sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
69552386a8ca09d680aec77bf57834c07946c7d998a5fc8cbec26327adca4cb5 | R | 632 | 15 | test_that(
"add_qc_metrics returns modified spe",
{
if (!exists("spe")) spe <- fetch_data("spe")
# run metrics spe
spe_qc <- add_qc_metrics(spe, overwrite = TRUE)
expect_equal(ncol(spe), ncol(spe_qc)) ## same number of spots
expect_equal(ncol(colData(spe)) + 7, ncol(colD... |
4113db4cb887b9d086b4e30e3c96c472a08d5505fb6604b4923f78e65a2cf594 | R | 634 | 23 | sandwich.var.est.stderr<-function (x, y, betainit, Z)
{
n <- nrow(x)
p <- ncol(x)
if (!isTRUE(all.equal(rep(1, p), colSums(Z * x)/n, tolerance = 10^-8))) {
rescale.out <- score.rescale(Z = Z, x = x)
Z <- rescale.out$Z
}
if (length(betainit) > ncol(x)) {
x <- cbind(rep(1, nrow(x)), x)
}
else {... |
f9c76b53f0fd3918771fa60f6b00c86e897e02866c42daa6d431581fe76bf60f | R | 636 | 21 | test_that("Test GetCellsInside", {
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
package = "SpNeigh"
))
boundary_points <- GetBoundary(
data = coords, one_cluster = 2, eps = 120, minPts = 10
)
boundary_points_sub <- subset(boundary_points, region_id == "2")
... |
3a744f41cf9412bf632ca2e737e70e4ef53de3257b7e558f8d12cd5c95ba673f | R | 657 | 21 | #'
#'
#' @export
#'
#'
require(tidyr)
require(fuzzyjoin)
require(data.table)
##
add.correct.ends <- function(bam.lortia, correct.strings.5p='correct', correct.strings.3p='correct') {
bam.lortia$correct_tes <- F
bam.lortia$correct_tss <- F
bam.lortia$correct_tss[grepl(paste(correct.strings.5p, collapse='|')... |
7d60007c1c9c74137a73f5975bbbed3784c99b540bd18144cc00083eb30b83ed | R | 658 | 18 | #'
#'
#' @param
#'
#' @return
#'
#' @export
nested_xgboost_cox_cv_model_matrix <- function(x, event_incident, event_agediff, ...) {
dset <- x %>%
dplyr::filter(!is.na(!!rlang::sym(event_incident))) %>%
dplyr::mutate(event = ifelse(!!rlang::sym(event_incident) == 1,
!!r... |
e073d9ae2e6bd921a49762a7cc285830fdc9647e4755dc2b3589654557ee88ad | R | 660 | 20 | #!/usr/bin/env Rscript
library(tidyverse)
args <- commandArgs(trailingOnly = TRUE)
dat <- read_tsv(args[1], col_names = F)
names(dat) <- c("CHR", "Position", "Depth")
p <- ggplot(dat, aes(Position, Depth)) +
theme_minimal() +
geom_vline(xintercept = seq(1, max(dat$Position), 5000), colour = "grey96") +
g... |
b7dee9881ee3e6704ea6ea69199152d2adf08608a999602882f375207362fe26 | R | 664 | 24 | UminPd<-function (U, CovS)
{
if (is.null(dim(CovS))) {
Tu <- sum(U^2/CovS)
if (is.na(Tu) || is.infinite(Tu) || is.nan(Tu))
Tu <- 0
pTu <- as.numeric(1 - pchisq(Tu, 1))
}
else {
Tu <- as.vector(abs(U)/(sqrt(diag(CovS)) + 1e-20))
k <- length(U)
V <... |
62011f6f6a6741932b80be34113d7a7891e89e101fe65988a74213222232d71e | R | 665 | 25 | ## Obtain the necessary data
if (!exists("modeling_results")) {
modeling_results <- fetch_data(type = "modeling_results")
}
## query spatialDLPFC modeling results
query_modeling_results <- fetch_data(
type = "spatialDLPFC_Visium_modeling_results"
)
## Compute the correlations
cor_stats_layer <- layer_stat_cor... |
8e567f21bf61463627e0a28f127ec3e6c8b35feb71be1c50dce30da1ccfe96b2 | R | 667 | 26 | # -----
# UMS IPSIT BIOINFO - Licence GPL-3
# https://github.com/fdumbioinfo/moal
# -----
#
# moal install
source("https://raw.githubusercontent.com/fdumbioinfo/rtools/main/moal-demo/0-moal-install-r-universe.r")
library(moal)
# change working directory to data_analysis folder
setwd(".")
# input data
"inputdata/DESeq20... |
1508dd336e3c01f6385c967102e86f8f349a45fdcefc8b0fa8ee58519fad1928 | R | 670 | 27 |
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(
"step6_check_results.Rmd",
params = list(cell_type_name = cell_type_name, graph_weight=graph_weigh... |
f3b6320cb4547187c7120a77db6fd718895cf20aef43ff6c585dd8edc14aed70 | R | 670 | 20 |
## create Rdata file used in tests and examples
library(conos)
library(dplyr)
library(pagoda2)
small_panel = readRDS("panel.rds") ## located in older versions of Conos
## simply subsetted panel.rds
small_panel[[1]] = small_panel[[1]][c(1:1000), c(1:100)]
small_panel[[2]] = small_panel[[2]][c(1:1000), c(1:100)]
#... |
85e7e7ca8e3e3c8736467fb4219815dda230da3dbafb901bd7e545f0cdada678 | R | 671 | 20 | library(seqinr)
var_muts = function(){
var_aligned = read.fasta("Datasets/var_aligned.fasta", seqtype = "AA", forceDNAtolower = F)
#need to match variants up, the only length change is a pair of deletions, none have insertions
var_aligned = lapply(var_aligned, function(x){x[1:201]})
refseq = var_aligned$Wu... |
4f82a01f12ce76cba39df3a8be6bb8e7dbf89fdbcbd0e5c55746ddc0440151de | R | 672 | 14 | # function to convert distance data from 'dist' to 'dataframe' format.
