sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b0efce91c5f285510730e8f568f17efebc4b5551daa4799c8239f6cc6e8d0613 | R | 3,474 | 59 | ### DTS script to extract the PI learningscore values for the period specified in the dataset yaml file (and some other important PIs)
if(PIs_present & !is.null(learningscore)){
PIstat <- list()
CatStat <- list()
for(x in 1:NofGroups){
PIstat[[x]] <- grouped.PIprofiles[[x]][[learningscore]]
CatStat[[x]] <... |
e254c38a6f606acec7f68a54f9af81126d12dc895619619a799a47c87ed4bcdf | R | 3,474 | 95 | #' Multivariate multilevel imputation using \code{jomo}
#'
#' This function is a wrapper around the \code{jomoImpute} function
#' from the \code{mitml} package so that it can be called to
#' impute blocks of variables in \code{mice}. The \code{mitml::jomoImpute}
#' function provides an interface to the \code{jomo} pack... |
974333df7ee1ea6e57afc3366cd7d7d42f48436064e2703d9f4cf23c04071a05 | R | 3,480 | 96 | ---
output: github_document
---
# hBayesDM
[](https://www.repostatus.org/#active)
[![R](https://github.com/CCS-Lab/hBayesDM/actions/workflows/R.yaml/badge.svg... |
aba78270486f455d31647b5fb137b031886d6a9a1d8cda9c778822a949590c18 | R | 3,481 | 111 | ---
title: "Add external networks to HuMMuS object"
author: "Trimbour Rémi"
date: "2023-05-16"
output:
html_document:
css: pkgdown/extra.css
github_document: default
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(eval = TRUE)
#devtools::install_github("cantinilab/HuMMuS")
```
##... |
da2cf0324a361aa2f937dc10564da6544cd1c841a73b9eb04b3eabbba0cf9f88 | R | 3,492 | 109 | ---
title: "Weight analysis"
output:
pdf_document: default
html_document: default
date: "2023-10-21"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Loading data
```{r}
library(pacman)
p_load(tidyverse, ggsignif, multcomp, broom, emmeans, DescTools, readxl)
setwd("~/Documents/Systemic ESY... |
fd5d490c82cde29b2175729d997cc1108f4de8ae98ce9054c82f1380fe78c573 | R | 3,492 | 96 | library(data.table)
library(devtools)
load_all()
# copied from MotrpacRatTraining6mo
list_available_data = function(package=NULL){
res = utils::data(package=package)
obj = res$results[,3]
# remove objects that can't be called directly
obj = obj[!grepl("\\(", obj)]
return(obj)
}
# list all available data obj... |
95165a565676080ac0d6b31733811a2c28c7ab3cf8742cde32e78aa4ffee0659 | R | 3,498 | 68 | #!/usr/bin/env Rscript --vanilla
#load the libraries
library(proBatch)
library(readr)
library(dplyr)
library(tidyr)
source('transition_fragment_conversion.R')
#load the data
essential_columns = c('peptide_group_label','filename','aggr_Fragment_Annotation', 'aggr_Peak_Area')
cols_to_get <- rep(list(col_guess()), lengt... |
4c5be65bfe4a235af408a9699d7c4e50b6aeae20ef19d5a73b250b9a7c8f19ec | R | 3,500 | 60 | #' @templateVar MODEL_FUNCTION pstRT_rlddm1
#' @templateVar CONTRIBUTOR \href{https://github.com/hydoh/}{Hoyoung Doh} <\email{hoyoung.doh@@gmail.com}>, \href{https://github.com/sanghoon-kang/}{Sanghoon Kang} <\email{sanghoon.kang@@yale.edu}>, \href{https://jihyuncindyhur.github.io/}{Jihyun K. Hur} <\email{jihyun.hur@@y... |
068637f1ebf3aac7b4d9f5b5f0afaf427fd3caa367640061abe5232ac18e3029 | R | 3,508 | 119 | ---
title: 'Saldana, Montano, et al. -- ATAC-seq: Additional plots and tables'
author: 'Florian Halbritter'
date: '`r format(Sys.time(), "%B %d, %Y %H:%M:%S %Z")`'
output:
html_document:
toc: yes
toc_float: yes
code_folding: show
highlight: pygments
df_print: paged
---
# Intro
In this noteboo... |
e7cb29ce3a55a1ec88f49a2b36ea34c650b1c383d8d4a8340734182130e6162c | R | 3,508 | 81 | #! /usr/bin/Rscript
args1 <- commandArgs(trailingOnly=TRUE)[1] # sample.txt
args2 <- commandArgs(trailingOnly=TRUE)[2] # target2gene.txt
dir.create('output')
# sleuthパッケージを読み込む
library("sleuth")
# サンプル情報を読み込む
s2c <- read.table(args1, header=T, stringsAsFactors=F, sep="\t")
s2c$condition <- gsub(" ","_",s2c$conditio... |
f5e1427ff0becf264d5b9ba74423d141000ff3433bde5f219be39c8527191e68 | R | 3,517 | 113 | #' Imputation of unordered data by polytomous regression
#'
#' Imputes missing data in a categorical variable using polytomous regression
#'
#' @aliases mice.impute.polyreg
#' @inheritParams mice.impute.pmm
#' @param nnet.maxit Tuning parameter for \code{nnet()}.
