sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
38fd067cb18308cfd5eccd3019b2f53da8a142b9658fa1c97896235e3263bdc6 | R | 1,705 | 88 | # Test check_library_installed ----
test_that(
"check_library_installed - works - TRUE", {
expect_no_condition(
object = check_library_installed(
x = c("tools", "base"),
error = FALSE
)
)
expect_no_condition(
object = check_library_installed(
x = c("tools", "ba... |
8afa3ed9fc1486be53fc8eaeb7ac4f8f5ba282160c727918a95554c25215aa24 | R | 1,707 | 61 | # Hua Sun
# Seurat v5.1
library(Seurat)
library(Signac)
library(GenomicRanges)
library(dplyr)
library(stringr)
library(stringi)
library(EnsDb.Mmusculus.v79)
library(EnsDb.Hsapiens.v86)
rdir <- 'out_cellranger_arc'
seed <- 42
ref <- 'mm10'
regress <- NULL # set it by data
outdir <- 'out_multiome_integrated'
set.se... |
7f053c31b3ce8d6b57fd32759b2575bf4d8493e5638bd7bc7b1e80ccb1552796 | R | 1,709 | 42 |
# Function to add HPO terms to results table
add_HPO_cols <- function(RES, sample_id_col = 'sampleID',
gene_name_col = 'hgncSymbol', hpo_file = NULL){
require(data.table)
filename <- ifelse(is.null(hpo_file),
'https://www.cmm.in.tum.de/public/paper/drop_analysis/reso... |
f3f29594c0c3be786bef96ce91ed6129dbbb7462b196312a110b6d98932f8f5a | R | 1,716 | 59 | #'---
#' title: Create datasets from annotation file
#' author: Christian Mertes, mumichae
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AS" / "{dataset}" / "00_defineDataset.Rds")`'
#' params:
#' - setup: '`sm cfg.AS.getWorkdir() + "/config.R"`'
#' - ids: '`sm lambda w: sa.getIDsByGroup(w.dataset, assa... |
339db2d8efd0a87a91412f16c7676a89ffecc524564b769681123cbf66eb9480 | R | 1,718 | 44 | #' richR publication-ready ggplot2 theme
#'
#' A clean, consistent theme for all richR visualizations. Designed for
#' publication-quality figures with legible fonts and minimal clutter.
#'
#' @param base_size base font size (default: 12)
#' @param base_family base font family (default: "")
#' @return A ggplot2 theme o... |
041bd4f5aeb5f74da5f2f0d3c006da1c47b7e0721879f55af39c1b97820e8f97 | R | 1,719 | 61 | setwd("/media/user/disk21/completeAnalysis/")
data = read.table("cellbrowser/scRNA-Seq/l3.coords.tsv",
row.names = 1, header = T)
meta = read.table("cellbrowser/scRNA-Seq/meta1.tsv",
row.names = 1, header = T, sep = '\t')
identical(rownames(meta), rownames(data))
library(ggplot2... |
a1d123fac1553d6ab0910e8a48be52f1edfa7e3604c243881d0e08d9c702cbc0 | R | 1,719 | 55 | # Calculate current coverage
coverage <- covr::package_coverage(path = "OlinkAnalyze")
coverage_value <- covr::percent_coverage(x = coverage) |>
ceiling()
if (coverage_value > 0L && coverage_value < 50L) {
coverage_badge_color <- "red"
} else if (coverage_value >= 50L && coverage_value < 70L) {
coverage_badge_col... |
efb1c84ad82a261926a7e429ebe419ae0d04ba4d56f68667abb3aed6694b5974 | R | 1,726 | 50 | library(tidyverse)
library(openxlsx)
## set directories
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
input_dir <- file.path(root_dir, "data")
output_dir <- file.path(root_dir, "tables", "results")
mb_dir <- file.path(root_dir, "analyses", "molecular-subtyping-MB", "results")
## read file
hist <- read_... |
2ef870532c85ba954e46a5964705a153520b839795ff583eb68197a654b1ea04 | R | 1,728 | 87 | # Hua Sun
library(infercnv)
rds <- 'pre_infercnv_obj.rds'
func <- 'hmm'
sd_amp <- 2
cutoff <- 0.1
outdir <- 'out_infercnv'
dir.create(outdir)
infercnv_obj <- readRDS(rds)
infercnv_obj2 <- ''
# fast
if (func == 'default'){
infercnv_obj2 <- infercnv::run(
infercnv_obj,
cutoff=cutoff,
o... |
4463ec3c239c195ba41a15c1070d5fad25cffd8509e212481c1261fe24492ecc | R | 1,730 | 56 | #' Remove a metadata from being included in the shiny app
#'
#' Remove a metadata from being included in the shiny app.
#'
#' @param scConf shinycell config data.table
#' @param meta.to.del metadata to delete. Users can either use the original
#' metadata column names or display names. For more information regarding... |
b10584337f681426ea797ab178c89d6dd36b6c416efcf9cbb2002bd6d60f3515 | R | 1,731 | 50 | # load libraries
suppressPackageStartupMessages({
library(tidyverse)
library(pheatmap)
})
# function to create heatmap of average immune scores per cell type per cancer and gtex group
heatmap_by_group <- function(deconv_output, annot_colors, output_file) {
# create a generalized group column
deconv_output ... |
114b6c8805f85f678c14ac45dcd4d37676f818139b8050467674862ec02902a7 | R | 1,740 | 59 | #' Modify the legend labels for categorical metadata
#'
#' Modify the legend labels for categorical metadata.
