sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
de4ea67066e958e3b7d5669b7cbf4137bee068c43407511c79a0971f955083a7 | R | 1,654 | 24 | ### DTS script to attempt to pool some PI data into two groupsif there are more than two groups
PoolGrps=FALSE
if(NofGroups>2 & length(unique(groupdescriptions))==2 & PIs_present){
PoolGrps=TRUE #we have several groups, but only one control and one experimental group
#find out which group belongs to which pool
... |
630b7dbbcab99d7a59d8e87d432baf88efb4ca397e1fd5a566b402744926a99c | R | 1,657 | 53 |
# Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
# Load data ---------------------------------------------------------------
prop_ers_per_tissue <- readr::read_csv(
here::here("results", "ers", "prop_ers_per_tissue.csv")
)
# Main -----------------------... |
a34d16e6519d818c3230ae32a6dffe9feab54e6d39d47e3989c6e358c23dfe8c | R | 1,657 | 62 | ## Preprocessing data of Velmeshev et al., Science (2023)
rm(list = ls())
library(tidyverse)
# Lineage and branch-specific genes.
df = readxl::read_excel('~/Dropbox/CWAS_paper_WD/Data/raw_annotations/Velmeshev2023_TableS2.xlsx')
#df %>%
# group_by(lineage) %>%
# dplyr::count() %>% View()
table(df$lineage)
mge = c('... |
4d2fb9255c051232a557f415c5cb356d817072ffa52c77181a6ebb71f42dc917 | R | 1,658 | 67 | #!/usr/bin/env Rscript
library(MOFA2)
library(argparse)
library(tidyverse)
get_args <- function() {
# create parser object
parser <- ArgumentParser()
parser$add_argument("--dataset-name", type = "character", default = NULL, help = "dataset key from params.yaml to use")
args <- parser$parse_args()
... |
f3a7de64221dce334b85a59de1013d5b2e9054ac412e3106caf9f65338117299 | R | 1,658 | 50 | #' Extract Model Comparison Estimates
#'
#' @param model_data Object returned by \code{'hBayesDM'} model function
#' @param ic Information Criterion. 'looic', 'waic', or 'both'
#' @param ncore Number of cores to use when computing LOOIC
#'
#' @importFrom loo relative_eff loo waic
#' @importFrom posterior as_draws_array... |
e44b34637bef612bd5006765bd7b7ffd11b4dd220eb170c3a378b1d889624abd | R | 1,665 | 42 | #!/usr/bin/env Rscript --vanilla
#load the libraries
library(tidyverse)
library(proBatch)
#load the data
#TODO: update "m_score" according to proper JPP outputpro
good_columns =c("peptide_group_label", "filename", "Intensity", "m_score",'ProteinName')
cols_to_get <- rep(list(col_guess()), length(good_columns))
names(... |
c5a6cc50036e48af4900300b1b167f14a7bba939783a2d65d38bce055bdcafef | R | 1,670 | 33 | #!/usr/bin/env Rscript --vanilla
#load the libraries
library(readr)
library(dplyr)
library(stringr)
#load the given annotation
sample_annotation_original <- read_delim('data_PanCancer/1_original_data/annotation_col_anova_PCP_final.txt', delim = '\t')
#adapt sample annotation
sample_annotation_PanCancer = sample_anno... |
6c5e802ec1cbda1b5468d440555e6ca3a0d3ad58e79bc9976c8d83cd91748b1d | R | 1,674 | 43 | ## Summarizes data.
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
## data: a data frame.
## measurevar: the name of a column that contains the variable to be summariezed
## groupvars: a vector containing names of columns that contain grouping variable... |
22c30c18276599b590324e6755155dbb98aae3a0473fae5f787acf9808a0fc00 | R | 1,675 | 45 | #9 Macrophage
# 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','org.Hs.eg.db')... |
f94816d68c4bca9575c431f4df3c299bb5ce832b34fcb27ff215003c604edb4e | R | 1,679 | 63 | ##
## subjective arousal data
##
library(ProjectTemplate)
load.project()
arousal.long <- arousal %>% gather(condition,arousal,-Participant) %>%
separate(condition, into=c("condition","time"), sep="_") %>%
mutate(condition=relevel(factor(condition), ref="BL"),
time=relevel(factor(time), ref="before"))
a... |
f1f2dd6f9518eba0d4603530b595fff99e5ae5f872e16a58904de7c58acc804b | R | 1,685 | 52 | library(UpSetR)
library(jsonlite)
library(yaml)
#library(ggplot2)
#library(tidyverse)
build_combinedModuleList<-function(stages){
#Parameters
# stages:named list of modules containing filepath to model module json file containing gene/protein names/symbols
combined_modules<-c()
for (stage in names(stag... |
6a0f1241352f6b128b0fa8c4f2101bd0866b889c036ebd33fe74c1b3d00b338f | R | 1,688 | 47 |
# Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
# Load data ---------------------------------------------------------------
df_lncRNA_genes_IDB_tidy <-
readRDS(
here::here("results", "intron_db", "prop_genes_IDB_threshold_5.rds"))
# Main ---------... |
ed6b6d64bc6ebc0ec6f9ea1d806433ac3a308d6b4c9147a05013642afcdf7200 | R | 1,688 | 41 | #' @templateVar MODEL_FUNCTION ug_bayes
#' @templateVar TASK_NAME Norm-Training Ultimatum Game
#' @templateVar TASK_CODE ug
#' @templateVar MODEL_NAME Ideal Observer Model
#' @templateVar MODEL_CODE bayes
#' @templateVar MODEL_CITE (Xiang et al., 2013)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMN... |
28ad519fdbf212154d59f569376f94e2dd8cda547e50f816f322d9986268f830 | R | 1,690 | 53 | wiggle2Hilbert <- function(wiggleRle, image.dir, datName) {
# Requires an rle object imported using rtracklayer::import.bw - v.slow and
# lots of memory required for bp resolution bigwig files.
# remove non-canonical contigs
# generate hilbert curves and plot as png - save in image.dir
suppressPackageS... |
37bc3b2fe5696e3ca5fb39774e301e270d8875f1789f3f656b046ef3d88674b4 | R | 1,699 | 53 | #3 Annotation
# 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'))
# Annotation---------------------------... |
34604b19dd84ce696c6c8bc8e857b6fa57ab680196b6e30126ce0429ba52f09c | R | 1,701 | 26 | #' Fit generalized linear model under the null hypothesis for unrelated samples.
