sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
db232b5f9009773e2d87530c45e761633d219dd452409470cf0a50636f73645f | R | 8,879 | 291 | #' Search for Differentially Methylated Regions (DMRs) using DMRcate
#'
#' This function searches for Differentially Methylated Regions (DMRs)
#' in DNA methylation data using the DMRcate package.
#' It takes a set of CpG sites with associated statistical information
#' and annotates them for DMR analysis.
#'
#' ... |
e46092b4203c03dcc9188db8fdbb8170d90a119bea3e2a9d7e9a114e5904f03e | R | 8,893 | 203 | library(tidyverse)
library(rstatix)
library(ggpubr)
library(cowplot)
library(patchwork)
scale_factor <- 2
# ==== Prepare data =====
data_path <- "Figure_1/data"
csv_files <- list.files(data_path, full.names = F)
all_d <- data.frame()
for (i in 1:length(csv_files)) {
cfile <- paste0(data_path, "/", csv_files[i])
... |
a6cd9c118e729d95d6bab2e766227e7f745c6a5d154403fe8b25f169f7b04af8 | R | 8,896 | 208 | ---
title: "Update clinically reviewed subtype for PNOC003 samples"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
author: Krutika Gaonkar for D3b
---
As part of molecular-subtype-HGG analysis we assign a HGG or DMG subtype from looking for K28M histone variants, in this notebook we are identifying sample... |
f118713887d8ec9abb5deee4d76d05aea31d32228a0d15ec1dbce59050da5b62 | R | 8,901 | 214 | #' Add PAF self-alignments to a SVbyEye miropeat style plot.
#'
#' This function takes a \code{ggplot2} object generated using \code{\link{plotMiro}} function and adds PAF self-alignments
#' stored in the `paf.table` to the plot.
#'
#' @inheritParams addAnnotation
#' @inheritParams breakPaf
#' @inheritParams plotMiro
#... |
97db6788a58697132aea01740546fa30f2b781c81b554ff7b0acdbd94f78d181 | R | 8,904 | 260 | # ==============================================================================
# S11_co_visualization.R
# Server logic for Step 7: Multi-feature Visualization
# Handles RGB/Pseudocolor mapping for visualizing up to 3 features simultaneously.
# ======================================================================... |
a18b2648f4e920c963feb52dc6a4bf93b1643eec6591c1ac586e23af10e1f336 | R | 8,912 | 250 | ---
title: "Molecularly Subtype Craniopharyngiomas into Adamantinomatous or Papillary"
author: "Daniel Miller <millerd15@email.chop.edu> and Jo Lynne Rokita <rokita@chop.edu> for D3B"
date: 2020, 2022
output:
html_notebook:
toc: TRUE
toc_float: TRUE
---
# Background
This notebook looks at the defining lesio... |
55aaaee6fbe93c6f2956c52eb56cf14a2883eb807fdfcdfcedb4dccd2616e648 | R | 8,913 | 285 | ---
title: "script01_quality_control"
author: "Shamini Ayyadhury"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
### R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. F... |
fdef0fa1b27b68d5d94c725a16d3df172927e6756bbf1741132adcfb6f8a025e | R | 8,919 | 236 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
dpi = 70
)
```
## Overview
```{r, eval=T, include=F}
start... |
b67d58e3dfcecc63aff2f505d08c5838a70641292b6e6ac50e970a3001cd90f2 | R | 8,939 | 198 | # pROC: Tools Receiver operating characteristic (ROC curves) with
# (partial) area under the curve, confidence intervals and comparison.
# Copyright (C) 2010-2019 Xavier Robin, Matthias Doering,
# Alexandre Hainard, Natacha Turck, Natalia Tiberti,
# Frédérique Lisacek, Jean-Charles Sanchez and Markus Müller
#
# This pr... |
e37ca996d2906f31ae95abf3233fe3056666386de59b1bb6a8d1becc6be8ce8e | R | 8,951 | 213 | # Function to show a notification in a Shiny app
# Parameters:
# - msg: The message to display in the notification
# - id: Optional ID for the notification (default is NULL)
# - duration: Duration (in seconds) for which the notification will be displayed (default is 2 seconds)
# - closeButton: Set to FALSE to hide the ... |
8f61f10a4e12d1b9bb9ff362793d892f8dc1453b4d8f0a49fb6799e8b50d2588 | R | 8,953 | 186 | # Function to generate a permutation map from a set of cortical regions of interest to itself,
