sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
ca0a6cd5fd5dfbe8aa1798fed4d692d218475f9a8fce50fd7bb80e6e7a538243 | R | 1,568 | 53 | ####################################
## Reverse MR - tier #2 and tier #3 traits
## William Reay (2023)
####################################
library(dplyr)
library(data.table)
library(TwoSampleMR)
setwd("~/Desktop/Retinol_GWAS/MR_all_cis_acting_SNPs/")
Tier_3 <- fread("Processed_output/Tier_3/Passing_traits_tier_3... |
bd49a17d5c2c85a61d543079aedf8cc77fbd8a74d07c58b6038139991d00b042 | R | 1,576 | 81 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
setClass(
"familiarRandomVimp",
contains = "familiarVimpMethod"
)
setClass(
"familiarNoneVimp",
contains = "familiarVimpMethod"
)
setClass(
"familiarSignatureVimp",
contains = "familiarVimpMethod"
)
setClass(
"familiarNoFeaturesVimp... |
4569800220dc0e8e99787ce23a3ae4b6636b5b0fa8297836d6f1ddd8c6511720 | R | 1,590 | 41 | # NOTE: please create a '.Rprofile' file in your project directory in the same location as your '.Rproj' file.
# Include a single line with the following code: `R_PROFILE_USER=""`
# This will address any normalizePath warnings looking for the home directory which is not bound within the container
# This script is cale... |
7c6acf395e53318320416192728403406686420fe4cbd7c8156190fcfecfced6 | R | 1,613 | 33 | library(stringr)
align2='human'
input_path=paste0('/scratch/users/banwang3/celllines/analysis/ASE/',align2,'/')
output_path=paste0('/scratch/users/banwang3/celllines/analysis/ASE_norm/',align2,'/')
humangenes=read.csv('/scratch/users/banwang3/celllines/scripts/RScripts/All_Human_Genes.csv',sep=',')
samplenames<-c('RNA3... |
0443f560ec9d627eb2e34c2d12723e20e89a2cf1e9e179d9d5d40721ae4b1282 | R | 1,617 | 36 | iDeepViewLearn_train <- function(python_path, myseed=1234L,
X_train, y_train=NULL, X_tune=NULL, y_tune=NULL,
search_times=0L, best_comb=NULL, impt=NULL, outtype = NULL,
top_rate=0.1, edged=NULL, vWeightd=NULL,
... |
344d8342c9f79ac3adaa1eecbe674083275d3ba8cb142dec405cabbc4febf2b6 | R | 1,643 | 43 | # peak
identify.peak.events = function(dt, t, x, sd.peak.n = 3, sd.peak.width = 1, prefix="peak", ...) {
n = round(sd.peak.width / dt[, shift(get(t), n = -1) - get(t)][[1]], digits = 0)
.x = as.vector(dt[, get(x)])
.xs = as.vector(dt[, shift(get(x), n = round(-n/2))])
.mean = frollmean(.x... |
0019a0a7bd9cebe928b064c989a5fb038d440e6f127bac338afe9ba492177e6e | R | 1,646 | 89 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# add_feature_info_parameters (NULL, ANY) --------------------------------------
setMethod(
"add_feature_info_parameters",
signature(
object = "NULL",
data = "ANY"
),
function(
object,
data,
...
) {
# This is when ... |
8e4bf5c521be48688180f012bc186912e7bb1b2718f68a7274493971bdf6c54e | R | 1,654 | 41 | #' compare_ucobias
#'
#' Compares taxa by codon usage bias.
#' @export
compare_ucobias<-function(featuredata=NULL, contigsdata=NULL, taxlevel="LKT", taxaofinterest=NULL, ucobias=NULL, method="tSNE", frac=0.25){
taxlevels<-colnames(opt$LKTdose)[colnames(opt$LKTdose) %in% c("Domain", "Kingdom", "Phylum", "Class", "... |
89462762a8a19fe2afad0b3fb61c29a6cd2955ae769af75c674e08a5f96a26d5 | R | 1,659 | 42 | context("Dissimilarity calculation")
test_that("Dissimilarity calculation", {
data(GlobalPatterns, package="mia")
tse <- GlobalPatterns
# Test if Unifrac dissimilarity works with external tree
expect_equal(getDissimilarity(tse, method = "unifrac"),
getDissimilarity(tse, tree = rowTree(tse), metho... |
30b3e93d1f27049559e35e606736876442f43925d0ef945e71cae293c30d5ed0 | R | 1,663 | 61 | library("muxViz")
library(igraph)
#Or build by specifying layers
Layers <- 10
Nodes <- 100
NodeTensor <- list()
g.list <- list()
g.list[[1]] <- barabasi.game(Nodes, m = 1, directed = F)
#generate additional layers with increasing % of overlapping edges, while preserving degree distribution
ov.fractions <- seq(0.1, 0... |
7ce566167d2c8bb8c33ea51823dab02e3588ea72d409d5c26f311daecb2a31f5 | R | 1,690 | 31 | library(ggplot2)
library(Seurat)
library(harmony)
library(dplyr)
library(Matrix)
library(patchwork)
#
outdir <- 'output/'
#Read in pre-merged count matrix data
SeuratObject <- readRDS("PD.paper.seuratObject.RDS") #data available at https://www.synapse.org/#!Synapse:syn52911948
SeuratObject@assays$RNA@data <- SeuratObje... |
bf596cdae74f0c939034688c8545c5cdb328304b31e02d38d44adb44955ee76a | R | 1,700 | 47 |
test_that("Test getReducedDimAttribute", {
data(GlobalPatterns)
tse <- GlobalPatterns
#
res <- getReducedDimAttribute(tse) |> expect_error()
# Calculate MDS
tse <- runMDS(tse, assay.type = "counts")
# Get reference values
mat <- reducedDim(tse, "MDS")
ref <- attributes(mat)
ref ... |
1faa07ad3a9421df93c15496467025f0acc1e22eb410aa9d7b331dce72fd04f5 | R | 1,702 | 16 | #' glom_completeness(completeness_df = NULL, taxlvl = NULL)
#'
#' JAMSalpha function
#' @export
glom_completeness <- function(completeness_df = NULL, taxlvl = NULL, HQ_completeness_threshold = 90, HQ_contamination_threshold = 5, MHQ_completeness_threshold = 70, MHQ_contamination_threshold = 10, MQ_completeness_thresh... |
37ffc41ee1f15467151b44b59753f3809b4b766d5447bc482528cbbe79046569 | R | 1,705 | 42 |
library("limma")
setwd("/Users/hongbin/Desktop/r work document")
#10.3 limma分析需要 行为待测物、列为样本
df <- read.csv("完整数据.csv",header = TRUE,row.names = 1)
df <- as.data.frame(lapply(df,as.numeric),row.names = rownames(df))
#要把rownames带上，否则会乱
#过滤含有0的行
row_sub <- apply(df, 1, function(row)all(row !=0))
df2 <- df[row_sub,] #... |
73e0861244caff9e8f322276ecd9d84d0cad3a53938ecd52d70165c516b03282 | R | 1,708 | 42 | setwd("/Users/zhangyuan/Google Drive/2021_HCP_Gender_DNN")
rm(list=ls())
library(dplyr)
library(ggplot2)
library(ggpubr)
library(ggforce)
hcp_sessions = c("LR_S1","LR_S1","RL_S1","RL_S1","LR_S1","RL_S1")
test_sessions = c("LR_S1","LR_S2","RL_S1","RL_S2","nki_645","nki_645")
#i = 1
for(i in 1:6){
fname1 = paste("... |
8386c9eb3ee0202328859d01679c36d3089187192765f2fbe973893e2affc604 | R | 1,714 | 42 | #' plot_readscatter(opt = NULL)
#'
#' Makes a scatterplot of percentage of reads assembled into contigs by sequencing depth.
