sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
3b34710ff061a7a4cc436a73af277133cdf1e0db5db18e2c47d5388ca8e2c74d | R | 14,584 | 569 | #' Compute the activity score of each TF based on the observed activity of its targets.
#' (Similar to ChEA3; it is based on a meta-analysis using 4 separate datasets)
#'
#' @param scores Matrix of genes x cell type/state
#'
#' @return Matrix of TF x cell type/state indicating inferred TF activity scores
#'
#' @example... |
34746dacf86fb513a21fa1375bb8bbc2016d94af9d35c9d720a9fe293ea1acf8 | R | 14,613 | 456 |
colTypes <- cols( .default = col_double(), `...1` = col_character())
table_tmp <- read_delim("/groups/umcg-bios/tmp01/users/umcg-pdeelen/nod2genotypes_1000g.txt.dosages.txt", delim = "\t", quote = "", col_types = colTypes)
dosages1000g <- as.matrix(table_tmp[,-1])
rownames(dosages1000g) <- table_tmp[,1][[1]]
rm(tabl... |
89c68fac1e04974f18d28ae1518a1c2f9642c346da1f9642daf323abdc4fd4d1 | R | 14,670 | 365 | #this code sees if upregulated DEGs and increased centrality can be comparable and draws Venndiagram for each
#there are very small DEGs...lets just see if these gene are actually in the non zero pos or neg diff ranked genes of networks
df.rank.cond <- read.table('/home3/junhacha/HumanNetv3/Velemeshev2019/condition_me... |
336e3ce19ad0e50d335ea9ce9a91c4cfb5da602ca42a2db609fa6403dba5951a | R | 14,681 | 368 | # TF expression and binding to the DHS, particularly intron
#load libraries
libs<-c("tidyverse","cowplot","GenomicRanges","data.table","reshape2", "broom", "ape", "ggtree", "readr","geiger","nlme","phytools","org.Hs.eg.db","DESeq2","flashClust","topGO", "biomaRt","vsn","GGally","pheatmap","RColorBrewer")
sapply(libs,... |
b4f239a9577f136b7dabaf54591bc6c1986d2216c2b3ea8a7df08037f36a9fb8 | R | 14,687 | 385 | # uses marker lists downloaded from Panglao and CellMarker
library(biomaRt)
human = useMart("ensembl", dataset = "hsapiens_gene_ensembl")
mouse = useMart("ensembl", dataset = "mmusculus_gene_ensembl")
# fix the table------------
# mgenes<-getBM(attributes = c("mgi_symbol"),mart = mouse)
# h2genes<-mgenes$mgi_symbol[gr... |
47d137d4332383303eef13475b9f3d445331fce42b4a86012130958d8c123b59 | R | 14,697 | 440 | library(MungeSumstats)
library(tibble)
library(tidyr)
library(readr)
library(purrr)
library(plyr)
library(dplyr)
library(stringr)
library(forcats)
library(tabulizer)
library(fuzzyjoin)
data(sumstatsColHeaders)
# Snakemake input & output
lambert.path <- snakemake@input[['lambert']]
kunkle.path <- snakemake@input[['kun... |
6da9a805d387defcf2dad6b75c60e35ba4f355018673ae35b0cdf13e22eeb0f0 | R | 14,751 | 303 | library(plyr)
library(ggplot2)
library(reshape2)
library(lme4)
library(lmerTest)
library(plotrix)
library(coin)
## read data file
cb.df = read.csv('data_pnas_chengetal2023.csv',header = TRUE)
cb.df$ROI.hm = paste(cb.df$ROI, cb.df$hemisphere,sep = "")
## calculating individual summed adjusted volume, cortical thickn... |
c1910a60462bc188f71fd7b08e32a6b816648cff71b3dc887fc54dc0bd3fac2e | R | 14,858 | 221 | ---
title: "Understanding the HCP ICA-FIX Pre-processing Pipeline"
author: "Damon Pham"
date: "1/01/2021"
output: html_document
---
```{r}
source("../analysis/0_SharedCode.R")
stopifnot(SharedCode_version == c(10,0))
scan_dir <- "/N/dcwan/projects/hcp/103818/MNINonLinear/Results/rfMRI_REST1_LR"
```
__Goal: to unders... |
7c3bf6eda3188a986eb448d524eb1f9c1aace8a1b411459d751aa7f101271944 | R | 15,206 | 317 | # Bayesian Data Analysis on Steady State Rates in OH family of tasks
# Based on code and analysis from "Doing Bayesian Data Analysis, 2nd Edition",
# John Kruschke, 2014.
# Copyright (C) 2022 Richard Hugh Moulton
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU G... |
5e55a6eafee18a0ca3963e861b18071af9cfc24c24fd3a74b844b58d37819179 | R | 15,214 | 360 | ###--------
### r.a.aguirre.gamboa
### DeconCell
### Functions
#' Pre-process RNASeq based expression data.
#' \code{dCell.expProcessing} Performes a TMM normalization, log2(counts+1) and scaling.
#'
#' @param count.table A table of quantified gene expression, samples by columns and genes by row.
#' @param trim boolea... |
2d13a101785cf04c1be4529edded8d80ea2403b5a5f9ef5c57f6ab0e046090d7 | R | 15,317 | 513 | #' A wrapper function to call all main functions of the ACTIONet
#'
#' @param ace Reduced `ACTIONetExperiment (ace)` object (output of reduce.ace() function).
#' @param k_max Maximum depth of decompositions (default=30).
#' @param min_specificity_z_thresh Defines the stringency of pruning nonspecific archetypes.