#'
#' @param scConf shinycell config data.table
#' @param meta.to.mod metadata for which to modify the legend labels. Users
#' can either use the actual metadata column names or display names. Please
#' s... |
c26108d6ac33bde89c86e436ccedb8c204bcde90d63e93d4dad1c5196cdfa563 | R | 1,745 | 74 | multi_defined <- function() {
return(1)
}
multi_defined <- function() {
return(2)
}
# Test comment
multi_defined_multi_ways1 <- function() {
return(1)
}
# Test comment
assign("multi_defined_multi_ways1", function() { return(1) })
`<-`(multi_defined_multi_ways2, function() { return(1) })
# Test comment
assign("m... |
a310ca68cf621a0c141e7b08d0d1effdddd99ec70df44faf2c670f57627f1522 | R | 1,749 | 49 | # Author: Komal S. Rathi
# Function: Script to perform MB molecular subtyping
# load libraries
suppressPackageStartupMessages(library(optparse))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(medulloPackage))
suppressPackageStartupMessages(library(org.Hs.eg.db))
# source cla... |
8d390d0408ad04190b4bd54c2753a06776a38d8c2128bcc1582206a3e272f0ed | R | 1,751 | 43 | #' Read nucmer coordinates file in to PAF formatted table
#'
#' This function takes alignments produced by nucmer (parameters: --mum --coords) and loads
#' reported coordinates file (suffix .coords) into a PAF formatted table (see PAF specification).
#'
#' @param nucmer.coords A path to a nucmer coordinates file contai... |
1d049186a495115292b08d3919af08d4e3600dc9c3a88a4a0330d8f0c05f44df | R | 1,752 | 54 | # ref for this file:
#
# * https://r-pkgs.org/testing-design.html#testthat-helper-files
# * https://r-pkgs.org/testing-design.html#testthat-setup-files
# LightGBM-internal fix to comply with CRAN policy of only using up to 2 threads in tests and example.
#
# per https://cran.r-project.org/web/packages/policies.html
#
... |
e3a2179eea10927e79dd873a089cd85553a42c8abf211275c51c8e99bfbef6ea | R | 1,754 | 56 | #' Reverse-combine logical columns into gene lists
#'
#' For each logical indicator column, extracts the values of \code{input_col}
#' where the indicator is \code{TRUE}, deduplicates them and returns one gene
#' list per indicator column bound side by side.
#'
#' @param inputDF A \code{data.frame} with an identifier c... |
e414f8bdb6765200583aa308f4ce23baee5802cb822d49fcae289b48a94884b2 | R | 1,757 | 62 | #' @title Modify the colour palette for categorical metadata
#' @description Modify the colour palette for categorical metadata.
#' @param scConf shinycell config data.table
#' @param m metadata for which to modify the colour palette. Users
#' can either use the actual metadata column names or display names. Please ... |
172b6506fd46b37b99be6b8b915102658a6f081237231d0463062aa42a117eaa | R | 1,759 | 56 | # AR1
ar1=function(n,rho=0.9) rho^toeplitz(0:(n-1))
# CS
cs=function(n,rho=0.9) {r=matrix(rho,n,n);diag(r)=1;r}
# trace function
tr=function(x) sum(diag(x))
# function to return P-values for sum(zs^2) using method of moments to find null distribution
gent=function(zs=NULL,LD,A=NULL,chisquares=NULL) {
# find null dist... |
48c098d9346d4004c1c8bb5d6bdad8b38e59ac517cc0bc666297e51fbdafa874 | R | 1,766 | 46 | DEgenesMAST <- function(Data, Labels, Normalize = FALSE, LogTransform = FALSE){
# This functions applies a differential expression test to the data using one vs all
# The training data should be used a an input
# The output is a matrix with marker genes where the columns are the cell populations and the rows a... |
c8aaa65530cf9fa7c0188208afd5f92239c43373f9b39c8494943e53112398c0 | R | 1,766 | 55 | library(pROC)
data(aSAH)
test_that("var with delong works", {
expect_equal(var(r.wfns), 0.00146991470882363)
expect_equal(var(r.ndka), 0.0031908105493913)
expect_equal(var(r.s100b), 0.00266868245717244)
})
test_that("var works with auc", {
expect_equal(var(auc(r.wfns)), 0.00146991470882363)
expect_equal(v... |
96390b371b719bd097df16cb2b82ab72cdaa99081ccaee4cb78ada8826f4ce66 | R | 1,768 | 59 | ###### load data
load("imp.mlmi.RData")
#TRUE still has some missing values otherwise FALSE
data_imp <- complete(imp.mlmi, "long", include = FALSE)
# load packages
library(mice)
library(dplyr)
library(flextable)
library(crosstable)
library(caret)
library(skimr)
library(pROC)
library(purrr)
library(tidyr)
library(GGa... |
661cfa07231c5d86a13a89a6ff84a9ffa3059d158612a4b5973ec0f88908b1dd | R | 1,773 | 30 | GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin,
draw_group = function(self, data, ..., draw_quantiles = NULL) {
data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth * (xmax - x))
grp <- data[1, "group"]
newdata <- plyr::arrange(transform(data,... |
992f99f1f11e25e3eae203d9674a52c1ab43b21bb4d04584b75629804f03deca | R | 1,776 | 55 | # Create a subset for the the histologies-base.tsv with WGS samples missing
# in the GATK CNV calls to generate consensus calls if present in Manta SV caller
# Load libraries
suppressPackageStartupMessages(library(optparse))
suppressPackageStartupMessages(library(tidyverse))
# set up optparse options
option_list <- l... |
b55623afa924c11e7081bb08d9bffc4f2237ba4938eb735115762e8bf3d0bed1 | R | 1,788 | 69 | library(tidyverse)
library("GPA")
library(readxl)
library(data.table)
library(parallel)
library(foreach)
library(doParallel)
library(writexl)
# Set up parallel processing
num_cores <- 10
cl <- makeCluster(num_cores)
registerDoParallel(cl)
# Read data
cmd_pair <- read_excel("/path/cmd_heart_brain.xlsx")... |
f5b8584d65f14396f55518ae2811668983fd1934596377e8a35179d041828b45 | R | 1,789 | 55 | #' @title Modify the display name of metadata
#' @description Modify the display name of metadata. It is possible that the original
#' metadata name is not so informative e.g "orig.ident" or too long e.g.
