sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
7581c5b9a78b1d87b276002cc06378524dc37a2601ab6dc9e0371068bdde5fef | R | 13,917 | 457 | ---
title: "SpNeigh analysis of human healthy liver MERFISH data"
author:
- name: Jinming Cheng
affiliation:
- &duk_nus Centre for Quantitative Medicine,
Duke-NUS Medical School,
Singapore, 169857,
Singapore
# email: jinming.cheng@outlook.com
date: "`r f... |
9c960d080dce02bb2bdd5f561bd4d5498b38bb3ee0634a372a679ab35d75a33d | R | 13,928 | 366 | #!/usr/bin/env Rscript
source("banc/banc-startup.R")
version <- "746"
# File paths
save.path <- '/n/data1/hms/neurobio/wilson/connectomes/banc'
dir.create(save.path)
####################
### GET SYNAPSES ###
####################
# csv info
desired_columns <- c('id', 'size', 'pre_root_id', 'post_root_id', 'ctr_x', 'c... |
b51e11dbc8ae1b4892ce622eceec35eed587b63ca77cd5f135f50ab91c0db9c0 | R | 13,945 | 354 | ## Private (for now) helper functions
flywire_cloudvolume_url <- function(cloudvolume.url=NULL, graphene=TRUE) {
if(is.null(cloudvolume.url)) {
u=getOption("fafbseg.cloudvolume.url")
# the current option points to a graphene URL (should be flywire or zetta) so use that
cloudvolume.url <- if(grepl("graphe... |
d9915cf1e42d0581293d30191ebd251934a939fe3cece5481cf1a683610749dd | R | 13,982 | 328 | # -------------------------------------------------------------------
# Organoid vs EV Combined Proteomics Pipeline (R)
# -------------------------------------------------------------------
# This script imports batch‑1 and batch‑2 proteomics exports, attaches
# clinical metadata, creates unified sample labels, merges ... |
fd054bd7c879742b7e9ae7b1e9bd3f0247758d01db288c8defdedbd9f1b33e1a | R | 14,011 | 293 | ##################################
# Examine distributions of the CARE IDH-mutant cell states across genetic alterations while controlling for grade
# Author: Kevin Johnson
# Date Updated: 2026.04.03
##################################
# Reproduce the following figures: 4a
library(tidyverse)
library(ggpubr)
library(En... |
de92ace82b9f87e42fd168110ace21f1d129fae861fa3c86be48a7116af0f425 | R | 14,014 | 550 | ---
title: "Get association between DNAm residuals and RNA residuals in ROSMAP data"
subtitle: "We correlate probes and genes on opposite strands"
author:
- David Lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
rmarkdown::html_document:
hi... |
70b29064b7656c243742678394c2479ce8711a7d4231168854717940499607a3 | R | 14,027 | 270 | library(metacell)
# devtools::load_all('~/src/metacell.flow')
library(metacell.flow)
suppressPackageStartupMessages(library(ComplexHeatmap))
# wd <- '/home/feshap/raid/proj/mmcortex'
wd <- '.'
# setwd(wd)
db_path <- file.path(wd, 'scdb')
scdb_init(db_path, force_reinit = T)
scdb_flow_init()
SEED <- 1337
K ... |
0e9d57ee5128e18dcf180050f6fc5a7889c2cf5cec048a994cb4337066153e86 | R | 14,076 | 273 | #load the libraries
library(enrichR)
library(dplyr)
#load in data this set is without the ENS genes
both.genes <- read.csv("D:/PEA/List.of.genes.csv")
#organize data
ADvsNC.genes <- both.genes$CONvsAD.genes
ADCHCHD10vsAD.genes <- both.genes$ADvsADD10.genes
#first try with AD vs NC neuron DMR genes
result... |
12ed5e5c84d94278fa32e8ad6b3b3b30ab300d5790fce6cebbc49e60dd35983d | R | 14,086 | 250 | # ----------------------------------------------------------------