#' @param nnet.trace Tuning parameter for \code{nnet()}.... |
ea9e01194b8ff87710c7177759dd7c7bf0d8d9ab14cdac40de1ab922ae4be12f | R | 3,526 | 94 | #' @title Calculate length of tree using MorphyLib
#'
#' @description Uses the MorPhy library to calcuate the length of a phylogenetic tree
#'
#' @param \list {
#' \item{tree} A tree in \code{PhyDat} format;
#' \item{data} A object of class phyDat containing sequences
#' \item{site} return either 'pscore' or 'sit... |
e5df638bbdc5cc139c6e863cfeede40d239394917955bea9455deb32367004ad | R | 3,528 | 30 | library(pheatmap)
library(ggplot2)
library(RColorBrewer)
library(ggrepel)
setwd("/Users/timothymartin/Desktop/Screens/U2OS Khurana Lab CRISPR screen/E3K screen june 2019/edgeR")
#For the heatmap I made cutoffs where the log2 foldchange difference (WT-E3K) was <-1, the E3K FDR<0.25, and log2 foldchange in GFP and... |
6595030bbc89518d0e1692be3a5a915924d8e27c534490b385983d7e77c14285 | R | 3,539 | 61 | ### DTS script to collect data from each group in their respective lists and empty the variables after all group evaluations are done
#### -- Period sequence design meta-data -- ####
#### -- Dwelling times -- ####
#### -- Performance Indices -- ####
#### -- Categories for plotting -- ####
#### -- Power spectra -- ####
... |
d1eda6ca59649a6783348fc59129e147f6e03a147f3ac561591872854ba30a85 | R | 3,539 | 78 | ################################################################################
# Written by Loïc Labache, Ph.D. #
# Holmes Lab, Department of Psychiatry - Rutgers University #
# February 7, 2024 ... |
56d836212adced0b5f01ff8f88f2d0a7450e542cdf0ee9a6a12edd412a45d83e | R | 3,566 | 114 | #' Relative importance
#' @title Relative importance
#' @param spec,env a data.frame object.
#' @param type one of "lmg", "last", "first", "betasq", "pratt", "genizi" or "car".
#' @param family a link function to be used in the model, see \code{\link[stats]{family}}.
#' @param na.action a function which indicates what... |
a4f625cf9d1fb13100762abab6a155086b0cdf990ef7713c4378b7fe5340a31f | R | 3,569 | 104 | ---
title: "RNAscope validation plots"
author: "Yuvarani Masarapu"
date: "2023-11-01"
output: html_document
---
```{r load-libraries}
library(ggplot2)
library(readr)
library(ggpubr)
library(readxl)
```
# For channel green, No. 488, gene id Adcy1
* Detected in ST data, upregulated for spaceflight in Hippocampal CA3 ... |
a96315ac6cd37ba62be97b5a99b59b55f775205c180b99681ad46aa73d320cee | R | 3,570 | 85 | # The following test was contributed by jawitte
# https://github.com/amices/mice/issues/226
set.seed(1)
A <- rnorm(100)
B <- 0.1 * A + rnorm(100)
fit1 <- lapply(1:5, function(m) lm(B ~ A))
fit0 <- lapply(1:5, function(m) lm(B ~ 1))
# hardcoded lrtest() result to avoid lmtest dependency
x1 <- structure(
list(
`#D... |
2f541631c1610581384545076d3481d2251b4cc2d128acb8805c30411354eb91 | R | 3,571 | 114 | #' Enlarge number of imputations by combining \code{mids} objects
#'
#' This function combines two \code{mids} objects \code{x} and \code{y} into a
#' single \code{mids} object, with the objective of increasing the number of
#' imputed data sets. If the number of imputations in \code{x} and \code{y} are
#' \code{m(x)} ... |
bd8c1c2f6596aa151e8e87de592278358f8d53bdf0fef53d3a9f9a52b77d3171 | R | 3,571 | 86 | library(ashr)
library(mashr)
library(gplots)
library(viridis)
library(ggplot2)
library(corrplot)
library(rmeta)
set.seed(2020)
current = getwd()
setwd(current)
folder = "5_mashr"
CORR_TYPE <- "withCorr"
MODEL_FOLDER <- "popDR_QN"
MODEL_TYPE <- c("popDR_QN")
COV_TYPE <- c("canonical_and_dataDriven")
## folders
INPUTS... |
63e3def358d0e5167454a206d1713d9293072893756b0b266c6cc6bcd3eb0bfc | R | 3,586 | 82 | # Laura Aarnos 190515
# Plotting burst detection results
# attach packages from library
#library(IGM.MEA)
library(Hmisc)
library(meaRtools) #attach meaRtools after IGM.MEA to get right parameter names
# import data
# location of RDS file (e.g. 'Z:\Public user documents\User\Folders between\Analysis\R objects\Filena... |
47ccf5825da43b5214459b6054fb07ed7b65be89a533cadab75aa79623ccccb6 | R | 3,611 | 74 | library(dplyr)
library(data.table)
library(getopt)
library(phenoscanner)
command<-matrix(c(
'help', 'h', 0,'logical', 'help.',
'coloc_res', 'r', 1, 'character', 'susie coloc result.',
'exp_ma_file', 'e', 1, 'character', 'input GWAS .ma file.',
'ce... |
18931ebe62093ac57594de1645e3750a31d712478e09be20f59a6f03483c4c5d | R | 3,618 | 73 | #!/usr/bin/env Rscript
## Load libraries
library(ggplot2)
library(EnhancedVolcano)
## Load datasets for differential expression results in genes with and without covariates
gene <- read.csv(file = '../../../data/bernardo/processed/04.deseq2/genes_annotated_results_M_vs_F.tsv', sep="\t")
## Load datasets for differ... |
f99654d9851d023265a4a11ac0380ce5fbc12d3ea787ba5a98463716becd85fb | R | 3,618 | 90 |
miceadds::load.Rdata(objname = "imp", "/data/pt_life_whm/Results/Tables/CVR_WMH_progression/imputed_data_6.12.23/imputed_data_6.12.23.Rdata")
i=1
comp_imp=mice::complete(imp, "long")
tmp=comp_imp[comp_imp$.imp==i,]
#generate quantiles
DBP_cutpoints = quantile(imp$data$DBP_base, probs = c(0,.33,.66,1), na.rm = TRUE)
... |
614318bf819a08274b0312aaf557c6b1f5689e7fb83003d35dca024e8dc682ce | R | 3,628 | 86 | library(ashr)
library(mashr)
library(gplots)
library(viridis)
library(ggplot2)
library(corrplot)
library(rmeta)