#' "seurat_clusters" and users want to shorten the way they are displayed
#' on the shiny app. This function all... |
672b72dc3997120bd2670c6218a01d5631abc7ac340116e014047f620affcd73 | R | 1,794 | 44 | rm(list=ls(all=TRUE))
library(mvnfast)
source('simulations/gent/functions.R')
##################################################################################
# changing ms, ngwas, LD density
ms=c(50,100,150)
ngwas=c(10000,50000,100000)
rhos=c(0.1,0.5,0.9)
h2=0
nref=500
niter=10000
#m.=n.=r.=1
for(m. in 1:length(ms))... |
a24f3d3f0a4551f3b70a1bcbb9ee7d87b3f77c9e4c16cd0ea25d0957c0bc98ba | R | 1,798 | 44 | # function to create clustering plot using either umap or t-SNE
suppressPackageStartupMessages(library(uwot))
suppressPackageStartupMessages(library(Rtsne))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(irlba))
suppressPackageStartupMessages(library(tidyverse))
clustering_plo... |
c919a5d9ad8be7495f396364a3d301f6605ce72f3e0afb87e7b926c0e74a5591 | R | 1,799 | 54 | #' Column-bind data frames or vectors of unequal length
#'
#' Binds a list of data frames or vectors by column, padding the shorter inputs
#' with \code{NA} rows so that all share the number of rows of the longest one.
#' Written in base R (no external dependencies), so it works identically on
#' Windows, macOS and Lin... |
b85e7f7c492647fad9c571d4028b637178c651b30fe96c9c69dd2dda7cce6d77 | R | 1,805 | 52 | library(ggplot2)
library(ggpubr)
# Data comes from the below link:
# https://kb.10xgenomics.com/hc/en-us/articles/360001378811-What-is-the-maximum-number-of-cells-that-can-be-profiled-
### Path to dataset:
pathtodpcsv<-''
dp<-read.csv(paste0(pathtodpcsv,'/DoubletPrediction.csv'))
plot(dp$MultipletRate, dp$... |
4654074aeb100767dfab51252b2d37aac154e3c3caa702ae5ec21844e715bbbe | R | 1,812 | 53 | library( psych )
ants <- read.csv( "../../Kirby/antsThickness.csv" )
antsxnet <- read.csv( "../../Kirby/antsxnetThickness.csv" )
visitPairs <- c( '01', '25',
'02', '37',
'03', '22',
'04', '11',
'05', '31',
'06', '20',
... |
30da3c7bdb02382fcc0e7f8cd9db3afd30fda930a804462ac7ce3ac7a7555f6a | R | 1,814 | 69 | require(luz)
require(torch)
require(torchvision)
npx <- 96
classes <- 3
convnet_dropout_5_10 <- nn_module(
"convnet_dropout_5_10",
initialize = function() {
self$features <- nn_sequential(
nn_conv2d(3, npx, kernel_size = 3, padding = 1),
nn_relu(),
nn_max_pool2d(kernel_size = 2),
nn_dr... |
16eeee48ca720366f1840c97d561d2f49879021588cb9545e34df8fbc797d704 | R | 1,816 | 59 | #' Set the default metadata to display
#'
#' Set the default metadata to display in the shiny app. Default1 is used when
#' plotting metadata with gene expression or when plotting two metadata
#' simultaneously. Default2 is used when plotting two metadata simultaneously.
#'
#' @param scConf shinycell config data.tabl... |
a667c2a5949d95f761ffb777c9e125a3dff9b2d2a657abd53637c41be1678cc6 | R | 1,820 | 64 | # to run on laptop #
set.seed(33)
library(tidyverse)
Exposure_Data <- data.table::fread("~/Documents/SGG/Projects/SampleOverlap/Data/Simulations/noY/GWAS_X.tsv")
Outcome_Data <- data.table::fread("~/Documents/SGG/Projects/SampleOverlap/Data/Simulations/noY/GWAS_Y100.tsv")
# take 750,000 SNPs
SNPs <- sample(x = 1:nr... |
0914e96d8a14b750a248e09beb5b2bf2e875ac4a4db3cbca7ea36bd806b111e8 | R | 1,827 | 46 | test_that(".species_table returns valid data.frame", {
tbl <- richR:::.species_table()
expect_s3_class(tbl, "data.frame")
expect_true(all(c("species", "dbname", "kegg", "msigdb", "reactome") %in% colnames(tbl)))
expect_true(nrow(tbl) >= 20)
expect_false(any(duplicated(tbl$species)))
expect_false(any(is.na(t... |
50d7898ebfcd4e66ec3182228e4fd235342eb076a688c258816ebde068941f23 | R | 1,829 | 51 | #'---
#' title: Fitting the autoencoder
#' author: Christian Mertes
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AS" / "{dataset}" / "05_fit.Rds")`'
#' params:
#' - setup: '`sm cfg.AS.getWorkdir() + "/config.R"`'
#' - workingDir: '`sm cfg.getProcessedDataDir() + "/aberrant_splicing/datasets/"`'
#' threa... |
e3476429b36cccd9ad04e384af8d60acb4c339d067ccb23b2e72a7d8c868a979 | R | 1,833 | 52 | library(tidyverse)
library(readxl)
library(optparse)
library(data.table)
arguments <- parse_args(OptionParser(), positional_arguments = 1)
base_dir<- arguments$args[1]
base_dir<-"/home/jbrenton/nextflow_test"
sample_info <-
read_excel(
path =
file.path(
base_dir,
"20201229_MasterFile_Sa... |
19d8d07ccb49b99adb160f39295c65d0091f7575f401d602791e7d7ae18fe819 | R | 1,837 | 57 | # prepare_seg_for_gistic.R
#
#
# Purpose: Generate seg files that are compatible with GISTIC
# Currently, if we use the cnv consensus seg file as is, since we define copy.num=NA for neutral calls
# a lot of NAs are present in the file and will cause GISTIC to error out.
# Those changes were made for the focal CN mod... |
0e5cbc0277f37fe47341af358af418bd9382750919863f976a5f7e33b7f87c83 | R | 1,838 | 55 | #' @title Set the default metadata to display
#' @description Set the default metadata to display in the shiny app. Default1 is used when
#' plotting metadata with gene expression or when plotting two metadata
#' simultaneously. Default2 is used when plotting two metadata simultaneously.
