sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
3f429ea5584c9c34789aa8d43efcb6325fd5cce0dfe33896016ca121536ae999 | R | 10,063 | 184 | #!/usr/bin/env Rscript
# Variables
table <- ""
cond1 <- ""
rep1 <- "" # Number of replicates for cond1, automatically determined
cond2 <- ""
rep2 <- "" # Number of replicates for cond2, automatically determined
output <- ""
siglfc <- 1 # |Log2| fold change to be used as significant. Set at 1 by default,... |
80330cb24ff4787beac0b3a812b12f7fb1ad061831e2748b2bb848d9c07f6163 | R | 10,077 | 134 | # Primary analysis: refuses to run without a verified pre-results freeze.
.libPaths(c(normalizePath('.Rlib'),.libPaths()))
suppressPackageStartupMessages({library(DESeq2);library(fgsea);library(jsonlite);library(ggplot2);library(digest)})
source('src/transcriptomics/helpers.R')
if(!file.exists('provenance/analysis_free... |
f78681d80452740afd7350356c78b3c517009b85aafd81eddbd066e2f7557473 | R | 10,085 | 313 | ################################################################################
### 1. script to process dnn predictions ---------------------------------------
### Marc Grünig --- 17.09.2024 -------------------------------------------------
#############################################################################... |
ee1bda04c7acbf227f909a106ab6d44c4e995344cbe371429686b27209cf677e | R | 10,127 | 280 | ---
title: "High-Grade Glioma Molecular Subtyping - Defining Lesions"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
author: Chante Bethell for ALSF CCDL
date: 2019
---
This notebook looks at the defining lesions for all samples for the issue of
molecular subtyping high-grade glioma samples in the OpenPB... |
359213dac65f2097d2e5da1f8298e1b1cde50d17a5361a9e72d06e7ad3cdbee7 | R | 10,156 | 188 | ---
title: "tICA_Interspecies_Correlations"
author: "AZ"
date: "`r Sys.Date()`"
output: html_document
---
This script performs a comparative analysis of functional brain components derived via temporal Independent Component Analysis (tICA) from movie-driven fMRI data in marmosets and humans. The goal is to assess both... |
fedd8717796844f96d65666020fb88795dc5d750efcff2d55a28478e9ee7527c | R | 10,159 | 220 | block <- function(){ # Preprocessing of detailing the metadata when available (Unused)
##### Detailing metadata #####
setwd("D:/valentin/main")
BDR_all = read.csv("raw_data/BDR_FULL_PHENOTYPING.csv", sep = ";") #709 samples
pheno_PFC = read.csv("./BDR_pheno.csv", sep = "\t") #1221 samples
BDR_filter = BDR_all[... |
3ad5257fa98f54098b8686dbc564919d45e2465157e6229c03f4ad978e72761d | R | 10,181 | 391 | test_that(
"olink_qc_plot - works - OSI",
{
skip_if_not_installed(pkg = "ggrepel")
osi_data <- get_example_data("example_osi_data.rds")
# ----------------------------
# OSICategory invalid value
# ----------------------------
df_bad_cat <- osi_data |>
dplyr::mutate(
OSICatego... |
53404ee03ccc3b62ab9ef2e25dce91953b0f0545593bc74fab193e40b981bd54 | R | 10,189 | 340 | ---
title: "Gather CNV changes to subtype LGAT biospecimens"
author: "K S Gaonkar (D3B)"
output: html_notebook
---
In this notebook, we will look for the following CNV changes that define subtypes of LGAT
- LGG, FGFR
harbors FGFR1 TKD (tyrosine kinase domain tandem duplication)
- LGG, CDKN2A/B
harbors focal CDK... |
5cabf33a47f8a11aa0f3eb10b827e53734499a06210bc47c0294f08cbec8350b | R | 10,196 | 345 | #' Bridge selection function
#'
#' @description
#' The bridge selection function will select a number of bridge samples based
#' on the input data. It selects samples with good detection that pass QC
#' and cover a good range of the data. If possible, Olink recommends 8-16
#' bridge samples. When running the selector, ... |
409e4e5862ca5dc4360ed839c4f98bad0600e887a64199da56c2911f5b54d1a7 | R | 10,204 | 270 | #---------------------------------------------------------------------------------------------
# R (version 4.2.1) code for visualizing hitgrams of climate datasets.
# This code is developped for the following paper:
# 'Predicting dominant terrestrial biomes at a global scale:
# Assessments of machine learning alg... |
d1bcf915ad272ecb0163a1cebcb10422d090fae4677bb6bc9b87e6c5ddda85ee | R | 10,223 | 366 | test_that(
"olink_heatmap_plot - works",
{
skip_if_not_installed("ggplotify")
skip_if_not_installed("pheatmap")
skip_if_not_installed("vdiffr")
# Load data with hidden/excluded assays (all NPX=NA)
npx_data_format_oct <- get_example_data("npx_data_format-Oct-2022.rds")
check_log_oct <- check... |
7123339eebf2d1648e1d333c81855d54ce03a367360804ec16c616eafffe302a | R | 10,234 | 222 |
### version of some panels without genetic insight filter
cat(file=stderr(), 'Generating alternate pipeline tables without genetic insight filter...')