#'
#' The \code{fit_null_glm} function is a wrapper of the \code{\link{glm}} function from the
#' \code{\link{stats}} package that fits a regression model under the null hypothesis for
#' unrelated samples, which provides the preliminary s... |
e5cfbb4279c470e78740da81611006953fd21a813809de2ee84593a742c3fb9c | R | 1,706 | 54 | #' Datasets with various missing data patterns
#'
#' Four simple datasets with various missing data patterns
#'
#' Van Buuren (2012) uses these four artificial datasets to illustrate various
#' missing data patterns.
#'
#' @name pattern
#' @aliases pattern1 pattern2 pattern3 pattern4
#' @docType data
#' @format \descri... |
888e81375816bface3d67a4cedd172b69724994fe4d4bd9666dacb3d072e5966 | R | 1,711 | 47 | data('factRsample')
samples <- c("SampleA.Rep1", "SampleA.Rep2",
"SampleB.Rep1", "SampleB.Rep2")
# simulate library size
set.seed(1987)
lib.sizes <- sample(10000:14000, size = length(samples))
# get list of transcripts
txsData <- featureData(factRsample[["transcript"]])
txs.length <- txsData$width
txs.n... |
32a3d98dbc07b3393602cf174de524367a91abfccc2cd5f556b70df974e62e4e | R | 1,712 | 41 | ##' @title Filter out duplicated samples
##' @description Filter out samples that are sequenced for two or more times
##' @param metadata metadata parsed from \code{\link{gdcParseMetadata}}
##' @return A filtered dataframe of metadata without duplicated samples
##' @export
##' @author Ruidong Li and Han Qu
##' @example... |
8693b677e93a05cc3796c7fb037248061e77d85b6287dfa9893bb62f7f7d1a99 | R | 1,715 | 57 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/gene_counts_unfiltered.tsv", sep="\t", row.names="gene_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed/04.deseq2/e... |
4f31d54aa026faded0b945cbba5bbc91c84e41bc5675ee74bb17db8ac0dbe3c0 | R | 1,717 | 51 | args = commandArgs(TRUE)
eta=as.numeric(args[1])
max_depth=as.numeric(args[2])
gamma=as.numeric(args[3])
min_child_weight=as.numeric(args[4])
nrounds=as.numeric(args[5])
data_folder=as.character(args[6])
sex_split=try(as.logical(args[7]))
BrainAgeFull = function(eta, max_depth, gamma, min_child_weight, nrounds, data... |
60959b975b9ee5adeef493301a207e37825ebe16955a5f7d198137a891fd4d8e | R | 1,717 | 56 | #' Expand axis limits
#' @description Force to extend the coordinate range of the ggplot object.
#' @param x,y NULL (default) or numeric vector.
#' @rdname expand_axis
#' @examples
#' quickcor(mtcars) + geom_square() + expand_axis(x = -3)
#' quickcor(mtcars) + geom_square() + expand_axis(y = 16)
#' @author Houyun Huang... |
62ef8eeb942ca7596c5d80e0b7b2d77c5e6ea8b46a8a383f70d12ecb795cfc44 | R | 1,718 | 49 | ---
title: "Transcript_profile"
---
This section of the report details all of the profiles that have been collected and collapsed accross the transcript profile.
```{r Transcript_profile, comment=FALSE, echo=FALSE, cache=FALSE}
library(knitr)
library("RSQLite")
library("DBI")
library(stringr)
library("rmarkdown") # ... |
15b74c8d030971c130db1097b5febabab6bf170c67594c4b9a2be30c613cb39e | R | 1,721 | 53 | #### Functional enrichment analysis with g:Profiler
library(dplyr)
library(gprofiler2)
save_ggplots <- function(fn, p, w, h){
for(ext in c('.pdf', '.png', '.svg')){
ggsave(paste0(fn, ext), plot=p, width=w, height=h)
}
}
load_eqtl <- function(){
fn <- "../../_m/BrainSeq_sexGenotypes_4features_3reg... |
3b2362b6a094bd9c08d72142056c284ae748fb1ee95ee5a4ef942be814781c1e | R | 1,727 | 23 | ### DTS script to Initialize empty lists where data are collected
grouped.PIprofiles <- list() #PIProfile data frames in a list of length NofGroups
grouped.Categories <- list() #For saving categories within groups
grouped.PIcombined <- list() #For categorical color coding of PIs
grouped.periods <- list() #Pe... |
79ac10734512787c9c13c16105524690c15b16e20580fb564ec12c974e14bb89 | R | 1,730 | 44 | # this top-level script may be used to run (knit) the individual
# Rmd files
# set up output path for the reports
config <- yaml::read_yaml("config.yaml")
# # example 1: knit one specific file
# rmarkdown::render(input = 'Rmd/example.Rmd',
# output_dir = report_dir,
# knit_root_dir... |
0cd89e3da125e8b89aae814ae2db014a6e81f25f38ad9927d6cf91f839073bcd | R | 1,731 | 36 | ##
## descriptive plots for paper
##
library(ProjectTemplate)
load.project()
## ACC
learn %>% filter(ACC>=0) %>% group_by(Participant, condition, pair) %>%
summarise(ACC=mean(ACC),RT=mean(RT)) %>% group_by(condition,pair) %>%