# from https://stackoverflow.com/questions/23474729/convert-object-of-class-dist-into-data-frame-in-r
DistToDataframe <- function(inDist) {
if (class(inDist) != "dist") stop("wrong input type")
A <- attr(inDist, "Size")
B <- if (is.null(attr... |
59c0af6d0675211dcf3dd76729550ae52e8e1b5c1610dfb5d3f13d8a5a87f212 | R | 672 | 20 |
datos <- read.csv("/home/usuario/Documentos/Doc/matlab/resultados/onset/metadata.csv")
require(ggplot2)
require(GGally)
library(dplyr)
ggcorr(datos[,-1], palette = "RdBu", label = TRUE)
data_filtrada <- datos %>%
dplyr::select(-tipo_marca,-participante, -orden_preg, -Var1, -offset_samples, -outliers, -ti... |
fbc5e7502ff13cd62df3d7b8c33924a54bed0d4afccb830beb727fcdbcdeb4b7 | R | 672 | 21 |
# Function to add a new column with the rightmost non-NA value
add_rightmost_non_na <- function(dt, columns, new_col_name) {
dt[, (new_col_name) := apply(.SD, 1, function(row) {
rev_row <- rev(row)
rightmost_non_na <- rev_row[which(!is.na(rev_row))[1]]
return(rightmost_non_na)
}), .SDcols = columns]
}
... |
19a88699911f2ba0006a13ff534c286e76a76b8aed88fed1a8afe121da238a53 | R | 673 | 21 | test_that("Test PlotWeights", {
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
package = "SpNeigh"
))
cells_c2 <- subset(coords, cluster == 2)[, "cell"]
weights_cen <- ComputeCentroidWeights(data = coords, cell_ids = cells_c2)
expect_silent(PlotWeights(data = coords, wei... |
75924cedd4f1f2f0844fbe1a041c983c5cf126b34d64009663dc5fbe30412fb6 | R | 673 | 23 | library(ggridges)
library(ggplot2)
library(viridis)
G_L <- paste(dir, "Group_wr_pa.csv", sep = "/") %>% read.csv()
G_L$Age <- as.factor(G_L$Age)
G_L$G_1 <- as.numeric(G_L$G_1)
# Plot
graph <- ggplot(G_L, aes(x = G_1, y = Age, fill = after_stat(x))) +
geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +... |
28b42fa871881dec538eda2ad8c4bbef5a361334f69cc39286b14879f01e57c9 | R | 675 | 16 | library(SOTK)
library(testthat)
context("Checking the initionalization function.")
test_that("Invalid/exclusive arguments", {
pvNmfObj <- readRDS("../inst/testdata/PV.RDS")
dataL <- list(PV = pvNmfObj)
rankL <- list(PV = c(2:6))
expect_error(SOSet(NMFobjL = pvNmfObj, NMFrankL = rankL,... |
4689ed97f053976487d4925d4bea72897bd3344ff90ce7a071520fc56b04565c | R | 677 | 25 |
### Split column into new columns based on a separator, if the number of new columns is unknown using data.table
library(data.table)
dt.splitcol <- function(DT, coltosplit, newcolpattern, sep) {
# Ensure DT is a data.table
setDT(DT)
# Split the specified column by the separator
split_results <- DT[, tstr... |
70a00410de4bb6d8f167973176693ceb5518f009cd4edc2df2c2db1bbc26ca77 | R | 680 | 27 |
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(
"step4_evaluate_gene_sets.Rmd",
params = list(cell_type_name = cell_type_name, graph_weight=graph_... |
3027d135474a84e2d6bd66d1937be198e7a7d32a7a14060aad575c18a6e1bd1d | R | 688 | 23 | #After quality control
library(Seurat)
data <- readRDS("/home/rstudio/shared_data/nodose/latest-integration-230403/nodose-integrated.RDS")
DefaultAssay(data) <- 'integrated'
data <- RunPCA(data, npcs = 100, verbose = F)
DIMS<-c(30, 40, 50, 60)
resolution.list<-seq(0.6, 2.0, 0.2)
for (i in DIMS) {
#data <- RunUMA... |
c5952781aa148da623eb36ba53d04ea1ad57d4a62deb3c9092df31959c374ebd | R | 688 | 21 | #============================================================
# Function vecvel() -- Microsaccade Toolbox 0.9
# (R-language Version)
# Authors: Ralf Engbert, Petra Sinn, Konstantin Mergenthaler,
# and Hans Trukenbrod
# Date: February 20th, 2014
#============================================================
vecvel <- fu... |
c8a9080f220917c73138d2d6dad3efa2fc60a021957d98efff716f7ae232a881 | R | 688 | 18 | #' Combine multiple continuous variables by proportion of positive values
#'
#' To summarize multiple features, the proportion of features with positive
#' values for each spot is computed.
#'
#' @param cont_mat A \code{matrix()} with spots as rows and 2 or more continuous
#' variables as columns.