set.seed(2020)
current = getwd()
setwd(current)
folder = "5_mashr"
CORR_TYPE <- "withCorr"
MODEL_FOLDER <- "popDE_continuousAncestryModel_QN"
MODEL_TYPE <- c("popDE_continuousAncestryModel_QN_combined")
COV... |
b2cd5fde7843dcee97dd53b63492a918a5d74e3ebe63f2537f7dc115dcc21f5b | R | 3,629 | 104 | #!/usr/bin/env Rscript
############
# load data
############
e.keep = readRDS('filtered_expression_matrix.rds')
keep.genes = readRDS('keep_genes.rds')
meta = readRDS('cayo_bulkbrain_combined_metadata.rds')
meta = meta[order(match(meta$LID, colnames(e.keep))),]
library(BRETIGEA)
library(biomaRt)
library(expss)
libra... |
26d00f01eddceb1ff2ed6d7e11c37392e85306169bfe7eefb4d880e05817df9c | R | 3,638 | 73 | #!/usr/bin/env Rscript
## Load libraries
library(ggplot2)
library(EnhancedVolcano)
## Load datasets for differential expression results in genes with and without covariates
gene <- read.csv(file = '../../../data/bernardo/processed/04.deseq2/genes_annotated_results_M_vs_F_no_intron.tsv', sep="\t")
## Load datasets ... |
8d32ee3c428abb037c5b33b8035de193d0bf06ac927cd00a1b201d8858b90d13 | R | 3,644 | 97 | ## Taken from https://github.com/nloyfer/meth_atlas
## To run:
## Rscript process_array.R {idat directory} {output csv path} {path to ref_sample.RData}
## Note: Requires minfi package.
## To install minfi:
## try http:// if https:// URLs are not supported
## source("https://bioconductor.org/biocLite.R")
## biocLite("m... |
a68f6b3fe19c3c5125229a3fd0d9769a8d3a3b5f03759b57dfc7c5f82ffa4728 | R | 3,654 | 86 | library(ashr)
library(mashr)
library(gplots)
library(viridis)
library(ggplot2)
library(corrplot)
library(rmeta)
set.seed(2020)
current = getwd()
setwd(current)
folder = "5_mashr"
CORR_TYPE <- "withCorr"
MODEL_FOLDER <- "popDE_continuousAncestryModel_QN_cisRegressed"
MODEL_TYPE <- c("popDE_continuousAncestryModel_QN_c... |
fd20f3a6006d02cb624dda86a954f88ee2984aa321fa65485fa5af1d8444bb77 | R | 3,659 | 97 | ---
title: "Help for old friends"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Help for old friends}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
<!-- oldfriends.md is generated from oldfriends.Rmd. Please edit that file -->
```{r, echo = FALSE}
... |
f0db1445e0d86c306d0520a427c87e487aea6fe1ec99e59345c4fb42367cf16c | R | 3,661 | 74 | ---
title: "BMI_Pulit_README"
author: "William Bone"
date: "3/24/2022"
output: html_document
---
## date: September 01, 2020
This is the documentation for how the Pulit et al. BMI data was modified to be compatible with ColocQuiaL
I downloaded this file from: https://zenodo.org/record/1251813#.X06L-hNKhTZ
#### adding... |
6fbdb48d54955bc411f3676e9d65c8b3c185361d530766148faeca4793fdc564 | R | 3,667 | 62 | #this R config file allows you to set the paths to all the dependency file you need to run the ColocQuiaL pipeline
#this config file is required for running the colocalizer pipeline on one locus
#provide the path to plink reference files to be used for plink commands
plink_bfile = "/project/voight_selscan/ksiewert/Car... |
52f40aef2e7c6644addeac210551bbb7490174c10753c3fabdc44ac8dc517efe | R | 3,679 | 117 | library(data.table)
library(PRROC)
edges <- fread("C:/STUDIES/RESEARCH/neural_ODE/ground_truth_simulator/clean_data/edge_properties_350.csv")
edges <- edges[, .(from, to, activation, EC50, n)]
withhold_perc <- 0
num_rows_to_change <- round(nrow(edges) * withhold_perc)
rows_to_change <- sample(1:nrow(edges), num_rows_... |
88ee5a3d5eccc045dbf695f6740b6babad7d4d54029fa8f05d7d8c82bc56e128 | R | 3,684 | 97 | #'#################################################################################
#'#################################################################################
#' Evaluate biological relevance of NetActivity weights
#'#################################################################################
#'##########... |
2ec302b2a4c73373980085c15cbeca9a8e205000ef559ceba83324a61b6c8e10 | R | 3,685 | 109 | library(org.Mm.eg.db)
library(org.Hs.eg.db)
library(tidyverse)
library(argparse)
source(file.path("src","shared","WGCNA","perform_WGCNA_functions.R"))
get_args <- function() {
# create parser object
parser <- ArgumentParser()
parser$add_argument("--dataset-name", type = "character", default = NULL, help =... |
f4fb42b768e03f66fce2152352e45526c6dbb68b6a534c92e9632ba2e088c8aa | R | 3,692 | 100 | library(ggplot2)
library(tidyr)
library(reshape2)
library(plyr)
library(dplyr)
library(grid)
library(gridExtra)
library(resample)
current = getwd()
folder = "2_calculate_residuals_and_DE_analyses"
setwd(current)
system(paste0("mkdir -p outputs/",folder,"/2_1_DE_infection_results/"))
celltypes <- c("B","CD4_T","CD8_T"... |
3f12d35a44f4b5aa183cd8f4a7b00fd7be9e00748d5154285172c554663420c1 | R | 3,694 | 110 | context("Test user-facing API (plots, diagnostics, IC, HDI helpers)")
library(hBayesDM)
skip_if_not_installed("cmdstanr")
skip_on_cran()
# Fit one small hierarchical model and reuse across assertions to avoid
# repeated cmdstanr compile cost.
fitted <- suppressWarnings(suppressMessages(
dd_hyperbolic(data = "examp... |
0ac822b483c9b89fb48d605dbf3ade918556a83a1d93cc28bc257210568169e6 | R | 3,699 | 119 | #' @title WaveICA2.0
#' @description Removing batch effects for metabolomics data without
#' using batch information
#' @author Kui Deng
#' \email{dengkui_stat@163.com}
#' @param data Sample-by-Peak matrix untargeted metabolomics data.