combined_ti_germline_unfiltered = pipeline_best(merge2, phase='combined', basis='ti', require_insight=F, include_missing=F, verbose=F)
combined_ti_omim_unfiltered... |
a73121c9ea2bb046f30fe99c2805ee070d3b2331072e622312f0e69c21947961 | R | 10,262 | 209 | ---
title: "Using WHO 2016 CNS subtypes to improve LGAT harmonized diagnosis"
output:
html_notebook:
toc: true
toc_float: true
author: JN Taroni for ALSF CCDL (code) ; K Gaonkar, J Rokita updated for LGAT
date: 2021, 2022
---
CNS lgat have subtypes per the [WHO 2016 CNS subtypes](https://link.springer.com/c... |
822ff2a096606fb388b5b879bace5d14fe9056217250ca51074201dc8eeab5a3 | R | 10,295 | 223 | # Author: Sangeeta Shukla
# This script servers as a precursor to the DESeq analysis step, as it calculates the GTEx_index, Hist_Index values
# This script also creates Histology and Counts data subsets which satisfy given clinical criteria
# Load required libraries
suppressPackageStartupMessages({
library(optpar... |
63378606ff860fe83422fd4779a9094b7e93826904b610d5a427ddbbcb542290 | R | 10,307 | 256 | #'---
#' title: "Counts Summary: `r paste(snakemake@wildcards$dataset, snakemake@wildcards$annotation, sep = '--')`"
#' author:
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AE" / "{annotation}" / "{dataset}" / "count_summary.Rds")`'
#' input:
#' - ods: '`sm cfg.getProcessedResultsDir() +
#' "/... |
2a357f112d5cb206bc812533933dfb998fcd235d24d1a923176fff197c3910ca | R | 10,325 | 202 | library(tidyverse)
library(data.table)
library(dplyr)
library(mice)
library(survival)
library(bigreadr)
#read into files
setwd("/path")
# Logistic Regression Functions
# Function for brain imaging traits (includes scanner position covariates)
log_reg_brain <- function(phenotype, inputdata){
logres <- a... |
2ba1f9c68a30473c7db4ee6e13781dda04f4565ff0e07f9ce540c25636f5c6b8 | R | 10,340 | 291 | 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... |
8c60ce4d9694ba1739fc66afce18d1a52408d522eb7526d77095f1e0863df399 | R | 10,369 | 210 | #Author: Kim Kundert-Obando for questions please reach out to me at k.rogge.obando@gmail.com
#This code will be a function that runs mixed model while correcting for age,
#gender, and, ethnicity in the FC comparisions with state and trait anxiety and outputs the beta an p-values results
#install and open packages
#i... |
80b3c964375f8ddcd97a4d289154f5e5fb7c7af1e1b73dadcf9ccf45b38c2037 | R | 10,407 | 173 | # similarity analysis of ciliopathies using propagation scores
# Libraries ----
library(tidyverse)
library(ComplexHeatmap)
# Load necessary files ----
pageRankScores = readRDS('data/pagerankScores.rds') #calculated network propagation scores for ciliopathies
traitAnnotation = read.csv('data/traitOverview.csv') #la... |
9ae2c628d1915eff8295cbb46ba8b601ec95364aa797ad1a32885307ff493903 | R | 10,421 | 275 | ---
title: "NanoFlow: cDNA Transcriptome Report"
date: 'Report created: `r Sys.Date()`'
#bibliography: Static/Bibliography.bib
output:
html_document:
css: Static/UoG.css
df_print: paged
highlight: null
keep_md: yes
number_sections: yes
self_contained: yes
theme: default
to... |
9c118bcec4b03555fdb25b549e98e8005777b9cbaa746eccd91b29e985635275 | R | 10,435 | 357 | #' Convert Illumina IDAT Files to Beta Values Using sesame
#'
#' This function takes Illumina IDAT files,
#' processes them using the sesame package,
#' and returns beta values (DNA methylation values)
#' along with quality control statistics and plots.
#'
#' @param idat The path to the directory containing Illum... |
96a14f6a8914cbc0bc21d0ce8ac6399e2c0c43d8a56d026125e4d2099e8f3f70 | R | 10,440 | 289 | #!/usr/bin/env Rscript