#'
#' @return A \code... |
907f5b9ca30bf28f784c8e6308e7e6c209a8f1ad37f2502f0bc93122c2959ff3 | R | 690 | 27 | create_conda_env <- function(name,
path,
channels = list("defaults"),
conda_deps = list(),
pip_deps = list()) {
conda_deps$pip <- pip_deps
deps <- list(
name = name,
channels = channels,
d... |
a59ce0128ef196bb740c11dc8e6391f9de21748db9c9b0ee93c61e96375ff496 | R | 690 | 25 | #######################################################
# Add ssm2 coverage counts to db
# Date: 2022.02.28
# Author: Kevin Johnson
#######################################################
# Necessary packages:
library(parallel)
library(tidyverse)
library(DBI)
library(odbc)
con <- DBI::dbConnect(odbc::odbc(), "Verhaak... |
fb7dec10a27d01fbf043e43ad8f3b9059f535783e47bdfcd88aaf0e60ccb703b | R | 690 | 25 | library("DR.SC")
library(Seurat)
library(anndata)
library("scater")
ST <- read_h5ad("./ST.h5ad")
ST$var_names_make_unique()
count <- t(ST$X)
meta_data <- data.frame(row=ST$obsm$spatial[,1], col=ST$obsm$spatial[,2])
row.names(meta_data) <- colnames(count)
seu <- CreateSeuratObject(counts=count, meta.data = meta_data)... |
922d6e54e88cc85eb596d9312c81ddceef005843165ea1597464fcaf30e2f642 | R | 692 | 25 | 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... |
e4a3666b5600a7530f596336d0f48fe8fd71b080b491c53b97c34bede9b64417 | R | 694 | 21 | #' @name GOmouse
#' @title GOmouse
#'
#' @description List containing gene symbols for 10 GO function
#'
#' @docType data
#'
#' @format
#' \describe{
#' \item{genesets}{List containing gene symbols for 10 GO function (GO:0016853
#' , GO:0005615, GO:0005768, GO:0007067, GO:0065003, GO:0042592, GO:0005929,
... |
a1dca49b7c1b8c13ff0078168dd161ed3a8c51d1d2e31f40644a972423f240e7 | R | 697 | 23 | test_that("Test plotWeights", {
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
package = "SpNeigh"
))
cells_c2 <- subset(coords, cluster == 2)[, "cell"]
weights_cen <- computeCentroidWeights(data = coords, cell_ids = cells_c2)
p <- plotWeights(data = coords, weights = we... |
c3c7ae65d0b90cf742a891498a421a06108fe272c75cf883cb5413d61931c71d | R | 698 | 21 | ---
title: "License"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
What license are you using for your code? See [choosealicense.com][choose] for
help deciding. It's a convention to save a file `LICENSE` in the root of your
Git repo that contains the license text.
Wh... |
7e205db1cb408c1ffaef4dce44a383ca8ff76adc3152027703fd1e8861000b37 | R | 699 | 30 | test_that("computeSpatialInteractionMatrix computes correct counts", {
# Deterministic coordinates
df <- data.frame(
x = c(0, 1, 5, 6),
y = c(0, 0, 0, 0),
cell = c("A1", "A2", "B1", "B2"),
cluster = c("A", "A", "B", "B")
)
res <- computeSpatialInteractionMatrix(df, k = 1... |
ba68613b1e286c0789081164d80d28e6a6ec8f2beaf6b58da09acbe66f0deede | R | 701 | 22 | # Suppress R CMD check NOTEs for data.table NSE variables
utils::globalVariables(c(
".", ".N", ":=",
"adjusted_influence", "adjusted_influence_minmax_by_seed",
"adjusted_influence_minmax_by_target",
"adjusted_influence_norm_by_sources_and_targets",
"id", "influence_original", "influence_summed",
"is_seed", ... |
c8c4700b4d9917b0e5ff8dc2d60251b091a237bcad715a5d876c6a4c1c691342 | R | 702 | 27 |
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(
"step5_evaluate_gene_sets_cell_types.Rmd",
params = list(cell_type_name = cell_type_name, graph_we... |
d8c63bf96287679201db558f3e158ba30117c1ba58d9bdc3955951b0cc62a781 | R | 702 | 17 | # R / Bioconductor dependencies for the hemostasis pipeline (R 4.5.2)
# Run: Rscript R_dependencies.R
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
# Bioconductor packages
BiocManager::install(c(
"clusterProfiler", # 4.18.4 — KEGG / GO enrichment
"org.Hs.eg.db", # h... |
c5f68068a88a16958480a9c173435018246a45ddfb99bb2545fd45fbc9d29e5b | R | 705 | 33 | library(dplyr)
library(readxl)
library(ggpubr)
library(rstatix)
RANKL_TEC <- read_excel("data/RANKL_TEC.xlsx")
p <- ggplot(RANKL_TEC, aes(x = cTEC, y = gMFI)) +
geom_point() +
stat_cor(method = "pearson", label.x = 0.5) +
geom_smooth(method = "lm", se = FALSE) +
theme_pubr() +
ylab("RANKL gMFI")
ggsave(
f... |
7ca56499b0baa55d3dcdd1ab07f7d075172ab230ac53dd1b9f8c369e7c940306 | R | 708 | 16 | test_that("flywire nuclei works", {
token=try(chunkedgraph_token(), silent = TRUE)
skip_if_not_installed('reticulate')
skip_if(inherits(token, "try-error"),
"Skipping live flywire tests")
skip_if_not(reticulate::py_module_available("cloudvolume"),
"Skipping live flywire tests requiring p... |
fe4e840862d557c52532a3bfcde896c4d49c14dbe7b4f6bbe22147a8c0d383b5 | R | 711 | 16 | suppressMessages(library(monocle3))
test_monocle3 <- function(X,genes,cells,UMAP,pr_graph_cell_proj_closest_vertex,F,B,n_jobs,...){
rownames(X)=cells
colnames(X)=genes
cds = new_cell_data_set(t(X))
cds@int_colData@listData$reducedDims@listData$UMAP = UMAP
cds@principal_graph_aux[["UMAP"]]$pr_graph_... |
c8587be7dc133c49fbe3e94d192d6cecf1a50bed9bd4fc43a15ffd6d52969ec6 | R | 715 | 21 | ####################################################
# Plotting the correlation matrix between factors #
####################################################
library(MOFA2)
library(ggplot2)
library(dplyr)
library(circlize)
library(reshape2)
# Retrieving MOFA
load("C:/Users/User/Documents/Drive_PC/Lucas/GitH... |
40b172a1846192912855e15f8ea5d6e1139f6b2e9c41e041f031def1c0cd51ac | R | 716 | 23 | test_that("Test getCellsInside", {
coords <- readRDS(system.file("extdata", "MouseBrainCoords.rds",
package = "SpNeigh"
))
boundary_points <- getBoundary(
data = coords, one_cluster = 2, eps = 120, minPts = 10
)
boundary_points_sub <- subset(boundary_points, region_id == "2")
... |
0e37ccf2d926fa2e0b4767b9c5d417ee1f6a81df95936bbffbc22d55982ea76d | R | 717 | 32 | .openDev <- function(fileName, devType, ...)
