sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
7d1bfcd364cbdd23308ab7e7182695aacccb423a3552f240826a32e3db054159 | Quarto | 20,364 | 574 |
Perform DEG analysis using WM score as a covariate
```{r setup}
rm(list=ls())
library(limma)
library(ggplot2)
library(VennDiagram)
outDir <- "/home/zhijiany/workdir/2026/0402_AD_PaperReviews/out/3.DEG_w_covariate"
dir.create(outDir, recursive = TRUE, showWarnings = FALSE)
dataDir <- "/home/zhijiany/workdir/2026/040... |
6201c2fb4cfd9deef328f1e117f12fded430fd310d6bf9446da3d191d84dd1f7 | Quarto | 20,635 | 743 | ---
title: "Fig-2D: T4 Window - Tail Regression"
author: "Andrea Mariossi"
format:
html:
embed-resources: true
toc: true
toc-location: left
toc-depth: 5
code-fold: false
code-line-numbers: true
page-layout: article
grid:
sidebar-width: 100px
body-width: 2000px
margin-... |
7ef3c5011b54222254019982a6a1426cc6647b91708ac82305fa6290404b35b6 | Quarto | 21,587 | 502 | ---
title: "PPRT Survival Analysis"
description: "To evaluate if there is a significant difference in **overall** survival between the PPRT intervention and control group"
date: last-modified
date-format: "[Last Updated:] MMM DD, YYYY"
format:
html:
self-contained: true
title-block-banner: true
title-bl... |
4e681daa8cfcf026442227ae3774d65db733770e3fef57e94bf0fef1a030931a | Quarto | 22,626 | 731 | ---
title: "Endodermal strand experiments"
author: "Andrea Mariossi"
format:
html:
code-fold: false
embed-resources: true
engine: knitr
editor: visual
grid:
sidebar-width: 100px
body-width: 2000px
margin-width: 100px
gutter-width: 3.5rem
execute:
fig-align: center
fig-format: png
editor_options... |
ec8955bbdfc7d13a81e9141fe93f934c4665f263bebdefaa43a9ad960d125c10 | Quarto | 23,083 | 746 | ---
title: "Fig-S5H_thr_RT-qPCR_nkx2.1-KD"
author: "Andrea Mariossi"
format:
html:
embed-resources: true
toc: true
toc-location: left
toc-depth: 5
code-fold: false
code-line-numbers: true
page-layout: article
grid:
sidebar-width: 100px
body-width: 2000px
margin-width... |
e06f98d83b0626df091f4f2122a23c82e1a33dfd24f5bc4350dfaac787a54c3b | Quarto | 25,315 | 812 | ---
title: "Fig-2BC_tail regression- T4 & T3"
author: "Andrea Mariossi"
format:
html:
embed-resources: true
toc: true
toc-location: left
toc-depth: 5
code-fold: false
code-line-numbers: true
page-layout: article
grid:
sidebar-width: 100px
body-width: 2000px
margin-wi... |
31d23d96a1cd4ffb9a61eaa0670f1fa0b1e15ec201f7b9ee59b5630b9965b6d8 | Quarto | 27,388 | 843 | ---
title: "Fig-4D_rescueExp_photoreceptor"
author: "Andrea Mariossi"
format:
html:
embed-resources: true
toc: true
toc-location: left
toc-depth: 5
code-fold: false
code-line-numbers: true
page-layout: article
grid:
sidebar-width: 100px
body-width: 2000px
margin-widt... |
2df98276bb4628f864db18a4248f41564bf9d29613bb102b2e6c8d93c68bdec0 | Quarto | 29,672 | 1,011 | ---
title: "T4 Tail Regression Analysis"
author: "Andrea Mariossi"
format:
html:
embed-resources: true
toc: true
toc-location: left
toc-depth: 5
code-fold: false
code-line-numbers: true
page-layout: article
grid:
sidebar-width: 100px
body-width: 2000px
margin-width: 1... |
df12a4b1340f282abb0eb76232956926b5e5a3f9fa94a3aed7593c72c6902257 | Quarto | 30,253 | 898 | ---
title: "Perchlorate and T4 rescue"
author: "Andrea Mariossi"
format:
html:
embed-resources: true
toc: true
toc-location: left
toc-depth: 5
code-fold: false
code-line-numbers: true
page-layout: article
grid:
sidebar-width: 100px
body-width: 2000px
margin-width: 100... |
7b98146f4d111a012d104532da7c3f1fe7632e957e334760d9ee17312d61d721 | Quarto | 36,122 | 1,053 | ---
title: "Fig-4C_OEexp_photoreceptor"
author: "Andrea Mariossi"
format:
html:
embed-resources: true
toc: true
toc-location: left
toc-depth: 5
code-fold: false
code-line-numbers: true
page-layout: article
grid:
sidebar-width: 100px
body-width: 2000px
margin-width: 10... |
21726c10dcf9e11a1bc65ffab39bb4df9a068d87d16f1946d8f54d44860395a5 | R | 5 | 1 | NULL
|
01fe6a0c52a2317ad79b67b2e74ec4571d629332f090d58406907c83a172150a | R | 18 | 1 | library(httptest)
|
224ab2be1de4fe5e510570b01a903ec6f58824436bf463145eaa8e3056ecec1f | R | 25 | 1 | #' @param run_id Run ID.