# Load a Visium HD dataset into Seurat, run QC, normalization, clustering, UMAP,
# and save the resulting Seurat object as an RDS file.
library(Seurat)
library(hdf5r)
library(ggplot2)
library(future)
library(argparse)
# give good tracebacks on errors
options(error = function() traceback(2))
err... |
ca14c09ddef46b22838a87ff7d1f8cd7a0e871e83ab87eb236e894d44cd4e95a | R | 10,448 | 263 | # Hua Sun
# Edited original 'ScType' codes (https://github.com/IanevskiAleksandr/sc-type)
# v3.2 2024-07-26 (Updated to support Seurat v4 & v5)
library(dplyr)
library(HGNChelper)
library(readxl)
## gene_sets_prepare
gene_sets_prepare <- function(path_to_db_file, cell_type){
cell_markers = openxlsx::read.xlsx(... |
5fa89756a9c49f9ce5ab94bc0c42bca0fda86e96865d11135492172cea9ed245 | R | 10,458 | 345 | ---
title: "Circos Plots Examples for Visualizing SV and CNV data"
output:
html_notebook:
toc: true
toc_float: true
author: Candace Savonen for ALSF - CCDL
date: 2020
---
This notebook shows examples of how to use the circos_map_plot function for
mapping data that corresponds to chromosomal coordinates.... |
cc1f2ef12ef85bd95c451eabdd319d8c8d1dad1fe313f062997b56c0026c07f0 | R | 10,466 | 341 | # This script displays an oncoprint displaying the landscape across PBTA given
# the relevant metadata. It addresses issue #6 in the OpenPBTA-analysis
# github repository. It uses the output of 00-map-to-sample_id.R. It can
# accept a gene list file or a comma-separated set of gene list files that will
# be concatenate... |
0092db9c4952daf4246045f133cdb94d337a8400fe4ea8e2de014da3e8ddde09 | R | 10,488 | 315 | ---
title: "Survival analysis by immune scores and molecular subtypes"
authors: Run Jin (D3B), Stephanie Spielman (CCDL), Jo Lynne Rokita (D3b)
output:
html_notebook:
toc: true
editor_options:
chunk_output_type: inline
---
## Setup
#### Packages
```{r Set up library}
library(survival)
library(ggpubr)
libra... |
405231ba542cc54190227d264ebeeebb21d101553a919cd1d1f099a85834891c | R | 10,529 | 272 | library(catmaid)
library(data.table)
library(tidyverse)
# get list of cell types in project --------------------------------------------
# assumes they have an annotation starting with "celltype:"
get_celltypes <- function(pid) {
annotations <- catmaid_get_annotationlist(pid = pid)
celltypes <- annotations$annotat... |
dc1bce398801f95cd170a073b95b1cec6f1692ecdbbe2743b44b569e9a34fe1e | R | 10,533 | 294 | ---
title: "Statistics Functions"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output: rmarkdown::html_vignette
theme: united
vignette: >
%\VignetteIndexEntry{Statistics Functions}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
***
<style>
p.caption {
font-size: 0.9em;
}
</style>
`... |
d2ea109f7ba346e13cec343c382df329a560493a60982923d63c5cd6a0029d23 | R | 10,566 | 191 | Boxplot2 <- NeuroMET %>%
filter(visit == "t1" )%>%
select(record_id, diagnose, glu, gaba, glu_gaba, pTau_v1_group, diagnose_group, glu_CRLB, gaba_CRLB)%>%
pivot_longer(cols = 3:5, names_to = "biomarker", values_drop_na = TRUE)%>%
mutate(biomarker = factor(biomarker, levels = c("glu", "gaba", "glu_gaba"))) %>% #... |
240ae9aff47fa2c0b0363650436ddd5273fd54b2e8976bd1947d2425e2a62e93 | R | 10,570 | 332 | #' Function to simulate the protracted speciation process
#'
#' Simulating the protracted speciation process using the Doob-Gillespie
#' algorithm. This function differs from pbd_sim_cpp that 1) it does not
#' require that the speciation-initiation rate is the same for good and
#' incipient species, and 2) that it simu... |
f6b9eb02bc467e323656f690a4d6de7dc30b49a4ab568162b6354a295d9c3ab4 | R | 10,576 | 223 | #' Function to parse CIGAR string into a set interval ranges.
#'
#' @param cigar.str A character string containing alignment represented as a CIGAR string.
#' @param coordinate.space A used defined coordinate space given CIGAR should be parsed against, either 'reference' or 'query'.
#' @importFrom GenomicAlignments exp... |
550e9c6ab0978f8b007b21378ff17562e08652254cca487b9975a177dcdfb316 | R | 10,581 | 263 | # Functions for chromosomal instability plots
#
# C. Savonen for ALSF - CCDL
#
# 2020
make_granges <- function(break_df = NULL,
sample_id = NULL,
samples_col = "samples",
chrom_col = "chrom",
start_col = "start",
... |
d10fa418fc144becb8776833c88333ff7cd077f111ae80c9f9314ffd05613f9a | R | 10,582 | 267 | # Meta-analysis of scRNA-seq data of Neocortex developmental time points - Part 2 : Clustering ---------------------
# E10-P4
# Rahul Jose
# SCB, RGCB
# October 2024
# Primary Aim :
# For the identification of NIHes1 and NDHes1 cells across developmental time points
# Data -----------------------------------... |
f08b54728ad97357171331a469ed9116442c173975fc571fe2f35be2cc89a4f5 | R | 10,582 | 353 | #' Function to plot an overview of a sample cohort per Panel.
#'
#' @description
#' Generates a facet plot per Panel using ggplot2::ggplot and
#' ggplot2::geom_point and stats::IQR plotting IQR vs. median for all samples.