#' @param wf wavelet functions. The default is "haar".
#' @param Injection_Order I... |
1236bc3964b51f27182a06eca1a7e28194695ee16372256b025a83a4bda2cf7c | R | 3,707 | 118 | #' @noRd
draw_key_square <- function(data, params, size) {
if (is.null(data$size)) {
data$size <- 0.5
}
grid::rectGrob(
x = 0.5,
y = 0.5,
width = grid::unit(data$r0, "npc") - grid::unit(data$size, "mm"),
height = grid::unit(data$r0, "npc") - grid::unit(data$size, "mm"),
gp = grid::gpar(col... |
8bbd646506b1eb00d0acb3cfd89417a6e454ce9337d26b0303962e74ca5303c1 | R | 3,713 | 99 | # To identity DEG in cell-type corrected data
# using Mann Whitney Wilcoxon test (a.k.a Mann Whitney U test and Wilcoxon Rank Sum test)
rm(list = ls())
library(tidyverse)
library(optparse)
# Loading global variables ------------------------------------------------
option_list <- list(
make_option(c('--br'), help ... |
ee518913b0916c6f9bd2727c62e5b82508de99525ad84c99badfd35853eee8eb | R | 3,718 | 100 | #Trying to get GeneWeaver API to work:
#my API key is in this code - for public release I've replaced it with [MyAPIKey]
https://geneweaver.org/api/get/publication/byid/[MyAPIKey]/26/
https://geneweaver.org/api/get/geneset/bygenesetid/8/
#Not found
#adding geneset->genesets doesn't help
https://genew... |
d046d72c611654eabdc43b717aa640ba6627c0272515746227c7bf77c1c7abb7 | R | 3,720 | 64 | #Example usage of Brain.GMT:
#Example Code written for R 4.3.3. using fgsea v.1.2.1
#Link to the fast gene set enrichment analysis (fGSEA) documentation:
# https://bioconductor.org/packages/release/bioc/html/fgsea.html
#installing the R package fast Gene Set Enrichment Analysis (fGSEA):
if (!require("BiocManager", ... |
d37ed21be23a7a5e206d62e0f0ec9536f8ad6b0a1909fb427cd4e5c69ef38741 | R | 3,725 | 72 | ################################################################################
# Written by Loïc Labache, Ph.D. #
# Holmes Lab, Department of Psychiatry - Rutgers University #
# January 26, 2024 ... |
d7ca3dcec999241735fe42bcdb2a364b615dd811e40f517fcd5c74f0e62f7880 | R | 3,730 | 152 | # Marker terms ----
imm=c("myeloid cell differentiation
T cell activation
response to interleukin-1
response to lipopolysaccharide
response to interferon-gamma
response to tumor necrosis factor
antigen processing and presentation
response to virus
response to type I interferon
regulation of innate immune response
regu... |
8b7b4edff3b78e8be93a9702d69e8feec6448fb477381bfca953a933f5576f74 | R | 3,731 | 73 | #!/usr/bin/env Rscript
## Load libraries
library(ggplot2)
library(EnhancedVolcano)
## Load datasets for differential expression results in genes with and without covariates
gene <- read.csv(file = '../../../data/bernardo/processed/04.deseq2/genes_annotated_results_AD_vs_CT.tsv', sep="\t")
## Load datasets for diff... |
c4a7b85ee31af733efe082c797a9b0a371952f867af361ca78ddd9db5a87f25c | R | 3,731 | 57 |
HumanPsychiatricGeneSets_HumanOrthologs_JustSymbols_NoNA
HumanPsychiatricGeneSets_RatOrthologs_JustSymbols_NoNA
HumanPsychiatricGeneSets_MouseOrthologs_JustSymbols_NoNA
HumanPsychiatricGeneSets_Mouse_Temp<-list()
HumanPsychiatricGeneSets_Rat_Temp<-list()
HumanPsychiatricGeneSets_Human_Temp<-list()
names(table(Human... |
e98de7210e501bb1bcd92fb35dc7093c1acca753d2e7026d02ac05aade0339c3 | R | 3,731 | 151 | library(Signac)
library(Seurat)
library(tidyverse)
# Ref: https://stackoverflow.com/a/5577647/4638182
load_multiomics_rdata <- function(file_path) {
mo_env <- new.env()
mo_obj_names <- load(file_path, mo_env)
stopifnot(identical(mo_obj_names, c("seurat_multi")))
mo_seu <- mo_env[["seurat_multi"]]
return(mo... |
4e976febf90ee12046710d79e024f5758f073dbd15a35eea4b0d9b45e6e11369 | R | 3,734 | 77 | ################################################################################
# Written by Loïc Labache, Ph.D. #
# Holmes Lab, Department of Psychiatry - Rutgers University #
# March 28, 2024 ... |
6481139660a5b71b34f12316549cccbcabeaf0678978673b5562d5575ad9134e | R | 3,738 | 96 | #4 Compare Young vs Elderly
# Setup---------------------------------------------------------------
rm(list = ls()) ; gc() ; gc()
set.seed(1234)
wdir <- 'set your working directory'
setwd(wdir)
save.path <- 'object folder path'
# load library
easypackages::libraries(c('Seurat','tidyverse','fgsea','msigdbr','escape','... |
17b3924eaf1fe73f9c82fc6c7056e7e2f3315c959e95a0189d274d6fe35a24dc | R | 3,746 | 98 | # Sconda conda activate TensorFlow_CPU_01; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/traditional_ml_methods; R
#!/usr/bin/R
# All.vs.All.R
wkdir="/mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/traditional_ml_methods/"
setwd(wkdir)
library(ggplot2... |
2ceac94dbd1c349129e0c576cc31977c11861fbe0a0fd674c7587b2887ed25a1 | R | 3,746 | 105 | #'#################################################################################
#'#################################################################################
#'
#' Prepare mCRPC PROMOTE vis2 SummarizedExperiment object
#' with updated gene SYMBOL names ... |
21da794fb2203532630d0919d8865086923329630a711601909e9c5922e2d805 | R | 3,752 | 99 | #'#################################################################################
#'#################################################################################
#' Analyze PRAD
#'#################################################################################
#'##############################################... |
71d7c2b194949838fbd52baea71f62ebc02aa92c43e329cfeabeac532f38a112 | R | 3,759 | 88 | # This is an example to show how to normalize T2 template with Allen Brain Atlas data.
