na(dg$rna.logp) ,]
x$mut.b6.strict = 0
x... |
31dd544c6eb96f31675dd07e3d3be549571f63651abccd34b14904ad92fbc3ed | R | 2,562 | 68 | # from Lorena Pantano's original template: hbc_kressler_sRNAseq_anxiety_brain_mouse_hbc02185
# run:
# interactive session with 20G of RAM
# cd to project/final/project
# conda activate r
# Rscript 01.bcbio2sse.R
library(SingleCellExperiment)
library(Matrix)
library(AnnotationHub)
library(tidyverse)
species = "Macaca... |
eec29e5dfd489bdd0501b01dbbfdd5d37c01f4e3a15df1c50e3293e9853c26f4 | R | 2,563 | 74 | library(tidyverse)
library(Seurat)
options(Seurat.object.assay.version = "v5")
options(future.globals.maxSize = 5e9)
library(SeuratObject)
library(Matrix)
Sys.setenv("_R_USE_PIPEBIND_" = TRUE)
library(future)
projdir <- here::here()
rdir <- file.path(projdir, "package/R")
import::from(.from = "cembav2env.R", .director... |
65b5720a8a5d9f6fe3268cef5b94e36b208760f267f7aa40f9b0f7b9d4ff6922 | R | 2,564 | 69 |
# This code is to calculate time of day effect on the MRS data for sub2
# WANG. 25-April-2023
# Remove all objects created before to prevent clusing
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Research_projects/BBSC/MRS/Output_v2_4/QuantifyResults")
# create a ... |
b6236357b67610cc10dbb8f91a1369d1cf0c32b0f0af4a7205e6b9310f30c936 | R | 2,566 | 70 |
# This code is to calculate time of day effect on the MRS data for sub1
# WANG. 25-April-2023
# Remove all objects created before to prevent clusing
rm(list=ls())
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Research_projects/BBSC/MRS/Output_v2_4/QuantifyResults")... |
86573946198b750b4f0a29d9461928967eda1d94fd0bee0d3438c295d653316e | R | 2,570 | 68 | library(ape)
library(geiger)
library(phytools)
library(evomap)
library(caper)
library(ggplot2)
library(nlme)
library(stats)
library(DescTools)
setwd("your directory")
##Import tree and data
data<-read.table("final_compilation with percentage values.R1.csv",header=T,sep=",",row.names=1)
data<-data[whic... |
aaad80b23876b95b5dcc2f626e0d1954f92ce9873fbcc5b4e98bc51e0d853a34 | R | 2,573 | 71 | #!/usr/bin/env Rscript
# I'm not currently using this script, because I'm interested in sumdiv and
# these summaries aren't actually all that informative.
# But, if you want to use them the following targets may be helpful:
# @nest.add_target_with_env(localenv)
# def test_sumrep(env, outdir, c):
# test_sumrep_pat... |
7ccfa64996a16fb135dc9c7448bad79ca8dfa2b6be852dec3a29fd22c4898313 | R | 2,577 | 81 | # merFISH pipeline
library(Seurat)
library(ggplot2)
library(data.table)
library(SeuratData)
library(dplyr)
library(DoubletFinder)
merFISH_pipeline = function(dir, proj){
# generate expr matrix
expr1 = fread(paste0(dir, "/countsPerCellCorrectedIn.csv"), header = F, data.table = FALSE)
expr2 = fread(paste0(dir... |
25fd6c2e7c93d0b5bf2ee3579bfd6f27093cfcbcb6a7df41a6175f42e777a639 | R | 2,582 | 37 |
#' @title Principal Component Analysis
#' @description A wrapper for irlba::prcomp_irlba(). Performs efficient computation of a principal component analysis of a given data matrix.
#' @param m A numeric matrix or data frame.
#' @param npcs The number of principal component vectors to return. Must be less than 'min(d... |
265e15ac4f3951cd2f77f1028f9c64c59b44d929816923709d55799841759871 | R | 2,582 | 71 | ## 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... |
59c9a5fcae597cf61c3c8a560dac8f863a7723c584b25c79b410d090cb99949c | R | 2,594 | 53 | #' Plots results from \code{oncodrive}
#'
#' @description Takes results from \code{oncodrive} and plots them as a scatter plot. Size of the gene shows number of clusters (hotspots), x-axis can either be an absolute number of variants
#' accumulated in these clusters or a fraction of total variants found in these cluste... |
962dc8c1515d3e991443681168cf3575ac1fa14e335132caa206f46847a800ec | R | 2,596 | 94 | #' @export
splitByChunk <- function(nondecVec, chunkSize = 2012) {
n <- ceiling(max(nondecVec) / chunkSize)
r <- lapply(seq(n), function(i) {
left <- chunkSize * (i-1)
right <- chunkSize *i
index <- (nondecVec > left) & (nondecVec <= right)
if(sum(index) < 1) {
return(NULL)
} else {
... |
24e030212d31ce30119a3d20ac4e0a5e10d7ad1727b70f313e5e806f214ece85 | R | 2,599 | 64 |
# function taken from TCGAbiolinks package
#' @title Get hg19 or hg38 information from biomaRt
#' @description Get hg19 or hg38 information from biomaRt
#' @param genome hg38 or hg19
#' @param as.granges Output as GRanges or data.frame
#' @importFrom biomaRt getBM useMart listDatasets useEnsembl
#' @export
getGRCh <-... |
2d99d22a5e268be267e07cebfdcf55acc5da07f75218a19ad077243ec6623fa9 | R | 2,599 | 68 | #### Check correlation of patterns with cell features