#' @export
plot_readscatter <- function(opt = NULL){
readdata <- opt$readdata
phenotable <- opt$phenotable
phenolabels <- opt$phenolabels
readdata <- readdata[opt$phenotable$Sam... |
ce615ef3d3a659e1cbcb1c9f7dadc704697ea116acc5edb2f985eaee2feb7c45 | R | 1,717 | 54 | library("muxViz")
#Or build by specifying layers
Layers <- 3
Nodes <- 100
NodeTensor <- list()
#Generate an edge-colored network
g <- barabasi.game(Nodes, m=1, directed=F)
g.list <- list()
for(l in 1:Layers){
g.list[[l]] <- delete_edges(g, sample( E(g), floor(0.2*length(E(g))) ) )
NodeTensor[[l]] <- get.adja... |
a3c0f7c00b386219ea5309f54ddf8b99693ebe5d4d8c88a8aeb4a726bd7025b0 | R | 1,721 | 39 | test_that("getQC and addQC", {
data(GlobalPatterns, package = "mia")
tse <- GlobalPatterns
mat <- assay(tse, "counts")
# Samples
res <- getQC(tse)
expect_s4_class(res, "DataFrame")
expect_identical(rownames(res), colnames(tse))
expect_equal(res[["total"]], unname(colSums(mat)))
expec... |
59c2cacecf6ff60cc5a84cba4d921afee304ee738c0a001f305f56252d983734 | R | 1,740 | 45 | df = read.csv('/scratch/users/banwang3/celllines/analysis/ASE_norm/human/filtered/ASE_value_fromrawcounts.txt',sep='\t')
rawcounts = read.csv('/scratch/users/banwang3/celllines/analysis/ASE_norm/human/filtered/rawcounts.txt',sep='\t')
idxa=grep('total',colnames(rawcounts))
rawcounts=rawcounts[,-idxa]
genenamesall=df$ge... |
b2beeb01d2077b1a5168a0f9d96cf7641c6c0967a1e1655906a0137e34540660 | R | 1,741 | 75 |
#' mini Giotto object for spatial single-cell resolution data
#'
#' Mini Giotto object created from the seqFISH+ data.
#'
#' @docType data
#'
#' @usage data(mini_giotto_single_cell)
#'
#' @format An object of class \code{"giotto"}; see \code{\link[Giotto]{createGiottoObject}}.
#'
#' @keywords datasets
#'
#' @referenc... |
952f439b2a304382e07030703b20b96fe036099deaabd90460fdf4274b7857da | R | 1,746 | 53 | # Don't perform any further tests on CRAN due to time of running the complete
# test.
testthat::skip_on_cran()
testthat::skip_on_ci()
debug_flag <- FALSE
familiar:::test_plots(
plot_function = familiar::plot_shap_dependence,
data_element = "shap",
shap_max_iterations = 10L,
outcome_type_available = c("continu... |
7405926e4f1f6fc8037e00a9e5f743b1812d655806e63aa2cd0077be9655805a | R | 1,776 | 37 | #'
#' Calculates the sPCC as proposed in the iENA paper
#'
#' @param y the dataframe with preprocessed expression data; the rows are genes and the columns samples
#' @param sample for which sample (number according to the columns in y) must the sPCC be calculated
iENA <- function(y, sample){
sample_name ... |
f749d728410c75e56ca84771e08a6d6f63c65c775155b79cfcc492d9719cdc9d | R | 1,778 | 47 | setwd("/Users/zhangyuan/Google Drive/2021_HCP_Gender_DNN")
rm(list=ls())
source("scripts/brainbeh/2022CCA/myRFunc_updated.R")
library(ggplot2)
# library(ggrepel)
# library(reshape2)
#### load existing results for plotting ####
midfix = "HCP_LR_S1_CCA_msubjs_sFC_cognition"
fname = paste("results/brain_behav/sFC_PCvers... |
508208228f996d0fb08015cb1fb8a99ff7d1c0af558838425b3c28e49e370afa | R | 1,787 | 45 | getARvsDiff <- function( tmpMatI, tmpMat , n_row, n_col){
tmpAR <- vector()
tmpLatDiff <- vector()
tmpMatrix<- matrix( 0 , n_row, n_col )
for( i in 1:dim( tmpMat)[1]){
if( is.na( tmpMat[ i, 2])){
if( length( tmpAR ) == 0 ){
tmpAR[ length( tmpAR ) + 1 ] <- tmpMat[i,1]
}
else{
... |
f6e4690fd7d748ae618d571ad2e6cb8f33cfcb5219baccdbb80443b25b6ff92b | R | 1,801 | 64 | combine=function(lst, alpha = 0.05, collapse = F){
n=length(lst)
seed=do.call("c",lapply(lst, function(x){x$seed}))
nperm=do.call("c",lapply(lst, function(x){x$nperm}))
nlocations=do.call("c",lapply(lst, function(x){x$nlocations}))
alternative=do.call("c",lapply(lst, function(x){x$alternative}))
if (leng... |
ba46e22c3c9a20d996afd30c1f273553a8ffc3c9e6bac8492affc91ba88ab9a5 | R | 1,803 | 30 | #####################
## ROSE - super enhancer detection code
#####################