#' The... |
99105b7da5734be7272c4bf7f0c0dbc9f54d3fbe7c597fe3c038dde768c1b100 | R | 15,349 | 330 | ---
title: "Models with Mixed Response Types"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Models with Mixed Response Types}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, ... |
3d0331b11ba9c1db89a73b275cc8f899ada623d94ed30bd0a2cbb0a9f36dfab3 | R | 15,355 | 386 | # Tests for visualization functions
# library(SingleR); library(testthat); source("setup.R"); source("test-heatmap.R")
colnames(test) <- sprintf("cell_%i", seq_len(ncol(test)))
pred <- SingleR(test=test, ref=training, labels=training$label, genes="de")
test.exp <- assay(test,1)
training.exp <- assay(training,1)
test_... |
af10c85ef0e0157c058df5d5095a5bfd1e550558edb1a4a7caff45cd554616c7 | R | 15,357 | 407 | ---
title: "Semiparametric Latent Variable Modeling"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Semiparametric Latent Variable Modeling}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8... |
bfd5816985ea287d872a3bd84c0368c0275152a6996acdad074b92ee02449867 | R | 15,406 | 442 | ---
title: "Generalized Linear Mixed Models with Factor Structures"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Generalized Linear Mixed Models with Factor Structures}
%\VignetteEngine{knitr::rmarkdo... |
1b05326788acad9c14de5c7198d177932e863c4d7cf5012441c1686cded98dfc | R | 15,424 | 513 |
#remoter::server(verbose = T, port = 55556, sync = T)
remoter::client("localhost", port = 55556)#55556 55501
setwd("/groups/umcg-fg/tmp01/projects/downstreamer/depict2_bundle/")
library(readxl)
read.depict2 <- function(path, potential_traits=NULL) {
if (is.null(potential_traits)) {
potential_traits <- ... |
62ec97264ae72448a3090f1f982d86da780c208c1d3f935a54d7d9556203fd1f | R | 15,460 | 368 | ###################################################################################################################
#This script is based on and modified from Jeremy Miller's "Meta-analyses of data
#from two (or more) microarray data sets" tutorial
#Miller's tutorial and related files can be found at:
#https://... |
41caa9db0e2d5740d33efe0e4e99b2827769ec5c7884215c010e93a3b6135027 | R | 15,548 | 596 |
map.clusters <- function(Labels, clusters) {
N = length(Labels)
cluster.ids = sort(unique(clusters))
if (is.factor(Labels))
Label.ids = levels(Labels) else Label.ids = sort(unique(Labels))
W = sapply(Label.ids, function(label) {
idx1 = which(Labels == label)
n1 = length(idx1)
... |
829a5ccb841e3b8100545980b1c009ddcaaafb02f61086b90092a1bea1d549ba | R | 15,645 | 421 | getGenes <-
function(out_file_name, segmentation, pval_threshold, baf, ensembl_dataset,
mart_database, html, correction){
## This function generates the html file reporting the list
## (with the respective details) of each gene overlapping a significant region
eps <- 0.00000001;
message("Connecting to ... |
17f0b8f936920dd482c4ba264d1746009f9dbf55771ef00184a56a5f6ea34b18 | R | 15,697 | 516 | ---
title: "Customized_upset_plot"
author: "Chenxin Li"
date: "01/19/2023"
output:
html_notebook:
toc: yes
toc_float: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
This is a workflow to generate upset plots with custom aesthetics.
# Dependencies ... |
acc587719701a35972fdc4f94be0a2a9e3f3f452944191efe2b92e6555802288 | R | 15,701 | 311 | ---
title: "08_PRSxGREx.Brain"
output:
html_document:
df_print: paged
---
## Install packages #
```{r}
rm(list = ls())
options(stringsAsFactors = FALSE)
library(tidyverse)
library(lme4)
library(lmerTest)
library(ggseg)
library(sjPlot)
library(interactions)
```
## Load data #
```{r}
## Load data
load("ABCD.Rele... |
0ed30666752415296454ad06782e8699343b9f9b7520922461c0069fac89838e | R | 15,738 | 543 | #' @srrstats {G5.0} Datasets from PLmixed package are used for testing, and
#' results from the functions in this package are precomputed for comparison,
#' in cases where PLmixed and galamm support the same models.
#' @srrstats {G5.4} It has been confirmed that PLmixed returns the same results.
#' PLmixed is not... |
04f5b0b77d0ec8e44f335248d3fa81fafc73f0453fc27f2178250781b5de16b9 | R | 15,990 | 461 | #' Gets the logged journal of events for a future
#'
#' _WARNING: This function is under development. It can change at any time.
#' For now, please, do not depend on this function in a published R package._
#'
#' @param x A [Future] object.
