sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
77cfe0ec06ebdb41e04d7bbbf3291edbb6b3c8cc61898bb5b1220aa7125ea6df | R | 4,552 | 137 | ---
title: "EPM"
output:
word_document: default
html_document: default
pdf_document: default
date: '2022-12-05'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load data
```{r}
setwd("~/Documents/Systemic ESYT1 project locomotion, behavior, survival, anatomical data/Behavioral paradigms da... |
fe716ef880f7da0dcfbd8d632ca4115feb95240146c8bf8b80fcf0d0fff30a9b | R | 4,559 | 115 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
# Load data ---------------------------------------------------------------
# GTEx gene level expression data
gtex_filtered <-
readRDS("/home/egust/Projects/GBA_GBAP1/results/gtexv8_GBA_GBAP1_tpm.Rds") %>%... |
00847f1dd6397ad7ddbea1ef32134fddc31046c0587a37240a62e4dcedd95be3 | R | 4,571 | 168 | ---
title: "Spectral reflectance curves and flower colors in perceptual color spaces"
author: "Martin Dessart"
date: "11/09/2023"
output:
html_document:
toc: true
theme: united
pdf_document: default
word_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,fig.width=15,fig.... |
28d9e25ba030d2c9401b03d640b147182d11d591b3e5928bf43c088a6616590f | R | 4,572 | 80 | #!/usr/bin/Rscript
# Source: https://github.com/opain/Calculating-FUSION-TWAS-weights-pipeline/
options(verbose=TRUE)
library("optparse")
option_list = list(
make_option("--PLINK_prefix", action="store", default=NA, type='character',
help="Prefix of PLINK files for the target sample [required]"),
mak... |
b3bd92ee061202ae71930c50d952e3ad5aad78bce537cdf943e2c6d18117ebee | R | 4,580 | 129 | ### Frame-by-Frame package ###
### sleep parameter ###
# function sleepParameter(...) --------------------------------------------
# it expects dn, i.e. frame-by-frame data splitted by day/night in a list
# first, detectNaps(...) converts each element of dn into booleans asleep/not asleep, called ffz for zzz (for a... |
90c29879c0a5e09547a0d9f9067741f1f885d45ab7053b1ba3741da9bce77146 | R | 4,583 | 118 | library(ggpubr)
library(ggplot2)
library(scatterpie)
library(RColorBrewer)
library(grDevices)
library(Seurat)
library(viridis)
library(tibble)
library(data.table)
set.seed(1234)
library(dplyr)
library(stringr)
file_path = '/Users/cao/Desktop/PDAC/'
#-----------------------------------------------------------
# inpu... |
da75c8f2fa8d03c543de169aca7d4f13f3736056bb4dbc98f6cd3dd72f38f455 | R | 4,585 | 92 | ---
title: "README"
author:
output: github_document
date: "`r Sys.Date()`"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Altered Glia-Neuron Communication in Alzheimer's Disease Affects WNT, p53, and NFkB Signaling Determined by snRNA-seq
Peer-reviewed manuscript:
[
library(dplyr)
library(ggpubr)
save_ggplots <- function(p, fn, w=7, h=7){
for(ext in c('.svg', '.pdf')){
ggsave(p, filename=paste0(fn, ext), width=w, height=h)
}
}
get_pheno <- function(){
fn <- here("input/phenotypes/_m/phe... |
28c2691cc0fec7d812a83675632a7236d95f474f9205a42a866858ed3892c246 | R | 4,606 | 119 | library(dplyr)
library(gridExtra)
library(ggplot2)
library(plyr)
library(reshape2)
library(tidyr)
library(limma)
current = getwd()
setwd(current)
folder = "6_calculate_cisRegression_effect/predicted_population_differences"
## outputs
system(paste0("mkdir -p outputs/",folder,"/"))
results_dir <- paste0("outputs/",fold... |
1b9373cb18fb35b0db81b4b4f6a8d4959e99727692b19564e3bd531a0a214c68 | R | 4,610 | 145 | rm( list=ls(all=TRUE ) )
# Load and install necessary packages
load_required_packages <- function(packages){
new.packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
invisible(lapply(packages, library, character.only = TRUE))
}
requ... |
4861e6125d4fa3a2e5dba9b7af29d9ddc77a2bbd89918181a69aba1c636e3e91 | R | 4,610 | 145 | rm( list=ls(all=TRUE ) )
# Load and install necessary packages
load_required_packages <- function(packages){
new.packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
invisible(lapply(packages, library, character.only = TRUE))
}
requ... |
451421a1d9656a7b82f229d7a1a444459f1fb6d7ad903edad615d142066c3a2c | R | 4,612 | 128 | ######
# Parameters
#
path_home <- './'
# Working directory with temperature data
setwd(path_home)
# Libraries to use
library(readr)
library(tidyr)
library(purrr)
library(ggplot2)
library(lubridate)
library(jsonlite)
library(dplyr)
library(stats4)
library(gridExtra)
library(grid)
library(stats4)
library(bezier)
libra... |
a741255a8f4cf869ae561f24b01e7ba5f2bfac011fb0526eaa96c9b5a9a2649d | R | 4,633 | 139 | ##-------------------------------------------------------
## GEO UPLOAD
##-------------------------------------------------------
# module purge && module load shared slurm python/3.7.x-anaconda
# module load gcc/8.3.0
# module load hdf5_18/1.8.17
# module load R/4.1.1-gccmkl
##---------------------------------------... |
9308592b91492fb2286c0d8476a1dbcb223949ff8b3811563881ebc29bba745e | R | 4,636 | 123 | #############################################
##
## mash modeling in R
## Author: Kynon J Benjamin
##
#############################################
suppressPackageStartupMessages({
library(mashr)
library(dplyr)
library(ggpubr)
})
save_img <- function(image, fn, w, h){
for(ext in c(".svg", ".pdf")){
... |
84cc3acb6ff1cdb42efbe241623c76cb44afd20688a8199367223e4a2aa36bf1 | R | 4,641 | 167 | # this script is for selecting out 5 or 3 samples of the same sex for a given
# and seeing if DTU genes detected changes much
library(tidyverse)
library(IsoformSwitchAnalyzeR)
library(ComplexHeatmap)
library(viridis)
library(org.Mm.eg.db)
library(gprofiler2)
library(styler)
library(lintr)
library(here)
source("src/dt... |
f012e8079263e3be5530cd921d69c2511f0750f1d022cef3cb5296ad3f9cd79c | R | 4,641 | 97 | #' Multivariate amputation based on discrete probability functions
#'
#' This function creates a missing data indicator for each pattern. Odds probabilities
#' (Brand, 1999, pp. 110-113) will be induced on the weighted sum scores, calculated earlier
#' in the multivariate amputation function \code{\link{ampute}}.
