sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
17a02dbe6672a9790f92101c545e47dc5899a20350596b4742337af585aa2ef2 | R | 308 | 12 | library(testthat)
library(fafbseg)
if(nzchar(Sys.getenv("FLYWIRE_PRINCIPLES")))
download_flywire_release_data()
if(nzchar(Sys.getenv('CI')) && !identical(Sys.info()[["sysname"]], "Darwin"))
dr_fafbseg()
op <- options('fafbseg.cachedir'=tempfile('fafbseg-tempcache'))
test_check("fafbseg")
options(op)
|
65b1485c84231f4e1d21813518aa260c31914b3c1436a279f96ed6ba240d1a2c | R | 311 | 12 | nodewise.getlambdasequence<-function (x)
{
nlambda <- 100
p <- ncol(x)
lambdas <- c()
for (c in 1:p) {
lambdas <- c(lambdas, glmnet(x[, -c], x[, c])$lambda)
}
lambdas <- quantile(lambdas, probs = seq(0, 1, length.out = nlambda))
lambdas <- sort(lambdas, decreasing = TRUE)
return(lambdas)
} |
4a6cd2549098802157934180c4c82e557689114f7eb1c1ade6aaa576c358ec96 | R | 313 | 11 | library(mlflow)
# define parameters
my_int <- mlflow_param("my_int", 1, "integer")
my_num <- mlflow_param("my_num", 1.0, "numeric")
my_str <- mlflow_param("my_str", "a", "string")
# log parameters
mlflow_log_param("param_int", my_int)
mlflow_log_param("param_num", my_num)
mlflow_log_param("param_str", my_str)
|
0ed5570ba94cfe8e30eadefb57684e83b68308308343ec26fe110e307e7c7546 | R | 323 | 12 | #### Section: Utility Helpers ####
anova_protect_vars <- function(vars) {
if (is.null(vars) || length(vars) == 0) return(vars)
vals <- vapply(vars, function(v) {
if (is.null(v) || is.na(v) || !nzchar(v)) return("")
if (grepl("^`.*`$", v)) v else paste0("`", v, "`")
}, character(1))
vals[nzchar(vals)]... |
af732fc6decf85944ef88ce4e2bdf43920226e28e09d29a460d6e6d58639d27b | R | 331 | 12 | # Set options here
options(golem.app.prod = FALSE) # TRUE = production mode, FALSE = development mode
# Detach all loaded packages and clean your environment
golem::detach_all_attached()
# rm(list=ls(all.names = TRUE))
# Document and reload your package
golem::document_and_reload()
# Run the application
spatialLIBD:... |
44f2ece0a218ed67a19b2f1d0faa2fe6fdc92ebadc503e77ec1e207dae51fbc6 | R | 336 | 14 | shinyUI(
navbarPage("premessa",
header = list(
tags$head(tags$script(src = "shinyoutputbindings.js")),
singleton(tags$head(tags$link(rel = 'stylesheet', type = 'text/css', href = 'spinner.css')))
),
tabPanel("Debarcode data",
uiOutput("debarcoderUI")
... |
b4495769b76ed8a85932e8121a2a5c05cdaa60c5520d355706723304f17bb3f6 | R | 336 | 7 | unlink("man", recursive = TRUE)
roxygen2::roxygenise()
# remove mlflow-package doc temporarily because no rst doc should be generated for it.
file.remove("man/mlflow-package.Rd")
source("document.R", echo = TRUE)
# roxygenize again to make sure the previously removed mlflow-packge doc is available as R helpfile
roxygen... |
d7ba5b4f49a1e3990f5470e043e4f4fc9b2d279c0eede16ddb7521e88f630bf2 | R | 336 | 11 | #' Add random number columns in data frame with specific seed to validate machine learning algorightms
#'
#' @param
#'
#' @return
#'
#' @export
add_randomized_column <- function(df, n) {
stringr::str_c("rnorm_", seq_len(n)) %>%
purrr::reduce(~tibble::add_column(.x, '{.y}' := rnorm(n = nrow(df), mean = 0, sd = 1))... |
1f1c9c19c6f5ec8fa4f5f4435c23ff341f43c85de36cfa22b14b2210bff68472 | R | 341 | 15 | #'
#'
#' @param
#'
#' @return
#'
#' @export
xgb_linear_scores <- function(model, matrix) {
yhat <- predict(model, matrix)
y <- xgboost::getinfo(matrix, "label")
rmse <- sqrt(mean((y - yhat)^2))
mae <- mean(abs(y - yhat))
r2 <- 1 - sum((y - yhat)^2) / sum((y - mean(y))^2)
tibble::tibble(RMSE = rmse, MA = mae... |
111e0aeaec37b23fb12a8589aca6109e02d7cc646359b474ebc0ad469f368c36 | R | 344 | 11 | ## code to prepare `DATASET` dataset goes here
library(nat)
JRCFIBVNC2020MNP.surf <- read.hxsurf('data-raw/VNC_tbars_scale-5.resampled.500k.bin.surf')
usethis::use_data(JRCFIBVNC2020MNP.surf, overwrite = TRUE)
# alias - easiest way just to make a copy sadly
MANC.surf <- JRCFIBVNC2020MNP.surf
usethis::use_data(MANC.... |
88a94b19f967ebe39b4a893aa6377ffd9bfaab6b21201f9259952b7aca81889b | R | 348 | 11 |
ucsc_gene_track<-read.table("ucsc_gene_track.txt",header=T,sep="\t")
exomes<-ucsc_gene_track[,c("chrom", "txStart","txEnd")]
dup<-paste(exomes$chrom,exomes$txStart,sep="_")
dup<-paste(dup,exomes$txEnd,sep="_")
exomes<-exomes[!duplicated(dup),]
#now write file exomes.txt
write.table(exomes,"data/exomes.txt", row.name... |
e7bf6a29b7e0888855b2add925cb1f035de5d54e8f856ea7764a4d1f92974520 | R | 349 | 16 | library("spatialLIBD")
## Obtain the necessary data
if (!exists("sce")) sce <- fetch_data("sce")
pdf(here::here("data-raw", "logo.pdf"), useDingbats = FALSE)
