sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
22aa1067716d5f835e6ca9998f5ae7299bd951ba223b7afbc9efa9118f5bae90 | R | 1,951 | 57 | #####################################
## Write TITAN segments to database
## Author: Kevin Johnson
## Updated: 2022.02.25
#####################################
library(tidyverse)
library(DBI)
library(odbc)
## Current project.
projdir <- "/projects/verhaak-lab/USERS/johnsk/glass4"
setwd(projdir)
## Connect to the cor... |
4c4bd1a90ddbe4920a70cfb1063340d75c4a37f8295ef14d051f6ed7d52a5f8a | R | 1,951 | 84 | context("test-merge-groups")
test_that("find agglomeration groups for google segmentation", {
skip_if_not_installed('fafbsegdata')
skip_on_os("mac")
# confirmed by Peter Li Sep 2018
# see https://fafb.slack.com/archives/GCSHW2YFK/p1537818312000100?thread_ts=1537809139.000100&cid=GCSHW2YFK
peterli_segs <-
... |
d7ecacdffa3b74886d6d81afbc27ba16248385dd9006ad3f2730ecf1bdb6378b | R | 1,951 | 69 | # ===============================================================
# 📏 Submodule — Subplot Size Controls
# ===============================================================
subplot_size_defaults <- function() {
list(
width = list(
label = "Subplot width (px)",
value = 400,
min = 200,
max = ... |
55b9663c137070fb6b776909121e13cd076551cff1af2dbe9d85e90b00d6892f | R | 1,952 | 49 | library(dplyr)
library(ggplot2)
inverseLogitFn = function(score) {
v = 10.0^(-score/10.0)
v / (1.0 + v)
}
args <- commandArgs(TRUE)
verbose = TRUE
variant_eval_filename <- args[1]
output_basename <- tools::file_path_sans_ext(args[2])
sample <- args[3]
make_sample_df = function(filename, sample) {
df = ... |
4be1c0ca4f4c16364b1d9e9728fb4a5709daf18c1327e623e755e2577ab360c8 | R | 1,954 | 52 | # Call marker peaks for disaeses within each subC
library(GenomicRanges)
library(ArchR)
library(parallel)
addArchRThreads(threads = 20)
addArchRGenome("hg38")
library(ComplexHeatmap)
library(ggsignif)
library("RColorBrewer")
library(ggplot2)
library(ggpubr)
Dxs <- c("Control","AD","bvFTD","PSP_S")
majorCs <- c("ast","... |
c5fb097a16079b944ee040c71a64f62596329063fb6f81f0100c730414a394e2 | R | 1,955 | 48 | ---
title: "Detecting differential modules using ALPACA"
author: Megha Padi
date: "2017-10-19"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Detecting differential modules using ALPACA}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
Install and load netZooR package
```{r,eva... |
304faede464d2e1abf5374a3af1bdec087cda51afeb1833338dfc323ce8b55ff | R | 1,956 | 96 | ---
title: "05_signif_probes"
author: "David Lukacsovich"
date: "2024-04-08"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Set Up
## Define Directories
```{r}
library(dplyr)
base_dir <- "~/TBL Dropbox/David Lukacsovich"
analysis_dir <- file.path(base_dir, "AD-Aging-... |
c0c8ed9ef30265b428d742fe857ee71bb56cad1dec9b8668400979f4f69aea45 | R | 1,957 | 45 | library(tidyverse)
library(tximport)
library(DESeq2)
library(readr)
# Prepare your data
annot <- read_tsv("nv2.func-04.04.23.tsv")
tx2geneFile <- "Trinity.fasta.gene_trans_map"
tx2gene <- read.table(tx2geneFile, header = FALSE, col.names = c("gene_id", "transcript_id"))
tx2gene <- select(tx2gene, c(2,1))
samples <- li... |
fcc2ac9b040769642e2d84443d95b79fed4c10f5e24243f1320c18e28a772394 | R | 1,959 | 76 | ## Ensure reproducibility of example data
set.seed(20220907)
## Generate example data
sce <- scuttle::mockSCE()
## Add some sample IDs
sce$sample_id <- sample(LETTERS[1:5], ncol(sce), replace = TRUE)
## Add a sample-level covariate: age
ages <- rnorm(5, mean = 20, sd = 4)
names(ages) <- LETTERS[1:5]
sce$age <- ages[... |
60f7fbf9dfe2bf82b98d2ccf834e21daf9aa4d9797b3f427810a819d9e77124f | R | 1,960 | 63 | #!/usr/bin/env Rscript
suppressPackageStartupMessages(library("argparse"))
# create parser object
parser <- ArgumentParser()
# specify our desired options
# by default ArgumentParser will add an help option
parser$add_argument("-i", "--input", required=TRUE, help="load clustered RData")
parser$add_argument("-c", "--... |
f9b29997cb61fbbed730ee1a9ad7fb46153c41f1326e911a1610182877c75802 | R | 1,964 | 56 | ### PCA analysis on lipidomics of qNSC Mboat2 overexpression in vitro
rm(list=ls())
library(tidyverse)
setwd(rstudioapi::getActiveProject())
source("./Scripts/Function_scripts/Effect_size_functions.R")
load("./Output_data/M2PM_MsD.Norm_Impt_log2_conc_397_lipids.Rdata")
#### ==== PCA of Mboat2 overexpression samples... |
e1c5d5e457860749f9a8908652895472ab1d0620cc9278fbf2e91db6af42fa5c | R | 1,967 | 53 | #' Marker Richness Function
#'
#' This function calculates the richness of markers within clusters, either using all genes or a specified set of variable features.
#'
#' @param ann An object containing annotation data.