# while (approximately) preserving contiguity and hemispheric symmetry.
# The function is based on a rotation of the FreeSurfer projection of coordinates
# of a set of regions of interest on the sphere.
#
# Inputs:
# coord.l... |
579731194f712b352059aa34cf1e540b86d873fbe5cf26019d3f60bed85348b0 | R | 8,966 | 171 | setwd("D:/valentin/main/")
sources = dir("./scripts/",full.names=TRUE)
for(s in sources){
source(s)
}
load("FANS_UKBBN/current.Rdata")
pheno_UKBBN_FANS$subtype = pheno_UKBBN[match(pheno_UKBBN_FANS$Individual, pheno_UKBBN$BBNId),]$subtype
list_EWAS = list()
for(i in list(c("red","Control"), c("blue", "Cont... |
8a0b4a6bf49b46ad0932e113d65e10049ca6dbdf423a2f6ba40ca3e8c5e2ec92 | R | 8,978 | 290 | #########################################################
## RareComb pipeline: multi-cohort pathogenic variant pairs
## - Per-cohort pathogenic filtering (no rare/MAF filtering)
## - Cohort merging
## - RareComb boolean input matrix
## - Running pyRareComb (example commands)
## - Downstream analysis of RareComb p... |
30c2e0dc221a725eae6df4d4f6cff63fb1f09965d0b802553e3d852c2c274939 | R | 8,992 | 236 | #!/usr/bin/env Rscript
#Run like this:
#Rscript --vanilla tests/IVIMmodels/unit_tests/compare.r test_output.csv test_reference.csv reference_output.csv test_results.csv
# If this script fails:
# 1. Save the "Comparison" file from the run on Github, OR run this file directly
# 2. Find the file producted "test_referenc... |
14ce22922528f6d6ee9e55d298e22ff0b6e5b96bf1c3012bf529e0fee52cfe22 | R | 8,993 | 121 | ############################################################
## Main Figure 4
############################################################
## Load helper functions
source("path/to/function_definition.R")
## Packages
library(tidyverse)
library(ggpubr)
library(egg)
#####################################################... |
02d217ca71d59ffc426657e40664ad07d50b07e1edb8f0a79e479160d913ee4f | R | 9,004 | 266 | library(scSeqComm)
library(scrattch.hicat)
library(scrattch.vis)
library(scrattch.io)
library(Matrix)
library(Seurat)
library(dplyr)
library(rhdf5)
library(pbmcapply)
library(OmnipathR)
library(graphite)
library(data.table)
library(corrplot)
library(ComplexHeatmap)
library(stringr)
library(ggplot2)
setwd("/mnt/DD/Sc... |
ccd322ba2d09be03878df7390013e38ac21bcf9226a8e90ea57126f0122b6d4b | R | 9,020 | 293 | # Test olink_lmer_plot ----
test_that(
"olink_lmer_plot - works - 6 assays, 1 page",
{
skip_on_cran()
skip_if_not_installed("vdiffr")
skip_if_not_installed(pkg = "lme4") |> suppressPackageStartupMessages()
skip_if_not_installed(pkg = "lmerTest")
skip_if_not_installed(pkg = "broom")
skip_if_... |
37fb0d84939cf7a42c195e87ad5f9c2d2a6a3445083a7130d2bec92befe3b9a1 | R | 9,022 | 212 | #### libraries
# required for linear mixed effects models
library(lme4);
# required for significance testing in LMMs
library(lmerTest);
# required for pretty plotting
library(ggplot2);
# required for colour-blind friendly palettes
library(viridis);
# required for estimating marginal means
library(emmeans);
# requi... |
a6fddc44dfa812b24e5d990f0654316d9963b87112664f069d6ffde4458fa1ff | R | 9,041 | 231 | # JN Taroni and SJ Spielman for ALSF CCDL 2021-2022
#
# Create panels for representing sample distribution:
# - Cancer group
# - Experimental strategy
# - Tumor distribution
#
# Each broad histology display group has an individual panel
#### Libraries -------------------------------------------------------------... |
674978a0fe8eeb8d859c5c6cd47fbd34d7923ffe11259bfc97c154d38323ed68 | R | 9,054 | 369 | # constants that control naming in lists
.EVAL_KEY <- function() {
return("eval")
}
.EVAL_ERR_KEY <- function() {
return("eval_err")
}
#' @importFrom R6 R6Class
CB_ENV <- R6::R6Class(
"lgb.cb_env",
cloneable = FALSE,
public = list(
model = NULL,
iteration = NULL,
begin_iteration = NULL,
end_i... |
5bb929a5170ada2d0d39542a0af8b00cfecf8739b5f15ee51a17f8b26881b674 | R | 9,066 | 203 | library( ggplot2 )
library( ANTsR )
library( brainGraph )
library( ggradar2 )
# dataSets <- c( "SRPB1600", "IXI", "Kirby", "NKI", "Oasis" )
# demoFiles <- c( "srpb1600.csv", "ixi.csv", "kirby.csv", "nki.csv", "oasis.csv" )
dataSets <- c( "SRPB1600" )
demoFiles <- c( "srpb1600.csv" )
pipelineNames <- c( "ANTs", "ANT... |
b94930055d6d58a6ac34973c1b555f8a8360a383fdbba55c33913f9551aa6949 | R | 9,073 | 186 | #' Prepare PAF alignments for plotting.
#'
#' This function takes loaded PAF alignments using \code{\link{readPaf}} function. Such alignment could be post-processed
#' using \code{\link{filterPaf}}, \code{\link{breakPaf}} and \code{\link{flipPaf}} functions. Subsequently such alignments are
#' expanded in a set of x an... |
441c18d10a3230c06f7c8e1d5d5351f3481164ce02a970017f05eb4c20016a70 | R | 9,075 | 177 | #' @title Create configuration (cfg) structure for analysis
#' @description This function compiles the relevant settings and file paths to be fed into analysis functions.
