sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
88b462dda160b9a971beea316c144524de82cfe3e086151964e5f8a1eda66dcb | R | 1,527 | 43 | test_that("neuroglancer annotations work", {
skip_if_not_installed('purrr')
xyz=matrix(rnorm(18), ncol=3)
# layers with colour palette
df=data.frame(point=xyzmatrix2str(xyz),
layer=rep(LETTERS[1:3], 2))
colpal=c(A="red", C="green", B="blue")
expect_is(ann <- ngl_annotation_layers(df, rawcoo... |
939faae6b13359883102e72d141915383ed7cfd01ce71370ee6151ab3ed04008 | R | 1,530 | 40 | # 06_pca_analysis.R
# PCA plots for all samples and per brain region
rm(list = ls())
library(ggplot2)
library(FactoMineR)
library(factoextra)
library(RColorBrewer)
library(openxlsx)
data <- as.matrix(read.csv("data/峰面积表达谱_填充_排序.csv", row.names = 1))
data <- log2(data)
# Sample groups (as defined before)... |
4d8fc67b193477ba9b199ae64ef953e6eb640ccaf84b956c6d3e0bbe8aa5ca39 | R | 1,532 | 45 |
### Quantification of cholesterol concentration from GPMV lipidomics
setwd(rstudioapi::getActiveProject())
rm(list = ls())
library(tidyverse)
library(ggbeeswarm)
library(ggpubr)
load("./Output_Data/GPMV.MsD.ClassSum.Rdata")
sum.df <- GPMV.class.sum %>%
pivot_wider(., names_from = "Class", values_from = "ClassSum")... |
a19769170a57ea047340978f4d7be7af381f2c97817fc8800af3357614849976 | R | 1,534 | 18 | structure(list(url = "https://globalv1.flywire-daf.com/nglstate/123",
status_code = 502L, headers = structure(list(server = "nginx/1.15.10",
date = "Sun, 19 Jul 2020 21:47:52 GMT", `content-type` = "text/html",
`content-length` = "157", connection = "keep-alive",
`strict-transport-securi... |
8e95eb92debee96c82cb970c39c4d902615f864b153675232fd48be5b47aa143 | R | 1,535 | 59 | RNGkind("L'Ecuyer-CMRG")
set.seed(123)
# Set up fake events
events <- data.frame(
subjects = rep(1, 10),
run = rep(1, 10),
onset = seq(0, 90, by = 10),
condition = rep(c("A", "B"), 5),
rt = runif(10, 0.5, 1.5),
accuracy = sample(0:1, 10, replace = TRUE)
)
# Reshape events to long format
reshaped <- reshap... |
ab09ee543adb55a97418353c4d3615151729c3f1f6deb256835f72a229f11924 | R | 1,538 | 39 | #' Dummy query marker data
#'
#' Data contains
#' @format A dataframe with ...:
#' \describe{
#' \item{X}{Running index of gene*cluster ID}
#' \item{p_val}{Uncorrected p value for gene in cluster-of-interest vs. ALL}
#' \item{avg_log2FC}{Average log2FC of expression for gene in cluster-of-interest vs. ALL}
#' \ite... |
b31a771e012d952884d0fd975a45dd054bcc168735a2b91798fb7607140082ec | R | 1,539 | 52 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
exprpath = args[1]
dbpath = args[2]
resPrefix = args[3] # All_junction
method = args[4] # gsva
print(args)
library(GSVA)
parse_gmt = function(gmt_path){
db = readLines(gmt_path)
geneset = list()
for (line in db){
line ... |
752e086d3c804215e2cbebfec83ba93f214de7bf57690d8a53ae11e5efd2bb5e | R | 1,541 | 51 | library(tidyverse)
library(SummarizedExperiment)
library(edgeR)
library(nlme)
library("XML")
library(GEOquery)
datExpr <- fread("Labonte_GSE102556_HumanMDD_fpkmtab.txt.gz")%>%
as.data.frame()
rownames(datExpr) <- datExpr$gene_id
colnames(datExpr) <- paste("X", colnames(datExpr), sep = "_")
# Read in metadata and cr... |
3651a8164d547fa1c5a3da8350e35b68f78e6e581c049294e1a8e53b68871525 | R | 1,542 | 37 | #' An S3 class constructor of representing Venn plot components.
#'
#' @param x data source of a VennPlotData object
#'
#' @slot shapeId shape id
#' @slot type type of shape
#' @slot nsets number of sets
#' @slot setEdge a data.frame, the coordinates of set edges, can be retrieved by `venn_setedge()`
#' @slot setLabel ... |
e6d804d1c3b870e6bd3ed90c45c3c5362732d12af3bcdda06409ce901b8e907c | R | 1,542 | 44 | #!/usr/bin/env Rscript
source("essential_functions.R")
library(dplyr)
#' Author: Sumaiya Nazeen (sumaiya_nazeen@hms.harvard.edu)
#' This program generates the pairwise lookup table for
#' a given gene network
#'
#' @param Network_matrix_file a .tsv file containing the network adjacency matrix.
#' @param num_alpha_le... |
5e5b4323860f2b04067b6e1a2a4b33bc1c86d516e25a962f775b45bf10d6958a | R | 1,545 | 46 | #' Register an external MLflow observer
#'
#' Registers an external MLflow observer that will receive a
#' `register_tracking_event(event_name, data)` callback on any model tracking
#' event such as "create_run", "delete_run", or "log_metric".
#' Each observer should have a `register_tracking_event(event_name, data)`
#... |
3d8da7bf5cfbe4232567b955ccf5487e3fb2f3672d7fbc14246e1b1553a94edf | R | 1,547 | 36 | #' Plot the distribution of module sizes
#'
#' Plots the distribution of module sizes (i.e. the number of target genes assigned to each regulator) as a histogram.
