sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c2e23d449a7dfe16ea630e41eb37a64876990a75a98896bba3b544c282ef3276 | R | 2,007 | 43 | #' @templateVar MODEL_FUNCTION ra_prospect
#' @templateVar TASK_NAME Risk Aversion Task
#' @templateVar TASK_CODE ra
#' @templateVar MODEL_NAME Prospect Theory
#' @templateVar MODEL_CODE prospect
#' @templateVar MODEL_CITE (Sokol-Hessner et al., 2009)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMNS... |
e8668cbf1f13f1fdba12a625334a9a67f628c1d4fefa45fb4db932dfed6f3f86 | R | 2,007 | 69 | #' Remove axis elements.
#' @description A simple wrapper of the \code{\link[ggplot2]{theme}} function
#' to quickly remove axis elements.
#' @param index 'all' (default), 'x' or 'y' axis will be removed.
#' @return The theme.
#' @importFrom ggplot2 theme element_blank
#' @rdname remove_axis
#' @examples
#' quickcor(mt... |
fd3dcfb02a57ca84ba91227154d81d25e661dc4786a8ab4b88cce4fd76f33850 | R | 2,007 | 59 | #' @include factRObject-class.R
show.factR <- function(object){
cat(sprintf("class: factRObject [version %s]\n", object@version))
cat(sprintf("# transcriptome:\n "))
ngenes <- length(object[["gene"]]$gene_id)
ntxs <- length(object[["transcript"]]$transcript_id)
nnovel <- sum(object[["transcript"]... |
271229a2feb7c6520ab0aee6e88013c9354d8a504cf3ac7857cc9a5da49a1afc | R | 2,010 | 37 | ###DTS script to extract single fly data from a DTS XML file and claculate some derivative variables needed later
#extract fly meta-data
fly <- singleflydata$fly
flyname = fly$name[1]
if(exists("flynames")){flynames[l,x] = paste(flyname)} #copy the name into the list of all flynames
#extract sequence meta-data
NofPeri... |
581d74dd2ebf7840e419dd243910851f9f84f9440df513d3121e09e9b88799f0 | R | 2,014 | 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... |
4b96a17a84a2f467f725124149f7fb6ad9de77a98944ca8f214d854d0f58d12c | R | 2,019 | 43 | #' @templateVar MODEL_FUNCTION dd_hyperbolic
#' @templateVar TASK_NAME Delay Discounting Task
#' @templateVar TASK_CODE dd
#' @templateVar MODEL_NAME Hyperbolic Model
#' @templateVar MODEL_CODE hyperbolic
#' @templateVar MODEL_CITE (Mazur, 1987)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMNS "subj... |
cfb1aefb084b1250501bf12550125a8aa66e2f4f95000580f4803aab9ad2375d | R | 2,026 | 65 | library(ggplot2)
library(scales)
library(ggthemes)
source("./utils.r")
demog_data <- read.csv("./outputs/demog_summary.csv")
demog_data <- subset(demog_data, sex != "")
demog_data$sex <- factor(demog_data$sex, levels = c("M", "F"))
demog_data$dataset <- factor(
demog_data$dataset, levels = c("camcan", "lemon", "c... |
c0abcfa12311b922b8b9f55e565349f58d08acfe3ebbe2ac8f33bf4def0ad199 | R | 2,027 | 96 | ---
title: "Prep ST data for deconv"
author: "Asa Bjorklund"
output:
html_document:
self_contained: true
highlight: tango
df_print: paged
code_folding: hide
toc: yes
toc_depth: 3
toc_float:
collapsed: false
smooth_scroll: true
---
Load each of the R objects for st data an... |
3ad32f636e5b920d515745c75631233ed6ebb56b27da0cbd03a780f8d41ed163 | R | 2,033 | 74 | #' Square Geom
#'
#' @inheritParams ggplot2::layer
#' @inheritParams ggplot2::geom_rect
#' @section Aesthetics:
#' \code{geom_square()} understands the following aesthetics (required aesthetics are in bold):
#' \itemize{
#' \item \strong{\code{x}}
#' \item \strong{\code{y}}
#' \item \code{r0}
#' ... |
18c665c6e5ad24ca53feaecfe7855277de710611abad35a2cbba4cd3f4c38a97 | R | 2,037 | 75 | library(tidyverse)
library(magrittr)
library(gprofiler2)
# Import parameters from snakemake
# min_log_base_counts = 1
direction = snakemake@params[['up_or_down']]
model = snakemake@params[['model']]
# Import compiled limma results and subset tissue of interest
limma_results <-
# here::here("results/limma_compile_re... |
db12b7529d02fd4d3c525da1bcfba939423555e2cf6c00cf06ebc92046509616 | R | 2,040 | 68 | #!/usr/bin/env Rscript
predictor='sexm'
########################
## choose data to upload
########################
# gene level
emma.results = readRDS('emma_results.rds')
keep.genes = readRDS('keep_genes.rds')
# cell type corrected gene level
emma.results = readRDS('emma_results_cell_type.rds')
keep.genes = readRDS... |
1efd10d928424ccc5133765e76d6eea2a31305802d2d0767315c42ad83a5c783 | R | 2,044 | 43 | #' @templateVar MODEL_FUNCTION dd_hyperbolic_single
#' @templateVar TASK_NAME Delay Discounting Task
#' @templateVar TASK_CODE dd
#' @templateVar MODEL_NAME Hyperbolic Model
#' @templateVar MODEL_CODE hyperbolic
#' @templateVar MODEL_CITE (Mazur, 1987)
#' @templateVar MODEL_TYPE Individual
#' @templateVar DATA_COLUMNS ... |
0d4256caa62ed09f097e10fe22f5fc66cd60f47f83a64fc0368f671641c07b4d | R | 2,045 | 63 | .compLvls <- function(object, A, B = NULL, ident = NULL,
col_prefix = NULL,
set = NULL, return_df = FALSE){
# get levels data based on set
if(is.null(set)){
set <- object@active.set
} else{
# check if given set is part of object
if(!set %i... |
7ad7346e05baaf5391105b5c76ff5a5fecd50ba29403ccc57ca6cfd01482b2b0 | R | 2,045 | 48 | #' @templateVar MODEL_FUNCTION gng_m1
#' @templateVar TASK_NAME Orthogonalized Go/Nogo Task
#' @templateVar TASK_CODE gng
#' @templateVar MODEL_NAME RW + noise
#' @templateVar MODEL_CODE m1
#' @templateVar MODEL_CITE (Guitart-Masip et al., 2012)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMNS "subj... |
a8366513d1b6ba37d211047f56eaa19a34c80fad00054220aba81a0d52fa3700 | R | 2,049 | 49 | library(EpiDISH)
# data(centEpiFibIC.m)
# data(DummyBeta.m)
# out.l <- epidish(beta.m = DummyBeta.m, ref.m = centEpiFibIC.m, method = "RPC")
setwd('/home/yyasumizu/nanoporemeth')
x_ref <- read.csv('./results/230531_compare_algorithms_test_indsamples_min3/ref_nnls.csv', row.names=1)
x_test <- read.csv('./results/230... |
e1021d0d17161d9f572a58cf8d9b44c09a8aa3c0627866ab6b671cf75af65368 | R | 2,053 | 86 | #' Extract order
#' @title Extract order
#' @param x any \code{R} object.