#'
#'... |
1127d346cf76106694e8f9d66233d1866859514540a535d6726be682093e6fb1 | R | 4,653 | 134 | #### This script explores sex steroid hormone levels
#### in the brain
suppressPackageStartupMessages({
library(dplyr)
})
get_expression <- function(region){
fn <- here::here("differential_expression",
tolower(region), "_m/genes/voomSVA.RData")
load(fn)
return(v)
}
memVOOM <- memo... |
d646cde7612f738fa70c6a0117b28da58c9bc62a626c2f6fd0da5652ddf7d426 | R | 4,658 | 109 | # Sconda conda activate TensorFlow_CPU_01; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/results/deeplift_analysis; R
workdir = '/mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/results/deeplift_analysis/'
file_data = paste0(workdir,"deeplift_scores.RD... |
4839f863b1ee11c5ead32c2724a94cc16191d86b57852f384afe3dd273e88cb3 | R | 4,660 | 160 | #' Coerce to a cor_tbl object
#' @description Functions to coerce a object to cor_tbl if possible.
#' @param x any \code{R} object.
#' @param extra.mat any other matrix-like data with same dimmsion as \code{x}.
#' @param byrow a logical value indicating whether arrange the 'spec' columns on y axis.
#' @param ... extra ... |
a577545779a22d0c346b3b75c5096d4f7d0e439ba06107e78db5bc6ca7be18a0 | R | 4,677 | 96 | #!/usr/bin/env Rscript --vanilla
#load the libraries
library(readr)
library(ggplot2)
library(dplyr)
library(proBatch)
# load data
sample_annotation_AgingMice = read_csv("data_AgingMice/3_data_for_plots/sample_annotation_AgingMice.csv")
normalized_df_AgingMice = read_csv(file = "data_AgingMice/3_data_for_plots/normal... |
a92e83418f9c2ae935416280e221af1e212418cbc709ad003e6505316060ebb8 | R | 4,684 | 102 | 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 <- "eQTL_QN"
MODEL_TYPE <- c("eQTL_QN_combined")
COV_TYPE <- c("canonical_and_dataDriven")
## folders... |
7d08a8d804f37e009174c30052d55b6c921c088fd1628cd3a5272de18f4c87cb | R | 4,694 | 127 | #' Age transformation function
#'
#' This function performs age transformation used in epigenetic clock calculations.
#'
#' @param x Numeric vector of ages to transform
#' @param offset Numeric offset value (default: 0.06)
#' @param adult.age Numeric adult age threshold (default: 1.2)
#' @return Numeric vector of tran... |
23941477db7a037ce8927fab8bf9849e82bc8486d6b7197935494cf8de23feaa | R | 4,699 | 151 | library(colorspace)
require(RNOmni)
require(glmnet)
require(caret)
library(plyr)
library(RColorBrewer)
# library(WGCNA)
library(tidyverse)
library(readxl)
outfolder = 'out_AROCM_0.1L'
freq = 5 ##### to include lemurs
anAge <- read.csv("/Users/feiz/Dropbox/MammalianMethCombined/StuffCaesar/anAgeUpdatedCaesarVersion51.c... |
5a39248f9bcc5f2176d8aee74fa3933ce71c8c1e1bda67b03447f082230417cc | R | 4,704 | 157 | expect_warning(imp1 <- mice(nhanes[1:13, ], m = 2, maxit = 1, print = FALSE))
test_that("RBIND-001: Constant variables are not imputed by default", {
expect_equal(sum(is.na(complete(imp1))), 6L)
})
expect_warning(
imp1b <- mice(
nhanes[1:13, ],
m = 2,
maxit = 1,
print = FALSE,
remove.constant =... |
13e6a95a816d5c7d06a69d3042c6bd94e4997f72b81785a4a05791ccb257be2d | R | 4,721 | 111 | #!/usr/bin/env Rscript
source(here::here("src/shared/R-helpers/get_settings.R"))
suppressPackageStartupMessages({
library("dplyr")
library("tidyr")
library("readr")
library("tibble")
})
settings <- get_settings(omic = "phosphoproteomics", stage = "organize_samples", dataset_name = NULL) # dataset_name = "c9"... |
cc86647bf5553cdf4d3610f504acc5c4d7b24d70314b21837ce8e85687476803 | R | 4,728 | 139 |
library(data.table)
library(ggplot2)
resultsDir <- function(...) {
paste0(config$out_root, "/atac/", ...)