#' Horizontal dashed lines indicate \eqn{\pm}\var{IQR_outlierDef} standard
#' deviations from the ... |
a3d857eb212425aa25b131cfe575d985bc9c8ec3c0da1a28f199cb6253359eda | R | 10,598 | 314 | ---
title: "Scanner_plots"
author: "HannahSavage"
date: "2023-08-21"
output: html_document
---
## SET ENV
```{r setup, include=FALSE}
library(readxl)
library(dplyr)
library(tidyverse)
#library(ggplot2)
library(reshape)
#library(scales)
library(sjmisc)
library(scatterpie)
library(showtext)
library(psych)
library(tidyr)... |
8c5b6cdbfd26c4abb960a112a59b7da9d584155aab9058b4857b156aef23c741 | R | 10,601 | 378 | # This script performs the following functions:
# 1. DESeq2 tumor-only analysis with RUVg by molecular subtype
# Authors: Komal Rathi, updated by Adam Kraya
suppressPackageStartupMessages({
library(optparse)
library(tidyverse)
library(DESeq2)
library(RUVSeq)
library(EDASeq)
library(edgeR)
library(stringr... |
8f8f6b4e9548fe9943beb1de68242b60081a853b3fdfebd50e38eda212a1089e | R | 10,613 | 266 | # Code to generate Figure 1 Supplements of the Jokura et al 2024 Ctenophore apical organ connectome paper
# source packages and functions ------------------------------------------------
source("analysis/scripts/packages_and_functions.R")
# show just a subset of celltypes, just the interesting ones
celltype_map <- ... |
a3a6ac24bd909eeee49dd053a7ce7ed3faec23a6ed2569f2a759be5b49cae65c | R | 10,613 | 227 | # modes that olink normalization functions may have
olink_norm_modes <- list(
"bridge" = "bridge",
"subset" = "subset",
"ref_median" = "ref_median",
"norm_cross_product" = "norm_cross_product"
)
# pre-populated dataset with column names and classes that the reference medians
# input dataset may have
olink_norm... |
e341feddcf58052ddce74cd3cd0da5fd048d59ce4f4365bb4b4290d27bd5e5e5 | R | 10,623 | 362 | # Instructions for creation of package msigdb gene lists
# Create Gene Symbol Lists --------------------------------------------------------------------
library(dplyr)
library(msigdbr)
msigdbr_species()
msig_dbr <- msigdbr(species = "Homo sapiens", category = "H")
msig_oxphos_direct <- msig_dbr %>%
dplyr::filter... |
c67cd993bb5cd2736b1602a9856301a970e8c5b823396d2fa9fe03bcea2ddccd | R | 10,632 | 291 | ---
title: "Pathway_Enrichment"
author: "Tingting Wang"
date: "2/28/2025"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = F)
library(tidyverse)
library(readxl)
library(dplyr)
library(ggsankey)
library(tidyverse)
library(cols4all)
```
# Create a list of selected metabolites (PATHWAY... |
7347b8ec6e0c780f7b258447ea1a8587317b552e8a44452b567fd6dd331565fe | R | 10,664 | 275 | # Intended for import only
# Chante Bethell for CCDL 2019
#
# Assign function to perform the dimension reduction techniques
perform_dimension_reduction <- function(transposed_expression_matrix,
method,
model_filename,
... |
d19b4c52fc84b36e5ce5c6be7b260e22bd026be457110da2005388445319a2d0 | R | 10,676 | 198 | source('src/figures/figure_style.R')
.libPaths(c(normalizePath('.Rlib'), .libPaths()))
suppressPackageStartupMessages({
library(ComplexHeatmap)
library(circlize)
library(DESeq2)
library(jsonlite)
library(grid)
})
out_dir <- 'figures/transcriptomics'
dir.create(out_dir, showWarnings=FALSE, recursive=TRUE)
sp... |
b6144c5c74bbe4dfc446d9390c2ead93db8d9cc46c8c39507837b3e5a87673cb | R | 10,688 | 375 | #' Help function to read long or wide format
#' `r ansi_collapse_quot(x = get_olink_data_types(), sep = "or")` data from
#' delimited
#' `r ansi_collapse_quot(x = get_file_ext(name_sub = "delim"), sep = "or")`
#' files exported from Olink software in R.
#'
#' @description
#' The function can handle delimited files in l... |
702cb7da9718d0683fc909a5fec0a563f093c3a07f7538869933f3b4a27768a7 | R | 10,713 | 227 | rm(list = ls())
library(SummarizedExperiment)
library(ComplexHeatmap)
library(circlize)
library(corrplot)
library(limma)
library(tidyverse)
library(xlsx)
library(ggrepel)
#differential analysis function
limma_comparsion <- function(data, meta, group_col, control = 'Control', case = 'Disease'){
compair.me... |
20b61ba51ca3d2a103f82ae6e141e353c070f523de8565aa88c82f9c7c27fc9d | R | 10,721 | 350 | #' Function which performs a t-test per protein
#'
#' @description
#' Performs a Welch 2-sample t-test or paired t-test at confidence level 0.95
#' for every protein (by OlinkID) for a given grouping variable using
#' stats::t.test and corrects for multiple testing by the Benjamini-Hochberg
#' method (“fdr”) using stat... |
8229dd296311c17e2cafa07e43b471fbd3147fcf54d10eabbfcc6d0d9a1f79da | R | 10,722 | 297 | ## Load data and wrangle
# load packages
library(tidyverse)
library(readxl)
library(gtsummary)
library(ggdist)
library(modelsummary)
library(lmerTest)
library(performance)
library(datawizard)
library(glmmTMB)
library(emmeans)
library(Hmisc)
library(ggdist)
# load data
df_raw <- read_xlsx("Base de datos rTMS S1.xlsx",... |
e6c1d1ea04287a324ffc2a086755d31dd4eeab2bc7862ae91f404e35ca7d29fc | R | 10,730 | 312 | #' @export mean_difference
mean_difference <- function(x, y) {
if (length(x) != length(y)) {
stop("Vectors must be the same length")
}
mean_diff <- mean(x - y)
return(mean_diff)
}
#' @export all_differences
all_differences <- function(x, y) {
if (length(x) != length(y)) {
stop("Vectors must be the ... |
810bc60d33dcc5f8dcbb3eb05dd18c5f0a05cb5dddf57a5b1ef5abe8a5cc7ce9 | R | 10,754 | 206 | 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... |
cf977fc20bce4ede82845e54c2f741d29b15a0d4872713c3b4d0341adfb0b9fa | R | 10,769 | 269 | library(pROC)
data(aSAH)
context("power.roc.test")
# define variables shared among multiple tests here
test_that("power.roc.test basic function", {
res <- power.roc.test(r.s100b)
expect_equal(as.numeric(res$auc), as.numeric(r.s100b$auc))
expect_equal(res$ncases, length(r.s100b$cases))
expect_equal(res$ncontr... |
58a7ea30fb87cbd65697c60b5299d48c772933a513c9751343fb92568d014f44 | R | 10,772 | 338 | ---
title: "High-Grade Glioma Molecular Subtyping - Mutations"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
author: Chante Bethell and Jaclyn Taroni for ALSF CCDL
date: 2020
---
This notebook prepares consensus mutation data for the purpose of subtyping HGG samples
([`AlexsLemonade/OpenPBTA-analysis#24... |
5d28d4f76b1884972f5ba4b13fdb161707aafd6a1b4caff892742101f50b2ef5 | R | 10,780 | 200 |
#############################################################################################################################################################
#############################################################################################################################################################
###... |
b39df5a6e6c938f1f887dc79a6eae1632a94e5c6a3327f8871d72f5fb08da359 | R | 10,785 | 337 | # Analysis c) to investigate whether an increased PAD at baseline is associated with future conversion from CN/MCI to MCI/AD.