#'
#' @param varfeat A vector of variable features (genes) to consider. If NULL, all genes are used.... |
8af05bb43b582e502fd5ca9ba4df84ebbe5f67c6981518352d68e691f1aaaa63 | R | 1,973 | 48 | test_that(
"multi_gene_z_score",
{
# With two good columns but 1 zero-variance column, the zero-variance
# column should be dropped with a warning
cont_mat <- matrix(c(1, 0, 3, 3, 2, -5), ncol = 3)
colnames(cont_mat) <- c("good1", "bad", "good2")
expect_warning(
... |
218421af0d5684bc935e1337f72b00fe86984ddfd704178cc43beaad5f9acee1 | R | 1,975 | 43 | # Load necessary libraries
library(dplyr)
directory_path <- "/home/joonho345/1_Epilepsy_RNA/RNA_Human/09.Deconvolution/06.DEG_02DESeq"
cohort_GSE160189_186538_Integration <- c("Astro_H", "ExN_H", "InN_H", "Oligo_H", "Micro_H", "OPC_H", "Endo_H")
cohorts <- list(
"GSE160189_186538_Integration" = cohort_GSE160189_186... |
ebb21650c79062f9b454d0b1a06e788769d8a3893d9e433184292e49ce3cfec9 | R | 1,975 | 46 | ################################################################
# Plot AUC histograms & default tsne
#' @title plotTsne_AUCellHtml
#' @description Wrapper for the 'AUCell_plotTSNE' function: plots the regulon activities on the t-SNE.
#' @param scenicOptions Fields used: aucell_regulonAUC, aucell_thresholds (int), and... |
711e05e4b0193958be238acc4b5434ac13bc6ea5d6222681c5f7d0ec988b967f | R | 1,976 | 46 | #' Compares gene expression between query cluster and database cell type
#'
#' Generates a scatter plot comparing gene expression levels between a specified query cluster
#' and a database cell type. Highlights top expressed genes and outliers.
#'
#' @param ann An object containing normalized gene expression data in `a... |
8bcf40e12f9b95bc3b6e3b1621ea6564e2470cb85959ef1b208e353405dabd1b | R | 1,976 | 59 | library(ggplot2)
library(forcats)
# V2
data_countsV2A <- data.frame(
Category = c(">2.0m", "1.6-2.0m", "1.2-1.6m", "0.8-1.2m", "0.4-0.8m", "<0.4m"),
# Count = c(5.69, 0.38, 11.66, 4.31, 2.66, 275.28)
Count = c(1.9, 0.13, 3.89, 1.44, 0.89, 91.77)
)
data_countsV2B <- data.frame(
Category = c(">2.0m", "1.6-2.0m"... |
9f45a68f5133f57cfea14a9a7fdfe5496963cb29b01b473e6d74ecdfebd254e6 | R | 1,981 | 52 | library(DESeq2)
library(ggplot2)
library(pasilla)
### Choose one matrix to analyze ###
coldata_df_M_KAI_IP_A_HA <- coldata_df_M %>% filter(TYPE_2 == "M_KAI_IP_A_HA")
filtered_samples_M_KAI_IP_A_HA <- rownames(coldata_df_M_KAI_IP_A_HA)
adjusted_df_M_KAI_IP_A_HA <- adjusted_df_M[, colnames(adjusted_df_M) %in% filtered_... |
cd68bdf0e94a14b4d24549efa6cbd73566a71769e32c4eb05619247e6cf7d65c | R | 1,982 | 53 | library(DESeq2)
library(ggplot2)
library(pasilla)
### Choose one matrix to analyze ###
coldata_df_R_KAI_IP_A_CR <- coldata_df_R %>% filter(TYPE_2 == "R_KAI_IP_A_CR")
filtered_samples_R_KAI_IP_A_CR <- rownames(coldata_df_R_KAI_IP_A_CR)
adjusted_df_R_KAI_IP_A_CR <- adjusted_df_R[, colnames(adjusted_df_R) %in% filtered_... |
32d4490c98e9ea32a9a12d132d4724c4d97ae2b425614a5cb0d7e7b59b42d298 | R | 1,984 | 56 | here::i_am("scripts/1.neural_differentiation_dataset/1.1.mapping_and_QC/1.1.3.filter_liftoff_gtfs.R")
library(tidyverse)
library(plyranges)
library(here)
# helper function to get transcript length
get_transcript_length<-function(gtf) {
gtf %>%
as_tibble() %>%
group_by(transcript_id, gene_name) %>%
... |
9aefaeee9bb9150a9b56dc6cafab6d36ee4da3b0c627262b5d50d8b1080dff99 | R | 1,985 | 56 | ## ------------------------------------------------------------
## Compute the genomic interval to visualise for one gene
## • gene_id – gene (or cluster) to plot
## • end_type – "TSS" or "TES"
## • clusters_dt – data.table with the columns used below
## Returns a named list: seqnames, strand, start, en... |
36bb7a701222569c02cdb1ebc1f02b6ec1d8ae47989bef96d8b5cc6fec44ac36 | R | 1,986 | 54 | here::i_am("scripts/2.validations/2.1.regulator_interactions_and_module_overlaps/2.1.2.calculate_module_overlaps.R")
library(tidyverse)
library(foreach)
library(doParallel)
library(here)
dd <- here("data/neural_differentiation_dataset/CroCoNet_analysis/")
wd <- here("data/validations/regulator_interactions_and_module... |
81dd58f2718ef3f44ac286f3741a4fcd04150ebd304d9712f7097586fa6357d4 | R | 1,989 | 53 | library(DESeq2)
library(ggplot2)
library(pasilla)
### Choose one matrix to analyze ###
coldata_df_R_PILO_IP_A_CR <- coldata_df_R %>% filter(TYPE_2 == "R_PILO_IP_A_CR")
filtered_samples_R_PILO_IP_A_CR <- rownames(coldata_df_R_PILO_IP_A_CR)
adjusted_df_R_PILO_IP_A_CR <- adjusted_df_R[, colnames(adjusted_df_R) %in% filt... |
f911acd5dc4cdd721b1cdd3752ad075e17ef7548c3bb71a8e20254a6ca1d2da3 | R | 1,991 | 51 | library(epicalc);
