sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
b94b55fdebf1fdb4daad01bd3fe8cbd9acf330998eb6b90db84d26614941ab6f | R | 861 | 17 | # construct a mirroring registration from landmarks
mirror_banc_tps <- function(anns) {
if(is.character(anns)) {
anns=ngl_annotations(anns)
}
AB=rbind(banc_raw2nm(anns$pointA), banc_raw2nm(anns$pointB))
BA=rbind(banc_raw2nm(anns$pointB), banc_raw2nm(anns$pointA))
# have decided to use this rather than "of... |
d7e6b7b93216973d6817d70c346f1ea4b13cf9b8560449d5ad2b370ff2ce800c | R | 862 | 30 | library(data.table)
merge2 <- function(x, y, by, ncol_y, ...) {
# Convert data frames to data.tables
x <- as.data.table(x)
y <- as.data.table(y)
# Perform the merge
merged_dt <- merge(x, y, by = by, ...)
# Get the names of the columns in y that were not used for merging
y_cols <- setdiff(names(y), by)
... |
6527f4e2e4154ff5329f04e5f6d1584adf76da8ef56902e20163c8cea7c36a5a | R | 865 | 40 | # region items and polygons
#' all possible combinations of n sets
#'
#' @param n dim
#'
#' @importFrom utils combn
combinations <- function(n){
l <- lapply(seq_len(n), function(x){
m <- combn(n,x)
matrix2list(m)
})
unlist(l, recursive = F)
}
matrix2list <- function(matrix){
lapply(seq_len(ncol(matrix... |
e7eb03aee61f8a586cff97f856cb9bf3f473a612e78ab42c42e1a4ab2051b303 | R | 867 | 25 | library(Seurat)
library(tibble)
library(dplyr)
SSE1_filtered <- readRDS("SSE1_5_seurat_final_sse7.rds")
SSE1_filtered <- subset(SSE1_filtered,Condition_mCherry_strict %in% c("Control_No","SSE7_No","SSE7_Yes"))
for (i in unique(SSE1_filtered$cell_line)) {
a <- subset(SSE1_filtered,cell_line == i)
a <- NormalizeD... |
307953b5d0dd32b4a176c40bd9d7f31bc10c142ae5c911dca2a5d6badb971bf9 | R | 872 | 23 | # Function to save a ggplot figure to a specified file
save_figure <- function(plot, output_file, fig_size = c(10, 3)) {
library(ggplot2) # Load ggplot2 for saving figures
# Ensure results directory exists before saving
results_folder <- dirname(output_file)
if (!dir.exists(results_folder)) {
dir.create... |
f87c8c57cd981ce21c746a90a29ce26476133b4d92a06811dcf26e20ea901e8f | R | 872 | 30 | #######################################################
# Add ssm2 coverage counts to db
# Date: 2022.07.07
# Author: Kevin Johnson
#######################################################
# Necessary packages:
library(parallel)
library(tidyverse)
library(DBI)
library(odbc)
con <- DBI::dbConnect(odbc::odbc(), "Verhaak... |
29736cbefec5dc3ec950223ffecebbd35eedfc4557a5f78358f4cc639252b46b | R | 873 | 18 | test_that("skeletor works", {
skip_if_not_installed('reticulate')
skip_if_not(reticulate::py_module_available("cloudvolume"),
"Skipping live flywire tests requiring python cloudvolume module")
skip_on_os("mac")
skip_if_not(reticulate::py_module_available("skeletor"),
"Skipping live f... |
7cc966667777f07a0fe3940cae6837fb7ebbd8aed56f58ff406950be4520238e | R | 878 | 22 | # ================================
# Load or Install All Libraries
# ================================
packages <- c(
"readxl","conflicted", "readr", "tidyr", "tibble", "pheatmap", "ggbreak",
"imputeLCMD", "openxlsx", "limma", "writexl", "showtext", "jsonlite", "curl",
"ggplot2", "scales", "ggrepel", "sva", ... |
54212e6931b734b8f4a3e2f70884329cfd27e7b3028227b54bad2f857fd0cee0 | R | 879 | 23 | ### Calculate the mol% of each individual lipid
setwd(rstudioapi::getActiveProject())
rm(list=ls())
source("./Scripts/Function_scripts/Effect_size_functions.R")
source("./Scripts/Function_scripts/Pre-processing_functions.R")
library(tidyverse)
##########################################################################... |
bb489d246a0ce8f642291f53575930205b7d55a1c8b33e629eaddb2a6063a77b | R | 879 | 32 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Clean sc DRG data; only keep naive samples
# Author: Christina Meier
# Date: 2025-05-25
# Email: 19cm51@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load StdEn... |
3a7f9a094ddb36d471f86b37969a1012271e0a893091b242d35a80cc20deb078 | R | 886 | 22 | library(ggplot2)
# Example data (replace with your actual data)
observed_counts <- c(13825, 592, 299, 170, 114) # Summed observed counts from all runs
total_observed <- sum(observed_counts)
expected_counts <- rep(total_observed / 5, 5) # Assuming equal distribution
# Create a data frame for plotting
data <- data.fr... |
519490903cb664cdd636d248ba39c0b5892d4a82bfb7caed4430fba59a118eb0 | R | 890 | 26 | library(ggplot2)
library(viridis)
# Your data
data_time <- data.frame(
Year = c(1950, 1955, 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2024),
Value = c(226, 374, 451, 563, 786, 1490, 2150, 3170, 4170, 5410, 8090, 13700, 16000, 16200, 16500, 17300)
)
# Create a line plot
ggplot(d... |
7a5c0019e69d1f2cabb858fb9a4c328e0250da4683fb4b029d5420bcd11a41a4 | R | 892 | 38 | ## code to prepare `tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer` dataset goes here
load(
here::here(
"data-raw",
"tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer.Rdata"
),
verbose = TRUE
)
head(tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer)
write.csv(
tstats_Human_DLPFC_snRNAseq_Nguyen_topLa... |
7bcc264f954e889afb8dc0327921660ef95571a1b1553fb139fd55c4a370ff6a | R | 893 | 24 | design_data <- design(data = data.frame(forstmann, CO = 1:nrow(forstmann)),
model=LBA,matchfun=function(d)d$S==d$lR,
formula=list(v~lM,sv~lM,B~E+lR,t0~E2 + CO),
contrasts=list(v = list(lM=matrix(c(-1/2,1/2),ncol=1,dimnames=list(NULL,"d")))),
constants=c(sv=log(1)),
... |
7e06f4326315fedcc51b8935182a4ce5de6c61e4cab26115cb684bebb4661c1b | R | 894 | 14 | #' @param model_uri The location, in URI format, of the MLflow model.