sce_image_clus(
sce = sce,
clustervar = "layer_guess_reordered",
sampleid = "151673",
colors = libd_layer_colors,
... = " LIBD Layers",
... |
af3de9a4f3cb6f21cd08cca782ea3f1f2ba6467b29307234b82c683ad0726695 | R | 351 | 13 | #'
#'
#' @param
#'
#' @return
#'
#' @export
nested_xgboost_cox_cv_save <- function(x, saver = mysavefactory(), name = "nested_xgboost_cox_cv") {
x %>%
purrr::map(~purrr::list_modify(.x, matrix = purrr::zap())) %>%
purrr::map(~purrr::list_modify(.x, model = xgboost::xgb.save.raw(.x$model, raw_format = "json"))... |
249cc8246990f63c84edf67a04fead9a532fb7676a07e25be33a38bbe4784d2b | R | 352 | 7 | # ===============================================================
# 🧮 Linear Model (LM) — fixed effects only
# ===============================================================
lm_ui <- function(id) regression_ui(id, "lm", allow_multi_response = TRUE)
lm_server <- function(id, data) regression_server(id, data, "lm", a... |
f64f9c90e2cf9f234b0424d5038c2dddbf5b7f794e2d28d0a3a24674d43c2584 | R | 353 | 10 | install.packages("easypackages")
library("easypackages")
libraries("readr", "data.table", "dplyr", "tidyr", "gtsummary","openxlsx", "readxl", "MASS", "ggplot2", "forcats", "hrbthemes", "viridis")
edges %>%
pivot_longer(!Real_random, names_to = "type", values_to = "value") %>%
ggplot( aes(x=Real_random, y=value,... |
ab850d871c3cbd9179c27153746d516ed0f12344c1f2ce6b3650cbc13d32ebe0 | R | 355 | 11 |
ucsc_mirna_track<-read.table("hg_miRNA.txt",header=T,sep="\t")
mirnas<-ucsc_mirna_track[,c("chrom", "chromStart","chromEnd")]
dup<-paste(mirnas$chrom,mirnas$chromStart,sep="_")
dup<-paste(dup,mirnas$chromEnd,sep="_")
mirnas<-mirnas[!duplicated(dup),]
#now write file exomes.txt
write.table(mirnas,"data/mirnas.txt", r... |
f89d5eff5e9669bd65f6d5f4de3fcdac37962de910ec05d5bd57cbeafbd986b4 | R | 357 | 11 | op <- choose_malevnc_dataset('MANC')
on.exit(options(op))
test_that("manc_leg_summary works", {
expect_s3_class(mls <- manc_leg_summary(c(10126, 10118)), 'data.frame')
expect_true(nrow(mls)==2)
expect_true(ncol(mls)==13)
expect_s3_class(mss <- manc_side_summary(c(10126, 10118)), 'data.frame')
expect_equal(m... |
88d2996ae8e2bc19759beee4c8a135542f3ba8331e7376ddbf6f80e4f9a392d3 | R | 360 | 16 | prior <- get_prior(samples_LNR)
design <- get_design(samples_LNR)
RNGkind("L'Ecuyer-CMRG")
set.seed(123)
test_that("priorS3", {
expect_snapshot(prior)
expect_snapshot(summary(prior))
vdiffr::expect_doppelganger("prior_plot", plot(prior, map = FALSE, N = 1e2))
})
test_that("designS3", {
expect_snapshot(design... |
89312dab0a9aa8cbb38a1fae03334de2d27517ad8e888564aff63c5ff9048840 | R | 360 | 13 | #'
#'
#' @param
#'
#' @return
#'
#' @export
nested_xgboost_cox_cv_load <- function(x) {
readRDS(x) %>%
purrr::map(~purrr::list_modify(.x, matrix = nested_xgboost_cox_cv_model_matrix(.x$data, .x$event_incident, .x$event_agediff, .x$vars)$dmatrix)) %>%
purrr::map(~purrr::list_modify(.x, model = xgboost::xgb.loa... |
f30070b336a8ff4bb2483e7ec7ad472e317ec35f0be64a2159d714a3893107a6 | R | 362 | 18 | context("UI")
test_that("mlflow can launch the UI", {
skip_on_travis()
url <- mlflow_ui()
response <- httr::GET(url)
status <- httr::status_code(response)
if (status != 200L)
{
ui_content <- httr::content(response)
stop("Status ", status, " retrieved with contents: ", ui_content)
}
expect_e... |
1c0a123de67ebabda71582b81032b26914c429775839eed04aae44b83b6b600a | R | 365 | 8 | check_package = function(package = "ggVennDiagram"){
if (!requireNamespace(package, quietly = TRUE)){
stop(paste("The", package, "package is not found in your library paths.",
" It is required to show intersections interactively.",
" Please run `install.packages('package')` and ret... |
54ad1f463f59c75cc50927979bf580a79485f0a6f72ee5a48d11a50b51d2878f | R | 366 | 16 | #'
#'
#' @param
#'
#' @return
#'
#' @export
xgb_model_matrix <- function(x, index, outcome) {
dset <- x[index,]
labels <- dset %>% dplyr::pull({{outcome}})
matrix <- dset %>%
dplyr::select(-{{outcome}}) %>%
data.matrix()
dmatrix <- xgboost::xgb.DMatrix(matrix, label = labels)
list(dmatrix = dmatrix, d... |
ff73fd4cb89123e89a9979d79a458963c25182433787dd51493b9e1110153bf0 | R | 367 | 13 | prepare.data<-function (x, y, standardize, family)
{
x <- scale(x, center = TRUE, scale = standardize)
dataset <- switch(family, gaussian = {
list(x = x, y = y)
}, {
switch.family(x = x, y = y, family = family)
})
x <- scale(dataset$x, center = TRUE, scale = FALSE)
y <- scale(dataset$y, scale = FAL... |
836f9a1e8e34a38fd4f1edd5530f74dea0ba856bcb0a3e3c1b563d2a46fdf9a0 | R | 368 | 12 | # This file is part of the standard setup for testthat.