summarize(mACC=mean(ACC),semACC=sem(ACC), mRT=mean(RT), semRT=sem(RT)) %>% ungroup %>%... |
dabfbf48a267b6461f5795572f9aa9e37b0c4f46740bca1d5f4a5ecce1a9c6c2 | R | 1,731 | 71 | # ---- D1 ----
imp <- mice(nhanes2, print = FALSE, m = 10, seed = 219)
fit1 <- with(
data = imp,
expr = glm(hyp == "yes" ~ age + chl, family = binomial)
)
fit0 <- with(data = imp, expr = glm(hyp == "yes" ~ 1, family = binomial))
empty <- with(data = imp, expr = glm(hyp == "yes" ~ 0, family = binomial))
# stat1 <-... |
2a3a7b32d87279d403ae81f5b7050946ab6f429b93f8a97c69afc44fdee3bbc5 | R | 1,732 | 34 | ###DTS script to create superimposed histograms from the test periods of two groups
if (NofGroups==2){
if('yt' %in% sequence$type || 'sw' %in% sequence$type) #for yt or sw experiments, collect fly data
{
if(x==1){
histo1 <- data.frame(pooled.data[[learningscore]][["fly"]])
histo1$v2 = groupnames[x]... |
fa117e2fcec1a8830d965cfda3999df12e3114412bd21d76e839a3464ff513c6 | R | 1,735 | 54 | ## mean transition probabilities ##
act_id_1 <- sc_5@active.ident
act_id_2 <- sc_6@active.ident
rv_1 <- nsc1_tp_5 #Step 3 result for control
rv_2 <- nsc1_tp_6 #Step 3 result for TBI
exp_1 <- "Control"
exp_2 <- "TBI"
int_cl_name = "NSC-stage1" #interested cluster name
neighbours = c("NSC-stage1","NSC-stage2","RG-l... |
a624a5866a149a078306923de7006dc1a0e289712d627d22cf923d0cd8dff7a1 | R | 1,736 | 52 | test_that("QUICKPRED-001: returns square binary matrix", {
predictorMatrix <- quickpred(nhanes)
expect_true(is.matrix(predictorMatrix))
expect_equal(nrow(predictorMatrix), ncol(predictorMatrix))
expect_in(predictorMatrix, c(0, 1))
})
test_that("QUICKPRED-002: mincor supports scalar, vector, matrix", {
n_col... |
1016d955709e4e122ac0061a0cbb4b78f2334291cc5101a235ba74d0b5f25d44 | R | 1,742 | 59 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/gene_counts_unfiltered.tsv", sep="\t", row.names="gene_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed/04.deseq2/e... |
7be29b653fe476e1be8d1dd35b3285830fb60ec92783231397915ba22abd3b10 | R | 1,746 | 57 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/gene_counts_unfiltered_no_introns.tsv", sep="\t", row.names="gene_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed/... |
cf35e8195f4cae25cbd82e33d8014db827becb6cfceaba30588c096000fbe558 | R | 1,752 | 77 | ## Plot frequency of intersecting responses for timepoint by tissue
library(UpSetR)
library(tidyverse)
library(magrittr)
library(janitor)
ALPHA = snakemake@params[["ALPHA"]]
MIN_LOGFC = snakemake@params[["MIN_LOGFC"]]
filtered_results <-
snakemake@input[[1]] %>%
# here::here("results/limma_maternal_lung/fold_c... |
fcec54888bdd6037290bb98f97e3e416f5ac767f82700768c56f10109cb72284 | R | 1,752 | 44 | ## Download the supplementary table for Darbandi et al., bioRxiv (2023) and convert it to bed files
## https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE248876
library(tidyverse)
d0 = readxl::read_excel('~/Dropbox/Resources/Human_5TF_20230829/TableS3_5TRa_Peaks_May23.xlsx', sheet = 'Human_5TRa_Wide')
table(d0$pro... |
9525c36a06e249068aefb0f1b1169fb1aeeada9a7a8918d6da3f92fbb1bc3fde | R | 1,756 | 64 | #' Corece to a graph_tbl object
#' @description Functions to coerce a object to graph_tbl if possible.
#' @param x \code{R} object.
#' @param directed logical value, whether or not to create a directed graph.
#' @param ... extra params.
#' @return tbl_graph object.
#' @importFrom tidygraph tbl_graph as_tbl_graph
#' @r... |
fc6bbae6a6c0f39dfcb9982e71dcafa5a7c8abef0199f5120e2861ff51cb7883 | R | 1,760 | 60 | #' Complete case indicator
#'
#'
#' The complete case indicator is useful for extracting the subset of complete cases. The function
#' \code{cci(x)} calls \code{complete.cases(x)}.
#' The companion function \code{ici()} selects the incomplete cases.
#'
#' @name cci
#' @param x An \code{R} object. Currently supported ar... |
32acd0e8c6478e551e1e2643446fb71f77b5326efeca50b82c6df08c48da2a8e | R | 1,762 | 59 | ## caching single variables based on filename
## http://stackoverflow.com/a/15373917/972791
this.file.name <- function() {
cmdArgs <- commandArgs(trailingOnly = FALSE)
needle <- "--file="
match <- grep(needle, cmdArgs)
if (length(match) > 0) {
# Rscript
return(normalizePath(sub(needle, "", cmdArgs[matc... |
23866c3f288ac980c62c976ae7a2a5326a6daef210ab6bf4727c11a489635179 | R | 1,764 | 58 | ## This script generates plots for GSEA analysis.