{
if(devType=="pdf")
pdf(paste0(fileName, ".pdf"), ...)
if(devType=="png")
png(paste0(fileName, ".png", type="cairo"), ...)
if(devType=="cairo_pfd") # similar to Cairo::CairoPDF?
grDevices::cairo_pdf(paste0(fileName, ".pdf"), ...)
}
.openDevHeatmap <... |
a0036007ed0c7947fb696a42216921cf8d873aaa54204cc0f58b84a8fc1a2059 | R | 721 | 26 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
# test if there is at least one argument: if not, return an error
if (length(args) < 1) {
stop("Please provide an input", call.=FALSE)}
matrix_path <- args[1]
gct_out <- args[2]
#Create gct file of gene expression matrix
data <- read.delim(matrix_path)
... |
e5e811d5dd0cdfc62722eef46344b8edd3458fc5af997d582b1db5a1408383c2 | R | 722 | 22 | library(memes)
genome <- BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38
PATHDB<- "/geschwindlabshares/RexachGroup/SharedData/motif_databases"
motifdb <- file.path(PATHDB, "CIS-BP_2.00/Homo_sapiens.meme")
memeBin <- "/home/xhan/apps/meme-5.4.1/bin"
run_TFenrich <- function(gr, label){
seqs <- get_sequence(g... |
9514748e77aeef4edf948c6384f0d18ded48dba64f180d0afc923ea6321c665b | R | 723 | 26 | #!/usr/bin/env Rscript
#######################################################
library(tidyverse)
library(odbc)
library(DBI)
#######################################################
#Establish connection
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB4")
args = commandArgs(trailingOnly=TRUE)
# test if there is at l... |
f8f4e5feaf3f8323ae09e2760079955d5e7f90d0ba23ef152ec867afd0d7d321 | R | 724 | 25 | test_that("Test PlotStats functions", {
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)
... |
550e868763eb98bf92cdf3812f925c94f568b30d7e6d2c1d8a78b30e340a39bf | R | 730 | 17 | library(seqinr)
mai_seq_align = function(){
var_seqs = read.csv("Datasets/variant_sequences.csv")
var_seqs$sequence = trimws(var_seqs$sequence)
short_seqs = strsplit(var_seqs$sequence, "")
short_seqs = lapply(short_seqs, function(x){x[13:213]}) #Mai's sequences are R319-N577. Starr's are N331-T531
#note, ... |
afeebaa5b79d4d4875206225b8991a3fa0f5854281c3fa4c0c5ccec22ee91654 | R | 730 | 29 | #' banc-fix-major-cell-type-errors — Integrate external tracing decisions (ORN + others).
#'
#' Manual spot-fixer; not part of any automated run.
#'
#' @section Reads:
#' - `banc_edgelist()`, SeaTable `banc_meta`
#'
#' @section Writes:
#' - SeaTable `banc_meta` (per-block targeted columns)
#'
#' @section Notes:
#' ... |
570363e32dec017ef23e48757e0b326b403ed03582986e218bfa90869e3f22d8 | R | 732 | 14 | RNGkind("L'Ecuyer-CMRG")
set.seed(123)
# Generate some prior predictives
dat <- get_data(samples_LNR)
pp <- predict(get_prior(samples_LNR), n_post = 25, data = dat)
drt <- function(data) diff(tapply(data$rt,data[,c("S")],mean))
test_that("plot_fit", {
vdiffr::expect_doppelganger("CDF_plot_fit", plot_cdf(dat, prior_p... |
61444bbe89db3b2c45d254831235bbaaa2354905a485425da127bebb70ec4d65 | R | 732 | 28 | ART.A <- function(P, k, L) { # "ART-A"
wgt <- rep(1,k)
z <- P
z[1] <- ( 1 - P[1] )^L
for(j in 2:L) z[j] <- ((1-P[j]) / (1-P[j-1]))^((L-(j-1)))
p <- (1-z)[1:k]
k = length(p)
sumZ <- rep(0, k)
y <- qnorm(p)
z <- y
gz <- z[1] * wgt[1]
sumZ[1] <- gz
for(i in 2:k) {
gz... |
71abe9dec90ea851c883b72d736e3938d693c447517d515192b7ff4608bbfd03 | R | 738 | 26 | score.getZforlambda<-function (x, lambda, parallel = FALSE, ncores = 8, oldschool = FALSE)
{
n <- nrow(x)
p <- ncol(x)
Z <- matrix(numeric(n * p), n)
if (oldschool) {
message("doing getZforlambda oldschool")
for (i in 1:p) {
glmnetfit <- glmnet(x[, -i], x[, i])
prediction <- predict(glmnetf... |
eef2a83e3b66937e3f476edeb9ad6cab1a1a9bef9f490ebe9a385a6b36e2e516 | R | 738 | 32 | #' @rdname mlflow_save_model
#' @export
mlflow_save_model.crate <- function(model, path, model_spec=list(), ...) {
if (dir.exists(path)) unlink(path, recursive = TRUE)
dir.create(path)
serialized <- serialize(model, NULL)
saveRDS(
serialized,
file.path(path, "crate.bin")
)
model_spec$flavors <- a... |
b760b39a21cae4ce9bbf5ed2fee0eb8e5e8ab2595f3f3930e4186d3fde3e2d94 | R | 740 | 17 | #' Looks up marker overlap between query cluster and database cell types
#'
#' This function calculates the number of markers from a specified query cluster that overlap with each cell type in the database.
#'
#' @param cluster The name of the query cluster.