|
b852b94d205222ff9bbe3be5223fa2f1b4940d50b5153f994800a846c0531f33 | R | 29 | 3 |
.onLoad <- function(...) {
} |
7a51852a9f976dc4201c18395be88a6f4e013f64a4e8c101f4e6fdee3bc38bf1 | R | 34 | 2 | library(sesame)
sesameDataCache()
|
a36c85606947264a2d98ac57df261d919c07031720686575b9884cc84d2f6261 | R | 34 | 1 | source("testthat.R", echo = TRUE)
|
924d11d5c2516c093a3fb6e9693f0d2934417b65279897efbcbc2e9cdf8ffbd5 | R | 41 | 1 | .globals <- new.env(parent = emptyenv())
|
168f04e89cb10fc2afc36cd84f32dc9e749d47aa187c211212b7a991470fcc1e | R | 44 | 5 |
context("Test helpers")
library(Scillus)
|
813c6f1bc8af5a380e9a38dad58424047a7ede172a69b299d52f85a12af1ad37 | R | 45 | 3 | library(conos)
testthat::test_check("conos") |
f005114900355e1390113c02aad052cacfdfa063b0135eb0000c912d2ebd1641 | R | 50 | 4 | library(testthat)
library(bhr)
test_check("bhr")
|
0dd83c8a47b1ea9693b0987a869bd53c6a0cbffc05c0d768ba00c21c82c095e3 | R | 52 | 4 | library(testthat)
library(COSG)
test_check("COSG")
|
457e171f40123132d7751c7c92bf20b9af83d96b7340e58128ac752a0eceb629 | R | 53 | 4 | library(testthat)
library(ArchR)
test_check("ArchR") |
b61defd86a4cec973208ea4304502d94ac6d05cdda23380c5cb47ad237866791 | R | 54 | 4 | library(testthat)
library(fancr)
test_check("fancr")
|
0e604a95642713e4a27002cbe717abce97e3133c86ef5731eaa6dd7c69b8687a | R | 56 | 4 | library(testthat)
library(sleuth)
test_check("sleuth")
|
a17f2f93cc26140dd10409927497e8404b43580618ea8241781b58b65f61f917 | R | 58 | 4 | library(testthat)
library(Scillus)
test_check("Scillus")
|
33b8c6ea85dd17de0e6c71288c22d95e40d08edbb1797bc704f91f8979138a17 | R | 66 | 3 | test_that("multiplication works", {
expect_equal(2 * 2, 4)
})
|
7bd1f78dcf40df64604c650437f9cbaf3dbf8bd556717007131215be849ef002 | R | 66 | 4 | library(testthat)
library(spatialLIBD)
test_check("spatialLIBD")
|
2cf48c08eb304c91509de6b4f5b18d8610f6bc6ab4cf982f785a44145438ec29 | R | 70 | 4 | library(testthat)
library(ggVennDiagram)
test_check("ggVennDiagram")
|
0d5fd598742b5428ec741c50756924e4619c79a16b970c9c2f465eca6bd210ad | R | 72 | 4 | library(testthat)
library(MetaNeighbor)
test_check("MetaNeighbor")
|
1777dbe324efa48c8b4d6ed369c97e21ec651fffd3b55e5ab5fb200381bc35b8 | R | 75 | 3 | .onLoad <- function(lib, pkg) {
library.dynam("TitanCNA", pkg, lib)
}
|
5b6d389a627127de3268c1faa4568579f483beed9b89ee25c17d8996f03d5147 | R | 87 | 2 |
utils::globalVariables(c("mm_hs", "pathways.hallmarks", "exhaust_genes", "cc.genes"))
|
aae50836c403a37bdee708042d2288a8d965e226d6d2cec35a258edcef3518be | R | 100 | 6 | require(ggplot2)