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
})
save_plot <- function(p, fn, w, h){
for(ext in c('.svg', '.pdf')){
ggsave(file=paste0(fn,ext), plot=p, width=w, height=h)
}
}
get_top_GO <- function(tissue){
new_tissue <-... |
873ad4604f6edfbb32500b2745480196035fc92cd42ab40f6efe09bce0c3d470 | R | 1,766 | 67 | #!/usr/bin/env Rscript
source(here::here("src/shared/R-helpers/get_settings.R"))
source(here::here("src/shared/R-helpers/cohort_to_factors.R"))
source(here::here("src/shared/R-helpers/deseq2_analysis.R"))
suppressPackageStartupMessages({
library("dplyr")
library("readr")
})
settings <- get_settings(omic = "rnas... |
2e060caffce2858e23ebb154c69b43af76c38845ab03e9ae808bb99a2a9d557d | R | 1,774 | 54 | #############################################################################
#
#Updated by: Yiqin Wang
#TODO
#to add similiar functionalities in annot
#
##############################################################################
args <- commandArgs(trailingOnly = TRUE)
var <- read.table(args[1], header = T, sep="... |
40f2e3e650e54c194413c71ada7b473faef3a35d4f4ca281088f4fbdb7d3d0eb | R | 1,780 | 42 | library(scMarkerViz)
library(ggplot2)
library(dplyr)
# Cell type colors (consistent with 07_plot.R)
celltype_cols <- c("Oligodendrocytes" = "#E3EDC0", "Microglia" = "#F2CA8D", "Astrocytes" = "#C1E6F3")
csv_pattern <- 'all_DEGs\\.csv$'
fc_cutoff <- 0.25 # |log2FC| threshold
pval_cutoff <- 0.05 # p-value threshold
... |
ae9a2bcf065d6d8e532cb26b297f861fa2687ae6ebfd20dfc5f3dd145f84f6fc | R | 1,786 | 48 | library(dplyr)
library(tibble)
# Make sure R's working directory is in the correct spot to make relative paths resolve correctly
if (rstudioapi::isAvailable()) {
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
} else {
# If running as a script, finding the file is harder
# https://stackoverflow.com/a... |
7fc05ff4fa344c980d5040230c27109e114c58e6e8ee4ab8eea7eb667ff0113f | R | 1,787 | 68 | set.seed(1)
td <- matrix(rnorm(20), nrow = 5, ncol = 4)
dimnames(td) <- list(1:5, LETTERS[1:4])
y <- td[, 1]
ry <- rep(TRUE, 5)
# data frame for storing the event log
.mice.state <- list(it = 0, im = 0, dep = "y", meth = "test", log = FALSE)
loggedEvents <- data.frame(it = 0, im = 0, dep = "", meth = "", out = "")
f... |
5045c060ef93bacbae9ffd4369671c8062c484f15ee24fce61af7d11f37b3e17 | R | 1,789 | 70 | #' Creates a \code{visitSequence} argument
#'
#' This helper function creates a valid \code{visitSequence}. The
#' \code{visitSequence} is an argument to the \code{mice} function that
#' specifies the sequence in which blocks are imputed.
#' @inheritParams mice
#' @return Vector containing block names
#' @seealso \code... |
ccaac8a8a6ac018786c30026d2a2bca54303f04c44053008bab857e4f22d83d8 | R | 1,790 | 57 | #!/usr/bin/env Rscript
source(here::here("src/shared/R-helpers/get_settings.R"))
source(here::here("src/shared/R-helpers/cohort_to_factors.R"))
source(here::here("src/shared/R-helpers/deseq2_analysis.R"))
suppressPackageStartupMessages({
library("readr")
})
settings <- get_settings(omic = "srna", stage = "deg", da... |
e7a0d037bb450d7dd680269b6d5112d5306640cfdaa258e28b33dfea5b8ceaf6 | R | 1,790 | 46 | #' Plots the histogram of MCMC samples.
#'
#' @param sample MCMC samples
#' @param title Character value containing the main title for the plot
#' @param x_lab Character value containing the x label
#' @param y_lab Character value containing the y label
#' @param x_lim Vector containing the lower and upper x-bounds of ... |
edfceecee0bdf56757e39c0db38ecc4ef6cd1538d82baf853dda6f576840aa12 | R | 1,790 | 65 | #' Corece to a igraph object
#' @description Functions to coerce a object to igraph if possible.
#' @param x \code{R} object.
#' @param directed logical value, whether or not to create a directed graph.
#' @param ... extra params.
#' @return igraph object.
#' @importFrom igraph graph_from_data_frame as.igraph
#' @rdna... |
3da4a2f91ec7c277b155b1295654d8b6fb6d9b9c72c1ec898b7befcbda5b8e12 | R | 1,794 | 51 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
# Load data ---------------------------------------------------------------
# Parent genes
parentgenes <- readRDS(here::here("results", "pseudogenes", "parentgene_annotated.rds"))
# Main --------------------... |
96ec80c268d3ea464511a15495098092ae7219e69910fd44a2f045cf255c53cd | R | 1,802 | 49 | #install.packages("BiocManager", repos = "https://cran.r-project.org", dependencies=TRUE)
#install.packages("dplyr", repos = "https://cran.r-project.org", dependencies=TRUE)
#install.packages("tibble", repos = "https://cran.r-project.org", dependencies=TRUE)
#install.packages("tidyr", repos = "https://cran.r-project.or... |
c3050b938a79adac98e2543f104db7af9cd85146d8905468e4fe0326166d5a4c | R | 1,807 | 73 | #
# regression model with random intercepts per subject for RTs
#
library("ProjectTemplate")
load.project()
library(rstan)