#' @details The URI scheme must be supported by MLflow - i.e. there has to be an MLflow artifact
#' repository corresponding to the scheme of the URI. The content is expected to point to a
#' directory containing MLmodel. The follow... |
beb9dc5848617ad54134d65e35d7840e069837ab913e6a96e0688f3c587f9996 | R | 896 | 22 | ### Append concentration to each internal standards
setwd(rstudioapi::getActiveProject())
rm(list=ls())
library(tidyverse)
library(stringi)
##Input IS concentration
load("./Output_Data/Exp2.MS-Dial_IS.all.Rdata")
labelled.conc <- read.csv("./Input_Data/KO_Labelled_IS_concentration_082720.csv", stringsAsFactors = F... |
f2d92e8cda917f3ea2e3cc9433c86b9e1e5d7bb26fc4b1f4eb6d06d7ec12d7f4 | R | 901 | 26 | RNGkind("L'Ecuyer-CMRG")
set.seed(123)
matchfun <- function(d) as.numeric(d$S)==as.numeric(d$lR) |
(d$lR=="pm" & as.numeric(d$S)>2)
designLBA <- design(
factors=list(subjects=1,S=c("left","right","leftpm","rightpm"),RACE=2:3),
Rlevels=c("left","right","pm"),
matchfun=matchfun,
model=LBA,constants=c(v_RACE3=0... |
5f8a1ea1630eacaafb5b9f83a16e5d71af2150a226522a105368d56d0a875792 | R | 903 | 24 | dGNG <- design(Rlevels = c("left","right"),
factors=list(subjects=1,S=c("left","right")),
functions=list(
TIMEOUT=function(d)rep(2.5,nrow(d)),
Rnogo=function(d)factor(rep("left",nrow(d)),levels=c("left","right")),
Rgo=function(d)factor(rep... |
5bd64208dd3b8717c69dd51f3bd4c6c20e6616f89281d53b77052b6f25f32746 | R | 904 | 21 | ### Append concentration to each internal standards
rm(list=ls())
library(tidyverse)
setwd(rstudioapi::getActiveProject())
load("./Output_Data/IS_invivo_samples_Int.MsD.Rdata")
IS.label <- read.csv("./Input_Data/Labelled_IS_concentration_2020_052020.csv", stringsAsFactors = F,
check.names = F, c... |
afbd726af439f29e64ad097ffb9c99f4d490107a2134f989a548ca3fc0b74625 | R | 905 | 31 | #' Determine if you have enough RAM memory
#'
#' This function determines if you have enough RAM memory on your system.
#'
#' @param how_much The number of bytes you want to compare against.
#'
#' @return A `logical(1)` indicating whether your system has enough RAM memory.
#' @export
#'
#' @details If `benchmarkme::get... |
b7f0dac7cd70ec1d28f70b39d04c48500851e1f46eb7e5adf8c1d15cd3eea6f4 | R | 909 | 24 | #
# sleuth: inspect your RNA-Seq with a pack of kallistos
#
# Copyright (C) 2015 Harold Pimentel, Nicolas Bray, Pall Melsted, Lior Pachter
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software F... |
3fcd2af2dfb0dae12ed3d9913e21770d69156365c96a42fe300db71f700b77af | R | 910 | 40 | #!/usr/bin/Rscript
args <- commandArgs()
# print(args)
dir <- args[6]
Name1 <- args[7]
all <- as.integer(args[8])
dedup <- as.integer(args[9])
Df <- data.frame(sample=Name1, total=all, unique=dedup)
Df$dup <- 1 - Df[ ,3]/Df[ ,2]
colnames(Df)[4] <- "duprate"
Df$temp <- 0
colnames(Df)[5] <- "libsize"
# head(Df)
for... |
e176f43215b090b93f87ea8cc1d79ec8076b605737f2a52a20f4136c13672672 | R | 911 | 28 | ########################## Step.02 QC & Integrated All Samples ##########################
if(T){
sce[["pMT"]] = PercentageFeatureSet(object = sce, pattern = "^MT-")
summary(sce[["pMT"]])
summary(sce[["nFeature_RNA"]])
summary(sce[["nCount_RNA"]])
nFeature_lower = 300
nFeature_upper = 10000
nCount_lower =... |
2eb90d165da79231c064b550fa55406d7c632e39bb4a6f3cb215d89193f644bc | R | 916 | 22 | #' Read one or more FANC neuron meshes
#'
#' @param ids One or more root ids
#' @param savedir An optional location to save downloaded meshes. This acts as a
#' simple but effective cache since flywire neurons change id whenever they
#' are edited.
#' @param ... Additional arguments passed to
#' \code{fafbseg::\l... |
f56182a3f2e03c8b11bdebaf246c44df3f716055e32a4d7b66e60965d356d46a | R | 921 | 34 | here::i_am("extra/Spearman_on_NPC/network_inference/1.4.1.prepare_data_for_Spearman.R")
library(tidyverse)
library(SingleCellExperiment)
library(foreach)
library(doParallel)
library(glue)
library(here)
wd <- here("data/neural_differentiation_dataset/Spearman_network_inference_and_analysis/input/")
dir.create(wd)
# l... |
1ae25036a8a4d4a96708ceea5255c9d993b11c88bcb2bbcce2402ab0af13ecd3 | R | 924 | 27 | #-------------------------------------------------
# Title: Get a named list of paths given a directory and pattern
# Author: Amanda Zacharias
# Date: 2022-12-31
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 ... |
8990537e07f945ded61dcee0fba13f2f395ba19e6483e60a35de9122c1f38e70 | R | 927 | 31 | #' @keywords internal
#' @import nat
#' @section Registration: For information about left-right mirroring and
#' symmetrising registrations within MANC, please see
#' \code{\link{mirror_manc}}.