# It is recommended that you do not modify it.
#
# Where should you do additional test configuration?
# Learn more about the roles of various files in:
# * https://r-pkgs.org/tests.html
# * https://testthat.r-lib.org/reference/test_package.html#special-files
libr... |
1b7e0060413b3c2a049adbec02f04972d840027a843b2c5241621a01e06205f9 | R | 380 | 22 | test_that("Test FactorNaturalOrder", {
x <- FactorNaturalOrder(10:1)
expect_true(is.factor(x))
})
test_that("Test my_theme_ggplot", {
p <- ggplot2::ggplot() +
my_theme_ggplot()
expect_silent(p)
})
test_that("Test SafeColorPalette", {
n1 <- length(SafeColorPalette(5))
expect_equal(... |
7d0925ec4758e37ec83ee42b7dc94c8f9c33cfd5a1d73539c229fa864ce93579 | R | 382 | 17 | #' Surface model of neuropils in the MANC dataset
#'
#' The \code{\link{MANC.surf}} model was generated in Amira by Kazunori Shinomiya and shared with Greg 1 October 2020. It should be regarded as provisional
#'
#'
#'
#' @name MANC.surf
#' @aliases JRCFIBVNC2020MNP.surf
#' @docType data
#' @examples
#' \dontrun{
#' # ... |
562d6e8155ef57c516e95e1f94ae9db89d53f3403e2a7cf31e64a4c7c569cdd1 | R | 388 | 12 | # This file is part of the standard setup for testthat.
# It is recommended that you do not modify it.
#
# Where should you do additional test configuration?
# Learn more about the roles of various files in:
# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
# * https://testthat.r-lib.org/articles/spec... |
dfc6bf1dadb5f811a68043ac9cba673bfebdcb75836c530c6d1226ddc240a718 | R | 390 | 19 | library(purrr)
load("./metadata.RData")
tissues = c("dlpfc","hippo","caudate","amygdala","dACC","sACC")
#tissues = c("sACC")
#### run jobs
purrr::walk(tissues,function(tt){
arrays = paste0("1-",length(metadata.PRS.sub))
#arrays = 1
print(paste0("running job for: ",tt))
cmd = paste0("sbatch -a... |
3143a245e76c8677cba3409edc862bfe768dae6f0aa0fb0c072ccfa50aa1e724 | R | 391 | 13 | #!/usr/bin/env Rscript
args <- commandArgs(trailingOnly = TRUE);
cvPATH <- args[1];
bedname <- args[2];
spec <- args[3];
x <- file('stdin');
cv2 <- read.table(x);
#p-site
cv2[,2] <- cv2[,2]+12;
cv2$flen <- cv2[,3]-cv2[,2]+12;
write.table(cv2,file=paste0(cvPATH,bedname,"sorf.tp.txt"),sep="\t", ... |
ceb8bb43e182c495939eca9899c870557791809de65306cc2494388f285dbb8f | R | 392 | 16 | #'
#'
#' @param
#'
#' @return
#'
#' @export
generage_pca_grouping <- function(df, size, seed = 2024) {
set.seed(seed)
n_centers <- nrow(df) %/% size
pca_result <- df %>%
dplyr::mutate(dplyr::across(dplyr::everything(), ~ ifelse(is.na(.), mean(., na.rm = TRUE), .))) %>%
prcomp()
pca_data <- as.data.frame... |
493fc7f88e28ad442ab833cdf20d69755599b5579ecd7effe457c0516f27a37c | R | 393 | 13 | mlflow_clear_test_dir <- function(path) {
purrr::safely(mlflow_end_run)()
mlflow:::mlflow_set_active_experiment_id(NULL)
if (dir.exists(path)) {
unlink(path, recursive = TRUE)
}
deregister_local_servers()
}
deregister_local_servers <- function() {
purrr::walk(as.list(mlflow:::.globals$url_mapping), ~ .... |
5a15f3b4f3f416b7d9722ae1ae8dec95f934c47db8be5888c4398cecc4d9aeda | R | 394 | 12 | # This file is part of the standard setup for testthat.
# It is recommended that you do not modify it.
#
# Where should you do additional test configuration?
# Learn more about the roles of various files in:
# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
# * https://testthat.r-lib.org/articles/spec... |
9517b6e64fa283624833a3caa4dc4c4741148de56a4d661382f4e2a0edb8a462 | R | 398 | 16 | ### Inspect nodomap output
library(Seurat)
data_dir <- '/cephfs/scratch/bruening_scratch/gdowsett/data/nodomap/'
data <-readRDS(paste0(data_dir, 'nodomap_cleaned_20251025.RDS'))
data <- DietSeurat(
data,
assays = 'originalexp',
dimreducs = 'umap',
graphs = c('integrated_nn', 'integrated_snn'),
misc = FALSE... |
b60bf6269195755718b98ea5c820318395268fd659abfb062c73b21348699cef | R | 398 | 17 | test_that(
"vis_clus",
{
if (!exists("spe")) spe <- fetch_data("spe")
# Bad spatialCoords
spe_temp <- spe
colnames(spatialCoords(spe_temp)) <- c("a", "b")
expect_error(
{
p <- vis_clus(spe_temp, clustervar = "sample_id")
},
... |
e393c425f3c24d1afecbe235e38a30b07040d8aed027ddc88900afa1e60a8a6b | R | 399 | 8 | test_that("changelog works", {
skip_if_offline()
skip_if_not(fanc_token_available(),
message="Unable to obtain a FANC access token")
expect_s3_class(res <- fanc_change_log("648518346473954669"), "data.frame")
expect_named(res, c("operation_id", "timestamp", "user_id", "before_root_ids",
... |
d65a6d080ca9a8622f72f80cf4e8130d33f0998421baaf48d39f6f10533d5d8d | R | 402 | 14 | context("test SAMBAR result")
test_that("SAMBAR function works", {
suppressWarnings(load("./testDataset.RData"))
data("exon.size")
data("mut.ucec")
data("genes")
expect_equal(sambar(mutdata=mut.ucec, esize=exon.size, signatureset=system.file("extdata", "h.all.v6.1.symbols.gmt", package="netZooR", mustWork... |