# Load required libraries
library(mvtnorm)
library(data.table)
library(LMMstar)
library(mets)
library(riskRegression)
library(dplyr)
library(lava)
### Script
# Load excel file
file_path <- her... |
d3b8629ea9ea99d88ba430dd185baa75591619ec0b79957c0963fd930e486f57 | R | 10,812 | 253 | # pROC: Tools Receiver operating characteristic (ROC curves) with
# (partial) area under the curve, confidence intervals and comparison.
# Copyright (C) 2010-2014 Xavier Robin, Alexandre Hainard, Natacha Turck,
# Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez
# and Markus Müller
#
# This program is free soft... |
b07ac2f9719405fe4766c883f9b36e5d918a6ed9dcd6293f98c877e423fdb66a | R | 10,860 | 319 | # S. Spielman for CCDL 2022
#
# Makes pdf panels for supplementary Figure S2, specifically those that are derived from the `snv-callers` analysis module.
library(tidyverse)
# Directories -------------------------------------------------------------------
# Establish base dir
root_dir <- rprojroot::find_root(rprojroo... |
32b09c67e5a015d24352116db59e59b1f6ab6f13a77c840a881c035cfa15a8ce | R | 10,875 | 306 | ########################### Ground truth disease simulator ################
#
# Objective: Simulate cancer targets, background mortality data, and relative
# survival data for ground truth model
########################### <<<<<>>>>> ##############################################
rm(list = ls()) # Clean environmen... |
cfc0c3d3d23edcb84c891f5f24724108487b9952868a66ecd5ccb15d8951b137 | R | 10,882 | 292 | ---
output: html_document
author: "Delphine Potier"
output:
html_document:
code_folding: hide
code_download: true
editor_options:
chunk_output_type: console
---
#################
Script for histone marks (H3K27ac, H3K4me1, H3K4me3) in Jurkat WT and Jurkat CRISPR-edited clones (1D9,2G5 and 1B6).
Made with ... |
dce9cd8b1368d9f7f1900433a5af85b02ac5fd52eb70aebbeba61717ae89368b | R | 10,885 | 359 | #' Function which performs a Mann-Whitney U Test per protein
#'
#' Performs a Welch 2-sample Mann-Whitney U Test at confidence level 0.95 for
#' every protein (by OlinkID) for a given grouping variable using
#' stats::wilcox.test and corrects for multiple testing by the
#' Benjamini-Hochberg method (“fdr”) using stats:... |
f5b8bd817034a860d365e4b413c84c19a37bd6d5651a9ab0bb1d577f20ae37c6 | R | 10,934 | 386 | # This script performs the following functions:
# 1. DESeq2 tumor-only analysis with RUVg by molecular subtype
# Authors: Komal Rathi, Adam Kraya
suppressPackageStartupMessages({
library(optparse)
library(tidyverse)
library(DESeq2)
library(RUVSeq)
library(EDASeq)
library(edgeR)
library(stringr)
librar... |
42c117927f545affe6975524eb418b5cf04ffed9e3431bc8416cf4b0ecf1c05a | R | 10,944 | 332 | ### 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... |
768323cfac84dedd013c449096f3cea2ec4240d202fb9e04eb85f6e60aa8c799 | R | 10,962 | 187 | library(dplyr)
library(parallel)
library(tidyr)
source("/cbica/projects/luo_wm_dev/two_axes/code/results/main_figures_functions.R")
source("/cbica/projects/luo_wm_dev/two_axes/code/results/supp_figures_functions.R")
# Spin tests for supplementary figures: tract-level Pearson's (age of maturation vs. S-A rank), and par... |
ff4fb7fba5f546022eca33c7b4fe216e56993e38ad5fda636267df655443704f | R | 10,983 | 237 | #!/usr/bin/env RScript
library(ggplot2)
library(ggiraph)
library(grid)
library(optparse)
# Getting options from command line
option_list = list(
make_option(c("-e", "--expMatrixTrans"), type="character", default=NULL,
help="transformed expression matrix file path", metavar="character"),
make_option(c... |
0465c23a90fdc97a9f2e3866f1ed0299a9d23e1154356da3a55f28d8ab5d3440 | R | 10,996 | 239 | #' Enrichment analysis for any type of annotation data
#' @param x vector contains gene names or dataframe with DEGs information
#' @param object annotation data
#' @param ontology ontology type
#' @param pvalue cutoff pvalue
#' @param padj cutoff p adjust value
#' @param organism organism
#' @param keytype keytype for... |
d2bf8c20e5638f5b88fc23fc4868fe7961831d4d21108ac3a6104d6f04c8fc87 | R | 11,006 | 267 | setwd("./2ndCohort/spacexr")
#### convert seurat object to anndata for neigborhood enrichment analysis
#### use R 4.2.2
options(stringsAsFactors = FALSE)
.libPaths(c("./R-4.2.2-latest/", "./Rlib4.2.2/"))
library("Seurat", lib.loc = "./R-4.2.2-latest//")
library('SeuratData')
library(SeuratDisk)
library(ggplot2)
librar... |
d7ab4d2f0c83f36307a4613d7308197432400f372d487bc9c4f4fe7331d28fa6 | R | 11,078 | 333 | library(MOFA2)
test_that("a model can be created from a list of matrices", {
m <- as.matrix(read.csv('matrix.csv'))
expect_warning(create_mofa(list("view1" = m))) # no feature names provided
rownames(m) <- paste("feature", seq_len(nrow(m)), paste = "", sep = "")
expect_s4_class(create_mofa(list("view1" = m)), "MO... |
15356a8f1c630501bbdf4e3f8c51f29f3b2277f2b4259056ad8a3ca4e04d1e9b | R | 11,085 | 388 | #' Generic function for plotting channels
#'
#' This generic function provides a common interface for plotting channels. The
#' behavior of this function depends on the class of the input data.