#'
#' @param modules Data frame of the pruned modules, required columns:
#' \describe{
#' \item{regulator}{Character, transcriptional regulator.}
#' \item{mo... |
0ea3d443e469bf93b52b0b4fb4b5ec7d2bfc80a6d6115cef8dc79c0895636472 | R | 1,548 | 45 | #This code combines atc and covariates data frames to QT variants
#projectID: VL20243010
#by: Aleksi Kristian Winstén
#date: 20.11.2024
#University of Turku
setwd("/home/ivm/Projects/VilleLangen/EKG-MachineLearning/")
library(data.table)
library(dplyr)
library(tidyr)
library(vcfR)
#load qt variants
vcf_file <- "./Data... |
9bfd72e68a64774b214758325981eef4b9f0eb2b5fa377f182acbf289b766817 | R | 1,548 | 41 | #' @title dotHeatmap
#' @description Plots a dot-heatmap for enrichment results
#' @param enrichmentDf Input data.frame
#' @param var.x Variable (column) for the X axis
#' @param var.y Variable (column) for the Y axis
#' @param var.col Variable (column) that will determine the color of the dots
#' @param var.size Varia... |
bf5c2806c9dadade9fedca15dafcf5d61fa1663237fe127f8b2e0858758d45c6 | R | 1,550 | 68 |
dt.from.ps <- function(ps,
glom_taxa=F, i=NA, t=NA, ignore_lineage = T, keep.all=T,
melt = T,
glom_samples=F, summarise.samples = NULL, by.var = 'sample'
) {
if(glom_taxa) {
if(is.na(i)) { i <- 2 }
if(is.n... |
7e1de6887042bd461df6b6dd86cfc20b14ed6567d640556712920d5992103d38 | R | 1,554 | 66 | # Cast utility functions to replace deprecated forge functions
# These functions use modern rlang/vctrs functions instead of deprecated ones
cast_string <- function(x, allow_na = FALSE) {
if (is.null(x)) {
if (allow_na) return(NA_character_) else stop("Value cannot be NULL")
}
if (is.na(x) && !allow_na) {
... |
6c8045beefcaf0c777ad1da1ae529c4443fc43f0afe71e1ceee54b63c1060384 | R | 1,555 | 40 | #' banc-make-proofread-ids — Emit a txt file of proofread BANC root_ids.
#'
#' Used by the proofread-redo workflow: each downstream NBLAST script
#' honours `BANC_TEST_IDS_FILE` and processes only the listed IDs.
#'
#' @section Reads:
#' - `banc_meta.csv` (override path via `BANC_META_PATH`)
#'
#' @section Writes:
#'... |
113a3c2dfea53287c56b4196f6408489362496b237fdf543983794bed5c07085 | R | 1,561 | 50 | ####################################################################################
###############--------------- DESCRIPTIVE STATISTICS ---------------###############
####################################################################################
### In this script:
# (1) Demographics
# (2) Psychiatric questi... |
c219303a8aa4994306ffcf69bec86f35d08946f49e21ea9276c243e3889f92b3 | R | 1,564 | 41 |
ason_pk_rna = read_tsv('data/PRP230911.tsv', col_types=cols()) %>%
mutate(animal = gsub('-','',animal))
ason_pk_rna %>%
mutate(conc_ug_g = pk_ug_g) %>%
mutate(day = as.integer(gsub('D','',day))) %>%
mutate(dose_color = '#FF56AD') -> ason
ason %>%
filter(tx != 'PBS') %>%
mutate(day = as.integer(gsub('D','... |
e305cbea7ed84408a040a14f91701216f42cea4501ceeabcc552269a5f2e44b8 | R | 1,566 | 40 | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library("argparse"))
# create parser object
parser <- ArgumentParser()
# specify our desired options
# by default ArgumentParser will add an help option
parser$add_argument("-g", "--gene", required=TRUE, help="input gene symbol list")
parser$add_argument("-o", "-... |
669a60426d93607f95d7450d4901008b38b73781e9d63118610ea7a77c36efeb | R | 1,567 | 43 | #' Creates a dendrogram for self-similarity analysis
#'
#' This function generates a dendrogram based on the self-similarity matrix for either
#' marker-based or average expression data. It also attempts to rename the dendrogram
#' labels based on a provided annotation data frame.
#'
#' @param ann An object containing ... |
3992ce40b6e17ce50765cd9d187181dc348f3f87e86bd202a355521a648ed30b | R | 1,568 | 41 | plot_beads_over_time <- function(beads.data, beads.normed, beads.cols) {
beads.data <- data.frame(beads.data, type = "Before", check.names = F, stringsAsFactors = F)
beads.normed <- data.frame(beads.normed, type = "After", check.names = F, stringsAsFactors = F)
m <- rbind(beads.data, beads.normed)
m ... |
5fc14a73c54f0f59251016f4dee27e0fa80040546f3426bb644e1311963effaa | R | 1,568 | 45 | ## =========================================================
## Data-generating mechanisms for NL-HSIM simulations
## （continuous / SNP，lienar / nonlinear，CWSM / DSSM）
## =========================================================
#' Internal: AR(1) correlation matrix
#' @keywords internal
.ar1_sigma <- functio... |
83f7b67daf5d449dfb4c97bda9b1dad7945491685d49a630bf6e47864456af33 | R | 1,569 | 57 | setwd(Sys.getenv("HOME"))
source("R_func/mutate_load.R")
library(tibble)
library(arrow)
library(GenomicRanges)
library(ChIPpeakAnno)
# Load TEs
all.human.TE <- read_feather(
"project/multiomics/PeakAnlys/PeakAnnot/all.hg38.TE.feather"
)
peak.df <- all.human.TE[, c('chr', 'start', 'end')]
peak.df %<>% unique() %>... |
bd576e55f17237e4c87632e72b0b6c2f3525c90f6af12b682ecc5524d622f5ae | R | 1,569 | 43 | #' Creates a dendrogram for self-similarity analysis
#'
#' This function generates a dendrogram based on the self-similarity matrix for either marker-based or average expression data. It also attempts to rename the dendrogram labels based on a provided annotation data frame.