b09d17fcfdf961c46a007b19f84f42519c7a42be26e783256e95c883da0b92db | R | 404 | 8 | test_that("reading a file works", {
skip_if_not_installed('git2r')
expect_is(anns <- flywire_sirepo_file('supplemental_files/Supplemental_file1_annotations.tsv', read = TRUE, version=630), 'data.frame')
expect_type(
flywire_sirepo_file('https://github.com/flyconnectome/flywire_annotations/blob/staging/supplem... |
3bffe6f695dfcc1a5b4c0ef33f53f260c275a10b4b24bdfd3e913476bfcd934a | R | 409 | 14 | calcM<-function (x, lambdas, parallel, ncores)
{
if (parallel) {
M <- mcmapply(FUN = calcMforcolumn, x = list(x = x),
j = 1:ncol(x), lambdas = list(lambdas = lambdas),
mc.cores = ncores)
}
else {
M <- mapply(FUN = calcMforcolumn, j = 1:ncol(x), x = list(x = x),
... |
1bda52f4b8b50c04fa2f84c79d033aca3ec4f4f236867f2080fd99a90620f265 | R | 410 | 14 | #'
#'
#' @param
#'
#' @return
#'
#' @export
xgb_save <- function(x, name = "nested_xgboost", dir = "rds") {
dir.create(dir, showWarnings = FALSE)
x %>%
purrr::map(~purrr::list_modify(.x, train_obj = purrr::zap(), test_obj = purrr::zap())) %>%
purrr::map(~purrr::list_modify(.x, model = xgboost::xgb.save.raw(... |
523911a6296972c4f12faa4d53f81fc8da1ac697ab010cfa8b06f56ca429b819 | R | 410 | 16 | RNGkind("L'Ecuyer-CMRG")
set.seed(123)
test_that("compare", {
expect_snapshot(
compare(list(samples_LNR), cores_for_props = 1)
)
})
test_that("savage-dickey", {
expect_snapshot(
round(hypothesis(samples_LNR, parameter = "m", do_plot = F, H0 = -1), 2))
expect_snapshot(
round(hypothesis(samples_LNR,... |
5570f834a9dd643a5a3e991ba436a340cfaca8469c60bfddf4a4a402f2de3632 | R | 413 | 13 | op <- choose_malevnc_dataset('VNC')
on.exit(options(op))
test_that("somapos works", {
skip_if_offline()
skip_if(inherits(try(clio_token(), silent = T), 'try-error'),
message = "no clio token available")
expect_silent(res <- manc_nearest_soma(cbind(20540, 18544, 1968), details = TRUE))
expect_equal(re... |
8cf84aec25183ee9c5a6ba87f6cb97cdca1bb04d44546759ed88fb06b645f3e4 | R | 413 | 14 | # To remove axes in UMAP plots
umap_theme <- theme(
axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.ma... |
d7fda0ae90fb9aeef239fce72e069a91d8ff5c30ff71f2a8a0d99b2d005f9fe2 | R | 413 | 12 | # This file is part of the standard setup for testthat.
# It is recommended that you do not modify it.
#
# Where should you do additional test configuration?
# Learn more about the roles of various files in:
# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
# * https://testthat.r-lib.org/reference/tes... |
1557a83a38e7932129bf45b1d04d1e5d5ad631d01b4b0d78c904ba32663dea0c | R | 415 | 17 | library(openxlsx)
library(DESeq2)
library(tidyverse)
options(device=pdf)
setwd("Z:/charliew/da/gsea")
rnkFile <- read.xlsx("huMPG_neu_rnkStock.xlsx", colNames=TRUE, rowNames=TRUE)
names <- colnames(rnkFile)
for (i in names){
d<-rnkFile %>% select(i)
d$'#gene' <- row.names(d)
d <- d[,c(2,1)]
w... |
94d35a35d48b9314111ecf37199e900184dcf5bff6d40172157eb81eeb112427 | R | 416 | 14 | #'
#'
#' @param
#'
#' @return
#'
#' @export
nested_xgboost_cox_cv <- function(data, index = 1, n_inner_folds = 5, n_outer_folds = 5, ...) {
nested_xgboost_cox_cv_outer_loop(data = data,
iteration = index,
n_outer_folds = n_outer_folds,
... |
dd4319eea6f9858ef0bb911bd3ce431f8f0ae0920f06e7d0b8d477f4847e8c9a | R | 423 | 14 | est.stderr.despars.lasso<-function (x, y, Z, betalasso, sigmahat, robust, robust.div.fixed = FALSE)
{
if (robust) {
stderr <- sandwich.var.est.stderr(x = x, y = y, Z = Z,
betainit = betalasso)
n <- nrow(x)
if (robust.div.fixed)
stderr <- stderr * n/(n - sum(b... |
660ae0fbdf7476080dbe36ed200d1c59985bff4b7daef53ab04ccc498ca92cdb | R | 429 | 11 | test_that("fafb14_to_flywire_ids works", {
skip_if_not(spine_ok())
rid=flywire_rootid('79730879731420093')
expect_equal(
df <- fafb14_to_flywire_ids(5038796, conn = catmaid::vfbcatmaid()),
data.frame(root_id = rid, hits = 57L, skid = "5038796"))
expect_equal(fafb14_to_flywire_ids(5038796, conn = catmai... |
856ab8ae6b3f7a68073a284536f1b91372c0abc2dc8e6a27828205cda61ca03a | R | 430 | 9 | test_that("memory usage for large list", {
large_list <- list(Score = as.character(rnorm(130000, 80, 20)),
Length = as.character(rnorm(6000, 200, 50)),
Charge = as.character(rnorm(90000, 2000, 800)),
Math = as.character(rnorm(100000,90,20)),
... |
3a76b5f846db228d84c4896f28b5c2b22435707c21f2bd6bd0168d76027ecb53 | R | 432 | 13 | .onLoad <- function(libname, pkgname) {
# make FANC4<->FANC3 bridging registrations available
register_fanc3to4()
invisible()
}
.onAttach <- function(libname, pkgname) {
packageStartupMessage(
'Do `choose_fanc()` to use many fafbseg::flywire_* functions!\n',
'Use dr_fanc() to get a report on your inst... |
5e2d0fb8dda421f04b4f255d6645422761a4eaa7821b9d2a5616ba9388a4a83d | R | 438 | 15 | #### !!! IMPORTANT !!! RENAMING BAMFILES !!!