# The following script loads and processes data from:
#
# Kim, Minsuk, et al. "Fecal metabolomic signatures in colorectal
# adenoma patients are associated with gut microbiota and early
# events of colorectal cancer pathogenesis."
# MBio 11.1 (20... |
caf5d0c8d4ad04543e5ae1f2e66a4e6d87170370fec9ceb05f3968dc9d7758bc | R | 14,093 | 314 | #!/usr/bin/env Rscript --vanilla
# Script for importing and processing transcript-level quantifications.
# Written by Lorena Pantano, later modified by Jonathan Manning, and released
# under the MIT license.
# Loading required libraries
library(SummarizedExperiment)
library(tximport)
################################... |
20b8d9dbbe192a5ac93ae955fb9414105e4cc430144c2c1c37e3f3b5dee4301c | R | 14,104 | 293 | # ------------------------------------------------------------------------------
# NeuroConnectivity - Profiling
#
# Author: Marlies Verschuuren
# Modified by: Marlies Verschuuren
# Creation date: 2023-12-01
# Last Modified: 2023-12-20
# ------------------------------------------------------------------------------
#-... |
1bf4f35736753a77650ec3e0aabd9ebdcbcbdb9558ebe15fabda290cb5bf75da | R | 14,126 | 291 | # R code for "Brain structure and function in Homo naledi"
# (Cofran et al., Brain Structure & Function)
# Estimate missing landmarks for LES1 endocast
# geometric morphometric analyses
# comparing LES1 to recent humans and fossil hominins
# First, download landmark data Neubauer et al. 2018 dataset: < https:... |
34df8f1643716717675ba716d7ccd5058eaf8b80c86d0fb3e76f6aebc737f778 | R | 14,133 | 415 | ############################################
# Dyadic LME (frequentist) with df corrections
# - outcome: dz = atanh(STTC_rebound) - atanh(STTC_late)
# - fixed effect: probe (optrode vs contra)
# - random effects: (1 + probe | mouse) + multi-membership (unit_i + unit_j)
# - inference: Satterthwaite df and Kenward... |
bbeaf3a5eaff24113cd7580ed25ada806d4b45b73ea10f4137d3bfda74350fc5 | R | 14,138 | 329 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# Wrangling Mo's connectivity data with dn taxono... |
5c24f60dbcf815a90d98732fae10328b7caaa502a4f26c6526b94bf2ee53195e | R | 14,149 | 398 | # Behavior Analysis - Paper Figures
# Figure overview:
# - Fig. 2 (main): Normalized bar plots pairing each vehicle with its
# drug (saline_lira/lira_sd, veh_sd/dan_sd, saline_orfo/orfo,
# saline_licl/LiCl) for select behaviors (and more behaviors in extended).
# Each drug group is scaled to its o... |
099828f6fbc24d43216eef5d73a7f1f9fd182d77eb839a0dc3ce634b6b5071d7 | R | 14,151 | 521 | ---
title: "Getting Started with SpNeigh"
author:
- name: Jinming Cheng
affiliation:
- &duk_nus Centre for Quantitative Medicine,
Duke-NUS Medical School,
Singapore, 169857,
Singapore
# email: jinming.cheng@outlook.com
date: "`r format(Sys.time(), '%d %B... |
a67e30a117198ebfb86b9a8c5e80259ec0d1a399a99a3b1567bdf9f029fbea9b | R | 14,162 | 414 | library(tidyverse)
library(glmnet)
library(ggplot2)
# ============================================================================
# FUNCTION 1: Load and scale mean results
# ============================================================================
create_combined_scaled_results <- function(metrics_config, base_di... |
9af012f1df6e20250cf4ed6cc8a9334a9daf8ccde229664838d6b9bbadf23c86 | R | 14,176 | 355 | library(ggplot2)
library(dplyr)
library(svglite)
#### Read outlier files ####
outlier_dir <- "/home/joonho345/1_Epilepsy_RNA/RNA_Human/06.Selection/03.PCA_Outliers"
# Try to read the combined all outliers file first (most efficient)
all_outliers_file <- paste0(outlier_dir, "/03.ALL_Outliers_PCA.txt")
if (file.exists... |
ca5116125e5e43a0a564dc96a95d307c9c3d17f0d62f0e47fe28a911b3412f57 | R | 14,177 | 411 | ## Run this on JHPCE at
## /dcs04/lieber/lcolladotor/with10x_LIBD001/HumanPilot/Analysis/spatialLIBD_files
library("SingleCellExperiment")
library("here")
library("sessioninfo")
## Output directory
dir.create(here(
"Analysis",
"spatialLIBD_files"
), showWarnings = FALSE)
## Load data
load(
here(
... |
4841a59ce8d0e4bceee4944b05fe0d15f39e11d2c2dc5b182db5788f75db78c2 | R | 14,181 | 311 | library(bancr)
# Deformetrica folder
deform.folder <- "/Users/abates/projects/flyconnectome/bancpipeline/deformetrica/mirror"
deform.folder.points <- file.path(deform.folder,"Points")
deform.folder.meshes <- file.path(deform.folder,"NeuronMesh")
deform.folder.skeletons <- file.path(deform.folder,"Neuron")
deform.folde... |
e2bfa6c546b6c254400652f19bb3acd1fcf2d61cfa59f43c41b9694c5a052ea9 | R | 14,182 | 379 | #' banc-cx-fix — Fix fafb_/hemibrain_ matches for central-complex neurons.
