sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
77362cb2849eeb047948b0758e55f7f7db2d0e2e34b5dd00e8a1af5ec396c359 | R | 3,258 | 104 | # Central location for parameter aliases.
# See https://lightgbm.readthedocs.io/en/latest/Parameters.html#core-parameters
# [description] List of respected parameter aliases specific to lgb.Dataset. Wrapped in a function to
# take advantage of lazy evaluation (so it doesn't matter what order
# ... |
e33cc88d26eac4350aff0efd59a219ee935c550ac709e76de158e3aeee3739a0 | R | 3,258 | 80 | # Stephanie J. Spielman and Jaclyn Taroni for ALSF CCDL 2020
#
# This script subsets the files required for subtyping non-MB and non-ATRT
# embryonal tumors. The samples that were subset in
# [`01-samples-to-subset.Rmd`](./01-samples-to-subset.Rmd), based on specific
# conditions outlined in that notebook, will be inc... |
c463d2e1b8591148886356fa574abb44507b3550f623bfa16c1e215646824e53 | R | 3,259 | 100 | #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#################### DEPRECATED FUNCTIONS ####################
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' Deprecated functions `r lifecycle::badge("deprecated")`
#'
#
# @description
# Use [FeatureS... |
8284bcede3e05ef73ec5c0fcc178e8db2e4054f919113f3047e3d15b189110e0 | R | 3,261 | 88 | #!/usr/bin/env Rscript
# Copyright (c) 2015 Tobias Neumann, Philipp Rescheneder.
#
# This file is part of Slamdunk.
#
# Slamdunk is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
... |
df5f2f147dccdde0da0655bee54171c19bc53ad1fe89619cd7e7b37b846f1c9b | R | 3,262 | 130 | ---
title: "Celltype_Abundance"
author: "AF"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(Signac)
library(Seurat)
library(tidyr)
library(dplyr)
library(ggplot2)
library(rstudioapi)
set.seed(17)
```
Celltype abundance plots, r... |
b1eec4f4bf5660303c2e6f97c52e3173aa099fd5ea79a4bc48651f5be60ffeba | R | 3,270 | 91 | #' @export
pairwise_glmer <- function(clr, y = "microbe",
model = "~ . + (1|ID)",
metadata,
posthoc.method = "BH",
features.as.rownames = FALSE, CI = TRUE, verbose = TRUE){
#Generate output data.frame
out_df ... |
1ee2ea610e356013253d4e3695f0f34af1e5b10d9c232ecb4e1f8a600833904c | R | 3,273 | 131 |
library(plink2R)
library(grid)
readmat <- function(f)
{
con <- file(f, "rb")
p <- readBin(con=con, what="integer", n=1)
K <- readBin(con=con, what="integer", n=1)
matrix(readBin(con=con, what="double", n=p * K), nrow=p, ncol=K)
}
dat <- read_plink("data", impute="random")
# Price 2006 standardisation
X ... |
a34ce896655fde75671ac6a24a7390f35e4a25fda33d9af6b7b9e93a6e7ead4f | R | 3,285 | 92 | #' Plot cell type counts means versus variances
#'
#' This function returns a plot of the log10(mean) versus log10(variance) of
#' the cell type counts. The function takes a matrix of cell type counts as
#' input. The rows are the clusters/cell types and the columns are the samples.
#'
#' The expected variance unde... |
c510902f784ba8909a4bc01591867c336a4b07f37fccc1f975aed863c67deaee | R | 3,286 | 92 | ########################### Coverage Analysis Diagnostics ##########################
#
# Objective: Program to check which parameter sets were in bounds
#
########################### <<<<<>>>>> #########################################
rm(list = ls()) # Clean environment
options(scipen = 999) # View data without s... |
25dd907c37f9c395b920e4c71867c8c9c29ee7c0a6b6151918f5750a6014ff0d | R | 3,290 | 67 | ##' generate network based on Enrichment results
##' @rdname richNetmap
##' @param richRes list of enrichment object
##' @param gene vector contains gene names or dataframe with DEGs information
##' @param top number of terms to display
##' @param top.display top number to display
##' @param pvalue cutoff value of pval... |
5a5f77c1aaf47d973897892c065f579823c176da8976ef701438dda7271dacbc | R | 3,293 | 127 | ---
title: "Domain segmentation (STARmap PLUS mouse brain)"
output: BiocStyle::html_document
# output: pdf_document
vignette: >
%\VignetteIndexEntry{Domain segmentation (STARmap PLUS mouse brain)}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
... |
dfabaea2b7b6b274163d665f79594d3a908e8adbe3738fe7ddfbd95b1c78ad37 | R | 3,297 | 127 | ---
title: "Temporal GO Term Analysis from NPCs to Day14 Neurons"
author: "AF"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
```
```{r libraries}
library(clusterProfiler)
library(dplyr)
library(ggplot2)
```
```{r para... |
b8cb84bcf8c03145c3d32d27a7c9a1d3813c7255b603d573fa3043ba588842e6 | R | 3,312 | 77 | args <- commandArgs(TRUE)
run_scID<-function(DataPath,LabelsPath,CV_RDataPath,OutputDir,GeneOrderPath = NULL,NumGenes = NULL){
"
run scID
Wrapper script to run scID on a benchmark dataset with 5-fold cross validation,
outputs lists of true and predicted cell labels as csv files, as well as computation time.