#
# ANTsR requires us to use the same data geometry (x,y,z) and same orientation.
# Therefore, you need to work with resampling (resampleToTarget) provided by ANTsR and re-orientation by
# itksnap -> Tools -> Reorient image...
#
# ... |
5303c151d4057d3c415f561f776d3b1f438bd2fef9fde78348bcf8c7e2c607b0 | R | 3,762 | 73 | library(readr)
library(proBatch)
library(reshape2)
library(ggplot2)
library(dplyr) # Load "%>%"
sample_annotation_AgingMice = read_csv("data_AgingMice/3_data_for_plots/sample_annotation_AgingMice.csv")
color_list_AgingMice = readRDS("data_AgingMice/3_data_for_plots/color_annotation.rda")
loess_fit_75_df = read_csv(fi... |
ef791e80823a964e137374b606191c3addba9f496a08293f11f2dc670d31acc8 | R | 3,765 | 116 | #!/usr/bin/env Rscript
########################
## choose data to upload
########################
# gene level data (primary analyses)
e.keep = readRDS('filtered_expression_matrix.rds')
keep.genes = readRDS('keep_genes.rds')
meta = readRDS('cayo_bulkbrain_combined_metadata.rds')
meta = meta[order(match(meta$LID, col... |
6b3ba6d7cdfbd925e6dccb34a80c5172fdf4a53b50625df76748e6681e036c6e | R | 3,766 | 142 | xname <- c("age", "hgt", "wgt")
br <- boys[c(1:10, 101:110, 501:510, 601:620, 701:710), ]
r <- stats::complete.cases(br[, xname])
x <- br[r, xname]
y <- br[r, "tv"]
ry <- !is.na(y)
wy1 <- !ry
wy2 <- rep(TRUE, length(y))
wy3 <- rep(FALSE, length(y))
wy4 <- rep(c(TRUE, FALSE), times = c(1, length(y) - 1))
test_that("MI... |
8a7af53855f3cba27afad732a25e1a0817d90c906931e06ebf42ae8ea29c7593 | R | 3,771 | 95 | # script for running WGCNA on biocluster
args <- commandArgs(trailingOnly = TRUE)
library(WGCNA)
# The following setting is important, do not omit.
options(stringsAsFactors = FALSE)
###############################################################################
#### STEP 2. Automatic construction of the gene netwo... |
1d64b48bdf27708840dde1552797df4bafe2d3f370d04201c8a657d4bd6a24ca | R | 3,779 | 195 | ###################################################
# ~~~ FramebyFrame package ~~~
# STATISTICS UTILITIES
# small functions statistics-related
# Francois Kroll 2022
# francois@kroll.be
###################################################
# function mad2(...) ------------------------------------------------------
# ... |
3dec5bac57fc915fdca8f4249e9ff6a523d9a8263436ef267411c41c75cf0171 | R | 3,784 | 101 | #### This script generates boxplots for permutation analysis
#### (male downsampling to female levels)
suppressPackageStartupMessages({
library(dplyr)
library(ggpubr)
})
save_ggplots <- function(p, fn, w=6, h=6){
for(ext in c('.svg', '.png', '.pdf')){
ggsave(p, filename=paste0(fn, ext), width=w, h... |
d57b6ef242e04b20db1355d45f8dd7517a66d9391f48a8e643eba1e0ee02f53a | R | 3,787 | 160 | #### Code for plotting survival curve in Lam, Ndayisaba et al 2024 Neuron - FIGURE S7E
#### Graph contains data from IL-HCNT61-A53T
# 2/8/21: Re-plotting survival curves for manuscript figure
# HCNT61-A53T
library(survival)
setwd("~/Desktop/20210208-Survival_plots_for_Figures")
####################################... |
5201fd16911af4c4c6f516ab8ca5aed888b01c6202a8ad06ef4569c0ef6fbad6 | R | 3,791 | 132 | #' Wrapper for VennDiagram with basic customization and command line functionality
#'
#' WARNING: This script is work-in-progress
#'
#' Example usage:
#'
#' cgat sc-counts2counts --counts-filename=featurecounts.tsv --phenotypes-filename=phenodata.tsv --factor=group,mouse_id,collection_date,slice_depth,slice_number,pi... |
a9eac1698b8c9016dd4c96b10cd3dcb5da096e7d7746aa40d403b7b882de8d41 | R | 3,798 | 93 | #' Convert to cor_tbl based on input type.convert
#' @description The fortify_cor function is a deep encapsulation of
#' the \code{as_cor_tbl} function and also supports converting raw
#' data into cor_tbl objects by calculation.
#' @param x any \code{R} object.