#'
#' @param x Input data. Depending on its class, behavior varies.
#'
#' @return A plot representing the channels.
#'
#' @exp... |
f439bc0826fdf948238ae9b5eced74f066b6fc60cddea12174f3bb335453f032 | R | 11,087 | 396 | ############################################################
## Main Figure 3
############################################################
## Load helper functions
source("path/to/function_definition.R")
## Packages
library(ggplot2)
library(dplyr)
library(tidyr)
library(tibble)
library(data.table)
library(anndata)
li... |
9d8700f5545af05b570489b9099b7d23a9eeb9a0b97cc7c88ddecba73e02abb8 | R | 11,088 | 297 | #' Create CNVRs in a set of CNVS based on network analysis
#'
#' A network is defined a set of CNVs that overlap with each other. Usually there
#' are several ones in each chromosomal arm. Given a minimum IOU value
#' each pairs of CNVs is considered as two connected nodes. The CNVRs are
#' computed using community the... |
4fd7f08b49d8526e70582af9085089455fb5887114748763c92b53201da8e056 | R | 11,094 | 291 | ########################################################################
### 4. mapping of competitive strength ---------------------------------
########################################################################
### libraries
library(tidyverse)
library(dplyr)
library(ggplot2)
library(tidyterra)
library(sf)
li... |
69a232b5a9908a82e2f49c53fcd77f1a785c7bf0a81a2de8d70b8be9b3dbce1b | R | 11,121 | 394 | ############################################################
## Burden tests for DNV and rare variants in EUR / EAS
## 1) DNV
## 2) DNV in constraint genes
## 3) Rare inherited variants
## 4) Rare inherited variants in constraint genes
## 5) Ultra-rare inherited variants
## 6) Ultra-rare inherited variants ... |
4599245ede9d878be4c8fe4f4b87e238b6c8b53a7bf8eb818c1866a2da6aa233 | R | 11,144 | 381 | ---
title: "MSLc Primed Genes Heatmaps in Neurons and Astrocytes"
author: "AF"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
suppressPackageStartupMessages({
library(circlize)
library(ComplexHeatmap)
library(rstudioapi)
library(dplyr)
library... |
16442504b1844fcdcdc51decc8da562a98bccd61f9d401842935a2221dbfefc4 | R | 11,183 | 246 | test_that("lgb.convert_with_rules() rejects inputs that are not a data.table or data.frame", {
bad_inputs <- list(
matrix(1.0:10.0, 2L, 5L)
, TRUE
, c("a", "b")
, NA
, 10L
, lgb.Dataset(
data = matrix(1.0:10.0, 2L, 5L)
, params = list()
... |
451e3ff07871e367a51611188ac2920fdc902f757f642a44eed488e6373a6a16 | R | 11,187 | 507 | #Video1 of the Jokura et al ctenophore AO paper
#Sanja Jasek, Kei Jokura, Gaspar Jekely
#load packages and functions ------------
source("analysis/scripts/packages_and_functions.R")
#create temp dir to store video frames -----------
mainDir = getwd()
dir.create(file.path(mainDir, "videoframes"), showWarnings = FALSE)... |
81fffe8584a98ea1ec5e50a3eb816b3588c5ad18df78cf7d682bf86a4623e01b | R | 11,196 | 387 | #' Volcano Plot for DMPs/DMRs
#'
#' Publication-ready volcano plot showing log2(fold-change) vs -log10(p-value)
#'
#' @param df Data frame with DMPs/DMRs
#' (columns: deltabetas or maxdiff, adj.P.Val or HMFDR)
#' @param fdr_threshold FDR significance threshold (default 0.05)
#' @param deltabeta_threshold Delta beta t... |
8aa680ed68ddae9234839d48404fbd6d65ed8fba06731418700da995921310c3 | R | 11,218 | 255 | ---
title: "HeMoVal: Recomputation of the Primary Outcome Analysis"
date: "2025-01-28"
authors: "David Kronthaler"
output: html_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE, echo=FALSE}
knitr::opts_chunk$set(echo = TRUE,
cache = TRUE,
... |
0cafece199aac3b7fa2609f36d661d07c96e63a814cc6b69082f1e37d3dea3a7 | R | 11,259 | 290 | ---
title: "03-subtyping"
author: "Aditya Lahiri, Eric Wafula, Jo Lynne Rokita"
date: "11/14/2022"
output: html_notebook
---
## Objective
To subtype `Neuroblastoma`, `Ganglioneuroblastoma`, and `Ganglioneuroma` biospecimen into either `MYCN amplified` or `MYCN non-amplified`.