#read HPV.csv from input
args<-commandArgs(TRUE)
hpv_csv = sprintf("%s", args[1])
HPV<-read.csv(hpv_csv)
#########################################################################
#Now select HPVs and correct accorring to stop codons and chimeric redas#
#WARNING: stop codon presence here are only app... |
df5d9eaaa34cac1f3fd2158cf51a118236b30d82cb50492321ca3e528eb95f1c | R | 1,992 | 52 | library(DESeq2)
library(ggplot2)
library(pasilla)
### Choose one matrix to analyze ###
coldata_df_R_KAI_SUB_I_IM <- coldata_df_R %>% filter(TYPE_2 == "R_KAI_SUB_I_IM")
filtered_samples_R_KAI_SUB_I_IM <- rownames(coldata_df_R_KAI_SUB_I_IM)
adjusted_df_R_KAI_SUB_I_IM <- adjusted_df_R[, colnames(adjusted_df_R) %in% filt... |
9432c80a50fd53982dee78eb519e2159e212d7e1970a4cba96135ac423f567e3 | R | 1,995 | 43 | here::i_am("scripts/4.paper_figures_and_tables/suppl.figureS8.R")
library(CroCoNet)
library(tidyverse)
library(patchwork)
wd <- here("data/neural_differentiation_dataset/CroCoNet_analysis/")
tree_stats_jk <- readRDS(here(wd, "tree_stats_jk.rds"))
lm_overall <- readRDS(here(wd, "lm_overall.rds"))
lm_human <- readR... |
9bfa4e84d074d44ab0984a5287f23e02d7171b38e71ffbc682ef29e71764705d | R | 2,000 | 51 | # R 4.4.2
# install.packages("lme4")
# install.packages("Matrix")
# install.packages("lmerTest")
# install.packages("mediation")
library(lmerTest)
library(lme4)
library(Matrix)
library(mediation)
data <- read.csv("/data/summarydata_ahahmmreinst.csv")
df <- data.frame(
subject = as.factor(data$subject),
parcel = as... |
aa94ed1325e9bdc3a0502438b199b5a26c1188fec85fb16e10f88c14c8f42f57 | R | 2,003 | 52 | library(DESeq2)
library(ggplot2)
library(pasilla)
### Choose one matrix to analyze ###
coldata_df_R_TBI_IPSI_A_CR <- coldata_df_R %>% filter(TYPE_2 == "R_TBI_IPSI_A_CR")
filtered_samples_R_TBI_IPSI_A_CR <- rownames(coldata_df_R_TBI_IPSI_A_CR)
adjusted_df_R_TBI_IPSI_A_CR <- adjusted_df_R[, colnames(adjusted_df_R) %in%... |
2fc4d59609865871e9094b104befbd6ce766eeb2acd47165ef50d4758b339731 | R | 2,006 | 50 | #-------------------------------------------------
# Title: Helper: Determine whether 'marked' outliers actually should be removed
# Author: Amanda Zacharias
# Date: 2022-12-31
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load... |
a05c2bb3aef44abafb4f497830ee59222b7c48984b3b51a567428a22b4dcb21c | R | 2,006 | 63 | library(ggplot2)
library(dplyr)
library(readr)
library(ggpubr)
library(EnhancedVolcano)
library(rstatix)
library(tibble)
data <- read_csv("data/TREC_KREC_combine_Baby_4.csv")
subtype_names <- colnames(data)[23:36]
protein_names <- colnames(data)[37:124]
data %>%
filter(!is.na(Neutrophils)) %>%
dim() # check t... |
3f4c99043b624ef7b097c2b55f7bec85eae96d199d931d8c951113d9c521cf3f | R | 2,007 | 55 | here::i_am("scripts/2.validations/2.7.POU5F1_LTR7_enrichment/2.7.6.get_POU5F1_motifs.R")
library(tidyverse)
library(universalmotif)
# POU5F1 motifs
pou5f1_motifs <- readRDS(here("data/validations/motifs/motif2TF.rds")) %>%
dplyr::mutate(SYMBOL = toupper(SYMBOL)) %>%
dplyr::filter(SYMBOL == "POU5F1") %>%
pull(mo... |
50bb8fb633288143c4766eb74caf53690def31d718e2edf1a816af0ef6639942 | R | 2,007 | 72 | #!/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$add_argument("-p", "--pmat", required=TRUE, help="load pmat in cnt, cpm, ra")
parser$add_argument("-n",... |
39cd85105aed3335f1aafdbcceb5f07b95e4e80082714fc830502233ff7f023b | R | 2,010 | 52 | # 04_trend_clustering.R
# Temporal expression trend clustering, LOESS fitting, GO/KEGG
rm(list = ls())
library(openxlsx)
library(ggplot2)
library(dplyr)
source("scripts/utils.R")
dat <- read.csv("data/峰面积表达谱_填充_排序.csv", row.names = 1)
# Fix specific rownames
rn <- rownames(dat)
rn[rn %in% c("P01756_","P06... |
55beedb95f29a7c2b468957b0411a2caa05a186e53cd0e707a49b5e5dea4e969 | R | 2,010 | 55 | ### PCA analysis on lipidomics of qNSC with young plasma membrane lipid supplementation in vitro
setwd(rstudioapi::getActiveProject())
library(tidyverse)
rm(list=ls())
load("./Output_data/M2PM_MsD.Norm_Impt_log2_conc_397_lipids.Rdata")
PM.sup.Lipid <- M2PM.log2.impt.norm.conc.MsD %>%
select(matches("_meth|lpd")) #... |
9a07aba7af15a53b176a1744586d253dab2762a48e8967e5f4f4eca63340479b | R | 2,012 | 53 | library(DESeq2)
library(ggplot2)
library(pasilla)
### Choose one matrix to analyze ###
coldata_df_R_AMG_IPSI_A_CR <- coldata_df_R %>% filter(TYPE_2 == "R_AMG_IPSI_A_CR")
filtered_samples_R_AMG_IPSI_A_CR <- rownames(coldata_df_R_AMG_IPSI_A_CR)
adjusted_df_R_AMG_IPSI_A_CR <- adjusted_df_R[, colnames(adjusted_df_R) %in%... |
e8c1e94f3f3ad7daafa9a6494737bf343c573441f1b0a47560d2b3c80752e93c | R | 2,013 | 71 | #Filter the aligned Curio nodose spatial transcriptomic data