#### One hot encoding method.
### For each pattern, compute correlation between two vectors. One is continuous (cell pattern weight)
### and one is binary (yes/no is that cell in that group)
library(ComplexHeatmap)
library(circlize)
#as implemented, features shoul... |
2ea1f1602781c731304d216d88acb8474c7a024fa4c8c25ef7bd4cd94d3af492 | R | 2,599 | 78 | library(pls)
library(mvdalab)
library(dplyr)
library(haven)
library(rlist)
options(max.print=1000000)
rm(list = ls())
# load SPSS data set
UJ <- read_sav("http://fizyka.umk.pl/~tpiotrowski/complexity/UJ_gf_complexity.csv")
# sex==1 males, sex==2 females
# for overall group (males+females), the foll... |
2f5c82dabfbcff8a3016be52fc1fe6cbfdb45eb693b84fa3514344e843cbdf38 | R | 2,600 | 56 | #!/usr/bin/env Rscript
# Usage:
# Rscript --vanilla GetDivergences.R file1.csv file2.csv [out_filename]
# If out_filename is not specified, it saves the divergences to "divergences.csv"
# Note that this does full NN distance calculation, an O(n^2) operation, so don't
# try this for anything too big!
args <- command... |
5fc45a439bbf9efed5950418836de3195b1a9bffe94fed6d9881d5c83812610d | R | 2,601 | 81 | #-----------------------------------------------
# LICENSE
#-----------------------------------------------
# Copyright 2018 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 ... |
b6051c512a53258109e4644b823d09da10daaae1901d19c779703c6b5fd98145 | R | 2,601 | 63 | ## Defines colors used for reference cell types plotting
pbmc_colors = c("B" = "#66C2A5",
"DC" = "#FC8D62",
"HSC" = "#8DA0CB",
"MK" = "#E78AC3",
"Mono_CD14" = "#A6D854",
"Mono_CD16" = "#f2ec72",
"NK" = "#62AAEA",
"T_CD... |
2db3f8fa0ddf5934ef32f78c8ca97646a9455ba10f106cf74a4eeae967863bdd | R | 2,608 | 77 | #' Replace the names use match table
#'
#' @param match_table default NULL; character or data.frame; matching table used.
#' @param abund_new default NULL; data.frame; the abundance table used.
#' @return new abundance table.
check_match_table <- function(match_table = NULL, abund_new = NULL){
# read according to the ... |
602b8f32c2c36f97baa17d6987aad13f1384d5c145fa604f5d10aa54f048fed0 | R | 2,608 | 66 | #' @title Functional enrichments
#' @description Functional enrichments by hypergeometric test
#' @param test_gene_sets (list of) character vector gene IDs
#' @param ref_gene_sets (list of) character vector gene IDs
#' @param universe all genes considered. If NULL, uses all genes in test_gene_sets. Default: NULL
#' @p... |
3acf26cd39e58213caf28507c5fd4640ff5df4f24326ce0ba0b19d6b7078bb06 | R | 2,609 | 64 | # SCRATCH1=/mnt/g/scratch
# docker run -it \
# --workdir $HOME \
# -v /mnt/g/ckd:$HOME/project \
# -v /mnt/g/cellranger_atac_counts:$HOME/cellranger_atac_counts \
# -v /mnt/g/reference:$HOME/reference \
# -v $HOME:$HOME \
# -v $SCRATCH1:$SCRATCH1 \
# -e SCRATCH1="/mnt/g/scratch" \
# -v /mnt/g/scratch:$HOME/scratch \
# ... |
1d8c1f3e6a70d5cfb8201f26895214dc4aae5aff9bd254a870c6e9fa567e1a75 | R | 2,613 | 65 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
library(ggpubr)
library(data.table)
library(fgsea)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors ... |
97a4d8c920b9708dacb78f3cba67f7851802b921e08e4243bb1a174919a2ba7f | R | 2,619 | 65 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
library(ggpubr)
library(data.table)
library(fgsea)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors ... |
98cf84a35b2cdffaf0e92a1f472f84395520005e8f4b2fa61e29bb06f455c400 | R | 2,619 | 71 |
# This code is to calculate time of day effect on the MRS data for sub3
# WANG. 25-April-2023
# Remove all objects created before to prevent clusing
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Research_projects/BBSC/MRS/Output_v2_4/QuantifyResults")