#This function calculates the cutoff by sliding a diagonal line and finding where it is tangential (or as close as possible)
calculate_cutoff <- function(inputVector, drawPlot=TRUE,...){
inputVector <- sort(inputVector)
inputVector[... |
398a29cb343cace338ebed87ca4ad0b3409b97d428f349c20c3c62eca1906b34 | R | 1,807 | 61 | # Stability selection methods, as in Wald R, Khoshgoftaar TM, Dittman D, Awada
# W, Napolitano A. An extensive comparison of feature ranking aggregation
# techniques in bioinformatics. 2012 IEEE 13th Int. Conf. Inf. Reuse Integr.,
# IEEE; 2012, p. 377-84. These methods are based on Haury AC, Gestraud P, Vert
# JP. The ... |
8b4e962b741577c2fa8372067270c7fb58a18663a28d42905a87bf9358150c06 | R | 1,808 | 68 | .get_available_rank_aggregation_methods <- function() {
return(c(
"none",
"mean",
"median",
"best",
"worst",
"stability",
"exponential",
"borda",
"enhanced_borda",
"truncated_borda",
"enhanced_truncated_borda"
))
}
.optimise_feature_occurrence_threshold <- function(vim... |
98c7dc0401f050f0d01ce52cbba87747e9dabdd74b77d33ecfb5ec2d291e9051 | R | 1,824 | 47 | # shiny stuff for saving a dataset in the data tab
shinyFileSave(input, "data_save", root=c(`Working Directory`='.', getVolumes()()), filetypes=c("csv", "xlsx"))
shinyFileSave(input, "data_saveall", root=c(`Working Directory`='.', getVolumes()()), filetypes=c("xlsx"))
observeEvent(input$data_save, {
if (is.... |
eed5cdc638a7dc0ecafcf1bdf12c5e0cbfec7ca76c4ac9e9f585e13d420b3ba3 | R | 1,858 | 59 | #################################################
## Retinol gene priortisation - ABF finemapping
## William Reay (2022)
#################################################
library(dplyr)
library(data.table)
## Load ABF function from GitHub - https://github.com/Williamreay/Pneumonia_meta_GWAS/blob/master/Finemappin... |
0deb6436635506d62043c7114b05c0c277f59fc42389fece4be479b5b280ad55 | R | 1,865 | 76 | ####################################
## Retinol ~ protein colocalisation
## William Reay (2022)
#####################################
setwd("~/Desktop/Retinol_GWAS/ARIC_MR_and_coloc/")
library(dplyr)
library(data.table)
library(readxl)
library(coloc)
## Read in retinol GWAS
Ret_GWAS <- fread("../Meta_analyses/Co... |
7050aa2136f3f853bfc3ae464d5ef15f908bac199a1d1e579748febf62567736 | R | 1,871 | 71 | testConditionValid = function() {
t = T
tryCatch({
.test = data$raw[[input$events_dataset]][, eval(parse(text=input$events_conditions))]
}, error = function(e) {
t = F
}, warning = function(e) {
t = F
})
t
}
condition.events = function(.dt, .el, .t, c, if.al... |
de443906ac4f17b80c2eda05afdc4da001d17b4a610310945e881146890c4e71 | R | 1,882 | 58 | library(ggplot2)
library(plotly)
library(tidyverse)
cases <- c("RB-1745-2486", "RB-1745-2911", "RI-1933-1142", "RI-1933-2449", "RI-1933-2462",
"RI-1933-2463", "RI-1933-2470", "RI-1933-2487", "RI-1933-2611", "RI-1933-2630",
"RI-1933-2687", "RI-1933-2701", "RI-1933-2712", "RI-1933-2852", "RI-193... |
4d4a1e62947d667a047ea3fb0e6772f9d4de326a6f2179ddd4eb20c1fe905d9b | R | 1,896 | 38 | #' process_MAGs(opt = NULL)
#'
#' JAMSalpha function
#' @export
process_contig_LKTs <- function(opt = NULL){
if (opt$analysis == "isolate"){
opt <- evaluate_LKTs(opt = opt, contigsdata_name = "contigsdata", output_list_name = "taxonomic_completeness_and_counts", taxlvls = "LKT")
} else if (opt$analy... |
55bb0d9af337743166406038e4cc3669600d67f6bbe85df34b0ea5a69d44b938 | R | 1,907 | 54 | library(stringr)
#library(dplyr)
foldername='FABP7'
input_path=paste0('/scratch/users/banwang3/references/outputs/',foldername,'/')
align2='hg38'
#align2s=c('hg38','pantro6')
filename=paste0('SNPlist_',align2,'_4096bp.bed')
SNPs=read.csv(paste0(input_path,filename),sep='\t',header=F)
SNPs=SNPs[,1:4]
colnames(SNPs)=c('c... |
2c644fc867393a93b16e0e2da1b4c309f8661ada6938527a23c0c4cf4cdc068f | R | 1,910 | 84 | # Simple ensemble aggregation, as in Wald R, Khoshgoftaar TM, Dittman D, Awada