###
rename.bamfiles <- F
if(rename.bamfiles) {
from <- list.files(bamdir, pattern, recursive = T, full.names = T)
to <- gsub('PRV_MdBio_', '', from)
to <- gsub('_virus_only_filtered', '', to)
file.rename(from, to)
bamfiles <- grep('.bai',
... |
c20a59a19ba8dfdb4bd0137169aa91cb2b70f7e9e1b1d4bd64a5046b9b75ef80 | R | 438 | 17 | # script for downloading data for reproducing figures of the mmcortex paper
options(timeout = 100000)
download.file("https://mmcortex.s3.eu-west-1.amazonaws.com/mmcortex.tar.gz", "mmcortex.tar.gz")
system("tar -xvzf mmcortex.tar.gz")
folder_names <- c('figs',
'output/paper_figs')
for ... |
026104f3f505783466e225dad2d6aeb20e9b384bc925bea597c0e894da3ed4fc | R | 447 | 18 | # Hello, world!
#
# This is an example function named 'hello'
# which prints 'Hello, world!'.
#
# You can learn more about package authoring with RStudio at:
#
# http://r-pkgs.had.co.nz/
#
# Some useful keyboard shortcuts for package authoring:
#
# Install Package: 'Cmd + Shift + B'
# Check Package: ... |
5606cae0b9f97d1051581493d971c57eb8cadcaedf993c21ad4b6e47e8a040cf | R | 450 | 11 | #' premessa: A package for normalization and debarcoding of CyTOF data.
#'
#' For the normalizer GUI refer to the documentation for the \code{normalizer_GUI} function
#' by typing \code{help('normalizer_GUI')} in your R session. For the debarcoder GUI refer
#' to the documentation for the \code{debarcoder_GUI} function... |
50793b9fd6caaaccce22679377e8e72dccaa5d77ae8e4c3e5e7cfa0ed44f962b | R | 452 | 16 | #!/usr/bin/env Rscript
library(tidyverse)
library(odbc)
library(DBI)
#######################################################
rm(list=ls())
#Establish connection
con <- DBI::dbConnect(odbc::odbc(), "GLASSv3")
new_dat <- read.delim("/projects/verhaak-lab/GLASS-III/results/mutect2/consensusvcf/consensus.normalized.sor... |
1898620401a181de6002a0a8fe6ce6a4c8a2ed863ada7ef4a49b0796446cb7c6 | R | 455 | 12 | p.adjust.wy<-function (cov, pval, N = 10000)
{ # 检查 cov 和 pval 是否为标量
if (is.null(dim(cov)) || (length(cov) == 1 && length(pval) == 1)) {
# 对于标量情况，直接返回 pval
return(pval)
}
zz <- mvrnorm(N, rep(0, ncol(cov)), cov)
zz2 <- scale(zz, center = FALSE, scale = sqrt(diag(cov)))
Gz <- apply(2 * pnorm(abs(zz2),... |
7a166e20803e8db865eca0651d8532fbd51dc555cbda289e5c0ff1edeca92222 | R | 456 | 21 |
Sys.setenv(RSTUDIO_PANDOC="/Applications/RStudio.app/Contents/MacOS/pandoc")
render_report = function(graph_weight) {
rmarkdown::render(
"step3_check_results.Rmd",
params = list(graph_weight=graph_weight),
output_file = paste0("step3_check_results_", graph_weight, ".html")
)
}
weights = c("0.0", "0... |
da66b7b40432f1c7a656a7d3e4556bd1c9bdbcb6c679374454b9bd18e6532791 | R | 459 | 12 | library(magrittr)
options(repos = c(CRAN = "https://cloud.r-project.org"))
list_packages <- c("xgboost", "pdp", "smoof", "mlrMBO", "lhs", "shapviz", "ggfittext", "gggenes", "BBmisc", "ParamHelpers", "DiceKriging")
dir.create("cran/sources", showWarning = FALSE, recursive = TRUE)
miniCRAN::pkgDep(list_packages) %>%
... |
64ec39a107f6b4e7813cb31772ec570ad1f94fb9c8576d850369ebc5694226d1 | R | 461 | 21 | ###################################################Scoring helper functions#########################
rmse = function(x){
return(sqrt(mean(x^2)))
}
rmsr = function(x, e){
return(sqrt(mean((x/e)^2)))
}
wmse = function(x, w){
#w = 1/e or 1/e^2
x = x[is.finite(w)]
w = w[is.finite(w)]
return(sqrt(sum(w*(x)^2)... |
65aa0c0d3e20a954f605fb366b4ed3f5e3199001459906ee60dc4fccdb25e2cf | R | 463 | 14 | #' Add random number columns in data frame with specific seed to validate machine learning algorightms
#'
#' @param
#'
#' @return
#'
#' @export
add_randomized_binom_column <- function(df, n, prob) {
random_values <- function(n, prob = runif(1, min = 0, max = 1)) {
factor(rbinom(n = nrow(df), size = 1, prob = prob... |
0447d0bd0a02b52b343ca766f9f3b7ad55b78095c82b62e1340ddd532b60e85c | R | 465 | 15 | #' @importFrom utils capture.output
skip_if_flywire_materialize_unavailable <- function(
message = "Skipping live FlyWire tests: materialize service unavailable") {
cave_versions <- try({
capture.output(
capture.output(
invisible(flywire_cave_client()$materialize$get_versions(expired = TRUE)),
... |
8069104c76b23ff7561fd5c2233c77b28bf1959a7c85231447e3438d12393dc8 | R | 468 | 23 |
#' @title Mouse Human gene symbol lookup table
#'
#' @description A 2-column tibble of mouse and human gene symbols
#' @name mm_hs
#' @format A 2-column tibble
NULL
#' @title Hallmark genes
#'
#' @description Human gene symbol lists for hallmarks
#' @name pathways.hallmark
#' @format A list of 50 vectors
NULL
#' @... |
a6554aa3db523cc5f11c77116a984b2661ef24f83ebec6cd38d32b977a17836c | R | 469 | 15 | #' A small example Seurat object for testing
#'
#' A subsetted version of a 10X Genomics PBMC dataset, containing 80 cells
#' and 230 genes. This is the same dataset as SeuratObject::pbmc_small.