#' @param index one of "row" or "column".
#' @param name old name.
#' @param ... ignore.
#' @return a numeric vector.
#' @rdname get_order
#' @examples
#' hc <- hclust(dist(mtcars))
#' get_order(hc)
#' @author Houyun Huang, Lei Zhou, Jian Chen, T... |
7439f5d8c25d06e8d9ec91fa67e6a90e4fcbb47a4006729b249fa8c2d6338b43 | R | 2,054 | 63 | #' Extract list of fitted models
#'
#' Function \code{getfit()} returns the list of objects containing the repeated analysis
#' results, or optionally, one of these fitted objects. The function looks for
#' a list element called \code{analyses}, and return this component as a list with
#' \code{mira} class. If element ... |
e5608043b8f625eb74f66287ab7cdd4b9b918884378e06e52287e802a3032555 | R | 2,055 | 47 | ##' @title TMM normalization and voom transformation
##' @description
##' Normalize raw counts data by TMM implemented in \pkg{edgeR}
##' and then transform it by \code{\link[limma]{voom}} in \pkg{limma}
##' @param counts raw counts of RNA/miRNA expression data
##' @param filter logical, whether to filter out... |
360031359ff00807272a08cfb2277ee40c16823bff92eefec747a741cc7d9ef8 | R | 2,060 | 79 | mean.dbeta <- function( alpha, beta ){
alpha/(alpha+beta)
}
var.dbeta <- function( alpha, beta ){
(alpha*beta)/(( (alpha+beta)**2)*(alpha+beta+1))
}
mean.dgamma <- function( alpha, beta ){
alpha/beta
}
var.dgamma <- function( alpha, beta ){
alpha/(beta**2)
}
posterior.mode <- function(s){
if( class(s)=="mc... |
3d03e3bad9c204bdce59be00bcef7deb8a30ddc5ac2c716f46cda9c057e0b868 | R | 2,061 | 43 | #' @templateVar MODEL_FUNCTION dd_exp
#' @templateVar TASK_NAME Delay Discounting Task
#' @templateVar TASK_CODE dd
#' @templateVar MODEL_NAME Exponential Model
#' @templateVar MODEL_CODE exp
#' @templateVar MODEL_CITE (Samuelson, 1937)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMNS "subjID", "del... |
aef19cba91b906051709686745f15726ca18f79860576d6ff9ffc78fdc5d2799 | R | 2,069 | 63 | #' Quick heatmap
#' @title Quick heatmap
#' @param data a cor_tbl object.
#' @param mapping aesthetic mappings parameters.
#' @param geom short name of layer function, the default is "tile".
#' @param zoom_x,zoom_y the ratio of zoom.
#' @param ... extra parameters for layer function.
#' @note This function is mainly us... |
fbf0ddd270c9c035d21886babc3db4b2523153176e86ba43dbfc2e1347a05b12 | R | 2,077 | 49 | #' Potthoff-Roy data
#'
#' Data from Potthoff-Roy (1964) with repeated measures on dental fissures.