# W, Napolitano A. An extensive comparison of feature ranking aggregation
# techniques in bioinformatics. 2012 IEEE 13th Int. Conf. Inf. Reuse Integr.,
# IEEE; 2012, p. 377-84.
.compute_rank_mean_score <- function(x) {
# Aggregate using m... |
2cca03e0bdb7ce59ffa62acdb639bbd9b668d594ab88d2b5d2bccb2b47d89e83 | R | 1,911 | 44 | ---
title: "Setup Infomap Fanmod executables"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Setup Infomap Fanmod executables}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
In order to ... |
d2bd0127331a89f2d48f838a7cc0b7d266d4a963ca1ef446db60b2debf05000f | R | 1,914 | 66 | library(shiny)
library(pheatmap)
library(grid)
library(stringr)
library(RColorBrewer)
library(circlize)
library(ggplot2)
# Load Data
load("data.Rdata") #object called data
# tf names
all_tfs <- rownames(data)
# Server
server <- function(input, output) {
# Reactive values to store the selected tfs
selected_tfs_da... |
b75c4df245779695ff7ba1e509eb35aaa34dd9c9b6d1c2e9bbb3127be02e67da | R | 1,916 | 54 | library(stringr)
#library(dplyr)
foldername='GAD1_centeratHAR'
input_path=paste0('/scratch/users/banwang3/references/outputs/',foldername,'/')
align2='pantro6'
#align2s=c('hg38','pantro6')
filename=paste0('SNPlist_',align2,'_4096bp.bed')
SNPs=read.csv(paste0(input_path,filename),sep='\t',header=F)
SNPs=SNPs[,1:4]
colna... |
64938dc7bd2eb5a6deaf0921c2d1009c80cd3ba8725d53d82c278d7b2ce7c9ea | R | 1,921 | 39 | library(argparse)
parser <- ArgumentParser()
parser$add_argument("--generator", default="FABP7")
args <- parser$parse_args()
foldername=args$generator
input_path=paste0('/scratch/users/banwang3/references/outputs/',foldername,'/')
for (align2 in c('hg38','pantro6')){
filename = paste0(input_path,'SNPlist_',align2,'... |
8a0e80f63cef5a59fd79edfe34a6298c5e3c3d1a4d864b53643bcd16fb022578 | R | 1,928 | 44 | #' filter_sample_by_class_to_ignore(SEobj = NULL, variables = NULL, class_to_ignore = NULL)
#'
#' Filters a SummarizedExperiment object by several criteria.
#' @export
filter_sample_by_class_to_ignore <- function(SEobj = NULL, variables = NULL, class_to_ignore = NULL){
ptb <- as.data.frame(colData(SEobj))
all... |
762889656438bcc7d22d7411dcd1f54bd0bcf0d7485f66e7cd0e807f0ae7af7a | R | 1,929 | 74 | # buildNNmatrixDist=function(distMat, max.radius=20){
# p=nrow(distMat)
# dist_range = sort(c(ceiling(min(distMat, na.rm=T)),ceiling(min(c(max(distMat,na.rm=T),max.radius),na.rm=T))))
# dist_range_sequence = seq(dist_range[1], dist_range[2], by=1) # different radii to consider
#
# out=foreach(i=1:p,.combine=... |
dced432e9203f4ebc693671733ca4cb94e471de1f18e8e68da7126329376444a | R | 1,962 | 59 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
set_seed <- function(seed) {
invisible(.Call('_CLEAN_set_seed', PACKAGE = 'CLEAN', seed))
}
CleanMeanC <- function(ymat, NNmatrix, nperm, s) {
.Call('_CLEAN_CleanMeanC', PACKAGE = 'CLE... |
dd7b5dd8257f1b42b7d6a594f53c72bd4bdcd094e48ce04f6fb01b23f48943e4 | R | 1,982 | 53 |
# This script conducts testing & plotting on ROI-based MVPA
rm(list = ls())
source('Setup.R')
usedir = dirname(getwd())
figDir = file.path(usedir,'Figs')
SubInfo = read.xlsx(file.path(usedir,'SourceData.xlsx'),'SubjectConds')
Corr_diff_vals_df_summary = read.xlsx(file.path(usedir,'SourceData.xlsx'),'Fig6d')
#######... |
e4ebf3103b02544d12f61eb16f77c5d33f6649a2f2f085844dd374fec9ce5ea9 | R | 1,993 | 68 | library(data.table)
library(ggplot2)
library(ggtext)
library(tidyverse)
## https://danielroelfs.com/blog/how-i-create-manhattan-plots-using-ggplot/
args <- commandArgs(T)
gwas_data <- fread(
args[2],
sep="\t",
)
unique(gwas_data$CHROM)
gwas_data$CHROM <- as.numeric(recode(gwas_data$CHROM, X = "23"))
gwas_d... |
4b7f8bde53b667dbeab7d25d40a8e24c05aae3ff1fd452b7155ee407d73cae3c | R | 1,996 | 64 | familiar:::test_all_vimp_methods_available(
familiar:::.get_available_correlation_vimp_methods(show_general = TRUE)
)