# enabls --force for stallo etc.
options <- commandArgs(trailingOnly = TRUE)
if( "--force" %in% options)
uncache.all()
bname<-tools::file_path_sans_ext(basename(this.file.name()))
mod.fname=spr... |
4b65c2b7fbfed43dc36759f67ca6a67c97c1ea399f77b5d184ce4d4acfc89af5 | R | 1,808 | 41 | #' @templateVar MODEL_FUNCTION ug_delta
#' @templateVar TASK_NAME Norm-Training Ultimatum Game
#' @templateVar TASK_CODE ug
#' @templateVar MODEL_NAME Rescorla-Wagner (Delta) Model
#' @templateVar MODEL_CODE delta
#' @templateVar MODEL_CITE (Gu et al., 2015)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_... |
3e01ae776231fdd158f3bfd1289e4815cc1568bdb3acc36b5215742c5f75219d | R | 1,816 | 53 | #2 Normalization, Dimention reduction, Batch correction
# 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',... |
a1f1c152cfa1e63ee08f513cec6da3d350fae0229e782f8623a46d8b7844d2be | R | 1,829 | 62 | #' Compare several nested models
#'
#' @rdname anova
#' @param object Two or more objects of class \code{mira}
#' @param method Either \code{"D1"}, \code{"D2"} or \code{"D3"}
#' @param use An character indicating the test statistic
#' @param ... Other parameters passed down to \code{D1()}, \code{D2()},
#' \code{D3()} a... |
d420b3a8542ec720f46a9926ebebaf4be300ffc1c935ff1fecf4e7e7d7aff12e | R | 1,849 | 45 | #' Function for extracting Rhat values from an hBayesDM object
#'
#' @description
#' A convenience function for extracting Rhat values from an hBayesDM object. Can also
#' check if all Rhat values are less than or equal to a specified value.
#' If variational inference was used, an error message will be displayed.
#'
#... |
b6d5b03eb4c783609aacbb6b57407529289c1fb1659640caca758dbd22cfa712 | R | 1,850 | 39 | getwd()
df = read.csv("df_exp1.csv")
head(df)
library(ggplot2)
library(ggsignif)
df$Model=factor(df$Model,level=c("NetTCR-1.0","NetTCR-2.0","ERGO_LSTM","ERGO_AE","DLpTCR","HeteroTCR"))
p = ggplot(data=df,aes(x=Model,y=AUC,fill=factor(Score)))+
labs(x="",y="AUC",fill="VDJdb confidence score")+
stat_boxplot(g... |
d5d8332d1da70b7d9d4aab36f17918f2fff90dff50cb03cd0bf7b828d9b7b659 | R | 1,852 | 50 | ## This script runs a specific chunk
suppressPackageStartupMessages({
library(argparse)
library(mashr)
library(dplyr)
})
save_results_chunk <- function(chunk_num, outdir){
## Load data
load("model_variables.RData")
load(paste0(outdir,"/chunk_",chunk_num,"_bhat_shat.RData"))
## Run mash on c... |
7696e46258512f704d12b1bfb8d2157c81b57397fb29f50f80c53d5a510f5e32 | R | 1,856 | 72 | ---
title: "Mapping quality"
---
This page details the mapq results following running the tool bamstats.
```{r mapq_plot, echo=FALSE}
library(knitr)
library("RSQLite")
library("DBI")
library(stringr)
library("rmarkdown") # Make cool dynamic documents
library("knitr") # Run R Code Chunks
library("ggplot2") # For plo... |
55eb855ff00eb0bff0090f85845528bc679dd4185079c6eefeb57337dd0022a3 | R | 1,862 | 50 | #1 Data preparation, Quality control
# 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'))
# make seurat ob... |
3ed3d8846aecd9d672361f50cf71bb6b9c88d9d78a54909c7a6b6d53cea406b7 | R | 1,865 | 65 | ---
title: "Bcell PHX postprocessing"
author: "Jonas Fischer"
date: '`r Sys.Date()`'
output: html_document
---
```{r setup, include=FALSE}
require(ggplot2)
require(ggthemes)
library(ggrepel)
require(viridis)
require(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
library(pathview)
```
## B cell GRN analysi... |
cfbda91e9cc52af62b39cea85593062b7d4dd324842ab5ac924a4aeae6eb2b9a | R | 1,867 | 63 | get.dfcom <- function(model, dfcom = NULL) {
# Input: a fitted model
# residual degrees of freedom of model fitted on hypothetically complete data
# assumed to be the same across imputations
if (!is.null(dfcom)) {
return(as.numeric(max(dfcom, 1)))
}
# first, try the standard df.residual() function
d... |
332b6b6278506a9aa5fcd5ccb562d659d6c17fddbbcffa0b3e9b370f70c1ef49 | R | 1,869 | 49 | set.seed(66322)
y <- popmis$texp
y[rbinom(length(y), size = 1, prob = 0.5) == 1] <- NA
x <- popmis[, c("pupil", "school", "sex")]
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))
type <- c(1, -2, 1)
yn <- y
test_that("MICE-IMPUT... |
4022d84f0b61e1fef7936a1d01491821d125179e74f4a9ca5b8cfa307316e153 | R | 1,869 | 85 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
library(ggtranscript)
library(plyranges)
# Load data ---------------------------------------------------------------
probes <-
read_csv(here::here("data", "probe_design_final.csv"))
ref <-
rtracklayer::i... |
d00e630e192c19ca34f6b6c1cf65f9c9f451d4df3f9a000f6a07b0dbcab3dd65 | R | 1,871 | 42 | #' @templateVar MODEL_FUNCTION wcs_sql
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/dayeong-min/}{Dayeong Min} <\email{mindy2801@@snu.ac.kr}>
#' @templateVar TASK_NAME Wisconsin Card Sorting Task
#' @templateVar TASK_CODE wcs
#' @templateVar MODEL_NAME Sequential Learning Model
#' @templateVar MODEL... |
aa53d73d4ddf2f8e60a1a69ce8db2bf58b3ef0e5f6f94d2ec667ea54a21155c5 | R | 1,878 | 43 | library(biomaRt)
library(tidyverse)
library(here)
library(diffloop)
# Load data ---------------------------------------------------------------
# take the mim2gene list
mim2gene <- readr::read_delim("https://omim.org/static/omim/data/mim2gene.txt", delim = "\t", skip = 4)
# Main ------------------------------------... |
50b8647973ff8f9d0bdaecebf58ab3150630fab6be21278746b118545c701baf | R | 1,888 | 26 | #Note:
#To make a brain cell type GMT, for each species (mouse, rat, human) I will need to combine together DropViz_HC, DropViz_STR, BrainInABlender, Zeisel, and any relevant cell types in C8, and regional patterns from CombinedHCspecific_mouse
#Some of these things may be easier to do in Excel (e.g., adding Zeisel an... |
f7ac83ff99f0739bcce5db8dbe77d459f9c703bd91b3cc913dc520fe85090669 | R | 1,903 | 70 | #' Set the theme for the plotting Trellis functions
#'
#' The \code{mice.theme()} function sets default choices for
#' Trellis plots that are built into \pkg{mice}.