#' @param scConf shinycell co... |
835ee8c1f9b4b22c493e59da076360b7940cf6d73e3937be9b150d3aa0d10e88 | R | 1,843 | 51 | #' Estimate parameters of a Beta distribution from counts
#'
#' This function estimates the two parameters of the Beta distribution, alpha
#' and beta for each cell type. The input is a matrix of cell type counts,
#' where the rows are the cell types/clusters and the columns are the samples.
#'
#' This function is c... |
5d5b6fe7c29463ed1342d616b8df02fd9abadb41b8e83614b0242d3323c18adf | R | 1,845 | 51 | library(lightgbm)
# Load in the agaricus dataset
data(agaricus.train, package = "lightgbm")
data(agaricus.test, package = "lightgbm")
dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)
dtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label)
# Note: for ... |
7f53fcaf82a451ebb72493438c3c906a585f8e8d5e62fcf09b0cf94e653e067e | R | 1,847 | 22 | # The Osprey Workflow
**Osprey** was designed to have an easy, linear workflow with as little user input as possible. It has many built-in routines to recognize data formats and sequence types, and will be able to perform most required processing steps automatically.
Osprey consists of seven separate modules – Job, ... |
a4e8dc32de572ffe32ba609818620e8887fc5fcd3f55db8f45c135606c393b8f | R | 1,851 | 47 | library(Seurat)
library(ggplot2)
library(patchwork)
ba9.integrated = readRDS("ba9_array_integrated.rds")
# Read in tissue annotation info (BA46 vs BA9)
df.meta = read.csv("../U54_Metadata_Full.tsv", header=1, sep="\t")
res = sapply(df.meta$tissue_id, FUN=function(x){
parts = strsplit(x, '_')
first_elements = sapp... |
03cb938c1b3bd7e0b208496a4b1cb9dcf1a880a93fbb553ae4612a23a1ffcca8 | R | 1,854 | 57 | calc_flynet_activations <- function (videos, out_path, script, weights, output_type = "activations") {
# TODO: May still need to batch this in case there are too many videos to feed into one arg
video_paths <- paste(videos, collapse = " ")
command_args <- c(script,
"-l 132",
... |
56745aeb4e0dc5040e961f16770778f8be9dead1907bd286a188a346c4ec773c | R | 1,855 | 52 | # power as more SNPs are included ####
ms=c(5:200,300,400,500,600,700,800,900,1000,1250,1500,1750,2000) # number of tested SNPs
alpha=0.05 # Type I error rate
m0=3 # number causal SNPs
h2=0.0005 # h2 explained by gene
ngwas=30000 # GWAS sample size
cortypes=c('CS','AR1') # CS, AR1
rhos=c(0,0.3,0.5,0.9)
POWER=array(dim=... |
c6a91af334fd06cb9b96e10591c44c18ad09a52c9157dfd0085dbabd4d19f134 | R | 1,861 | 63 | #' Create a Scatter Plot for Visualizing Beta Values
#'
#' This function generates a scatter plot for visualizing
#' beta values using multidimensional scaling (MDS).
#' It takes beta values, group information, sample names,
#' color palette, point size, and other optional parameters
#' for customization.
#'
#'... |
aff1e404e49f4408791093ebc9a50049a34bad54710b14e85e61950732be3a21 | R | 1,872 | 75 | #checking assumptions
install.packages("readxl")
library(readxl)
#check my decoding accu normality using shapiro test
setwd("/Users/shahzad/Documents/tryANOVAAssumptions")
myData<-read_excel(path = "anatExt_decodAccu.xlsx")
# checking for normality
qqnorm(myData$anat_lS1)
qqline(myData$anat_lS1)
# test of normality
... |
8b7a69450134cb9dda6c6baf0126e1eb7f3393738ac7db90ff4d83967e7fe3b2 | R | 1,883 | 46 | setwd("/media/user/disk21/completeAnalysis/visium_2Jun/")
meta = read.csv("meta_12Mar2025.csv", row.names = 1)
info = read.csv("SNUH_AF_15Jun.csv", row.names = 1)
identical(rownames(meta), rownames(info))
meta$ravi_module = info$RaviModule
df1 = as.data.frame(table(meta$AF_mar2025, meta$greenwald))
colnames(df1) =... |
9d21dcdedcdb66df2edddd49b4a36f5e50b8d0aeaa9f9bcffcf43a54c078c0f3 | R | 1,887 | 70 | #! /usr/bin/Rscript --vanilla
library(MuMIn)
library(survival)
library("survminer")
source("DataSplitter.R")
source("Outcome.R")
source("MelanomeCSVParser.R")
source("FeatureReduction.R")
source("Model.R")
source("PredefinedFeatureReductionRuleSequences.R")
source("MelanomeSettings.R")
source("ModelTrainer.R")
sourc... |
a51353d7f3d5b62357cb7ba1dafe6e2a7e38369aad1b7461f8b902c30e155140 | R | 1,890 | 71 | # Scripts to create ensembl_id gene lists for IEGs
# Mouse gene list is from: SI Table 4 from \doi{10.1016/j.neuron.2017.09.026}. Human
# gene list was compiled by first creating homologous gene list using biomaRt and then adding some manually curated
# homologs according to HGNC. See data-raw directory for scripts ... |
d4d79c27b87c678ccade88dce700044a935870dd114735372d5dba52021dc332 | R | 1,893 | 56 | ---