#' @param pat_ids a vector of strings specifying patients' IDs (used as directories for output files)
#' @param lesion_paths a vector of strings spec... |
cd011b7e6bce345ec8b5faa728d9845d36cafa97d80104fbabb6c2266fb0d4cf | R | 9,092 | 283 | ---
title: "MOFA+: downstream analysis (in R)"
author:
name: "Ricard Argelaguet"
affiliation: "European Bioinformatics Institute, Cambridge, UK"
email: "ricard@ebi.ac.uk"
date: "`r Sys.Date()`"
output:
BiocStyle::html_document:
toc_float: true
vignette: >
%\VignetteIndexEntry{Downstream analysis: Overview... |
7c405c4255ffb0e358138367f03e2540897ab758a535148a54439ad3fedd44b2 | R | 9,095 | 225 | # using devtools to create package
# nice elementary tutorial
# https://uoftcoders.github.io/studyGroup/lessons/r/packages/lesson/
# adds documentation to package as a whole
# use_package_doc()
# in case of problems delete namespace file
# then do devtools::load_all()
# and then devtools::document()
# storing data i... |
0ba7d4e3427d7b7e7ff1a439d89225b7aeb6c97b52e04485cc6d87d1b95cc768 | R | 9,100 | 179 | make_targets_fmri_by.run <- function (n_runs, task = "controlled", additional_targets = NULL) {
# defining these separately instead of in a tar_eval together
# because we need them to be accessible as components of the list tar_map for naturalistic
# and because we need to only use boxcar for controlled
sub... |
1057e8756aa80142989de9678c0e7f98224b1440dd218052212bcbb3d0ab3d79 | R | 9,109 | 327 | # Test check_is_numeric ----
test_that(
"check is numeric works - TRUE",
{
expect_true(
object = check_is_numeric(x = 3.14,
error = FALSE)
)
expect_true(
object = check_is_numeric(x = 3.14,
error = TRUE)
)
expect_true... |
78eb8d0f497edce420a5726a53f933dd4dfec98adc76ed4f519658da7e4f1eaa | R | 9,110 | 253 | # SMIntegration: Spatial Multi-omics Integration Platform
# ==============================================================================
#
# Purpose:
# Bridges the Python-based SpatialData format and R-based Seurat analysis.
# It converts the Zarr output from the interpolation step into Seurat RDS
# obj... |
d23ab08d4d8cc6c2959980a1deb9ea0deee0fb1df9e4bbc93c302fbfb69520b7 | R | 9,123 | 204 | # look further into mouse phenotypes
# load libraries ----
library(igraph)
library(tidyverse)
library(pROC)
library(foreach)
library(doParallel)
library(ComplexHeatmap)
library(clusterProfiler)
library(RColorBrewer)
library(dendextend)
source('Code/networkPropagation.R')
'%notin%' = Negate('%in%')
# load files ---... |
de9ccde2c4ff72c257caf96472f70c57c30791bfcf00a1294777987b5c5f0cd8 | R | 9,130 | 315 | #' run a model on every row of your data. Made with microbiome data in mind.
#' @export
#' @examples
#'
#' metadata = data.frame(a = sample(letters[1:3], ncol(mtcars), replace=T),
#' b = sample(letters[4:6], ncol(mtcars), replace=T),
#' c = rnorm(ncol(mtcars)))
#'
#' fw_fit(... |
aaa878478f1d31cd70fb108d6b58df86295140868ee05390b92fbe9ecc766a5d | R | 9,137 | 257 |
# Analysis of ENS Progenitors (Morarach et al. 2021)
#
# Author: Anoohya Muppirala
# Date: 11-09-2025
#
# Description:
# This script processes and analyzes 10x Genomics single-cell RNA-seq data
# of enteric nervous system (ENS) progenitors from the developing mouse
# small intestine at embryonic day 15.5 (E15.5) and... |
afa654c6e963426100807944ab45bf7c78f98b9169fe1ad05b2e17eaeb8eff71 | R | 9,151 | 277 | #!/usr/bin/env Rscript
#
# Ballgown Differential Gene Expression Analysis
#
# This script performs differential expression analysis using the Ballgown R package.
# It requires:
# - StringTie output directories for each sample (containing *.ctab files)
# - A metadata CSV file with sample information and condition la... |
624e9b8cc4c724b858c2dbfee09cdca88ffac98c069c09946d10c8605c673a0d | R | 9,164 | 304 |
#' @export
#'
#'
lnc_RNARNA_scanner <-function(ENST_input, analyze_onlyDEtargets, ENST_targets ,nr_top_genes )
{
if(missing(nr_top_genes)){
nr_top_genes<-500
}
gc()
#input explanation
print(paste("Input explanation:"))
print(paste("1. ENST_input = ENST transcript numbers. RNARNAdb will automat... |
2a4682282a7b0cc663b6832f711e3a4d34b33a7d425b0eb1d591bdaef15c14bc | R | 9,178 | 268 |
# Calculting module scores for senescence flavors across cell types
flavors <- read.csv("flavors_of_sen_0911_SYMBOL.csv") %>%
dplyr::select(-San.Diego.TMC)
modules <- colnames(flavors)
flavors <- lapply(1:ncol(flavors), function(col) {
vals <- str_to_upper(flavors[flavors[,col]!="",col])
genes1 <- v... |
82f3c8d7389802579d4a2f9bcc009df9ce072ed60634c6e70fbd7d22afda9346 | R | 9,186 | 308 | ---
title: "Project specific filtering"
author: "K S Gaonkar (D3B); Jaclyn Taroni (CCDL); Kelsey Keith (DBHI), Jo Lynne Rokita (D3b)"
output: html_notebook
params:
histology:
label: "Clinical file"
value: data/histologies.tsv
input: file
group:
label: "Grouping variable"
value: cancer_group
... |
7f39fd813e5e23afb4ba24dec499643599dbb91e51db466eae1bacb4346ccca6 | R | 9,192 | 303 | ---
title: "Multi-sample analysis (10x Visium Human DLPFC)"
output: BiocStyle::html_document
# output: pdf_document
vignette: >
%\VignetteIndexEntry{Multi-sample analysis (10x Visium Human DLPFC)}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
... |
d23c464d0fcb4bfeb8ec9dfff290b19c058543b7b3c4d007e2215f2f904871fb | R | 9,193 | 282 | library(tensorflow)
library(keras)
library(survival)
library(survcomp)
#BiocManager::install("survcomp")
# neg_log_likelihd <- function(y_true, y_pred) {
# event <- y_true[, 1]
# time <- y_true[, 2]
# # time <- y_true[, 1]
# # event <- y_true[, 2]
#
# mask <- k_cast(time <= k_reshape(time, s... |
8fd13e1955b38aed756ed57fe70e0f956554cc46eca5460972120eb0181694f9 | R | 9,197 | 207 | ```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(WVPlots)
library(tidyverse)
library(dplyr)
library(tidyr)
library(ggplot2)
library(corrplot)
library(visreg)
library(ggseg)
library(ggsegSchaefer)
library(mgcv)
library(fastDummies)
library(lme4)
library(lmerTest)
library(car)
library(purrr)
k=3
loa... |
eda617179de1347b2deedda9d897af34d35202346df1404d49d1c4be28bc28d5 | R | 9,225 | 316 | ---
title: "MOFA+: downstream analysis in R"
author:
- name: "Ricard Argelaguet"
affiliation: "European Bioinformatics Institute, Cambridge, UK"
email: "ricard@ebi.ac.uk"
- name: "Britta Velten"
affiliation: "German Cancer Research Center, Heidelberg, Germany"
email: "b.velten@dkfz-heidelberg.de"
date: "`r Sys.... |
f558d2570a4bfe41ef49004df5135ce9d2486fd496ce4fe1a53a18b5a7dba54e | R | 9,225 | 204 | ## Creating Fibroblast species object (Fig7) with our Fibroblast scRNA-Seq data (7/22/82 wo ChP 4V&LV) and human snRNA-seq data (Yang et al.)