#'
#' For information about across-template registrations, please see
#' \code{\link{download_manc_registrations}}.
#'... |
39f09d766e85e3c0a75fe952aeb4ee7a7967648a29a69949cc445f23e3cb75a9 | R | 930 | 33 | #-------------------------------------------------
# Title: Reads in dataframes from a named list of paths
# Author: Amanda Zacharias
# Date: 2022-12-31
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 gcc/7.3.0... |
4a20fa1b41a4cc2265ae5781e157b3aa2b0d44c8ada08cfd98582c65c31075dc | R | 934 | 17 | ## Add aligned files
library(odbc)
library(DBI)
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB")
bamfiles = list.files("/projects/verhaak-lab/GLASS-analysis/results/align/bqsr", pattern = "^GLSS-MG.*bam$", full.names = T)
md5files = list.files("/projects/verhaak-lab/GLASS-analysis/results/align/bqsr", pattern = "^GLS... |
3fe500b07b8e21b678e782e965c84858baac66f12057de5c7c3399d7e7787676 | R | 939 | 28 | op <- choose_malevnc_dataset('MANC')
on.exit(options(op))
for(ds in c("MANC", "VNC")) {
choose_malevnc_dataset(ds)
test_that("manc neuprint works", {
expect_equal(manc_ids(10000, as_character = T), "10000")
expect_equal(manc_ids(10000, as_character = F), 10000)
expect_equal(manc_ids(10000, integer64 = T), as.... |
a5e56b8652690a332645ddadb06b24b2b7c76026ac0b80a2186a5a58b9ad6855 | R | 939 | 54 | ---
title: "Identify mitochodira genes"
author: "Wei Sun"
date: "`r Sys.Date()`"
output:
html_document:
theme: journal
highlight: tango
toc: true
toc_depth: 3
toc_float:
collapsed: true
smooth_scroll: false
number_sections: false
df_print: paged
---
```{r setup, include=FALSE}
... |
aeb53991c0bd1256bea4d3164e8d34fd0d6f73ff28b505010ddc31ab43561869 | R | 939 | 27 | library(readr)
library(tibble)
library(zellkonverter)
library(Seurat)
library(mixhvg)
library(SingleCellExperiment)
library(scater)
library(here)
#hvg_subset <- readH5AD(here("data", "whole_mouse_brain", "processed", "merged_before_HVG.01pct.h5ad"), reader = "python", X_name="counts")
#loading 5% of the cells due to R... |
220d6340420ad5978dd07bf4b0a28e8664d5af9999300088d6b04d888b6fab64 | R | 942 | 17 | test_that("multiplication works", {
pal=c(`Tergopleural-Pleural-promotor` = "#2280B8", `Pleural-remotor-abductor` = "#7B2DBA",
Tergotr. = "#EFE952", Sternotrochanter = "#CCF750", `Sternal-posterior-rotator` = "#AE22BA",
`Sternal-anterior-rotator` = "#33C0D0", `Sternal-adductor` = "#145E67",
`Tr-e... |
3cdb8f95e198e5290e70801e4a40d1f859729b4b7086b80d6491d576d6971345 | R | 943 | 41 | test_that("Test factorNaturalOrder for numeric input", {
x <- 10:1
f <- factorNaturalOrder(x)
expect_s3_class(f, "factor")
# Levels should be sorted in increasing order
expect_equal(levels(f), as.character(1:10))
# Factor values should preserve original order
expect_equal(as.character(f),... |
0d3f8abf4cb0ac5966a7d1b8728e12cb7e84196fa3a7b0b41e4ab30da59b47a0 | R | 944 | 35 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: VST Transformation
# Author: Amanda Zacharias
# Date: 2023-11-30
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 gcc/7.3.0 r/3.... |
8cafe8a660738c0efe2c4f7cf65ca5997b5e8ed9b6d36a8202e204215446e70e | R | 947 | 31 | old_root=root="C:/Users/Jun/Documents/R/AA_wSignal/" #you will need to change this
new_root="C:/Users/Jun/Documents/R/AA_wSignal/" #you will need to change this
next_experimentName= "AA_wSignal" #you will need to change this
###############################################################
newdir=paste(new_ro... |
1d549fdf7379530a4e229c98b5f9dd9b687b5fad74d387ecb1ced2b2db715fb6 | R | 950 | 41 | library(Seurat)
library(tibble)
library(dplyr)
library(SCopeLoomR)
SSE1_filtered <- readRDS("SSE1_5_seurat_final_sse7.rds")
SSE1_filtered <- subset(SSE1_filtered,Condition_mCherry_strict %in% c("Control_No","SSE7_No","SSE7_Yes"))
# scenic by library
for (i in unique(SSE1_filtered$library)) {
a <- subset(SSE1_filter... |
ffc6c7d3106101131b70930ade152f9f20a22d53778d47d89e190659510f2388 | R | 950 | 30 | rm(list=ls())
basedir <- '~/Dropbox/Cornblath_Bassett_Projects/MouseGeneExpressionABA/mouse_abi_tool/'
setwd(basedir)
params <- list(basedir=basedir)
source('code/misc/miscfxns.R')
params$source.save <- source.save
params$opdir <- paste('ABA_expression',params$c.max,'/',sep='')
dir.create(params$opdir,recursive = T)
#... |
e76a80a8d1bd1b718b04926740403354ae2c60b51d8e47ea218ad9c906709ca7 | R | 951 | 31 | library(dplyr)
#### import coldata_df from 00.Matrix_H.R
coldata_df
#### import HiRES matrix ####
## CellType1 (Integration version) ##
celltype <- 'ExN_H'
celltype <- 'InN_H'
celltype <- 'Astro_H'
celltype <- 'Micro_H'
celltype <- 'Oligo_H'
celltype <- 'OPC_H'
celltype <- 'Endo_H'
Dataset_H <- 'GSE160189_186538_In... |
c676c6543691ab1f351333efc6b62f1ba3895eb8ef487b63d6db0d811ad10c70 | R | 953 | 25 | library(Seurat)
library(dplyr)
library(Matrix)
args <- commandArgs(T)
input <- args[1]