#'
## @param baseline (POSIXct; optional) A timestamp to server as time zero
##... |
f41cc6f0fc1ef888ca5e14239c95ec9e4844bb3a1adcaa6a55f630fc438c5c7e | R | 15,993 | 195 | #!/bin/env Rscript
library(vegan)
library(foreach)
library(data.table)
library(coop)
write("Defining function distribution feature methods", stderr())
cosine_similarity = function(vec1, vec2){
num = sum(vec1 * vec2)
denom = sqrt(sum(vec1^2)) * sqrt(sum(vec2^2))
value = num/denom
return(value)
}
get_unique_c... |
9b34c11e29c7118a23eebbb09a4e7275d4d5c97b82a44b13469eb04564b1e879 | R | 16,042 | 517 | #' Computes feature (i.e. gene) specificity scores for each cluster
#'
#' @param ace ACTIONet output object
#' @param clusters Cluster
#' @param output_slot Name of the output in rowMaps(ace) to store results
#' @param renormalize.logcounts.slot Name of the new assay with updated logcounts adjusted using archetypes
#' ... |
4519cc1cadd24bfc723daa81a3be6cf22f6d61e3cb68799c334d57e7b4a662ed | R | 16,188 | 505 | isFALSE <- function(x) {
is.logical(x) && length(x) == 1L && !is.na(x) && !x
}
isNA <- function(x) {
is.logical(x) && length(x) == 1L && is.na(x)
}
assert_no_positional_args_but_first <- function(call = sys.call(sys.parent())) {
ast <- as.list(call)
if (length(ast) <= 2L) return()
ast <- ast[-(1:2)]
dots ... |
17a83b7e03f1888286f484e59829efa6d0995e32b30644bb21009003a11781ea | R | 16,216 | 466 | #START_OF_LICENSE_TEXT
######################
#License #
######################
# Copyright (c) 2020-2021 National Technical University of Athens
# Contact C. Gousiadou, email:cgousiadou@gmail.com
####################################
#https://opensource.org/licenses/BSD-3-Clause
#Redistribution and... |
6c8ef1bb50d6b51baa065fcbf23e2cecc9c9904972cf927cfa2a9aac9255e149 | R | 16,220 | 479 | ---
title: "Interactions Between Latent and Observed Covariates"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Interactions Between Latent and Observed Covariates}
%\VignetteEngine{knitr::rmarkdown}
... |
e356d061dfbc341e29004bbd99dad8d97d13e3d9c4c371e71aad4e41fa1ab38d | R | 16,371 | 445 | ---
title: "Linear Mixed Models with Factor Structures"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Linear Mixed Models with Factor Structures}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding... |
0491a93f0947b1994437cc0c9577195bb27b463a13be9c60832b4418197466ed | R | 16,414 | 436 | ###################################################################################################################
#This script has been modified from Jeremy Miller's "Meta-analyses of data
#from two (or more) microarray data sets" tutorial with only minor additions made
#Miller's tutorial and related files (inc... |
a3fd35c5f8dea96a82805b52a6749c5d36c1e553a6ce854b1699b6fe8d8d0d2e | R | 16,478 | 464 | #srun --cpus-per-task=20 --mem=200gb --nodes=1 --qos=priority --time=168:00:00 --pty bash -i
#remoter::server(verbose = T, port = 55556, sync = T)
remoter::client("localhost", port = 55001)#55556
library(DESeq2)
library(parallel)
library(viridisLite, lib.loc = .libPaths()[2])
library("havok")
setwd("/groups/umcg... |
2020b557deb62116f8ef0af80e2d39fbe1d8b4d8fc14f5ae3d55941384c236c6 | R | 16,521 | 264 | ---
title: "Misc TRAP analyses"
output: html_notebook
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
#load libraries
```{r}
library(tidyverse)
library(ggplot2)
library(ggfortify)
library(ggrepel)
librar... |
de98452a989641815bd0e5f432a529bb5d979d93e66139464e48cf1584c75a3d | R | 16,683 | 540 |
rm(list=ls());
options(echo=TRUE)
options(stringsAsFactors = FALSE);
library(WGCNA);
allowWGCNAThreads()
library(flashClust);
library(gplots)
library(RColorBrewer)
setwd(dirname(rstudioapi::callFun("getActiveDocumentContext")$path))
# load networks or start with input below
load("../Colon/processed_data/inputData.... |
d61ea659754ebd83293296366bf1566400e393fe9e29b06e60cd5cfd56477f4e | R | 16,697 | 378 | setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(readr)
table_tmp <- read_delim("pheno_9_66/pheno_9_geneMaxSnpScores.txt", delim = "\t", quote = "")
geneMaxSnpZscores <- as.matrix(table_tmp[,-1])
rownames(geneMaxSnpZscores) <- table_tmp[,1][[1]]
rm(table_tmp)
table_tmp <- read_delim("pheno_9_66/pheno_9_geneP... |
0743340442abc33b3c6e52859a6776d3cd8c449f15af9085812a52d490fd4853 | R | 16,920 | 533 | ---
title: "Customized_upset_plot"
author: "Chenxin Li"
date: "01/19/2023"
output:
html_notebook:
toc: yes
toc_float: yes
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
This is a workflow to generate upset plots with custom aesthetics.
This workflow use... |
28e8dca843c1afc606803ff367e882b311be6cf39ac7d9d322791315a63033b8 | R | 16,998 | 519 | #create an observed over expected matrix
obs.exp.matrix <- function( mat, correct=F, outlier.correct = 0.995, lowess = F ){
pos <- which(mat$z > -1, arr.ind=T)
val <- mat$z[mat$z > -1]
d <- abs(pos[,1]-pos[,2])
if(correct){
norm.factor <- tapply(val, d, function(x) mean( x[x < quantile(x, outlier.correct)] ) )
}... |
9a2ea6c2ec23ba87314aa953556118b3e300569aa81dda98080440339c23ac61 | R | 17,061 | 300 | library("optparse")
library(ggplot2)
library(ggpubr)
library(gridExtra)
library(matrixStats)
library(plyr)
library(data.table)
library(stringr)
# Get command line arguments
option_list = list(
make_option(c("-c", "--cisQTLs"), type="character",
help="eQTLProbesFDR0.05-ProbeLevel.txt.gz of cis-eQTLs", m... |
be696521c413a6bf3defe91f1799ed464d217f954f68fac6411aa435adf06f7c | R | 17,250 | 368 | library(oro.nifti)
library(neurobase)
library(dplyr)
library(readr)
library(stringr)
library(plotly)
library(AnalyzeFMRI)
library(oce)
library(R.matlab)
library(data.table)
library(reshape2)
library(ggsignif)
library(RColorBrewer)
library(boot)
library(limma)
setwd("/Users/philippehabets/Dropbox/Endo/fMRI.transcriptom... |
f0b398a792c5dbae188e9d9dee8192160c92279dd232e0db2a54c13b4393311c | R | 17,256 | 462 | library(here)
library(tidyverse)
library(lme4)
library(car)
library(emmeans)
library(performance)
library(ordinal)
library(RVAideMemoire)
# global settings
options(contrasts = c("contr.Sum", "contr.poly"))
theme_set(theme_bw())
font_theme <- theme(
axis.title.x = element_text(size = 20, face="bold"),
axis.title.y... |
2d60f23f33aec6ab8d55eb2b80a6bee74b11078f46bedff2b93ab6eb9cdb6641 | R | 17,535 | 430 | ```{r}
library(tidygraph)
library(ggraph)
source("downstreamer_functions.r")
setwd("network_plots")
source("data/source_olivier.r")
ensembl <- read.table(ensembl.file, sep="\t", header=T, stringsAsFactors = F)
ensembl <- unique(ensembl[,c(1, 5)])
rownames(ensembl) <- ensembl[,1]
ngwas <- 100
nhpo <... |
3722edb8de830f8c48d74264905269a6f6072653683f1595cbd0fcba0c50750b | R | 17,582 | 432 | ---
output: html_document
editor_options:
chunk_output_type: console
---
# Results
**Word limit**: `r wordcountaddin::word_count("03-results.Rmd")` of 1000.
The DCNN architecture with the highest top-1 accuracy `r df %>% pull('accuracy') %>% head(1) %>% signif(digits=3)` in the test set was `r df$Architecture[1]`... |
0fd1130e2e2a0ee99abec567226b797e328948b30473173dad05eff8154a2c39 | R | 17,637 | 581 | #' Resolve one or more futures synchronously
#'
#' This function provides an efficient mechanism for waiting for multiple
#' futures in a container (e.g. list or environment) to be resolved while in
#' the meanwhile retrieving values of already resolved futures.
#'
#' @param x A [Future] to be resolved, or a list, an ... |
aaf401e78943a15adbef00dd8b70bca5def21ae8e313c5232293d3fbfecc4b6e | R | 17,741 | 660 | ---
title: "Colon Crypt Respirometry"
output:
html_vignette:
df_print: "kable"
vignette: >
%\VignetteIndexEntry{Colon Crypt Respirometry}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
... |
72e47c50fc19d5b80455e867edfa86b9d932fd3851cfd49a856345defc557432 | R | 17,787 | 473 | library(lme4)
library(report)
library(texreg)
library(stargazer)
citation()
citation("report")
citation("texreg")
citation("stargazer")
# read the data
MVCs <- read.csv("MVCs.csv", header=TRUE, colClasses = c("integer","factor","factor",
"numeric","factor","fac... |
81790b85fb2d05ecbc72913599d92d09d359229edb9805d335c17e318fa3f399 | R | 17,894 | 473 |
# n.b. issue with biomaRt is preventing full reproducibility from raw data. The work-around which sets host = "https://dec2021.archive.ensembl.org/ results in one gene that is mapped from human to mouse differently (ENSG00000255439, VKORC1 And PRSS53 Readthrough is not mapped to mouse Vkorc1). The original annotation... |
95dcabdf7dfaccecba42f620059490695985ae80eddccf4e531385cb9ff92fd4 | R | 17,894 | 478 | #' A condition (message, warning, or error) that occurred while orchestrating a future
#'
#' While _orchestrating_ (creating, launching, querying, collection)
#' futures, unexpected run-time errors (and other types of conditions) may
#' occur. Such conditions are coerced to a corresponding FutureCondition
#' class to ... |
adab73419cb93397aea3bfc5fd906b223e3b83c89518c2ae331c301f71a5970b | R | 17,954 | 507 | # reticulate::use_python("/usr/local/bin/python3") # <-- this was necessary on my local system (disambiguates due the multiple versions of python installed)
require(tensorflow)
require(keras)
# re-train PCA models using specified gene lists present in target sample:
retrain_autoencoder_gtex = function(gene_list = NUL... |
a0a36b93d1852fabd0ca66b44a52fb3be5c6a4e26dc4263db0008c33b4172a94 | R | 17,985 | 441 | #this code reads GRNboost2 PIDC and SAVER output and compare them with HumanNet BC celltype network
library(igraph)
library(dplyr)
library(RColorBrewer)
library(UpSetR)
library(enrichR)
library(viridis)
library(grid)
library(ggplot2)
#Tcell
t.grnboost2 <- read.table('/home3/junhacha/HumanNetv3/Benchmark/GRNboost2/Tcel... |
992f7a5b27553049f6fb9e7f4746d6478c2495ccb7fe1b083bf8e0f220c4a485 | R | 18,079 | 282 | #!/bin/env Rscript
#
# Author: Alex Eng
# Date: 1/18/2018
###############################################################################
suppressPackageStartupMessages(library(argparse))
# Defining arguments and parser for command line arguments
parser = ArgumentParser(description = "Generate tables of measures sum... |
7e8777cd8fb98de53e6847c8d89dc1ad858c1bc0106874651ab0abed3f65d126 | R | 18,209 | 505 | ## AD Genetic Architecture for Neuron review
library(dplyr)
library(tidyr)
library(purrr)
library(janitor)
library(tibble)
library(glue)
library(stringr)
library(readr)
library(forcats)
library(glue)
library(ggrepel)
library(plotgardener)
`%nin%` = negate(`%in%`)
## Snakemake
### Input
# setwd("~/Dropbox/Research/Po... |
a78d49ccac1d11979b85835d94fcc18baebdae8f217a14facd62a57859b79433 | R | 18,212 | 449 | #' SortAddLLS
#'
#' @description Sort SCINET output edges alphabetically and add LLS weight derived from HumanNetv3 reference interactome
#'
#' @param Celltype.specific.networks SCINET output from function run.SCINET.clusters().