## Script attempted integration of the datasets via Seurat (CCA) and subsetted to common homologs
## CCA integration was finally not used, but this object was used as starting p... |
c2e2865af254ae81248899af30158b2ad4d46b8389bdcf7871f4571e8f9ea2d9 | R | 9,249 | 270 | # This script subsets the focal copy number, RNA expression, fusion and
# histologies` and GISTIC's broad values files to include only High-grade glioma
# samples.
#
# Chante Bethell for CCDL 2020
#
# #### USAGE
# This script is intended to be run via the command line from the top directory
# of the repository as follo... |
6486201d4a185ba10b4a741afe41f2f730778193c894c671a983e402aea3b0c5 | R | 9,253 | 246 | #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#################### NEBULOSA ####################
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' Nebulosa Density Plot
#'
#' Allow for customization of Nebulosa plot_density. Requires Nebulosa packag... |
f879790004bde9ce8a27153ee623ab0632dcab286f79299bacf5902033317d45 | R | 9,267 | 179 | library(data.table)
library(dplyr)
library(mgcv)
library(parallel)
library(rjson)
library(stringr)
library(tidyr)
source("/cbica/projects/luo_wm_dev/two_axes/code/fit_GAMs/gam_functions/GAM_functions_tractprofiles.R")
# This script fits developmental nodewise GAMs on tract profiles data using functions from GAM_functi... |
00ef23bc5d927dee73798a660ed085fe047eefc8d8de84b637a6084ab4ee15d2 | R | 9,271 | 181 | library(data.table)
library(dplyr)
library(mgcv)
library(parallel)
library(rjson)
library(stringr)
library(tidyr)
source("/cbica/projects/luo_wm_dev/two_axes/code/fit_GAMs/gam_functions/GAM_functions_tractprofiles.R")
# This script fits developmental nodewise GAMs on tract profiles data using functions from GAM_functi... |
4656805f102ff4f6358d39913850b8017caf5aa9a2274be1a36e127d94949b29 | R | 9,295 | 184 | ---
title: "`r params$project`"
subtitle: "Single Cell RNA-seq - QC"
date: "`r Sys.Date()`"
output:
html_document:
lightbox: true
toc: false
toc_float:
collapsed: false
toc_depth: 3
fig_width: 8
fig_height: 5
number_sections: false
params:
project: Project
seuratdir: directory
... |
af621d9f3d488c4cc6f31c24bdc399acfe16be0f91e610add324139b1814bb69 | R | 9,310 | 262 | # S. Spielman for ALSF CCDL 2023
#
# Makes pdf panels for TP53 and telomerase scores across cancer groups, focusing only
# on high tumor purity samples
library(tidyverse)
# Establish base dir
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
# Declare output directory
output_dir <- file.path(root_dir, "f... |
a879f1cdab5f0d196417ef89fb13fe53900011cbb2d6683683cf4c4493ab6ad9 | R | 9,314 | 268 | # Load related function
dir.base <- "."
script <- list.files(
path = file.path(dir.base,"function"),
pattern = "[.]R$",
full.names = T,
recursive = T
)
for (f in script) source(f)
n <- c(100, 200, 200)
p <- c(200, 500, 1000)
parameter <- list(
s1 = list(p.group = 2, q.group = 5, p.b = 0),
s2 = list(p.g... |
375fd7a2ee6546966e4e77f2010df371c29249dba355dceaada6f577fb8bf5ce | R | 9,329 | 284 | # Load related function
dir.base <- "."
script <- list.files(
path = file.path(dir.base,"function"),
pattern = "[.]R$",
full.names = T,
recursive = T
)
for (f in script) source(f)
n <- c(100, 200, 200)
p <- c(200, 500, 1000)
psel = c(20, 30, 50)
n_reps <- 50
parameter <- list(
s1 = list(rho = 0)
)
mod =... |
de5f7c8b03070379817904ffde1edaca1aa658897c3908cba327cce1698ba10b | R | 9,362 | 306 | ##########################################################################
### 6. mapping of competitive strength -----------------------------------
##########################################################################
### libraries ---
library(tidyverse)
library(dplyr)
library(ggplot2)
library(tidyterra)
libra... |
a88da0a13f4b456f3bfaea200699d68548e297021e80fe7739e273c558e8e6ab | R | 9,370 | 251 | library(tidyverse)
library(gtools)
library(Seurat)
######################
Mode <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
mapping_res <- read.csv("/mnt/vast/hpc/MenonLab/SenNet/Jason_work/p400_to_sennet_mapping_5sets.csv")
mapping_res$mode_cluster <- sapply(1:nrow(mappin... |
ef7daa12dcd76b04f290ffc45a982750dc7b98ea4a86ef74563e49bf31f432db | R | 9,403 | 222 | # SMIntegration: Spatial Multi-omics Integration Platform