}
pipelineDir <- function(...) {
paste0(config$out_root, "/pipe/", ...)
}
getBamPath <- function(pipe_name, genome_ver = basename(config$genome_chromatin)) {
return(pipelineDir("out/", pipe_name, "/align... |
11358e2c384d84c7b34e331c6e7a65a4cbaa979606a37e437bbb3e2e85634d36 | R | 4,732 | 123 | ## Generating eQTL boxplots
suppressPackageStartupMessages({
library(here)
library(dplyr)
library(ggpubr)
})
save_ggplots <- function(fn, p, w, h){
for(ext in c('.pdf', '.png')){
ggsave(paste0(fn, ext), plot=p, width=w, height=h)
}
}
plot_eqtl <- function(variant_id, gene_id, gene_name, eq... |
4a73e1f53c92e9163e8ee6c367967c0d30579b684fd9aeac2a5dbdd1980b23ae | R | 4,732 | 116 | ## This script generates the upset plot for mash sig results.
library(ComplexHeatmap)
get_sig_genes <- function(){
fn <- paste0("../../summary_table/_m/",
"differential_expression_schizophrenia_",
"by_sex_4features.sig.txt.gz")
return(data.table::fread(fn) |>
dp... |
3ec02679af6b7a1e069e39a41921e49f556613d07e706f6670021dd4d5f1b6f1 | R | 4,734 | 128 | #' Tidy co-occurrence network data
#' @description The function calculates correlation coefficient, statistical
#' significance level and filters according to conditions.
#' @param x a cor_network object.
#' @param corr correlation matrix.
#' @param p.value significant matrix of correlation.
#' @param directed log... |
5176d1b67ce895052d1472bb1b5e703bad51ba432c7e342e0f872275836fa9b4 | R | 4,755 | 132 | #' @importFrom dplyr filter
#' @export
dplyr::filter
#' Subset rows of a \code{mids} object
#'
#' This function takes a \code{mids} object and returns a new
#' \code{mids} object that pertains to the subset of the data
#' identified by the expression in \dots. The expression may use
#' column values from the incomplet... |
d252d0d9ab01f030c8fb07dee0e5d7ace6501203a0471b2be5b2237f07318802 | R | 4,758 | 112 | library(Seurat)
library(SeuratWrappers)
library(ggplot2)
library(velocyto.R)
library(dplyr)
library(ggpubr)
## AstroNeurontest - seurat object with defined clusters
# filtering out N-stage7,A-stage5,6,7
small_data <- subset(AstroNeurontest,idents = c("NSC-stage1","NSC-stage2","RG-like","N-stage1",
... |
c5022f9f5831150c95ef5831fbb21513dda6eb1662b9e4b3e8802e8af8ac60e7 | R | 4,760 | 136 | source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/atac.R')
source('~/scripts/perturbator/de.R')
library(Pando)
setwd('~/projects/cutntag/')
#### Read data ####
marks <- read_rds('data/all_marks_list_v3.3motifs.rds')
rna <- read_rds('data/RNA/RNA_all_srt_v2.2matched.rds')
drugs <- read_rds('data/dru... |
aa8c171e4c36a0f565ebc50105141c34381c72514d1b4be6f905e4c18f8c952b | R | 4,766 | 111 | #!/bin/R
# Nicole Gay
# 15 May 2020
# Fix and merge ATAC-seq QC
#Usage : Rscript src/merge_atac_qc.R -w ~/test_mnt/PASS/atac-seq/stanford/batch4_20200928/Output/final/sample_metadata_20200928.csv -q ~/test_mnt/PASS/atac-seq/stanford/batch4_20200928/Output/final/qc/atac_qc.tsv -m ~/test_mnt/PASS/atac-seq/stanford/bat... |
943071cbb5cab6558b82ff4bb2b3863fffb8f8dd14fc58880e22443789f2c72b | R | 4,777 | 129 | #' Significant marks Geom
#'
#' @param digits integer indicating the number of decimal places (round) or
#' significant digits (signif) to be used, the default value is 2.