require(stringr)
require(R.matlab)
require(cowplot)
require(gsubfn)
require(rjson)
|
b43586ca804d3d8760513cc8206463385a1d5cc84f9b2a1609f56df411d98ada | R | 102 | 6 | #' Import venn shape coordinates
#'
#' @return a data frame
vensets = function(){
venn::getSets()
}
|
98424272c29c1e83bab3eda27ea5451afbafc01856b1a61ef9fefc1968af743d | R | 108 | 5 | #' Default databases by organism
#'
#' @format Named character vector
#' @source GEO number
"defaultDbNames" |
98e1ea4a4c1eaeb1272e1a55b90ed1482bd1b70871ac7329315c43d7e43de372 | R | 111 | 7 | #' @keywords internal
"_PACKAGE"
## usethis namespace: start
#' @import Matrix
## usethis namespace: end
NULL
|
781df8b9380491c8ad745eebcb217ff8ebe9e715554c354578df961eac890794 | R | 114 | 5 | #' Small pre-processed data from Pagoda2, two samples, each dimension (1000, 100)
#'
"small_panel.preprocessed"
|
bd5cdca93959c68a68d744fd7ce2c6cf5da178400531f4a8229ef1d9bc2b71c3 | R | 114 | 5 | test_that("plot_chains", {
vdiffr::expect_doppelganger("chain_plots", plot(samples_LNR, selection = "mu"))
})
|
0ffc4f5030c338a88787d5baa1f5719fc63d36d27e18fd4b9369f7d15a57b8ff | R | 118 | 6 | library(nichenetr)
library(tidyr)
library(dplyr)
library(testthat)
test_check("multinichenetr", filter = "pipeline")
|
cf93695cf13375893d917d6134d2cde4a0b1f5ffccd24e63534a11b7f30542d8 | R | 121 | 7 | #' @keywords internal
"_PACKAGE"
## usethis namespace: start
#' @importFrom magrittr %>%
## usethis namespace: end
NULL
|
bfc5963028daa28a82300f0eab5c133d053fac053315673e983e83ad4c7ee78c | R | 122 | 5 | is_binary_column <- function(x) {
vec <- as.vector(na.omit(x))
check <- all(vec %in% c(0, 1))
return(check)
}
|
c4eb51e24b025210a7d71a254f053b6aeef0cf8669c6a34eb32dafccc0b2e0ae | R | 124 | 8 | #' @importFrom rlang %||%
rlang::`%||%`
#' @importFrom purrr %>%
purrr::`%>%`
#' @importFrom zeallot %<-%
zeallot::`%<-%`
|
ef406368b6427cab1636b0bf1b3f00f97a18f20fa18af959862d88ca0ef0fba9 | R | 131 | 4 | test_that("pairs_posterior", {
vdiffr::expect_doppelganger("pairs_plot", pairs_posterior(samples_LNR, selection = "sigma2"))
})
|
8317e89058970c1ee313fde1257cf73b3fc76a8f16ad42fa2561cd74e9b65b6c | R | 138 | 6 | if (requireNamespace("lintr", quietly = TRUE)) {
context("lints")
test_that("Package Style", {
# lintr::expect_lint_free()
})
}
|
813362a859e46806b02a404c41595b99d0727e184f73d5cf398298e30982b133 | R | 140 | 10 | ---
title: "About"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
Describe your project.