#'
#' @param ann An object containing SAHA a... |
e11f1d41d3c85681ffc7dddd9f4818a6936f07d1f14ccb03161fe2be2517c50c | R | 1,570 | 65 | setGeneric(
"plotCorrDensity",
function(
object,
filename = "pairwiseCorrDensityPlot.pdf",
breaks = NULL,
width = 15,
height = 15
)
standardGeneric("plotCorrDensity")
)
setGeneric(
"plotNetwork",... |
ae29292a88df84276799986736e6e9c255725ecc09b749f61febc83caaa7a6d6 | R | 1,571 | 77 | # nocov start
mlflow_swagger <- function() {
def <- c(
swagger_header(),
swagger_paths(),
swagger_defs()
)
jsonlite::toJSON(def)
}
swagger_header <- function() {
list(
swagger = jsonlite::unbox("2.0"),
info = list(
description = jsonlite::unbox("API to MLflow Model."),
version... |
20b49dd7efc4df644b946cf38d55b5a16f5f428b818d205931fd120a7b967830 | R | 1,574 | 43 | # ===============================================================
# ?? Regression shared UI helpers
# ===============================================================
reg_detect_types <- function(df) {
num_vars <- names(df)[sapply(df, is.numeric)]
fac_vars <- names(df)[sapply(df, function(x) is.factor(x) || is.char... |
44a038d3a0f57f2e5db5d5ea42bf31a8dd1edf7b4d12a86452fa2dfc034f0e45 | R | 1,579 | 54 |
```{r}
library(dplyr)
library(tidyr)
library(psych)
library(tibble)
# Keep only baseline weeks and complete pairs, then widen (one row per subject per acid)
df_icc_wide <- df_scfa_long_updated %>%
filter(week %in% c(1, 9)) %>%
select(study_id, group, acid_type, week, acid_value) %>%
mutate(week = as.integer(wee... |
1279f9cf39330085f18883d5f0e3fceab624259e55a8d16a7182b03b57058d29 | R | 1,581 | 47 | library(paletteer)
library(ggsignif)
#------------- Negative network in DLPFC----------------
so <- readRDS("Astro_obj.rds")
# FADS1 DLPFC pos
DLPFC_FADS1_pos <- readRDS("my_big_positive_GTEx_DLPFC_list.RData")
DLPFC_FADS1_pos <- DLPFC_FADS1_pos$ENSG00000149485_
DLPFC_FADS1_pos <- getBM(filters= "ensembl_gene_id", ... |
70b1fa1e4786d32e4229ffe41e2fd892e023184476970f9f1cee40bd5d0cd121 | R | 1,589 | 40 | #!/usr/bin/env Rscript
###############################################################################
## Regenerate all DE results tables from checkpoint ##
###############################################################################
## Purpose: Create consistent DE result CSV files from c... |
622c75c637bab684dbee603b9ec264e7f1469b2b5634c198b4171ab28fd6df79 | R | 1,590 | 51 | library(tidyverse)
library(odbc)
library(DBI)
library(GSVA)
library(qusage)
rm(list=ls())
myinf1 <- "/projects/verhaak-lab/GLASS-III/results/kallisto/kallisto/final/gene_tpm_matrix_all_samples.tsv"
myinf2 <- "/projects/verhaak-lab/USERS/johnsk/glassx/varnf/data/msigdb/h.all.v7.1.symbols.gmt"
myinf3 <- "/projects/verh... |
95753063afe1f686073f5329dfbdb9efff347619878ed8ff11dbbc1b718b02ae | R | 1,595 | 51 | # 加载必要的包
library(dplyr)
library(tidyr)
# 1. 读取数据
data <- read.csv("Immature_granule_CellChat_Prob.csv", stringsAsFactors = FALSE)
# 2. 筛选出 Immature Granule 的数据
immature_data <- data %>%
filter(celltype == "Immature Granule")
# 3. 提取 CON 和 GCS 两组的 prob 值
prob_CON <- immature_data$prob[immature_data$Grou... |
37a23a82929ea643895d6c03c8c004458073b58fd482cfd025a40a37c91927ed | R | 1,597 | 59 | #' Extract the XYZ coordinates from neuroglancer mesh objects
#'
#' @param x an ng_raw or ng_raw_list object
#' @param ... additional arguments (currently ignored)
#' @export
#' @importFrom nat xyzmatrix
#' @rdname xyzmatrix
#' @aliases xyzmatrix
#' @seealso \code{\link[nat]{xyzmatrix}}
xyzmatrix.ng_raw <- function(x, ... |
9af4cddc3726b840e7b6a13033f74148412224c2f965873e497a826e11c0c9b0 | R | 1,597 | 50 | library(ArchR)
library(parallel)
addArchRThreads(threads = 20)
addArchRGenome("hg38")
library(ComplexHeatmap)
library(ggsignif)
library("RColorBrewer")
projdir <- "projALL_rmDoublets"
proj <- loadArchRProject(projdir)
# add LSI
proj <- addIterativeLSI(ArchRProj = proj, useMatrix = "TileMatrix", dimsToUse = 1:30,
... |
d0c55fa9cec71ae8ff17a7dea09a60e9b51a602e2749a1f125d1561e8aaf1eaf | R | 1,599 | 45 | #' Export a column with cluster results
#'
#' This function creates a `clusters.csv` file similar to the ones created by
#' SpaceRanger at `outs/analysis/clustering` but with the `key` column that
#' combines the `barcode` and the `sample_id`, which is needed when the `spe`
#' object contains data from multiple samples... |
e48ff45752dfb0c65f585412fb6d258f306a7614c24409a77a3c77a9fb1c612f | R | 1,599 | 28 | # Increase the timeout length for `utils::download.file` because the default value (60 seconds)
# could be too short to download large packages such as h2o.
options(timeout=300)