# create a ... |
85b3c860be3c0160e8beb15a7c27a8b450e0b71f24f20b7389add1d05402dcf2 | R | 2,626 | 104 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(lavaan)
library(semPlot)
library(paran)
library(factoextra)
library(RColorBrewer)
source('functions/explained_variance.R')
source('functions/heatmaps.custom.R')... |
f206577c14f48bcfbe17835be0c1ebaacc4fe2786158a37d7d8a716b4980cb42 | R | 2,633 | 72 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RRHO2)
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/... |
a806dd519d48449fe52bf2dd0e33b92243dd2ce664c00e9e6cecc6fee793cd8b | R | 2,635 | 86 | # 05/23/2023 Fengyao Yan
# Make a dot plot to show the change of pcc for NNLS cmp 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/cmp/pcc_all_cmp_nnls.csv', row.na... |
7e728423ca402c77035d1b900c1487e2f46b41424c69228aa05448b9f82e0a47 | R | 2,637 | 103 | # install.packages("ggplot2")
# install.packages("tidyverse")
# install.packages("ggsignif")
# install.packages("RVAideMemoire")
# install.packages("lawstat")
# install.packages("ARTool")
library(ARTool)
library(lawstat)
library(ggplot2)
library(tidyverse)
library(ggsignif)
library(RVAideMemoire)
library... |
4e1269a11b964cc1421544f4d8d72192fcef15507cc94e9265164536cf22f05e | R | 2,642 | 99 | set.seed(321)
x <- replicate(m <- 25, runif(10, 0, 1))
# Add an anticorrelated column
x[, 24] <- seq(0, 1, length.out = 10)
x[, 25] <- seq(1, 0, length.out = 10)
rownames(x) <- paste0("spot", seq_len(nrow(x)))
colnames(x) <- paste0("type", seq_len(ncol(x)))
.checks <- function(p) {
expect_is(p, "ggplot")
expec... |
4a74c3844b44e5614569d4a473faacafb1e00df18aa764e2a284bfe8030b3f5b | R | 2,656 | 93 | ---
title: "Figure 1"
author: "Joe Raymond"
date: "April 2, 2021"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(tidy = TRUE)
knitr::opts_chunk$set(eval = TRUE)
knitr::opts_chunk$set(fig.width = 8)
knitr::opts_chunk$set(fig.height = 4)
knitr::opts_chunk$se... |
948d064f45ddec2dcb22896446918fa2ae73cfcb0bb091fc5a233f1f608fa20f | R | 2,663 | 89 | ---
title: "MG A549 experiments - volcano plots"
author: "Pierre-Luc Germain"
date: "3/1/2022"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
---
```{r}
suppressPackageStartupMessages({
library(SummarizedExperiment)
library(sechm)
library(ggplot2)
library(cowplot)
library(C... |
2e8ca16fc6dde6ba579f1afc6332bd7bfaaa45ad18f411a84eaf291a8916c3f9 | R | 2,664 | 117 | ## approximate equality
#' gmap wrapper
#'
#' @rdname ggmap
ggmap = function(m, ...) {
gmap(reshape2::melt(as.matrix(m)), ...)
}
#' sapply wrapper
#'
#' @rdname Sapply
Sapply = function(X, FUN, ..., simplify = FALSE, USE.NAMES = TRUE) {
sapply(X, FUN, ..., simplify = simplify, USE.NAMES = TRUE)
}
about_equa... |
1b8657e7f34c4a7443f89fe9112aa99aab930590e64dfa8d8cc5cf5d0c6cac23 | R | 2,665 | 67 | #############################################################
# Generate Figure 4D
# Plot 3D plots of Discrete 3D patterns
#############################################################
library(plotly)
library(scales)
library(plot3D)
InputData <- read.csv(".\\..\\..\\Data\\discrete\\Discrete_900by10_width2_qt88_N... |
4afa9dcb00cb1bfc2c1556b8550486ef4807d1dd628abde0db52794af06fd531 | R | 2,668 | 95 | library(igraph)
####GRAPH filename
genedet='../OscopeData/GenesLabels/Mapping/SVZq.txt'
netdirList=c('../OscopeData/dataAdjacencyMatrixe02/','../OscopeData/dataAdjacencyMatrixePiDiv9/','../OscopeData/FirstData/')
outdirList=c('../Communities/Networks02/','../Communities/NetworksPiDiv9/','../Communities/FirstData/')
fo... |
673c61ec76dacbff97e01b418039c42ed163f7116f61722a0ad7729fe88bee23 | R | 2,670 | 64 | #' Generates count matrix of mutations.