#'
#' @format A Seurat object with:
#' \describe{
#' \item{assays}{RNA assay with counts and data layers}
#' \item{meta.d... |
c33f3417ef77312e0bc57a9602d123d37e35dd12a08ea557d215ffe9f869b1eb | R | 470 | 9 | options(Ncpus = 8)
install.packages(c("devtools", "arrow", "randomForest", "jsonlite", "htmlwidgets", "ggplot2", "plotly", "BiocManager"))
BiocManager::install("preprocessCore", configure.args = c(preprocessCore = "--disable-threading"), force=TRUE, update=TRUE, type = "source")
BiocManager::install(c("minfi", "Rsamt... |
02a9bc2d9fb6c6a24b426de8168dbf968257e9fcd1e7df25c977ad57c79e883c | R | 473 | 12 | test_that("manc_mutations", {
skip("Skipping due to a regression in the MANC DVID setup.")
withr::local_options(choose_malevnc_dataset('VNC', set = F))
expect_s3_class(muts <- manc_mutations('956250c3062245f9b63a000dfe05289c'),
'data.frame')
expect_equal(manc_size(10000, node = '19fdf756b8c947... |
b14d58385d165e6e0eaba5f8eb8d94ec53b766acba67116c9eb9a519ff011905 | R | 473 | 13 | mancsomapos.orig=read.csv('data-raw/dd51_sphere_annotations_21-02-12.csv')
mancsomapos=mancsomapos.orig[c("X", "kind", "from", "to", "comment")]
colnames(mancsomapos)[1]="n"
mancsomapos[,c("X","Y","Z")] <- xyzmatrix(mancsomapos.orig$mid_point)
mancsomapos=mancsomapos[c("n", "X",'Y', 'Z', "kind", "from", "to", "commen... |
d44978236a43d9df6579850dbc52e74123238c346eba67504a31517506e2638d | R | 479 | 19 | install.packages("easypackages")
library("easypackages")
libraries("readr", "data.table", "dplyr", "gtsummary","openxlsx", "readxl", "MASS", "ggplot2")
ggplot(as.data.frame(mfa), aes(x=avg_Rcount, y=Pred/avg_Rcount)) +
geom_point() +
geom_hline(yintercept = 1)
ggplot(as.data.frame(mfa), aes(x=Pred, y=Pred/avg... |
ea21dc9bc7a6ebb68968d775306e9e869289b40694dc655cf7c61a2140d33966 | R | 487 | 17 | test_that("Test StatsCellsInside", {
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
package = "SpNeigh"
))
boundary_points <- GetBoundary(
data = coords, one_cluster = 2, eps = 120, minPts = 10
)
boundary_polys <- BuildBoundaryPoly(boundary_points)
cells_insi... |
1fb6fe5a78ee16f14631835b3798ecacd877fb165da85312b5c3db61d2e84794 | R | 493 | 10 |
# read whole file
txt <- readLines("/data/share/lab/lab specific overview and lists/Plasmid Sequences/p216_pAAVS1-TetOn-zKRAB-dCas9-P2A-mCherry/pAAVS1-TetOn-zKRAB-dCas9-P2A-mCherry.gbk")
# extract only the sequence portion (between ORIGIN and //)
seq_lines <- txt[grep("^ORIGIN", txt):length(txt)]
seq_lines <- seq_li... |
ecd4d37ff97a3ae6359eb623edf19af1ca3b23de799707924b2647abfe539c18 | R | 497 | 18 | #' Custom Color Palette
#'
#' A character vector containing 15 colors.
#'
#' @docType data
#' @name my_colors_15
#' @usage my_colors_15
#' @format A character vector of length 15.
#' @examples
#' plot(1:15, 1:15, pch = 16, col = my_colors_15)
#' barplot(rep(1, 15), col = my_colors_15)
#'
#' @export
my_colors_15 <- c(
... |
bf9b25cbfa6e35ab5686a77cf4e42ba950cc64e19a7daf7d7677a0493d985dc5 | R | 499 | 12 | library(ggplot2)
library(dplyr)
library(reshape2)
# params
thresh = 543 # dPixel>thresh -> "1" (moved) ## Example value; calibrate for each dataset if acquisition conditions differ
bout.thresh = 12 # in frame ## Example value; calibrate for each dataset if acquisition conditions differ
nframe = 56517
nhour = 1... |
db8d915891c9cf16d8d6eb94e149d2acd48af29dc7d1f3b9cc24ce4756031421 | R | 501 | 20 | #'
#'
#' @param
#'
#' @return
#'
#' @export
detect_factors <- function(df, max_distinct = 10, debug = FALSE) {
c_factors <- df %>%
dplyr::select(-ends_with("_YEAR"), -ends_with("_NEVT")) %>%
dplyr::summarise_all(dplyr::n_distinct) %>%
tidyr::gather(column, n_distinct) %>%
dplyr::filter(n_distinct <= m... |
7b778646dfd2f8fd239efcf11174c2b32a36fb7200bd29a42a02c08860ed2024 | R | 507 | 18 | #' Custom Color Palette
#'
#' A character vector containing 15 colors.
#'
#' @docType data
#' @name colors15_cheng
#' @usage colors15_cheng
#' @format A character vector of length 15.