#'
#' This data set is the famous Potthoff-Roy data, used to demonstrate MANOVA on
#' repeated measure data. Potthoff and Roy (1964) published classic data on a
#' study in 16 boys and 11 girls, who at ages 8, 10, 12, an... |
9630386851c63b08b0318e09a6486ee70ab4f6d88e3e5a51a075d178a709c672 | R | 2,089 | 55 | #!/usr/bin/env Rscript
#################### Differential Transcript Usage Analysis with DEXSeq
library(DRIMSeq)
source(here::here("src/rnaseq/DTU_Analysis/stageR_DTU_significanceTesting.R"))
DRIMSeq_method <- function(sample_info, filtered_dm_object, design_full, sex, organism){
if(organism == 'mmusculus'){
strp <-... |
c9631a442ecb49c1fda4bf4f0acbd38f1da42f403d7823fd1e7b7d4bfa362541 | R | 2,089 | 57 | #!/usr/bin/env Rscript
source(here::here("src/shared/R-helpers/get_settings.R"))
suppressPackageStartupMessages({
library("clusterProfiler")
library("ggplot2")
library("org.Hs.eg.db")
library("stringr")
})
settings <- get_settings(omic = "integration", stage = "cluster_profiler_compare", dataset_name = NULL)... |
8860a204ec8de02959630063b20423f2cd8db05d2cd44b8d2d994ba921b7c449 | R | 2,091 | 45 | #' @templateVar MODEL_FUNCTION bandit4arm2_kalman_filter
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/yoonseo-zoh/}{Yoonseo Zoh} <\email{zohyos7@@gmail.com}>
#' @templateVar TASK_NAME 4-Armed Bandit Task (modified)
#' @templateVar TASK_CODE bandit4arm2
#' @templateVar MODEL_NAME Kalman Filter
#' @te... |
254259cb3a5498bc48dc08afa099149fe465fceb14b804b38d06c4ad1fd46a21 | R | 2,095 | 89 | source('~/scripts/single_cell/de.R')
source('~/scripts/perturbator/de.R')
library(Pando)
library(destiny)
setwd('~/projects/cutntag/')
#### Read data ###
marks <- read_rds('data/all_marks_list_v3.2annot.rds')
rna <- read_rds('data/RNA/RNA_all_srt_v2.2matched.rds')
marks <- map(marks, function(x){
x$lineage2 <... |
5b2ab23017be5f739e01cbdb6432c780ab64ee77214f0b16ad7268ad78edb89a | R | 2,101 | 60 | suppressWarnings(library(Boruta))
args = commandArgs(trailingOnly=TRUE)
train_data_file = trimws(args[1])
test_data_file = trimws(args[2])
boruta_figs_dir = trimws(args[3])
boruta_tables_dir = trimws(args[4])
tmp_run_id = trimws(args[5])
train_df = read.table(train_data_file, sep=',', row.names=NULL, header=T)
test... |
39726752fe9c7e191bd2bc90ec098ec2dd5e23cec47baf2f8ca23b3b0a3f4446 | R | 2,103 | 58 |
guha_sim1 <- function(nburn=30000,nsamp=20000)
{
rm(list=ls())
#if (!require(timeR)) install.packages('timeR')
load("data/test/GuhaData.Rdata")
#### Xmat (Predictor Matrix) and y (continuous response vector) are loaded.
Xmat <- simdata$Xmat
y <- simdata$y
## For later comparisons, load 'true.b'
tru... |
f141a257b1cc23700b68fd2660c760633a1551b24a84fad804b44adfe1215739 | R | 2,103 | 49 | #' @templateVar MODEL_FUNCTION gng_m2
#' @templateVar TASK_NAME Orthogonalized Go/Nogo Task
#' @templateVar TASK_CODE gng
#' @templateVar MODEL_NAME RW + noise + bias
#' @templateVar MODEL_CODE m2
#' @templateVar MODEL_CITE (Guitart-Masip et al., 2012)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMN... |
b994ffe5a51d26646988fe59a5ba0b2e86b46ec2be22ce892b76058f23449510 | R | 2,115 | 44 | #' Toenail data
#'
#' The toenail data come from a Multicenter study comparing two oral
#' treatments for toenail infection. Patients were evaluated for the
#' degree of separation of the nail. Patients were randomized into two
#' treatments and were followed over seven visits - four in the first
#' year and yearly the... |
765864912c351148bf2565d5b8fbacb5aa9a192a7b822262e42d90c8ec9e1e75 | R | 2,118 | 54 | #This Script is supposed to analyze multiple omics datasets for shared components in order to decide whcih combinations to use later on
#BiocManager::install("STATegRa")
library(STATegRa)
library(yaml)
library(tidyverse)
library(ggplot2)
library(gridExtra)
#library(grid)
print("yeah! Lets start STATegra!")
params <... |
98612312df38c5b76fd330c3f8c191ee5bcc5a92377fdb8112eb8b9cf3db079d | R | 2,121 | 44 | #' Toenail data
#'
#' The toenail data come from a Multicenter study comparing two oral
#' treatments for toenail infection. Patients were evaluated for the
#' degree of separation of the nail. Patients were randomized into two
#' treatments and were followed over seven visits - four in the first
#' year and yearly the... |
1d50f9ccb42aa11c8763fde1642e017369fb7bb8de898ac6b7c7bbc580533a0e | R | 2,123 | 47 | #' @templateVar MODEL_FUNCTION prl_rp
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang (for model-based regressors)} <\email{jaeyeong.yang1125@@gmail.com}>, \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park (for model-based regressors)} <\email{hrpark12@@gmail.... |
53c0fe1e78d4ae6c483ea6d082afa74fad28ddaae493bf8f98629294a2fec7e7 | R | 2,126 | 70 | .plotLvls <- function(object, ..., set = NULL, group.by = NULL, slot = "data",
ncol=1){
# get levels data based on set
if(is.null(set)){
set <- object@active.set
} else{
# check if given set is part of object
if(!set %in% names(object@sets)){
set ... |
9909cf3c7226aa2a3bba73bcae2fd853bb3d4842a940513da3e3b38b3f434909 | R | 2,126 | 54 | test_that("CHECK-FORMULA-001: predictorMatrix and dot-formula yield same imputations", {
# predictorMatrix specification (baseline)
imp_pm <- mice(nhanes, seed = 51212, print = FALSE, m = 1)
form <- list(age ~ ., bmi ~ ., hyp ~ ., chl ~ .)
imp_f <- mice(nhanes, formulas = form, seed = 51212, print = FALSE, m = ... |
c571169712b983fd5d08a5402fb997df8be9727d901a6888fbf6c67c31f558a2 | R | 2,126 | 45 | #' @templateVar MODEL_FUNCTION bandit4arm_lapse_decay
#' @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). Added decay rate (Niv et al., 2015, J. Neuro).