#' @param nsmall the minimum number of digits to the right of the decimal
#' point in formatting real/complex numbers in non-scientific form... |
c5a2c19f6d44e19287a404754a2fa3e16ebae6814d15fcb44249a6fa42773d98 | R | 4,777 | 93 | #!/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/processed/deseq2/genes_annotated_results_AD_vs_CT_no_intron.tsv', sep="\t")
## Load datasets for differenti... |
e71603bf9e2c4017c310b6f597eed73ffffc1ff717ccd5f314f5add46884778b | R | 4,781 | 111 | library('phangorn')
dyn.load('../src/RMorphy.dll')
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',
'0',... |
2d36aade292c83b8ad1ac17c8fb71589e0a888582945be762da5041e8807a230 | R | 4,808 | 126 | ### Frame-by-Frame package ###
### import behaviour parameters ###
# function importBhvParams(...) -------------------------------------------
# to import parameter tables from directory(ies) bhvparams
# returns a list, each element is one parameter table
#' Title
#'
#' @param paDir
#' @param poolExp1
#' @param pool... |
4f9720759d19fa4979ae383f2ea2ce0461004c75fd1396ab92116bf09bc9ab4b | R | 4,811 | 137 | rm( list=ls(all=TRUE ) )
# Load and install necessary packages
load_required_packages <- function(packages){
new.packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
invisible(lapply(packages, library, character.only = TRUE))
}
requ... |
4485e0cb036013693f6eaa286df2f532d6d189ee6b920327a9852ea3b22850b3 | R | 4,814 | 137 | rm( list=ls(all=TRUE ) )
# Load and install necessary packages
load_required_packages <- function(packages){
new.packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
invisible(lapply(packages, library, character.only = TRUE))
}
requ... |
5d560a5a47c979e784fd4196628729cb786f4489768dbd135f0926b4a395dc28 | R | 4,814 | 137 | rm( list=ls(all=TRUE ) )
# Load and install necessary packages
load_required_packages <- function(packages){
new.packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
invisible(lapply(packages, library, character.only = TRUE))
}
requ... |
1d4c513ff132f2e115dea22e210d284f4a9ffb65fbced5b7562e84f5b405d67c | R | 4,826 | 128 | #### This script explores sex steroid hormone levels
#### in the brain
suppressPackageStartupMessages({
library(dplyr)
library(ggpubr)
})
get_expression <- function(region){
fn <- here::here("differential_expression",
tolower(region), "_m/genes/voomSVA.RData")
load(fn)
return(... |
abc5bed6481b21f77bb458f2303448a64aa9e75b863d5a5643d1ae8d4ca3c6af | R | 4,846 | 119 | library("gamlss")
library("mgcv")
x <- read.csv('global dispersion.csv')
centile <- data.frame(
age = seq.int(8, 89, length.out = 1000)
)
quantiles <-c(0.25,0.5,0.75)
centile_names <-c("low","mid","high")
min_age <-min(x$age)
max_age <-max(x$age)
############################################################... |
51ee5129d7af535f46c48afb79b092783ed996e6cbb9e1a2242f496627ea56e1 | R | 4,849 | 98 | library(mixOmics)
library(stringr)
require(caret)
set.seed(1)
args<-commandArgs(TRUE)
matrix1 <-args[1]
matrix2 <-args[2]
matrix3 <-args[3]
label_path <-args[4]
dataset<-args[5]
save_path<-args[6]
datasets<-paste('dataset',dataset,sep='_')
message('read count matrix: ', matrix1)
mat1 <- read.table(matrix1, header ... |
2c5a253c4e48444e34aa919edea9a3a4d96bb436486b69cf978c3ecb177f88ed | R | 4,857 | 186 | #' Creates a \code{method} argument
#'
#' This helper function creates a valid \code{method} vector. The
#' \code{method} vector is an argument to the \code{mice} function that
#' specifies the method for each block.
#' @inheritParams mice
#' @return Vector of \code{length(blocks)} element with method names
#' @seealso... |
5e1246cf85e8a82b014da426189061f3054ff68f65b498062d93ad362d9e3679 | R | 4,863 | 161 | suppressPackageStartupMessages({
library(tidyverse)
library(Seurat)
library(openxlsx)
})
setwd('/cluster/tufts/slonimlab/rbator01/covid-placenta-edlow/')
source('scripts/00_get_started.R')
# load data -----
so_file="analysis/seurat_object/seuratobj_subset_integrated_HBCs_USETHISONE.rds"
so = readRDS(so_file)
re... |
9462378a6060cec5ab5e47e4c7d41800f2718106c80f6cf4a21e7d5d0ac24c6c | R | 4,865 | 132 | #' Computes convergence diagnostics for a \code{mids} object
#'
#' Takes an object of class \code{mids}, computes the autocorrelation
#' and/or potential scale reduction factor, and returns a \code{data.frame}
#' with the specified diagnostic(s) per iteration.
#'
#' @param data An object of class \code{mids} as created... |
2d104fc28fd143af04686676121d8ee03c2dcb4338db5a6941251f13881c6806 | R | 4,885 | 138 | #'#################################################################################
#'#################################################################################
#' PROMOTE vis 2 results with NetActivity
#' Used in Figure 5; Supp. Fig 14 and Supp. table 6
#'
#'... |
27c5b3c9b8092ca88ea79dd2e431a9eed004fb2750f372e715f3002f5ed7fedf | R | 4,887 | 101 | library(limma)
library(edgeR)
library(tidyr)
library(reshape2)
library(plyr)
library(dplyr)
library(grid)
library(gridExtra)
current = getwd()
folder = "2_calculate_residuals_and_DE_analyses"
setwd(current)
system(paste0("mkdir -p outputs/",folder,"/2_2_popDE_results/"))
## inputs
meta_data_file <- paste0("inputs/",f... |
d1f84c2eaedfcf63f41d8342f034f93a9f821d4e9e036199feb8b88a1b024ffe | R | 4,893 | 117 | ## This script generates the upset plot for mash sig results.
library(magrittr)
library(ComplexHeatmap)
get_sig_genes <- function(){
fn <- paste0("../../summary_table/_m/",
"differential_expression_analysis_4features_sex.txt.gz")
return(data.table::fread(fn) %>%
dplyr::filter(Typ... |
730817696ea42915da3ad76aecf14572fa9077cfe2962a1fe5d60ed1ea5e8608 | R | 4,898 | 117 | library("mlxR")
project.file <- "./all_groups_104/loglogistic_logmu.mlxtran"
Te_pop <- 32.31109058
beta_Te_mu <- -0.06604329
s_pop <- 17.87627025
omega_Te <- 0.11431745
mu_10 <- 3.992731e-06
mu_90 <- 0.0001186991
mu_5 <- 2.144331e-06
mu_95 <- 0.0001649904
res1 <- simulx(project = project.file,
... |
1113a5ec8ea752852cb8be866ba5f43f19305c7ed53e5306ca7ac42b2b441fc6 | R | 4,931 | 158 | # This script plots expression of several gene modules from published data, Fig 3E
LIB='/cluster/tufts/patralab/rbator01/R_libs/4.0.0'
.libPaths(c("",LIB))
suppressPackageStartupMessages({library(org.Hs.eg.db)
library(tidyverse)
library(Seurat)
library(clusterProfiler)
library(org.Hs.eg.db)
})
source('script... |
a0125e83fe7335fd674d893f7fea725d9ed49a5e1640966fa89d0472409cbd6a | R | 4,948 | 110 | ---
title: "Figure 6. Ro/e tissue distribution analysis of glial subtypes (STARTRAC)."