# ==============================================================================
#
# Purpose:
# This is the main entry point for the SMIntegration Shiny application.
# It initializes the environment, loads necessary R libraries, defines the
# User... |
fbf21e6a5cd962b5e00ad343be930d2e1b9e3f861ed8954003a6cc1412180b10 | R | 9,423 | 255 | # This script defines custom functions to be sourced in the
# `rna-expression-validation.R` script of this module.
#
# Chante Bethell for CCDL 2019
#
# # #### USAGE
# This script is intended to be sourced in the
# 'analyses/focal-cn-file-preparation/rna-expression-validation.R' script as
# follows:
#
# source(file.path... |
9800fd0a0cd6663755949de24fc62a78aa43e0564b0e78ba872273d9d32b1c0a | R | 9,427 | 275 | # Genomic Prediction for Winter Wheat - Across Environments Analysis
# Prediction Type: ACR (Across environments)
# Models: Additive+Dominance and Additive+Dominance+Epistatic
# Cross-validation: 80/20 random split
# Load required packages
library(BGLR)
library(qs)
library(dplyr)
library(AGHmatrix)
library(feather)
#... |
2dee825e71c72e94f3ea94751fd84ed28d6b97004fe8c4042a171fdeb0c61e29 | R | 9,441 | 145 | ---
title: "Tables output for manuscript"
author: "Aditya Lahiri, Jo Lynne Rokita"
date: "2021-2024"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
params:
release: v15
---
Code adapted from: https://github.com/AlexsLemonade/OpenPBTA-analysis/blob/master/tables/output_tables.Rmd
```{r setup, include=FA... |
dd97802a721d73d0632a93a3b7e888839c4109b637d58675c6d0fb66124a7c5e | R | 9,444 | 313 | ---
title: "Manuscript stats for naturalistic loom object fMRI"
format:
html:
toc: true
toc_float: true
---
```{r setup, include=FALSE}
require(targets)
require(tidyverse)
require(rlang)
require(knitr)
target_store <- here::here("ignore", "_targets", "naturalistic")
target_store_controlled <- here::here("i... |
779e1960200ffaef81ac202ff3be79136284b6f3bd018725f755b2d6978fad88 | R | 9,448 | 235 | ################################################################################
# Script to apply COMBATLS on MIND networks
################################################################################
# Copyright (C) 2026 University of Seville
#
# Written by Natalia García San Martín (ngarcia1@us.es)
#... |
56d7219b0db6e79c087e25f27d25eb565079785a705c6047bad3a39a3fa29211 | R | 9,494 | 223 | ###Single Cell Object Creation and Integration###
#This script creates a Seurat object from cellranger filtered h5 files, marks doublets, and integrates the data using Seurat's RPCA method.
#It also performs quality control, normalization, variable feature selection, scaling, PCA, clustering, and UMAP visualization.
#... |
64218bea66a35f0a4475b989eba8d29778b6990239efe8c7c6b73e2252f15522 | R | 9,499 | 386 | #' Olink color panel for plotting
#'
#' @param alpha transparency (optional)
#' @param coloroption string, one or more of the following:
#' c("red", "orange", "yellow", "green", "teal", "turquoise", "lightblue",
#' "darkblue", "purple", "pink")
#'
#' @return A character vector of palette hex codes for colors.
#'
#' @ke... |
1f8ecd0d19124706e9c677b045c154275ab05114215f0543bc04b802a613caa6 | R | 9,501 | 347 | # Process mutations for interaction plot from MAF file.
#
# JA Shapiro for ALSF - CCDL
#
# 2019
#
# Generates a table of gene by gene co-occurence data with p values from Fisher's exact test.
# By default, performs analysis of the top 50 most mutated genes.
#
# Option descriptions
#
# --maf : File path to MAF file to... |
b896f888963f72c5745d68e5ad0c47ed4160b89a22cadaf24ee5f7b293ae6e62 | R | 9,518 | 226 | #' @title Standardize Column Names
#' @description Renames columns of input data frame to standard format (geneID, x, y, MIDCount) and ensures correct data types.
#' @param data Input data frame.
#' @return Data frame with standardized column names and types.
colname_change<-function(data){
colnames(data)[1]<-"g... |
8f1a301392940e758dd43cfc0ce8d0b846826649dafd2a68a4f7e9037c3b9907 | R | 9,528 | 240 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(edgeR)
library(dplyr)
library(readr)
library(tibble)
library(matrixStats)
library(ComplexHeatmap)
library(circlize)
library(ggplot2)
library(grid)
})
# ── [1] Settings ────────────────────────────────────────────────────────
WD <- ... |
18543212dbd155a58cc4138454b2695df7a6f2910ccd81dfb79df62fbade88d0 | R | 9,536 | 237 | ---
title: "MOFA+: tutorial on Gene Set Enrichment Analysis"
author:
name: "Ricard Argelaguet"
affiliation: "European Bioinformatics Institute, Cambridge, UK"
email: "ricard@ebi.ac.uk"
date: "`r Sys.Date()`"
output:
BiocStyle::html_document:
toc: true
vignette: >
%\VignetteIndexEntry{MOFA2: Gene Set Enric... |
f1963639ceb0fb7eb9d89a47293f19e1831f2f418fc8e65d75f898b36274caa1 | R | 9,536 | 225 | library(dplyr)
library(tidyr)
library(ggplot2)
library(ggprism)
# Tissue_spec :
# input : a vector of gene names and the gene tissue expression dataset from HPA
# output : a dataframe with SPM and CTM values from Pan et al (2013) and Tau value from
# Yanai et al (2004) and a classification of gene tissue specificity
... |
b242c36f7ff87d42e82d85b8b4f412e066b1ef66475c1aee6ab4e83b7d781b25 | R | 9,554 | 202 |
#' @title Preview Spatial Plot
#' @description Wrapper for iPlot to generate a spatial plot of a specific feature.