|
efcde0c2b070e06a2a790fde0a97fc3c4db93598350e3abe9b425b25c0b66417 | R | 142 | 4 | # Install MLflow for R
files <- dir(".", full.names = TRUE)
package <- files[grepl("mlflow_.+\\.tar\\.gz$", files)]
install.packages(package)
|
93ff3683bb118c94cd06899a9d9016125e3adcba0981db284ee5c8194cbf0818 | R | 148 | 5 | check_package_available <- function(pkg) {
if(!requireNamespace(pkg, quietly = TRUE)) {
stop("Please install suggested package: ", pkg)
}
}
|
d36b5c6dc934519073ed64c702c3bb33f738e83a8d8264e0743d99bb70d905dc | R | 155 | 5 | library(testthat)
library(malevnc)
# tests currently assume MALEVNC_DATASET=VNC
withr::with_envvar(new=c('MALEVNC_DATASET'="VNC"), test_check("malevnc"))
|
112fa0a8bf38b7a34ff6a9fe6c13a1fa4a7ef1e8c4acc30d33b33db3b6574c53 | R | 161 | 7 | despars.lasso.est<-function (x, y, Z, betalasso)
{
b <- crossprod(Z, y - x %*% betalasso)/nrow(x) + betalasso
if (ncol(b) == 1)
b <- as.vector(b)
b
} |
cfcc739c36ed9828be2cf4ae7189244cebcb563d8b4e5acecd528eb0913734df | R | 161 | 3 | if(requireNamespace('spelling', quietly = TRUE))
spelling::spell_check_test(vignettes = TRUE, error = FALSE,
skip_on_cran = TRUE)
|
90071b0b0756982ff398f4895842192ac747516856a2833f4137f37ced1846ab | R | 164 | 6 | score.rescale<-function (Z, x)
{
scaleZ <- diag(crossprod(Z, x))/nrow(x)
Z <- scale(Z, center = FALSE, scale = scaleZ)
return(list(Z = Z, scaleZ = scaleZ))
} |
c9f0606e1a37b64c38887dd03316f48a5df6d39111b87364a272beb934b8f0f3 | R | 166 | 4 | BiocManager::install(c("WGCNA", "devtools", "org.Hs.eg.db"))
install.packages('msigdbr', repos = 'https://cloud.r-project.org')
install_github('jdrudolph/PerseusR')
|
074fad3f7a35d46b474a171dce237b46518d63d6ce640f0ab6aecd0b58e8c915 | R | 169 | 6 | test_that("ann data can be created.", {
data("x")
data("y")
ann=Create_SAHA_object(query = x,db = y,data_type = "Markers")
expect_equal(typeof(ann),"S4")
})
|
041ae6645b65cd8cb7638daf294a2fc641cc1400d8b7132539185fcf335ea8d7 | R | 176 | 4 | test_that("fanc_partner_summary works", {
expect_s3_class(fanc_partner_summary(fanc_latestid("648518346494405175"), partners = 'outputs'),
"data.frame")
})
|
80b3dd492c364fb0ca16fb35a5e906e56a4d6454c43f17847f5ef979c0202f8f | R | 179 | 4 | # Custom color palette
library(RColorBrewer)
library(viridis)
custom_magma <- c(colorRampPalette(c("white", rev(magma(323, begin = 0.15))[1]))(10), rev(magma(323, begin = 0.18)))
|
99b4afe6e428e5147255c1506b99361807debe783294b698c9d5f54d6c18a850 | R | 187 | 6 | score.getZforlambda.unitfunction<-function (i, x, lambda)
{
glmnetfit <- glmnet(x[, -i], x[, i])
prediction <- predict(glmnetfit, x[, -i], s = lambda)
return(x[, i] - prediction)
} |
5fab70714d6ac036616a37ace17eeff04072274eee6ab006e60a234230e08e33 | R | 190 | 10 | #' Cantor pairing number for unique mapping
#'
#' @param dir: target directory
#'
#' @return save function
#'
#' @export
cantor_pairing <- function(x, y) {
(x + y) * (x + y + 1) / 2 + y
}
|
1c113330ca78ac3de67be17699c5743757467214ee996eab10b7c833700a6b3f | R | 197 | 6 | rm(list=setdiff(ls(),'params'))
basedir <- params$basedir
setwd(basedir)
# create directories for each set of analyses in advance
dir.create(paste(params$opdir,'diffmodel/',sep=''),recursive=TRUE) |
71a83e3b9c8570580b26e008aa059de8bca058fd66e741a69dd97901a80020ea | R | 202 | 8 | #' @keywords internal
"_PACKAGE"
# The following block is used by usethis to automatically manage
# roxygen namespace tags. Modify with care!
## usethis namespace: start
## usethis namespace: end
NULL
|
889bdb5c181351e378210c0625f4da860473e75677a189d710a3d211bc407bde | R | 209 | 7 | .onLoad <- function(libname, pkgname) {
try(choose_malevnc_dataset(dataset = getOption("malevnc.dataset"), set=TRUE))
# TODO make MANC<->manc/JRCVNC2018U bridging registrations available
invisible()
}
|
42da4a290d73c953b22f56a58e814017eda57ee77a68092fb0e7eeaad0a8ff95 | R | 210 | 10 | preprocess.group.testing<-function (N, cov, conservative)
{
if (conservative) {
NULL
}
else {
zz <- mvrnorm(N, rep(0, ncol(cov)), cov)
scale(zz, center = FALSE, scale = sqrt(diag(cov)))
}
} |
6a7af85df6b8d9db23ce302abb5162205ae424ff0b9ba4d1c5d995cfaabf36b1 | R | 211 | 9 | mlflow_is_verbose <- function() {
nchar(Sys.getenv("MLFLOW_VERBOSE")) > 0 || getOption("mlflow.verbose", FALSE)
}
mlflow_verbose_message <- function(...) {
if (mlflow_is_verbose()) {
message(...)