#'
#' @aliases mice.theme
#' @param transparent A logical indicating whether alpha-transparency is
#' allowed. The default is \code{TRUE}.
#' @param alpha.... |
c7194f957263f1f26ec7aacfd28fb01d5178ae0a761f2058dd6b86e320a10e2e | R | 1,904 | 65 | library(ggplot2)
library(dplyr)
if (rstudioapi::isAvailable()) {
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
} else{
# If running as a script, finding the file is harder
# https://stackoverflow.com/a/55322344/10930590
this_file <- commandArgs() %>%
tibble::enframe(name = NULL) %>%
tidyr... |
4797f38bef4ffb1122912e0718e866dbd6688714c3cfd010634ba716ba73ccd3 | R | 1,905 | 43 | #' @templateVar MODEL_FUNCTION bandit4arm_4par
#' @templateVar TASK_NAME 4-Armed Bandit Task
#' @templateVar TASK_CODE bandit4arm
#' @templateVar MODEL_NAME 4 Parameter Model, without C (choice perseveration)
#' @templateVar MODEL_CODE 4par
#' @templateVar MODEL_CITE (Seymour et al., 2012)
#' @templateVar MODEL_TYPE Hi... |
27f1871c0bc13898c06fec17f26f3d343f0ad98627b7927ab1cd56265aecc7d6 | R | 1,907 | 47 | library(data.table)
library(stringr)
### clean otter tf names to align with desmedt
otter_motif_prior <- fread("C:/STUDIES/RESEARCH/ODE_project_local_old/breast_cancer_data/cancer_breast_otter_motif.csv")
otter_tf_names <- data.table(tf = setdiff(names(otter_motif_prior),"Row"))
desmedt_gene_names <- fread("C:/STUDIE... |
ad727a2923aeba1ae0787189445681d7abdb644841d44fb179bb9f19cd0896f4 | R | 1,907 | 49 | library("ProjectTemplate")
load.project()
str(transfer)
chooseA <- transfer %>% mutate(Atrial=ifelse(symb1=="A"|symb2=="A",1,0)) %>%
filter(Atrial>0) %>% group_by(Participant,condition) %>% summarise(chooseA=sum(choice=="A", na.rm=T)/n()) %>% data.frame
avoidB <- transfer %>% mutate(Btrial=ifelse(symb1=="B"|symb2... |
3cf146ecf985054f24f6593791a7546ac51258bac941ba023b57b22b5f04f863 | R | 1,919 | 43 | ###########################################################################
## Author: Youtao Lu <luyoutao@sas.upenn.edu>
##
## Copyright (c) 2021-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2021-2023, James Eberwine, Perelman School of Medicine, University of ... |
3694d11dc0552d9a0f44a2f86ccddbad1ea23e508d1519277bd58b71937710de | R | 1,922 | 73 | source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/atac.R')
source('~/scripts/perturbator/de.R')
source('~/scripts/perturbator/enrichment.R')
library(Pando)
library(doParallel)
registerDoParallel(36)
filter <- dplyr::filter
select <- dplyr::select
dist <- stats::dist
Matrix <- Matrix::Matrix
setwd('... |
a8c1b1ca74acd8f3a9c53740cecee698b852a577ac4f199d93d52a65acaaee1f | R | 1,922 | 66 | ## Generate supplemental material for colocalization results
suppressPackageStartupMessages({
library(doParallel)
library(foreach)
library(dplyr)
library(here)
})
get_coloc_signal <- function(tissue){
cols = c("cluster_name", "N_SNPS", "eQTL_PIP", "GWAS_PIP", "GWAS_PIP_prior",
"RCP")
... |
5ce494b5d62a2b12a6863953b5a56234290f5394ce1101cc2b4bb467be331dbb | R | 1,927 | 93 | ---
title: "Supplementary material: Principal component analysis"
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.height=8)
```
# Pre... |
c66e6a678f84d940b19cdeafd16a065d28ed56a74c65b9dd9a33b5c743f059b5 | R | 1,928 | 56 | library(lme4)
library(multcomp)
library(lmeresampler)
library(readxl)
# -----------------------------------------------------
# Load in the data
# -----------------------------------------------------
data <- read_excel("/Users/jkosnoff/Library/CloudStorage/Box-Box/Joshua-Speller-Paper/Manuscript/nCommu/Source Data.xl... |
5f73887b9524cfa54551311b9c7d73fa826d573ec7fc3208608a50b6d4518b88 | R | 1,929 | 44 | #' @templateVar MODEL_FUNCTION bart_ewmv
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park} <\email{hrpark12@@gmail.com}>, \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang} <\email{jaeyeong.yang1125@@gmail.com}>
#' @templateVar TASK_NAME Balloon Analogue Risk T... |
31921b0f204dcda36452a7e682a5768484fae17a5e9ca3787cbdfc8f926f0831 | R | 1,930 | 71 | library(epitools)
# Check HERV-K feature association
# Replace with relevant values
dd <- matrix (c(10,20,30,40),
nrow=2, ncol=2, byrow=TRUE)
outc <- c("herv_als", "other_als")
herv <- c("down", "up")
dimnames(dd) <- list("HERV feats" = herv, "Outcome" = outc)
epitools::oddsratio.fisher(dd, rev = "c")... |
5374dc7dd46dc44480600ecfd011e23b3ea2d50de69063f11cb4aa5159109778 | R | 1,930 | 40 | #' Mammal sleep data
#'
#' Dataset from Allison and Cicchetti (1976) of 62 mammal species on the
#' interrelationship between sleep, ecological, and constitutional variables.