... |
2e2ac927f5c0e3a48ad2cc31e298069b9de235fe57623fc91a06d9b7c277f14d | R | 3,326 | 88 |
suppressPackageStartupMessages(library(tidyverse))
library(tidyverse)
suppressPackageStartupMessages(library(ComplexHeatmap))
library(ComplexHeatmap)
library(utils)
library(optparse)
option_list = list(
make_option(c("-f", "--interaction_file"), type="character", default="interaction_matrix.csv",
hel... |
dcb51f764d4be44e7add661f54429983461da915121fa54a71202ef38e9670de | R | 3,330 | 105 | ###############################################################
# Example script illustrating how to generate correlation
# dot plots with annotated p-values.
#
# This script uses only simulated data.
# It does NOT contain any real values or analysis from the study.
###############################################... |
06d85f2439257b2029fc76b252b545be6d74d70439a6c3ff0606aeec8764c2c7 | R | 3,336 | 112 | ---
title: "Select pathology diagnoses for inclusion"
author: "Candace Savonen for ALSF CCDL"
date: "2021"
output:
html_document:
toc: yes
df_print: paged
html_notebook:
toc: yes
toc_float: yes
---
## Background
In an upcoming release, `integrated_diagnosis`, which can be updated as the result of ... |
1be35c7ab7d6c5da0cf3de509d4683a6e2d7975d6b6730e23215638ae467def5 | R | 3,336 | 98 | ## =========================
## Experiment I: active vs. sham TUS (amygdala)
## =========================
if (!requireNamespace("here", quietly = TRUE)) install.packages("here")
source(here::here("stats","lme_models","_setup.R"))
## -------- acquisition: CS * TUS * TRIAL --------
res_acq_triple <- run_lmer_test(
da... |
4c6290788c62d499fae91d7d365d914d26f179dd041ffc7eeacf8d5084393033 | R | 3,338 | 66 | ################################# ED.Fig.13c and d
#The intermediate files 'VSN_sympathetic.gct' and 'CD8.gct' are available in the TCGA folder
#ED.Fig.13c: Survival probabilities of VSN+sympathetic###
library(ggpubr)
library(ggplot2)
library(survival)
#survival plot####
res.ssgsea <- as.data.frame(fread('VSN_sympathe... |
7cd84894237cc9ab39b4c5100879e9da62ff86d1d6da6d3a63d664c7cd563ac6 | R | 3,355 | 72 | #' @title Generate Pathway Annotation Dot Plot
#' @description Creates a dot plot (bubble chart) to visualize the top annotated KEGG pathways.
#' The plot displays the annotation count for both Genes and Metabolites across different pathways.
#' Top 20 pathways are selected based on total feature count.
#' @param d... |
e6f3bd641cf572ae5217abe3eddc577567e529b4d6bdadaa7c1e3f41277fa504 | R | 3,368 | 98 | ---
title: "Combo_Report"
output: html_document
---
## `r PID`
```{r, fig.dim=c(13, 6)}
combo <- Synergy_df
combo$Metric <- ifelse(combo$dcDSS_asym > 0, "dcDSS_asym > 0", "dcDSS_asym < 0")
CRA_report_list <- list()
d <- ggplot(combo, aes(x = reorder(.data$Drug.Name, -.data$dcDSS_asym), y = .data$dcDSS_asym, width = 0... |
8a462e25c926e7f3d1ed66b830bb317ad072a407be9d631b738b775c5204e6af | R | 3,370 | 102 |
#' Make Prediction on a set of CNVs
#'
#' This function uses a pre trained model to make a prediction on a new set
#' of CNVs
#'
#' @param model pre trained model loaded using `luz::luz_load()`
#' @param root root folder for the dataset, created using `save_pngs_prediction()`
#' @param cnvs cnv data.table in the usual... |
8729fc5ea1ca77b2db7f9ea79a661a2013ab1e3bd6aa439a6f459a2c1121e14e | R | 3,388 | 104 | # ==============================================================================
# S1_Tutorial.R
# Server logic for the Tutorial/Introduction tab.
# Handles loading of built-in demo datasets (TXT and RDS formats) and download handlers for tutorial materials.
# =======================================================... |
449e36710fe93d9e2cfe2a25d51662572217488bb77b906e3720574c482f1aac | R | 3,390 | 115 | # Test read_npx_format_colnames ----
test_that(
"read_npx_format_colnames - error - long read as wide",
{
withr::with_tempfile(
new = "cdfile_test",
pattern = "delim-file-test",
fileext = ".txt",
code = {
# write the coma-delimited file from a random data frame
dplyr::t... |
408940507f8a8c2ebb481d5d763fbb7bf6f986096510ec27b2e5a4cc11fe61d6 | R | 3,392 | 88 | # Import a single function from an R file without running the whole file
#
# Args:
# - source_code_R_file: path to the source code R file that contains the
# definition of the function to be imported
# - function_name: the name of the function to be imported
#
# Returns the imported function
#
# NOTES:
# - Only funct... |
05ff8798a69b3f8e4a46613fdd056a9491f13c0367f7e0cd4bab5107af0539c3 | R | 3,394 | 83 | context("Manuscript Benchmark Data Integrity")
# ==============================================================================
# Unit Test: Benchmark Data Integrity (Meta-Test)
# ==============================================================================
# Purpose:
# This script performs a 'meta-test' on the ben... |
833ad06d1738b9d67601188139d1fa852cb6bfa7dec7c80dde666f9264b678f8 | R | 3,395 | 107 | # Heatmap of Top 30 Shared DEGs per Regulatory Pattern (Vertical View)
# Description: Generates quadrant-specific vertical heatmaps (log2FC) for shared DEGs in OSNs and Fat Bod
library(readr)
library(dplyr)
library(tibble)
library(pheatmap)
# Custom diverging color palette
my_colors <- colorRampPalette(c("#0066CC", "... |
2c2d869a53ae1e4d0e6bc42945fb6a5d52d98f8b66e01f68acbc0e41e3341778 | R | 3,397 | 99 | ---
title: "NE_down_genes_expr_in_NPCs_Neurons"
author: "AF"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r setup}
library(dplyr)
library(rstudioapi)
library(ggplot2)
```
I want to look at the expression of the genes which are down in Neuroec... |
bd9058e50f7685f0093c7f4771a4d7aaf766f223cde36eb8b3ea6afe2471361a | R | 3,397 | 45 | # Separate post-primary targeted tier; never modifies frozen primary outputs.