title: "Readme_Wei et al"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Vagal sensory neuron single cell analysis
This repository contains the code used for the single-cell analysis of vagal sensory neurons from healthy and tumor-bearing mice. The data generated fro... |
ee842e2426fa7d4e5da7303b66ef17a8ebd6e75ad3460bbfad6595ca51f89a6a | R | 1,895 | 57 | ################################# ED.Fig.10a and b
###R data adapted from Zhao, Q., Yu, C.D., Wang, R. et al. A multidimensional coding architecture of the vagal interoceptive system. Nature 603, 878–884 (2022). https://doi.org/10.1038/s41586-022-04515-5
###Please refer to Extended.Data.Fig.2c.R for generation of "Lung... |
24a6495c594a01bde2ed87ff9a8343bf35510d06e55e16f1c37869b64817c717 | R | 1,901 | 47 | ---
title: "Figure 5"
author: "Maksym Zarodniuk"
date: "Compiled on `r format(Sys.time(), '%d %B, %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```
```{r, include=FALSE}
library(tidyverse) # dplyr, ggplot2, readr, tibble, pipes
library(ggplot... |
481ecd6f1affb3ed6cba2050fbeff5d181c1299da243291ffc8cde98676d138e | R | 1,901 | 47 | message("=================================================")
message("Check geom_miropeat and plotMiro wrapper function")
## Get PAF to plot ##
paf.file <- system.file("extdata", "test1.paf", package = "SVbyEye")
## Read in PAF
paf.table <- readPaf(paf.file = paf.file, include.paf.tags = TRUE, restrict.paf.tags = "cg"... |
9d0a5e67e663745659c457873c06e8b4fed76a8314ff1a959a8e07ad04c5c822 | R | 1,903 | 70 | library(lightgbm)
# We load the default iris dataset shipped with R
data(iris)
# We must convert factors to numeric
# They must be starting from number 0 to use multiclass
# For instance: 0, 1, 2, 3, 4, 5...
iris$Species <- as.numeric(as.factor(iris$Species)) - 1L
# We cut the data set into 80% train and 20% validat... |
63648d2e9d9610d9210826e95009aeb95e53e670ad6e64c20eda4a03b1c3f9eb | R | 1,908 | 70 | #' Help function checking whether a dataset contains NA or empty strings on
#' its column names
#'
#' @author
#' Klev Diamanti
#'
#' @inheritParams .read_npx_args
#' @inheritParams .downstream_fun_args
#'
#' @return Error is file contains problematic column names. `NULL` otherwise.
#'
#' @keywords internal
#'
read_np... |
871b8364fee2e52c835efaf73a22624d1485de272adb0580e2b88918cd7ad052 | R | 1,909 | 47 | #' @name lgb.restore_handle
#' @title Restore the C++ component of a de-serialized LightGBM model
#' @description After a LightGBM model object is de-serialized through functions such as \code{save} or
#' \code{saveRDS}, its underlying C++ object will be blank and needs to be restored to able to use it. Such
#' object ... |
45a0afaeb1d641ca0adf1b53a1e77ca36f58a110cce32a07b499eddb0ff450fd | R | 1,912 | 58 | #' Modify the display name of metadata
#'
#' Modify the display name of metadata. It is possible that the original
#' metadata name is not so informative e.g "orig.ident" or too long e.g.
#' "seurat_clusters" and users want to shorten the way they are displayed
#' on the shiny app. This function allows users to spec... |
6e52c8d0703b995a14ea3a03729575ed51602fe269f4d8f38cf2153b004234d9 | R | 1,917 | 76 | args <- commandArgs(TRUE)
name <- as.character(args[1])
params <- yaml::read_yaml("../Config/pbd_sim.yaml")
if (!dir.exists(name)) {
dir.create(name)
}
setwd(name)
dists <- params$dists
within_ranges <- params$within_ranges
nrep <- params$nrep
age <- params$age
proportion <- params$proportion
nworkers_sim <- par... |
984154831dc873a88230092bc82ff518a2262fa466e849ced18ca5b5539a5ecf | R | 1,923 | 60 | library(testthat)
library(biodiscvr)
# Setup paths [cite: 12, 16]
synth_data_root_dir <- system.file("synthdata", package = "biodiscvr", mustWork = TRUE)
path_to_pkg_files <- system.file("files", package = "biodiscvr", mustWork = TRUE)
pkg_synth_config <- "config_synth.yaml"
pkg_synth_dict <- "dict_suv_synth.csv"
tes... |
7c36ced3b0408514b7fed3918e1acec255fd9562e86dd7c7c86dac9e55bab72e | R | 1,927 | 61 | setwd("/Volumes/IqraMacFmri/visTac/fMRI_analysis/outputs/derivatives/decoding/glm_noResponse_forIMRF_includesAllSub")
# Remove all variables
rm(list = ls())
library(openxlsx) # import excel file
library(reshape2) # reshape data
library(ggplot2) # plots
library(lmerTest) # linear mixed model
library(emmeans) # multip... |
ab63327a4c7de3ca4b898328d29a87d5ed08aac902756548e9f812c9979f4f8d | R | 1,936 | 65 | #' Modify the colour palette for categorical metadata
#'
#' Modify the colour palette for categorical metadata.