#'
#' @param ann An object containing marker data in `ann1$q... |
8cfa13d61042bc8aa19fb66e84e812ae37f98dec76a939eb8794cf83dbf0d0b4 | R | 741 | 31 | # --------- Load libraries --------- #
library(tidyverse)
library(rjson)
data_list <- readRDS('Behavioural Analysis/scr/data/data_list.rds')
# -- Get Design Matrix Sanity Check -- #
# This design matrix contains events
# associated with the option selection.
# We expect to see motor-related activity.
test <- data_... |
8cb587c8f78a9a8563b598054bff9433a3094e2567b3eb4612282cc2ea4a13a2 | R | 746 | 18 | #' Looks up marker overlap between query cluster and database cell types
#'
#' This function calculates the number of markers from a specified query cluster
#' that overlap with each cell type in the database.
#'
#' @param cluster The name of the query cluster.
#' @param ann An object containing marker data in `ann1$qu... |
916af291dddc0addf0d30bd4940462068181194b3b615b74951e15d7f66f7447 | R | 746 | 20 | test_that("mirror_manc works", {
skip_if(inherits(try(nat::cmtk.bindir(check = TRUE), silent = T), 'try-error'))
hookr=cbind(33549, 45944, 50806)*c(8,8,8)/1000
hookl=cbind(8718, 40794, 52140)*c(8,8,8)/1000
hookrl=rbind(hookr, hookl)
expect_is(hookrl.m <- mirror_manc(hookrl), 'matrix')
ds=sqrt(rowSums((hookr... |
1774e7939dbf04d78c3e52e8e3697c57278c0976676adec9050caee95c92b635 | R | 748 | 20 | #'
#'
#' @param
#'
#' @return
#'
#' @export
xgb_load <- function(x, group_column = "group") {
load_model <- function(obj) {
model_raw <- xgboost::xgb.load.raw(obj$model, as_booster = TRUE)
feature_names <- obj$train_obj$matrix %>% colnames
model_raw$feature_names <- feature_names
model_raw
}
readR... |
a9760ac9c63ce7abfa3413ed754dc8172a6369bec40053b565733cf46c625a2b | R | 750 | 23 | library(tidyverse)
library(SingleCellExperiment)
library(scran)
library(data.table)
library(readr)
library(BiocParallel)
library(optparse)
option_list = list(
make_option("--input", default=NA, type='character',
help="File path to the input (logcounts saved as an RDS object)"),
make_option("--output"... |
1562ab174a5067fb117d1c8fe8df83dc2267859ded65ea45e9d94274e22aef79 | R | 752 | 29 |
foverlaps2 <- function(DTx, DTy,
by=c('seqnames', 'strand', 'start', 'end'),
by.x=by, by.y=by,
type='any',
minoverlap=20,
maxgap=0
) {
setkeyv(DTx, by.x)
setkeyv(DTy, by.y)
... |
9a469ef46a774e8bcb4284c987bee2e2522852a2a72341af8e8ba037d11ded5e | R | 754 | 21 | #' See the list of probes needed for each Dunedin Pace of Aging Methylation Score Model
#'
#' \code{getRequiredProbes} returns a list containing the IDs of the probes needed for each Dunedin Pace of Aging Methylation Score Model.
#' @param backgroundList (default: FALSE). If set to true, it will return the full set of... |
fcb57dde198f8b0b2b5db6b9f4d9f19e1a516b6f8ebceea3923c2ec635569cf2 | R | 758 | 36 | ######################
### Assign regions ###
######################
source("banc/banc-startup.R")
#################################
### Read synapses by neuropil ###
#################################
# Get BANC synapses
banc.syns.nps <- arrow::read_parquet(
file.path(banc.connectivity.save.path, "banc_synapses.par... |
1db7913522058241dc42b60a6d6b1b5e380671a17ceeb19e9dd2a3700121b4d1 | R | 759 | 25 | example("registration_pseudobulk", package = "spatialLIBD")
mod <- registration_model(sce_pseudo)
test_that("Checking for model terms works", {
expect_equal(sum(grepl(
"^registration_variable", colnames(mod)
)), ncol(mod))
expect_error(registration_model(sce_pseudo, "absent"), "Formula term")
... |
d850960c10fec0a02645d39e8e06e16a6a523651120f9ea7e86562ec529ecea6 | R | 760 | 22 | library(EpiModel)
g <- as.network(el, directed=FALSE) #generate network from synthetic edgelist
data <- data[match(get.node.attr(g, "vertex.names"), data$record_id), ] #make sure node indices match participant IDs
g %v% "venues" <- c(data$venues)
g %v% "HIV" <- c(data$hiv)
for (ii in 1:length(venues)){
g %v% venue... |
4584de7684dac63792192e5715a82ace69a2b19e97a1311c5ed4a16aa6a6eab2 | R | 761 | 28 | test_that("predict requires data for memory_saver fits", {
dat <- subset(forstmann, subjects == levels(forstmann$subjects)[1])
dat <- dat[seq_len(100), , drop = FALSE]
design_in <- design(
data = dat,
model = DDM,
formula = list(v ~ 0 + S, a ~ E, t0 ~ 1, s ~ 1, Z ~ 1, sv ~ 1, SZ ~ 1),
constants =... |
a33bc5b6d8af5393d423917b56fe0bc9f7b6bb2b55080876d3c985e2d2d0df74 | R | 761 | 27 |
Sys.setenv(RSTUDIO_PANDOC="/Applications/RStudio.app/Contents/MacOS/pandoc")
render_report = function(cell_type_name, graph_weight) {
file_tag = paste0(cell_type_name, "_BL_", graph_weight)
rmarkdown::render(
"s2b_check_results.Rmd",
params = list(cell_type_name = cell_type_name, graph_weight=graph_we... |
c3fa6be889d869405d366df15fb110416bcc86d43cffad2e12329a97b84020d7 | R | 761 | 24 | # Simple normal likelihood, the first half of the parameters are the means,
# the second half the variances. We exp them to ensure positive variances
custom_ll <- function(pars, dadm, ...){
idx <- dadm$S == "left"
ll_left <- dnorm(dadm$rt[idx], pars[1], exp(pars[3]))
ll_right <- dnorm(dadm$rt[!idx], pars[2], exp(... |