#' @templateVar MODEL_CODE lapse_dec... |
108307251ff2a51da9bd29f2149a2fa0bc57e4a8cc8ae33401bcf191346a24ca | R | 2,129 | 65 | ## This script estimates pi1 across brain regions.
suppressPackageStartupMessages({
library(dplyr)
library(qvalue)
})
load_eqtls <- function(){
fn <- "../../_m/lfsr.sex_interaction.txt.gz"
return(data.table::fread(fn))
}
load_nominal_eqtls <- function(tissue, egenes){
fn <- here::here("prep_eqtl_... |
62d71907bb3ddf6f7311ea876f681d9e1890f293da3ab3f4552c7109a4abd2e0 | R | 2,131 | 46 | #######################################################################################################################
################## R-script to read single XML raw data files, visualize and evaluate data. Reports in HTML ###########
################################################################################... |
75789ef2d24f194551d0629e37a88deb035eaf79773174db519649f31ae3b7b3 | R | 2,140 | 67 | #' Compare two nested models using D2-statistic
#'
#' The D2-statistic pools test statistics from the repeated analyses.
#' The method is less powerful than the D1- and D3-statistics.
#'
#' @inheritParams D1
#' @inheritParams mitml::testModels
#' @note Warning: `D2()` assumes that the order of the variables is the
#' s... |
01c9e25cc04cddcd52e60393bcac1dcef619f60a5a81bad78110e12abc08d3cd | R | 2,146 | 49 | # load data
df <- read.table("table_survival_ind_param_cov_log_prop_R.csv",
header = TRUE, sep = " ", as.is = TRUE)
df <- df[- which(is.nan(df$CTC)), ]
row.names(df) <- c(1:length(row.names(df)))
library("corrplot")
library("Hmisc")
# Correlation matrix for the biomarkers -----------------... |
e6d6f8ae24bf9bdb98d8cd538f837b558096cd74ae545af5e09ebb4bd74204af | R | 2,146 | 40 | # input files can be obtained through enrichment analysis performed using GeneTrail webservice (https://genetrail.bioinf.uni-sb.de/)
# required columns: X.Name (gene name), Regulation_direction (1 for enriched, 0 for depleted), P.value (adjusted enrichment p-value)
# up_file: categories correspond to all cell lines i... |
3c9af300822b65bbd12fe2fea07daab26f4beef430006ae9af5aa1f5a0549b91 | R | 2,149 | 80 | #
# logistic regression model with random intercepts per subject
#
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... |
c89983a9e815b89aab84058b92355d8dcaa829f851300fc1a86295db342ee567 | R | 2,150 | 54 | #'#################################################################################
#'#################################################################################
#' Define GO and KEGG terms to include in the model
#'#################################################################################
#'##########... |
73958c3da7b8959dc5beb55c052bcfed59bfa6bd81d91521cec589138167aadb | R | 2,152 | 47 | #!/usr/bin/env Rscript
library(tximport)
library(rhdf5)
library(biomaRt)
library(stringr)
samplesF=scan('female_ids.txt', what = "")
samplesM=scan('male_ids.txt', what = "")
samples=c(samplesF, samplesM)
files = file.path('kallisto',samples,'abundance.h5')
names(files) = samples
filesF = files[names(files) %in% sam... |
585f944862765d52393bdd8fc6349e999407b96def50ade58c9d4c3b5ef936ee | R | 2,158 | 51 | ---
title: "**Fragment library type**"
---
This page contains the library fragment type information that is generated using a CGAT specific . This information can be required for a number of downstream processes such as running featurecounts for RNA-seq. We use the nomenclature detailed in the salmon documentation whi... |
2e1a354bb92c58ea966ecfafdd9f3f63861c3b782be39fe1b476b83dfbf18eb2 | R | 2,162 | 48 | #' @templateVar MODEL_FUNCTION prl_ewa
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang (for model-based regressors)} <\email{jaeyeong.yang1125@@gmail.com}>, \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park (for model-based regressors)} <\email{hrpark12@@gmail... |
38ffe75c66ec8c8132eeeb69b85c60edae567951661234d1b6159335f8d6fb18 | R | 2,162 | 59 | ## Enrichment analysis
library(ggplot2)
library(tidyverse)
save_plot <- function(p, fn, w, h){
for(ext in c(".pdf", ".svg")){
ggsave(filename=paste0(fn,ext), plot=p, width=w, height=h)
}
}
load_enrichment <- function(){
return(data.table::fread("eGene_enrichment_fishers.tsv"))
}
memENRICH <- memoi... |
5fbc6da1a327657349377c51fc4147614c66bb0216f7b0cc319bdbbdfbfd86be | R | 2,167 | 44 | #' @templateVar MODEL_FUNCTION dd_cs
#' @templateVar TASK_NAME Delay Discounting Task
#' @templateVar TASK_CODE dd
#' @templateVar MODEL_NAME Constant-Sensitivity (CS) Model
#' @templateVar MODEL_CODE cs
#' @templateVar MODEL_CITE (Ebert et al., 2007)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLUMNS... |
5a9a5f0cb5f7a6b1302cd0eb1fa5f21bb4a4be74a339c821b2919ae4e31cf452 | R | 2,168 | 71 | #' Creates a \code{where} argument
#'
#' This helper function creates a valid \code{where} matrix. The
#' \code{where} matrix is an argument to the \code{mice} function.
#' It has the same size as \code{data} and specifies which values
#' are to be imputed (\code{TRUE}) or nor (\code{FALSE}).
#' @param data A \code{dat... |
66ed338bb2e231516a8a84cd21309ac57f2a5371fb8069cc8dd4941b990aad55 | R | 2,171 | 101 | ---
title: "cran-comments"
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Release summary
`mice 3.19.10` adds new features, bug fixes, and enhancements.
See <https://github.com/amices/mice/blob/master/NEWS.md>
## Test environments
```{r}
packageVersion("mice")
R.V... |
d3bbd7180d749fb5475fe9d37f25caf11fe36c8d20845ea8828d1ab80f1d000a | R | 2,171 | 82 | #' Select complete cases
#'
#' Extracts the complete cases, also known as \emph{listwise deletion}.