output <- args[2]
set.seed(42)
obj <- readRDS(input)
exprMat <- GetAssayData(obj, layer = 'counts')
#LogexprMat <- readRDS("LogexprMat.rds")
cellInfo <- obj@meta.data
cellInfo$CellID <- rownames(cellInfo)
# Sample 500 cells for eac... |
cb1c03fdeb288dea62b92f3a5b5f04de1a9e0de26fd23e2f7fe0c93b78a9dec4 | R | 953 | 21 | args = commandArgs(trailingOnly=TRUE)
infile = args[[1]]
out_prefix = args[[2]]
lds_seg = read.table(infile,header=T,colClasses=c("character",rep("numeric",8)))
quartiles=summary(lds_seg$ldscore_SNP)
lb1 = which(lds_seg$ldscore_SNP <= quartiles[2])
lb2 = which(lds_seg$ldscore_SNP > quartiles[2] & lds_seg$ldscore_SNP ... |
f66ae4f0cc8c0dcbba2ec045c73fdd99bcea067b1896c43230dd403a980e3d12 | R | 954 | 25 | library(Seurat)
library(dplyr)
library(Matrix)
args <- commandArgs(T)
input <- args[1]
output <- args[2]
set.seed(42)
obj <- readRDS(input)
exprMat <- GetAssayData(obj, layer = 'counts')
#LogexprMat <- readRDS("LogexprMat.rds")
cellInfo <- obj@meta.data
cellInfo$CellID <- rownames(cellInfo)
# Sample 500 cells for eac... |
ac1c678b692bfd77ae75e0dcbea1c07002694b05e791fa76fc0d19e6f9d19484 | R | 955 | 30 | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library("argparse"))
# create parser object
parser <- ArgumentParser()
# specify our desired options
# by default ArgumentParser will add an help option
parser$add_argument("-i", "--input", required=TRUE, help="load bam")
parser$add_argument("-o", "--output", req... |
463831e16d485c5d738649d2b19266cc22d57feaa5301b38d943c12d8c25b910 | R | 957 | 29 | # This scripts prunes the species tree so it only keeps the species that are present in the dataset (output of script 13.2).
# It produces a species tree for each site.
library(ape)
# Paths
path_datasets <- "Data/13.Variable_Sites/out/datasets_for_BT_pair_bonding/"
path_trees <- "Data/14.BayesTraits/in/trees/"
# Tre... |
4377a850b173d0768e83bf9d6619a34b514dc85ceed11a20ec42e2a377cceef5 | R | 958 | 25 | #' Locate image files
#'
#' Creates a named `character()` vector that can be helpful for locating
#' image files and used with `add_images()`. This function is not necessary if
#' the image files don't use the `spe$sample_id`.
#'
#' @inheritParams vis_clus
#' @param image_dir A `character(1)` specifying a path to a dir... |
cb7db8c7bd6183c12b2f6b908f2e69b379b39c977f8e55e62277c5b95efa99e1 | R | 958 | 35 | context("Params")
test_that("mlflow can read typed command line parameters", {
mlflow_clear_test_dir("mlruns")
mlflow_cli(
"run",
"examples/",
"--env-manager",
"uv",
"--entry-point",
"params_example.R",
"-P", "my_int=10",
"-P", "my_num=20.0",
"-P", "my_str=XYZ"
)
expect_tr... |
71def475da819eebca9b089962c68f54bf1a2d7de071516c3f3d59cfc97f4311 | R | 963 | 24 |
# Figure 3C ###################################################################
DVPV_MG = read.csv("DVvsWT_DE_MAST_RNAassay_MG_pct0.15_dim15.csv", header = T)
Cgas_MG = read.csv("Cgashet_DVvsDV_DE_MAST_RNAassay_MG_pct0.15_dim15.csv", header = T)
DVPV_MG_UP = filter(DVPV_MG, p_val_adj < 0.05&(avg_log2FC>=0.1))
DVPV_M... |
1179d7571e767a3833516428eae5547d3e98b24f4aadcd1a3c8d715cb89154dd | R | 971 | 21 | get.clusterGroupTest.function<-function (group.testing.function, x)
{
stopifnot(is.function(group.testing.function))
function(hcloutput, dist = as.dist(1 - abs(cor(x))), alpha = 0.05,
method = "average", conservative = TRUE) {
hh <- if (missing(hcloutput))
hclust(dist, method = method)
e... |
eefad40752fc147ce5c3af38b3d373d2db706a1dc71def6a2005a711e16004a1 | R | 973 | 37 | ---
title: "Seurat_tail_cell_cycle_scoring"
output: html_document
date: "2025-07-11"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Load libraries and tail file
```{r}
#library(dplyr)
library(Seurat)
tail <- LoadSeuratRds(file = "tail_final.rds")
```
# This code is based off of Tirosh et a... |
9631c267f74498d649b038464b66677076dc8f7c321706e0cc39680860a7b290 | R | 977 | 30 | if (!require("pacman")) install.packages("pacman")
pacman::p_load(caret)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
# IAPS neuroticism data
N_IAPS <- read.table("N_IAPS.txt")
set.seed(666)
testIndex <- createDataPartition(N_IAPS$V1, p = 100/nrow(N_IAPS),
list = F, ti... |
2a02b03248dd593acf848d570774f8e8763200a7144f8ff318aa8b423903f873 | R | 980 | 28 | context("Bypass conda")
test_that("MLflow finds MLFLOW_PYTHON_BIN environment variable", {
orig_global <- mlflow:::.globals$python_bin
rm("python_bin", envir = mlflow:::.globals)
orig <- Sys.getenv("MLFLOW_PYTHON_BIN")
expected_path <- "/test/python"
Sys.setenv(MLFLOW_PYTHON_BIN = expected_path)
python_bi... |
200aa14631a939105b7f6648db56606dbb610e14efa51e9e685d30f876f49953 | R | 981 | 28 | plot_color_coded_biaxial <- function(m, x.var, y.var, color.var, color.breaks = NULL) {
x.var.idx <- which(colnames(m) == x.var)
y.var.idx <- which(colnames(m) == y.var)