.libPaths(c(normalizePath('.Rlib'),.libPaths()))
suppressPackageStartupMessages({library(fgsea);library(jsonlite);library(digest)})
source('src/transcriptomics/helpers.R')
cfg<-fromJSON('config/targeted_redox_v1.json',simplifyVector=FALSE)
f<... |
e4a903cfb438a6d844dc199970c0def84c99e4def9fdbc74207dad77b9e15288 | R | 3,401 | 101 | #' @export
col_agrecounter<-function(inputDF, col_names, col_collapse , rows_collapse, control_col)
{
#example
#create dataframe for aggegation and counting (A&C)###
# dat1<- data.frame(
# mature_miRNA=c('hsa-miR-195-5p', 'hsa-miR-195-3p','hsa-miR-195-5p', 'hsa-miR-195-5p', 'hsa-miR-4753-5p', 'hsa-miR-475... |
21270f53e67314a8b94b2743162a987cb0c47642321b576c9acac93bd5210140 | R | 3,403 | 85 | library(ComplexHeatmap)
library(stringr)
library(ggplot2)
library(ggrepel)
library(data.table)
library(cowplot)
library(patchwork)
source("../Plot_theme.R")
# Load color scheme
colors <- fread("../Plotting/colors.csv", strip.white = F)
color_v <- colors$Color
names(color_v) <- colors$ID
# LAR dataset volcano plots
da... |
a3e87e7ccfdf378382598211fe95d026362ff563ba1e7f136cb920ffe4212928 | R | 3,404 | 67 | library(Seurat)
library(BioVenn)
library(ggplot2)
library(gridExtra)
setwd("/home/ubuntu/PDSCRBNG/26_03_24_Figure_1")
source("~/PD_project_analysis/manuscript_scripts/MV_utils.R")
color_palette_cluster <- c("DaN1" = "#0072B2",
"DaN2" = "#56B4E9",
"GabaN1" = "#CC79... |
91beca0369f486bf3e5167ace0b8b852bdc8a972436b7e7e9e2efed6e47eda5e | R | 3,405 | 68 | #' @section Package options:
#'
#' scCustomize uses the following [options()] to configure behavior:
#'
#' \describe{
#' \item{\code{scCustomize_warn_raster_iterative}}{Show message about setting `raster` parameter
#' in \code{\link{Iterate_FeaturePlot_scCustom}} if `raster = FALSE` and `single_pdf = TRUE`
#' due... |
68e1be4aa7d42dd12571501bc5becc517c9429e13175f6e0e83fef8f16f6ebb2 | R | 3,406 | 73 | run_SingleR<-function(DataPath,LabelsPath,CV_RDataPath,OutputDir,GeneOrderPath = NULL,NumGenes = NULL){
"
run SingleR
Wrapper script to run SingleR on a benchmark dataset with 5-fold cross validation,
outputs lists of true and predicted cell labels as csv files, as well as computation time.
Parameter... |
c1c933b4eaa06e806373485dbacf01f3429b90f521d85f9e4c7cd97dd8262c39 | R | 3,407 | 66 | #' @title Get parcel-based structural connectivity
#' @description This function computes parcel-based direct structural connectivity measures using an MNI-registered
#' brain parcellation and the curated HCP-842 structural connectome template described in Yeh et al., (2018 - NeuroImage)
#' @param cfg a pre-made cfg st... |
5b31e03aff06841817cc1d3c581f1902cc8bb57083fbff3050db85c696a58927 | R | 3,411 | 93 | #!/usr/bin/env Rscript
library(ggpubr)
library(cowplot)
library(tidyverse)
library(rstatix)
gene_data_file = "/Users/plezar/Library/CloudStorage/Box-Box/TIME Lab_Shared Folder/Personnel/Graduate Students/Maksym Zarodniuk/GBM ECM paper/qPCR/2025-01-11 IHA compression/targets.csv"
rq_results_file = "/Users/plezar/Libra... |
f663112546a24e15cb90ac541a53d8292dd5dfe2fb6514c2184ecb155a188297 | R | 3,416 | 99 |
rm(list=ls(all=TRUE))
library(REdaS)
sub_list = 1:20
for (ith in sub_list) {
result_raw_table <- read.table(paste("/Users/bo/Documents/data_liujia_lab/analysis_liuP1_greeble/sub", ith,"_mri_record.txt", sep = ""), stringsAsFactors = FALSE)
num_trial = length(result_raw_table[,1])
result_table <- data.frame(s... |
63848d76f009e3ff1b0368007f04479b2c751c075123625ab5fd5d2853940440 | R | 3,417 | 97 | # Author: Komal S. Rathi updated, 2020-07 Kelsey Keith
# script to perform immune characterization using R package immunedeconv
# load libraries
suppressPackageStartupMessages({
library(optparse)
library(tidyverse)
library(immunedeconv)
})
# parse parameters
option_list <- list(
make_option(c("--expr_mat"), t... |
76bc989c9b5b3a33d9bc788a156d411185f686cb436a58179e7814d0ae0655d0 | R | 3,417 | 117 | 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$Species)) - 1L
# Create imbalanced training data (20, 30, 40 exampl... |
5892c12b1c725503b9f4cdc49a4795d6158a975ad0eee9b83d6e97078ff92959 | R | 3,419 | 50 | setwd('/media/user/disk31/Nameeta_230810_Xenium_5samples_230816/FinalClus/')
xen = readRDS('stlearn_origSlide.rds')
meta = as.data.frame(fread('/media/user/disk31/Nameeta_230810_Xenium_5samples_230816/analysis_Dec/xenium_meta_24Jan_with_info.csv'))
rownames(meta) = paste0(meta$sample, '_', meta$bc)
info = meta[colnam... |
ce75fbd2124e2deab971f30efd040ed3cefcccd05fff1d764aa3e1f138af0786 | R | 3,422 | 123 | ---
title: "02_S1_pharynx_epithelium"
output: html_document
date: "2025-04-01"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
# Load custom plotting themes and required libraries
source("~/Rfunction/scTheme.R")
scThemes <- scThemes()
library(Seurat)