#'
#' @param scConf shinycell config data.table
#' @param meta.to.mod metadata for which to modify the colour palette. Users
#' can either use the actual metadata column names or display names. Please
#' ... |
148053ea38e8c8a8f05c15db9917c216b3203ba8165d8d832c9ab9747e42cc21 | R | 1,946 | 63 | library(dplyr); library(tidyr); library(broom)
# ==== 入力データ ====
dat_long <- read.csv("DFT_VAS_spellout.csv",
header = T,
stringsAsFactors = F,
fileEncoding = "SJIS")
value_col <- "VAS" # 例: "VAS"
# 前処理（Timeの順序を固定）
dat_long <- dat_long %>%
... |
19b9c10554b81e83de3538eb8bd6c3c46d714f3527950f2c3508161fbc0b968a | R | 1,952 | 66 | # -----------------------------------------------------------------------------------------------------------
# For reproducible research, please install the following R packages
# and make sure the R and BiocManager versions are correct
# ─ Session info ───────────────────────────────────────────────
# setting value... |
2950144f2dc703c06dccee509c4a80a3be1ca42e62382a219fed2c0640089148 | R | 1,954 | 50 | df = read.csv("res/predictive_connections.csv")
modul = c(structure(as.character(df$mod_A), names=as.character(df$reg_A)), structure(as.character(df$mod_B), names=as.character(df$reg_B)))
modul = modul[!duplicated(names(modul))]
modul = modul[order(modul, names(modul))]
mod_colors=c(
rgb(171/255, 112/255, 83/255), ... |
fe9a5d76bac3cdaa9436e100674fd42347d73f6159785fb659272de75d7011a0 | R | 1,957 | 80 |
setwd("/data/nas1/liuyiding_OD/project/01_project_147/11_Cor_GSEA/gsea")
library(data.table)
exp=fread("log2TPM.txt",header=T,data.table=F)
exp=column_to_rownames(exp,"V1")
exp=exp[,-c(1:40)]
library(stats)
library(clusterProfiler)
genelist <- c("PATZ1", "SIN3B", "BLK", "MTHFD2")
subFpkm <-as.data.frame(t(exp[genelist... |
98291e4db9060d6b50fc5f25dc3b4e34bb7731628bbc0e0ff8f9c878d86b3ef2 | R | 1,962 | 62 | #' Download example Seurat objects / single-cell data
#'
#' Download example Seurat objects / single-cell data required for
#' ShinyCell tutorials.
#'
#' @param type can be either "single" or "multi" or "h5ad" or "loom"
#' or "plaintext"
#'
#' @return downloaded Seurat object
#'
#' @author John F. Ouyang
#'
#' @im... |
43fa75d5416194b98e246b40f64352e63bfd14209deed0e71e4839eb42372a33 | R | 1,963 | 61 | #'---
#' title: Preprocess Gene Annotations
#' author: mumichae
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AE" / "{annotation}" / "preprocess.Rds")`'
#' input:
#' - gtf: '`sm lambda wildcards: cfg.genome.getGeneAnnotationFile(wildcards.annotation) `'
#' output:
#' - txdb: '`sm cfg.getProcessedDataDir()... |
90160f796183cad6d5a5ca17878c42bde2fc43043de4cd76b6ffea589aa4bbb4 | R | 1,964 | 59 | #' Reverse-combine logical membership columns into gene lists
#'
#' For each logical membership column, extracts the values of \code{main_col}
#' where the column is \code{TRUE}, deduplicates and sorts them, and returns one
#' gene list per membership column bound side by side.
#'
#' @param inputDF A \code{data.frame} ... |
417141e3ebd58a23acb7a5dafa06ba786a2307d2a5358757aad30be9e1e6a868 | R | 1,967 | 45 | # Clinically significant change
#......................................................
# Documentation
#' @title Clinically significant change
#' @description This easy function calculates Clinically significant change (clinical cut-off scores) as defined by Jacobson and Truax (1991).
#'
#' @param SD_0 standard devia... |
3a944727a76a86833c8935660228e511a2502f32e60f2e95af7e8afdca96f381 | R | 1,968 | 55 | #'---
#' title: Count Split Reads
#' author: Luise Schuller
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AS" / "{dataset}" / "splitReads" / "{sample_id}.Rds")`'
#' params:
#' - setup: '`sm cfg.AS.getWorkdir() + "/config.R"`'
#' - workingDir: '`sm cfg.getProcessedDataDir() + "/aberrant_splicing/datasets/"... |
2c31a82620718216731b8a07ee9896a4da3776614bf03d46085631241d8683a4 | R | 1,975 | 77 | # Load related function
dir.base <- "."
script <- list.files(
path = file.path(dir.base,"function"),
pattern = "[.]R$",
full.names = T,
recursive = T
)
for (f in script) source(f)
dir.data <- file.path(dir.base, "$data path$")
# Data folder
cohort <- "BRCA"
dir.data.raw <- file.path(dir.data, cohort, "raw/"... |
c7528088134b23a939884a9ef65c4ccab38c2ec842d3f37bf35099eeca1bc593 | R | 1,978 | 51 | #' Compute the overlap between a CNV table and a list of genomic bins
#'
#' This can be regular windows throughout the genome or exons / genes for example.
#'
#' @param cnvs usual CNV `data.table`
#' @param format format for the output table, "count" and "both" return a list
#' @param bins for computing the overlap, by... |
800eea267a2db5460d35d392b31ea2a5829aff3e3b9d39e20d15247112531354 | R | 1,988 | 47 | #!/usr/bin/env Rscript