library(Seurat)
library(ggplot2)
library(patchwork)
library(dplyr)
#load the data
##F1R1
F1R1 <- readRDS("../nodose_st_230918/F1R1_seurat.rds")
DefaultAssay(F1R1) = "RNA"
summary(F1R1@meta.data$nCount_RNA)
SpatialFeaturePlot(F1R1, "nCount_RNA", stroke = 0)
... |
ac4ddfe8969d7b6557db054636d1c731100b27aec74d5721418b2d6f887864fa | R | 2,017 | 85 | ---
title: "Process binomial independent data"
author: "Joonatan Palmu"
editor_options:
chunk_output_type: console
---
```{css, echo = FALSE}
.page-inner, #content{
max-width:1920px !important;
}
```
```{r options, include = FALSE}
knitr::opts_chunk$set(include = TRUE, echo = TRUE, message = FALSE, results='asi... |
b75cdb15740bb445bf3da4875c53a7758d081febcd77dda0e0c088d3a612923e | R | 2,020 | 55 | library(Seurat)
library(Matrix)
setwd('/home/joonho345/1_Epilepsy_RNA/scRNA_Human/')
GSE <- 'TLE_GSE160189'
#########################################################
#### Create Seurat Object ####
raw_counts <- read.csv("/data/project/1_Epilepsy_RNA/scRNA_Human/Raw_Data/TLE_GSE160189/GSE160189_Hippo_Counts.csv", row.... |
cbded7451ca8aa8a6089961a1424c79ad75d4cc134a4777595a4530cf5ce17db | R | 2,022 | 33 | # script for getting ratio of major cell type and subtype labelling in our iterative clusters
suppressMessages({
library(dplyr)
library(Seurat)
})
source('ClusterPlotFunctions.R')
process_table <- function(obj, major, sub){
table1 <- calculate_ratios(obj, order = levels(obj@meta.data$iter.clustering), majo... |
975cf9d2c5a252eb5ff116f29affa65103e9231d5ee07b3d8334e883332e98a4 | R | 2,023 | 59 | # ==============================================================
# PCA baseline module (comparison method)
# - Input: x1, x2 (screened test matrices), Y (centered test outcome),
# lasso_model_omnibus() must be available (already sourced).
# - Output: list(res_lasso_pca, num_PC1_pca, num_PC2_pca, PX_pca, PC... |
0371c532e7d71d539892b14092ae8ba13644e8f1f186750fa8cdc14ec84ac54a | R | 2,025 | 46 | ##### Unifying Tany-seq and Hypomap Column Names #####
tanycyte <- combined
#Before everything else we should fix column names.
tanycyte$orig.ident <- tanycyte$Sample_ID
tanycyte$orig.ident[tanycyte$Sample == "Blackshaw_Chow_1"] <- "Blackshaw_Chow_1"
tanycyte$orig.ident[tanycyte$Sample == "Blackshaw_Chow_2"]... |
c866ed364d8d2040ee9532aad4f3eff64605d032255018b9038bf8f2cb1bfb3a | R | 2,029 | 45 | rm(list=setdiff(ls(),c('params','fname')))
basedir <- params$basedir # make this whatever you like, end with /
setwd(basedir)
source('code/aba/aba_fxns.R')
abadir <- 'data/aba/'
dir.create(paste0(abadir,'expression'),recursive = T)
load(file=paste0(abadir,'ontologies/keys.RData'))
load(paste(params$opdir,'processed/con... |
47fb346a064d1e8903e0498176002f0725b2e15fd2057a9a1a0cf0cfd0eee06e | R | 2,034 | 55 | #' Summarize cell counts and proportions inside spatial regions
#'
#' Computes the number and proportion of cells from each cluster
#' inside boundaries or ring regions.
#' This function is useful for downstream visualizations such as bar plots or
#' pie charts showing the spatial composition of cell types per region.
... |
09c526d7d76db15267a57af10ae17303f5e9d8a2977a40135593301bcb539dc1 | R | 2,038 | 68 | #' shapes: shape data used to setup Venn plot
#'
#' a collection of geometric shapes, which defined the edge and label of sets in a Venn plot.
#' use `plot_shapes()` to see some of them.
#'
#' @format a list with several slots
#' see "?VennPlotData".
#'
#' @source
#' - The `venn` datasets authored by Adrian Dusa (<htt... |
2c6f35def81ddea1945c7b2d90e48a63393b8ea80997cb9b1a30e31ab2fbb6aa | R | 2,043 | 64 | #' Import cluster results
#'
#' This function imports previously exported clustering results with
#' `cluster_export()` and adds them to the `colData()` slot of your
#' [SpatialExperiment-class][SpatialExperiment::SpatialExperiment-class]
#' object.
#'
#' @inheritParams cluster_export
#' @param prefix A `character(1)` ... |
f96672493367d4ab5c5925012680afce442c6bc3d7cf60041f89f5131c901fb9 | R | 2,044 | 43 | ## ---- message=FALSE, warning=FALSE--------------------------------------------
install.packages('conosPanel', repos='https://kharchenkolab.github.io/drat/', type='source')
## ---- message=FALSE, warning=FALSE--------------------------------------------
library(conos)
library(pagoda2)
library(parallel)
library(ggplot... |
18b68f8ad462a6917e22c40ad4c7c37fd858d9b49dfdce85778f50b5eb625318 | R | 2,047 | 17 | #' Generate the Dunedin Methylation Pace of Aging Scores!
#'
#' The \code{PoAmProjector} function has been deprecated! Please use \code{PACEProjector} instead.