# Don't perform any further tests on CRAN due to running time.
testthat::skip_on_cran()
testthat::skip_on_ci()
familiar:::test_hyperparameter_optimisation(
vimp_methods = familiar:::.get_available... |
f7c70ff0c3913e5a43f40144a8b153cc77677f13b63e78d469355179f4aaa074 | R | 1,997 | 58 | context("addNMF")
test_that("addNMF", {
skip_if_not_installed("NMF")
data(GlobalPatterns, package="mia")
#
set.seed(123)
tse <- GlobalPatterns
tse <- agglomerateByPrevalence(tse, rank = "Phylum")
tse <- addNMF(tse, k = 2, seed = 123)
expect_named(reducedDims(tse),"NMF")
expect_true(is.matrix(reducedDi... |
b516f0c33b2f738798de1a9a695473f28d4083fb37e5344d935eceace3893b2a | R | 2,001 | 55 | # Load necessary packages
library(mia)
library(stringr)
library(S4Vectors)
### Defining FILE PATHS ###
path_to_assay <- "Tito2024QMP_assay.csv"
path_to_coldata <- "Tito2024QMP_coldata.csv"
path_to_rowdata <- "Tito2024QMP_rowdata.csv"
### Importing FILES ###
Tito2024QMP_assay <- read.csv(path_to_assay, row.names = 1... |
e208a6eb2a21ba881ed3a36adadbc2c3762f988a3ba816deefe1ddd31a6243e9 | R | 2,001 | 75 |
rm(list=ls())
library(openxlsx)
library(R2jags)
library(plyr)
# source data
sourceDir = '~/Desktop/SourceData'
# modeling dir
behmodelDir = '~/Desktop/BehavioralModeling'
dat = read.xlsx(file.path(sourceDir,'OdorPredRes.xlsx'))
datinfo = read.xlsx(file.path(sourceDir,'SubjectConds.xlsx'))
# exclude 4 subs: only ... |
79e803b0522d580650038d9ac4fb5407e22bad9c8f2a7b1031d5456589bdedf3 | R | 2,002 | 43 | #' kraken_classify_taxonomy
#'
#' JAMSalpha function
#' @export
kraken_classify_taxonomy <- function(opt = NULL, fastafile = NULL, confidence = 0){
#Default is kraken2
#Count kmers
flog.info(paste("Counting k-mers of query sequences with kraken2 and using confidence score", as.character(confidence)))
... |
99f9f5c0ac5ead791f82a31540a49fbea12dfeaa780779be6663118ccd7202ca | R | 2,011 | 56 | library(stringr)
#library(dplyr)
foldername='FABP7_centeratpromoter'
input_path=paste0('/scratch/users/banwang3/references/outputs/',foldername,'/')
align2='pantro6'
#align2s=c('hg38','pantro6')
filename=paste0('SNPlist_',align2,'_4096bp.bed')
SNPs=read.csv(paste0(input_path,filename),sep='\t',header=F)
SNPs=SNPs[,1:4]... |
edf89ecc378b9f399d74642cd19fa2548d81f99f93f1914a7e05b7c4602b6947 | R | 2,028 | 54 |
rm(list = ls())
source('Setup.R')
usedir = dirname(getwd())
figDir = file.path(usedir,'Figs')
SCdat = read.xlsx(file.path(usedir,'SourceData.xlsx'),'SFig1')
# this only contains 31 subjects
# 3 plea ratings are corresponding to 3 odors
# same for the intensity ratings
# what are the scales of the ratings?
# pleasant... |
b3a7909f4467f955c4dc1d2d2fe8b56d27a5af17bd79ae3d0b402ac510e72037 | R | 2,051 | 68 | library("muxViz")
#Remind to comment this line to have different results
set.seed(123456)
Layers <- 10
Nodes <- 100
cat("####################################\n")
cat("# Layer-Layer correlations\n")
cat("####################################\n\n")
layerCouplingStrength <- 1
networkOfLayersType <- "categorical"
g.lis... |
8a4a3c027b3ebf624e80880e637485cfc12310b053df8145e48c758e078316cd | R | 2,060 | 58 | #############################################################
##################### NETWORK FEATURES ######################
#############################################################
#######################################################
#### Auxiliary function to compute network features
## Main function to cal... |
2a9704e0cc265fba58e49849decac746a8009191b06550e8a9c5a4c1fb839dcd | R | 2,103 | 54 | library(ggplot2)
library(stringr)
library(dplyr)
library(patchwork)
align2='human'
TPM = read.csv('/scratch/users/banwang3/celllines/analysis/ASE_norm/human/filtered/rawcounts.txt',sep='\t')
idx =grep('total',colnames(TPM))
TPM = TPM[,-idx]
idx = grep('RNA',colnames(TPM))
CPM = sweep(as.matrix(TPM[,idx]), 2, as.doubl... |
59836d9ec9c801d039691cbbcb6d66a4f85517e8ed8fbc3931dace607d514657 | R | 2,106 | 59 | ###############################################################################
#################### GenR: residualization & weighted PCA #####################
###############################################################################
library(doParallel)
library(permute)
################################... |
312a22b68f121839cde8f19af4cfc35e9616c706c1b581d8f2e1f6f8c9c1fd3b | R | 2,110 | 43 | #library(UpSetR)
library(ggplot2)
library(ComplexUpset)
output_path= '/scratch/users/banwang3/celllines/scripts/RScripts/suppplots/'
input_path='/scratch/users/banwang3/celllines/scripts/RScripts/figscripts/suppfigscripts/ATAC_counttable/'
celltypes = c('CM','SKM','HP','PP','MN')
df = read.csv(paste0(input_path,'Peakl... |
a329f4a1bf0cbc2e0c9ef8c6e166341e51d3329dcc0082b18134a5d802870111 | R | 2,126 | 61 | #################################
## Partitioned h2 - GTEx/Franke tissues and cell types
## William Reay (2022)
#################################
library(dplyr)
library(data.table)
library(ggplot2)
library(ggpubr)
setwd("~/Desktop/Retinol_GWAS/Partitioned_h2/")
## Read in all three Stouffer meta partitioned h2
A... |
05ecf034b773afddd33c80ed24c8d12bd44bc3a75a7b249296c9aa5a1259d980 | R | 2,127 | 47 | #' make_classes_df(curr_pt = NULL, compareby = NULL, wilcox_paired_by = NULL)
#'
#' This function makes a data frame with sample to class mapping for use by calculate_matrix_stats.
#' @export
make_classes_df <- function(curr_pt = NULL, compareby = NULL, wilcox_paired_by = NULL){
#Create a standard data frame mapp... |
15c8be2244eeb9bba05502cfdca6b43217f46571e6319aedd97dde72cff9f441 | R | 2,128 | 78 | # Don't perform any further tests on CRAN due to time of running the complete
# test.
testthat::skip_on_cran()
testthat::skip_on_ci()
debug_flag <- FALSE
# Without setting the number of important features.
results <- familiar:::test_export_specific(
export_function = familiar:::export_permutation_vimp,
data_eleme... |
c6d78f34f3407097d0e41f172f09cc0cb990b40f13bc789e5a2315cd9df61a2d | R | 2,131 | 74 | library(MODifieR)
library(tidyverse)
library(biomaRt)
library(DOSE)
library(clusterProfiler)
top_results <- read.csv("local_data/burden_results/burden_vf_threshold_full_results.tsv", sep = "\t")
top_results <- top_results[order(top_results$p), ]
top_genes <- top_results[1:(nrow(top_results)*0.01), "gene"]
mart = use... |
7c61aa73a064705dce3e6f77061d99480ec87752c929ba92aac50b16f08030b6 | R | 2,142 | 64 | # Don't perform any further tests on CRAN due to time of running the complete
# test.
testthat::skip_on_cran()
testthat::skip_on_ci()
debug_flag <- FALSE
# Generic test
familiar:::test_plots(
plot_function = familiar:::plot_sample_clustering,
not_available_all_predictions_fail = FALSE,
not_available_some_predic... |
17aa56a262ec830eefced80a871a901ceddb84a9b3fce6aee5eb3a0999688952 | R | 2,176 | 54 | #' find_accession_from_keyterm(expvec = NULL, analyses = NULL, SEobj = NULL, keyterms = NULL)
#'
#' Returns the accession number(s) of a feature(s) matching a general expression present in a SummarizedExperiment or metagenomeSeq object.