1dc0ad9e94ee1fb2f9a7501bc0083cdfa321ee56e17a29c5c4a7ef5d3dc62f1d | R | 762 | 26 | #' Add random number columns in data frame with specific seed to validate machine learning algorightms
#'
#' @param
#'
#' @return
#'
#' @export
#'
shapviz_avg <- function(model, train_obj, n = 5) {
sv <- shapviz::shapviz(object = model, X_pred = train_obj$matrix)
groups <- generage_pca_grouping(train_obj$df, n)
... |
a26ddeaf125cb88939b5a72fa2931dd6e3d4831177772910e4d7ad00e335bb0b | R | 765 | 17 | test_that("fanc4to3 works", {
# https://radagast.hms.harvard.edu/catmaidvnc/?pid=13&zp=117630&yp=470101.3&xp=194569.2&tool=tracingtool&active_skeleton_id=1377198&active_node_id=18180879&sid0=10&s0=-2
# nb xyzmatrix sets column names
fanc3=xyzmatrix(cbind(194569.2, 470101.3, 117630))
fanc4=cbind(45224, 109317... |
5adaa8e0931627d2d571aa4c7598999409043e2d907ff6f6190adb22aec66c0d | R | 769 | 25 | # `trivial` is a dummy MLflow flavor that exists only for unit testing purposes
mlflow_save_model.trivial <- function(model, path, model_spec = list(), ...) {
if (dir.exists(path)) unlink(path, recursive = TRUE)
dir.create(path, recursive = TRUE)
path <- normalizePath(path)
trivial_conf = list(
trivial = ... |
e95406554a5e4e2213bd530b56b227da0ef5f5514ca571d99972438c74e982dc | R | 780 | 32 | library(dplyr)
library(ggpubr)
library(rstatix)
data <- readxl::read_excel("data/sjTREC_150kcells_21112024.xlsx") %>%
mutate(log10age = log10(Age))
# Fig 4c
data %>%
ggplot(aes(x = SNP, y = sjTREC)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha = 0.5) +
theme_pubr() +
ylab("log10 TREC... |
f237a1304c0784fef7d24f3696dbcf9fc44fcff4033d9c9bb7123148ecb7e44c | R | 780 | 28 |
Sys.setenv(RSTUDIO_PANDOC="/Applications/RStudio.app/Contents/MacOS/pandoc")
render_report = function(cell_type_name, graph_weight) {
file_tag = paste0(cell_type_name, "_BL_", graph_weight)
rmarkdown::render(
"s1_evaluate_gene_sets_goseq.Rmd",
params = list(cell_type_name = cell_type_name, graph_weight=... |
675c21a79d68f7ef15e9df61f1b83ef2d00c47699ebdc5ba78ce2c74c7da5db1 | R | 781 | 20 | matchfun <- function(d)d$S==d$lR
# design an "average and difference" contrast matrix
ADmat <- matrix(c(-1/2,1/2),ncol=1,dimnames=list(NULL,"diff"))
# specify design
design_LBABE <- design(data = forstmann,model=LBA,
formula=list(v~1,sv~1,B~E+lR,A~1,t0~1),
constants=c(sv=log(1)))
pmean... |
207ebdac472a71188d076df416e427e047d7a70fc1643aaa34f5b9bed3ac4570 | R | 782 | 33 | setwd(Sys.getenv("HOME"))
source("R_func/mutate_load.R")
library(tibble)
library(GenomicRanges)
# Load peak ages
peak.ages <- fread(
"project/multiomics/PeakAnlys/PeakAnnot/AgePeak.csv"
)
meta.features <- fread(
"project/multiomics/PeakAnlys/meta.features.csv"
)
all.peaks <- meta.features[, 1, ] %>% unlist %... |
aec89271459de068d042fecd0f8fc75acb916f4295ab8e32505ae88973dacb80 | R | 788 | 20 | # Kirjastot
library("plyr")
library("XML")
library("purrr")
# Listaa luettavat tiedostot, 5MIN kestää
setwd("/finngen/library-red/EA3_HEART_FAILURE_1.0/data/ecg")
files <- list.files(full.names = TRUE,recursive = T)
# Poimii halutut tiedot, tässä noden <RestingECGMeasurements> alla
parse_xml <- function(FileName){
... |
7dd34d486bc0f04c40d69c95d2bc2ecbc30819808f04741125da935b1abc232d | R | 794 | 28 |
Sys.setenv(RSTUDIO_PANDOC="/Applications/RStudio.app/Contents/MacOS/pandoc")
render_report = function(cell_type_name, graph_weight) {
file_tag = paste0(cell_type_name, "_BL_", graph_weight)
rmarkdown::render(
"s1b_evaluate_gene_sets_cell_types.Rmd",
params = list(cell_type_name = cell_type_name, graph... |
b8aa35cb042fdbe3e63c9dea55725c5ee741f992b4b3d2e56b06e12e2672a746 | R | 795 | 30 | test_that("Test PlotStats functions", {
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)
... |
4ef63b998f34aa110ef7bc2e1f964ac657158b82611db15ba15701000ece2a95 | R | 800 | 28 | suppressWarnings({
library(afvaper)
library(vcfR)
library(data.table)
library(ggplot2)
library(cowplot)
library(tibble)
library(stringr)
library(tidyr)
})
args <- commandArgs(trailingOnly=TRUE)
out_full <- args[1]
k_val <- args[2]
out_subset < paste0(out_full, "_LD50kb")
run_t... |
fce389719a028349e4f477961f0063a734484450936708a5e3e58dc110484abc | R | 800 | 45 | setwd("./BayesCafe-main")
source("R/BayesCafe.R")
library(anndata)
library(scater)
set.seed(101)
ST <- read_h5ad("./ST.h5ad")
out.path <- "./output_BayesCafe.csv"
ST$obs_names <- make.unique(ST$obs_names)
ST$var_names <- make.unique(ST$var_names)
count_ST <- (ST$X)
x<- ST$obsm$spatial[,1]
y<- ST$obsm$spatial[,2]
po... |
1e3a53f146c5e37b5a8067ac4add9396bef70f2eb96be4d312c827fe9ba7495a | R | 802 | 25 | library("spatialLIBD")
library("markdown") ## due to a shinyapps.io bug
## spatialLIBD uses golem
options("golem.app.prod" = TRUE)
## You need this to enable shinyapps to install Bioconductor packages
options(repos = BiocManager::repositories())
## Load the data (all paths are relative to this script's location)
spe... |
20ca339d774b0c0caeb1cc89e67ff2bfb584479b35e7d54715926e2a33dffe78 | R | 802 | 16 | # This script randomizes the dataset 200 times. It creates a new dataset with the same species and alleles but with randomized phenotypes.