date: "2026/9/16"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
# Clean workspace
rm(list=ls())
# Load setting and functions
source("../script/stat_plot_functions.R")
# Set output directory... |
c9c1fdfd6e0647cac3c20038122543b3aa12eb6a65cefd89edc7e48aabf4a7ad | R | 4,948 | 113 | # set working directory
setwd("~/Desktop")
maindir <- getwd()
datadir <- file.path("~/Desktop/")
# load packages
library("readxl")
library("ggplot2")
library("ggpubr")
library("reshape2")
library("emmeans")
library("hrbrthemes")
library("umx")
library("interactions")
library("car")
# import data
data_ppi <- read_exce... |
aa72e37b9cee63500185f7a1a3cfde6440c401a3d5f0e47fe723e1f682d925a4 | R | 4,957 | 150 | #!/usr/bin/env Rscript
source(here::here("src/shared/R-helpers/get_settings.R"))
source(here::here("src/shared/R-helpers/remove_ensembl_version.R"))
source(here::here("src/shared/R-helpers/gseGO_memoise.R"))
suppressPackageStartupMessages({
library("AnnotationDbi")
library("biomaRt")
library("clusterProfiler")
... |
5ca5719e7f94f73fa4dc3a739f8a421779a3c39d4ccc86ba74c6f85620b2f6e9 | R | 4,968 | 188 | rbind.mids <- function(x, y = NULL, ...) {
call <- match.call()
if (is.mids(y)) {
return(rbind.mids.mids(x, y, call = call))
}
# Combine y and dots into data.frame
if (is.null(y)) {
y <- rbind.data.frame(...)
} else {
y <- rbind.data.frame(y, ...)
}
if (is.data.frame(y)) {
if (ncol(y) ... |
ab58d132e3636a738f10844d1a2a17b14f50f1824bbfe43b4be50a57dfab7b4c | R | 4,969 | 123 | ---
title: "Figure 3. Differential expression analysis (MMT_D30 vs GF_D30) and volcano plots per tissue."
date: "2026/9/16"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
# Clean workspace
rm(list=ls())
# Load setting and functions
source("../script/stat_plot_functions.R")
# S... |
94a5ffcf2b414bc37595d024a9716a50625dbebc383d89cd00d9f15c647f9b7c | R | 4,979 | 109 | ############################################################################################
# Copyright (c) 2022 - Mucosal Immunology Lab, Monash University, Melbourne, Australia #
# Author: Matthew Macowan #
# This script is provided under the MIT l... |
24e51667d9b268e87dea565fe6f3e2ca5a0b475fea7d89b17e9520fd7218c4f5 | R | 4,985 | 138 | library(limma)
library(edgeR)
library(tidyr)
library(reshape2)
library(plyr)
library(dplyr)
library(grid)
library(gridExtra)
current = getwd()
folder = "2_calculate_residuals_and_DE_analyses"
setwd(current)
system(paste0("mkdir -p inputs/",folder,"/corrected_expression_QN/"))
## inputs
meta_data_file <- paste0("input... |
62beb88525abc6e10830d2ca9481e33fb8fd0d708a4b229be1045883589b29a4 | R | 4,986 | 103 | ---
title: "Overview"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
bibliography: refs.bibtex
vignette: >
%\VignetteIndexEntry{Overview}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
<!-- overview.md is generated from overview.Rmd. Please edit that file -->
## Software
1. [`mice`... |
27550628f625b741033cae51a02c9498d30c4225f42a37984310ca2d5035f367 | R | 4,988 | 168 | #' Coerce to a cor_network object
#' @description Functions to coerce a object to cor_network if possible.
#' @param x any \code{R} object.
#' @param directed logical value, whether or not to create a directed graph.
#' @param simplify logical value (defaults to TRUE) indicating whether to
#' delete nodes without ... |
799aedafe93577e9958439dfabe010008a70b02ee37944614f0dafac8b45a792 | R | 5,003 | 139 | ##
## anticipation/experienced improvements
##
library(ProjectTemplate)
load.project()
d<-antexp[complete.cases(antexp),]
d %>% select( AAntDirection, BAntDirection, AExpDirection,BExpDirection ) %>%
gather(variable, direction) %>% separate(variable, c("condition","question",'trash'),c(1,4)) %>%
mutate(condition=... |
18703ea771d53087b3eac8f35b86bf72f992e4465dc9393324fcddf601ee0664 | R | 5,010 | 138 | # general settings, package loading
library(lme4)
library(tidyverse)
library(dplyr)
library(ggplot2)
library(meta)
#' @param x outcome which should be min-max-normalized
#' @return min-max normalized outcome
min_max_norm <- function(x) {
(x - min(x)) / (max(x) - min(x))
}
#' performs a metaanalysis of all scores ... |
46c1b76a14d8a8860d1ca58c9d84f17fbcce77456b44bed738159c09b6b773a3 | R | 5,011 | 147 | # Internal functions used to define parameters and column descriptions.