#'
#' @description Generates a count matrix of mutations. i.e, number of mutations per gene per sample.
#' @param maf an MAF object generated by \code{\link{read.maf}}
#' @param includeSyn whether to include sysnonymous variants in ouput matrix. Default FALSE
#' @param countOnly ... |
d2a84466620dccbd0a0664c901a65d92dcae425779e8d5c57d35c365a606ab91 | R | 2,671 | 103 | # install.packages("ggplot2")
# install.packages("tidyverse")
# install.packages("ggsignif")
# install.packages("RVAideMemoire")
# install.packages("lawstat")
# install.packages("ARTool")
library(ARTool)
library(lawstat)
library(ggplot2)
library(tidyverse)
library(ggsignif)
library(RVAideMemoire)
library... |
e670712415a22b766284843b1622870b5273d816b80bc96746d42985faada234 | R | 2,671 | 102 | # load data from the MSK-IMPACT 2017 dataset
library(tidyverse)
library(fs)
source("scripts/utils.R")
Sys.setenv(VROOM_CONNECTION_SIZE = 500000L)
# Load data ---------------------------------------------------------------
# process command line arguments (1st argument = selected cancer type)
args <- commandArgs(T... |
180c59f53dfeeab053ef35de04e4c8eb6dd8be08bc49dad344571aacb4ffe30d | R | 2,673 | 93 | #source("https://bioconductor.org/biocLite.R")
#biocLite("Mus.musculus")
#biocLite("Homo.sapiens")
#biocLite("categoryCompare")
#biocLite("KEGG.db")
require("categoryCompare")
#require("Homo.sapiens")
require("Mus.musculus")
require("KEGG.db")
do.GOEA = function(cls,univ) {
univ = subset(select(Mus.musculus, keys=... |
1e8ea856261d1f4c80b1d8bf70a5228516b5d626604c96d93c92a5f2a526f4dd | R | 2,674 | 107 |
# 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... |
a6a8d346caebb8d2fd913f803e948e1e5a2268a259efdc860506f4e9de488ddd | R | 2,676 | 115 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(finalfit)
library(mice)
```
# Load Data
```{r}
brain.mri = read.csv("data/brain.mri.data.csv")
brain.tracts = read.csv('data/brain.tracts.csv')
```
# Prepare B... |
aab8abb5dd35d33051f96d39e7d8f91738921786e32be195d34e0b137aef9344 | R | 2,682 | 78 | # figure qc snATACseq
# supplemental figure qc metrics and lineage markers
library(Seurat)
library(Signac)
library(here)
library(ggplot2)
# create a plot dir
plots <- here("ckd","figures")
dir.create(here(plots))
atac <- readRDS(here("ckd","atac_aggr_prep","step6_atac_loy.rds"))
plotA <- VlnPlot(atac, features = "... |
78e57ef04699223542f38fe06f12f2138c88cca132a6d86d957367233182be4f | R | 2,683 | 102 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(finalfit)
library(mice)
library(purrr)
library(dplyr)
```
# Load Data
```{r}
covs.final = read.csv('data_derived/cleaned/covs_cleaned.csv')
vrfs.final = read.csv... |
e4c01e10361f2162756613b4c86f708d9e25526d2ee464886bfee129f3a966a9 | R | 2,683 | 91 | source('queryGo.R')
library(igraph)
netdir='../OscopeData/dataAdjacencyMatrixe02/'
#filenet='WaterfallbootstrapSequential_CPUDPSF_TF_2000Boot_data_s45res_C28G8442_edgeNetwork.csv'
filenet='SVZbootstrap_CPUDPSF_TF_2000Boot_data_aNSC_C32G7883_edgeNetwork.csv'
Thresh=8
Dircom='../Communities/Networks02/'
#file='Go_W45fast... |
1a8bda6f0473a02ea3f34632dd0f9ce10d10d129cc5a5e52ca184331441b760f | R | 2,685 | 70 | # singularity exec --bind /lab-share/RC-Data-Science-e2/Public/Daniel/p147-scRNAseq-Koehler:/data/,/lab-share/RC-Data-Science-e2/Public/DST_software/pipeline_R_snapshot/:/snapshot/ r-monocle3:1.0.0--r42h9f5acd7_3 R
snapshot <- "/snapshot/r4.2.3-20230324-bch-dst-v1"
.libPaths(snapshot)
library(Seurat)
library(monocle3)... |
6fab254e85a462b1e79d9392101cbff1f5bd80d4464ee0baad542314aec94702 | R | 2,686 | 57 | #install packages/load libraries
install.packages("DESeq2")
library(DESeq2)
install.packages("tidyverse")
library(tidyverse)
#if having trouble with DESeq2 installation in R 4.2.1, try:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DESeq2")
library("DESeq... |
e64f863c83716e3169b091b85ee6da02acd5cbe9a9e3e515c1365fd0a2bd594e | R | 2,686 | 107 | ---
title: "Cell cycle classification"