}
}
|
7f7ff27a472a8ab6f87656d6de4b88033ab09fec0d149213b4e2910395c815ee | R | 217 | 14 | shinyUI(
navbarPage("premessa",
tabPanel("Panel editor",
fluidPage(
fluidRow(
uiOutput("paneleditorUI")
)
)
)
)
)
|
728e81caef6f148441de8ce11cbe40d2db26d216f30ff8d9b937ed0d379d65c6 | R | 219 | 7 | test_that("manc_xyz2bodyid works", {
withr::local_options(choose_malevnc_dataset('VNC', set = F))
skip_if_not(nzchar(Sys.which('curl')))
expect_equal(manc_xyz2bodyid(c(24581, 35523, 62688)), 10000L)
})
dr_manc()
|
9b04fe8a4293fab4e80e6ea6d60459e88eafd224c13953b189c0076c5b6b6d4d | R | 220 | 11 | # #' Pipe operator
# #'
# #' See \code{magrittr::\link[magrittr]{\%>\%}} for details.
# #'
# #' @rdname pipe
# #' @keywords internal
# #' @export
# #' @usage lhs \%>\% rhs
# #' @name %>%
#' @importFrom magrittr %>%
NULL
|
f1612952ef585fb72f2381717a75f3500f5c51884c38c943b7fe128caeb64a18 | R | 220 | 8 | PowerUniv<-function (U, V)
{
n <- dim(V)[1]
x <- as.numeric(max(abs(U)))
TER <- as.numeric(1 - pmvnorm(lower = c(rep(-x, n)), upper = c(rep(x,
n)), mean = c(rep(0, n)), sigma = V))
return(TER)
} |
4f3a4895e3dbc09a2eec33d96b204fabc6620bd4281be085c06ad8d41b8f101a | R | 226 | 6 | RNGkind("L'Ecuyer-CMRG")
set.seed(123)
test_that("profile_plot", {
vdiffr::expect_doppelganger("profile_plot",
profile_plot(get_data(samples_LNR), get_design(samples_LNR)[[1]], credint(samples_LNR, probs = .5)[[1]]))
})
|
7cfdd9a6a7e9ddd45563a6d0df37721deacad18b37c6e0f287dd7ff15a034431 | R | 232 | 10 | #!/usr/bin/env Rscript
packs <- c("qqman","optparse","data.table","R.utils")
for (p in packs) {
if( !require(p, character.only = T)) {
print(p)
install.packages( p, repos = c(CRAN = "http://cran.r-project.org") )
}
}
|
9fbd6d523972dedfaa61d10406d0e7e804a173fbdd43f16458a2c86029e629c5 | R | 232 | 21 |
#' @export
#'
#'
rownames.from.1st.col <- function(x) {
x <- data.frame(x[,-1], row.names = x[,1])
return(x)
}
#' @export
#'
#'
luniq <- function(x) length(unique(x))
#' @export
#'
#'
dup <- function(x) x[duplicated(x)]
|
5f6488bbdc3c4e3c9236a201b1a8cff7733424a7be089efb5107ad826ae33edf | R | 233 | 7 | test_that("plot 4d Venn", {
genes <- paste("gene",1:1000,sep="")
set.seed(20210408)
x <- list(A=sample(genes,300),B=sample(genes,525),C=sample(genes,440),D=sample(genes,350))
p <- ggVennDiagram(x)
expect_is(p, "ggplot")
})
|
ef70b179055068ed86d2bdb6dac1974b3ca7e856d0b247738341dda7e340d73f | R | 242 | 7 | context("test-fetch-all.R")
test_that("read_ng_dump works", {
expect_is(d <- read_ng_dump('testdata/read_ng_dump/'), 'ng_raw_list')
expect_length(d, 3L)
expect_named(d, c("000000000003500b", "0000000000035042", "0000000000035008"))
})
|
dd11674ddec98f4db6c4637d671fd744ba0ce64dd2a30849693efb89039b601c | R | 248 | 7 | context("test pandaToCondorObject function")