library(tidyverse)
# Load full datas... |
e7ceef8c2a58bb02e56c1cfc7076618aad9a331128e05aeac66bfcfb29ecdd98 | R | 3,422 | 103 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
# path_sc = "/home/ubuntu/simulation_LN/sc_simu.h5ad"
# path_st = "/home/ubuntu/simulation_LN/st_simu.h5ad"
# params are
# ID clustering
# path in
# path out
path_in <- args[1]
dir_out <- args[2]
index_key = args[3]
path_sc <- paste(path_in, "/sc_simu.h5a... |
ee3578bbf88ea63be68a8cf725ce045b67cdbe938b4bfd2eae09f292fcadd15f | R | 3,426 | 92 | ## Integration with Campbell et al. 2017 dataset, neuronal cells only
library(Seurat)
library(Matrix)
library(data.table)
library(ggplot2)
library(patchwork)
ref <- readRDS('ARH_NN_neurons_integrated_RPCA.rds')
arh <- readRDS('GSE282955_ARH_Sex_by_Nutr_neurons_integrated_RPCA.rds')
arh <- subset(arh,subset = cell_typ... |
a98ac22e398958a78c41502807db8ab45516cada5e9eea9e0b7037680d6c4be0 | R | 3,428 | 172 | #' Help function checking if a variable is an R6 ArrowObject.
#'
#' @inherit .check_params params author return
#'
#' @keywords internal
#' @noRd
#'
check_is_arrow_object <- function(x,
error = FALSE) {
# check if input error is boolean vector of length 1
check_is_scalar_boolean(x... |
3f8137e894afc809b10e74e0b52c968541e247d64063f2cd1ef6b9b34d13b3c6 | R | 3,430 | 72 | ### Summary stats for the cross sectional analysis
# Load required libraries
library(readxl)
library(writexl)
library(here)
library(dplyr)
# Load excel file
file_path <- here::here("data", "megamastersheet_simulated.xlsx")
data <- readxl::read_excel(file_path)
print(dim(data))
# [1] 10802 36
# only keep the column... |
a76116e3b5058ce8c57771947765b570a8e753d26a21c0c1588d28e0b14c0c54 | R | 3,430 | 93 | #'---
#' title: "RNA Variant Calling Summary: `r paste(snakemake@wildcards$dataset, snakemake@wildcards$annotation, sep = '--')`"
#' author: nickhsmith
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "RVC" / "{dataset}" / "{annotation}_RVC_summary.Rds")`'
#' input:
#' - data_table: '`sm os.path.join(
#' ... |
74dc854dad8b29e0f56dda70854eb1f6c19b5a0a394d34f23bcc93a13cf0dcb9 | R | 3,431 | 71 | # The working directory is the directory that contains this test R file, if this
# file is executed by test_dir
#
# testthat package is loaded, if this file is executed by test_dir
context("tests/test_get_pcb_pot_csi.R")
# import_function is defined in tests/helper_import_function.R and tested in
# annotator/tests/test... |
d0d8703ae2a61f359c449acfe1b20901e2990c2a23bd19e160d352d8d7213da7 | R | 3,438 | 91 | library(data.table)
library(ggplot2)
library(lme4)
library(ggrepel)
library(cowplot)
source("../SM/src/custom_pvca.R")
source("../Plot_theme.R")
set.seed(42)
# Load color scheme
colors <- fread("../Plotting/colors.csv", strip.white = F)
color_v <- colors$Color
names(color_v) <- colors$ID
# Define variables and tissu... |
d81d41648d08d7d9c3f8f1286f0e2fde94e3351c07aaf1811e610f9aa1a89a8b | R | 3,441 | 90 | library(MOFA2)
library(data.table)
# (Optional) set up reticulate connection with Python
# reticulate::use_python("/Users/ricard/anaconda3/envs/base_new/bin/python", required = T)
###############
## Load data ##
###############
# Multiple formats are allowed for the input data:
## -- Option 1 -- ##
# nested list of... |
8154f8955d5f9cde212419939233a6bd23706eaaa18d384f268f219e5c3df1cf | R | 3,444 | 113 | # Figure 4B and Supplementary Figure 4A
library(tidyverse)
library(ggplot2)
library(ggridges)
library(dplyr)
library(forcats)
library(viridis)
library(tidygraph)
library(ggnetwork)
library(ggraph)
library(reshape2)
library(ggpubr)
# Custom function to create polar coordinate system for ggplot
# Acknowledgements to Et... |
ab16f6c4c070aa1796ebd1732c94b29cfc4fc905b02c8f5cf948e74dfb523381 | R | 3,457 | 94 | rm(list=ls(all=TRUE))
library(data.table);library(dplyr)
source('/home/lorincn/isilon/Cheng-Noah/software/corefunctions/functions.R')
#####
#####
tr=function(x)sum(diag(x))
xvegas=function(z,R,Z_xqtls) {
z=as.matrix(z);Z_xqtls=as.matrix(Z_xqtls)
m=nrow(R);p=ncol(Z_xqtls)
L=matrix(0,nrow=nrow(Z_xqtls),ncol=nrow(Z_... |
1e83dfa5e36e163a7d40f44fae7c4176b32a6d6c16d1d6773a3748a542c139d9 | R | 3,460 | 129 | #' Easy volcano plot with Olink theme
#'
#' @description
#' Generates a volcano plot using the results of the olink_ttest function using
#' ggplot and ggplot2::geom_point. The estimated difference is plotted on the
#' x-axis and the negative 10-log p-value on the y-axis. The horizontal dotted
#' line indicates p-value=... |
47e6750673763c0c9b6353c430836ad6daa8af57eb460d556fc681600cd6afde | R | 3,462 | 123 | set.seed(11235)
library(tidyverse)
data <- read.csv("D:/Program Files/MATLAB/Joint_Perception_Project_Final/Pupil/Post_Resp_Pupil.csv")
library(ggplot2)
library(ggprism)
library(gridExtra)
library(patchwork)