#'
#' CX neurons often have good `cell_type` but poor `fafb_cell_type` /
#' `fafb_match` and stale `hemibrain_cell_type` / `hemibrain_match`. This
#' script picks the best matching FAFB id and hemibrain bodyid per
#' cell_type using NBLAST score... |
6bb50579628364c9370826871081d1953d436788a6c43a6a51e0fdd84e52db95 | R | 14,196 | 363 | #!/usr/bin/env Rscript
###############################################################################
## Script: 3.6.viz_alluvial_ggalluvial.R
## Purpose: Classical alluvial diagram showing trajectory flow from early
## defects through mechanisms to late outcomes
## Output: 03_Results/02_Analysis/Plots/Trajec... |
1c100c6400ef3e35711a3313e3c22ea69e468b4fb265fda1854411da3794d3c3 | R | 14,200 | 377 | required_packages <- c(
"dplyr",
"UpSetR",
"data.table",
"VennDiagram",
"ggpubr",
"tidyr",
"ggplot2",
"ggalluvial",
"readr",
"pheatmap",
"stringr"
)
load_required_packages <- function(packages) {
missing_packages <- packages[!vapply(packages, requireNamespace, logical(1), quietly = TRUE)]
if ... |
238fa35c69e251ff927fa92da847f64062660c0f4ddc1761120ff8a14f91f664 | R | 14,210 | 367 | f = "/Users/danielesilvestro/Dropbox (Personal)/SpeciesConcept/empirical_fossil_clades/pyrate_mcmc_logs/CetaceaBD1-1_mcmc.log"
f = "/Users/danielesilvestro/Dropbox (Personal)/SpeciesConcept/tree_fossil_pyrate/pyrate_mcmc_logs/Ruminant_occurrences_crown_1BD1-1_mcmc.log"
f = "/Users/danielesilvestro/Dropbox (Personal)/Sp... |
9f92a509e8b45fc4197cbff3467026fabc5e87bb2b6f3aeb0428ea9ab3e97c9d | R | 14,244 | 335 | library(ArchR)
library(parallel)
addArchRThreads(threads = 20)
addArchRGenome("hg38")
library(ComplexHeatmap)
library(ggsignif)
library("RColorBrewer")
projdir <- "projrmSubset_subPeak"
proj <- loadArchRProject(projdir)
#Load in co-accessible peaks (for cell types)
proj$subC_Dx <- paste(proj$subClusters, proj$Clini... |
5d5180a72d4a669dc336bb5e71a41717ff2b5a9d14bcdf0d58f4e89a83aa9719 | R | 14,251 | 427 | #' Plot spatial cell coordinates with cluster boundaries
#'
#' Plots spatial cell locations and overlays cluster or population boundaries
#' if available.
#' If `boundary` is not provided and `one_cluster` is specified, the boundary
#' will be automatically generated using the `getBoundary()` function.