#' @export
find_accession_from_keyterm <- function(expvec = NULL, analyses = NULL,... |
62aca33bfd806f6e26454bfae2eb091657bcde6d63e04fa86c55f5d8c32ad00a | R | 2,176 | 76 | # First test if all selectable learners are also available
familiar:::test_all_learners_available(
learners = familiar:::.get_available_cox_learners(show_general = TRUE)
)
# Don't perform any further tests on CRAN due to time of running the test.
testthat::skip_on_cran()
testthat::skip_on_ci()
# Generic test
famili... |
02fbfcb83e5963afc6a6589ccc9039f14466e5656eb266f7979f53e38c42d227 | R | 2,181 | 53 | # import-rds.R
shinyFileChoose(input, "data_import_file_r", root=c(directories, `Working Directory`='.', getVolumes()()), filetypes=c("rds"))
observeEvent(input$data_rds, {
output$data_import_r_filename = renderText("select a *.Rds file from a previous session")
showModal(
modalDialog(title = "... |
6b65aeff124b16d4c494e554467368124c145e4a63f2012bb840b61e9c77b332 | R | 2,185 | 57 | library(xlsx)
basePath = "/data/bcu_projects/MelBrainSys_PostdocProject_Gruetzmann/publications/2022-my-MelBrainSys-paper/scripts-etc-for-publication/"
setwd(basePath)
immunePWs = read.xlsx(file = "annotation/pathway-definitions.xls", header = T, sheetName = "ImmunePathways")
colnames(immunePWs) = gsub("\\."," ",coln... |
cfa328fc5eed4dac5bdd084f0d1375254efe4628e9f77adf450d59c3bed809f6 | R | 2,194 | 84 | spLeverage=function(data, distmat=NULL, mod0=NULL, sacf="exp", nngp=T, nngp.J=50){
if (!is.null(mod0)){
data=t(lm(t(data)~mod0)$residuals)
q=ncol(mod0)
} else{
q=0
}
covreg.fit=CovReg(data, distmat, n.covariates=q, kernel=sacf)
if (nngp & nngp.J<nrow(distmat)){
NNGPinfo=constructNNGPinfo... |
44e4fb1204d89b3a0035d24e3b3d1ff68565b5e8da57398308327c741343ef6b | R | 2,199 | 62 | #############################################################
################# READ EMPIRICAL DATASET ####################
##############################################################
## load datasets
reats_url <- "http://www.timeseriesclassification.com/Downloads/Archives/Univariate2018_arff.zip"
download.file(rea... |
735b1df599bf987cfe2ab7b124b9384e4a7b277c342dc48d1653e882c3b10e46 | R | 2,216 | 60 | #' Create a \code{TreeSummarizedExperiment} object from \sQuote{DADA2} results
#'
#' @param ... Additional arguments. For \code{convertFromDADA2}, see
#' \code{mergePairs} function for more details.
#'
#' @details
#'
#' \code{convertFromDADA2} is a wrapper for the
#' \code{mergePairs} function from the \code{dada2} pac... |
9a7d86201646956bc810297dd2b4f335d2e5022eeebd76ed73f6a67acaa09deb | R | 2,222 | 48 | # Inference about theta( 4Nu) from an observation of the number of segregating
# sites at a locus with recombination.
#
# The following script will generate independent draws from the
# posterior distribution of theta conditional on the observed
# number of segregating sites is equal to 14 (nsam=10) and assuming... |
c13555bc363ce0520b8aea43e49e9a7559c91a1e8423fa4aff3b56c69c155eda | R | 2,223 | 56 | library(ggplot2)
backup_path='/scratch/users/banwang3/celllines/data_backup'
align2='human'
TGF = c('NGF','BDNF','NTF3','NTF4','NTRK1','NTRK2','NTRK3','NGFR')
length(TGF)
TGF = data.frame('gene'=TGF)
final=c()
for (celltype in c('CM','MN','PP','HP','SKM','RPE')){
deseq2 = read.csv(paste0(backup_path,'/scratch/use... |
c574734b9c56cd99f56e303ba73295cdce3514d8ff8356b32b5274848b4c02a3 | R | 2,226 | 102 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
setClass("familiarCoreLearnVimp",
contains = "familiarVimpMethod"
)
setClass(
"familiarCoreLearnGiniVimp",
contains = "familiarCoreLearnVimp"
)
setClass(
"familiarCoreLearnMDLVimp",
contains = "familiarCoreLearnVimp"
)
setClass(
"fami... |
998f3120999206c9f4e3b21f2d9f7c8c7f95cab79731657109ff9cdfc0112b50 | R | 2,233 | 86 | get_newp <- function(perm_stat, tref){
p.perm <- length(perm_stat[abs(perm_stat) >= abs(tref)])/length(perm_stat)
return(p.perm)
}
grouped_perm_glm <- function(tbl, formla, var_to_perm, permNum = 1000, seed = 42){
require(modelr)
require(dplyr)
set.seed(seed)
perms <- modelr::permute(tbl, permN... |
2be3c6a2dd37eb9e0d0d83beb8b1e082f1e29e9b04b7a67fa6058794b74aa97d | R | 2,242 | 56 | ####################################################################################
###################### Read the data, residualization, weighted PCA ################
####################################################################################
library(doParallel)
library(permute)
############### read the d... |
45c2fc2643ecd7444eeb369e28ef42c60c1444c9ed6fc7f2db7d1b4b729d587e | R | 2,242 | 48 | #library(UpSetR)
library(ggplot2)
library(ComplexUpset)
output_path= '/scratch/users/banwang3/celllines/scripts/RScripts/suppplots/'
input_path='/scratch/users/banwang3/celllines/scripts/RScripts/figscripts/suppfigscripts/ATAC_counttable/'
celltypes = c('CM','SKM','HP','PP','MN')
peaks=c()
for (celltype in celltypes){
... |
4d5f86c08b768f670452e1f5f73b963676bad8be961636e52e742ebfb600ec06 | R | 2,245 | 42 | #' rename_MAG_Accession(MAG_Accession = NULL, Cluster_Number = NULL, mag2species = NULL)
#'
#' Builds a Contextualized/Consolidated Species Bin (CSB) name for a MAG given its functional
#' cluster number. Used internally by contextualize_taxonomy (q.v.).