#' @param y NULL (default) or a matrix or data frame with... |
c6a7c9b1ded0176a0c92e9e6e2467587c9d7d9d97cfd933e2de946ddf885b6ae | R | 3,803 | 140 | #' Summary of a \code{mira} object
#'
#' @rdname summary
#' @param object A \code{mira} object
#' @param type A length-1 character vector indicating the
#' type of summary. There are three choices: \code{type = "tidy"}
#' return the parameters estimates of each analyses as a data frame.
#' \code{type = "glance"} return... |
2ace5dbdffd46bb1ae78e403ca4088c2d4d404d74283af7fd7ec017bacc0873d | R | 3,807 | 100 | library(dplyr)
# Glia
Glia <- read.csv('/mnt/data0/projects/biohub/hassan2022/output/omicsintegrator/csv/Glia.csv')
colnames(Glia)
#idx <- is.na(Glia$geneSymbol)
#grep("NA",Glia$geneSymbol)
#allgenes <- Glia$geneID
#Glia$geneID[2]
#allgenes[idx]
#Glia$geneSymbol[idx]
prize <- Glia$avg_log2FC
prize <- as.data.frame(pri... |
be6dc065acde6736d9a82a1ee5c496a4ab3126c1e71c68a94ceb82af97294565 | R | 3,809 | 114 | TipCol <- function (tip.label) {
pal <- palette.colors(pal = "Tableau 10")
blue <- amber <- red <- teal <- green <- gold <- lilac <- pink <- brown <-
grey <- character(0)
# List taxa against their desired colours here
blue <- c("taxon_1", "taxon_2")
amber <- c("taxon_3", "taxon_4")
red <- ... |
83ee63641bc2eaa1a06d1f2b6cbf6dd44611b2101557d77d4fccc4f1cd142ea7 | R | 3,811 | 100 | #' Graphical parameter for missing data plots
#'
#' \code{mdc} returns colors used to distinguish observed, missing and combined
#' data in plotting. \code{mice.theme} return a partial list of named objects
#' that can be used as a theme in \code{stripplot}, \code{bwplot},
#' \code{densityplot} and \code{xyplot}.
#'
#'... |
9c650eae57f196f31e87487dcc132627141ad973884f27f988a54ea137996d3e | R | 3,811 | 103 | library(ggplot2)
library(scales)
library(ggthemes)
library(patchwork)
library(kableExtra)
source('utils.r')
# fig 1
results_fnames <- list.files('results/', full.names = T)
results_fnames <- results_fnames[!grepl('ys.csv', results_fnames, fixed = T)]
results <- do.call(rbind, lapply(results_fnames, read.csv))
results... |
ae7c3c3cd7ce40d98f91e903d1ea6c48a20bad24734bd6b3c79e116b29f94bfa | R | 3,820 | 96 | #6 Deconvolution analysis
# Setup---------------------------------------------------------------
rm(list = ls()) ; gc() ; gc()
set.seed(1234)
wdir <- 'set your working directory'
setwd(wdir)
save.path <- 'object folder path'
# load library
easypackages::libraries(c('Seurat','tidyverse','spacexr','escape','msigdbr'))... |
e53c0bb23f6b76d128f5faa085c32945eeb3f37fccba3132b8e29586a0e78a87 | R | 3,821 | 116 | ---
title: "ncnb_coverage_plots"
author: "Peter Zoescher"
date: "12/15/2023"
output: html_document
---
```{r libraries, echo=F, warning=F}
# to be run in ccribioinf/dockrstudio:4.0.3-v1
# dockerhub manifest digest: sha256:055a81938337a3fc2a15039476094058f5691e02b529578041af508352264323
suppressPackageStartupMessages(... |
11331820e1cd37cca1ad3c843b0439e870c565fe03ca05c74c63849778d12c74 | R | 3,822 | 123 | suppressPackageStartupMessages({
library(clusterProfiler)
library(enrichplot)
library(DOSE)
library(org.Mm.eg.db)
library(org.Hs.eg.db)
library(ggplot2)
library(pathview)
library(tidyverse)
})
make_kegg_gsea = function(genelist, folder, organism, pvalueCutoff, pAdjustMethod,
scoreTy... |
d63f31cc53b4701d5ba87ea76c3d8eb9f69dcb21cb48cdbf239449ae58434121 | R | 3,828 | 110 | test_that("TALON mult-sample", {
file <- "../test_data/example_talon_read_annot_multiSample.tsv"
gene_id <- "SIRV1.1"
perc_As <- 0.80
abundance_file_type <- "TALON"
min_exp_perSample <- 0
min_exp_mean <- 0.3
min_sample_perc <- 0
x <- read.table(file, header=TRUE, sep="\t")
x.gene <- x %>%
... |
f40911579c7952a3972ea4ba55cc1ab673e2cb3caecb003307aa1c071f9a4a15 | R | 3,832 | 95 | #' Self-reported and measured BMI
#'
#' Dataset containing height and weight data (measured, self-reported) from two
#' studies.