#' @param reference.network reference network input, default is HumanNetv3.
#'
#' @return L... |
34c513a8a95a3cf80aba68758df292e639cae23bd428c4e8504a9da9c1ce5c28 | R | 18,218 | 327 | #' buildref
#'
#' Function to build a RefCDS object from a reference genome and a table of transcripts. The RefCDS object has to be precomputed for any new species or assembly prior to running dndscv. This function generates an .rda file that needs to be input into dndscv using the refdb argument. Note that when multip... |
86d301cec5f09f10e51caa59a9fe492cdc5e225fc5d5176fae9e66753a12ef11 | R | 18,234 | 403 | commonfactor <-function(covstruc,estimation="DWLS"){
time<-proc.time()
#function to create lavaan syntax for a 1 factor model given k phenotypes
write.Model1 <- function(k) {
Model1 <- ""
for (i in 1) {
linestart <- paste("F1"," =~ NA*",colnames(S_LD)[i], sep = "")
if (k-i > 0) {
... |
c48e246c12d4720c15f92ffc6bb02a9bfc16a31456d2ee6fca5bdb078220c0a3 | R | 18,246 | 293 | #' codondnds
#'
#' Function to estimate codon-wise dN/dS values and p-values against neutrality. To generate a valid RefCDS input object for this function, use the buildcodon function. Note that recurrent artefacts or SNP contamination can violate the null model and dominate the list of codons under apparent selection.... |
4c772e9af543455afe6efac9395009a68dea19281b757b64eddda3ae09b38336 | R | 18,329 | 597 |
load("regInputData.rda")
source("myMultivarFunctions.R")
library(sjPlot)
library(easystats)
library(report)
library(limma)
library(MASS)
library(ggplot2)
library(corrplot)
library(knitr)
library(broom)
library(flextable)
# Striatum --------
mod.age<-NULL
for(mod in names(str.MEs)){
s.aso<-summary(lm(str.dat[,mod... |
11654563cdcbd5866cdeba086b5437b46953113923abd0866c0a15940c8856d0 | R | 18,511 | 644 | ---
title: "AmpSeqR report"
date: "`r date()`"
output:
html_document:
code_folding: "hide"
toc: true
toc_depth: 3
---
```{r setup, include=FALSE}
#knitr::opts_chunk$set(echo = TRUE,warning=FALSE)
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
knitr::opts_knit$set(eval.after = 'fig.cap')
```
... |
29cbdc6af9881183a014008fb0d3cba56dfbfaffa5be27a1f24f3f64580ac861 | R | 18,592 | 669 | ---
title: "10X_clk856_TH_20210928"
author: "Dylan"
date: "10/1/2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# laod libs, setup
```{r}
library( tidyverse )
library( gtools )
library( magrittr )
library( ggrepel )
library( cowplot )
library( patchwork )
library( Se... |
66275b0e89deeb5daf8d4fd2c93a74649b2e3502e0da91817cc7ee41b3811eb9 | R | 18,764 | 548 | setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(plotfunctions)
sampleUmap <- read.delim("Umap/umap2.txt")
ensg <- read.delim("ensgR75_protein_coding.txt", stringsAsFactors = F)
row.names(ensg) <- ensg$Ensembl.Gene.ID
str(ensg)
exp <- readRDS("31.07.pc1.illumina.wantedgenes.DESeqNorm_log2.duplicatesRemoved.... |
1a5fd68810e5c6cc8c4cbe1cf88118566b35faf95d3fb3bff71f7fdf24e35c68 | R | 18,768 | 735 | #' @srrstats {G5.0} Datasets from PLmixed package are used for testing, and
#' results from the functions in this package are precomputed for comparison,
#' in cases where PLmixed and galamm support the same models. In addition,
#' dataset "sleepstudy" from lme4 package is also used here.
#' @srrstats {G5.4} It h... |
f5ec8992a1763f7d4ce753513a4badaa6c8babfef88f10b940aac54493672e4a | R | 18,878 | 469 | ## HubMAP
## David Degnan, Pacific Northwest National Laboratory
## Last Edited: 10/06/2022
# Load libraries
library(dplyr)
library(purrr)
library(tibble)
setwd("~/Git_Repos/spatially-resolved-TDP/")
######################################################
## STEP 1: Collapse down TopPIC and TDPortal Output ##
#######... |
74abc0fd82835a04dcc4014a1ffacf4fa67558f9fc6dca8de05fa895c87af0d9 | R | 18,942 | 598 |
# setup ---------
library(Biobase)
library(limma)
library(edgeR)
library(DESeq2)
library(matrixStats)
library(MASS)
library(pheatmap)
library(tidyverse)
library(gridExtra)
library(grid)
library(WGCNA)
# setup directory structure ---------
dirSetup<-function(out.name){
# outname is the name of the main directory
... |
6326e6476dbf1e397a4e78f47ddc6efc927bbe5284dc2b5f7d665677fa48fa32 | R | 18,988 | 532 | ####################################################################################
#' The Pre-processing Data.
####################################################################################
#'
#' After input data, RISC preliminarily filter datasets by using three criteria:
#' first, discard cells with too low... |
9cfbabdf8fbb6c119d109d121254a9e834e8f53f89809c16f2d00090b2451db7 | R | 19,020 | 615 | #' Imports data from a 10X experiment folder and constructs an `SingleCellExeriment` object
#'
#' @param input_path Folder containing input files.
#' @param mtx_file Count file in Matrix Market format (default='matrix.mtx.gz').