library(lme4)
library(ggpubr)
library(dplyr)
library(lmerTest)
library(sjPlot)
libra... |
2c7360ab3a19fb447dd3799c88b051a9663695f50c5ff914fc6490f643b147f2 | R | 3,464 | 105 | # Install required packages if not already installed
if (!require("VennDiagram")) install.packages("VennDiagram")
if (!require("readr")) install.packages("readr")
if (!require("dplyr")) install.packages("dplyr")
library(readr)
library(dplyr)
library(VennDiagram)
# Step 1: Load the raw p-value CSV
df <- read_csv("~/De... |
bbd828fc196474a1fcf3e0ac09b5b8278a5d7fafefe7acb86f4c04e4d461ee1b | R | 3,464 | 78 | library(ggplotify)
library(data.table)
library(ggplot2)
library(cowplot)
source("../Plot_theme.R")
# Load color scheme
colors <- fread("../Plotting/colors.csv", strip.white = F)
color_v <- colors$Color
names(color_v) <- colors$ID
# Load TERM DEG data
degs <- read.csv("results_TERM/degs_TERM_formated.csv")
degs$signif... |
b5bd6da706cae78a61da94ffaf0c386877299a3dfac19c83172fadd0df9c2573 | R | 3,471 | 95 | library(Biobase)
library(GEOquery)
library(Seurat)
library(readxl)
library(ggplot2)
library(dplyr)
library(harmony)
library(GenomicRanges)
library(Seurat)
library(patchwork)
library(cowplot)
library(data.table)
library(scales)
library(org.Hs.eg.db)
library(rtracklayer)
library(gghighlight)
library(dplyr)
library(Seurat... |
f3e9944d31577103fd82e4c99a14a5215899e171d94697f0214dff398872f2e3 | R | 3,473 | 89 | partialPlot <- function(x, ...) UseMethod("partialPlot")
partialPlot.default <- function(x, ...)
stop("partial dependence plot not implemented for this class of objects.\n")
partialPlot.randomForest <-
function (x, pred.data, x.var, which.class, w, plot=TRUE, add=FALSE,
n.pt = min(length(unique(... |
181546686aaec38e465f5ee4d7a3efa845a9cca92f67586cf487eecbc2012b52 | R | 3,474 | 91 | norGeneExp <- readRDS('norGeneExp.rds') # rows = genes, cols = samples
phenotypeDF<- readRDS('phenotype.rds') # one row per sample
safescale <- function(v) {
v <- as.numeric(v)
if (!any(is.finite(v))) return(rep(NA_real_, length(v)))
m <- mean(v, na.rm = TRUE); s <- sd(v, na.rm = TRUE)
if (!is.finite(s)... |
9ea1c15e350970038dddde8917da1f7e7843b39f55e86b36caa956aff0b719a5 | R | 3,475 | 127 | ---
title:
"Basic Walkthrough"
description: >
This vignette describes how to train a LightGBM model for binary classification.
output:
markdown::html_format:
options:
toc: true
number_sections: true
vignette: >
%\VignetteIndexEntry{Basic Walkthrough}
%\VignetteEngine{knitr::knitr}
%\Vignette... |
7f349ba9e1e90f1c267f767924751b1120523a43ef2b896944a469e4f8927769 | R | 3,483 | 132 | library("UMI4Cats")
library(tidyr)
library("ggplot2")
gene <- snakemake@params[['gene']]
waldout <- snakemake@output[['wald']]
fisherout <- snakemake@output[['fisher']]
diffplot <- snakemake@output[['diffplot']]
diffplotpng <- snakemake@output[['diffplotpng']]
diffplot_fisher <- snakemake@output[['diffplot_fisher']]
d... |
aa6ef8eb5b9318967a1d36999a4aba12a0689b7c85cf8f19dc77224d3accd375 | R | 3,487 | 91 | library(data.table)
library(ggplot2)
library(lme4)
library(ggrepel)
library(cowplot)
source("../SM/src/custom_pvca.R")
source("../Plot_theme.R")
set.seed(42)
# Load color scheme
colors <- fread("../Plotting/colors.csv", strip.white = F)
color_v <- colors$Color
names(color_v) <- colors$ID
# Define variables and tissu... |
2385d63a4d625774f929d6ca61785992c52e2c6f50830439770eafb82306addf | R | 3,489 | 99 | # S Spielman for CCDL, 2023
# Create panel A for Figure S7 that shows how histologies (cancer groups)
# are not balanced across RNA library preparations (polyA vs. stranded)
#### Libraries -----------------------------------------------------------------
library(tidyverse)
library(ggpubr)
#### Directories and files... |
4b94d31451580dbb44c92ecc71cea9c78731a4f3560547d9409f94b3f2f298de | R | 3,490 | 62 | # trait variants
'%notin%' = Negate('%in%')
library(AnnotationDbi)
library(org.Hs.eg.db)
# load variants from Open Targets ----
variants = read.csv('Datasets/associationByDatasourceDirect/associationAll.csv', row.names = 1) #data from open targets (ftp.ebi.ac.uk/pub/databases/opentargets/platform/24.09/output/etl/js... |
2b96a73089a043353381633bba2e89f190ee78039862e2d8790d55d78332797d | R | 3,499 | 118 | # Functions for chromosomal instability calculations
#
# C. Savonen for ALSF - CCDL
#
# 2020
map_breaks_plot <- function(granges,
y_val,
y_lab,
color,
main_title) {
# Given a GRanges object, plot it y valu... |
4fe579a5dbf0f6ad38628f76284322a6eea2db01635fc4f22fa0750260e61e1b | R | 3,499 | 119 | #basic functions
#exporting deduplicated gene lists in clipboard####
#' Copy a deduplicated gene list to the system clipboard
#'
#' Removes duplicates and \code{NA}s from a vector and copies the result to the
#' system clipboard, for pasting into Excel, Cytoscape or the Venny web tool.