#' @examples
#' plot(1:15, 1:15, pch = 16, col = colors15_cheng)
#' barplot(rep(1, 15), col = colors15_cheng)
#'
#' @export
colors15_che... |
78f2baa888b2ef44c422d6e92b2fb6ea1ef13b85aa1408460d5f2675bd6e5310 | R | 528 | 12 | # Install all the things
if (!require("devtools")) {install.packages("devtools")}
library(devtools)
if (!require("tidyverse")) {install.packages("tidyverse")}
if (!require("data.table")) {install.packages("data.table")}
if (!require("Matrix")) {install.packages("Matrix")}
if (!require("irlba")) {install.packages("irlba... |
cd1dfdbf26ca096e6b8013f333bb9f7c3359ec05a6e6c78905ae5f98f71f8bba | R | 535 | 14 | test_that("flywire_user_info works", {
# requires admin privileges since middle_auth v2.29.0 (April 2025)
res <- tryCatch(
flywire_user_info(60, datastack_name = "flywire_fafb_public"),
error = function(e) e
)
skip_if(inherits(res, "error") && grepl("403|admin", conditionMessage(res)),
"Requir... |
cdcabe5b976f9a2a7d6caf2ca0050ffc3e27538408eeffea26e9933a7641142c | R | 535 | 17 | library("DESeq2")
pheno <- read.csv('data/pheno.csv')
edata <- read.csv('data/count_tb.csv', row.names=1)
edata <- edata[, as.vector(pheno$id)]
table(names(edata) == pheno$id)
# run DESeq
dds <- DESeqDataSetFromMatrix(countData = edata, colData = pheno, design= ~ group)
dds <- DESeq(dds)
re <- results(dds, contrast=c... |
60ed2bb681bd6eb44be4d2bd9ffb0d2153739be47decd196dcd5c5acfa2041f7 | R | 537 | 24 |
get_sampling_trials_idx <- function(ls_json_i) ls_json_i$trial_type=="rdk-sampling"
sampling_idx <- lapply(ls_json, get_sampling_trials_idx) %>% unlist() %>% which()
ls_json[sampling_idx+0][[1]]$start_time +
ls_json[sampling_idx+0][[1]]$rt +
800 + # Start duel
ls_json[sampling_idx+2][[1]]$rt
ls_json[sampl... |
b6420e829b6811e4e4a8e62750824de83e7df615e6834aab8a2146c8376db4bc | R | 537 | 20 | #Bar plot of left and right cells from Buchanan and inhouse
library(ggplot2)
df <- data.frame(table(data@meta.data$Position))
df$Var1 <- c("Left","Right")
pdf("figure_leftright_barplot.pdf", width = 3, height = 3)
ggplot(df, aes(x = Var1, y = Freq)) +
geom_bar(stat = "identity", fill = c("yellowgreen","plum3"), wid... |
0fc28c814cbb928c6c972a4a191d8cd818361adb13d8891df80fa38d6d0eb141 | R | 543 | 17 | #Bar plot of fed and fasted cells from inhouse
##Already subset inhouse
library(ggplot2)
df <- data.frame(table(data@meta.data$Nutr.cond))
df$Var1 <- c("Adlib", "Fast")
pdf("Figure_fedfasted_barplot.pdf", height = 3, width = 3)
ggplot(df, aes(x = Var1, y = Freq)) +
geom_bar(stat="identity", fill = c('#117733', '#AA... |
5a49061b377251cf4d8a9feab0a2e86cc9aa891b2d555ac37cd3c9fb26cfe7e3 | R | 544 | 17 | test_that("Test PlotCellsInside", {
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
package = "SpNeigh"
))
boundary_points <- GetBoundary(data = coords, one_cluster = 2)
boundary_polys <- BuildBoundaryPoly(boundary_points)
cells_inside <- GetCellsInside(data = coords, bou... |
5d4c6f8b7eac5f6afa9245afb668e07c7802b922482f4cd1e2955590b20ae2be | R | 552 | 14 | cv.nodewise.totalerr<-function (c, K, dataselects, x, lambdas)
{
totalerr <- matrix(nrow = length(lambdas), ncol = K)
for (i in 1:K) {
whichj <- dataselects == i
glmnetfit <- glmnet(x = x[!whichj, -c, drop = FALSE],
y = x[!whichj, c, drop = FALSE], lambda = lambdas)
predictions... |
90599c0380b8841e3d46444f002c19baae698dbc060a4eb12bb37da3361ba570 | R | 554 | 29 |
#' Alignment IDX statistics from a list of .BAM files.
#'
#' @export
idxstats.bams <- function (bamfiles, bamnames=NULL) {
require(Rsamtools)
#require(plyr)
nbam <- length(bamfiles)
if (is.null(bamnames) ) {
pattern <- '.bam'
bamnames <- gsub('.*\\/', '', bamfiles)
bamnames <- gsub(pattern, ... |
f249ec1a3ca540e35b7f7fd2237926f7baf4d214610074597e6c71e45de396b9 | R | 566 | 15 | cv.nodewise.err.unitfunction<-function (c, K, dataselects, x, lambdas, verbose, p)
{
if (verbose) {
interesting.points <- round(c(1/4, 2/4, 3/4, 4/4) * p)
names(interesting.points) <- c("25%", "50%",
"75%", "100%")
if (c %in% interesting.points) {
message("The ex... |
8e50f1c2704954803a8d9b40c68b73f1c020f79c2d58ece5224ff82535a67d7e | R | 570 | 19 | library(purrr)
library(dplyr)
# Set working directory - adjust path as needed
# setwd("path/to/STEP7.Training_INGENE/")
dataset = "your_training_dataset_name"
regions = c("dlpfc")
walk(regions, function(region){
files = list.files(sprintf("%s/output/summary/%s", getwd(), dataset,region))
extra = bin... |
a019a2505fa6c3a5d4063330b88810e3b01d0c3fb4d163e79806d44091fff9aa | R | 582 | 21 | # base URLdecode gets very slow for long URLs
urldecode <- function(x) {
curl::curl_unescape(x)