#' The dataset contains missing values on five variables.
#'
#' Allison and Cicchetti (1976) investigated the interrelationship between
#' sleep,... |
cf1738efae576bf6ae09b2caebd4067eceb059cd065b4431fea2de6d0b392944 | R | 1,931 | 57 | library(lme4)
library(multcomp)
library(lmeresampler)
library(readxl)
# -----------------------------------------------------
# Load in the data
# -----------------------------------------------------
data <- read_excel("/Users/jkosnoff/Library/CloudStorage/Box-Box/Joshua-Speller-Paper/Manuscript/nCommu/Source Data.xl... |
aa3e51a21f71e71ac2d1c4c5bc9077605827ea664d59796708eea6fe2ffdb130 | R | 1,933 | 64 | ## code to prepare `OUTLIERS` dataset goes here
library(data.table)
library(sinew)
library(MotrpacRatTraining6moData)
OUTLIERS = fread("data-raw/outliers.tsv", sep='\t', header=T)
OUTLIERS[,viallabel := as.character(viallabel)]
# fix tissue
OUTLIERS[,tissue_code := gsub(",.*","",tissue)]
# remove MSSM outliers
OUTLIE... |
14345a35f0db86796593c758c203df3071459b6e1c54919be27b20fb0c37e582 | R | 1,935 | 51 | library(data.table)
library(stringr)
### clean otter tf names to align with miasedt
otter_motif_prior <- fread("C:/STUDIES/RESEARCH/ODE_project_local_old/breast_cancer_data/cancer_breast_otter_motif.csv")
otter_tf_names <- data.table(tf = setdiff(names(otter_motif_prior),"Row"))
mias_gene_names <- fread("C:/STUDIES/R... |
057d6c960c08c8f1d666ceeeda68ab2f6bec7e33ac40fbf1f241ca1c6497c015 | R | 1,942 | 52 | #' Missing data pattern by variable pairs
#'
#' Number of observations per variable pair.
#'
#' The four components in the output value is have the following interpretation:
#' \describe{ \item{list('rr')}{response-response, both variables are observed}
#' \item{list('rm')}{response-missing, row observed, column missin... |
86d16d44d3a67d7cd63c668d20c1993bc52783a44be9227499f3953b937b1d53 | R | 1,944 | 42 | #' @templateVar MODEL_FUNCTION bandit2arm_delta
#' @templateVar TASK_NAME 2-Armed Bandit Task
#' @templateVar TASK_CODE bandit2arm
#' @templateVar TASK_CITE (Erev et al., 2010; Hertwig et al., 2004)
#' @templateVar MODEL_NAME Rescorla-Wagner (Delta) Model
#' @templateVar MODEL_CODE delta
#' @templateVar MODEL_TYPE Hier... |
78de3b3ddec600eaac13c7c941ad2147bc6e009ee2bbd27934d807c6c9580fbf | R | 1,948 | 38 | library(rmeta)
meta_sldsc <- function (myrow,dir,analysis,mytraits=traits,mysd=""){
nbtrait = length(mytraits)
M = 5961159
res = NULL
for (t in mytraits){
data = read.table(paste(dir,"/",t,".",analysis,"results",sep=""),h=T)[myrow,]
log = read.table(paste(dir,"/",t,".",analysis,"log",sep="... |
18f78e3bcb4d04219291ce87a8095b0bbe9a212fd4875ab705bbf5593ea74cae | R | 1,956 | 66 | #' filtering single cell data based on QC metrics
#'
#' 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,pipette_visual,timepoint > filtered... |
35078aec5c673fb9938fc7d67e9cb50cd39d88946d87cb0107fb53fe866384fe | R | 1,958 | 72 | ## Preprocessing data of Herring et al., Cell (2022)
library(GenomicRanges)
library(data.table)
library(dplyr)
library(stringr)
atac_dir <- '~/Dropbox/CWAS_paper_WD/Data/raw_annotations'
peaks <- readRDS(paste0(atac_dir, "/Herring2022_cell_type_atac_peaks_filtered_anno_gr.rds"))
cell_types <- names(peaks)
stages <- ... |
de2af96da4c68f643f1170f03d11c2e2eaf05153c41166b7672fe57432924de8 | R | 1,959 | 72 | ## Preprocessing data of Trevino et al., Cell (2021)
library(GenomicRanges)
library(data.table)
library(dplyr)
library(stringr)
atac_dir <- '~/Dropbox/CWAS_paper_WD/Data/raw_annotations'
peaks <- readRDS(paste0(atac_dir, "/Trevino2019_cell_type_atac_peaks_filtered_anno_gr2.rds"))
cell_types <- names(peaks)
stages <-... |
3d20750990c8013d53952ebbff08a9ebb1b4a6467e072a14a608b0f6b78f08f3 | R | 1,960 | 44 | #' Imputation by linear regression through prediction
#'
#' Imputes the "best value" according to the linear regression model, also
#' known as \emph{regression imputation}.