#' \code{cc(x)} is similar to
#' \code{na.omit(x)}, but returns an object of the same class
#' as the input data. Dimensions are not dropped. For extracting
#' incomplete cases, use \code{\link{ici}}.
#'
#' @param x An \... |
0368286d8c3ca0084a847f897b67bf2a9c5bf2a04f744d5dd5b0361e5ea51c64 | R | 2,180 | 56 | ##' @title Export network for Cytoscape
##' @description Export nodes and edges of ce network for
##' \strong{Cytoscape} visualization
##' @param ceNetwork a dataframe generated from \code{\link{gdcCEAnalysis}}
##' @param net one of \code{'nodes'} and \code{'edges'}
##' @return A dataframe of nodes or edges
##' @e... |
4360c2b86149b47131553f272400919508b91e821da8ea05e9905e3ddef5eced | R | 2,184 | 51 | #' @templateVar MODEL_FUNCTION gng_m3
#' @templateVar TASK_NAME Orthogonalized Go/Nogo Task
#' @templateVar TASK_CODE gng
#' @templateVar MODEL_NAME RW + noise + bias + pi
#' @templateVar MODEL_CODE m3
#' @templateVar MODEL_CITE (Guitart-Masip et al., 2012)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_C... |
0ab0605e61a9568a842d4b73337501eeac5aa83a42c7dabc13aa2afa500418e1 | R | 2,185 | 55 | #' EnsembleAge: Epigenetic Age Prediction Using Ensemble Clock Methods
#'
#' @description
#' EnsembleAge provides tools for predicting epigenetic age using various clock methods
#' including Universal Clocks, Elastic Epigenetic clocks, static ensemble methods, and
#' dynamic ensemble clocks. The package automatically... |
fe3c13547674a4189de9307f16e8528bab7e1078ac62321f8ef3eea2fe982119 | R | 2,189 | 47 | #' @templateVar MODEL_FUNCTION banditNarm_4par
#' @templateVar CONTRIBUTOR \href{https://github.com/cheoljun95}{Cheol Jun Cho} <\email{cjfwndnsl@@gmail.com}>
#' @templateVar TASK_NAME N-Armed Bandit Task
#' @templateVar TASK_CODE banditNarm
#' @templateVar MODEL_NAME 4 Parameter Model, without C (choice perseveration)
... |
21243930226757802184b00f8c222c981e4794383fe7917b76953372bae2116c | R | 2,191 | 44 | #' @templateVar MODEL_FUNCTION alt_delta
#' @templateVar CONTRIBUTOR \href{https://github.com/lilihub}{Lili Zhang} <\email{lili.zhang27@@mail.dcu.ie}>
#' @templateVar TASK_NAME Aversive Learning Task
#' @templateVar TASK_CODE alt
#' @templateVar TASK_CITE (Browning et al., 2015)
#' @templateVar MODEL_NAME Rescorla-Wagn... |
e97acd46b87edbfdf7a4a24bb0e2047f6ace108ab4c3d42367ace909d059608f | R | 2,191 | 44 | #' @templateVar MODEL_FUNCTION alt_gamma
#' @templateVar CONTRIBUTOR \href{https://github.com/lilihub}{Lili Zhang} <\email{lili.zhang27@@mail.dcu.ie}>
#' @templateVar TASK_NAME Aversive Learning Task
#' @templateVar TASK_CODE alt
#' @templateVar TASK_CITE (Browning et al., 2015)
#' @templateVar MODEL_NAME Rescorla-Wagn... |
55557fced97e4dc3e96b9fbfbed4588d6aabde6987e77f52bd0c45a3af7f5d10 | R | 2,192 | 44 | #' @templateVar MODEL_FUNCTION dd_cs_single
#' @templateVar TASK_NAME Delay Discounting Task
#' @templateVar TASK_CODE dd
#' @templateVar MODEL_NAME Constant-Sensitivity (CS) Model
#' @templateVar MODEL_CODE cs
#' @templateVar MODEL_CITE (Ebert et al., 2007)
#' @templateVar MODEL_TYPE Individual
#' @templateVar DATA_CO... |
c007798c9ac194a9c3e3d183b196ac3a932cf7ee39432db3bdd7eb1807f12aba | R | 2,193 | 54 | #8 Ligand_receptor analysis
# Setup---------------------------------------------------------------
rm(list = ls()) ; gc() ; gc()
set.seed(1234)
wdir <- 'set your working directory'
setwd(wdir)
save.path <- 'object folder path'
# load library
easypackages::libraries(c("tidyverse","Seurat","liana","CrossTalkeR"))
# ... |
309d4954d8be77e616834be0887af2f895c30e5d369c56eecb4c3d573a1281cb | R | 2,196 | 48 | #' @templateVar MODEL_FUNCTION prl_fictitious_woa
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang (for model-based regressors)} <\email{jaeyeong.yang1125@@gmail.com}>, \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park (for model-based regressors)} <\email{hrpa... |
33a65a323cc1f113c3fbe591dafa52a8c7bc6e346ca30f781eba04ad2b8b2048 | R | 2,198 | 57 | # old implementation (with 1e-4 floor on lambda)
barnard.rubin_old <- function(m, b, t, dfcom = Inf) {
lambda <- (1 + 1 / m) * b / t
lambda[lambda < 1e-04] <- 1e-04
dfold <- (m - 1) / lambda^2
dfobs <- (dfcom + 1) / (dfcom + 3) * dfcom * (1 - lambda)
ifelse(is.infinite(dfcom), dfold, dfold * dfobs / (dfold + ... |
ea5140fdbae6937d73164ecfcb52714478382bc276abef9d5d0e0eb8dd6820bb | R | 2,203 | 75 | nhanes3 <- nhanes
rownames(nhanes3) <- LETTERS[1:nrow(nhanes3)]
imp <- mice(nhanes3, m = 2, maxit = 1, print = FALSE)
X <- complete(imp, action = "long", include = TRUE)
# create dataset with .imp variable as numeric
X2 <- X
# nhanes example
test1 <- as.mids(X)
# nhanes example
test2 <- as.mids(X2)
# nhanes example, ... |
8200ae2b8ce74532760033c4d1c57a07b6eb084d94e85eca8c0f2e0a8fa0f740 | R | 2,210 | 49 | #' @templateVar MODEL_FUNCTION prl_fictitious