#' Works on Windows, macOS and ... |
d4b1f6b7b0bd879ac7b67f6d742873a111984b68576f6641e5c9d5959f98218b | R | 3,506 | 71 |
#######################################################
## Functions to perform imputation of missing values ##
#######################################################
#' @title Impute missing values from a fitted MOFA
#' @name impute
#' @description This function uses the latent factors and the loadings to impute mi... |
0cd03b15ebc052f048abb6f746b95a1d130d5b9cd200a8b9e56bd1c607fd8e2c | R | 3,508 | 119 | #' PCA analysis
#'
#' Estimate correlation covariates vs. PCA axes
#'
#' @param mvals matrix of m values
#' @param pdata sampleSheet of data
#'
#' @importFrom stats prcomp
#'
#' @return betas matrix
#'
#' @export
#'
makepca <- function(mvals, pdata) {
tmvals <- t(mvals) # n x p required for prcomp
sel <- which(app... |
1b387264957c4fcc58e4ff25995747352a92e81c2bbbfefbde621dfc1078c6bd | R | 3,512 | 74 | # Author(s): Regina H. Reynolds
#---Load Libraries and data--------------------------------------------------------------------------------------------------------------####
library(tidyverse)
library(stringr)
library(optparse)
library(RNAseqProcessing)
# Main ---------------------------------------------------------... |
7928d20cbbf96baa7b528e0c0336bd50891e6ae2adf2dad49eece56d76cb93a9 | R | 3,514 | 102 | # S. Spielman for ALSF CCDL 2022-3
#
# Makes a pdf panel for the HGG Kaplan-Meier survival analysis for Figure 4
library(tidyverse)
library(survival)
library(patchwork)
# Establish base dir
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
# Declare output directory
output_dir <- file.path(root_dir, "figu... |
f1b19f0967b32e57ee8bf355f5e092624de8e24b6893b484162e76728d001101 | R | 3,514 | 150 | ---
title: "UMAPs"
author: "AF"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(Signac)
library(Seurat)
library(tidyr)
library(dplyr)
library(ggplot2)
library(rstudioapi)
library(scales)
library(purrr)
set.seed(17)
```
UMAP repre... |
a6e2070e3cc1252e65918508ec723d2796298ba9e74181d9ad0971ea05c56a73 | R | 3,520 | 90 | #' Plot cell type proportions for each sample
#'
#' This is a plotting function that shows the cell type composition for each
#' sample as a stacked barplot. The \code{plotCellTypeProps} returns a
#' \code{ggplot2} object enabling the user to make style changes as required.
#'
#' @param x object of class \code{SingleCe... |
df3f11204f22ab5348730558597c52e534a8af9d90e554c6575dc2567e69669d | R | 3,522 | 114 | ---
title: "Seurat Merge and Integration Comparison"
subtitle: "`r params$project`"
author: "`r params$author`"
output:
rmdformats::html_clean:
lightbox: true
number_sections: false
gallery: true
code-fold: true
toc_depth: 3
fig_width: 10
fig_height: 8
date: "... |
4d01f3b0f96fe3a95940b45c12018a52eff82b736c9d2b05fe4baa2730e0d6a7 | R | 3,533 | 133 | ---
title: Two brain systems for the perception of geometric shapes
subtitle: MEG Behavior analysis
author:
- Mathias Sablé-Meyer
- Lucas Benjamin
- Fosca Al Roumi
- Cassandra Potier Watkins
- Chenxi He
- Stanislas Dehaene
output: rmdformats::readthedown
---
```{r echo = FALSE, cache = FALSE, message=FALSE... |
dfb51dacee81ba2223cc4c7319a02b7d7769166dec438e13193be4bc6c3e434e | R | 3,533 | 94 | ## Exploration of public human snRNA-Seq data (Yang et al.) and checking Fibroblast markers
## Data obtained from https://twc-stanford.shinyapps.io/scrna_brain_covid19/
library('Seurat')
library('dplyr')
library('gridExtra')
library('scater')
library("clustree")
source('/home/clintdn/VIB/DATA/Sophie/RNA-seq_Sandra/CI... |
302f6247a9bf5ce3d37bae43d36ff53051041124a39a97f23c4a37647966d7a9 | R | 3,538 | 125 | # Install R dependencies, using only base R.