This script loads the table `input/altera... |
ee18b61452332fc2837659e01505ba71b4f06a8c27497a5b97490fc9db090518 | R | 11,265 | 418 | ---
title: "ATRT Molecular Subtyping - Data Prep"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
author: Chante Bethell for ALSF CCDL
date: 2019
---
This notebook addresses the issue of molecular subtyping ATRT samples.
# Usage
This notebook is intended to be run via the command line from the top direc... |
51e7d4d29889694d88589a7939dd79ae0766af802c0a35941fe6e9dba12c26d5 | R | 11,286 | 409 | #' Multiple plot functions
#' @param dat A data frame or matrix
#' @param group Class group
#' @param position Legend position. The default is "bottomright"
#' @inheritParams graphics::plot
#' @return A plot object
#'
#' @export plot_tSNE
# plot tSNE
plot_tSNE <- function(dat, group = NULL, label_group = T, position =... |
5c70e7c771bad2e6045c3f1837391a56011cd7bdc2da202d651eebbd1ac37e16 | R | 11,298 | 272 | ---
title: "Preliminary QC Report for Sample `r params$sample`"
subtitle: "Initial Sample Preprocessing"
date: '`r Sys.Date()`'
output:
rmdformats::robobook:
lightbox: true
number_sections: true
gallery: true
code-fold: true
toc_depth: 3
params:
seuratdir: seurat
sample: ... |
5e62c17372da7424f8f8fd8e0d5637bb1ba13597b52f7381a2dd59517cc7a2c6 | R | 11,329 | 324 | # Calculate TMB for a given SNV consensus MAF file from Strelka2, Mutect2,
# Vardict, data Lancet callers
#
# Eric Wafula for Pediatric OpenTargets
# 12/10/2021
# Adapted from AlexsLemonade OpenPBTA-analysis snv-callers analysis module
#
# Load libraries:
suppressPackageStartupMessages(library(optparse))
suppressPac... |
9f613578f1acb492c197791d5f9cac36ae7ab889537046971f92814e9189124c | R | 11,368 | 284 | # pROC: Tools Receiver operating characteristic (ROC curves) with
# (partial) area under the curve, confidence intervals and comparison.
# Copyright (C) 2010-2014 Xavier Robin, Alexandre Hainard, Natacha Turck,
# Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez
# and Markus Müller
#
# This program is free soft... |
833e206cabe667dfcbc4fd21e726c6dfddd15ea3c9e03d769972b5bee9edf64e | R | 11,369 | 282 | # Author: Run Jin
# Add PedCBio Sample Name Column Addition
suppressPackageStartupMessages({
library("optparse")
library("tidyverse")
library("readr")
library("tidyr")
})
#### Parse command line options ------------------------------------------------
option_list <- list(
make_option(c("-i","--hist_file"),t... |
73d569c3f4c0323d7a543e309f1a85742a3d412a3b53728c4524bcfed50dd3d0 | R | 11,370 | 331 | # gets statistics about synapses and mitochondria and which itochondria are part of a synapse
source("analysis/scripts/packages_and_functions.R")
# calculate average number of post-synaptic sites per synapse -----------------
pre_connectors <- catmaid_fetch(
path = paste(pid, "/connectors/", sep = ""),
body = l... |
0c846be06cf5bbc07afe8b156813903e80829b2726cc6467e6d60b53ea5d822e | R | 11,416 | 224 | #' Export FASTA sequences from a set of alignments reported in PAF formatted file.
#'
#' @param alignment.space What alignment coordinates should be exported as FASTA, either 'query' or 'target' (Default : `query`).
#' @param order.by Order alignment either by `query` or `target` coordinates.
#' @param bsgenome A \pkg{... |
07a27ced3cc86d0905100fbba843dd37b666ba06abd7913a104358232cf6730f | R | 11,418 | 175 | #!/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
... |
40acfd6a4c6f3a0e68072bf586df843087ae9c8af71183a6d6e66603a44b737c | R | 11,453 | 276 | # targets-safe tidymodels wrapper functions ----
# to fit cross-validated models small enough not to be run in matlab
# assumes that all predictor cols and all outcome cols share a respective prefix. easy enough
make_recipe <- function (in_data, x_prefix, y_prefix, additional_steps = NULL) {
in_recipe <- recipe(in_d... |
50ae45612651037dbe5ffe89ad8bffba82e785167f2dfbd28ce3abce9ca1a632 | R | 11,464 | 298 | ########################### Create prior distributions ##########################
#
# Objective: Refine data-driven prior distributions for model parameters
########################### <<<<<>>>>> #########################################
rm(list = ls()) # Clean environment
options(scipen = 999) # View data without ... |
affffb5f470b3d7f31730ee1527027b71f2715c583fa13c150496ecaf6a44827 | R | 11,476 | 319 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(data.table)
library(edgeR)
library(limma)
})
# ============================================================
# infer_handedness_multiROI.R
#
# Goal:
# Infer handedness / language laterality orientation from RNA-seq
# expression patterns across m... |
55545f9112bd4fafa23e4e6614534184405a048cc0ac3765843b1f9b7f6f5824 | R | 11,491 | 369 | # Test check_columns ----
test_that(
"check_columns - works - tibble",
{
tmp_data <- dplyr::tibble(