# Please add functions in alphabetical order.
#
# To render:
# tissue character, tissue abbreviation, one of [MotrpacRatTraining6moData::TISSUE_ABBREV]
# assay character, assay abbreviation, one of [MotrpacRatTraining6moData::ASSAY_ABBREV]
# ... |
a67ba4425e28087716c462527bc00b30ff6a9de5e221ba584f42073066fa23c7 | R | 5,019 | 96 | #' \pkg{mice}: Multivariate Imputation by Chained Equations
#'
#' The \pkg{mice} package implements a method to deal with missing data.
#' The package creates multiple imputations (replacement values) for
#' multivariate missing data. The method is based on Fully Conditional
#' Specification, where each incomplete vari... |
f41617a5581c4f068adec2e662c98bc74ca1f91f4e02b9b5422cbc8161d6b763 | R | 5,019 | 111 | library(tidyverse)
library(readr)
library(GenomicRanges)
library(plyranges)
library(diffloop)
library(rtracklayer)
# Arguments ---------------------------------------------------------------
args <-
list(
path_to_GENCODEv10 = here::here("data", "gencode.v10.annotation.gtf.gz"),
path_to_OMIM = here::here("re... |
c206e297ab0e20da02ad89d3433f9c28bd9acb89e255cf5fc56c11db4aba42f1 | R | 5,044 | 167 | ordered_cp_dotplot <- function(ck, marker_term_select_combine, showCategory){
ck_select=ck %>% dplyr::filter(Description %in% marker_term_select_combine)
#ck_select@compareClusterResult$Description = factor(ck_select@compareClusterResult$Description,
# level... |
443df1112e83c7cb696ffbefe9ad48b4363ce79d003b4ba60a9122dde282e71c | R | 5,047 | 59 | library(DiagrammeR)
all_wMRI_fu=nrow(LST_overview[!is.na(LST_overview$LPA_long_complete),])
all_complete=length(LST_overview[!is.na(LST_overview$LPA_long_complete)&LST_overview$LPA_long_complete==1,"LPA_long_complete"])
missing_baseline_MRI=8
missing_fu=2
procissue=1
grViz("digraph flowchart {
node [shape = rectangle,... |
baa9d0bcd9d739e68aea09a0fc571d3f7831a9c360dec4a549ded4f4938eb1a1 | R | 5,050 | 174 | #' Creates a \code{predictorMatrix} argument
#'
#' This helper function creates a valid \code{predictMatrix}. The
#' \code{predictorMatrix} is an argument to the \code{mice} function.
#' It specifies the target variable or block in the rows, and the
#' predictor variables on the columns. An entry of \code{0} means that... |
3329a6e811dceb1457f4543b2cbc524d64e461502067892c6c21508ba23b7bd1 | R | 5,053 | 134 | library(limma)
library(edgeR)
library(ggplot2)
library(cowplot)
library(tidyr)
library(reshape2)
library(plyr)
library(dplyr)
library(grid)
library(gridExtra)
current = getwd()
folder = "7_WOS_effects"
setwd(current)
system(paste0("mkdir -p inputs/",folder,"/pseudobulk/"))
system(paste0("mkdir -p outputs/",folder,"/7_... |
605ab70a99bad0b459db3c835f8b5dd55ce024168bee9a1a81567950b84ed517 | R | 5,068 | 161 | library(readxl)
library(dplyr)
library(DESeq2)
library(ggplot2)
library(EnhancedVolcano)
library(ggpubr)
######################################
# Read meta data
MetaData = readxl::read_xlsx("meta_data.xlsx")
##############################################
options(digits = 3)
# Format MetaData3 to have distinct indi... |
a6a221f722aaf70722964d1df7c9d0d2a79416dd8f9fcc2ee3c25d32f56cc5e8 | R | 5,068 | 175 |
unbiased_stICA <- function(X,k=10,alpha) {
#
# [A B W] = unbiased_stICA(X, k,alpha)
#
# Compute factorization of X =A*B' using Jade algorithm
#
# Inputs:
# - X (matrix p*n) is the matrix to factorise
# - alpha (scalar between 0 and 1) is the trade-off between spatial ICA (alpha = 0) et temporal ICA... |
fa8ff69e9bcd1c80354f165f56d283dd23cdb5574baf72d1df046fcc4fe963d0 | R | 5,081 | 117 | #'#################################################################################
#'#################################################################################
#' Explore hsa00430
#'#################################################################################
#'##########################################... |
db85cc18870bea3838a6fad577049d62c91f9ab98e077a06b7a2bbd27e400528 | R | 5,093 | 136 | #
#Universal mammalian clock2 of age
#
#author for developing clock2: Ake T. Lu
#author for developing clock1 and clock3: Zhe Fei
#
rm(list=ls())
options(stringsAsFactors = F)
#
myinput.list=readRDS('mydata_GitHub.Rds')
# The following labels the coefficient values of the three universal clocks
beta.name=c('beta_clock1... |
eb71fdffe172c1e06e7e5278514ff7b313e494b158bf94b43f65d27fdd310c25 | R | 5,105 | 144 | source('~/scripts/single_cell/atac.R')
source('~/scripts/single_cell/wrapper.R')
source('~/scripts/single_cell/markers.R')
source('~/scripts/single_cell/celltype.R')
source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/graphs.R')
source('~/scripts/grn/models.R')
setwd('~/projects/cutntag/')
#### Read s... |
165133edc6db5adb12de46bf10904f4c50167b5e63f6fddb3252c7b3a2a11dce | R | 5,124 | 177 | library(tidyverse)
source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/wrapper.R')
source('~/scripts/single_cell/atac.R')
source('~/scripts/perturbator/de.R')
library(Pando)
library(doParallel)
library(BSgenome.Hsapiens.UCSC.hg38)
registerDoParallel(24)
rename <- dplyr::rename
filter <- dplyr::filter
... |
bcc09dbc41c85cb8c31b2bb3f29184760e4dfc574c7d07a8f418b2ba3aca3d37 | R | 5,138 | 149 | ---
title: "Untitled"
output:
pdf_document: default
html_document: default
date: '2023-04-18'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(pacman)
p_load(tidyverse, readxl)
spinalcord <- read_excel("~/Documents/Systemic ESYT1 project locomotion, behavior, survival, anatomi... |
5ab02de5c56c9a81d3304deba40b0f25a3c21779d3101e251afd457fd3d432b0 | R | 5,175 | 110 | #'#################################################################################
#'#################################################################################
#' Analyze PRAD
#'#################################################################################
#'##############################################... |
da170ee4f6d5f4847894c6a6530cf1b9c07269d4b438b26406ba9f2d4850f9c7 | R | 5,181 | 129 | ##-------------------------------------------------------
## SEURAT ANALYSIS | MODULES & LIBRARIES
##-------------------------------------------------------
# module purge && module load shared slurm python/3.7.x-anaconda
# module load gcc/8.3.0
# module load hdf5_18/1.8.17
# module load R/4.1.1-gccmkl
##-------------... |
46f81c0472c7ea2c41db71df6bf23f0e72adb9c87b81b4cc59ccafa1f6757e5b | R | 5,184 | 137 | #' Imputation at level 2 by Bayesian linear regression
#'
#' Imputes univariate missing data at level 2 using Bayesian linear regression
#' analysis. Variables are level 1 are aggregated at level 2. The group
#' identifier at level 2 must be indicated by \code{type = -2} in the
#' \code{predictorMatrix}.