#'
#' @param betas A numeric matrix containing the percent-methylation for each probe. Missing data should be 'NA's. The rows should be probes, with the probe... |
cb8c055b49c3822612a84ba87441e3d54c00fb281997c61e8c95e7e82a269d3e | R | 2,047 | 38 | ##################################
# Plot the difference in genetic distance (private mutations) versus pseudobulk state-controlled transcriptional distance
# Author: Kevin Johnson
##################################
library(tidyverse)
library(ggpubr)
library(EnvStats)
proj_dir <- "/vast/palmer/pi/verhaak/kcj28/car... |
49ec9f092ca7626cc4094f102cb01b5154bf3189841077c4316a3ecae94126c5 | R | 2,048 | 61 | ### PCA plot on In vitro and In vitro Experiment #2 together
rm(list = ls())
library(tidyverse)
source("./Scripts/Function_scripts/Pre-processing_functions.R")
load("./Output_Data/zscore.lpd.Exp2CTRL.for.cmb.PCA.MsD.frmt.Rdata")
load("./Output_Data/zscore.lpd.Invitro.for.cmb.PCAMsD.frmt.Rdata")
z.Invitro <- z.Invitro ... |
fcb8a2ef67976cc16a48b541fec463fa0ddf395e71d89e70d4ca0a515fbf8fdb | R | 2,050 | 99 | # Tests for functions in src directory
# change in random number generation in R3.6, this ensures tests will pass under older and newer Rs
# (this was implemented in Seurat's testthat)
library(ArchR)
library(testthat)
library(Matrix)
suppressWarnings(RNGversion(vstr = "3.5.3"))
set.seed(1)
#Context
context("test_cpp"... |
94afe1db2f668e26752b051171c84ed780ea45ec64f0eda0df4f396a93a033fc | R | 2,053 | 25 | library(Matrix)
library(Seurat)
#
counts = readRDS("preprocessed.rds") #read in preprocessed count matrix (genes x cells)
meta.data = readRDS("preprocessed_meta.rds") #read in preprocessed meta data (cells x variables)
#
seurat_obj = CreateSeuratObject(counts = counts, meta.data = meta.data, project = "seurat_obj... |
ebfa76ed301b2f114dfca3debec4bc2f93cb3fbffe50a128570fad3e40fd61b3 | R | 2,066 | 58 | #' SynGO Running Sum Plot (DEPRECATED)
#'
#' This function is deprecated and maintained only for backward compatibility.
#' Use `gsea_running_sum_plot()` from the RNAseq-toolkit instead, which now
#' works correctly for all databases including MSigDB, SynGO, and MitoCarta.
#'
#' @param gsea_obj SynGO gseaResult object
... |
d2223998630d7d8f8bde5687f28210f51d53133c0db58b3bce8446bbbf4e1197 | R | 2,067 | 52 | #!/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 preprocessed RData")
parser$add_argument("-o", "... |
fade3fcccc0a73b32e8334dc439954718d558b319deb3b06a5beea8bb8519e12 | R | 2,070 | 57 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# `CroCoNet` <img src="man/figures/logo.png" align="right" width="1... |
0f7239e89c32df0f6ef391585d129d6a39c3e82e8a9df8f9d33b315d09241ed7 | R | 2,073 | 60 | #####################################
## Write TITAN GLSS-LX segments to database
## Author: Kevin Johnson
## Updated: 2023.03.20
#####################################
library(tidyverse)
library(DBI)
library(odbc)
## Current project.
projdir <- "/projects/verhaak-lab/USERS/johnsk/glass4"
setwd(projdir)
## Connect to... |
24f960e18468b163b31fe24ea48f11abd2069a9597a291bb9d3b44c08477cfbf | R | 2,073 | 60 | #####################################
## Write TITAN segments to database
## Author: Kevin Johnson
## Updated: 2022.07.06
#####################################
library(tidyverse)
library(DBI)
library(odbc)
## Current project.
projdir <- "/projects/verhaak-lab/USERS/johnsk/glass4"
setwd(projdir)
## Connect to the cor... |
f47f2471c169f62d6809ee9bd9f3f508d95fc8614f357fd00c51a6157feebfad | R | 2,073 | 60 | #######################################################
# Enumerate cumulative coverage per aliquot for WGS/WXS
# Date: 2022.03.01
# Author: Kevin Johnson
#######################################################
options(scipen=999)
## Parse snakemake
if(exists("snakemake")) {
files = snakemake@input[["metrics"]]
... |
f8eff3809f0b6e5b953f46264c02cbd91a31c076189784b9736364bf0d71aa8f | R | 2,075 | 58 | #!/usr/bin/env Rscript
# Load required packages
require(furrr)
require(data.table)
require(purrr)
# Load configuration and utilities
source("config.R")
source("utils.R")
# Parse command line arguments
args <- commandArgs(trailingOnly = TRUE)
if (length(args) != 1) {
stop("Usage: Rscript 1.coTWAS_prep... |
9b106d0403da2d1bc9435ff59a5681918e7e5bf566d82b41fb4962dc98154389 | R | 2,082 | 46 | rm(list=setdiff(ls(),c('params','fname')))
basedir <- params$basedir # make this whatever you like, end with /
setwd(basedir)
source('code/aba/aba_fxns.R')
abadir <- 'data/aba/'
dir.create(paste0(abadir,'expression'),recursive = T)
load(file=paste0(abadir,'ontologies/keys.RData'))
load(paste(params$opdir,'processed/con... |
8a178c29783596da6cbd151b883e4eb55443ffd402e5e2ff5955b8d0dd629ee2 | R | 2,083 | 54 | ## TAU - Topomaps
## Load the files --------------------------------------------------------------
tablepath = "Desktop/Tau_data/"
cohorts = c('exploratory', 'validation', 'adults')
for (cohort in cohorts) {
if (cohort != "adults") {
if (cohort == 'exploratory') {
taudata = readr::read_csv("Desk... |
0ba0476ae6c86b998260db637bb093af0f8465c402a6551694269c46bc26d94a | R | 2,084 | 71 | asd_sfari <- utils::read.csv(
system.file(
"extdata",
"SFARI-Gene_genes_01-03-2020release_02-04-2020export.csv",
package = "spatialLIBD"
),
as.is = TRUE
)
asd_sfari_geneList <- list(
Gene_SFARI_all = asd_sfari$ensembl.id,
Gene_SFARI_high = asd_sfari$ensembl.id[asd_sfari$gene.... |