author: Jared Slosberg
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}
library(monocle3)... |
50518757689560beb95f593e5764927cdd94a78ee83de1d28abe7c585ad2c2d0 | R | 2,687 | 98 | 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)
projdir <- here:... |
26d34fda1d6f8c590861d00442d92f1e9077484b8571b93f13800d663a74910d | R | 2,688 | 94 | # install.packages("ggplot2")
# install.packages("tidyverse")
# install.packages("ggsignif")
# install.packages("RVAideMemoire")
# install.packages("lawstat")
# install.packages("ARTool")
# install.packages("effsize")
library(ARTool)
library(lawstat)
library(ggplot2)
library(tidyverse)
library(ggsignif)
library(RVAid... |
80609329fdd0e71ad7da53a80771211845fa925d2ba18c09ce1c4a268d90d58c | R | 2,705 | 108 | ---
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... |
5f4fbba00e7aa7c22c5036f56fc9b3630955de44a6a0a9154bc69c1e55f34c9f | R | 2,707 | 99 |
plot_counts_matrix_log2_dist = function(matrix_file) {
data = read.table(file=matrix_file, com='', row.names=1, header=T)
conditions = colnames(data)
colors = rainbow(length(conditions))
plot(density(log2(data[,1])), col=colors[1], main=matrix_file, xlab='log2(frag_counts)', ylab='density')
for (i in 2:len... |
885d64c7d879b649df0e3a44deed5e2e65bb8f9015cec912fd8ad3bbac940d29 | R | 2,707 | 96 | ---
title: "Marker identification"
author: "Sergey Naumenko"
date: "`r Sys.Date()`"
output:
html_document:
code_folding: hide
df_print: paged
highlight: tango
theme: default
number_sections: true
toc: true
toc_float:
collapsed: true
smo... |
97a5b0989a0487d301226eaf29b285b703a50c5b14276fd950f5622d7cc77225 | R | 2,712 | 79 | # figure kpmp qc and dotplot
library(Seurat)
library(Signac)
library(here)
library(ggplot2)
# create a plot dir
plots <- here("ckd","figures")
dir.create(here(plots))
rna <- readRDS(here("ckd","rna_aggr_prep","kpmp","kpmp_prep.rds"))
plotA <- VlnPlot(rna, features = "nCount_RNA", group.by = "specimen_id", pt.size =... |
7e74054d5d17c1846eb8e890b48698c02a139c55d148ad38ec3f946fa6d928d2 | R | 2,725 | 78 | # 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(... |
e3a79f54824aaf03b03e7222ea2ea7ee3d4cfeb5d77e748919732984fed72a09 | R | 2,726 | 81 | #############################
# Create metadata spreadsheet
#############################
# -------------------------
# Metadata for each dataset
# -------------------------
# Spatially-resolved transcriptomics (SRT) dataset
# saved as SpatialExperiment object
df_Visium <- data.frame(
Title = "WeberDivechaLCdata_V... |
b2c19022aec19991759cec2cebe433eb8a5a10e7b9e643f8cd67474b1c7efe3f | R | 2,729 | 54 |
# contributed by: Satyajeet Khare satyajeetkhare@gmail.com
# May 3, 2018
# If you modified your gene count matrix or transcript count matrix to add functional annotations using ...
# Trinity/Analysis/DifferentialExpression/rename_matrix_feature_identifiers.pl script, you will get an error
# if you run Trinity/Analy... |
8aabcb3bab82139b99cba15fa2ac2869163b597dff3c5d64bbb83955fe65a647 | R | 2,734 | 66 | #' Compares identified denovo mutational signatures to known COSMIC signatures
#'
#' @description Takes results from \code{\link{extractSignatures}} and compares them known COSMIC signatures. Two COSMIC databases are used for comparisons - "legacy" which includes 30 signaures, and "SBS" - which includes updated/refined... |
462e0392180d43bb78232003283aecd8b5270afd3a7f98cf607578df407f6857 | R | 2,735 | 74 | library(monocle3)
library(dplyr)
library(ggplot2)
#Within Monocle framework, do either LRT or Wald Test
#Provide monocle object, method ("LRT" or "wald"), minimum percent of cells expressing a gene to consider,
#and the linear models to be fit
#Minimum number of cells expressing a gene for the gene to be tested for ... |
814dc1aed25900db5e84727048b8d3b0585b72df53397690f1f7fbe182169664 | R | 2,736 | 114 | #05/23/2023 Fengyao Yan
#make the pie chart to show to change trend of genes from normal tissue to tumor.