# TODO: Remove once pak 0.9.4 is live on packagemanager.rstudio.com's focal/latest mirror.
# The mirror currently serves pak 0.9.3-1, which bu... |
f21c0c7999bc31f13ea1cbe8f55984e36a1167efbea59bafe8d8d00ec81fe4ee | R | 1,599 | 32 | # Load necessary libraries
library(dplyr)
directory_path <- "/home/joonho345/1_Epilepsy_RNA/RNA_Animal/06.Deconvolution/06.DEG_02DESeq"
cell_types <- c("ExN-M", "InN-M", "Astro-M", "Micro-M", "Oligo-M", "OPC-M", "Endo-M")
# Loop through each cell type
for (cell_type in cell_types) {
cell_files <- list.files(path = ... |
1857281f10fe0231c1e5b1bbd45a27949041e90e6df468905d0a384c22e12f45 | R | 1,604 | 51 | # Computes path to Python executable from the MLFLOW_PYTHON_BIN environment variable.
get_python_bin <- function() {
in_env <- Sys.getenv("MLFLOW_PYTHON_BIN")
if (in_env != "") {
return(in_env)
}
# MLFLOW_PYTHON_EXECUTABLE is an environment variable that's defined in a Databricks notebook
# environment.
... |
2ffc7e173ad7cf12434cc5c7982eeef070364ae3f47659af52d6d4060d112174 | R | 1,612 | 32 | #################################
## Create a final metaprogram table based on initial metaprogram derivation and iterative NMF for testing in the Undifferentiated compartment
## Author: Kevin Johnson
## Updated:2026.04.02
#################################
library(dplyr)
proj_dir <- "/vast/palmer/pi/verhaak/kcj28/... |
9b5154f8aa2bff6f63fb396dfa4487736083b2d0f0b4707fbcf070faf92eeda9 | R | 1,612 | 48 | #### Section: Summary Calculations ####
anova_summarise_stats <- function(df_subset, resp_name, factor1, factor2) {
if (is.null(factor1) || !factor1 %in% names(df_subset)) {
return(tibble::tibble())
}
if (is.null(factor2) || !factor2 %in% names(df_subset)) {
df_subset |>
dplyr::group_by(.data[[fac... |
09f4c78c0db79ebcfe87d6e6b97cb6d152e50ec9ad619918244db95cef06c7bf | R | 1,617 | 46 | #' Check input sce_layer
#'
#' This function checks that the `sce_layer` object has the appropriate structure.
#' For more details please check the vignette documentation.
#'
#' @inheritParams run_app
#' @param variables A `character()` vector of variable names expected to
#' be present in `colData(sce_layer)`.
#'
#' @... |
e995ddc9a3deece5705328ded99c6c178171778b9fbfe6bea5ae3a766e53621f | R | 1,619 | 34 | #' Computes Pearson correlations between query and database expression profiles
#'
#' This function calculates Pearson correlation coefficients between all query and
#' database cell type expression profiles stored in the `ann@results$marker_free$norm_merge` data
#' frame within the `ann` object. The results are stored... |
01e3d4604dad8c2d9e696e285c697146d90259727615af4eb5c48cac9a103d36 | R | 1,622 | 49 | #Add Interval between last cognitive visit and death
#Alaina Durant - 5 2025
#directory
directory <- "C:/Users/durantam/VUMC/Research - Hohman - CNT/"
my_directory <- paste0(directory, "Team/Alaina_Durant/")
#library and functions
source(paste0(my_directory, "Scripts/load_libraries.R"))
source(paste0(my_dire... |
b17da6cfaba9b00893164fb4265fad7492c5c569814be87aa6d96ba20da72099 | R | 1,622 | 61 | #
# pliu 20150509
#
# module for processing ChIP-seq data
#
main <- function() {
name2func <- list(
'guessFqEncoding' = guessFqEncoding
)
argv <- commandArgs(trailingOnly=T)
name2func[[argv[1]]](argv[2:length(argv)])
}
guessFqEncoding <- function(argv){
.libPaths(c(argv[4], .libPaths()))
suppress... |
b81fc9209c58c98dbf6bf87f86401e065bebdf5ea792e41f29a11ade6eba7f6d | R | 1,622 | 67 | ---
title: "R Notebook"
output: html_notebook
---
This notebook complements `train.R` by enabling the same functionality from the MLflow tutorial using an R notebook.