#' @param feature_metadata Table of the same size as number of rows in the count matrix (def... |
a0eb89186099023fde6d7d0f4470e1312f8367c943c6eca8ac37cc69cd25714c | R | 19,583 | 575 | #remoter::server(verbose = T, port = 55557, password = "laberkak", sync = T)
remoter::client("localhost", port = 55557, password = "laberkak")
setwd("/groups/umcg-wijmenga/tmp04/projects/depict2/")
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(gatepoints)
#if(FALSE){
# #don't run by accident
# table_t... |
60b75e2dc783c19b16fef66bd49830a165adff1f6bc26533cd9526f2d11fd2e4 | R | 19,916 | 566 | library(ggplot2)
library(gridExtra)
library(readxl)
library(pheatmap)
library(RColorBrewer)
library(gridExtra)
library(data.table)
# Function definitions
# ------------------------------------------------------
# NPG friendly ggplot theme
theme.nature <- function(p, base_size = 11, base_family... |
09a95e84ba6311e18237bdbe5a38a97c98b007400dd0ba704747ab757dd57999 | R | 19,958 | 532 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("evaluating_coregulation")} else {setwd("../evaluating_coregulation")}
source("../downstreamer_functions.r")
source("data/source_olivier.r")
library(UpSetR)
library(tidygraph)
library(ggraph)
library(clusterProfiler)
library(org.Hs.eg.db)
library(ReactomePA)
... |
5ccfc036810323fe108939e144c203ca9b6798ed677217706868e302cbe54d04 | R | 20,140 | 564 | library(MungeSumstats)
library(tidyverse)
library(tabulizer)
library(fuzzyjoin)
library(plotgardener)
data(sumstatsColHeaders)
setwd("/Users/sheaandrews/Dropbox/Research/PostDoc-MSSM/ADGenetics/")
#Chang 2017
# chang_raw <- extract_tables("resources/Chang2017.pdf")
#
# chang_munged <- bind_rows(
# chang_raw %>%
#... |
86850d63f5d5d6b88b804becc628f2d711200b3c7b1dd9a0097c14e1344b1f36 | R | 20,203 | 510 | library(parallel)
setwd("/groups/umcg-fg/tmp01/projects/genenetwork/recount3/")
load(file = "Metadata/combinedMeta_2022_09_15.RData", verbose = T)
load(file = "Recount3_QC_2ndRun/PCA_Patrick/pcs.RData", verbose = T)
load(file = "Recount3_QC_2ndRun/PCA_Patrick/eigen.RData", verbose = T)
#save.image("predictionSession... |
dda5df19d3954620cbbd8f89c6bb3ab2d440d102cfdfd73472738ed9de9f8fdf | R | 20,523 | 483 | ## ---------------------------
##
## This script was created with R version 4.2.1 (2022-06-23)
##
##
## Title: Patient-specific identification of genome-wide DNA-methylation differences between intracranial and extracranial melanoma metastases
##
## Purpose of script: Helper functions for script MainScript.R
##
## Auth... |
9197d5e58b769a0a7f7e83e1fcee61d4e309a5dc6b660963a28ab4cd3191ef40 | R | 20,541 | 520 | #srun --cpus-per-task=20 --mem=100gb --nodes=1 --qos=priority --time=168:00:00 --pty bash -i
#remoter::server(verbose = T, port = 55556, sync = T)
remoter::client("localhost", port = 55508)#55506
library(DESeq2)
library(parallel)
library(viridisLite, lib.loc = .libPaths()[2])
library(preprocessCore)
setwd("/gr... |
c7a464952c17ad71343617df06ef2569f5d57a1f1542fa02bba63e14b39a3b32 | R | 20,595 | 415 | ---
title: "Optimization"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Optimization}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
``` r
library(galamm)
```
The purpose of th... |
1bb25632720dcf4ce0b8e0943202b7f9ee853814e37a00167353c275469636c9 | R | 20,636 | 550 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("evaluating_coregulation")} else {setwd("../evaluating_coregulation")}
source("../downstreamer_functions.r")
source("data/source_olivier.r")
enrichments <- read.downstreamer.enrichments("../data/final_paper_cov_correct/enrichments/",
... |
e3807884cbbc3e30c3fc9b0f55845de217a538938c480789a6e4fd356462a69d | R | 21,394 | 626 | # Genetic Architecture of Alzheimer's disease
library(dplyr)
library(tidyr)
library(purrr)
library(janitor)
library(tibble)
library(glue)
library(stringr)
library(readr)
library(forcats)
library(glue)
library(ggrepel)
library(plotgardener)
`%nin%` = negate(`%in%`)
## Snakemake
### Input
adgwas_loci.path = "results/a... |
d8a0a5c1049ab3800960c1d8b865e39f4c333fc12c85f07deec2e429d8ede3fb | R | 21,420 | 546 | library(tidyverse)
library(glue)
library(ggrepel)
library(plotgardener)
`%nin%` = negate(`%in%`)
setwd("/Users/sheaandrews/Dropbox/Research/PostDoc-MSSM/ADgenetics/")
ad_loci <- read_csv("/Users/sheaandrews/Dropbox/Research/PostDoc-MSSM/ADGenetics/intermediate/ad_loci.csv")
adgwas <- read_csv("/Users/sheaandrews/Drop... |
086d56d25ab3cf7a65086ccc7c01978a34116745c93fe49c47620a49836f0a8f | R | 21,461 | 520 | library(readr)
library(rlang)
library(tidyverse)
library(reshape2)
library(ggthemes)
library(rgdal)
library(spatstat)
library(nabor)
##A function, for images imaged at 40x obj, 3.5x digital zoom, 1-micron z-step size,
##to compile puncta detected by SynapseJ v.1
##input is the label of the image as a s... |
0b32708bca50a5f8a2cbce3775abe35a11745ef9c85059f1f3ad8e8ce8d357ad | R | 21,575 | 570 | #!/usr/bin/env R
#This script is designed to create a bubble plot of the proportion of predicted reads >95% cutoff
#per class from PACIFIC summary.txt files. It also plots data from Wesolowska-Andersen et al. 2017
#using PACIFIC and BWA-MEM.