test_that(" pandaToCondorObject works or not", {
suppressWarnings(load("./testDataset.RData"))
condor.obj <- pandaToCondorObject(T4Panda_subset)
expect_equal(condor.obj$edges, T4Condor$edges)
})
|
239a576ebf1ffaa4fbb74494dd56fa1e25c7edc11fc6b264af16302df7101381 | R | 249 | 10 | context("test pandaDiffEdges() result")
test_that("pandaDiffEdges() works or not", {
suppressWarnings(load("./testDataset.RData"))
diffPanda <- pandaDiffEdges(T4Panda_subset, T10Panda_subset)
expect_equal(diffPanda,T4T10diffPanda)
}) |
e905c59cfc0037dbcdf7c7d0aff5914509d1d7b7994fd4530a4cbe753147012c | R | 250 | 7 | context("test panda.to.alpaca function")
test_that(" panda.to.alpaca works or not", {
suppressWarnings(load("./testDataset.RData"))
alpaca <- pandaToAlpaca(T4Panda_subset,T10Panda_subset,NULL,verbose = F)
expect_equal(alpaca, T4T10Alpaca)
})
|
d1420a9a4183b9103e4495e069dcb18ecff928a0b60bf17dab79db9aae544bff | R | 253 | 6 | test_that("plot_pars", {
RNGkind("L'Ecuyer-CMRG")
set.seed(123)
vdiffr::expect_doppelganger("density_plots", plot_pars(samples_LNR, selection = "sigma2"))
vdiffr::expect_doppelganger("density_alpha", plot_pars(samples_LNR, all_subjects = T))
})
|
4a2552bfafde8dd6f56a6eee71b4684675f1e4426867aa1404ad2c83293af4d3 | R | 256 | 11 | Sum<-function (U, CovS)
{
if (all(abs(sum(U)) < 1e-20))
pTsum <- 1
else {
a <- rep(1, length(U))
Tsum <- sum(U)/(sqrt(as.numeric(t(a) %*% CovS %*% (a))))
pTsum <- as.numeric(1 - pchisq(Tsum^2, 1))
}
pTsum
} |
ad9bb074201bb4c6d150f45401b65624f7caea052d04209b0ba641452eacf811 | R | 260 | 13 | #'
#'
#' @param
#'
#' @return
#'
#' @export
nested_xgboost_cox_cv_harrell <- function(x) {
pred <- predict(x$model, x$matrix)
time <- xgboost::getinfo(x$matrix, "label")
index <- ifelse(time > 0, 1, 0)
intsurv::cIndex(abs(time), event = index, pred)
}
|
66db3db8d0a849795da6634e57a7e9af8f79b555a802e465c80dfeef72e1d936 | R | 261 | 11 | test_that("Test PlotInteractionMatrix", {
mat <- matrix(1:25, nrow = 5, ncol = 5)
colnames(mat) <- paste0("C", 1:5)
rownames(mat) <- paste0("C", 1:5)
expect_silent(PlotInteractionMatrix(mat))
expect_error(PlotInteractionMatrix(1:10))
})
|
8b81a276da8eeff139b96f40ff6c5e32bbed8bcfe4c1a8590a8c5c489193a3fc | R | 262 | 10 | #' @keywords internal
"_PACKAGE"
# The following block is used by usethis to automatically manage
# roxygen namespace tags. Modify with care!
## usethis namespace: start
## usethis namespace: end
if (getRversion() >= "2.15.1") {
utils::globalVariables(".")
}
|
20397b7ed4246a3c09905acfe5c55e3f767051276ffd73e7621829aeaaae881b | R | 263 | 11 | #' @keywords internal
#' @import fafbseg
#' @import nat.flybrains
#' @import bit64
"_PACKAGE"