#' This function... |
8480f72466234f82f0c00ec2a5174fb0f2656e2a6b132271605b1fde1708035e | R | 14,256 | 453 | ---
title: "S2: analysis of pupil sizes with brms"
author: "I. S. Plank"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
fontsize: 10pt
#geometry: margin=0.5in
---
```{r settings, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, fig.align = 'center', f... |
bc73e6cb9b8df60275506b46cb3e36b295a11493dc808706f83a975122560657 | R | 14,261 | 228 | library("here")
library("sessioninfo")
outdir <- "spatialLIBD_files"
pkgname <- "spatialLIBD"
meta <- data.frame(
Title = c(
"spatialDLPFC_Visium_spe",
"spatialDLPFC_Visium_example_subset",
"spatialDLPFC_Visium_pseudobulk_spe",
"spatialDLPFC_Visium_modeling_results",
"spa... |
10cdcab0a2e0b435f466b981a8da1aece7b650dd5b9d512a480776c0007339ee | R | 14,295 | 403 |
```{r setup, echo=FALSE, warning=FALSE, message=FALSE}
rm(list=ls())
library(tidyverse)
library(ggpubr)
library(ggthemes)
library(RColorBrewer)
library(rstatix)
library(ggnewscale)
library(patchwork)
library(ggbeeswarm)
knitr::opts_knit$set(root.dir = "~/Dropbox/NSC_aging_lipidomics/")
knitr::opts_chunk$set(
... |
8208f989bd55afe0af3cf0f01b44e8c87ea66abc5d9c4a2f6dec022df0fd9676 | R | 14,303 | 353 | library(ggplot2)
library(dplyr)
library(svglite)
#### Read outlier files ####
outlier_dir <- "/home/joonho345/1_Epilepsy_RNA/RNA_Human/06.Selection/05.TPM_PCA_Outliers"
# Try to read the combined all outliers file first (most efficient)
all_outliers_file <- paste0(outlier_dir, "/05.ALL_Outliers_TPM_PCA_Common.txt")
i... |
facd06b250dc818dd7ad57ad319fb014f2195459ab3c42747a9f518e0dfda0ef | R | 14,304 | 455 | #
# sleuth: inspect your RNA-Seq with a pack of kallistos
#
# Copyright (C) 2015 Harold Pimentel, Nicolas Bray, Pall Melsted, Lior Pachter
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software F... |
1a97c6e618e89f53ec5860f02d09e06cd1b37f4fcd43af7ab893af5b22cdd1fd | R | 14,310 | 291 | #' banc-update-abdominal-neuromere-types — Push FAbG abdominal-neuromere types into SeaTable.
#'
#' Loads the annotated xlsx files from the FAbG project (Jiayi) and pushes
#' cell_type / sexually_dimorphic / status updates to BANC SeaTable. Only
#' rows with `FAbG_match` populated (and not `Not in AbG` / `BANC_unmatche... |
cd31845aeee23fe7e8b7b8ec3f85b69f4f95cbc89f4d9862bfc034b028d3618a | R | 14,350 | 405 | ---
title: "Fast, Windows‑safe coverage from transcript intervals"
output: html_notebook
---
```{r}
library(data.table)
# Windows-safe parallelism by group with multisession
library("future.apply", quietly = TRUE)
library("future", quietly = TRUE)
```
# Fast coverage from [start, end] intervals (inclusive)
# - dt:... |
b0738efd99adf0d3be8864cbb7594c469ef6148e0baca711742350ff587dbb49 | R | 14,354 | 402 | #
# Control F and then replace "Factor1" with Factor_
rm(list = ls())
# BiocManager::install("edgeR")
?effsize::cohen.d
library(tidyverse)
library(edgeR)
library(corrr)
library(tibble)
library(ggpubr)
library(readr)
library(circlize)
library(beepr)
library(ggplot2)
library(dplyr)
library(effsize)
library(here)
condi... |
1a7b9755c8226ac68eaba09d1e76e32e6f433360580e0b842024eb2a129a7740 | R | 14,377 | 432 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# For testing out figures for Figure 3/4 of the b... |
f4175cfca08cd9e2d9d762728a36ac84f8c7680e0555258fc552eda70f06471c | R | 14,379 | 365 | gatePlot <- function (outputId) {
HTML(paste("<div id=\"", outputId, "\" class=\"shiny-gateplot\"><canvas id=\"gatePlotCanvas\"></canvas></div>", sep=""))
}
render_concatenate_ui <- function(working.directory, ...) {renderUI({
fluidPage(
fluidRow(
column(12,
#selectizeInput(... |
5dd8ef83f491eb684bd151a75fb0f6ae28fa37749d349d83eeeebf55c85468a2 | R | 14,386 | 482 | library(numDeriv)
fitKd = function(conc, resp, bidirectional = FALSE, verbose = FALSE, nofit = FALSE, force_sd = F){
logc = log10(conc)
fenv <- environment()
pars <- paste0(c("tp", "ga", "b", "er"))
sds <- paste0(c("tp", "ga", "b", "er"), "_sd")
myparams = c("success", "aic", "cov", "rme", "modl", pars... |
7464051c9834c887c14bde19ab4180847441e02a1b86250756d7ca2295c248df | R | 14,393 | 464 | ---
title: "tumor_analysis_inferCNV_P16_Post"
output: html_document
date: "2025-12-12"
---
```{r}
library(Seurat)
library(dplyr)
library(ggplot2)
library(readr)
library(infercnv)
```
# Define functions
This outputs annotations files for cell ranger cell ids and seurat clusters and returns a gene expression matrix
``... |
887d3152ef3111e6cbc1d9737dca06d61214281ec7a4f5988d1c409febe90e9b | R | 14,408 | 294 | # ----------------------------------------------------------------