```{r}
library(mlflow)
library(glmnet)
set.seed(40)
```
Read the wine-quality csv file
```{r}
data <- read.csv("wine-quality.csv")
```
Split the d... |
f82efa56070f5eb9309457ff5e7a1478438fcbfa4eb693c54a5369ac880919dd | R | 1,624 | 36 | library(ArchR)
library(Matrix)
outp <- "/geschwindlabshares/RexachGroup/Xia_Data/atac_cellrangerOut/project0318/cisTopic"
projdir <- "/geschwindlabshares/RexachGroup/Xia_Data/atac_cellrangerOut/project0318/projrmSubset_subPeak"
proj <- loadArchRProject(projdir)
dropSubCs <- c("undefined","ast.C6","mg.C8","mg.C10","mg.... |
bb71c3e232758a09fe1ff1012a9b9feca7b8a8f22b66c90b7b2251dc41d172ef | R | 1,629 | 40 | ### Side Chain composition analysis
setwd(rstudioapi::getActiveProject())
rm(list = ls())
library(tidyverse)
source("./Scripts/Function_scripts/Pre-processing_functions.R")
load("./Output_Data/M2PM_backtoRAW_MsD_Norm_Impt_397_lipids.Rdata") #from script #6
## ==== Aggregate lipids with the same number of double bond... |
b35c21f3a25bad93e93274a06d18835f97fe3228d03bc461820bd3e40ed4eb9d | R | 1,630 | 69 | library("SPOTlight")
library(SingleCellExperiment)
library(SpatialExperiment)
library(scater)
library(scran)
library("anndata")
st_path <- "./simulated_gene_expression_average.h5ad"
sc_path <- "./SC.h5ad"
celltype_key <- 'cell_type'
output_path <- "output.csv"
st<-read_h5ad(st_path)
st$var_names <- make.unique(st$va... |
a49e9cf7b8ae663d98bd72e5264b29cfec8d1e7822f5ae4bf32bafb6ebd296b8 | R | 1,631 | 43 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# Code to explore morphological classes of DNs
#... |
ac353f19c7c1a2ae9e20dfd86f77e4c2409663ccd4bef08a9133ecfa755f1871 | R | 1,632 | 53 | library(scater)
library(DropletUtils)
library(scDblFinder)
library(Seurat)
library(dplyr)
library(cowplot)
library(ggplot2)
library(hdf5r)
library(tidyr)
data<-list()
setwd("~/mouse-nuc-seq/nodose/data/processed")
files<-dir(pattern = ".RDS")
for (i in files)
{
data[[i]]<-readRDS(i)
}
data<-lapply(X=data, assay = '... |
79b7ed6d681d0886c89a0098e573cd599e1a4a375cd0a34de57973a8c3fb3c74 | R | 1,633 | 51 | rm(list=ls())
library(data.table)
peakLoader <- function(peakFolder = './Input_Data/Cleaned_title_all_spectra'){
#aggregate all files
massDT <- rbindlist(
lapply(
list.files(peakFolder,full.names = T),
function(i)
fread(i,skip = 1)[,name:=gsub(".txt","",gsub(".*/","",i))]))
setnames(ma... |
bad62ffd6fc367c996fbf63f613d43c7f0fb35f463c7e66127866799c4c921d9 | R | 1,633 | 54 | ## Obtain the necessary data
## reference human pilot modeling results
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 corr... |
98db07984a6e4d082e1b9948e18f57f1dc9189357a672a677b13aa968b434521 | R | 1,639 | 29 | library("data.table")
library("R.utils")
library("GWASTools")
library("dplyr")
Args <- commandArgs(TRUE)
resloc <- as.character(Args[2])
phenoname <- as.character(Args[1])
# Running the loop
for(method in c("M1","M2")){
for(pn in phenoname){
res <- fread(paste0("/hihg/studies/AD/analysis/projects/ADGC/Neuropath/GWAS... |
8db65c96f7f3155721a4a71f7404e59b832527cf7a4db483f5a6aa88ef377753 | R | 1,640 | 39 | #!/usr/bin/env Rscript
### This script will calculate nearest sequenced taxon index (NSTI) between study
### sequences in a tree and the nearest reference sequence.
# Read in quietly to avoid outputting "Loading required package: Rcpp" to stderr.
library(castor, quietly = TRUE)
Args <- commandArgs(TRUE)
# Read in ... |
2797461a7402e68aec4f30248e79a9ffc860bede61e8d1b645baa17d2525754b | R | 1,641 | 39 | ## ----setup, include=FALSE----------------------------------------------------------------------------------------
## ----Libraries and Data------------------------------------------------------------------------------------------
suppressWarnings({
library(vcfR)
library(data.table)
library(ggplot2)
... |
3c8bd59f952e3d9fc4b07fb48637618ae3a5129ea5a8db0d53c89b9973d5b9e1 | R | 1,649 | 49 | #!/usr/bin/env Rscript
# read results from ksklearn
suppressPackageStartupMessages(library("argparse"))
# create parser object
parser <- ArgumentParser()
# specify our desired options
# by default ArgumentParser will add an help option
parser$add_argument("-i", "--input", required=TRUE, help="input statH")
parser$a... |
839d9e9b326ec745f982eefe0d974ac408f4a29b6752406b1a46857bcdc2032a | R | 1,651 | 42 | ### Calculate Z score on lipids from 2 in vitro lipidomics
rm(list=ls())
library(RColorBrewer)
library(tidyverse)
setwd(rstudioapi::getActiveProject())
source("./Scripts/Function_scripts/Pre-processing_functions.R")
####Load log2 transformed data from In vitro Experiment #2 dataset####################################... |
31e457281ebc84a689f6227fab27bea3306c053f442b00851e4986db12132e03 | R | 1,659 | 47 | #TODO: revrite for command line arguments
library(epicalc)
#######################
#Read PB final results#
#######################
PB<-read.csv("prot.blastx_results",sep="@")
PB<-PB[order(PB$Queryid,PB$positives, PB$alignment.length, PB$identity, decreasing = TRUE), ];
PB<- PB[match(unique(PB$Queryid), PB$Queryid), ]... |
7f5e6072daf67aaeb035167443c7579ea81acb6a25a60d0eec2a6695f3eb13cb | R | 1,660 | 55 | library(tidyverse)
library(doParallel)
library(foreach)
load("Microarray_MDD_Normalized_CR_regressed.RData")
prsCor = function(i, gene, datExpr.local) {