#' @param object Seurat object.
#' @param feature Character. Name of the feature or metadata column to plot.
#' @param pointSize Numeric. Size of the points.
#' @param breakseq Numeric. Break sequ... |
4e587fbebe1df7096cc13415d3f1a4c7d183b0501f05b18f439dd206901b2daf | R | 9,591 | 229 | suppressPackageStartupMessages(library(tidyverse))
# Format numbers to percentage characters
# Adapted from @Richie Cotton's answer at
# https://stackoverflow.com/a/7146270/4638182
num_to_pct_chr <- function(x, digits = 2, format = "f", ...) {
stopifnot(!is.null(x))
if(length(x) == 0) {
stopifnot(is.numeric(x... |
651a3cb8af9653b05481a989b99ceb4f0c082e152955c5bed372afc0a762da67 | R | 9,598 | 244 | ---
title: "Marker Identification & Cluster Annotation Helpers"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output: rmarkdown::html_vignette
theme: united
df_print: kable
vignette: >
%\VignetteIndexEntry{Marker Identification & Cluster Annotation Helpers}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEnc... |
94eb4ee8552d4f4c25f3cf7cb5c52622a0ac6d099c83e5e3288547033d93668f | R | 9,607 | 198 | # Yang Yang 2019
# This script is for generating sv file in shatterseek-read/signature-read input format
#
# input files are:
# 1.independent-specimens.wgs.primary-plus.tsv
# this file is used to choose independent specimens
# 2. pbta-sv-manta.tsv
# this file is the original sv file generated by manta call
#
# output... |
d5be3433eb09debba8c67193083475e5f13019605cb670d1ed2d365fa66c6316 | R | 9,615 | 186 | library(data.table)
library(dplyr)
library(mgcv)
library(parallel)
library(rjson)
library(stringr)
library(tidyr)
source("/cbica/projects/luo_wm_dev/two_axes/code/fit_GAMs/gam_functions/GAM_functions_tractprofiles.R")
# This script fits developmental nodewise GAMs on tract profiles data using functions from GAM_functi... |
f9f9c463c27f8b8473aea714d7a2d4e4c20f0769ee0daec26ff95d63f48c0bb0 | R | 9,652 | 263 | ---
title: "Find CNV losses that overlap with TP53 domains"
author: "K S Gaonkar, Jo Lynne Rokita"
output: html_notebook
params:
base_run:
label: "1/0 to run with base histology"
value: 0
input: integer
---
In this script we will find if there are a Structural Variant breakpoints within TP53 or cover... |
876db200d929d8699a7b1f815db91747dca0684e58bd3dde564522489737eae0 | R | 9,672 | 311 | getwd()
options(scipen = 999)
library(ggplot2)
library(ggpubr)
library(ggExtra)
library(tidyverse)
setwd("/data/nas1/liuyiding_OD/project/01_project_147/11_Cor_GSEA")
exp=fread("log2TPM.txt",header=T,data.table=F)
exp=column_to_rownames(exp,"V1")
exp=exp[,-c(1:40)]
data=exp
x=as.numeric(data["PATZ1",])
gene="PATZ1" ... |
01c0a6b253822ca2bf4b53e12dae1183348ed8e3159d76e5899e2c5fd9a5f8ab | R | 9,675 | 255 | ##' build annotation database
##' @name buildAnnot
##' @rdname buildAnnot-methods
##' @title make annotation database
##' @param species species for the annotation
##' @param keytype key type export
##' @param anntype annotation type
##' @param builtin use default database (TRUE or FALSE)
##' @param OP BP,CC,MF default... |
ac12a389c40e457cd81e71d311d169c1d3325d6232b1915953010de87c4a8583 | R | 9,698 | 251 | #' Create the matrix for the PNG image
#'
#' This function create the pixel matrix that can be saved
#' as a PNG for further use. It will also perform some preprocessing, e.g.
#' reduce the LRR interval to [-1.4 , 1.2],
#'
#' @param cnv see load_snps_tbx() documentation
#' @param samp see load_snps_tbx() documentation
... |
7d1043f950933a8bfcbde81f0ffa030bd52495bb21604e1d741440fbfa026ab4 | R | 9,723 | 309 | # --------------------
# title: Figure1 Code
# author: Hu Zheng
# date: 2026-01-01
# --------------------
library(Seurat)
library(scCustomize)
library(ggplot2)
library(ggpointdensity)
library(viridis)
library(cowplot)
library(sciRcolor)
source('bin/Palettes.R')
source('bin/includes.R')
all.Adult <- readRDS('../data/r... |
98127fab9e86612fab27a16c0d603a927a37213815735e63ab16f894ae4f5d52 | R | 9,724 | 312 | ---
title: "Ca Imaging data analysis"
author: "Slesinger Lab"
date: "5/15/2023"
output:
html_document: default
pdf_document: default
---
```{r load packages}
#Run chunk at the start of every new session to load the R packages used in the analysis
library(readxl)
library(readr)
library(ggplot2)
li... |
8a942dac7d94d46e792d9b9197f8dc8b24644882c243da8544564d2f09b4e8f6 | R | 9,747 | 271 | ---
title: Visual Search, all pairs, behavior analysis
author:
- Mathias Sablé-Meyer
- [...]