library(ggplot2)
library(ggrepel)
library(scales)
library(tidyverse)
df_tss <- read.csv('../../data/raw/TSS_genes.csv', row.names=1)
tss <- unique(unlist(df_tss['gene']))
res_cancer <- data.frame(matrix(ncol=3,... |
39258a57a9d01c74ef19d88e446a6d3d0eb2d9b888042e60a195205abdf9aec4 | R | 2,747 | 100 | ########################################################
# LC project
# Script to plot top snRNA-seq markers in Visium samples
# Lukas Weber, June 2022
########################################################
# module load conda_R/4.1.x
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001... |
723cd03fc951e0d1b6f2b4aa0518a61b681eac31c0340aee2ac44cff1c6f2ecc | R | 2,749 | 95 | # install.packages("ggplot2")
# install.packages("ggsignif")
# install.packages("RVAideMemoire")
library(ggplot2)
library("ggsignif")
library(RVAideMemoire)
###Setting of the Graph###
#label
lab_homo = expression(paste(italic({piezo ^ {"KO"}})))
print(lab_homo)
lab_hetero = expression(paste(italic({piezo ^ ... |
450170868f961b6667b51fa52e36819adaea572728b3a598ca902db76c97272a | R | 2,750 | 95 | # 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:$HOME/cellranger_atac_counts \
# -v /mnt/g/cellranger_multi_counts:$HOME/cellranger_multi_counts \
# -... |
4fe30a9183aba2ae45fc116a575177d3d409a934b05ba8c9fef2dca7733c9246 | R | 2,751 | 94 | # install.packages("ggplot2")
# install.packages("tidyverse")
# install.packages("ggsignif")
# install.packages("RVAideMemoire")
# install.packages("lawstat")
# install.packages("ARTool")
# install.packages("effsize")
library(ARTool)
library(lawstat)
library(ggplot2)
library(tidyverse)
library(ggsignif)
library(RVAid... |
e0343802faeaa6d7c6d9b3c82aa0c93313455f99d467f6c3730e545bdffa5075 | R | 2,752 | 73 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(rlang)
library(writexl)
## main differential gene expression package
library(limma)
library(SingleCellExperiment)
library(edgeR)
library(swfdr)
library(jaffelab)
ss <- function(x, pattern, slot = 1, ...) {
sapply(s... |
2e38babf61aaa92f42ed4a634f0f77dcee764bfa8b112e9b3d5a2c3d442a8063 | R | 2,755 | 77 | ##functions to take cell data set and prepare expression and annotations matrices for
##heatmap visualization with scrattch.vis sparklines plots
###Define functions to prepare matrices for sparklines plots ----
prep_annotation_matrix <- function(cds_obj, facet_by, colors = NULL, labels = NULL){
if(is.null(labels... |
832659f42b9222a04e004eac311e964ebaf1a57723ddd5688b9cc8d48c0df0c4 | R | 2,755 | 108 | #####you need to install Biomart if you dont have it and load the library
#source("https://bioconductor.org/biocLite.R")
#biocLite("biomaRt")
#source("http://bioconductor.org/biocLite.R")
#biocLite("GO.db")
library(GO.db)
######################
library(biomaRt)
#####################Read the file name containing the gen... |
cd6078a9e5c495c73692da09a189f1e479b74fea17e22cf4048f995394611bd6 | R | 2,756 | 99 | ########################################################
# LC project
# Script to plot top snRNA-seq markers in Visium samples
# Lukas Weber, June 2022
########################################################
# module load conda_R/4.1.x
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001... |
16e770af29a31f493f5beadbae3e4fe393c3d202f6b3e18bdb1b99ab8ce32714 | R | 2,761 | 98 | 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)
# Create SpatialExperiment
spe1 <- SpatialExperiment::SpatialExperiment(
assay = list(counts = SingleCellExperiment::counts(spe)),
colData = SummarizedExperiment::colDat... |
dbc6b698e74defe3d8f783df847541608a9a17748ffe5df45d79c803de248cf3 | R | 2,775 | 70 |
library(ggplot2)
library(hrbrthemes)
library(splines)
data <- read.table("site2.txt",head=T)
color <- c("grey30", "grey50", "#00CC66","#339933")
ggplot(data = data,aes(x = name/1000000, y = num,group=group)) +
geom_point(aes(fill = group,size=4),shape = 21,alpha=.8,size=4)+
#color = "transparent")+
#... |
c82924d6304b521d1223e98071a1eb7ac87bf83d49a8f4732c6a22c2df77fb64 | R | 2,781 | 108 | #####you need to install Biomart if you dont have it and load the library
#source("https://bioconductor.org/biocLite.R")
#biocLite("biomaRt")
#source("http://bioconductor.org/biocLite.R")
#biocLite("GO.db")
library(GO.db)
######################
library(biomaRt)
#####################Read the file name containing the gen... |
aea4d73e92c08b12ffb561f3d98ed0f26f711a4ff2e671b12c812114b067cbf4 | R | 2,784 | 55 | #' Sex Ratio Heatmap
#'
#' Given the data frame obtained after apply the \code{sexRatio} function, a
#' heatmap representing the sex ratio results is obtained.