604dc35a3579eb8369173882dfad9201ade69eecfbcd95e23ce1b2bfc23d94b9 | R | 2,085 | 53 | # ============================================
# Script: 2_subset_and_save.R
# Purpose: Remove NGN5 update metadata
# ============================================
# Load required packages ----
library(Seurat)
library(ggplot2)
library(stringr)
# Set up paths and config ----
data_dir <- '/cephfs/scratch/bruening_scratc... |
65ab7cc8756736aac60285dbf9721a24db0d248dc5ff8187942f6f93a4f0c07f | R | 2,087 | 57 | library(ggplot2)
library(reshape2)
library(viridis)
library(forcats)
# Create the data for light attenuation sensitivity analysis
data_exp2 <- data.frame(
Zone1 = c(6, 7, 6, 5, 7),
Zone2 = c(0, 0, 0, 0, 0),
Zone3 = c(5, 4, 2, 14, 19),
Zone4 = c(0, 5, 26, 15, 13),
Zone5 = c(1, 79, 266, 266, 261),
Zone6 = c(... |
b009d91167af1d39eceef12f3453ef430eb8841bc7a7667fdb224b53eb0e7064 | R | 2,088 | 54 | #####################################
## Write GATK segments to database
## Author: Kevin Johnson
## Updated: 2022.02.25
#####################################
library(DBI)
library(tidyverse)
## Current project
projdir <- "/projects/verhaak-lab/USERS/johnsk/glass4"
setwd(projdir)
## Connect to the correct* internal d... |
5ef34f882c523c6d496d559b6ba9c0abc62bb82aed1e2a7e5381c2890408eb9b | R | 2,089 | 63 | library(Biostrings)
library(rtracklayer)
library(BSgenome.Mmusculus.UCSC.mm10)
setwd("./SpaDC/GRN")
get.seqs <- function (org, regions, no.cores=1) {
## Function for chromosome
get.seq.chr <- function (chr) {
seq <- org[[chr]]
if (class (seq) == 'MaskedDNAString')
seq <- unmasked (seq)
## Get ... |
9df61710b0947b6026943f7dd4eabbd186caebf33c0fa3fc616cd908b6656c6a | R | 2,091 | 37 | #' Retrieves plot data from a SAHA analysis object
#'
#' This function returns plot data based on the specified plot type and data type.
#' If `data_type` is not provided, it attempts to determine the data type from the `ann` object.
#'
#' @param ann A SAHA analysis object containing results.
#' @param plot_type A char... |
a2f7ecb4da45d2c3ca74f2fd71c755d83db35fbf8dda9a68288b1693c76d7a42 | R | 2,095 | 57 | #QC1
#Aim: draw a line plot with the average of asw at each resolution from each PCs
#Calculate the overall average and SD of the asw at different PCs and resolutions
setwd("/home/rstudio/2023_April_Integration/asw_2023_Jun")
name <- c("Run", "ASW", "PC_Res")
data <- list.files(path = ".", pattern = "[0-9].asw.txt")
... |
ecc99b1e7858e42d9751089af99f9f15f50fabc921ff376cfd771ecbbce997b2 | R | 2,095 | 75 | ################################################################################
###############----------- CONVERGENCE CHECK FUNCTION -----------###############
################################################################################
## Computes multiple model convergence checks ------------------------------... |
13a8b53195817b4e20e56a9fab5ac0a6988b8531c965ee2c8896860e2575685c | R | 2,096 | 62 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Filtering helper funcions
# Author: Amanda Zacharias
# Date: 2023-02-27
# Email: 16amz1@queensu.ca
#-------------------------------------------------
# Notes -------------------------------------------
# module load nixpkgs/16.09 gcc/7.3... |
d809760d62dc8eabf5a1f6755518d4545008bdefacbbaf683d84e8ba0c594b2f | R | 2,097 | 43 | ##################################
# Inspect the longitudinal differentially accessible peaks across major cell types
# Author: Kevin Johnson
# Date Updated: 2026.04.07
##################################
library(tidyverse)
library(ggpubr)
library(EnvStats)
proj_dir <- "/vast/palmer/pi/verhaak/kcj28/care_idh_mut"
f... |
e965b2d507bc8103fd6ba9cd0cad56830ed70310790e74ca3861edcf55314209 | R | 2,099 | 56 | ##### ##### ##### ##### ##### ##### ##### ##### ##### #####
# Parameter settings
##### ##### ##### ##### ##### ##### ##### ##### ##### #####
setwd(file.path(getwd(), "R"))
source("00_directorySettings.R") # load baseDir, xeniumDir
source("00_settings.R")
dataDir <- file.path(baseDir, "data")
outDir <- file.path(baseDi... |
adbbda3cab6be52142f6347b420d9e1103ab69883dd669b47b17a7406c18ec94 | R | 2,101 | 49 | library(SpatialDecon)
library(anndata)
library(Seurat)
library(scater)
h5adpath <- "./simulated_gene_expression_average.h5ad"
dlpfc <- read_h5ad(h5adpath)
st_count <- t(dlpfc$X)
st_count <- round(st_count)
andersson_g1 = CreateSeuratObject(counts = st_count, assay="Spatial")
pos <- dlpfc$obsm$spatial
andersson_g1@... |
73a793c775fc3ac05017da5609fb164c9dae43b207f3776d4779197339476f2d | R | 2,105 | 49 | setup_environment <- function(requirements_file) {
# Stop if no requirements file is provided
if (missing(requirements_file)) {
stop("No requirements file provided. Specify the path to R_requirements.txt")
}
# heck if requirements file exists
if (!file.exists(requirements_file)) {
stop("Requirement... |
c97fcf0817d89580ca4f72c4f50b5e269a795f82a289e8fbe4faa1ca07d61e1b | R | 2,106 | 57 | context("Cast utils")
test_that("cast_string works correctly", {
expect_equal(cast_string("test"), "test")
expect_equal(cast_string(123), "123")
expect_equal(cast_string(TRUE), "TRUE")
expect_equal(cast_string(NA, allow_na = TRUE), NA_character_)
expect_error(cast_string(NA, allow_na = FALSE))
expect_er... |
75483219f870596d1acae16b99d9c3c05097493c0022e7fc6a3ab4a5c4053bcf | R | 2,114 | 52 | ### Effect size calculation on lipids and double bond composition
### Mboat2 overexpression and plasma membrane lipid supplementation
setwd(rstudioapi::getActiveProject())