# This is a helper script to run the pipeline.
# Choose how to execute the pipeline below.
# See https://books.ropensci.org/targets/hpc.html
# to learn about your options.
# Leaving USE_SLURM as FALSE will build each pipeline _in series,_
# one intermediate object at a time. Nothing wrong with ... |
cc7d45ec5a4a851ee6f14b27cfa99a052e0b41ea4f4e4743b2e28b3c1b7a10e4 | R | 1,995 | 58 | #'---
#' title: Filter Counts for OUTRIDER
#' author: Michaela Mueller
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AE" / "{annotation}" / "{dataset}" / "filter.Rds")`'
#' input:
#' - counts: '`sm cfg.getProcessedDataDir() +
#' "/aberrant_expression/{annotation}/outrider/{dataset}/total_counts.... |
670a4a5e1f86258ef58a1eda9291c42b987748b8270d06a38c4a53875b92e5d8 | R | 2,003 | 111 | ## code to prepare internal dataset goes here
## based on https://r-pkgs.org/data.html#sec-data-sysdata
## Acceptable Olink platforms ----
# this tibble contains generic information about the Olink platforms that
# Olink Analyze accepts, their names, regular expressions to determine them from
# the data, quantificati... |
ee27eb2a6fc32adc593c091c081a8aec0d1e4508b0a142e26dba68d1bccd1174 | R | 2,008 | 68 | #!/usr/bin/env Rscript
library(SummarizedExperiment)
## Create SummarizedExperiment (se) object from Salmon counts
args <- commandArgs(trailingOnly = TRUE)
if (length(args) < 2) {
stop("Usage: salmon_se.r <coldata> <counts> <tpm>", call. = FALSE)
}
coldata <- args[1]
counts_fn <- args[2]
tpm_fn <- args[3]
tx2g... |
ba053f43331467d2052a1a6954e1374cc6aca3cd1c475f327c1177e9af3a731a | R | 2,015 | 62 | setwd("/Volumes/IqraMacFmri/visTac/fMRI_analysis/outputs/derivatives/decoding/glm_noResponse_forIMRF_includesAllSub")
# Remove all variables
rm(list = ls())
library(openxlsx) # import excel file
library(reshape2) # reshape data
library(ggplot2) # plots
library(lmerTest) # linear mixed model
library(emmeans) # multip... |
433c113e5a68e022d4747d3d84376c739ad1f409427b4d3f610a8203cd149efe | R | 2,016 | 84 | ---
title: "1p/19q co-deleted oligodendrogliomas"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
author: Jaclyn Taroni for ALSF CCDL
date: 2020
---
This notebook will look at 1p/19q codeletions in the entire OpenPBTA cohort.
The purpose is to identify samples that should be classified as 1p/19q co-delete... |
54ef341c224ca1a03229dda4c8ebc5cde42bf03778234e5c5fa917b1f5b87532 | R | 2,017 | 53 | context("Prepare the model from different objects")
library(MOFA2)
test_that("a MOFA model can be prepared from a list of matrices", {
m <- as.matrix(read.csv('matrix.csv'))
# Set feature names
rownames(m) <- paste("feature_", seq_len(nrow(m)), paste = "", sep = "")
# Set sample names
colnames(m) <- paste("sampl... |
12ce987a45a929dce01d3d9dffcd9f043d176d2b010217eeea0066307753bf21 | R | 2,026 | 53 | #'---
#' title: Nonsplit Counts
#' author: Luise Schuller
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AS" / "{dataset}" / "nonsplitReads" / "{sample_id}.Rds")`'
#' params:
#' - setup: '`sm cfg.AS.getWorkdir() + "/config.R"`'
#' - workingDir: '`sm cfg.getProcessedDataDir() + "/aberrant_splicing/datasets"... |
1983a8825dcd25c62d3bcc71a8c16a46bded5f02d0b900177159f5e07e8cc3c8 | R | 2,027 | 58 | # pROC: Tools Receiver operating characteristic (ROC curves) with
# (partial) area under the curve, confidence intervals and comparison.
# Copyright (C) 2010-2014 Xavier Robin, Alexandre Hainard, Natacha Turck,
# Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez
# and Markus Müller
#
# This program is free soft... |
a0e1345bd83d081d27466a3c931f92460a6aaaad21ec8be9f135031ab1cb6ba0 | R | 2,028 | 63 | ##-------------------------------------##
## FEATURE SELECTION TAB ##
##-------------------------------------##
calculate_hvg <- function(mat){
hvg <- modelGeneVar(mat)
hvg <- as.data.frame(hvg)
hvg <- hvg[order(hvg$bio, decreasing = TRUE),]
return(hvg)
}
plot_varvsmean <- function(hvg, top, p... |
b2a3763fd654bc580f6632c4b445e60a68e08e8f40c599515d00b3e3cbfb6219 | R | 2,029 | 72 | rm(list=ls())
## COMMON LIBRARIES AND FUNCTIONS
source("100.common-variables.r")
source("101.common-functions.r")
source("300.variables.r")
source("301.functions.r")
## SCRIPT SPECIFIC LIBRARIES
##
##
library("doSNOW")
library("foreach") ## loaded by doSNOW
## SCRIPT SPECIFIC FUNCTIONS
if( 1 ) {
if( exists("GA... |
bbf205b5e19bd1afdcb4421e0345154d27ddd87f337bb10ec2423aede5a7707f | R | 2,031 | 78 | test_that(".validate_enrichment_input rejects empty gene vector", {
expect_error(
richR:::.validate_enrichment_input(character(0)),
"empty"
)
})
test_that(".validate_enrichment_input rejects all-NA genes", {
expect_error(
richR:::.validate_enrichment_input(c(NA, NA, NA)),
"NA"
)
})
test_that("... |
3fac210bab981f7e43264bad3e84551a0788e6cdda3bbfe6da75c6a3fe2f3777 | R | 2,032 | 59 | rfImpute <- function(x, ...)