#
# Supported arguments:
#
# --all Install all the 'Depends', 'Imports', 'LinkingTo', and 'Suggests' dependencies
# (automatically implies --build --test).
#
# --build Install the packages needed to build.
#
... |
ea0b5d290c2ac9c2795e8e04bf462ce60a6f3382c3838fea459ad6cf51314a6e | R | 3,543 | 113 | # ------ PARAMETERS FOR PLOTS
# ------ Function definition
"%||%" <- function(a, b) {
if (!is.null(a))
a
else
b
}
geom_flat_violin <-
function(mapping = NULL,
data = NULL,
stat = "ydensity",
position = "dodge",
trim = TRUE,
scale = "area",
... |
b423ac1f69f15be2ff07dfb1f8012c1fdd5b7818162f4e4ce94cbe300a57cd3f | R | 3,545 | 124 | # The working directory is the directory that contains this test R file, if this
# file is executed by test_dir
#
# testthat package is loaded, if this file is executed by test_dir
context("tests/test_helper_import_function.R")
# import_function is defined in tests/helper_import_function.R and tested here
#
# testthat:... |
2c8dda9b29e5e670f73e83e78fed1fcd27e3e619df1f7f139520184fbdaf3437 | R | 3,547 | 102 | library("optparse")
args = commandArgs(trailingOnly=TRUE)
path_umi_count_matrix_directory <- args[1]
path_output <- args[2]
quantile <- args[3]
# convert from string to double
quantile = as.double(quantile)
library(Seurat)
# load
my_data.htos = Read10X(path_umi_count_matrix_directory, gene.column=1)
rownames(my_d... |
71c50b60f5e4f2dc12146ac82445bdc8a10bb8b4370241d51b5821484c672472 | R | 3,547 | 76 | #' Synchronize orientation of genomic ranges given the desired majority direction.
#'
#' This function takes a set of ranges in \code{\link{GRanges-class}} or \code{\link{GRangesList-class}} object
#' and return the same ranges in same class object such that total length of plus (direct, '+') and minus (minus, '-') .
#... |
9d92394a9638ee5c51c5fe38d478ef2b6ec2e1236fc613ecb6b62bd39bf21b19 | R | 3,556 | 93 | args <- commandArgs(TRUE)
name <- as.character(args[1])
params <- yaml::read_yaml("../Config/eve_sim.yaml")
if (!dir.exists(name)) {
dir.create(name)
}
setwd(name)
dists_pd <- params$dists_pd
dists_ed <- params$dists_ed
dists_nnd <- params$dists_nnd
within_ranges_pd <- params$within_ranges_pd
within_ranges_ed <-... |
9e77643e7a3a5461adb1c9b82df54f5e5f22937cb62f6b12377f1fc096612cb1 | R | 3,561 | 158 | ---
title: "Criterion task analysis"
author: "Marcos Moreno Verdú"
date: "2024-04-05"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Load packages and data
Packages
```{r}
library(tidyverse)
library(modelsummary)
library(ggdist)
theme_set(theme_ggdi... |
19f09063d6153ac6afb271a0ff4e3074fc314fdff429989427c89aecae38bab9 | R | 3,563 | 120 | ## Density plot for frequency of observation ----
# Prepare data frame for density plot
graph_data <- cbind(glm.probs$Pos, rf.probs$Pos, kernlab.probs$Pos, ridge.probs$Pos)
colnames(graph_data) <- c('Logistic', 'RandomForest', 'SVM', 'Ridge')
graph_data <- as.data.frame(graph_data)
graph_data <- mutate(graph_data, sub... |
39bcaebf8fd9851a85f2756aed051a7bd8ef09944938c1ccc268b54534e5f6ca | R | 3,563 | 148 | # Test get_df_output_print ----
test_that(
"get_df_output_print - works",
{
expect_true(
object = read_npx_df_output |>
stringr::str_replace_all("arrow", "ArrowObject") |>
(\(.) . %in% get_df_output_print())() |>
all()
)
}
)
# Test get_file_ext_summary ----
test_that(
"g... |
c0b2683c28e5b43b57c5c8ce7c0e3fffc904cb3a9efb70cd689cb895e2f25e2a | R | 3,565 | 73 | library(devtools)
library(roxygen2)
library(ggplot2)
library(patchwork)
ggthemr::ggthemr("flat", layout = "minimal")
detach("package:eveGNN", unload=TRUE)
install_github("EvoLandEco/eveGNN", dependencies = FALSE)
test_control <- readRDS("D:/Habrok/Data/14102023/Data/qt2/10_dsce2_0.6_0.1_0.0_0.0.rds")
test_no_n <- re... |
cea70953fa18a2bf5bac4c0694afb29e44da225a796a9ca9c6dd85062fd29c5f | R | 3,568 | 107 | # Script for plotting H2O2 measurements ++++++++++++++
# Authors: Meike Bielfeldt, Kai Budde-Sagert
# Created: 2025/05/09
# Last changed: 2025/11/12
# Delete everything in the environment
rm(list = ls())
# Close all open plots in RStudio
graphics.off()
# Show warnings as they appear
opt... |
1ad87c2ad3a58292515a5fbba16b982a3b9a9dd2832ac37353d34a819defb996 | R | 3,570 | 113 | rm(list=ls(all=TRUE))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
# distribution of angle of random vector in 2d space ####
library(plotly)
ar1=function(n,rho) rho^toeplitz(0:(n-1))
tr=function(x) sum(diag(x))
rho=0.9
R=ar1(2,rho)
lam=eigen(R)$values
x=seq(1e-1,2,0.... |
3246c0a56ab687fc0b71977c08d437e11aaa49975a841fca50d71c4d6a904e36 | R | 3,578 | 86 | # Author: Francois Aguet
library(peer, quietly=TRUE) # https://github.com/PMBio/peer
library(argparser, quietly=TRUE)
WriteTable <- function(data, filename, index.name) {
datafile <- file(filename, open = "wt")
on.exit(close(datafile))
header <- c(index.name, colnames(data))
writeLines(paste0(header,... |
24edf949b38e0f839272b5fcb3251a33b5de57cc4d834d633f6a8eef3e117990 | R | 3,580 | 77 | run_scID<-function(DataPath,LabelsPath,CV_RDataPath,OutputDir,GeneOrderPath = NULL,NumGenes = NULL){
"
run scID
Wrapper script to run scID on a benchmark dataset with 5-fold cross validation,
outputs lists of true and predicted cell labels as csv files, as well as computation time.