#'
#' This dataset combines two datasets: \code{krul} data (Krul, 2010) (1257
#' persons) and the \code{mgg} data (Van Keulen 2011; Van der Klauw 2011) (803
#' persons). The \code{krul} datas... |
caab22e3e429947b5ff3200a82624581f1b6857e7f633913707baf6b5f44c40b | R | 3,833 | 61 | ############################################################################################
# Copyright (c) 2022 - Respiratory Immunology Lab, Monash University, Melbourne, Australia #
# Author: Matthew Macowan #
# This script is provided under the MIT l... |
eca7b1e6e6c3ac4cd06a86196d84be00b55eb1d7ffc614c4de1a8213872ea5b0 | R | 3,834 | 63 | #' @templateVar MODEL_FUNCTION pstRT_rlddm6
#' @templateVar CONTRIBUTOR \href{https://github.com/hydoh/}{Hoyoung Doh} <\email{hoyoung.doh@@gmail.com}>, \href{https://github.com/sanghoon-kang/}{Sanghoon Kang} <\email{sanghoon.kang@@yale.edu}>, \href{https://jihyuncindyhur.github.io/}{Jihyun K. Hur} <\email{jihyun.hur@@y... |
3df2c0fb930d7dac027306adddd5fcc534646a3ac10382aa65da5159045c1db1 | R | 3,837 | 115 |
ncs = length(oldprops)
oslopes = matrix(nrow = nrow(SpeciesMat), ncol = ncs)
oslopes = as.data.frame(oslopes)
colnames(oslopes) = c(paste0("OldSlope",oldprops))
oslopes$m0 = oslopes$M = NA
yslopes = matrix(nrow = nrow(SpeciesMat), ncol = length(props))
yslopes = as.data.frame(yslopes)
colnames(yslopes) = c(paste0("Yo... |
fe6fafb420bdc243b0d15ef131d4ff7d3d1bc68341a666d4763b92c80ec7e27d | R | 3,840 | 126 | ---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
message = FALSE,
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
fig.align = "center",
out.width = "100%"
)
```
# ggcor
The goal of `ggcor` is to provide a set of functions that can be used to visualize a co... |
4c5ec6ced1d5aa5dde3d588e7f1356c42314618db5deafc95e8a3040d88fa94b | R | 3,841 | 96 | source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/atac.R')
source('~/scripts/perturbator/de.R')
library(Pando)
library(destiny)
library(dtw)
library(doParallel)
registerDoParallel(20)
filter <- dplyr::filter
select <- dplyr::select
setwd('~/projects/cutntag/')
mark_colors <- c('H3K4me3'='#CB9ACA',... |
74b4f5450fcab90659804eaa6a0cf2c6b88cd506d1d5408b91d40c899326a09c | R | 3,844 | 83 | #!/usr/bin/env Rscript
source(here::here("src/shared/R-helpers/get_settings.R"))
source(here::here("src/shared/R-helpers/remove_ensembl_version.R"))
library(dplyr)
settings <- get_settings(omic = "integration", stage = "rnaseq_srna_intersection", dataset_name = NULL) # dataset_name="c9"
message("Reading srna de res... |
38969721659f8138f64ee309d9575071151305a27f07cf6ce0a7f584fce24089 | R | 3,845 | 81 | library('phangorn')
dyn.load("../src/RMorphyEx.dll")
source("../src/Morphyex.R")
tree <- read.tree(text="(((a, b), c), (d, (e, f)));")
matrix_as_grid <- t(matrix(c('0', '0', '1', '1', '0', '0',
'0', '0', '-', '-', '-', '1',
#'0', '+', '?', '-', '1', '1',
... |
b83544300e313ae6a174d584475e2d062e7524e0ac0b03ffe50756756fc147bc | R | 3,848 | 113 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(GenomicRanges)
library(plyranges)
library(here)
library(rtracklayer)
library(patchwork)
# Load data ---------------------------------------------------------------
load(here::here("results", "pseudogenes", "pseudog... |
4a6a2bc0994c75703ddbb52ddca5ca1fb82406c16a8ab615b9d76ad8315eb1ea | R | 3,855 | 109 |
requireNamespace("jsonlite")
requireNamespace("tibble")
requireNamespace("dplyr")
requireNamespace("limma")
limma_analysis <- function(
x,
col_data,
limma_settings,
output_directory,
results_callback = NULL
) {
# x has samples in rows, proteins in columns
dir.create(output_directory, showWarnings = FALS... |
deb74caf23f473bb2f498c3c1d070cfdd89c13b9511b957c14ca490e0b777d96 | R | 3,855 | 95 |
slopes_mat[SpeciesMat$MammalNumberHorvath=="10.1.3" & SpeciesMat$Tissue =="Skin", 11:13] =
NA
slopes_mat[SpeciesMat$MammalNumberHorvath=="8.7.9", 1] =
2*slopes_mat[SpeciesMat$MammalNumberHorvath=="8.7.9", 2]
slopes_mat[SpeciesMat$MammalNumberHorvath=="8.7.10",11] = NA
slopes_mat[SpeciesMat$MammalNumberHorvath... |
b106f9aaf8746bf41da7881b07516aec228f97d33b812fa956bd9c2e91a768a6 | R | 3,862 | 106 | # Create synthetic data for publication
library(mice)
library(dplyr)
library(miceadds)
#Starting with the original dataset
wml_long_excl_a=read.csv("/data/pt_life_whm/Results/VRF_cSVD/imputed_data/imputed_data_6.12.23/full_dataset_long_excl_a.csv")
#Prepare data frame for imputation
test = wml_long_excl_a %>%
mut... |
6550dc3ae115e10a9e42fbbc625c758e2893c49a29c523f1701dcfb7572fd00f | R | 3,883 | 122 | #' Ring Geom
#'
#' @param remain.fill the fill colour of the remaining area.
#' @param start.radius inner circle radius.
#' @param end.radius outer circle radius.
#' @param steps radians per step.
#' @inheritParams ggplot2::layer
#' @inheritParams ggplot2::geom_polygon
#' @section Aesthetics:
#' \code{geom_ring()} unde... |
2d389a25650d14625936e22dbd77bd1dc6a394fa4796e0464ead9a5b5f87c8a9 | R | 3,888 | 173 | #########################
# TEST 1: Simple problem #
#########################
set.seed(123)
# generate data
n <- 1e3
y <- rnorm(n)
x <- y * .3 + rnorm(n, 0, .25)
x2 <- x + rnorm(n, 2, 3)
x <- cbind(x, x2)
# make missingness
y[sample(1:n, n * .3)] <- NA
ry <- !is.na(y)
# Use univariate imputation model
set.seed(123)... |
60a4d96535bcd081d82904179eda51adfb5c821275a70413a79937895ad4af70 | R | 3,891 | 104 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
library(rstatix)
library(ggpubr)
# Load data ---------------------------------------------------------------
GCase_activity <-
read_tsv(
here::here("results", "biochem", "GCase_activity_formatted.txt"),... |
58b0cdd1ebf32eb90ac20fa21b4f2f14700d81107c6dfe0e9967bf8cfe4ddd9f | R | 3,895 | 137 | ---
title: "**Idx Stats**"
---
This section of the report details all of the stats that have been collates from the running **samtools idxstats**.