#'
#' Hoisted to package top-level (see cluster_strains for the r... |
409b3dea3aa9feee9e2f6dd989573411775cfb96be14d1564169a83c617f3f38 | R | 2,247 | 53 | # Gina Norato; gina.norato@nih.gov
library(plyr)
library(dplyr)
library(tidyr) # separate()
library(stringr) # str_pad()
library(ggplot2)
read.csv(file="source.data.supp.fig.9e-9g.csv")
############
## Supp Fig 9E
#############
d<-subset(t2,timepoint!="Cross-sectional",select=c("analysisID","Group",... |
12330cda0bdff5db4168e30782ba1eaafe7707c05062ba02c6b7aeb63cb58855 | R | 2,248 | 59 | # Codes for two-dimensional projection of patient malignant cells by cell state
# Can be run in R
library(ggplot2)
# Read rds file contained results of cell state signature scores (can be obtained using example codes in "MalignantCellStateAssignment.R"), cell state assignment (can be obtained using example codes in "... |
58e255a1ff2a4b037509d3f89522ae94adcf2f03d2405c8744309daa3bf8d301 | R | 2,249 | 33 | #' gather_run_info
#'
#' JAMSalpha function
#' @export
gather_run_info <- function(opt = NULL){
flog.info("Gathering information on JAMS analysis run.")
useful_parameters <- c("analysis", "verstr", "JAMS_Kdb_Version", "krakenconfidencescore", "prefix", "threads", "contigs", "assemblyaccession", "readorigin", ... |
d78c1ac9afedfa36fa731688016b6d0adfc7a08778ad9adf32d70b65e12e23bf | R | 2,256 | 63 | context("addLDA")
test_that("addLDA", {
skip_if_not_installed("topicmodels")
data(GlobalPatterns, package="mia")
#
tse <- GlobalPatterns
tse <- addLDA(tse)
expect_named(reducedDims(tse),"LDA")
expect_true(is.matrix(reducedDim(tse,"LDA")))
expect_equal(dim(reducedDim(tse,"LDA")),c(26,2))
red <- reduced... |
4805751a8a53bd0429b21123288686ff6b158522992d26eb938cd7fc7d8794aa | R | 2,268 | 57 | library(ggplot2)
library(stringr)
backup_path='/scratch/users/banwang3/celllines/data_backup'
df = read.csv(paste0(backup_path,'/scratch/users/banwang3/celllines/analysis/ASE_norm/human/filtered/TPM.txt'),sep='\t')
output_path='/scratch/users/banwang3/celllines/scripts/RScripts/Rplots/'
idx =grep('total',colnames(df))
... |
bef160f47a0dd94bea638f8c4dde0ccb0c312fdf8045b0e5f5759772ef60e74e | R | 2,272 | 50 | #' make_featuredata_from_bedfile
#'
#' JAMSalpha function
#' @export
make_featuredata_from_bedfile <- function(opt = NULL, bedfile = NULL){
if (is.null(bedfile)){
bedfile <- opt$bedfile
}
featuredata <- fread(file = bedfile, header = FALSE, stringsAsFactors = FALSE, fill = TRUE, sep = "\t")
... |
a07cd438cad915b45deac03526aed1b1a3ff9cc12afa99d20dddaf33337f3e68 | R | 2,275 | 52 | # load libraries -----------------------------------------------------------
library(data.table)
library(Seurat)
library(ggplot2)
library(dplyr)
library(viridis)
# read in bulk RNA-seq data ------------------------------------------------
PBRAclusters<-read.delim("input_data/PBandRA_clusters.tsv")
RAvControl<-read.d... |
11fb65045c952c4e5bae8bc1dabd278602744ecb21c93ad22440ddf2e9dc5756 | R | 2,276 | 56 | library(JAMS)
#Read classes into list
atbclasses <- scan("antibiotic_classes.txt", what="", sep="\n")
atbclasses <- strsplit(atbclasses, "\t")
names(atbclasses) <- sapply(atbclasses, `[[`, 1)
atbclasses <- lapply(atbclasses, `[`, -1)
#Read phenotypes into table
atbphenotypes <- read.table(file = "phenotypes.txt", hea... |
05397906a0ab6933ab2c958779a126d412558019ddd048f8852a0bd4ee0444e6 | R | 2,280 | 78 | # Don't perform any further tests on CRAN due to running time.
testthat::skip_on_cran()
# power.transform and other packages are required.
if (!rlang::is_installed("power.transform")) testthat::skip()
# Create data.table.
data <- familiar:::test_create_good_data(
outcome_type = "binomial",
to_data_object = FALSE
... |
acb81923ef2d50a109fefa180ae5b26878af991c89d11a22b1013ca433696d8c | R | 2,280 | 80 | ###########################
## CAUSE creatinine and retinol
## Retinol IVW
## Dr William Reay (2023)
###########################
library(data.table)
library(dplyr)
library(cause)
library(genetics.binaRies)
library(tidyr)
library(ggplot2)
library(ieugwasr)
setwd("~/Desktop/Retinol_GWAS/")
Retinol_df <- fread("Met... |
c5bec66376b020c739d708b7da22534195ef1cf3fc110c74213d3ecc06337587 | R | 2,288 | 74 | # Don't perform any further tests on CRAN due to time of running the complete
# test.
testthat::skip_on_cran()
testthat::skip_on_ci()
debug_flag <- FALSE
familiar:::test_plots(
plot_function = familiar::plot_shap_force,
data_element = "shap",
shap_max_iterations = 10L,
outcome_type_available = c("continuous",... |
d8479f3bd163c7c1f67b0da2e8aba3fc7204f0fe8bcfef55ac400508be3ff7a8 | R | 2,292 | 65 | get_zdFF <- function(reference, signal, smooth_win=10, remove=200, lambda=5e4, itermax=50, differences=1)