#Load required libraries
library(tidyverse)
library(reshape2)
library(gridE... |
5e8601f889ff79f518420072ea452c1024ce9620fcc6bf2960dd57b96a01065c | R | 21,636 | 579 | ldsc <- function(traits, sample.prev, population.prev, ld, wld,
trait.names = NULL, sep_weights = FALSE, chr = 22,
n.blocks = 200, ldsc.log = NULL, stand = FALSE,select=FALSE,chisq.max = NA) {
time <- proc.time()
begin.time <- Sys.time()
if(is.null(ldsc.log)){
logtrait... |
63b8bed9643b658e09697634761cd6cdd0818c130500471b68eeb4c4b3b639c1 | R | 22,303 | 607 | ## Prelim ----------------------------------------------------------------------
source("0_SharedCode.R")
source("0_Analysis_helper.R")
stopifnot(SharedCode_version == c(11,0))
## Load mean FD-----------------------------------------------------------------
meanFD_fname <- file.path(dir_data_misc, "HCP_meanFD.rds")
if... |
940d555a4864957ae43e2ba7399b9746208f315b40153e0ca1d619a5f4d12f00 | R | 22,409 | 627 |
.reverseRows = function(Matrix)
{
ind = seq(from=dim(Matrix)[1], to=1, by=-1);
Matrix[ind,];
#Matrix
}
.extend = function(x, n)
{
nRep = ceiling(n/length(x));
rep(x, nRep)[1:n];
}
# Adapt a numeric index to a subset
# Aim: if 'index' is a numeric index of special entries of a vector,
# create a new inde... |
fb7b9918b691f3bf7463b403ca179db1c93bb982740e713a13edaf5f99efccce | R | 22,467 | 680 |
setwd(dirname(rstudioapi::callFun("getActiveDocumentContext")$path))
# NanoStringNorm needs to be installed from source
install.packages(pkgs="NanoStringNorm_1.2.1.1.tar.gz", type="source", repos=NULL)
library(NanoStringNorm)
## load data
load("NSraw.rda")
Nano_SampleInfo<-readRDS("NanoSampleInfo.rds")
ns.dat... |
a2f07f4a7530ca4857cae8f188e0c892b2ce3e1cbd2e0d5ca61a8bb0520b3e64 | R | 22,609 | 350 | #' withingenednds
#'
#' This function uses Poisson and Negative Binomial regression models at single-site level to study selection across different regions (coding and non-coding) within a gene.
#'
#' @author Inigo Martincorena (Wellcome Sanger Institute)
#' @details Martincorena I, et al. (2017) Universal patterns o... |
a566ca02259fcfce183bded11613f517cbcdf91c955b35b5b59f41d890028527 | R | 22,938 | 702 |
#remoter::server(port = 55556, sync = T)
#remoter::server(port = 54001, sync = T)
#ml R/4.2.1-foss-2022a-bare
remoter::client("localhost", port = 55556)#55556 55501 54104 54001
setwd("/groups/umcg-fg/tmp01/projects/downstreamer/")
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(readxl)
read.depic... |
a80bb5703e43e510daa2ac4c26b615cd80b4dd5ffd3f7ef4b1ae7731352e3c73 | R | 22,958 | 563 | require(treeio)
require(ggtree)
require(dplyr)
source("clean_functions.R") # contains no_()
beast218 <- read.beast("./BEAST2_analysis/beast_218_tree_asterophryinae.nex")
iqtree218 <- read.iqtree("./IQTREE2_analysis/iqtree_218_timetree_asterophryinae.contree")
d <- read.csv("Table1.csv")
if(dir.exists("out")!=T... |
93f762a9ee686c2f40289a30703e37d4def06c1ae52e15aa731333510d3b9dab | R | 23,112 | 699 |
#remoter::server(port = 55556, sync = T)
#remoter::server(port = 54001, sync = T)
#ml R/4.2.1-foss-2022a-bare
remoter::client("localhost", port = 54001)#55556 55501 54104
setwd("/groups/umcg-fg/tmp02/projects/downstreamer/")
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(readxl)
read.depict2 <- fu... |
fcaaf3063c5599cf0b884a9a6a78eb7e6e6bdbdf6eb309066236bcb6d2f923bd | R | 23,121 | 607 | # Utility programs for use with the book,