## **Mapped reads per contig from samtools idxstats**
<br>
This section will provide information from the output of **samtools idxstats** tool. It outputs the number of ... |
0249c1b3ebf96557f2dfcf7db5b514a367fb69a218d29a8de99d1a9ebd05da38 | R | 3,907 | 74 | GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin,
draw_group = function(self, data, ..., draw_quantiles = NULL) {
data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth * (xmax - x))
grp <- data[... |
d67f0ceed05aa705a829f49f2d0295bf29e7b22ea9832b40d8cff6bdfe2e5953 | R | 3,907 | 98 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts_transcript <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcript_counts_unfiltered.tsv", sep="\t", row.names="transcript_id"))
cts_gene <- as.matrix(read.csv("../../../data/bernardo/process... |
a445895da9836ebc1d61561f64d3db5077001f8c4c7f16a6cf802ecb1e232231 | R | 3,931 | 82 | ################################################################################
# Written by Loïc Labache, Ph.D. #
# Holmes Lab, Department of Psychiatry - Rutgers University #
# January 26, 2024 ... |
ee6cf5ce8cc1aa56c5ac74b8d256e2ab05d3ac753ea4a4dd7d09db79eb07078b | R | 3,933 | 110 | test_that("SQANTI multi-sample", {
file <- "../test_data/sqanti_classification_multiSample.txt"
gene_id <- "ENSG00000TEST"
perc_As <- 80
abundance_file_type <- "SQANTI"
min_exp_perSample <- 0
min_exp_mean <- 0.3
min_sample_perc <- 8
x <- read.table(file, header=TRUE, sep="\t")
x.gene <- x %>%... |
e97d2a44689728d1e4eab3adafa1817fc7efae97e65347bd00917662e1fef81d | R | 3,934 | 105 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
library(rstatix)
library(ggpubr)
# Load data ---------------------------------------------------------------
GCase_activity <-
read_tsv(
here::here("results", "biochem", "GCase_activity_formatted.txt"),... |
43bb88d29b6c4c30f5becd3c1228a15582a860f262d70d473376894b748649fb | R | 3,947 | 120 | ## This script analyzsis gene set enrichment with permutation
suppressPackageStartupMessages({
library(DOSE)
library(here)
library(dplyr)
library(biomaRt)
library(org.Hs.eg.db)
library(clusterProfiler)
})
get_biomart <- function(){
ensembl <- useMart("ensembl", dataset = "hsapiens_gene_ense... |
50b80e72c865d87ee33575674302db7d82ed939a616c952db0cbf17d44a8053b | R | 3,947 | 114 | #' Imputation by classification and regression trees
#'
#' Imputes univariate missing data using classification and regression trees.
#'
#' @aliases mice.impute.cart cart
#'
#' @inheritParams mice.impute.pmm
#' @return Vector with imputed data, same type as \code{y}, and of length
#' \code{sum(wy)}
#' @param minbucket ... |
b8929cbe3141b91e7e50c71b1498c673ac98cc1fc3ed1990179b69dfe65dcd49 | R | 3,948 | 112 | #' Pools R^2 of m models fitted to multiply-imputed data
#'
#' The function pools the coefficients of determination R^2 or the adjusted
#' coefficients of determination (R^2_a) obtained with the \code{lm} modeling
#' function. For pooling it uses the Fisher \emph{z}-transformation.
#'
#' @param object An object of clas... |
9f9fb012d4e569191a1826a044ab5ed17344ac15cb65cbb2d846c8a52b73a209 | R | 3,953 | 98 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts_transcript <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcript_counts_unfiltered.tsv", sep="\t", row.names="transcript_id"))
cts_gene <- as.matrix(read.csv("../../../data/bernardo/process... |
3b27a7859dcbfc5ddf0c7d1e3cf80a20af0492598f61cbc7cfe2cf26fa396b3f | R | 3,967 | 77 | library(dplyr)
library(data.table)
library(getopt)
library(phenoscanner)
command<-matrix(c(
'help', 'h', 0,'logical', 'help.',
'coloc_res', 'r', 1, 'character', 'susie coloc result.',
'exp_ma_file', 'e', 1, 'character', 'input GWAS .ma file.',
'cells','c',1,'ch... |
0b93f6f4e1869f296134032566e617aa993c6e0e2a3f7e82ff846b4094331ea4 | R | 4,010 | 112 | description <- "consistent logging and command line interface for R scripts"
library("futile.logger")
library("optparse")
library("uuid")
library("yaml")
suppressMessages(library("jsonlite"))
EXPERIMENT_ENV <- new.env()
experiment_start <- function(option_list, description="",
automount... |
a951e032ba91cdbcc25f2fa39be5d3c5ce794752ccf1b84ecb1a347d482cd2a5 | R | 4,012 | 76 | ## R-Script to read ASCII performance index data and plot them
library(ggplot2)
library(reshape2)
#read data file
PIprofile <- read.csv(file=choose.files(), header=TRUE, sep="\t")
#identify sorting columns, use them to define plot colors and then remove them
if("genotype" %in% colnames(PIprofile))
{
#Define colors... |
bee1090017363862bd0e3e5c5f2cc87a209f2b677582ccb0229725fe774d4093 | R | 4,021 | 172 | keep.in.model <- function(y, ry, x, wy) {
(complete.cases(y, x) & ry) | (complete.cases(x) & wy)
}
impute.with.na <- function(x, wy) !complete.cases(x) & wy
check.df <- function(x, y, ry) {
# if needed, writes the df warning message to the log
df <- sum(ry) - ncol(x) - 1
mess <- paste(
"df set to 1. # o... |
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