UseMethod("rfImpute")
rfImpute.formula <- function(x, data, ..., subset) {
if (!inherits(x, "formula"))
stop("method is only for formula objects")
call <- match.call()
m <- match.call(expand.dots = FALSE)
names(m)[2] <- "formula"
if (is.matrix(eval(m$data, p... |
884c6d9b8b2125ff9b7ce3de36eaac4c56cb440106162569657ca427ec479bfb | R | 2,033 | 56 | #'---
#' title: P value calculation for OUTRIDER
#' author: Ines Scheller
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AE" / "{annotation}" / "{dataset}" / "pvalsOUTRIDER.Rds")`'
#' params:
#' - ids: '`sm lambda w: sa.getIDsByGroup(w.dataset, assay="RNA")`'
#' - parse_subsets_for_FDR: '`sm str(projectDir ... |
c476d6f7b926f230361b4c22edcda4e4f13bcbf13e81ad72f378f629de885d5e | R | 2,034 | 59 | #Adapting the plot function from
# https://github.com/PediatricOpenTargets/OpenPedCan-analysis/blob/785c3224de29f701b1c1b62841d0f84d46f7eaca/analyses/molecular-subtyping-embryonal/03-clean-c19mc-data.Rmd#L56-L112
# Apply this code to chromosome 2 since MYCN is on Chromosome 2
plot_chr2 <- function(cn_df, biospecimen... |
e8471ed0e3e430ef6634e972d5d22421a7ac32d967c663667785c80c7b4603bb | R | 2,038 | 63 | ###############################################
# Mfuzz Clustering of Age-Binned TPM Profiles
###############################################
# --- Load libraries ---
library(Mfuzz)
library(Biobase)
# --- Input parameters ---
outn <- "TPMs_by_AgeGroup5_Hours_CENGEN_zscore" # Base file name (no .csv)
mfuzz_dir <- "/M... |
7a65f61f9b03d1df1c515d7310bcb5e8d0bcc980a6429c1b10b9f5de14736bd5 | R | 2,041 | 78 | # Extreme gradient boosting
# Parallel processing -----------------------------------------------------
library(doParallel)
all_cores <- parallel::detectCores(logical = TRUE)
# for 8 core 16 thread machine, good performance running more than
# physical but less than all logical
registerDoParallel(cores = all_cores - ... |
2741adc84abae58c1591c54227a34889d892460d21248318c2f3d023b94ef688 | R | 2,043 | 52 | run_SCINA<-function(DataPath,LabelsPath,GeneSigPath,OutputDir){
"
run SCINA
Wrapper script to run SCINA on a benchmark dataset,
outputs lists of true and predicted cell labels as csv files, as well as computation time.
Parameters
----------
DataPath : Data file path (.csv), cells-genes matrix w... |
7473043ebb6dcc2667c748daa13dc05269001b8a39e8d0f01e873bf274094853 | R | 2,044 | 55 | #'---
#' title: RNA Variant Calling
#' author: nickhsmith
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "RVC" / "Overview.Rds")`'
#' params:
#' - annotations: '`sm cfg.genome.getGeneVersions()`'
#' - datasets: '`sm cfg.RVC.groups`'
#' - htmlDir: '`sm config["htmlOutputPath"] + "/rnaVariantCalling"`'
#... |
ec505a2d7e8738b5dc09eb0bc0b0575cd401e5e1f272f66df852fe327759f5a2 | R | 2,046 | 61 | # Author: Komal S. Rathi
# Function: Function to classify MB subtypes
# load libraries
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(medulloPackage))
suppressPackageStartupMessages(library(org.Hs.eg.db))
# function to run molecular subtyping
classify_mb <- function(exprs_ma... |
1260d8251a62d4e14c6414776208a8c43dfaf8dd8abce776170cc2d729d1d785 | R | 2,048 | 66 | ##-------------------------------------##
## NORM TAB ##
##-------------------------------------##
tab_DEPTH <- tabItem(
tabName = "Depth Normalization",
sidebarLayout(
sidebarPanel(width = 3,
selectInput("norm_method",
label = "Select m... |
0eafbd625da57c2e741d7483369beccd92942eff01fd55ce9f0b29384ba49650 | R | 2,061 | 59 | test_that(".clean.char removes newlines", {
expect_equal(richR:::.clean.char("foo\nbar"), "foo bar")
expect_equal(richR:::.clean.char("no newlines"), "no newlines")
})
test_that(".filter_ora_result filters by pvalue", {
df <- data.frame(
Annot = c("A", "B", "C"),
Pvalue = c(0.01, 0.05, 0.1),
Padj = c... |
c8a0045be7a2a44ab3efb0c84380cf5380d688d39a5ca090e7c03bf258a5e346 | R | 2,061 | 66 | ########################### Process configuration file ################
#
# Objective: Process parameters from configuration file
########################### <<<<<>>>>> ##############################################
# Load libraries
library(yaml)
# Read configuration file
load_configs <- function(path) {
# Read ... |
b2d063aadd0d12460305f95e99c5035241dfa1245896cb46ae474d4d7f15eab1 | R | 2,063 | 85 |
library(doMC)
library(fields)
library(ggplot2)
registerDoMC(cores=20)
# Your FAM file here
famfile <- ""
# Your phenotype file here
phenofile <- ""
# Number of eigenvectors used
ndim <- 10
lpx <- list.files(pattern="pcsX")
lpy <- list.files(pattern="pcsY")
lpu <- list.files(pattern="eigenvectorsX")
names(lpx) <- ... |
2f065e096431ba6119be7957eb0f5d1ec31fd2fd4f1b8c9533163a999d32ed5e | R | 2,066 | 64 | ## =========================
## Experiment I vs. II: sham (amygdala) vs sham (hippocampus)
## =========================
if (!requireNamespace("here", quietly = TRUE)) install.packages("here")
source(here::here("stats","lme_models","_setup.R"))
# acquisition: CS
run_lmer_test(
data_name = "scr_df_exp_acq_sham_CS",... |
d78033e11465c5a234503dc8ff556ac0e6c0f25bac34a838a1b45f08e347b6c7 | R | 2,068 | 60 | DA_plot_prepper = function(DA_df,
limit_p = 0.1,
limit_e = 0.25,
name_cutoff = ".*ales_",
sig_symbols = c("*" = 0.1,
"**" = 0.01,
... |
89b064be888ad4732dfb17af3741226e8664e1890bafca0e3410a82d742e3ec7 | R | 2,072 | 76 | # 00-subset-for-EPN.R
#
# Josh Shapiro for CCDL 2020
#
# Purpose: Subsetting Expression data for EPN subtyping
#
# Option descriptions
# -h, --histology : path to the histology metadata file
# -e, --expression : path to expression data file in RDS
# -o, --output_file : path for output tsv file, optionally gzipped wit... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.