#'
#' @aliase... |
03b9e5d36d2bc5a5497d923f4292fdae8d3ff08f8854d500349791d0813180ea | R | 5,190 | 172 | ---
title: "Question 2: Are pollinators able to discriminate invasive flowers species from native flowers species?"
author: "Martin Dessart"
date: "11/09/2023"
output:
html_document:
toc: true
theme: united
pdf_document: default
word_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set... |
ce810ec64473992a13456f4c5fa5d3c5d68cad18e4c941f94c473adf7d553a46 | R | 5,202 | 98 | #What's in GeneWeaver:
setwd("~/Documents/SideProjects/BrainGMT/GeneWeaver")
GeneWeaver_GeneSets<-read.delim("publications2symbols.dat", sep="\t", header=FALSE, stringsAsFactors = FALSE)
str(GeneWeaver_GeneSets)
head(GeneWeaver_GeneSets)
# 'data.frame': 693 obs. of 4 variables:
# $ V1: chr "Differential effects i... |
e69bd946a8fd4f49c57d4f2f7b29a5f739207bf0bcf11ce20ff243c2f0b22bb5 | R | 5,226 | 138 | ---
title: "sc2marker with HCA-BM (Human)"
author: "sc2marker"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: yes
toc_depth: 3
toc_float:
collapsed: false
smooth_scroll: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# load data
You can... |
1def3624c9a2aef5f1a83b4f645fd07f64fb011f516b9520a5a3d1856211e4d3 | R | 5,241 | 126 | #' Combines estimates from a tidy table
#'
#' @param w A \code{data.frame} with parameter estimates
#' in tidy format (see details).
#' @param dfcom A positive number representing the degrees of freedom of the
#' residuals in the complete-data analysis. The \code{dfcom} argument is
#' used for the Barnard-Rubin adjustm... |
66ac5bcbd47456402ede868982ccb73585436809f742b455b5f81d44a0e6dcd2 | R | 5,252 | 165 | #!/usr/bin/env Rscript
source(here::here("src/shared/R-helpers/get_settings.R"))
source(here::here("src/shared/R-helpers/gseGO_memoise.R"))
source(here::here("src/shared/R-helpers/gseKEGG_memoise.R"))
suppressPackageStartupMessages({
library("AnnotationDbi")
library("dplyr")
library("purrr")
library("ggplot2... |
0abf64e52007e48fd3ca4a928eefd1627b0e6425531ec74ff4d6c21ee9912bf3 | R | 5,253 | 119 | #!/usr/bin/Rscript
library("optparse")
option_list = list(
make_option("--RDat_dir", action="store", default=NA, type='character',
help="Directory where FUSION LD reference files are stored [required]"),
make_option("--coordinate_file", action="store", default=NA, type='character',
... |
111053a138f11e98f1ad1b415d5ea5d08606b3e02325f3f81092f3b2346166af | R | 5,260 | 183 | #########################
# TEST 1: Simple problem #
#########################
set.seed(123)
# generate data
y <- rnorm(10)
x <- y * .3 + rnorm(10, 0, .25)
x2 <- x + rnorm(10, 2, 3)
x <- cbind(1, x, x2)
# make missingness
y[5:6] <- NA
ry <- !is.na(y)
set.seed(123)
svd <- .norm.draw(y, ry, x, ls.meth = "svd")
set.see... |
5ff75a87957787ff1ad73dffeebd7412c39c44dadb6098efb10e5259c3cac512 | R | 5,261 | 130 | library(mixtools)
library(ggplot2)
library(tidyr)
library(reshape2)
library(plyr)
library(dplyr)
library(grid)
library(gridExtra)
library(ggrepel)
library(stringr)
current = getwd()
folder = "2_calculate_residuals_and_DE_analyses"
setwd(current)
system(paste0("mkdir -p outputs/",folder,"/2_0_bimodal_proportions/"))
#... |
88496e033cca3fc1d6fad1b1e055e5fbca086f39784eaac499088b76fa301d89 | R | 5,293 | 135 | #'#################################################################################
#'#################################################################################
#' Process TCGA gene expression for deep learning
#'#################################################################################
#'################... |
bed97b3211d4245e6493bc11ce33688dd5ca57b39592eace4bbd812e983c4331 | R | 5,293 | 142 | ## Prepare covariates for FastQTL / tensorQTL analysis
suppressPackageStartupMessages({
library(here)
library(argparse)
library(tidyverse)
library(SummarizedExperiment)
})
#### Functions
getRPKM <- function(rse, length_var = "bp_length", mapped_var = NULL) {
mapped <- if (!is.null(mapped_var)) colD... |
ee3f66e7a386cb9a636aa908ff9838f24252c612a7891b825b39fec7e4486804 | R | 5,311 | 229 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(ggtranscript)