color.var.idx <- which(colnames(m) == color.var)
m <- data.frame(m)
m <- hexbin_downsample(m, x.var.idx, y.var.idx)
if(is.... |
0dab84815245320b67ff9200ddac89eda22f8ff0325bfafb3d16710f62735901 | R | 982 | 28 | # install_R_requirements.R
# Script to install all CRAN and Bioconductor dependencies for TSProm
# ---- 1. Install base package manager ----
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager", repos = "https://cloud.r-project.org")
if (!requireNamespace("pacman", quietly = TRUE))
... |
46bb52087457442befdb97a0c2ca139d3227154ee8b3138b2567bb68c54aae41 | R | 983 | 35 | test_that("Test ComputeSpatialEnrichmentIndex", {
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
package = "SpNeigh"
))
logNorm_expr <- readRDS(system.file("extdata", "LogNormExpr.rds",
package = "SpNeigh"
))
bon_c0 <- GetBoundary(data = coords, one_cluster = 0)
... |
6871783d81aea077342f6ba26be900f4d00b013bf9d6601a6efa350ab66b9788 | R | 985 | 34 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# Parameter settings
##### ##### ##### ##### ##### ##### ##### ##### ##### #####
setwd(file.path(getwd(), "R"))
source("00_directorySettings.R") # load baseDir <- "/WORKING_DIR"
dataDir <- file.path(baseDir, "data")
outDir <- file.path(baseDir, "results")
##... |
12b0b2d336da78ef91790c475eb47db565f58be34af9a4db9284cb0bd193748c | R | 986 | 23 | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library("argparse"))
# create parser object
parser <- ArgumentParser()
# specify our desired options
# by default ArgumentParser will add an help option
parser$add_argument("-i", "--input", required=TRUE, help="input matrix")
parser$add_argument("-o", "--output",... |
59cbd25bbc2eaeb780d0aabf2146b28f7c0914b6ee21c2850ea2f3a8e04ab5c2 | R | 987 | 35 | #!/usr/bin/env/ Rscript
library(MendelianRandomization)
mvdata <- mr_mvinput(bx = cbind(ldlc, hdlc, trig), bxse = cbind(ldlcse, hdlcse, trigse),
by = chdlodds, byse = chdloddsse)
mvdataout <- cbind(
mvdata@betaX ,
mvdata@betaXse,
mvdata@betaY,
mvdata@betaYse )
colnames(mvdataout) <- c(past... |
69794f80583d1284b410da3960930243f4ec0e3e994efab585e9cbd0927a94fa | R | 989 | 47 |
library(STdeconvolve)
library(anndata)
library(scater)
set.seed(101)
#Read SC data
sc_path = "./SC/SN_SC.h5ad"
SC <- read_h5ad(sc_path)
k <- length(unique(SC$obs$cell_type))
#Read ST data
h5adpath <- "./simulated_gene_expression_average.h5ad"
dlpfc <- read_h5ad(h5adpath)
cd <- t(dlpfc$X)
cd <- round(cd)
pos <- dl... |
470af62fe094914f5ea976639bb312d1ff17a9c9fdecd61342cbedb08e94cc1b | R | 990 | 27 | ### Extract internal standards from all samples of Mboat2 overexpression and plasma membrane lipid supplementation lipidomics
setwd(rstudioapi::getActiveProject())
rm(list=ls())
library(tidyverse)
library(stringi)
library(matrixStats)
##Input IS concentration
## ==== Ion list for internal standards ====
load("./Out... |
f26d04ea20c7cafdcbbf1807341aa172385fdb3a5fa1af8903f5d9f6f54335cf | R | 991 | 29 | library(devtools)
library(sva)
#### matrix and coldata ####
merged_matrix_M
merged_matrix_R
coldata_df_M
coldata_df_R
#### vectors ####
## Mouse
vector_Sequencing_M <- coldata_df_M$Sequencing
vector_Treatment_Lateral_M <- coldata_df_M$Treatment_Lateral
# gene name
adjusted_merged_matrix_M <- ComBat_seq(merged_matrix... |
b1d0841fc1e20be2b2eaa11806250ec76ef6bdab67bc40bba5e2d45cabf11740 | R | 995 | 31 | #This code combines atc and covariates data frames to QT variants
#projectID: VL20243010
#by: Aleksi Kristian Winstén
#date: 20.11.2024
#University of Turku
setwd("/home/ivm/Projects/VilleLangen/EKG-MachineLearning/")
library(data.table)
library(dplyr)
library(tidyr)
df.scores <- fread("./Data/PRS/PGS002120_scores.tsv... |
8a1d21cfe0e71f2cfcc6b2594f565b6e833c2ad0da5e1702e76c14dc4d2c0b3a | R | 997 | 30 | #######################################################
# Segments to database
#######################################################
options(scipen=999)
## Parse snakemake
if(exists("snakemake")) {
files = snakemake@input[["seg"]]
outfn = snakemake@output[["tsv"]]
} else {
files = list.files("results/cnv/call... |
0d7fbf9b8c397fe57c18368996706fbedab05409b1dbeceb534c03be467a82ee | R | 1,000 | 29 | # ===============================================================
# ?? Regression shared formula builders
# ===============================================================
reg_compose_random_terms <- function(random, nested = FALSE) {
random <- reg_protect_vars(compact_chr(random))
if (length(random) == 0) return(... |
3cd79708f0b32779f3932321d9bf1d267f09bc464f236f973b8a32eecbfe583a | R | 1,009 | 31 | # -----
# UMS IPSIT BIOINFO - Licence GPL-3
# https://github.com/fdumbioinfo/moal
# -----
#
# moal install
source("https://raw.githubusercontent.com/fdumbioinfo/rtools/main/moal-demo/0-moal-install-r-universe.r")
library(moal)
# change working directory to data_analysis folder
setwd(".")