rm(list=ls())
source("./Scripts/Function_scripts/Effect_size_functions.R")
source("./Scripts/Function_scripts/Pre-processing_functions.R")
library(... |
ca7009f77a8e3d818d60ce4080c32d51a9990064683be4f8c3877d42e9bdfccc | R | 2,116 | 49 | cv.nodewise.bestlambda<-function (lambdas, x, K = 10, parallel = FALSE, ncores = 8, oldschool = FALSE,
verbose = FALSE)
{
n <- nrow(x)
p <- ncol(x)
l <- length(lambdas)
dataselects <- sample(rep(1:K, length = n))
if (oldschool) {
message("doing cv.nodewise.error oldschool")
totalerr <- num... |
bd8ce7ea2fe3d73c99957a54c2ecb4b32e79eea1a22323c21c3cdc8ff2fb1e5a | R | 2,117 | 87 | library(testthat)
library(dplyr)
library(janitor)
source("../R/module_upload_helpers.R") # contains preprocess_uploaded_table()
test_that("preprocess_uploaded_table cleans column names and orders factors", {
df <- tibble(
"ID " = c("b", "a"),
"Group Name" = c("Low", "High"),
"Value 1" = c(1... |
0dbf48ee5d455b618e4a6eb99b67aa68a606ff897cfba2c41e435cbc0575d36f | R | 2,120 | 63 | #If version conflicts between anndata and giotto python environments:
# .rs.restartR()
#
# reticulate::use_python("/usr/bin/python3.8", required = TRUE)
library(Giotto)
library(DescTools)
library(anndata)
st_path <- "./ST.h5ad"
ST<- read_h5ad(st_path)
ST$var_names_make_unique()
st_adata <- t(as.matrix(ST$X))
x<- ST$... |
cccfc20dd17ef5a761dff6ddc0a0b3e590d36a9008679ca76a81a4f8aa899fd5 | R | 2,120 | 55 | ##############################
# Set working directory and base directory
# setwd("path/to/project/root")
library(purrr);library(limma);library(dplyr);library(tidyverse)
regions = c("dlpfc","dACC","sACC","caudate","hippo","amygdala")
walk(regions, function(region){
## Performance and Predict... |
c6287eb6589039ec3d5f98ae8b8752bde6879ff6b7103656344b0e0e9893c805 | R | 2,125 | 47 | #' @importFrom magrittr %<>% %$% %>%
#' @export
Read10x <- function(path, feature.name="features") {
cm <- Matrix::readMM(file.path(path, "matrix.mtx")) %>% as("dgCMatrix")
colnames(cm) <- data.table::fread(file.path(path, "barcodes.tsv"), header=F)[[1]]
rownames(cm) <- data.table::fread(file.path(path, paste0(f... |
e3024d7a2662b76dec05df975304d6bfeffdbeb311d34311e7acd9ac0259593b | R | 2,127 | 61 | #######################################################
# Enumerate cumulative coverage per aliquot for WGS/WXS
# Date: 2023.03.22
# Author: Kevin Johnson
#######################################################
options(scipen=999)
## Parse snakemake
if(exists("snakemake")) {
files = snakemake@input[["metrics"]]
... |
6bddf929df0973f1b5d661a486f9f1227d1b9812e3e1825c51c189f6dbd3c187 | R | 2,129 | 66 | # Building a Prod-Ready, Robust Shiny Application.
#
# Each step is optional.
#
# 1 - On init
#
## 1.1 - Fill the descripion & set options
##
## Add information about the package that will contain your app
golem::fill_desc(
pkg_name = "spatialLIBD", # The Name of the package containing the App
pkg_title = "LIB... |
15a02118e660abc86275a02844abdb7fb607f94bf9463b84cb69c1e7020c30a4 | R | 2,132 | 70 | #' Run OTTER in R
#'
#' Description:
#' OTTER infers gene regulatory networks using TF DNA binding
#' motif (W), TF PPI (P), and gene coexpression (C) through
#' minimzing the following objective:
#' min f(W)
#' with f(W) = (1-l... |
4e844136bbece2b2d9c8f24165ac36ef091165aa4534ad0d458d4a9edb2eb185 | R | 2,135 | 52 | library(ggpubr)
library(MOFAdata)
library(MOFA2)
library(tidyverse)
setwd("/pastel/Github_scripts/ROSMAP-MOFA")
load("/pastel/projects/MOFA/model/standart_50f.RDATA")
phenotypes = readRDS("/pastel/resources/data2share/basic_Apr2022_selected_list_Jul2024.rds")
phenotypes$projid = rownames(phenotypes)
load("/pastel/res... |
6d4a856b369475b23248fc51a7c4d568249c67be7fe06647034b1828dd593ac8 | R | 2,135 | 65 |
single_out_joint <- function(joint_samples_list, i){
single_samples_list <- lapply(joint_samples_list, return_single_sampler, i)
attr(single_samples_list[[1]]$prior, "design") <- get_design(joint_samples_list)[[i]]
class(single_samples_list) <- "emc"
return(single_samples_list)
}
get_joint_names <- function(e... |
75f65524c34567d0fe8c3f7630f3029a378127a5379526581ced1e4743c3dab4 | R | 2,136 | 59 | library(dada2)
library(dplyr)
library(readr)
gtdb.for.dada <- "../references/gtdb/sbdi_16s_gtdb_database/gtdb-sbdi-sativa.r07rs207.1genome.assignTaxonomy.fna.gz"
datasets <- c("JACOBS_IBD_FAMILIES_2016","HE_INFANTS_MFGM_2019","KIM_ADENOMAS_2020","KOSTIC_INFANTS_DIABETES_2015","POYET_BIO_ML_2019","KANG_AUTISM_2017","WA... |
1ef0787803f2da8f9edd6da5f432a25d7bea18d957d808f84d190a980e378623 | R | 2,138 | 80 |
library(data.table)
library(stringr)
# ------------------------------------------------------------------------
# read in file information
# ------------------------------------------------------------------------
sdrf = fread("E-MTAB-9357.sdrf.txt")
dim(sdrf)
names(sdrf)
sdrf[1:2,1:30]
w_data_file = which(names(sd... |
3aa32ce8de4eba167e712f7bd77189ffa784508d348ceabd3d7a9c8bdfef9ed3 | R | 2,138 | 53 | ##############################################
# Plotting total variance explained by views #
##############################################
library(MOFA2)
library(ggplot2)
library(dplyr)
library(ggsci)
# Retrieving MOFA
load("C:/Users/User/Documents/Drive_PC/Lucas/GitHub/ROSMAP-MOFA/MOFA_real/Raw_data/mofas/... |
003c0f57fda69599592caa320baa7697430d04cb509264f502bba6da4c9a8334 | R | 2,147 | 55 | ## This script produces the mean area and mean volume of all cells of a specific animal.