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang (for model-based regressors)} <\email{jaeyeong.yang1125@@gmail.com}>, \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park (for model-based regressors)} <\email{hrpark12... |
038fdc94a353a09f3527a0319a6f118db026572fe17e4106d169135fac6f7545 | R | 2,216 | 66 | #!/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... |
524d46c84076ea294749f0a1464d04b230591286c9c86dd87175d4bf982d9ae1 | R | 2,219 | 77 | # Load required libraries
library("TreeTools", quietly = TRUE)
library("TreeDist")
library("TreeSearch")
library("Rogue")
# Check working directory
getwd() # Should match location of data / tree files
setwd("../TNT") # Select TNT directory - modify path if necessary
outgroup <- c("Tubiluchus") # Specify tax... |
513e30317608e1acaf5579a1ea5089e31d244a2091a82e90b283adf74c6c88da | R | 2,222 | 67 | ##---------------------------------------------------------------------------##
## Modules
# module purge && module load shared slurm python/3.7.x-anaconda
# module load hdf5_18/1.8.17
# module load gcc/8.3.0
# module load htslib
# module load gsl/2.4
# module load macs/2.1.2
# module load R/4.1.1-gccmkl
##-----------... |
fc079cae679939ef04a6c8b1f70774d1b4cb4a73a2c4b6d5194df5bac9dfb2bc | R | 2,222 | 53 | #### Script to subset expression
suppressMessages({
library(magrittr)
})
#### MAIN analysis
# Set variables for filtering data
feature <- "Gene"
# Generate output directory
outdir <- paste("out", sep="/")
dir.create(outdir... |
bbb0697b8f22278f46e386d6cc97a72c8e7ce156ea6aeac6cec623813b4200ed | R | 2,226 | 78 | ---
title: "**NH tag**"
---
The following page details the distribution of NH tags in your bam sample. These stats come from running bamstats, information for this tool can be found <a href="http://bamstats.sourceforge.net/">here</a>
The NH (number of hits) tag can be useful for determining the number of occurances o... |
e2787c0b082199d02f45033a53b4a6332f0cb375b6744ac5017e4b1dff73b112 | R | 2,226 | 48 | #Now we need to cut out all of the less informative gene sets (and/or more likely to be confounded comparisons):
grep("organism_part", HC_GeneSets_Temp$Factor_Contrast)
grep("sex", HC_GeneSets_Temp$Factor_Contrast)
grep("developmental_stage", HC_GeneSets_Temp$Factor_Contrast)
HC_GeneSets_Temp_Informative<-HC_GeneSets... |
190a239d7796924699dc7f3d04a9bb51ef1a379f6cf443efff3f5bf7ee0145bb | R | 2,235 | 44 | #' Multivariate amputation under a MCAR mechanism
#'
#' This function creates a missing data indicator for each pattern, based on a MCAR
#' missingness mechanism. The function is used in the multivariate amputation function
#' \code{\link{ampute}}.
#'
#' @param P A vector containing the pattern numbers of the cases' ca... |
630bc7e95f5e5611a2394fffe278df4cc1fd16f8f3b2e2689231812e900fb769 | R | 2,236 | 73 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcript_counts_multiple_median_cpm_1.tsv", sep="\t", row.names="transcript_id"))
cts_med <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcr... |
f10f26deed3fe9c923e74919ed15c8060ac91fe838ad7bbd36dbb84731364f61 | R | 2,236 | 39 | #### Code for plotting FDR vs differential of log2 fold change SNCA-3K-sfGFP vs SNCA-WT-sfGFP for Lam, Ndayisaba et al 2024 Neuron - FIGURE 8D
# 7/6/22 Re-plotting graphs for manuscript figures
# 3K vs WT differential fold-change
load("/Users/isabellam/Dropbox (Partners HealthCare)/Isabel-Lindquist:Khurana:Vidal/Da... |
55351500c227e4855325ee514b3fcf2caad40fb60f7c39a376881d6a6431c9b8 | R | 2,237 | 52 | #' @templateVar MODEL_FUNCTION gng_m4
#' @templateVar TASK_NAME Orthogonalized Go/Nogo Task
#' @templateVar TASK_CODE gng
#' @templateVar MODEL_NAME RW (rew/pun) + noise + bias + pi
#' @templateVar MODEL_CODE m4
#' @templateVar MODEL_CITE (Cavanagh et al., 2013)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar D... |
8232fbb7e03b63824ee0183f79008bbc5d9573da0703f2a21057591d4e9d6984 | R | 2,238 | 66 | #!/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... |
ac948911990438de61d8cd47309f1791717e19ffb4323e9b0b860e23fd647099 | R | 2,238 | 53 | rm(list = ls())
# Global variables --------------------------------------------------------
path_results <- "/data/kailash/DDP/results/"
path_data <- "/data/kailash/DDP/data/"
BR <- "BM44"
# Loading libraries ------------------------------------------------------
.libPaths(c("/data/kailash/DDP/libraries/", .libPath... |
dd65449aff34037436e8076604989a3e988fc0be09274fe9d36064ccd2404005 | R | 2,244 | 79 | ---
title: "**Insert size**"
---
This page describes the base distribution of each of the samples. This is ran as part of picard metrics.For more information on the definitions of each of the metrics please see the following
<a href="https://broadinstitute.github.io/picard/picard-metric-definitions.html">link</a>.