#
"inputdata/1-DESeq2_RNA_4_197... |
840095b50085c8448a1d5ca63326fe82866fe134cf624e56070b3de7a4c491cd | R | 1,010 | 37 | shinyUI(
navbarPage("premessa",
tabPanel("Concatenate files",
fluidPage(
fluidRow(
uiOutput("concatenateUI")
)
)),
tabPanel("Normalize data",
tags$head(tags$script(src = "gate-plot.js")),... |
cbbf0870aedc6b732911642705622be283b9150f258d9825089b9db03fb83cfc | R | 1,010 | 32 | #' @title Check headers of files stored on disk
#'
#' @description
#' Function to check that all filenames in a vector
#' point to existing files with the same resolution,
#' orientation, size, and origin.
#'
#' @param files character vector of filenames
#' @param showError logical whether to show an error (True) or
#'... |
ee3508fe319e42b1c082c1d1d83fadd044876be09d4543e9e2a2dce2751ab005 | R | 1,013 | 37 | #'
#'
#' @param
#'
#' @return
#'
#' @export
nested_xgboost_cox_cv_pdp <- function(results, pred_vars) {
pdp_data_function <- function(x) {
feature <- x[[1]]
model_number <- x[[2]]
model <- flatten_results[[model_number]]$model
vars <- flatten_results[[model_number]]$vars
data <- flatten_results[[... |
18bf60f85fd01e3d7a2ca2c2ef442fc075ae4776cec347dd8227d44ca56da70f | R | 1,014 | 27 |
#' @title runCorrelation
#' @description Calculates the spearman correlation on the input expression matrix & saves the results in SCENIC format
#' @param exprMat_filtered Expression matrix (filtered)
#' @param scenicOptions Fields used: Intermediate file name "corrMat"
#' @return Writes the output in the file name st... |
dc17735e60ed0174bf711f4ff56f360e8106936ce2d618ec23eae2e13a53dbdf | R | 1,016 | 38 | library(Redeconve)
library("anndata")
library("Matrix")
library("MatrixExtra")
library("dplyr")
set.seed(1)
#memory.limit(size = 400*1024) # in Megabytes (edited)
args<-commandArgs(trailingOnly = TRUE)
st_path = args[2]
sc_path <-args[1]
celltype_key <- args[3]
output_path <- args[4]
sc<-read_h5ad(sc_path)
sc$var_n... |
034182756cb5dbca0075611441713afcb209f503619af661cb0ce8a84ec1471a | R | 1,017 | 34 | library(tidyverse)
library(odbc)
library(DBI)
library(GSVA)
library(qusage)
rm(list=ls())
myinf1 <- "/projects/verhaak-lab/GLASS-III/results/kallisto/kallisto/final/gene_tpm_matrix_all_samples.tsv"
myinf2 <- "/projects/verhaak-lab/USERS/johnsk/glassx/varnf/data/msigdb/h.all.v7.1.symbols.gmt"
expr <- read.delim(myinf... |
1d3ccb9783141171271902d529dca8e55f56b60143b239629af01d515cd90695 | R | 1,017 | 23 | calculate.Z<-function (x, parallel, ncores, verbose, Z, do.ZnZ = FALSE, debug.verbose = FALSE)
{
if (is.null(Z)) {
message("Nodewise regressions will be computed as no argument Z was provided.")
message("You can store Z to avoid the majority of the computation next time around.")
message("Z only depends ... |
a722d5a833407aafb1c01859cccdac9ae5f92da0c731118370e508889f21fc57 | R | 1,021 | 41 |
source('./R/20211129_init.R')
setwd("C:/.../20211103_mutant-analysis/")
source('./R/20211201_functions_data_processing.R')
library(gridExtra)
library(readr)
library(ggplot2)
library(dplyr)
setwd("C:/.../data")
df = NULL
ID_elements = NULL
thresh=543
gap2fill=0
for (i in 1:length(dir_list)){
for (... |
fe316350985300f328aef1b2afc21dd25c01399294fc4cb8653dbd7e6bd8da3a | R | 1,024 | 33 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Make id2name file
# Author: Amanda Zacharias
# Date: 2023-08-07
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 gcc/7.3.0 r/3.6... |
34934041180446ecb023bd3288d3ec541e6e6d7a4a9e6a11a67ba1c2e1dec87f | R | 1,029 | 24 | #' helper-load — testthat helper: source alignment-data-sources.R for tests.
#'
#' Auto-loaded by testthat before each test file in `alignment/assessment/`.
#' `testthat::test_dir()` walks up from the test file to find `helper-*.R`
#' files, so this works whether you run tests from the repo root or
#' anywhere else.
#'... |
8c5257c245c5f6a9def9c1528b412f59b3099d8ec22bf838b90ea85921a93220 | R | 1,029 | 27 | # R script for calculating annualized percentage change in hippocampal volume based on longitudinal MRI data
# Author: Maryam Hadji, University of Eastern Finland, Kuopio, Finland (maryamh@uef.fi)
# Last updated: 17-Feb-2025
#
# Requirements:
# - R version 4.3.1 or later
#
# Usage:
# - Set working directory to the dire... |
5ec569178ea9d92bc1de631b8a33da96e567a66f5ab27893b5f071f3e6e5bd5e | R | 1,030 | 37 | # ===============================================================
# ?? Regression shared utilities
# ===============================================================
compact_chr <- function(x) {
if (is.null(x) || length(x) == 0) return(character(0))
x
}
reg_protect_vars <- function(vars) {
if (is.null(vars) || l... |
8771e2fb7c587587ebe7842cb080e8bb7a8907c590fd2d65589af7640508fc37 | R | 1,030 | 28 | library(ggplot2)
# Manually specify column names if they are missing or incomplete
dataeli1 <- read.csv("copieddata.CSV", sep = ";", dec = ",")
# Create the line graph for AvgFitness and MaxFitness over Generation
ggplot(dataeli1, aes(x = Generation)) +
geom_line(aes(y = AvgFitness, color = "Average Fitness"), size... |
bde1757bb5159ad090d8d26e84d10089ee482b2054f98db96f228b554fb0507d | R | 1,033 | 15 | # Install MLflow for R
files <- dir(".", full.names = TRUE)
package <- files[grepl("mlflow_.+\\.tar\\.gz$", files)]
install.packages(package)
mlflow:::mlflow_maybe_create_conda_env(python_version = "3.7")
# Install python dependencies
reticulate::conda_install(Sys.getenv("MLFLOW_HOME", "../../.."), envname = mlflow:::... |
faff01281c57ea720b1edd29d19efb5bd63b71cc8eddff505aa6ad95d990f5f6 | R | 1,039 | 44 | # Deploy a Prod-Ready, Robust Shiny Application.