# The following block is used by usethis to automatically manage
# roxygen namespace tags. Modify with care!
## usethis namespace: start
## usethis namespace: end
NULL
|
94cd0d1874eea6aa5c2b9601004cbed40f1ea95aa9afd0ddff399f4bc15b13db | R | 266 | 10 | context("lints")
test_that("mlflow package conforms to lintr::lint_package() style", {
if (nchar(Sys.getenv("COVR_RUNNING")) > 0) {
skip("No linting during code coverage")
}
# removing lint check until lint is cleaned up
# lintr::expect_lint_free()
})
|
aaf8cfca8a1d30909659a78d5cb7e8b6fc5845009aa57c5ed3bf8af0e48f210a | R | 268 | 14 | #' Launch Reactor Data Shiny App
#'
#' @return a shiny app
#' @export
launch_app = function(){
check_package("shiny")
app = system.file(
"shiny",
"shinyApp.R",
package = "ggVennDiagram",
mustWork = TRUE
)
if(interactive()) shiny::runApp(app)
}
|
db44589c25644a814356f90e9af5db56ebd570452b71e8f907461d721c332f19 | R | 269 | 7 | test_that("flywire_sirepo_file", {
skip_if_not_installed('git2r')
expect_s3_class(
flywire_sirepo_file("https://github.com/flyconnectome/flywire_annotations/supplemental_files/Supplemental_file3_summary_with_ngl_links.csv", read = T),
"data.frame"
)
})
|
4f10d7365345820cb66246885c245a2257f1cf6ae396c2745edf071151613e79 | R | 270 | 13 | #'
#'
#' @param
#'
#' @return
#'
#' @export
nested_xgboost_cox_cv_concordance <- function(x) {
pred <- predict(x$model, x$matrix)
time <- xgboost::getinfo(x$matrix, "label")
index <- ifelse(time > 0, 1, 0)
intsurv::cIndex(abs(time), event = index, pred)$index
}
|
9bf3f812e88c465403e337176fe0d6d0deed040ebc0a1a3fe47ef03f997d2a03 | R | 274 | 9 | test_that("Test ComputeSpatialInteractionMatrix", {
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
package = "SpNeigh"
))
interaction_matrix <- ComputeSpatialInteractionMatrix(coords)
expect_true(is.matrix(interaction_matrix))
})
|
db3ba2095e0be25e4ae7575da7492176fc86eda71f78e6ed589dbdb01a13b66e | R | 283 | 5 | # Make sure the current working directory is 'mlflow/R/mlflow'
print(R.version)
install.packages('remotes')
saveRDS(remotes::dev_package_deps(".", dependencies = TRUE), "depends.Rds", version = 2)
writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), "R-version")
|
2d2b3ea46cc557ff27feaeca8206dbd8450c0eb641d842476c68e15512dd3438 | R | 285 | 13 | test_that("Test plotInteractionMatrix", {
mat <- matrix(1:25, nrow = 5, ncol = 5)
colnames(mat) <- paste0("C", 1:5)
rownames(mat) <- paste0("C", 1:5)
p <- plotInteractionMatrix(mat)
expect_s3_class(p, "ggplot")
expect_error(plotInteractionMatrix(1:10))
})
|
b04148d643fe754754d54a3c3f7c9a0bc89f2ba233a4bdc92a15cb2c597d0a5d | R | 286 | 13 | library(mlflow)
# read parameters
column <- mlflow_log_param("column", 1)
# log total rows
mlflow_log_metric("rows", nrow(iris))
# train model
model <- lm(Sepal.Width ~ iris[[column]], iris)
# log models intercept
mlflow_log_metric("intercept", model$coefficients[["(Intercept)"]])
|
cf03d58c99776fad9d7967ce0d014325b277fb49f8fd765232d45620d2495490 | R | 288 | 3 | imgurl <- "data-raw/logo.png"
sticker(imgurl, p_size=19, s_x=1, s_y=1.16, s_width=.63, p_y = 0.49, h_color = "#91c36f", h_fill = "#eaf4e4", package = "CroCoNet", p_color = "#385723", p_family = "sans", h_size = 0.6, p_fontface = "bold",
filename="inst/figures/CroCoNet_logo.png")
|
9484ee76357a7374c8d326ca10377a33b71e35631f14b9166eed9ea30e241c7c | R | 289 | 7 | test_that("url shortening works", {
skip_if_offline()
expect_is(lu <- malevnc::flyem_expand_url('https://neuroglancer-demo.appspot.com/#!gs://flyem-user-links/short/2023-12-15.090703.json'), 'character')
expect_length( flyem_shorten_url(c(lu,lu), title=c("test1", "test2")), 2)
})
|
9ca506f17c1ad362eaf01f670ff96febb22df1c645e2acd43dcad6a086a5c5b0 | R | 289 | 4 | test_that("plot_relations", {
vdiffr::expect_doppelganger("plot_relations_cred", plot_relations(samples_LNR, only_cred = TRUE, plot_cred = TRUE))