# The following script loads and processes data from:
#
# Mars, Ruben AT, et al. "Longitudinal multi-omics reveals
# subset-specific mechanisms underlying irritable bowel syndrome."
# Cell 182.6 (2020): 1460-1473.
#
# The output tables are:
# 1) m... |
3747a202b4dfff763aacb7eab78e7aa029a4ca78522307a00ae110bc5d3a4195 | R | 14,420 | 404 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# get neuron to neuropil synapses
##############... |
bacd0cb501310e2f547ab0a92e61fcd46971a852d5590beaf5d84c6acdbb236e | R | 14,446 | 253 | ########################################################################################################
#### Trial dataset for this R script can be downloaded from ####
#### https://data.mendeley.com/datasets/454s2vw255/1/files/ed8fb3e5-656e-43f1-9e5f-a763a4c61121 ####
##... |
b77f2e9907b5045317c0c96953e086012a41c99f5dec7c0812eb6d921cf77d5c | R | 14,448 | 252 | ########################################################################################################
#### Trial dataset for this R script can be downloaded from ####
#### https://data.mendeley.com/datasets/454s2vw255/1/files/def460b5-2f2b-4ab2-bc0b-a734ca869cb1 ####
##... |
bb38c920b5d83c9fd8b7483bc19df971286cc429d173ae1e4d60e0396e0b19c7 | R | 14,449 | 253 | ########################################################################################################
#### Trial dataset for this R script can be downloaded from ####
#### https://data.mendeley.com/datasets/454s2vw255/1/files/bba53b25-29fe-4c55-9564-8ad4fcb6ec62 ####
##... |
95f302767866aae89a9889f6db38720c16dd7aa706a1ba8a092222e210e6041a | R | 14,451 | 253 | ########################################################################################################
#### Trial dataset for this R script can be downloaded from ####
#### https://data.mendeley.com/datasets/454s2vw255/1/files/9e44d795-e4b3-4a66-a10a-cd15c6dea754 ####
##... |
4715caa3ef0ab57c2eaef478fa5386936c06cae93c2b76e114473908eda91d83 | R | 14,452 | 320 | ---
title: "Steps taken to generate Figure 2 of Koeber et al, Nature 2026"
author: Alhafidz Hamdan
output: html_document
date: "2026-02-01"
editor_options:
chunk_output_type: console
---
# This document details the methods/steps used to generate figures used in Figure 2 (Koeber et al. Nature, 2026):
## Load require... |
00fee7162d0e4fe78c92588353a5e07d9e1b3155bc0506431b1aa318f908399f | R | 14,453 | 348 | # see https://dvidtools.readthedocs.io/en/latest/
# https://github.com/flyconnectome/dvid_tools
# https://emdata5.janelia.org/api/help/
#' Interface to the python dvid_tools module from Philipp Schlegel
#'
#' @param user Default DVID user names
#' @param node default DVID node to use for queries (see
#' \code{\link{... |
8e330e6fdb46de94413c15931b9b38ee22a0521184d84f5bf15d82c63598f8a0 | R | 14,462 | 240 | here::i_am("scripts/4.paper_figures_and_tables/suppl.figureS5.R")
library(tidyverse)
library(CroCoNet)
library(ggsignif)
library(patchwork)
library(cowplot)
library(here)
dir_cor.kIM <- here("data/neural_differentiation_dataset/CroCoNet_analysis/")
dir_cor.adj <- here("data/neural_differentiation_dataset/CroCoNet_ana... |
224f2134950b2f4631c29a3db268d60d144e2ba9516275ce65f2dd6a2052933d | R | 14,513 | 352 | library(rstudioapi)
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(pafr)
library(gggenomes)
library(GenomicRanges)
library(IRanges)
library(purrr)
library(tibble)
library(clue)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
# --- 1. Compute scaffold pairing ---------------------... |
9ff08cdc70ec9fa923025aac6fb03e09caa032b74b5634352a32c164466d8c87 | R | 14,563 | 477 | ###################################################################
#### CAREmut study utils - scoring cells and generating null distributions
###################################################################