#
# 4. Test my package
devtools::test()
rhub::check_for_cran()
# 5. Deployment elements
## 5.1 If you want to deploy on RStudio related platforms
golem::add_rstudioconnect_file()
golem::add_shinyappsio_file()
golem::add_shinyserver_file()
## 5.2 If you want to deploy... |
229586db19245fb0dfb8195aa7cf36a393bb74419e579087a6df4a6a9b1ed9a7 | R | 1,040 | 38 | ## Ensure reproducibility of example data
set.seed(20220907)
## Generate example data
sce <- scuttle::mockSCE()
## Add some sample IDs
sce$sample_id <- sample(LETTERS[1:5], ncol(sce), replace = TRUE)
## Add a sample-level covariate: age
ages <- rnorm(5, mean = 20, sd = 4)
names(ages) <- LETTERS[1:5]
sce$age <- ages[... |
a9f0781873cc1cb8252ffd7bfd9a32dc0436af8d5d4ec9b7392b4f39aa80cc33 | R | 1,040 | 33 | library(ggplot2)
library(dplyr)
library(viridis)
# Your new data
data2 <- data.frame(
DispersalType = c("Passive Dispersal", "Combination of Passive and Active Dispersal"),
Percentage = c(64.9, 35.1)
)
# Create the ring (donut) chart with a larger hole in the middle
ggplot(data2, aes(x = 2, y = Percentage, fill ... |
db1558d6c5ea7dfdf09224a99156e6874d4721fac3f3e559b604dc1651ec26a5 | R | 1,041 | 26 | # Import
csv_file <- "/data/project/1_Epilepsy_RNA/RNA_Human/Raw_Data/Patients_sheet_FTP_2.csv"
file_data <- read.csv(csv_file, stringsAsFactors = FALSE)
files_and_labels <- setNames(file_data$Filepath, file_data$Group)
# Initialize an empty list to store dataframes
list_dfs <- list()
# Loop through each file path a... |
9ac91095ddff8fd5d299a13071746679e196ced45f7b92425d9339abe36a7c58 | R | 1,043 | 32 | library(dplyr)
library(ggplot2)
DataName <- 'DESeq_1_0.05_FILTERED_1_MTLEALL_NL_Up'
DataName <- 'DESeq_1_0.05_FILTERED_1_MTLEALL_NL_Down'
InputFile <- sprintf("/home/joonho345/1_Epilepsy_RNA/RNA_Human/08.Enrichment/GO_02.ToppFun/ToppFun_%s.txt", DataName)
GO_type <- "GO: Biological Process"
GO_name <- "Biological"
... |
2a029ed83a7b9fc59b9f8b938d93d6a63a09bf47abeacb78b136e4895ca78c23 | R | 1,045 | 30 | RNGkind("L'Ecuyer-CMRG")
set.seed(123)
lIfun <- function(d) factor(rep(2,nrow(d)),levels=1:2)
# All staircase (indicated by NA)
mySSD_function <- function(d)SSD_function(d,SSD=NA,p=.25)
designSSexG <- design(model=SSexG,
factors=list(subjects=1,S=c("left","right")),Rlevels=c("left","right"),
... |
9d24639a64b02c90e415ef1fb423b34ab849839d57e6b601edc3963ab423e290 | R | 1,045 | 31 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
# test if there is at least one argument: if not, return an error
if (length(args) < 1) {
stop("Please provide an input", call.=FALSE)}
titan_file <- args[1]
aliquot_barcode <- args[2]
solutions_out <- args[3]
# Interactive run
#titan_file <- "/projects... |
3aed7ed6f56a12f82a932fafc33372d601ce9407a077796813346b477e11fa01 | R | 1,048 | 34 | .onLoad <- function(libname, pkgname) {
op.fafbseg=choose_segmentation('flywire31', set=FALSE)
# set a default location for sqlite databases if user has not specified their
# own
op.fafbseg=c(op.fafbseg, list('fafbseg.sqlitepath'="~/projects/JanFunke/"))
op.fafbseg=c(op.fafbseg, list('fafbseg.cachedir'=fafbs... |
9516f035000a0fcb1debf66289f1c3d72c10d271c850fbcf33f8965519f9d003 | R | 1,049 | 16 | library(GeoTcgaData)
library(dplyr)
# https://github.com/YuLab-SMU/GeoTcgaData
load("/home/joonho345/resources/GeoTcgaData/gene_cov.rda")
# v1.992
#### import matrix ####
merged_matrix <- as.matrix(read.table("/home/joonho345/1_Epilepsy_RNA/RNA_Human/02.Quantification/merged_matrix.txt", sep = "\t", header = TRUE, ro... |
970ac098c6e22c0795daabf8cda3997a8b33025ff2a20e39997ea33e35255e54 | R | 1,055 | 31 | context("annotations")
op <- choose_malevnc_dataset('VNC')
on.exit(options(op))
test_that("manc_meta", {
skip_if(inherits(try(clio_token(), silent = T), 'try-error'),
message = "no clio token available")
res <- manc_meta('DNp01')
expect_true(is.data.frame(res))
expect_true("dvid_group" %in% colnames(r... |
49cd176ed6d3acb89f06c45e8bf8a133235e0a4017236a85b6cc4492a3d4c2f4 | R | 1,061 | 28 | library(ggplot2)
library(viridis)
# Create a dataframe for the CCA sensitivity analysis results
data <- data.frame(
CCA_Concentration = c("100%", "75%", "50%", "25%", "0%"),
Settlement_Success = c(96, 96, 72, 52, 1)
)
data$CCA_Concentration <- factor(data$CCA_Concentration,
level... |
812731f992ecc361ee417b0164a9416ee1e5861230272192021240065228583f | R | 1,065 | 28 | context("test source PPI")
test_that("sourcePPI works", {
skip_if_offline()
skip_if_not_installed("STRINGdb")
tf <- data.frame(matrix(c("Rv0022c","Rv0023","Rv0042c","Rv0043c","Rv0047c","Rv0054"),
nrow=6, byrow=T),stringsAsFactors=FALSE)
# STRINGdb Version 10
if(R.Version()$major=="... |
c04c0d2b7262b4a4c7eb30970a5769af7ee5da3de6942f54917a8187e56621f7 | R | 1,065 | 35 | library(tidyverse)
library(qs)
library(survival)
library(survminer)
result_df = data.frame()
para_df = qread('res/lasso_model_para.qs')
for(cohort in tcga_cohorts){
final_tpm <- qread(str_glue("data/UCSC_GDC/TCGA-{cohort}_clean_tpm.qs"))
genes = intersect(rownames(para_df),rownames(final_tpm))
surv_df... |
84f3b6a631c8ca217b240ab8f37a05a9a33ce089b23fefb150d24b41c31580b5 | R | 1,066 | 28 | ## venn diagram -hacky way by saving a csv in matlab and loading it here