c <- cor.test(datExpr.local[i,], datExpr.local[gene,], use = 'pairwise.complete.obs')
dfPrs <- data.frame(Gene = rownames(datExpr.local)[i], R = c$estimate, P = ... |
319a045e47df28ede4878c4d58bad7513a387f9372e931e3c005a1fac70549cc | R | 1,667 | 48 |
# 2025.12.27
# R codes for generating vignette data
#######################
# #
# Seurat analysis of the mouse brain Xenium data
# download data
# The (Fresh Frozen) Mouse Brain Tiny Xenium data from 10x Genomics website
# was used in this analysis:
# https://www.10xgenomics.com/datasets/fresh-... |
1922b1d787e536dde24bb557d6a4b3fbfc17548c79c02882e2f96eb019e4334a | R | 1,669 | 53 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Write list of paths to GTFs
# Author: Amanda Zacharias
# Date: 2023-06-30
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 gcc/7... |
1c477367d3df05a995b4e08790e9672d041a5f25d39d4f65b3fbbf0ba721349b | R | 1,671 | 50 | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library("argparse"))
# create parser object
parser <- ArgumentParser()
# specify our desired options
# by default ArgumentParser will add an help option
parser$add_argument("-i", "--input", required=TRUE, help="load snap RData")
parser$add_argument("-o", "--outpu... |
1a14c7fac9ab8bfcf9587b7183800974718ca459afd146d6af6cfd92f09f9a64 | R | 1,672 | 58 | # ===============================================================
# 🔁 Multi-Response Selector Module (final simple version)
# ===============================================================
multi_response_ui <- function(id) {
ns <- NS(id)
tagList(
with_help_tooltip(
checkboxInput(
ns("m... |
17b600a30aaf3d63c3c2653c31daaae93c58f40438123a7bce602d0406a799b5 | R | 1,674 | 48 | #' @include model-utils.R
NULL
#' @rdname mlflow_save_model
#' @export
mlflow_save_model.xgb.Booster <- function(model,
path,
model_spec = list(),
conda_env = NULL,
... |
2f643a1e612e9710f34e2c802c9cedcd0c0438c3960a3180c52ba1c797d8749d | R | 1,674 | 38 | #!/usr/bin/env Rscript
## Author: Luuk Harbers
## Date: 2022-10-22
## Script to process allelic counts
## Load/install packages
packages = c("data.table", "argparser")
invisible(sapply(packages, function(x) suppressMessages(require(x, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE))))
## Parse argumen... |
a502f583954424da2cc30285b6b9620d94276e65595498514990d832fd064f4c | R | 1,674 | 62 | library(purrr)
library(dplyr)
library(RSQLite)
# Get base directory for relative paths
# base_dir = "path/to/project/root"
base_dir = getwd()
# Configuration settings
CONFIG = list(
train_name = "your_training_dataset_name",
db_file_path = file.path(base_dir,"./PredictDB_LD/MODULE/"),
... |
ef892af9339966dd716a4dcdb8148bb43dcf2439a8522ac66e7d81ffb7afc508 | R | 1,674 | 32 | library(grDevices)
library(here)
library(ggplot2)
library(reshape2)
library(magrittr)
library(dplyr)
library(MetaMarkers)
library(readr)
library(conflicted)
conflicts_prefer(dplyr::filter)
conflicts_prefer(dplyr::first)
conflicts_prefer(dplyr::count)
Sys.setenv("VROOM_CONNECTION_SIZE"= 21000000)
greedy_tests_Mac <- r... |
67ad479d68cbc7c8c6fa80365d6f4597bcfd43ddd882fdf41350627d1c0319f2 | R | 1,677 | 45 | library(dplyr)
library(Seurat)
library(patchwork)
# Load the malt dataset
# rename features.tsv to genes.tsv
malt.data <- Read10X(data.dir = "MALT10k_filtered_feature_bc_matrix/")
# Initialize the Seurat object with the raw (non-normalized data).
malt <- CreateSeuratObject(counts = malt.data$`Gene Expression... |
156ed2a72706738fdb1bfb3d8c0630f8e816590a4eb5192a74699e6d135c2e0d | R | 1,680 | 52 | library(dplyr)
cohort <- 'GSE160189_186538_Integration'
target <- paste0(cohort, "_scRNA_matrix_DEGs_CellType1")
celltype <- "ExN_H"
celltype <- "InN_H"
celltype <- "Astro_H"
celltype <- "Micro_H"
celltype <- "Oligo_H"
celltype <- "OPC_H"
celltype <- "Endo_H"
window <- 'Window28'
# Set the working directory
setwd(p... |
d1c1946be152f47e7461e3118cb81d58b3ff7c72e4566702445d70c88e281eca | R | 1,680 | 37 | args<-commandArgs(TRUE)
self_blast = sprintf("%s", args[1])
#Creat cytoscape network file
self_blast<-read.csv(self_blast,sep="@",header=F)
colnames(self_blast)<-c("Queryid","gi","identity","Coverage","Strain","alignment.length","Chimera","Strand","q.start","q.end","s.start","s.end","e.value","bitscore","Length") ... |
7e2135d1347cffc6006cf7a0997406c46e1cac723645b920ad72b66565cf651c | R | 1,681 | 48 | #' Make plot data for the genome plot of plot.genome.regions function
#'
#' @export
gggenome.from.dataframe <- function (plot.data, visfrom = NA, visto = NA, genename.toupper=T
) {
require(gggenes)
require(tidyverse)
require(tidygenomics)
require(fuzzyjoin)
plot.data ... |
038cbbc48d578e94c09f5fdfa0901edcbdeb3b9eeca8d0ac9a12ddf49d34432a | R | 1,682 | 51 | #!/usr/bin/env Rscript
library(tidyverse)
library(odbc)
library(DBI)
#######################################################
rm(list=ls())
#Establish connection
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB4")
dat <- read.delim("/projects/verhaak-lab/USERS/johnsk/glass4/results/mutect2/consensusvcf/CARE-MD/consens... |
0f47b55c2a7b5f94b90825d5ce4805d3bc2da707bd7bf01bc5e8a77269f11497 | R | 1,682 | 51 | #!/usr/bin/env Rscript
library(tidyverse)
library(odbc)
library(DBI)
#######################################################
rm(list=ls())
#Establish connection