Parameters
----... |
01683996c8b355a37dedb172611c9444485492971533ba630fade22276a8b310 | R | 3,587 | 118 | ################################################################################
# This plotting script pre-renders the cell type plot. The plot can be
# pre-rendered because it always has all the cells and is not filtered so it
# never changes. It needed to be pre-rendered because it would exceed the
# shinyapps.io ... |
c181395c854d351689bed17067f802c7bc733810d6a69f484afed04b934f37ac | R | 3,587 | 106 | ##-------------------------------------##
## VIOLINS TAB ##
##-------------------------------------##
plot_grid_violins <- function(mat, genes, metadata, var, subvar, cols, pt.size,
gene_list){
#https://stackoverflow.com/questions/31993704/storing-ggplot-objects-... |
0fde452de383ee48ae54c61c67b44793b56423fc3de2a7dddf46858cb46eff26 | R | 3,602 | 105 | ---
title: "QC independent samples"
output:
html_notebook:
toc: true
toc_float: true
---
## Load libraries
```{r load_libraries}
suppressPackageStartupMessages({
library(tidyverse)
library(DT)
})
```
## Output files
```{r wgs_only}
wgs_only_files <- c("independent-specimens.wgs.primary.eachcohort.tsv... |
10bc49ca79bfa8f38a31623815b7a7fbaae5786940e9d0a1d10ef6197e80a72c | R | 3,607 | 129 |
##################
## Factor Names ##
##################
#' @title factors_names: set and retrieve factor names
#' @name factors_names
#' @rdname factors_names
#' @export
setGeneric("factors_names", function(object) { standardGeneric("factors_names") })
#' @name factors_names
#' @rdname factors_names
#' @aliases fac... |
a26bc3645bcda4b685a691ba70cf17dc56478fc5b30821ed770beca76416b3e6 | R | 3,610 | 128 | # Returns the coords as a data.frame in the right ordering for ggplot2
get.coords.for.ggplot <- function(roc, ignore.partial.auc) {
df <- coords(roc, "all", transpose = FALSE, ignore.partial.auc = ignore.partial.auc)
df[["1-specificity"]] <- ifelse(roc$percent, 100, 1) - df[["specificity"]]
return(df[rev(seq(nrow... |
b7144d6dbdbd74ae1ec2ac4e98ddad3fe2219fae185d2592aa8622d93d0e08bd | R | 3,625 | 76 | setwd("/home/pranali/Documents/glioma_manuscript/")
data = read.csv("patient_info.csv", row.names = 1, check.names = F)
data = data[, -c(1:3)]
library(stringr)
for(i in 1:ncol(data)){
if(colnames(data)[i] == 'Diagnosis'){
next
}
class_type = class(data[, i])
if(class_type == 'character'){
data[, i] ... |
d9a5525e626fa72ac2ef2bee2c97e41df723a391af8286273ce4c0641f629345 | R | 3,626 | 95 | 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
## SCRIPT SPECIFIC FUNCTIONS
## SCRIPT CODE
##
##
if( 1 ) { ## this block fits all models (and associated bfp-versions)
... |
e3eb03c4d92c46ec3aa344bfb61a062e1aebfe761801b5375dc96a2286e32a98 | R | 3,631 | 117 | ### Summary stats for the cross sectional analysis
# Load required libraries
library(readxl)
library(writexl)
library(here)
library(dplyr)
library(tidyr)
# Load excel file
file_path <- here::here("data", "megamastersheet.xlsx")
data <- readxl::read_excel(file_path)
print(dim(data))
# [1] 10802 36
# only keep the c... |
6899915413c8580831e1fa19fe2bf6e78f1e1ce8bd08bfc7433c680b2e9cf7dc | R | 3,640 | 110 | if (!requireNamespace("here", quietly = TRUE)) install.packages("here")
source(here::here("stats","learning_models","_setup.R"))
data_acq <- subset(scr_df, PHASE == "acquisition" & TUS == "active" & CS == "threat")
data_acq$CS <- relevel(data_acq$CS, ref = "control")
data_acq$US <- relevel(data_acq$US, ref = "unreinf... |
dce530dd6c76d861382d2cd0e74a4ebc3bc611062b29a70566d1bd5978c7e392 | R | 3,646 | 108 | #'---
#' title: Aberrant Splicing
#' author:
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AS" / "Overview.Rds")`'
#' params:
#' - annotations: '`sm cfg.genome.getGeneVersions()`'
#' - datasets: '`sm cfg.AS.groups`'
#' - htmlDir: '`sm config["htmlOutputPath"] + "/AberrantSplicing"`'
#' input:
#' ... |
cd5bf338420077b9002833b6996e6b32165f9d5783f971987b99df1956d64603 | R | 3,650 | 164 | ### Load packages
library(PMA)
library(mixOmics)
library(RGCCA)
library(caret)
library(truncnorm)
# MRF
sim.fn.mrf3.m <- function(dat, ...){
mrfinit <- mrf3_init(dat$X, ...)
imp_init <- plyr::llply(
c("filter", "mixture", "test"),
.fun = function(m) {
w <- mrf3_vs(mrfinit, dat.list = dat$X, me... |
fe04844d16a707bf8a6254ab8ac76f4104d8fe68ca3ee6d276992a83a5da4007 | R | 3,650 | 109 | ## =========================
## Questionnaires
## =========================
source(here::here("stats","lme_models","_setup.R"))
#### CS ratings ####
# Compute threat - control difference scores per subject and TUS, for all three variables
cs_diff_df <- cs_quest_df %>%
filter(CS %in% c("control", "threat")) %>%
... |
fe9fc1cbc4da65f3035e750778bd7d3f668449e3bb6731582e79b1135772f392 | R | 3,656 | 143 | #' Help function to read NPX data from long format parquet Olink software output
#' file in R.
#'
#' @author
#' Klev Diamanti
#' Kathleen Nevola
#' Pascal Pucholt
#'
#' @inherit .read_npx_args params return
#' @param file Path to Olink software output parquet file in long format.
#' Expecting file extension
#' `r... |
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