}
# unfortunately curl::curl_escape escapes slashes in URLs!
# and urltools::url_encode munges the #! that neuroglancer needs
# if you give it a whole URL
urlencode <- function(x) {
pu=httr::parse_url(x)
if(is.null(pu... |
ff54d4dbf6e5c5b91ae94fd8f40be5ad6a1949f07106beb226794d12d8570a54 | R | 582 | 22 | test_that("Test statsCellsInside", {
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
package = "SpNeigh"
))
boundary_points <- getBoundary(
data = coords, one_cluster = 2, eps = 120, minPts = 10
)
boundary_polys <- buildBoundaryPoly(boundary_points)
cells_insi... |
2405741557c288c69de119b6e6ec9fc163f4122fa66c6afa9ed976436420c87f | R | 586 | 23 | #' @keywords internal
"_PACKAGE"
## usethis namespace: start
#' @importFrom data.table :=
#' @importFrom data.table .BY
#' @importFrom data.table .EACHI
#' @importFrom data.table .GRP
#' @importFrom data.table .I
#' @importFrom data.table .N
#' @importFrom data.table .NGRP
#' @importFrom data.table .SD
#' @importFrom ... |
34c3d858c54619c02d56f42f741653dafb7000f2500be58af1e3ea8120d673c6 | R | 586 | 14 | test_that("upset plot works", {
list = list(A = LETTERS[1:10],
B = LETTERS[8:15],
C = LETTERS[10:13])
venn = Venn(list)
data = process_upset_data(venn,
order.intersect.by = "size",
order.set.by = "name")
expect_equal(data$top_da... |
a9aed64fb401c60f5456ef7c92b2d182d066a524cd7b62a159c28ec0c5ac1988 | R | 587 | 28 | library(reticulate)
names = list(
"data/pancreas/pancreas_multi_celseq2_expression_matrix_table.txt",
"data/pancreas/pancreas_multi_celseq_expression_matrix_table.txt"
)
datasets <- list()
genes_list <- list()
for (i in 1:length(names)) {
datasets[[i]] <- t(as.matrix(
read.table(names[[i]], sep="\... |
0e77880d37104e9018c916720da597870a75d98681443f7171d3414b87111567 | R | 592 | 17 | test_that("nginfo works", {
df=data.frame(id=c(10000,10002), type=c("DNp01"), side=c("L", "R"))
tf=tempfile(pattern = 'mancinfo.json')
on.exit(unlink(tf))
expect_silent(write_nginfo(df, f = tf))
bl = list(`@type` = "neuroglancer_segment_properties",
inline = list(
ids = c("10000", "1... |
cd710151299286daf78ccb510408f6074472cfa10d1d369e7440c9d05ec404a0 | R | 595 | 17 | # -----------------------------------------------------------------
# Zip data files that are to large for GitHub
# -----------------------------------------------------------------
library(zip)
all.data.files <- list.files("../data/processed_data", recursive = T, full.names = T)
file.sizes <- file.size(all.data.file... |
25baaa5d25d062094a3d3447272f819db64322142979fd042abc23f7851d4327 | R | 614 | 14 | connections_princeton_no_threshold <- memoise::memoise(function() {
fcd=fafbseg:::flywire_connectome_dir(version = 783)
cpn=file.path(fcd, 'connections_princeton_no_threshold_v783-zstd19.feather')
if(!file.exists(cpn)) {
message("downloading princeton synapses!")
ud='https://flyem.mrc-lmb.cam.ac.uk/flycon... |
2653b0a4440c820db563bf538d6f3ea818345a972c85a482fddc3ae9c69e4d6c | R | 614 | 19 | library(sceasy)
library(Seurat)
library(reticulate)
use_python("/home/zoo/ball6395/software/miniconda3/envs/scpy39/bin/python3.9")
loompy <- reticulate::import('loompy')
parser = argparse::ArgumentParser(description="Test paralog importance on cell marker genes")
parser$add_argument('-I','--input', help='input h5ad')... |
51f7c28b657827a707b017688ba523cadd1b2abc001ba01bd9e0ccc7c60b43ad | R | 614 | 19 | #' @name GOhuman
#' @title GOhuman
#'
#' @description List containing gene symbols for 71 GO function
#'
#' @docType data
#'
#' @format
#' \describe{
#' \item{genesets}{List containing gene symbols for 71 GO function (GO slim terms containing between 50 and 1,000 genes) downloaded from the Gene Ontology
#' ... |
20922af1ae2124519275e5edb1c6fa9c0758bba7acd6e38dc045b78e9a314be1 | R | 615 | 20 | #' banc-updateids — Refresh root_ids in SeaTable via bancr::banctable_updateids.
#'
#' Thin wrapper around `bancr:::banctable_updateids()` that resolves stale
#' root_ids in `banc_meta` and pushes the refresh back to SeaTable.
#'
#' @section Reads:
#' - SeaTable `banc_meta`
#' - CAVE (root_id resolution)
#'
#' @sec... |
ba0e62a9ad770a9aecdca3c7b17722e4d57ca05693a91c9d685b46e2410ffb27 | R | 622 | 16 | test_that("fanc_scene works", {
expect_type(sc <- fanc_scene("648518346498932033"), 'character')
})
test_that("switching between banc and fanc works", {
op=choose_fanc()
on.exit(options(op))
expect_match(fanc_cloudvolume_url(), "mar2021_prod")
expect_match(with_banc(fanc_cloudvolume_url()), "wclee_fly_cns_00... |
220e4aa0746257c7b73815324a4d99875fb54cfb277101df15b31368bd126b24 | R | 623 | 26 | example("registration_block_cor", package = "spatialLIBD")
results_enrichment <- registration_stats_enrichment(sce_pseudo,
block_cor,
"age",
gene_ensembl = "ensembl",
gene_name = "gene_name"
)
results_enrichment_nan <- registration_stats_enrichment(
sce_pseudo,
block_cor = NaN,
"age",
g... |
3505c0e0d66365f6e6968f7506243add0a7d0da34b601cd506e0693729542f5a | R | 624 | 16 | # Simple joint test
# Take the first entry of the design list
# emc stores a design list (with length 1 for non-joint models)
# or for joint models length x where x is the number of joint components
design_in <- get_design(samples_LNR)[[1]]
prior_in <- get_prior(samples_LNR)
data_in <- get_data(samples_LNR)
prior_join... |
982fe93e27bc4bcd00f0281736736640622dd444d84cc1a449475f030978052f | R | 624 | 22 | ---
title: "Home"
site: workflowr::wflow_site
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
Table of content:
- Pre-processing
- [Raw data filtering](./prep_filtration.html)
- [Alignment of the datasets](./prep_alignment.html)
- Analyses
- [Overview of the dat... |
d26187a342b5400d6b11ec84638ddb60f2556ca4567b6714e508fe7994065651 | R | 625 | 26 |
Sys.setenv(RSTUDIO_PANDOC="/Applications/RStudio.app/Contents/MacOS/pandoc")
render_report = function(cell_type_name, graph_weight) {
file_tag = paste0(cell_type_name, "_", graph_weight)
rmarkdown::render(
"step2_evaluate_gene_sets_goseq.Rmd",
params = list(graph_weight=graph_weight),
output_file = ... |
02980a3ff4074ef9ff15cac1ebe41c1a0943918be6c80700804d2998c6482bfc | R | 629 | 15 | # sccoda 数据准备=========================
Epilepsy19_cell_counts <- as.data.frame(table(Epilepsy19@meta.data$idents, Epilepsy19@meta.data$Anno_Idents))
library(dplyr)
library(tidyr)
#pandas.Dataframe：第一列为样本名，其余每列各代表一种细胞类型，值表示细胞数量
Epilepsy19_cell_counts <- Epilepsy19@meta.data %>%
count(idents, Anno_Idents, name = "n") %... |
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