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB4")
dat <- read.delim("/projects/verhaak-lab/USERS/johnsk/glass4/results/mutect2/consensusvcf/CARE-PS/consens... |
6c6b04a7af9493b6dd794449860241eddc0887f848978985e1dfb388e87ca817 | R | 1,682 | 51 | #!/usr/bin/env Rscript
library(tidyverse)
library(odbc)
library(DBI)
#######################################################
rm(list=ls())
#Establish connection
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB4")
dat <- read.delim("/projects/verhaak-lab/USERS/johnsk/glass4/results/mutect2/consensusvcf/CARE-PT/consens... |
7e4a370d212ed1751d717aad5ec5b1035236039897e6da19fc2c6872da2516ee | R | 1,682 | 51 | #!/usr/bin/env Rscript
library(tidyverse)
library(odbc)
library(DBI)
#######################################################
rm(list=ls())
#Establish connection
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB4")
dat <- read.delim("/projects/verhaak-lab/USERS/johnsk/glass4/results/mutect2/consensusvcf/CARE-TO/consens... |
b5f4bdabd356b44ae0b9580d2d317a26a4199acb61c4cca94117033a33e83177 | R | 1,682 | 51 | #!/usr/bin/env Rscript
library(tidyverse)
library(odbc)
library(DBI)
#######################################################
rm(list=ls())
#Establish connection
con <- DBI::dbConnect(odbc::odbc(), "VerhaakDB4")
dat <- read.delim("/projects/verhaak-lab/USERS/johnsk/glass4/results/mutect2/consensusvcf/CARE-TK/consens... |
0dd0ceb38923f54aa4acc93acc43e2538d5e872d443699e1ed56bacf5d3bed2d | R | 1,683 | 55 | require(tidyverse)
require(ggplot2)
require(clusterProfiler)
require(org.Hs.eg.db)
require(enrichplot)
#input deseq2 file:the one for volacano plot
DEG_3<- fam3_im_3110_uqcrc2_331_vs_321_ev_deseq2_padj0.05
head(DEG_3)
genelist<-DEG_3$external_gene_name
genelist<- genelist %>% bitr(fromType = "SYMBOL",
toTyp... |
5ad820e5a7098d4d065e24171422946a0a60592ef76286f9011a526742292120 | R | 1,685 | 47 | # 01_data_preprocessing.R
# Data input, missing value imputation, sample aggregation (technical replicates)
rm(list = ls())
library(openxlsx)
source("scripts/utils.R") # if needed for later, but not used in this script
# Input
all_protein_imput <- read.xlsx("data/protein_Samplematrix_imputeNA.xlsx", rowNames... |
62b768688e92df1f80b3c34062d69985fe2612478a9ec363027c9dc623ee08ea | R | 1,685 | 55 | #######################################
library(DESeq2)
library(ggplot2)
library(pasilla)
#### Wihtout Outliers -> New DESeq ####
### Choose one matrix to analyze ###
### raw data
dds_data <- adjusted_df_1
dds_coldata <- coldata_df_1
Target_comparison <- 'FILTERED_1_MTLEALL_NL'
contrast_vector <- c("Diagnosis","MTLEAL... |
7bea8da7504b0d73d601402cacebe094b8d1e52d15b280a755a50ea01033aa94 | R | 1,685 | 40 | #' Combine multiple continuous variables by averaging Z scores
#'
#' To summarize multiple features, each is normalized to represent a Z-score.
#' Scores are averaged to return a single vector.
#'
#' @param cont_mat A \code{matrix()} with spots as rows and 2 or more continuous
#' variables as columns.
#'
#' @return A \... |
20a9192f33d3f8dffe3d696e0f061d4fb1148a9b90585a2bb56f7196c6c4dd4b | R | 1,686 | 50 | ---
title: "Capturing neuroglancer meshes from a browser scene"
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## Background
Originally **fafbseg** had no support for reading objects directly from a
neuroglancer scene URL. The only approach was to capture an interactive ... |
9ace12cbb16394ce2c84c539d592340a079b2d058d0d201393eb46ad4b47bc07 | R | 1,688 | 42 | library(ggplot2)
library(reshape2)
library(viridis)
# Create the data for sound sensitivity analysis (full distribution)
data_exp3 <- data.frame(
Near_Sound = c(2, 5, 5, 3, 1),
Zone2 = c(6, 5, 10, 3, 9),
Zone3 = c(11, 22, 11, 10, 19),
Zone4 = c(40, 43, 31, 22, 49),
Far_Sound = c(441, 425, 443, 444, 422)
)
#... |
2108c63ce02cb2bfdb8683f798b345dcf2facd0b6915e9dc55462400611281ad | R | 1,691 | 40 | #' Spatial registration: block correlation
#'
#' This function computes the block correlation using the sample ID as the
#' blocking factor. This takes into account that cells in scRNA-seq data or
#' spots in spatially-resolved transcriptomics data from Visium (or similar)
#' have a sample ID batch effect.
#'
#' @param... |
6f36169da6d5bffd09288cd4f3821b353337cece1774b01fbfa6e9c6e57b3a12 | R | 1,694 | 34 | #' @title Compare headers between mask and other images
#'
#' @description
#' Function to check if mask is in the same space as inputs
#'
#' @param lesions.list list of antsImages or character vector
#' of filenames
#' @param mask antsImage of mask to check
#'
#' @return Nothing is returned, function stops with error i... |
72dc21c2b00356f393a91787712a87f67f8c2b9c6d99b00142b307cfeefad4f9 | R | 1,696 | 55 |
# Set working directory - adjust path as needed
# setwd("path/to/STEP2.Training_cis/")
library(purrr)
library(dplyr)
library(data.table)
library(limma)
## Get unique snp_annot all chromosomes and convert genotypes files in RDAta
walk(c("dlpfc"), function(region){
eqtls_all <- fread(sprintf("./output_LIB... |
f041da77338dd7783e693b51d06f0c16f11daccf237253e35f19afa0b32d002c | R | 1,703 | 52 | #' Load Data for LK Searchlight Analysis
#'
#' Loads train/test datasets, embeddings, gradients, and searchlight IDs.
#' Returns a list containing all loaded data.