<b... |
fddd445646fa81b91d92bc288b47152f1d5cb86333134a1955f700b078b3283a | R | 2,246 | 73 | ---
title: "convert seurat to anndata"
author: "Yuvarani Masarapu"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r read_spatial_data}
library(Seurat)
library(SeuratObject)
library(STutility)
library(SeuratDisk)
indir <- getwd()
st.brain <- read... |
8bc8cee4d8daec861465d3794c9ecb75fb5ad69b409ec6659bc226c1f37be9df | R | 2,248 | 55 | #' General Purpose Plotting for hBayesDM. This function plots hyper parameters.
#'
#' @keywords internal
#'
#' @param x Model output of class hBayesDM
#' @param type Character value that specifies the plot type. Options are: "dist", "trace", or "simple". Defaults to "dist".
#' @param ncols Integer value specifying how ... |
3c8054db2e8afbee4fccd76bf4acba9cb69de5dc58592144cb90150fb7cabbb5 | R | 2,252 | 73 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcript_counts_multiple_median_cpm_1.tsv", sep="\t", row.names="transcript_id"))
cts_med <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcr... |
de685af8b59cd6b3d3bf2eeafe3fb1b38e760b87484d17b5f575c27be94e5c14 | R | 2,256 | 46 | #' @templateVar MODEL_FUNCTION banditNarm_2par_lapse
#' @templateVar CONTRIBUTOR \href{https://github.com/cheoljun95}{Cheol Jun Cho} <\email{cjfwndnsl@@gmail.com}>
#' @templateVar TASK_NAME N-Armed Bandit Task
#' @templateVar TASK_CODE banditNarm
#' @templateVar MODEL_NAME 3 Parameter Model, without C (choice persevera... |
e7e204dc14c6a621834d9fe140e29648f06e1f49bf05821d4a3c0164f6405fe3 | R | 2,256 | 56 | #' Brandsma school data used Snijders and Bosker (2012)
#'
#' Dataset with raw data from Snijders and Bosker (2012) containing
#' data from 4106 pupils attending 216 schools. This dataset
#' includes all pupils and schools with missing data.
#'
#' @name brandsma
#' @docType data
#' @format \code{brandsma} is a data fra... |
113e2853bf6b24e72ca0066ce9eec657e9bf466c48b055e838269203e6126677 | R | 2,257 | 48 | #' @templateVar MODEL_FUNCTION banditNarm_lapse
#' @templateVar CONTRIBUTOR \href{https://github.com/cheoljun95}{Cheol Jun Cho} <\email{cjfwndnsl@@gmail.com}>
#' @templateVar TASK_NAME N-Armed Bandit Task
#' @templateVar TASK_CODE banditNarm
#' @templateVar MODEL_NAME 5 Parameter Model, without C (choice perseveration)... |
a061a0a419666bd644bfc8ba64deeb8a147efe07514bddc0500da0b1602c0458 | R | 2,261 | 53 | #' Imputation by linear regression without parameter uncertainty
#'
#' Imputes univariate missing data using linear regression analysis without
#' accounting for the uncertainty of the model parameters.
#'
#' @aliases mice.impute.norm.nob norm.nob
#' @inheritParams mice.impute.pmm
#' @return Vector with imputed data, s... |
675d18bfbfd095bb2c5e511edd33953cf0369b37f5d972b9b38e6504b0faebe5 | R | 2,273 | 62 | library("DHARMa")
##Three different ways to fit lesion load for baseline
usable=subset(bl,(bl$Age_all>50&(!is.na(bl$ADULT_BP_SBP.1))&(!is.na(bl$waist2hip))&(!is.na(bl$icv))))
fit1 <- lm(log10(usable$lesionload/1000) ~ Age_all, data = usable)
fit3 <- glm(lesionload/1000 ~ Age_all, data = usable, family = gaussian(li... |
c9c67fb3800bd7ef2d208fccb2acbd6a57f2487a3d6d74f28bb1ba206c74361e | R | 2,280 | 43 | #' @templateVar MODEL_FUNCTION bart_par4
#' @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}>, \href{https://ccs-lab.github.io/team/ayoung-lee/... |
7278990cc5f122377784c36a410fbda9f9e6d3ff2fabc7c1ee91abc442f62444 | R | 2,283 | 47 | #' @templateVar MODEL_FUNCTION banditNarm_singleA_lapse
#' @templateVar CONTRIBUTOR \href{https://github.com/cheoljun95}{Cheol Jun Cho} <\email{cjfwndnsl@@gmail.com}>
#' @templateVar TASK_NAME N-Armed Bandit Task
#' @templateVar TASK_CODE banditNarm
#' @templateVar MODEL_NAME 4 Parameter Model, without C (choice persev... |
b15fd10bd6aa6fdeea4f72abd7000282d15f7112920b4dc8a09894f310893126 | R | 2,285 | 57 | install.packages("tidyverse")
library(tidyverse)
design_matrix <- read.delim("dwi_over_55.tsv", tryLogical = F) %>%
arrange(SCD, participant_id) %>%
mutate(EV1 = ifelse(SCD == 0, 1, 0),
EV2 = ifelse(SCD == 1, 1, 0)) %>%
rename(EV3 = age, EV4 = homeint_storyrecall_d) %>%
select(EV1, EV2, EV3, EV4)
#me... |
854f0a4a2feaa4ac98440d53c13818b40fb9e27aec7aa545f5fce029c6f0224a | R | 2,301 | 109 | ---
title: "revisions"
author: "Emma Jones"
date: "2024-04-03"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
The purpose of this script is to respond to some reviewer comments asking about the biological processes of DTU-exclusive genes. Therefore, I'm getting the unique... |
31cf2e5451ae52372ce2e3c4b6192d44baaec0d428aba40c2cbcdb4c12d30696 | R | 2,303 | 71 | #' Imputation by the random indicator method for nonignorable data
#'
#' Imputes nonignorable missing data by the random indicator method.