generate_null_dist <- function(umi_data_list, md, sigs, genes_subset = NULL, n_iter = 20, n_cells = 10000, v... |
1bde8374a9509225fc9e5da1ade897e3ffb11612d69030a0de4083b39beb4339 | R | 14,572 | 256 | ---
title: "acesinabcd_setup"
author: "FB"
date: "2024-03-11"
output:
html_document: default
word_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Setup file for creating adversity scores in ABCD
Running this code will create an RDS file including all relevant variables... |
9661149cfd9055af0e1e6560a34f63c527e97f58afe415c020ecf07350679962 | R | 14,581 | 337 | if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!requireNamespace("sva", quietly = TRUE))
BiocManager::install("sva")
if (!requireNamespace("bladderbatch", quietly = TRUE))
BiocManager::install("bladderbatch")
library(ggplot2)
library(stats)
library(dplyr)
library(sva) # ... |
f3a65ded8339a0f3edb2ff57330efae4bef27de9f4e347d8219295ad5a747b0a | R | 14,597 | 302 | # Load required libraries
library(ggplot2)
library(ggrepel)
library(dplyr)
library(tibble) # To use rownames_to_column
########## Human gene count ##########
# Define Target Comparison
Target_comparison <- 'FILTERED_1_MTLEALL_NL'
Target_group_name <- '1'
# File path for the DESeq results
file_path <- paste0("/home... |
f148897d2eaff5f2fc1dda34c1d2fbf7488aff69ed91b95ec22ef06eff276c28 | R | 14,631 | 397 | # R 4.2.2
# XH Sep 9 2025
library(ComplexHeatmap)
library(dplyr)
library(ggplot2)
library(data.table)
library(enrichR)
library(memes)
library(colorRamp2)
outp <- "/geschwindlabshares/RexachGroup/Xia_Data/atac_cellrangerOut/project0318/cisTopic"
peakmat <- readRDS(file.path(outp, "Microglia_peaks_by_cells.rds"))
meta <... |
540a2fc0cba85dcd5ea22a6326c8ebea8f00d9a35352268d12f8798efb3c6686 | R | 14,632 | 574 | ---
title: "Run coMethDMR of Rosmap and BDR DNA methylation data"
author:
- David Lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
rmarkdown::html_document:
highlight: breezedark
theme: lumen
toc: true
number_sections: true
... |
32d5804c4f224b127ffd9e504820437eb5f43f055aeeb5601d696f3b49a69166 | R | 14,636 | 358 | # Support for queries from canned flywire connectivity datasets
flywire_connectome_basedir <- function(d=getOption('fafbseg.flywire_connectome_dir', NULL),
create=NA, check_contents=!create) {
if(is.null(d)) {
if(is.na(create)) create=TRUE
d=fafbseg_userdir('flywire_co... |
5dd2af0c500a88961c38c232c245d0c6a8c4bfee38fc460978daa200aa9b8eca | R | 14,642 | 453 | # ==============================================================================
# Script 05: Figure 1F - Alteration Frequency Barplots
# ==============================================================================
# Description: Creates barplots comparing alteration frequencies of key genes
# across ICL... |
c0eeed4848c3ac636fffcf04a6763c59a12e6a8a42f18b58f907752bbed9863e | R | 14,685 | 424 | ###############################################################################
## Temporal Trajectory: MitoCarta Pathway Recovery (D35→D65) ##
## Separation of Concerns: Focused on mitochondrial pathway dynamics ##
###############################################################################
##... |
812c63693ec11fa11217be5ac91f61b1e79352f2f0a24c683f873fe410d0aa0e | R | 14,694 | 291 | library(MOFA2)
library(reshape2)
library(stringr)
library(circlize)
library(RColorBrewer)
library(ComplexHeatmap)
library(tidyverse)
# Function to add asterisks to significant terms
my_cell_fun = function(j, i, x, y, width, height, fill, table_1) {
if(!is.na(table_1[i, j]) & abs(table_1[i, j]) > -log10(0.... |
9b9117d01b75fcf718b054d5c016484fb250b275aec609412ab2637f0a3dfebe | R | 14,694 | 373 | #' Spatial registration: pseudobulk
#'
#' Pseudo-bulk the gene expression, filter lowly-expressed genes, and normalize.