- Stanislas Dehaene
lang: en
output: rmdformats::readthedown
---
```{r settings, echo = FALSE, message=FALSE}
knitr::opts_chunk$set(echo = FALSE)
if (!require("pacman")) install.packages("pacman")
pacman::p_load(ggpl... |
f332be9aa5ccfb98ae819e92268ad992ad9c3bd136f67024f519875f65dc0644 | R | 9,752 | 199 | # ==============================================================================
# U2_upload.R
# UI definition for the "Overall Distribution Analysis" and "File Upload" tab.
#
# Purpose:
# Provides the interface for:
# - Uploading spatial multi-omics data (Transcriptomics + Metabolomics).
# - Supporting bo... |
7e62f3edd53fe2fad9f5df9f461aae13471738a10ed70fff30c60f063aaa997b | R | 9,767 | 253 | # 6. Neurotransmission ---------------------------------------------------------
## 6.1 Load packages and functions ---------------------------------------------
source("./codes/my_packages.R")
source("./codes/my_functions.R")
# install packages from github:
#devtools::install_github("jinworks/CellChat")
#d... |
d72ec277bec985863e05458dcfd1709e33123f524030b3217982c0584e8820c4 | R | 9,787 | 209 | suppressPackageStartupMessages({
library(GenomicRanges)
library(dplyr)
library(tidyverse)
})
resolve_duplicate_annotations <- function(overlap_annotation = overlap_annotation) {
# This function takes a standardized data.frame output from the `process_annotate_overlaps.R`
# script and attempts to resolve dup... |
962008f156c826927baf7df7d7ccc3f9c4c5fd28b9df4e76d8a5b02f5bd50e78 | R | 9,792 | 188 | filter_datasets_section_1<-function(datasets, N_sites, N_ROIs, N_min, N_min_sites){
dataList<-list()
dataList_to_remove<-list()
for (d in 1:length(datasets)){
cat("Filtering dataset:", names(datasets[d]), "\n")
dataList[[d]]<-datasets[[d]]
tbl<-data.frame(matrix(ncol=0,nrow=N_sites))
tbl<-table(da... |
35855278e0059b98f9b7c1020080623d489a665ba428f0d4b636ef51d8299586 | R | 9,809 | 275 | ######################################################
## Set the current working directory
######################################################
library(rstudioapi) # make sure you have it installed
current_path <- getActiveDocumentContext()$path
setwd(dirname(current_path ))
print(current_path)
base_dir = dirname(... |
ed4478ea6de62292d948077f739ca69437ed43fd480b88996807f41966d541c4 | R | 9,812 | 256 | ---
title: "MotiMus Headset Data Processinf"
Me: Ségolène M. R. Guérin
output:
html_notebook:
code_folding: hide
toc: yes
pdf_document:
toc: yes
html_document:
toc: yes
word_document:
toc: yes
editor_options:
markdown:
wrap: sentence
---
# Preamble
```{r message=FALSE, warning=FALSE}... |
2e58483358da2fe440c68232d613c2430fdff0c9c0121000ad3252d404939b08 | R | 9,817 | 208 | source('src/figures/figure_style.R')
.libPaths(c(normalizePath('.Rlib'), .libPaths()))
suppressPackageStartupMessages({
library(ggplot2)
library(jsonlite)
library(patchwork)
})
update_geom_defaults('text',list(family=figure_style$font))
update_geom_defaults('label',list(family=figure_style$font))
dir.create('fi... |
fd150cccb6bca9cf78fd027e17468f67c84ac5d65d92b9dc5c6cf625f6eb805d | R | 9,828 | 292 | ### analysis nuclear intensities single channel with different thresholds
# go to main directory (parent directory of scripts)
if (basename(getwd())== "00_scripts"){setwd("../.")}
#load packages
library("tidyverse")
library(colorRamps)
library(lmerTest)
#define working directories
in_dir = "./04b_int... |
a4ae3801d9e2d130163b4793152f4eed092f6b2f8fa7bda6396504d0e6760d88 | R | 9,843 | 298 | ################################################################################
# Functions to load and process data
################################################################################
################### Background mortality ###################
#' Read lifetable text file from Human Mortality Database
... |
ea05ad577548ceb20fb8ee6c6e1d44c2c68bf649eed802c7b86088c32eb366dd | R | 9,854 | 307 | ---
title: "Find most focal recurrent copy number units"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
author: Chante Bethell and Candace Savonen for ALSF CCDL
date: 2020
---
This notebook defines the most focal recurrent copy number units by removing focal changes that are within entire chromosome arm l... |
ad82464b5f758bf201408eca2d324da7d91150a0d87b8be1b62490821daed31f | R | 9,863 | 305 | # Load required libraries
library(tidyr)
library(ggplot2)
library(ggtree)
library(dplyr)
library(aplot)
library(stringr)
set.seed(2019-11-07)
# plotting parameters
label_font_size = 4
title_size = 6
dendrogram_thickness = 0.2
border_line_width = 0.5
tick_linewidth = 0.3
bar_width = 0.6
main <- funct... |
7d629863e1928411c721dd3ba9a7e54e6b772f381023e9fe0e237562a5b3960d | R | 9,864 | 299 | #
# Figueroa-Vargas, Navarrete et al., 2025
#
#
# Cargar las librerías necesarias
#library(tidyverse) # Para la manipulación de datos
rm(list = ls())
library(stats) # Para el PCA
library(ggplot2) # Para la visualización
library(dplyr)
setwd("~/Documents/GitHub/Figueroa-Vargas_Navarrete_2025_Scientific... |
3f73c49130a4461398bf1ea825517bfb1f4992da084cd1f6d34acd4693cbf5c2 | R | 9,871 | 218 | #......................................................