#'
#' @aliases mice.impute.ri ri
#' @inheritParams mice.impute.pmm
#' @param ri.maxit Number of inner iterations
#' @return Vector with imputed data, same type as \code{y}, and of l... |
5acdb13eabb356a309f20feb01bda3d303f71c0ced869f60147bb6800a2c6dff | R | 2,304 | 69 | suppressPackageStartupMessages({
library(dplyr)
library(eQTpLot)
library(argparse)
})
## Create parser object
parser <- ArgumentParser()
parser$add_argument("-t", "--tissue", type="character",
help="tissue to run analysis on")
parser$add_argument("-f", "--feature", type="character",
... |
260b88b80c1c41d269df61d8908d661c7a73ec7e553aa5f5a59761f2f445e2e4 | R | 2,307 | 86 | ```{r}
#.libPaths("/home/asish/R/x86_64-pc-linux-gnu-library/4.2")
library(MuSiC)
library(Seurat)
library(SingleCellExperiment)
library(SpatialExperiment)
library(scater)
library(scran)
library(scuttle)
library(SPOTlight)
```
load sc_ref_data
```{r}
sc_data_1 = read.csv("sc_ref_hippo.csv",
... |
54b04b75e8becf06c28d600b5565a7c213a33f870f16cbdfd6e0b1c4511cdc80 | R | 2,313 | 66 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(GenomicRanges)
library(plyranges)
library(here)
library(ggrepel)
# Load data ---------------------------------------------------------------
load(here::here("results", "pseudogenes", "pseudogene_annotated.rda"))
#... |
d5f1f2f1f934f8c3e729579da3c2afaf1c7052f91443cf61a48ec263a21fe7df | R | 2,313 | 48 | #' @templateVar MODEL_FUNCTION prl_rp_multipleB
#' @templateVar CONTRIBUTOR \href{https://ccs-lab.github.io/team/jaeyeong-yang/}{Jaeyeong Yang (for model-based regressors)} <\email{jaeyeong.yang1125@@gmail.com}>, \href{https://ccs-lab.github.io/team/harhim-park/}{Harhim Park (for model-based regressors)} <\email{hrpark... |
28733dc48376301cba9ecf5b7ce53dfcf4ec1d7aa65d9a90624aea37cd7c98e4 | R | 2,315 | 81 | #' Circle Geom
#'
#' @param n the number of circle path points.
#' @inheritParams ggplot2::layer
#' @inheritParams ggplot2::geom_polygon
#' @section Aesthetics:
#' \code{geom_circle2()} understands the following aesthetics (required aesthetics are in bold):
#' \itemize{
#' \item \strong{\code{x}}
#' \it... |
dacc3ca32d770f19991fd20086b8ab0c0877ad69a87b73bf8d6578baaa848b0d | R | 2,315 | 79 | #' Ellipse Geom
#'
#' @param n the number of ellipse path.
#' @inheritParams ggplot2::layer
#' @inheritParams ggplot2::geom_polygon
#' @section Aesthetics:
#' \code{geom_ellipse2()} understands the following aesthetics (required aesthetics are in bold):
#' \itemize{
#' \item \strong{\code{x}}
#' \item \... |
458da9c3ada28754ea15e49dbd63f37f5134a1bb3862229a5e585e1a8f950938 | R | 2,319 | 57 | #Calculate Power for specific N and effsize of WHR
#Change input csv files according to the N, effsize you want to estimate power. These files are generated by calc_models_on_simdata.R
library(dplyr)
results_dat=read.csv("/data/pt_life_whm/Results/VRF_cSVD/LME/simulations/WHR_effsize_1/n_1000_50_freq.csv")
n_sim=50
t... |
48a5cdfc5774e91d804b1948fede3192b99b2be207d27100eb50d5482e0b0dc5 | R | 2,322 | 49 | #' @templateVar MODEL_FUNCTION choiceRT_ddm
#' @templateVar TASK_NAME Choice Reaction Time Task
#' @templateVar TASK_CODE choiceRT
#' @templateVar MODEL_NAME Drift Diffusion Model
#' @templateVar MODEL_CODE ddm
#' @templateVar MODEL_CITE (Ratcliff, 1978)
#' @templateVar MODEL_TYPE Hierarchical
#' @templateVar DATA_COLU... |
cceccd9cd9f063f80470480a1c1c4abc98808f735710ddf1c63c2167a999391a | R | 2,323 | 66 | suppressPackageStartupMessages({
library(dplyr)
library(ggpubr)
library(argparse)
})
## Create parser object
parser <- ArgumentParser()
parser$add_argument("-f", "--feature", type="character",
help="feature name used in eQTL analysis")
parser$add_argument("-p", "--perm_pval", type="doub... |
6f6f2ae8715cbc9aa1f4caaac12bb56f85bd9f91c059ec07d62364f51599e2c8 | R | 2,324 | 67 | library(data.table)
library(stringr)
library(PRROC)
### will want genes to also have prior info so...
long_prior <- fread("/home/ubuntu/neural_ODE/breast_cancer_data/clean_data/otter_clean_harmonized_full_prior.csv")
long_prior[,prior_pred := 1]
all_prior_affiliated_genes <- unique(c(long_prior$from, long_prior$to))... |
8958a5551049b0055d4c235528e054a8f20efe4d3e10accc36244de8cb296ae1 | R | 2,327 | 117 | ## Limma results
library(tidyverse)
library(stageR)
library(limma)
library(magrittr)
limma_results <-
snakemake@input[["results"]] %>%
read_rds()
LOG_THRESHOLD = snakemake@params[['fold_change_threshold']] %>% log2
ALPHA = snakemake@params[['alpha']]
COEFS = 5:8
# Summarize results and filter by log fold chang... |
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