# Kruschke, J. K. (2015). Doing Bayesian Data Analysis, Second Edition:
# A Tutorial with R, JAGS, and Stan. Academic Press / Elsevier.
# This file contains several functions that are called by other programs
# or can be called directly by the user. To load all the functio... |
5dfb2e9a0696204d81968d4e41796c1245d79e69c0d1212cabac5741a75725c3 | R | 23,334 | 633 | ---
title: "Linear Mixed Models with Factor Structures"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Linear Mixed Models with Factor Structures}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding... |
497e63942212a168806af6046c2da8e70b966dcca224a8969d87a78c1f46250c | R | 23,335 | 711 |
#remoter::server(port = 55556, sync = T)
#remoter::server(port = 54001, sync = T)
#ml R/4.2.1-foss-2022a-bare
remoter::client("localhost", port = 54001)#55556 55501 54104
setwd("/groups/umcg-fg/tmp02/projects/downstreamer/")
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(readxl)
read.depict2 <- fu... |
5597eebb9784fdf167b807888e3be8b17a581b99a512ab025d1588cd7938d398 | R | 23,337 | 598 | #########
rm(list = ls())
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
##### generate a list of all 72 2-node circuit motifs (including redundant ones)
generate_motif_list <- function() {
motif_list = list()
motif_list[[1]] <- matrix(c(0,1,0,0), nrow = 2, ncol = 2)
motif_list[[2]] <- matrix(c(1,1,0,0)... |
e041dd92fe59acdad0d8fb1d562982d0ba15c1f4f58b1a2d38416ddad2973141 | R | 23,457 | 602 | #########
rm(list = ls())
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
##### generate a list of all 72 2-node circuit motifs (including redundant ones)
generate_motif_list <- function() {
motif_list = list()
motif_list[[1]] <- matrix(c(0,1,0,0), nrow = 2, ncol = 2)
motif_list[[2]] <- matrix(c(1,1,0,0)... |
00f9cf152d9bf3dcc6bca4664b2c0498ea984619a8647e013f56d3e6f834eb32 | R | 23,604 | 603 | #########
rm(list = ls())
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
##### generate a list of all 72 2-node circuit motifs (including redundant ones)
generate_motif_list <- function() {
motif_list = list()
motif_list[[1]] <- matrix(c(0,1,0,0), nrow = 2, ncol = 2)
motif_list[[2]] <- matrix(c(1,1,0,0)... |
10384473e8bf0a1819c44cf2e4ba51eaeb15c1200f63bcbf058d0810ca61ebca | R | 23,624 | 554 | #' Main trait pair analysis using LHC-MR
#'
#' @param SP_list List resulting from calculate_SP. Contains the filtered dataset (by every 'SNP_filter'th SNP), the starting points to be used in the pair trait optimisation, the traits' intercepts,
#' the traits' polygenicity if nStep = 2, as well as some extra parameters l... |
e797670c89f4723af7e9ec7a2c2e82b2a02fb725e15675b0c8e62f45fb420da2 | R | 23,811 | 491 | #' Train the SingleR classifier
#'
#' Train the SingleR classifier on one or more reference datasets with known labels.
#'
#' @param ref A numeric matrix of expression values where rows are genes and columns are reference samples (individual cells or bulk samples).
#' Each row should be named with the gene name.
#' I... |
0f437f1efcbb7ba138a7858865e29bc319a84ccbb457604d81eb3cad3377bf56 | R | 23,842 | 612 | #########
rm(list = ls())
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
##### generate a list of all 72 2-node circuit motifs (including redundant ones)
generate_motif_list <- function() {
motif_list = list()
motif_list[[1]] <- matrix(c(0,1,0,0), nrow = 2, ncol = 2)
motif_list[[2]] <- matrix(c(1,1,0,0)... |
3b2d2d811474275a328b119c8e1996a1d087aeef21c7815cd3942a4cac74bb6a | R | 23,871 | 616 | ############ stats for 2d enrichments ############
#########
rm(list = ls())
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
##### generate a list of all 72 2-node circuit motifs (including redundant ones)
generate_motif_list <- function() {
motif_list = list()
motif_list[[1]] <- matrix(c(0,1,0,0), nrow = ... |
740fc43df8120c7a93bcb5a7dbb412ee306d695981f04ff9a7faadd7f1dfac0b | R | 23,938 | 805 |
# Documentation -----------------------------------------------------------
#' @title Quantification of results
#' @name quantify
#'
#' @description A good amount of \code{discovery} objects can be quantified.
#' What exactly is to be quantified differs per \code{discovery} type.
#' \describe{
#' \item{saddle diso... |
1020e6125bebc5f5114f74b49ce4deaf7281450929eafaefb44d363440b095ce | R | 23,956 | 728 |
#MULTIVARIATE LOGISTICAL REGRESSUION#####################
# Load required libraries
library(readxl)
library(tidyverse)
library(ggplot2)
library(gridExtra)
library(reshape2)
library(FactoMineR)
library(factoextra)
library(dplyr)
library(ggeffects)
library(gtsummary)
library(flextable)
# Set directories
setwd("DIR")
da... |
5ec14a5d4b5e78f09099ee399ef6a965331a157008d1f2e6e065feec50b2155d | R | 24,108 | 475 | # For importing resultsanscript estimates.
library(tximport)
library(tximportData)
# For differential expression analysis.
library(edgeR)
# Import seqinr for editing fasta files.
library(seqinr)
# Load pheatmap.
library(pheatmap)
# For importing resultsanscript estimates.
library(tximport)
library(tximportData)
# For ... |
db1e5dc0c8259cbe8a3d1960aea410b81ddc2bff7fa31b193caf439914d83af1 | R | 24,268 | 569 | enrich <-function(s_covstruc, model = "",params,fix= "regressions",std.lv=FALSE,rm_flank=TRUE,tau=FALSE,base=TRUE){
time<-proc.time()
##determine if the model is likely being listed in quotes and print warning if so
test<-c(str_detect(model, "~"),str_detect(model, "="),str_detect(model, "\\+"))
if(any(test) !=... |
048ab1b1677f704a79797bfda26556c4fcf960bed9dd47681782e78ce61fe826 | R | 24,276 | 647 | ##---------------------------------------------------------------
## clean up and cluster variants based on allele frequency
##
sciClone <- function(vafs, copyNumberCalls=NULL, regionsToExclude=NULL,
sampleNames, minimumDepth=100, clusterMethod="bmm",
clusterParams="no.apply.ov... |
4a0e60b8a387dcb20e1a63664f25f2990c20e706f95db7171e4c46ec0698b2db | R | 24,391 | 561 | #' Retrieves global variables of an expression and their associated packages
#'
#' @inheritParams globals::globalsOf
#'
#' @param expr An \R expression whose globals should be found.
#'
#' @param envir The environment from which globals should be searched.
#'
#' @param tweak (optional) A function that takes an expre... |
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