vdiffr::expect_doppelganger("plot_relations_mean", plot_relations(samples_LNR, plot_means = TRUE, only_cred = FALSE, plot_cred = TRUE))
})
|
719f398b7811efec822333050d7db27dbc7192a0a13f6579b186ad921f8ab78a | R | 290 | 9 | test_that("Venn methods work", {
sets <- list(A=1:5,B=3:9,C=c(1,3,5,7,9,11))
sets <- lapply(sets, as.character)
venn <- Venn(sets = sets)
expect_equal(discern_overlap(venn), c("3","5"))
expect_equal(discern_overlap(venn,1:2), c("4"))
expect_equal(overlap(venn), c("3","5"))
})
|
015ec2f112f5f0529360072634642ec815e9c5ed52dfd371853aaf9c2d8d1a59 | R | 293 | 9 | require(GenomicAlignments)
cigar.sum <- function(cigar) {
cigar.df <- t(as.data.frame(explodeCigarOpLengths(cigar, ops=CIGAR_OPS)))
colnames(cigar.df) <- unlist(explodeCigarOps(cigar, ops=CIGAR_OPS))
rownames(cigar.df) <- NULL
cigar.df <- as.data.frame(cigar.df)
return(cigar.df)
}
|
be032d9b36c9f358ee524d16c3d114bd34c033dc035c23afdc2b7d06227d91af | R | 295 | 13 | #'
#'
#' @param
#'
#' @return
#'
#' @export
shap_rename <- function(obj, dict) {
features <- obj$X %>% colnames
colnames(obj$X) <- ifelse(features %in% names(dict), dict[features], features)
colnames(obj$S) <- ifelse(features %in% names(dict), dict[features], features)
invisible(obj)
}
|
098663e7863592ad5f5237216c9b81aeea888812685f30b49716ae7e1db88cd0 | R | 296 | 9 | calcMforcolumn<-function (x, j, lambdas)
{
glmnetfit <- glmnet(x[, -j], x[, j], lambda = lambdas)
predictions <- predict(glmnetfit, x[, -j], s = lambdas)
Zj <- x[, j] - predictions
Znorms <- apply(Zj^2, 2, sum)
Zxjnorms <- as.vector(crossprod(Zj, x[, j])^2)
return(Znorms/Zxjnorms)
} |
ac33d8da6a2cba5901e4137cc6265e53fadaedd81becbb60978c06e1f06a90fd | R | 297 | 7 | #load libraries
library(enrichR)
library(dplyr)
#load data and configure
Neuron.DEG <- read.table("D:/PEA/DE_S#NA#NA_G#group#APPPS1vsAPPPS1D10_A#NA_C#Neuron.txt", header = TRUE, row.names = NULL, encoding = "UTF-8")
colnames(Neuron.DEG)[colnames(Neuron.DEG) == "row.names"] <- "gene_symbol" |
d5178eab3036e33815a1cb8d83d6a68ab40353866b82468f04567d96a3f0454a | R | 297 | 8 |
ssGSEA.project.dataset(input.ds = "XXX.gct", output.ds = "customSets_ssGSEA", gene.sets.dbfile.list = "customSets.gmx")
rc.toPlot <- calculate_zscore(toPlot)
round(mean(rowMeans(rc.toPlot)),2)
round(mean(rowVars(rc.toPlot)),2)
writeLines(capture.output(sessionInfo()), "sessionInfo.txt") |
e0401624e8d59257047f60257a5b17302b216f1646a5acae0b78d1fe240cd2bb | R | 297 | 5 | ## Colors from Lukas M Weber adjusted for the purple background
libd_layer_colors <- c("#F0027F", "#377EB8", "#4DAF4A", "#984EA3", "#FFD700", "#FF7F00", "#1A1A1A", "transparent", "#666666")
names(libd_layer_colors) <- c(paste0("Layer", seq_len(6)), "WM", "NA", "WM2")
use_data(libd_layer_colors)
|
0d3b9c5b2c67a72114422a781e41a58c07e0254a5c6d37004d7373e9ebdb5cab | R | 300 | 10 | context("test OTTER result")
test_that("otter function works", {
suppressWarnings(load("./testDataset.RData"))
# Run OTTER algorithm
otterW <- otter(W, P, C, Iter = 1, lambda = 0.0035, gamma = 0.335)
expect_equal(as.integer(otterW*10**10)/10**10, as.integer(gt*10**10)/10**10)
})
|
9ac85bbc86c8cd49a2d17c81532187e4edc0b8aa23b3b0a985a1057325a81ae0 | R | 304 | 15 | test_that("multiplication works", {
bl=list(userMatrix = structure(
c(
-1,0,0,0,
0,0.3420201,0.9396926,0,
0,0.9396926,-0.3420201,0,
0,0,0,1
),
.Dim = c(4L, 4L)
),
FOV = 0)
expect_equal(manc_view3d(returnparams = TRUE),
bl, tolerance = 1e-4)
})
|
96a90c62d255fc7915099ae181780e6e7d51c1cace77a3e227654adc61aea6cd | R | 305 | 4 | #' @param client (Optional) An MLflow client object returned from \link[mlflow]{mlflow_client}.
#' If specified, MLflow will use the tracking server associated with the passed-in client. If
#' unspecified (the common case),
#' MLflow will use the tracking server associated with the current tracking URI.
|
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