"A" = c(1L, 2L, 3L),
"B" = c(TRUE, TRUE, FALSE),
"C" = c("A", "B", "C"),
"D" = c(FALSE, FALSE, TRUE)
)
# both A and B exist
expect_no_condition(
object = check_columns(... |
cc0c99f9d6807439239324687c2c039b1cf59669d3a813b0694ffaa08e2758cd | R | 11,560 | 430 | test_that(
"olink_umap_plot - works - snapshot",
{
skip_if_not_installed("umap")
skip_if_not_installed("ggrepel")
skip_if_not_installed("ggpubr")
skip_if_not_installed("vdiffr")
withr::local_seed(123)
cfg <- umap::umap.defaults
cfg$random_state <- 123
npx_df <- npx_data1 |>
... |
5ad7989619de63e66414dfff7ee4986a0fdc1d6932436506e14dc3973cd1f06d | R | 11,563 | 283 | library(pROC)
data(aSAH)
context("multiclass-roc")
test_that("univariate multiclass roc/auc works", {
expect_warning(uv.mr <- multiclass.roc(aSAH$gos6, aSAH$s100b), "2")
expect_equal(class(uv.mr), "multiclass.roc")
expect_equal(length(uv.mr$rocs), 6)
expect_equal(as.numeric(auc(uv.mr)), 0.6539999352)
expect... |
315430f69d5fc4a050b8d1913c2eea1fff1d451a59bba9186a4237a1630c653e | R | 11,564 | 257 | # ==============================================================================
# S5_cell.R
# Server logic for Step 3: Clustering Analysis and Cell Annotation
# Implements:
# - Step 3.3: Cell Type Annotation (Handles cell type assignment via SingleR, custom upload, or manual entry)
# ============================... |
6f800f24466cab5ff1dd9ec5ce6e7f0c3b8a39396507ad28a49c64d7b9f40037 | R | 11,568 | 281 | library(Seurat)
library(SummarizedExperiment)
library(ggplot2)
library(future)
library(scrattch.hicat)
library(data.table)
library(dplyr)
library(tibble)
library(pbmcapply)
library(gplots)
library(scales)
library(scubi)
library(paletteer)
library(SeuratWrappers)
plan("multicore", workers=10)
plan()
options(future.gl... |
8e3c3884bd2265dd7cef8eebacf7b451f44fc8d0e4d0b4e95b80b035ff1f0203 | R | 11,581 | 362 | ---
title: "Mutation Frequencies Table Summary and QC Checks"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
toc_depth: 4
author: Eric Wafula for Pediatric OpenTargets
date: 2022-05-01
params:
current_table:
label: "current mutation frequencies table"
value: current/gene-level-cnv-consensus-a... |
9bf35edb1ebb4ae26fbb4a8d436ec5096eb2c20e138b70bd9675f1de75e4acf5 | R | 11,595 | 280 | ---
title: "`r paste0('Preliminary QC Report for Sample ', params$sample)`"
subtitle: "Initial Sample Preprocessing - scATAC-seq"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float:
collapsed: false
number_sections: true
code-fold: true
toc_depth: 3
fig_height: 5
fig... |
d38b5a2e08f3c3201fe356aa864ef687f790d6845986a1f5d0d7f48bd0970db8 | R | 11,607 | 311 | library(RCurl)
library(stringr)
# Data processing for human
# download protein annotation from STRING v11.0 (9606.protein.info.v11.0.txt)
pinfo<- read.table(file = '9606.protein.info.v11.0.txt',
stringsAsFactors = F,
sep = '\t',
header = T)
pinfo$protein_extern... |
db26496867c262ed530800ba69201c0c74349c88ad882082c36197674fd1c34d | R | 11,615 | 234 | ---
title: "Customized Color Palettes & Themes"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output: rmarkdown::html_vignette
theme: united
df_print: kable
vignette: >
%\VignetteIndexEntry{Customized Color Palettes & Themes}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
***
<style>
p.... |
18fa0830955d7c60d2a7cd095803c461fcf665d290c511100ef4cc1eb070ac3f | R | 11,625 | 275 | "predict.randomForest" <-
function (object, newdata, type = "response", norm.votes = TRUE,
predict.all=FALSE, proximity = FALSE, nodes=FALSE, cutoff, ...)
{
if (!inherits(object, "randomForest"))
stop("object not of class randomForest")
if (is.null(object$forest)) stop("No forest compo... |
3c40c2ec5dde89b2848834764bc453563e3d6c9bfd9cc26bfc8c3942b27c34d2 | R | 11,641 | 419 | #' MRF variable selection
#'
#' @export
# This function performs variable selection for multi-omics data using MRF
mrf3_vs <- function(mod,
dat.list,
method = "filter", # Selection method: "filter", "test", "mixture"
se = NULL, # Sta... |
0d0d23b4dffde6b18ac85fb06b6c6db6ed30e120af9345f6bd74fa3e38fed3df | R | 11,656 | 337 | # Load all relevant libraries to the project
library(SNFtool)
library(Spectrum)
library(cluster)
library(ConsensusClusterPlus)
library(CancerSubtypes)
library(iClusterPlus)
library(ANF)
library(ggplot2)
library(factoextra)
library(grid)
library(gridExtra)
library(gtable)
library(gplots)
library(r.jive)
... |
1f17e221f00835e4bfa935187a3214c1664e5e884ddccafea4e35974642a6f41 | R | 11,668 | 247 | #!/usr/bin/env Rscript
################################################
################################################
## REQUIREMENTS ##
################################################
################################################
## PCA, HEATMAP AND SCATTERPLOTS FOR SAMPLES IN CO... |
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