{
# Calculates z-score dF/F signal based on fiber photometry calcium-idependent
# and -dependent signals.
#
# This program is a translation in R of the Python source code of get_zdFF.py
#
# Input
# ... |
2b6836f55ff01a34851cc2ff7f01f9831c02f695ad5b23ef5c552740b7f25b83 | R | 2,309 | 62 | context(".estimate_evenness")
test_that(".estimate_evenness", {
test_internal_.estimate_evenness <- function(tse){
# Check that every index is calculated by checking the column names from
# colData.
# Check that the order of indices is right / the same as the order
# in the input ... |
687e9829a1c583cec58cc03b7657a795edb9a0581e7e0f0a0f8f194334abb458 | R | 2,322 | 70 | # Don't perform any further tests on CRAN due to time of running the complete
# test.
testthat::skip_on_cran()
testthat::skip_on_ci()
debug_flag <- FALSE
# FDR-corrected p-values. Note that failure to predict survival probabilities
# does not influence the produced plots.
familiar:::test_plots(
plot_function = fami... |
1b51cbb6328edb4f711f2a16ec805971da81cbcbd2a6cebe05c2baea210d49a7 | R | 2,336 | 84 | testthat::skip_on_cran()
testthat::skip_on_ci()
debug_flag <- FALSE
# Simple test
vimp_methods <- c("none", "mim", "mim")
experimental_designs <- c("fs+mb", "fs+mb", "mb")
for (ii in seq_along(vimp_methods)) {
familiar:::integrated_test(
experimental_design = experimental_designs[ii],
vimp_method = vimp_met... |
071962459f572600315668110ab95e7724042cda91e8713cbe365befa3ce0e86 | R | 2,341 | 75 | setwd("/Users/zhangyuan/Google Drive/2021_HCP_Gender_DNN")
rm(list=ls())
source("scripts/brainbeh/2022CCA/myRFunc_updated.R")
#library(vegan) # for cca
library(CCP) # for cca statistical test
library(reshape2)
library(ggplot2)
library(psych) # for pca
################################
########## LOAD DATA ###########
... |
8fdb307afe8a7b7ceb268871dae91a60c954d6e41aee518600135483a4921d77 | R | 2,347 | 91 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# promote_detector -------------------------------------------------------------
setMethod(
"promote_detector",
signature(object = "familiarNoveltyDetector"),
function(object) {
learner <- object@learner
if (learner %in% .get_availab... |
2c4131ad19778c701ef06810e30df3fe626ba4881dd6303eb2e8469b73fa40ea | R | 2,355 | 64 | library(stringr)
#library(dplyr)
foldername='GAD1_centeratHAR'
input_path=paste0('/scratch/users/banwang3/references/outputs/',foldername,'/')
align2='hg38'
#align2s=c('hg38','pantro6')
filename=paste0('SNPlist_',align2,'_4096bp.bed')
SNPs=read.csv(paste0(input_path,filename),sep='\t',header=F)
SNPs=SNPs[,1:4]
colnames... |
0634d981e991e70abcd5f8d49520ad1f7575ed880f422c16cb3a759aaa47c8ef | R | 2,357 | 75 | # First test if all selectable learners are also available
familiar:::test_all_learners_available(
learners = familiar:::.get_available_survival_regression_learners(show_general = TRUE)
)
# Don't perform any further tests on CRAN due to time of running the complete
# test.
testthat::skip_on_cran()
testthat::skip_on_... |
6b60883b846267d5af4109971db9abd6cc19850b75c1beb088c0c07e1088426f | R | 2,371 | 80 | #################################################
## Retinol RapidoPGS style ABF calculation for PGS weighting
## William Reay (2023)
#################################################
library(dplyr)
library(data.table)
library(RapidoPGS)
## Load ABF function from GitHub - https://github.com/Williamreay/Pneumonia_m... |
1bdb33f7b45360b80240f300616bdd4ebfdb6d84d130a8a994dd896758bb045b | R | 2,376 | 77 | # Shiny server definitions
server = function(input, output, session) {
# markdown help documentation
#observe_helpers(help_dir = "help", withMathJax = T)
data = reactiveValues(
# metadata
meta = list(),
# data
raw = list(),
# model history
mo... |
35ce57c35401a32c071e0c9b8d3b173a10b4238dadc9941ee514796337cb9312 | R | 2,380 | 86 | ####################################
## Reverse MR - FinnGen
## William Reay (2023)
####################################
library(dplyr)
library(data.table)
library(TwoSampleMR)
setwd("~/Desktop/Retinol_GWAS/MR_all_cis_acting_SNPs/FinnGen_r8_MR/")
Ret_GWAS <- fread("../../Meta_analyses/Common_var/IVW_METSIM_INTERV... |
246d732c7d124ee4397d443cc070194f7d036dde25ebd756c7f601ac09d27a24 | R | 2,385 | 33 | #' rate_bin_quality(completeness_df = NULL, HQ_completeness_threshold = 90, HQ_contamination_threshold = 5, MHQ_completeness_threshold = 70, MHQ_contamination_threshold = 10, MQ_completeness_threshold = 50, MQ_contamination_threshold = 10, LQ_completeness_threshold = 0, LQ_contamination_threshold = 10, High_contaminati... |
d22c046018bbe83c78884c938e485014a4d1067ad078a936372cb09f3fbb65de | R | 2,406 | 61 | library(ggplot2)
input_path='/scratch/users/banwang3/celllines/scripts/RScripts/figscripts/suppfigscripts/EE/'
output_path='/scratch/users/banwang3/celllines/scripts/RScripts/suppplots/'
keyword='RNA'
includeRPE=T
if (includeRPE){
celltypes=c('CM','HP','MN','PP','RPE','SKM')
endname='includeSKM'
}else{
cell... |
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