#'
#' @param input Data frame obtained after applying the \code{sexRatio} function.
#' @param maleColor Determines the heatmap color for those comorbidities... |
3649430ee20b5e3ab6384b1fdd13c71a26afd1f2cbf698f3d6d6efed25d6a760 | R | 2,793 | 91 | ---
title: "ENCODE cCRE Analysis"
author: "Stephen P Plassmeyer"
date: "12/18/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../code/mpra_tools/mpra_tools.r')
```
Prepare variants as BED format to upload to ENCODE SCREEN
```{r}
variants <- fread('../../libr... |
086a27a8916dbab4889144101a8e60b1c45a0f17e156f103e93e2d59645f8967 | R | 2,794 | 57 | #R script for extracting mean fold-changes and deltaSVM scores
args<-commandArgs(trailingOnly=T)
input_prefix<-args[1] # "MPRA_minP.norm"
threshold<-as.numeric(args[2]) # q-value threshold: 0.01
inputfile1<-paste(input_prefix, "txt", sep=".")
inputfile2<-paste(input_prefix, "diff.pvalues.combined.sig", threshold, "t... |
fb45a72080bbc96499ab39348c1afd5f5b9e26a775ab9fed8316a2337864c86d | R | 2,796 | 74 | library(dplyr)
library(RColorBrewer)
library(gplots)
load("sc_exp.RData")
load("celltype.RData")
load("cell1_64.RData")
load("cell1_64_2.RData")
load("Single_copy_genes.RData")
load("cluster_1_2_3.RData")
mean2 = function(x){
mean(x,na.rm = T)
}
cell_list = list()
for(i in 1:nrow(cell1_64)){
cur =... |
72db9e229d2d3ca173e8e5199f9344c304b2f6c690879b3510a36a388e72c41b | R | 2,801 | 72 | "pwr.r.test" <-
function (n = NULL, r = NULL, sig.level = 0.05, power = NULL,
alternative = c("two.sided", "less","greater"))
{
if (sum(sapply(list(n, r, power, sig.level), is.null)) !=
1)
stop("exactly one of n, r, power, and sig.level must be NULL")
if (!is.null(r) && is.character(r))
... |
6820c288b8ebb85b549fe92cd7a6a1f2e0209c4b126dc68d28b829306bb6e1b4 | R | 2,804 | 84 | #####################################################################
# LC analyses: spot-level deconvolution using cell2location: plotting
# Lukas Weber, Apr 2022
#####################################################################
library(SpatialExperiment)
library(here)
library(ggplot2)
library(viridis)
# direc... |
e6ac688d9274f927f93c67edca995484c062139051e5b1cc54eff2df946e6927 | R | 2,809 | 66 | library(tidyverse)
library(knitr)
library(effects)
library(psych)
library(candisc)
#1 = Less than $5,000 ; 2 = $5,000 through $11,999 ;
# 3 = $12,000 through $15,999 ; 4 = $16,000 through $24,999 ;
# 5 = $25,000 through $34,999 ; 6 = $35,000 through $49,999 ;
#7 = $50,000 through $74,999 ; 8 = $75,000 through $99,999 ... |
b76233db41e6df1975e9cde0558ca63ee71b7264292137c909a8a46c561790d1 | R | 2,812 | 57 | ## 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_neura... |
adabddb3678c8e63f64937460cd055a7b4140a87d8186c2716d5b006a1bd80d5 | R | 2,817 | 76 | packages <- c("data.table","tidyverse","plyr","progress","parallel","doParallel","foreach","patchwork","lme4")
# Install packages not yet installed
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
install.packages(packages[!installed_packages],repos='http://c... |
146000ae1d9ca52ec86dbe9c2c0c44bc78eeb98eb54669f63ad3cdfae306fa6b | R | 2,824 | 71 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
## main differential gene expression package
library(SingleCellExperiment)
library(DelayedArray)
library(HDF5Array)
library(limma)
library(edgeR)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = p... |
215ee297d1755f08663b3ec1e42da21914b3a3877ad6a51f784787356ab9b77e | R | 2,834 | 72 | #Impute with 9022 patients
#Update on 7/24/21: for der_tri_rt_dx,create der_tri_rt_dx.v2, collapse the "Jan-Apr" and "May-June"
# b/c one missing in age, impute with age_center instead of der_age
#Update on 8/27/21: use New data
library(Hmisc)
library(rms)
library(kableExtra)
load("~/Box/CCC19_TS/48... |
e7a428f89ab038e67bebfd1445149df6bc8d1b17c643b39a8bf856b2823d07f7 | R | 2,834 | 94 | # install.packages("ggplot2")
# install.packages("tidyverse")
# install.packages("ggsignif")
# install.packages("RVAideMemoire")
# install.packages("lawstat")
# install.packages("ARTool")
# install.packages("effsize")
library(ARTool)
library(lawstat)
library(ggplot2)
library(tidyverse)
library(ggsignif)
library(RVAid... |
9f3d5897506cae7f82f374ace81abd0ea0378c1327c2b3d1c7ea0278ed514659 | R | 2,850 | 97 | # helper to record base R plot
# plotNetwork() ----
# record base R plot
. <- \(.) {
pdf(NULL)
dev.control(displaylist = "enable")
set.seed(1)
.