dataset <- "Path/to/dataset_file.txt" # Path to the dataset
# The dataset (output from script 14.2) contains three columns: 1) Species names, 2) Allele variant, 3) Phenotype state
... |
f4e96079b6e3f4f6e014a52194ce1a9238f881aa5847f189e2ebafb59fa42c55 | R | 803 | 29 | #######################################################
# Add ssm2 coverage counts to db
# Date: 2023.03.22
# Author: Kevin Johnson
#######################################################
# Necessary packages:
library(parallel)
library(tidyverse)
library(DBI)
library(odbc)
con <- DBI::dbConnect(odbc::odbc(), "Verhaak... |
7250ddfd31885346abea814bcc52846c14e09f38a34f548ff6d8688efd0016a1 | R | 808 | 35 | amos_ns <- 543893
bride_ns <- 543893
long_ns <- 543893
pat_ns <- 543893
quon_ns <- 543893
amos_i <- 20
bride_i <- 30
long_i <- 20
pat_i <- 19
quon_i <- 18
amos_het <- 0.14329768056384
bride_het <- 0.255855625571989
long_het <- 0.156552960008373
pat_het <- 0.187106414828695
quon_het <- 0.17847314076479
for ( lake in c... |
6ea5182c11aefc57d736de8acdf9a3144715c7630a070fcfcca881a11305f6ed | R | 811 | 23 | RNGkind("L'Ecuyer-CMRG")
set.seed(123)
dat <- forstmann
dat$covariate <- 1:nrow(forstmann)
des <- design(data = dat, formula = list(v ~ covariate*E, B ~ E, t0 ~ S),
model = LBA)
test_that("mapped_pars", {
expect_snapshot(mapped_pars(des))
expect_snapshot(mapped_pars(des, p_vector= rnorm(length(samp... |
bd53cdd376dd629f209e248535071f946893eb2fe91a35532d485dac50c45d9f | R | 816 | 25 | # Function to process a string containing multiple comma-separated entries
# and convert it into a cleaned character vector
multiple_entries_to_vector <- function(x) {
# Ensure input is in character format and handle NULL cases
if (is.null(x) || length(x) == 0) {
return(character(0)) # Return an empty charact... |
3c0c53295e2419489a3c88be18a894a8aa13ea4cce09f461f7e802c7ed9ef49f | R | 817 | 24 | context("test-ng-raw-io.R")
test_that("read_ng_raw works", {
expect_is(chunks <- read_ng_raw('testdata/read_ng_dump/chunk00789.raw'),
'ng_raw_list')
expect_is(chunksh <- read_ng_raw('testdata/read_ng_dump/chunk00789.raw', read_data = FALSE),
'ng_raw_list')
expect_equal(chunks[[1]]$h, chu... |
585414fdf172423ba5cbe0d54a1503cd8a10e0dfee50e8f1099c5290586fbeb3 | R | 818 | 21 | ### Calculate the mol% of each individual lipid
setwd(rstudioapi::getActiveProject())
rm(list=ls())
source("./Scripts/Function_scripts/Effect_size_functions.R")
source("./Scripts/Function_scripts/Pre-processing_functions.R")
library(tidyverse)
##########################################################################... |
a8bbe3b04072d6e0ad22172d3e08f5ec0898fc3599a74cf9b04866ded240dfd4 | R | 820 | 30 | #!/usr/bin/env Rscript
#######################################################
library(tidyverse)
library(odbc)
library(DBI)
#######################################################
#Establish connection
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB4")
args = commandArgs(trailingOnly=TRUE)
# test if there is at l... |
a458deddf4a6ed053c01349fddb00df4a0eba1f554c3f441304a294049c21971 | R | 825 | 24 | here::i_am("scripts/2.validations/2.3.binding_site_enrichment_and_divergence/2.3.8.create_blacklists.R")
library(plyranges)
library(tidyverse)
library(here)
input_dir <- here("data/validations/ATAC_seq_peaks_noBL/")
output_dir <- here("data/validations/ATAC_seq_blacklists/")
dir.create(output_dir)
# create custom b... |
a145c8e108ccea521bf30bb99da6fee0ca1a216658523812e6181952fe20800d | R | 832 | 16 | library(ggplot2)
##### Fig 5H dotplot for GSEA
data <- read.csv("Fig 5H LG815_TDI_MG_C5_Reactome_curated.csv")
ggplot() +
geom_point(data=data, aes(x=as.numeric(p.value.of.overlap), y=reorder(Gene.Set.Name,-log(FDR.q.value)), colour=as.numeric(p.value.of.overlap),size=X..Genes.in.Overlap..k.),
... |
9d42adc537808b551d39a426636029f1649b41a968b08ffedd1c1dfcd6f68720 | R | 833 | 20 | test_that("multiplication works", {
skip_if_not_installed('fastmap')
v64 <- vcache64('rhubarb-test')
expect_silent(vcache_mset(v64, '1', bit64::as.integer64(1L)))
expect_error(vcache_mset(v64, '1', 1L), regexp = "class mismatch")
expect_equal(vcache_mget(v64, '1'), bit64::as.integer64(1L))
expect_equal(vcac... |
531e3c9abf55b420a0a122f3a46b865253982a5ee95cb0f198d89b92841eee22 | R | 835 | 27 | SumSqU<-function (U, CovS)
{
if (is.null(dim(CovS))) {
Tscore <- sum(U^2/CovS)
if (is.na(Tscore) || is.infinite(Tscore) || is.nan(Tscore))
Tscore <- 0
pTg1 <- as.numeric(1 - pchisq(Tscore, 1))
}
else {
if (all(abs(U) < 1e-20))
pTg1 <- 1
else... |
0200a93e3ac068d9dd9f02304d4d4e8ac47a890e17da1d69afc7bd19ddc18f71 | R | 836 | 22 | ### Calculate the mol% of each individual lipid
setwd(rstudioapi::getActiveProject())
rm(list=ls())
source("./Scripts/Function_scripts/Effect_size_functions.R")
source("./Scripts/Function_scripts/Pre-processing_functions.R")
library(tidyverse)
##########################################################################... |
9439a46370a844de9cb3da8620cccf00f2bc2a1dcdad65cd8b19fe662923e659 | R | 837 | 31 | # CBIG_LBC_longComBat_FC_wrapper.R
# get env variable
LBC_rep_dir <- Sys.getenv("LBC_rep_dir")
if (LBC_rep_dir == "") {
stop("LBC_rep_dir environment variable not found. Did you source config.sh?")