#'
#' @aliases mice.impute.norm.predict norm.predict
#' @inheritParams mice.impute.pmm
#' @return Vector with imputed data, same type as \code{y},... |
e17ea730668dcf27d7ac8d49ea8fc211754dda92da33324c31edf8e757cca5f4 | R | 1,966 | 61 | #' Set axis labels on circular plot
#' @title Set axis labels
#' @param mapping NULL (default) or a list of aesthetic mappings to use for plot.
#' @param bcols colours of branchs.
#' @param ... extra parameters passing to \code{ggplot2::geom_text()}.
#' @rdname set_axis
#' @examples
#' p <- quickcor(mtcars, circular = ... |
0a15cee6e651d7423602bc29dcc21fc345bca589d85505171cf393a7d56999f3 | R | 1,967 | 91 | #----SimulationGRN----
setValidity('SimulationGRN', validSimulationGRN)
setMethod(
f = 'initialize',
signature = 'SimulationGRN',
definition = initSimulationGRN
)
setMethod(
f = 'show',
signature = 'SimulationGRN',
definition = function(object) {
mxp = 10
cat('Graph:', length(object@graph@nodeset), 'node... |
80c3bfd79e92b703f5ff0bde82b23141e59135c8cd3390db577b19e059fa71a8 | R | 1,969 | 62 | args = commandArgs(TRUE)
eta=as.numeric(args[1])
max_depth=as.numeric(args[2])
gamma=as.numeric(args[3])
min_child_weight=as.numeric(args[4])
nrounds=as.numeric(args[5])
data_folder=as.character(args[6])
sex_split=try(as.logical(args[7]))
BrainAgeCV = function(eta, max_depth, gamma, min_child_weight, nrounds, data_f... |
edebf4a1e17179503a5d41c90f9b51c14a9687595a13b013e636a8576bd228c2 | R | 1,971 | 42 | #' @templateVar MODEL_FUNCTION ra_noLA
#' @templateVar TASK_NAME Risk Aversion Task
#' @templateVar TASK_CODE ra
#' @templateVar MODEL_NAME Prospect Theory, without loss aversion (LA) parameter
#' @templateVar MODEL_CODE noLA
#' @templateVar MODEL_CITE (Sokol-Hessner et al., 2009)
#' @templateVar MODEL_TYPE Hierarchica... |
d104af4d16aedb57abf911960c10df1997933ccadf8be4d051c9e1a9cb763a7b | R | 1,972 | 42 | #' @templateVar MODEL_FUNCTION bandit4arm_2par_lapse
#' @templateVar TASK_NAME 4-Armed Bandit Task
#' @templateVar TASK_CODE bandit4arm
#' @templateVar MODEL_NAME 3 Parameter Model, without C (choice perseveration), R (reward sensitivity), and P (punishment sensitivity). But with xi (noise)
#' @templateVar MODEL_CODE 2... |
adbf81d7618cd487773396795a4731c442119ec47b40c0293758a6bff33f9e2b | R | 1,973 | 44 | #' @templateVar MODEL_FUNCTION bandit4arm_lapse
#' @templateVar TASK_NAME 4-Armed Bandit Task
#' @templateVar TASK_CODE bandit4arm
#' @templateVar MODEL_NAME 5 Parameter Model, without C (choice perseveration) but with xi (noise)
#' @templateVar MODEL_CODE lapse
#' @templateVar MODEL_CITE (Seymour et al., 2012)
#' @tem... |
6956ecb5f394447423c8a09b102124f0ded77435581844f871d4ddbc303ac6a3 | R | 1,976 | 50 | #' Fifth Dutch growth study 2009
#'
#' Age, height, weight and region of 10030 children measured within the Fifth
#' Dutch Growth Study 2009
#'
#' The data set contains data from children of Dutch descent (biological parents
#' are born in the Netherlands). Children with growth-related diseases were
#' excluded. The da... |
58a241c5408890db8cec5890ada55f8f7efa4336516000a6bc3167d21342a2f3 | R | 1,977 | 42 | #' @templateVar MODEL_FUNCTION ra_noRA
#' @templateVar TASK_NAME Risk Aversion Task
#' @templateVar TASK_CODE ra
#' @templateVar MODEL_NAME Prospect Theory, without risk aversion (RA) parameter
#' @templateVar MODEL_CODE noRA
#' @templateVar MODEL_CITE (Sokol-Hessner et al., 2009)
#' @templateVar MODEL_TYPE Hierarchica... |
bdc4f850d7e4562781226e1c5d94301019df9a0e204a471761899913cddbd2fe | R | 1,999 | 43 | #' @templateVar MODEL_FUNCTION bandit4arm_singleA_lapse
#' @templateVar TASK_NAME 4-Armed Bandit Task
#' @templateVar TASK_CODE bandit4arm
#' @templateVar MODEL_NAME 4 Parameter Model, without C (choice perseveration) but with xi (noise). Single learning rate both for R and P.
#' @templateVar MODEL_CODE singleA_lapse
#... |
82e8b0c9791a5fcbddbf03822d8307d772eb35b2acbe855fcbcd2435a4ba2c39 | R | 2,001 | 57 | #Author: Jan Hirtz 2020 - 2023
# this code makes use of the Dynamic tree cut algorithm as pubslihed in Langfelder et al. (2008): Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24:719-720.10.1093/bioinformatics/btm563
#install.packages("cluster")
#install.pa... |
4c39a18dcd65ddd2e4aac1246d2cdec8436367fb06154fc0fc2e526c789da14c | R | 2,006 | 69 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/gene_counts_unfiltered.tsv", sep="\t", row.names="gene_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed/04.deseq2/experimental_design... |
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