# Specify your working directory and the length of the animal names below:
wd <- "your_working_dir"
animal_name_length <- c(4)
library(writexl)
library(readxl)
library(dplyr)
library(stringr)
library(tidyr)
setwd(wd)
all_files<... |
9386fb12a1b6e57cc989cd4e35fa3671edcfcace34a584e1e4dc42337fd7cc46 | R | 2,147 | 82 | ############################################################
# MS-DAP analysis of DIA-NN output
# eGRASP hippocampal synaptosome proteomics
#
# This script performs the primary differential expression
# analysis using the MS-DAP pipeline.
#
# Required input files (place in data/):
# - diann_output.tsv.gz
# - sample_met... |
27c31880af342ad0f2d023b4ccd65f56206bc125da256b74e2ab52107e8a647c | R | 2,149 | 63 | #' Identify cells located within spatial boundaries or ring regions
#'
#' Returns the subset of cells from the input data that fall spatially
#' within a given boundary or ring.
#' The boundary can be provided either as raw boundary points
#' (from `GetBoundary()`), as polygons (from `BuildBoundaryPoly()`),
#' or as ri... |
74bab9546246db0d0e8540463ab6f2c9f81e8b52fd5c169b6966e11997b34b6c | R | 2,151 | 65 | #' Evaluate Lattice Kriging Model Performance
#'
#' Computes R^2 and correlation between observed and predicted values.
#' If new data is provided, predicts and evaluates on that data; otherwise, evaluates on training data.
#'
#' @param LKfit Fitted LKrig model object
#' @param locations_new Optional matrix of new spat... |
2b6107f93fa90e780ca04f1e99c60c87a969248791a69dfa35a1acc071a26ea9 | R | 2,155 | 71 | context("reading")
test_that("get kallisto path", {
dir_name <- "small_test_data/kallisto"
# the standard case
file_name <- file.path(dir_name, "abundance.h5")
result <- get_kallisto_path(dir_name)
expect_equal(result$ext, "h5")
expect_equal(result$path, file_name)
# user gives the full path
result <... |
8976b53d3f900efcd3bc9701b0c638acfd33a21e404df4a0524779b63e3700ac | R | 2,157 | 95 | ---
title: "WeightedCluster_Analysis"
author: "Michael Colwell"
date: "2025-09-20"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Weighted cluster analysis
Pre-processing and analysis script for the weighted cluster analysis
```{r cars}
library(metafor)
library(dplyr... |
9510c8d68ee78cf32fc1f31e9905d71aaff31500e37b46df22d0eef8f14866bc | R | 2,157 | 64 | # This script checks the convergence of the MCMC runs using the Geweke diagnostic. It creates a dataframe with the z-scores of the Geweke diagnostic for all sites
library(coda) # version 0.19-4.1
folder_datasets="Local/path/to/BayesTraits/datasets/" # Path to output of script 14.2
folder_out="Data/14.BayesTraits/out/"... |
b764d5718f26e115e1e899d168ee0464831d87992b518fd6358d0d833356b1ec | R | 2,157 | 58 |
## Figure S7 Tga20 expression level ####
tell_user('done.\nCreating Figure S7...')
resx=300
png('display_items/figure-s7.png',width=resx*3.25,height=3.5*resx, res=resx)
par(mar=c(1,3,1,1))
rbind_files('data/','21[46].tsv') %>%
mutate(plate = as.integer(substr(file,1,3))) -> elisa_raw
rbind_files('data/','21[46]_su... |
23cc5a68a286b77dcd5177290c5564c1dc2732efeb824563a8e6550595a1c1f8 | R | 2,160 | 76 | library(MOFA2)
library(MOFAdata)
library(data.table)
library(ggplot2)
library(tidyverse)
rna <- read.csv("MALT10k_rna_scaled.csv", header=TRUE, row.names=1)
protein <- read.csv("MALT10k_adt_scaled.csv", header=TRUE, row.names=1)
rna <- data.matrix(rna)
protein <- data.matrix(protein)
data<- list(rna,protein)... |
7b17208435ad93e8fcf09dae549663a4fc6adccfb9632ced550606b250ea3461 | R | 2,165 | 55 | #!/usr/bin/env Rscript
#-------------------------------------------------
# Title: Prep bulk data for deconvolution
# Author: Christina Meier
# Date: 2025-06-23
# Email: 19cm51@queensu.ca
#-------------------------------------------------
# Notes ------------------------------------------
#
# module load StdEnv/2023 r/... |
c4a882cb2e0adc279e1e77bb23bb4aa7dbd25b236668eec3234e6d62f7fcacf2 | R | 2,166 | 57 | library(dplyr)
library(ggplot2)
library(ggsankey)
library(readxl)
### use already defined coldata_df_1
coldata_df_1_MTLE <- coldata_df_1 %>%
filter(Diagnosis != "NL")
##
coldata_df_1_MTLE <- coldata_df_1_MTLE %>% mutate(ip_group = case_when(
IP == 'HT(Head Trauma)' ~ "Initial precipitating injury (Other)",
IP... |
e39e3ed0bf48fbb1120d784cf961d837bbd1b9772e7393cf856a514992840363 | R | 2,166 | 67 | #' banc-nblast-share-gcs — Publish per-dataset NBLAST feathers + reviewed CSVs to GCS.
#'
#' @section Reads:
#' - `banc_<target>_<ver>_nblast.feather` (FAFB, MANC, FANC, hemibrain, maleCNS, lr, native)
#' - reviewed match CSVs
#'
#' @section Writes:
#' - GCS `nblast/`
#'
#' @section Invoked by:
#' production v8... |
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