}
# Get repo root from environment variable defined in config.sh
CBIG_CODE_DIR <- Sys.getenv("CBIG_CODE_DIR")
if (CB... |
6ae6c7f71e9e950acebcfca6d2c826388221d30c4703d7149f899e35fa446dc1 | R | 843 | 31 | start_parallel <- function(parallel, cores) {
if (parallel &&
requireNamespace("doParallel", quietly = TRUE) &&
requireNamespace("parallel", quietly = TRUE)) {
if (Sys.info()["sysname"] == "Windows"){
cluster <- parallel::makeCluster(cores)
doParallel::registerDoParallel(cluster)
} els... |
4d0460cbe2e05b0bcb9c342c7777d8524086b6c685e739a3a4fc8e7615010494 | R | 845 | 31 | # run_longComBat_cog.R
# get env variable
LBC_rep_dir <- Sys.getenv("LBC_rep_dir")
if (LBC_rep_dir == "") {
stop("LBC_rep_dir environment variable not found. Did you source config.sh?")
}
# Get repo root from environment variable defined in config.sh
CBIG_CODE_DIR <- Sys.getenv("CBIG_CODE_DIR")
if (CBIG_CODE_DIR ... |
2b7ed4c16772de9d7a52b0001ded834eee33fd21cedf55c30716d7e7c72a5c7b | R | 847 | 29 | old_root="C:/Users/Jun/Documents/R/AA_behavior/" #you will need to change this
new_root="C:/Users/Jun/Documents/R/" #you will need to change this
next_experimentName= "PL_pPVT_opto" #you will need to change this
###############################################################
newdir=paste(new_root,next_exper... |
492a4628fbfd713d9bab521cb847e8a2b075dac48724534e1f935172bdfe3184 | R | 847 | 40 | #' @importFrom utils browseURL
mlflow_view_url <- function(url) {
getOption("page_viewer", browseURL)(url)
invisible(url)
}
#' Run MLflow User Interface
#'
#' Launches the MLflow user interface.
#'
#' @examples
#' \dontrun{
#' library(mlflow)
#'
#' # launch mlflow ui locally
#' mlflow_ui()
#'
#' # launch mlflow u... |
a1085cc8167d06b2a6e511047d34a39a8ee4722126fe48a933f5be8c234c60a6 | R | 853 | 36 |
dup <- function (x) x[duplicated(x)]
luniq <- function (x) length(unique(x))
is.0 <- function(x) x == 0
## add prime 3s and prime 5s based on start, end and strand
add.primes <- function(df) {
df$prime5[df$strand == '+' & !is.na(df$strand)] <- df$start[df$strand == '+' & !is.na(df$strand) ]
df$prime3[d... |
a46acfb7d9671ef68db0dcbdf27ed88d93c2f38b477f6156515a1c88da7df1cf | R | 858 | 22 | ## push blocklist to db
library(tidyverse)
library(DBI)
library(odbc)
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB")
tmp = read.delim("data/ref/block_review_allow_quality_lists_20181206.txt", as.is = TRUE)
df <- tmp %>%
select(aliquot_barcode,
fingerprint_exclusion,
coverage_exclusion = coverag... |
7c34fb7e2ebcb6db7a42a2678108761e37795a1f51ec07f6311821ea82464427 | R | 860 | 32 | library(SingleCellExperiment)
library(ggplot2)
library(BayesSpace)
library(anndata)
library(scran)
options(matrixStats.useNames.NA ="deprecated" )
set.seed(101)
ST <- read_h5ad("ST.h5ad")
q <- length(unique(ST$obs$ground_truth))
x <- ST$obs$array_row
y <- ST$obs$array_col
location <- data.frame('row' = x , 'col' = y)... |
919780604f920f315cefe507064f26f27dd3a950bdde25cecb3b7c3d6ae0c33f | R | 860 | 24 | ## Calculate the Z-score for each measure
setwd('/home/huyang/Projects/HuYang/HY_20250708')
atlas_names <- c('Schaefer300', 'DK308')
for (curr_atlas in atlas_names){
meas_names <- c('Morph_5F4S', 'Morph_5F1S', 'Morph_9F1S')
for (curr_meas in meas_names){
## Load raw data
in_fname <- paste('./PROCDATA/STATS/... |
1a5fdd3a75fa7e8c6e071e4088cf3e327d15758e10819c5fa4285a736e18e15e | R | 861 | 28 | library(tidyverse)
library(DBI)
#.libPaths("/home/barthf/R/x86_64-pc-linux-gnu-library/3.3")
#dyn.load("/projects/verhaak-lab/verhaak_env/anaconda/v4.2.0/envs/rvenv2018/lib/libodbc.so")
## database connection
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB4")
## input/output parameters
barcode <- snakemake@wildcards... |
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