library(patchwork)
library(rtracklayer)
library(plyranges)
library(here)
# Load data ---------------------------------------------------------------
Filtered_tx <-
list(
GBA = read.table(here::h... |
8f656ce0d20f012b83b36c2f878796984ee199a29dbd458ad66f961e2d000187 | R | 5,339 | 126 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
# Arguments ---------------------------------------------------------------
args <-
list(
ENCODE_files = list.files(here::here("data", "ENCODE_LR"), full.names = TRUE, recursive = TRUE),
path_to_pse... |
4fe1b705f5b12ffdacf48e9400e484a782d26c9a3c6232d07fa0dfaa77af7bb1 | R | 5,368 | 118 | #'#################################################################################
#'#################################################################################
#' PROMOTE vis 2 Differential gene expression analysis and enrichment
#' Used in Figure 5; Supp. Fig 13 and Supp. table 7,8 and 9
... |
0a9cd228372a5655841f4bc5f1eadc63e6f67b2717d42408a382af5c5d553260 | R | 5,374 | 103 | library(mixOmics)
library(stringr)
require(caret)
set.seed(10)
args<-commandArgs(TRUE)
matrix1 <-args[1]
matrix2 <-args[2]
matrix3 <-args[3]
label_path <-args[4]
dataset<-args[5]
save_path<-args[6]
datasets<-paste('dataset',dataset,sep='_')
message('read count matrix: ', matrix1)
mat1 <- read.table(matrix1, header ... |
aa78150510cc8305c6b1db0743cdfaf4a6f88502a07f37c36fd9ae94155a9818 | R | 5,380 | 137 | .findAS <- function(object) {
gtf <- slot(object, "transcriptome")
gtf <- c(gtf[gtf$type != "AS"], .runAS(gtf[gtf$type == "exon"]))
slot(object, "transcriptome") <- gtf
object@sets$AS@rowData <- as.data.frame(object@transcriptome) %>%
dplyr::filter(type %in% "AS") %>%
dplyr::mutate(coord = ... |
4e6d75adee3964e1acf8c5fbbd5dbc40c239452dfa2b3dd6ea8cc8e8014e8ba3 | R | 5,414 | 170 | ---
title: "R Notebook"
output:
html_document:
df_print: paged
---
#
#Universal mammalian clock2 of age
#
#author for developing clock2: Ake T. Lu
#author for developing clock1 and clock3: Zhe Fei
#
```{r}
rm(list=ls())
options(stringsAsFactors = F)
setwd('/Users/alu/Desktop')
myinput.list=readRDS('mydata_GitHub.... |
bd24e85bfc2a47835e0ed3079e057516f00bddfabb95a06db851a8fdb8594697 | R | 5,435 | 86 | run_LME<- function(dv, data, model, n_it){
# runs the model either with simple or extended set of covariates
if(dv=='log_ll'|dv=='asinh_ll' && model=="covariates"){
res <- lmerTest::lmer(formula = paste0(dv, '~ age_base + age_change +
SBP_base + age_change:SBP_base + SBP_... |
be583feaec5041efe9c7f426971288411ca28f1553815a8d89fff1ab3087e017 | R | 5,439 | 108 |
## As described in Li. C. Z. et. al. (2024), filter CpGs by detection p-values
## This code takes in all Mammalian 40K Array detection p-value output from Sesame normalization pipeline
## And take the median p-value (FDR adjusted) of each probe per species, and keep those with 85% of species Median value >= 0.05
## ... |
75a525b4b6691c5e6fecf12084a9346db698b9dc5fcbb466840dcb810e029602 | R | 5,449 | 163 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
# Load data ---------------------------------------------------------------
gtex_filtered <-
readRDS(here::here("results", "GTEx", "gtexv8_GBA_GBAP1_tpm.Rds")) %>%
dplyr::mutate(Organ = gsub("Brain - ",... |
92b0368b8dce6e38a95f20e03488d34e999d3f01271f8ead7c069d4d3bfd634d | R | 5,465 | 223 |
######## ADD DOCUMENTATION FOR ZENODO
# 2/8/21: Re-plotting survival curves for manuscript figure 6J
# (1) HCNT60-3K
library(survival)
setwd("~/Desktop/20210208-Survival_plots_for_Figures")
#### (1) HCNT60-3K input combined wells B02, C02, D02 inclusion plus
data = read.csv("HCNT60-3K_B02C02D02_inclusionplus.c... |
e5bd23bedfadd0ca37d064f6688754226c6456f3553a35b9e7e027be484d919a | R | 5,465 | 121 | library(data.table)
library(parallel)
simple_dot_prod <- function(i, this_path_binaries, this_phoenix_influence_scores){
(as.matrix(this_path_binaries) %*%
this_phoenix_influence_scores[sample(.N, replace = F),
influence])[1,1]
}
ecdf_fun <- function(x,perc){
ecdf(x)(perc)
}
... |
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