#'
load_data <- function(H, g, scale, side, group = "train") {
# Check that np and vgu are loaded
if (!exists("np", envir = .GlobalEnv) || !exists("vgu... |
829182e0ba95adb51972aba175e1686f0d995afc41022064c28506844e73aca3 | R | 1,708 | 63 | #!/bin/Rscript
args = commandArgs(trailingOnly=TRUE)
if (length(args)<2) {
stop("Two arguments must be supplied")
} else {
data_path = args[1]
detail_regions = args[2]
}
library(GenomicRanges)
library(htmltools)
library(conumee2)
library(sesame)
library(plotly)
message("Running: ", data_path)
message("Loadi... |
ea11d846350dbb0b5ff431642746e15dac013ce742a97f0ad12deb22d1a6e774 | R | 1,708 | 47 | #' @title Determine type of variable passed by user
#'
#' @description
#' Function to check a variable whether is composed
#' of an antsImage, list of antsImages, or simply filenames.
#' If none of the above, an error is returned.
#'
#' @param input the variable to be checked
#' @param checkHeaders make sure all images... |
0f946b71baf8436c46c1b4ce445ddbdb21319bf772da0cbb901ad57c6375ba8e | R | 1,710 | 58 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = NA,
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# SpNeigh <img src="man/figures/SpNeigh_logo.png" align="righ... |
e5e425efdea6f823c1a4c5456d7c6c1d13590e4c6123ce8766f1708d81bbc54a | R | 1,710 | 41 | # Load necessary libraries
library(Seurat)
library(matrixStats) # For efficient row/column operations
# Define the trinarize function
trinarize <- function(counts, cluster_ids, f = 0.2) {
# Extract count matrix from Seurat object (assumes RNA assay)
#counts <- GetAssayData(seurat_obj, layer = "counts")
#c... |
dc7a4deda18676dc3987285b61750b599537a36f61a2476529b91acf65d9a40c | R | 1,711 | 77 | ---
title: "1_data_import"
output: html_document
date: "2025-12-31"
---
```{r}
library(dplyr)
library(readr)
library(tibble)
library(readxl)
library(purrr)
```
# import sample metadata
```{r}
meta <- read_xlsx("/path/to/resources/metadata.xlsx")
block_ids <- meta$Block
block_lookup <- data.frame(Block = block_ids, ... |
ed3ed721019c12a71bf7b6521b1265467eda427f2794544e81517e70b92d988f | R | 1,711 | 49 | here::i_am("scripts/1.neural_differentiation_dataset/1.1.mapping_and_QC/1.1.4.remove_small_contigs.R")
library(glue)
library(tidyverse)
library(plyranges)
library(here)
wd <- here("data/neural_differentiation_dataset/genomes/")
# load genome index
fai <- read_delim(here(wd, "gorGor6.fa.fai"), col_names = F)
# plot ... |
490761831090a9faad4a3703386adb58d5255a22d9e92f6629ced0686bd1bb75 | R | 1,712 | 48 | args<-commandArgs(TRUE)
genome_species_list = sprintf("%s", args[1])
vir_taxa_file = sprintf("%s", args[2])
library(ape)
#####
codes_species<-read.table("codes_ffp_file_list.txt",sep=" ")
colnames(codes_species)<-c("ID","ffp_name")
codes_species$ffp_name<-as.character(codes_species$ffp_name)
##
family<-read.table(geno... |
04a2e33c01f061bfad1899878b049dcc18411eb239e392bb52dd4e21abffc65b | R | 1,714 | 45 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: run DRG deconvolution with Instaprism
# Author: Christina Meier
# Date: 2025-06-10
# Email: 19cm51@queensu.ca
#-------------------------------------------------
# Notes ------------------------------------------
# module load StdEnv/202... |
88a10d1c0b23b455c4e17c18958fc2ed6f6705e036c6bae99f50136ed986283b | R | 1,715 | 37 | # Moderate effect size genes project
# Author: Madison Caballero
# Desription: Performs equilibrium test
library(stringr)
`%ni%` = Negate(`%in%`)
args = commandArgs(trailingOnly=TRUE)
# The input file is a merge of data from all cohorts. Header:
#Gene1,Gene2,N_AB_ASD,N_AB_non_ASD,N_A*_ASD,N_A*_non_ASD,N_B*_ASD,N_B*_n... |
ab76cbfdd1d61a311f77bbd14913377b39b0b739f428ad0a0449a5d9492ab0cc | R | 1,718 | 49 | # The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality
# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
library(mlflow)
library(glm... |
b85a54e84b5f5d3bec814727684f8dc73b057f7719f1995e329a28ee08cd7cfe | R | 1,718 | 33 | # Please cite the following two papers if using CIE:
# 1) Farahman, S., O'Connor, C., Macoska, J., Zarringhalam, K. 'Causal Inference Engine: A platform for directional gene set enrichment analysis and inference of active transcriptional regulators', NAR doi: https://doi.org/10.1093/nar/gkz1046
# 2) Fakhry CT, Choudhar... |
a5f87414642b701699e16c477e2cb30e7cf5a4e76bfa69d54329a671102aa23b | R | 1,724 | 46 | op <- choose_malevnc_dataset('VNC')
on.exit(options(op))
test_that("manc_nodespec", {
mnc=manc_node_chain()
expect_equal(manc_nodespec("clio"), manc_dvid_node('clio'))
expect_equal(manc_nodespec("1ec355"), "1ec355123bf94e588557a4568d26d258")
expect_equal(manc_nodespec("1ec355:c9c1d9", include_first = T), mnc[1... |
0e3a3ddb5f20f43a0c77070a17e0a8db73d5d672db9dbc78c530d89877798ae9 | R | 1,728 | 48 |
library(dplyr)
df <- read.csv("03_Results/02_Analysis/master_gsea_table.csv")
# Check unique values of ever_significant
cat("Unique values of ever_significant:", paste(unique(df$ever_significant), collapse=", "), "\n")
# Create wide format
pathways <- df %>%
group_by(pathway_id, database, ID, Description) %>%
s... |
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