. <- recordPlot()
invisible(dev.off())
return(.)
}
# mock up some data
set.seed(321)
x <- replicate(m <- 10, rnorm(n <- 100, runif(1, -1, 1)))
# A... |
ed4f22a38bfc286d92105280b00e289249d75dc633d0bc1b6a94fb490f0b56fb | R | 2,851 | 103 |
#This script reannotates spatial data by DE gene labels. Annotation is
#limited by the small number of genes in the dataset,
#and not all labels are fully representative, specific markers
## ----receive input from nextflow----------------------------------------------
#Get parameters
args <- commandArgs(TRUE)
path... |
2ea45398d4cdc1649135562954b6c0be28e971f6d405b150c2df0bd30548d7e4 | R | 2,854 | 71 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
## main differential gene expression package
library(SingleCellExperiment)
library(DelayedArray)
library(HDF5Array)
library(limma)
library(edgeR)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = p... |
11f32c26d9fdf54747e65ff3bf14e793229e149ecc9612d6e7c5883104080d93 | R | 2,855 | 50 | # Import necessary libraries
library(tidyverse)
library(readxl)
library(plotly)
library(ggbeeswarm)
library(cowplot)
library(rvg)
library(svglite)
library(openxlsx)
library(gridExtra)
library(extrafont)
library(reticulate)
# Function to determine whether row is not outlier in group based on Tukey's fences
isnt_out_tuk... |
8ea94aa4ae3c443272ff27d0991371064e799b144d9caa28e187b6dd25808d94 | R | 2,857 | 73 | #' Loads a TCGA cohort
#'
#' @description Loads the user mentioned TCGA cohorts
#' @param study Study names to load. Use \code{\link{tcgaAvailable}} to see available options.
#' @param source Source for MAF files. Can be \code{MC3} or \code{Firehose}. Default \code{MC3}. Argument may be abbreviated (M or F)
#' @param ... |
27db35624a559ae58267a12f9a096023b67f65f5e24124b7a301753b6734d207 | R | 2,861 | 116 | ####################################################
# 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... |
69b4a784c3994eeb79a762bc0feb4e4d1210ff7aa80f84839cec3c7092ccbf68 | R | 2,861 | 71 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
## main differential gene expression package
library(SingleCellExperiment)
library(DelayedArray)
library(HDF5Array)
library(limma)
library(edgeR)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = p... |
cd702da1ccdeb055baec6788bba5adcbeeb2af1aa775b0bd20594c5b06b8eb2f | R | 2,862 | 70 | #' Creates a Genotype Matrix for every variant
#' @description Creates a Genotype matrix using allele frequcies or by muatation status.
#'
#' @param maf an \code{\link{MAF}} object generated by \code{\link{read.maf}}
#' @param genes create matrix for only these genes. Define NULL
#' @param tsb create matrix for only th... |
8451a9bb919b0dd4b52ede330c330cb6f6a3c860039723a073ac2cdb48bc3979 | R | 2,866 | 58 |
#' @title FUNCTION_TITLE
#' @description FUNCTION_DESCRIPTION
#' @param title.size PARAM_DESCRIPTION, Default: 16
#' @param text.size PARAM_DESCRIPTION, Default: 14
#' @param axis.title.size PARAM_DESCRIPTION, Default: title.size
#' @param legend.title.size PARAM_DESCRIPTION, Default: title.size
#' @param strip.text.s... |
c71e36151d01bc63bd797eaeb83a86da6617d929db26d3858f62ba53a82b8a4b | R | 2,868 | 82 | #' Convert gene-wise fasta files to species-wise fastas
#'
#' Function converts gene-wise fasta files to species-wise fastas
#' (e.g., multiple sp. per one gene file)
#' Relies on faSomeRecords
#' Download at http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/faSomeRecords
#' Put in /bin folder
#' Add execute permi... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.