# Documentation
#' Measurement invariance table
#'
#' @param group1_nam name of the first group. Used if per_group_models is TRUE.
#' @param group2_nam name of the second group. Used if per_group_models is TRUE.
#' @param ordered logical, if set to TRUE items will... |
6345ffe2263b354b3b98c5f1829cdc41b38c760ea79e7dd7dead09ec8f7327e8 | R | 9,882 | 191 | # independent-methyl-samples.R
#' Generate a vector of unique methylation samples
#'
#' The samples from this function will be unique with respect to participants
#' i.e. only no two samples will come from the same participant. The input list
#' should be pre-filtered by `composition` and `sample_type`.
#'
#'
#'... |
a8ac5becd288d1e60b4acce3a744a932946aab8d17ced36f9cbc7b827ce243f8 | R | 9,886 | 276 | ---
title: "flashpcaR"
author: "Gad Abraham"
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: tango
keep_md: true
toc: true
vignette: >
%\VignetteIndexEntry{flashpcaR}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, echo=FALSE, cache=FALSE}
optio... |
d45615e84f4d9dd960ebf8fb2207d0ed8ba1e1a7a78e3ab3675a459d9760a7dd | R | 9,911 | 132 | #' Function to process raw input data needed to run bayesReact
#' @description
#' The function processes raw expression, motif, and sequence data in order to produce sequence ranks and motif probabilities needed for numerical sequence representation.
#' The processed data is used by bayesReact to evaluate motif distrib... |
5e0c363e5ab732896df26cb1a90800829bc0e138d37333c4da673e6ca92596d9 | R | 9,928 | 244 | #' Report gaps between PAF alignments.
#'
#' This function takes loaded PAF alignments using \code{\link{readPaf}} function and then reports all gaps
#' in subsequent target and query alignments and classify them as either deletions ('D') or insertions ('I').
#'
#' @param min.gap.diff A user defined minimum gap size di... |
ef15e5d89a58c62b9c8ad58db6344e2df286ef29edcbaf93ebc6c2e9f81c7ab5 | R | 9,959 | 155 | ################################################################################
# Permutation Test Analysis for Disrupted Gene Pairs
# Step 1: Run 10,000 permutations per gene pair
# - Randomly shuffle case/control labels 10,000 times
# - Recalculate co-occurrence for each permutation
# Step 2: Calculate... |
f241faa40e84a9d476ea8b0b30e9b17054b871b13fd32fd725bd35e20e15fe1d | R | 9,963 | 277 | # Functions for conducting survival analyses
#
# C. Savonen and SJ Spielman for ALSF - CCDL
#
# 2019, 2022
#
# Attach this package
library(survminer)
# Magrittr pipe
`%>%` <- dplyr::`%>%`
survival_analysis <- function(metadata,
ind_var,
test = "kap.meier",
... |
c47814a9e03b1f8ea6b468072b725a7e51301a65c0cb03652c4b7df0a95e0213 | R | 9,970 | 243 |
##################################################################
## Functions to do dimensionality reduction on the MOFA factors ##
##################################################################
#' @title Run t-SNE on the MOFA factors
#' @name run_tsne
#' @param object a trained \code{\link{MOFA}} object.
#' @p... |
795f90ae511fc4e45ecfb0154f09cfa8fc59083eb60ecaa9f747ec03ec17bd92 | R | 9,987 | 250 | ################################################################################
# SUBSET OF PROFILES DATA + IMPUTATION
################################################################################
library(missRanger)
library(tidyverse)
# Define the path to where the output should be stored
out_path <- ""
# read f... |
6eb777f510a8097931886cf1a341b2b9426246a6b299df192a6daca642671ee4 | R | 9,998 | 256 | # 3. Genes Extraction
# Script to extract genes of interest
# Project: Clinical features, genetics, and pathology in a large series of movement disorder cases: a retrospective multi-ancestry brain bank cohort study
# Last updated in August 2025
# PART 1
---
# STEP 1: use plink2 to convert raw genotypes from each ance... |
b0898e1bc1ecade547fb5cfc0b7a6e3491770d17068934b1e249e9eed1bbde3d | R | 10,012 | 190 |
require(Seurat)
# ————————————————— MOUSE (Male) GLIA DATA —————————————————
# Load 10X data (matrix of gene expression counts)
male_glia_10x <- Read10X("/Users/anumuppirala/Downloads/AnuData/Male_1")
#combine all three male runs
# Create a Seurat object, requiring genes to be expressed in at least 3 cells
# and cel... |
0d83b2015b95899a6f5c26056565f7f4cba2993ee8c0c39520ab72911dbc1027 | R | 10,017 | 261 |
# install.packages("devtools")
#devtools::install_github("davidsjoberg/ggsankey")
library(tidyverse)
library(dplyr)
library(ggsankey)
library(cols4all)
library(ggplot2)
library(ggforce)
library(randomcoloR)
# Create a list of selected metabolites (PATHWAY_SORTORDER) with its sub-pathway and super pathway based on PA... |
f471ae62613707f5fb7186090c8420deb19c3c546ce11d379ef05ce37b57828a | R | 10,018 | 262 |
# install.packages("devtools")
#devtools::install_github("davidsjoberg/ggsankey")
library(tidyverse)
library(dplyr)
library(ggsankey)
library(cols4all)
library(ggplot2)
library(ggforce)
library(randomcoloR)
# Create a list of selected metabolites (PATHWAY_SORTORDER) with its sub-pathway and super pathway based on PA... |
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