## -don't have time to implement this in matlab.
# why does matlab suck so much
# r is so great
# library(venneuler)
# myVenn <- venneuler(c(A=10, B=11, C=13, D=12, 'A&B'=1, 'A&C'=13, 'B&C'=4, 'A&B&C'=1))
# myVenn$labels <- c("A\n22","B\... |
8f3b762c001882f887d66c5a2a9b034ff8563e9c8e39db74cd25aa05e7e3b906 | R | 1,066 | 16 | rm(list = ls())
indir <- '/sc/arion/projects/adineto/ase'
outdir<-'/sc/arion/projects/DiseaseGeneCell/Huang_lab_project/ADASE/ASE_individual/ASE_basic/'
### ASE analysis of each sample-variant by using binom.test
files <- setdiff(dir(indir),c('msbb.meta.tsv','msbb.meta.v2.tsv','ROSMAP'))
for (i in 1:length(files... |
5baf2066abd948990d28d5b3850ce5e75e54a29cba16a04ac439048b64017597 | R | 1,069 | 29 | library(stringr)
## Calculate MSN
setwd('/home/huyang/Projects/HuYang/HY_20250708')
atlas_names <- c('Schaefer300', 'DK308')
meas_names <- c('Morph_5F4S', 'Morph_5F1S', 'Morph_9F1S')
for (curr_atlas in atlas_names){
for (curr_meas in meas_names){
## Load raw data (Z-score)
in_fname <- paste('./PROCDATA/STATS/... |
f7dd9880eba4a7e7b857742607a187ea635bcb83655df33382ad277406c443d0 | R | 1,069 | 30 | #!/usr/bin/Rscript
args <- commandArgs()
#print(args)
dir <- args[6]
Name <- args[7]
#tail -n +5 RNA_2500.mm10.read_distribution.txt | head -n -1 > temp2.txt
#head -n 3 RNA_2500.mm10.read_distribution.txt | grep Assigned | sed 's/Total Assigned Tags//g' | sed 's/ //g'
Df <- read.table(paste(dir,"temp1.txt",sep="/")... |
165dbf8ea72dc724fde498bb251e431d416c47dee213beaaab925ad66658dadf | R | 1,073 | 33 | # simple get request
manc_get <- function(path, urlargs=list(), as='parsed', ..., show=NULL, body=NULL) {
u=manc_serverurl(path, urlargs=urlargs)
if(!is.null(body))
return(manc_get_body(u, body, as=as, ...))
if(!is.null(show)) {
show=match.arg(show, choices = c('all', 'time', 'user'))
u = paste0(u, "&... |
3c06e1e5e64e5d4bfa08075e9beb280d7e8027b4beba8400986c6ece2b54d9fe | R | 1,076 | 34 | library("rsconnect")
## Locate app_dir. Edit as needed
app_dir <- here::here("inst", "spe_wrapper_app")
## Or you can go to your shinyapps.io account and copy this
## Here we do this to keep our information hidden.
load(file.path(app_dir, ".deploy_info.Rdata"), verbose = TRUE)
## Authenticate to shinyapps.io
rsconne... |
32d017c039f0c54ae0cc79e6c9d408ceb73fb485dad59a53edc98f1e80b70fbb | R | 1,079 | 29 | propagate_transcript_filter <- function(filter_df, target_mapping,
grouping_column) {
filtered_target_mapping <- dplyr::inner_join(as.data.table(filter_df), # nolint
as.data.table(target_mapping), by = 'target_id') # nolint
filtered_target_mapping <- dplyr::select_(filtered_target_mapping,
grouping_colu... |
82430620ed197729b120c8a0c42f0f3620c49af4ce969c61e85578f8395ed331 | R | 1,080 | 34 | #After identified the most fitted PC and resolution
library(Seurat)
library(dplyr)
data <- readRDS("nodomap_cleaned.RDS")
DefaultAssay(data) = "originalexp"
data <- SetIdent(data, value = "seurat_clusters")
#Find markers of every clusters, only show positive ones
data.marker <- FindAllMarkers(data, only.pos = T, min.... |
2188eed98fc1fba02fc75af31187065602e62c9126666eb0b869b5c41c454855 | R | 1,081 | 28 | library(data.table)
# Inverse of sum() aggregation: repeat each row 'count' times
unsummarize <- function(DT, count,
cols = setdiff(names(DT), deparse(substitute(count))),
drop_zero = TRUE, validate = TRUE, keep_count = FALSE) {
stopifnot(inherits(DT, "data.table"))
... |
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