#' This is the first step for spatial registration and for statistical modeling.
#'
#' @param sce A
#' [SingleCellExperiment-class][SingleCellExperiment::SingleCellExperiment-class]
#' object or one t... |
373d84b07bbf9767fab15eb9b22518ed9f651514557870d6b5060aa96d7e325a | R | 14,706 | 305 | # ===============================================================
# Gene-panel across datasets -> tables + heatmaps (padj-based)
# Numbers shown in every cell; stars only if padj < 0.05
# Text: black & larger (significant) / gray & smaller (non-significant)
# Dedup for bulk genes + final (cell_type, gene) uniquenes... |
f8ee96c5ab4e4158fe378642e01d0bc5af60f93f86a23ff9ed9cdc5b4dab8a71 | R | 14,712 | 445 | library(Seurat)
library(ggplot2)
library(scCustomize)
library(patchwork)
library(RColorBrewer)
library(viridis)
# load data ---------------------------------------------------------------
data <- readRDS("diet_nodomap_cleaned.RDS")
DefaultAssay(data) = "originalexp"
View(data@meta.data)
data <- SetIdent(data, value ... |
7aec288d8805b535f4efd3e70596e31ba2062f7aff569673f0c0107dae2837d7 | R | 14,735 | 385 | ##########################################################################################
# Imputation Methods
##########################################################################################
#' Add Imputation Weights to an ArchRProject
#'
#' This function computes imputations weights that describe each ce... |
368e99c6798600b346b3454db01cec409ad0dc1ddb15b9d79582bc930d926f2b | R | 14,738 | 403 | #' Sample spatial gene visualization
#'
#' This function visualizes the gene expression stored in `assays(spe)` or any
#' continuous variable stored in `colData(spe)` for one given sample at the
#' spot-level using (by default) the histology information on the background.
#' To visualize clusters (or any discrete varia... |
c7d76b6fb5465bb192c7e986f7844d3d7d2ce569b389f1823be7261103d755c5 | R | 14,739 | 234 | ---
title: "An Introduction to the MONSTER Package"
author: "Daniel Schlauch"
date: "2017-09-22"
output: rmarkdown::html_document
vignette: >
%\VignetteIndexEntry{MONSTER}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
com... |
ffee361ec59f82e8bb898500537b316a6bdce912dbbb9e4d23becdeab8870b43 | R | 14,758 | 454 | ---
title: "MRS × Time Mixed Models, Spaghetti Plots, Comparison of baseline vars by MRS tertiles"
author: "LW"
date: "`r format(Sys.Date(), '%Y-%m-%d')`"
output:
html_document:
toc: true
toc_float: true
df_print: paged
code_folding: show
encoding: UTF-8
---
```{r setup, include=FALS... |
232045431a9bd879d1e546d2cda5efaeba4bcdd9bf21bde5ccfbe59fe1c17a7b | R | 14,762 | 380 | checkGenoInput_LDmat = function(bgenFile = "",
bgenFileIndex = "",
vcfFile = "",
vcfFileIndex = "",
vcfField = "DS",
vcfFilters = "",
savFile = "",
savFileIndex = "",
sampleFile = "",
... |
c3fe7f6d497eede442b2142e7dd5e108cf86361662a7e388b50b8e608f1ed228 | R | 14,813 | 276 | ---
title: "neural_figures"
output: html_document
date: "2025-07-24"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load
```{r}
library(ggplot2)
library(dplyr)
library(Seurat)
library(scCustomize)
library(xSelectCells)
```
#devtools::install_github('vikkki/xSelectCells@main', force = TRUE)
Lo... |
342165b2ba5a641a93863bc0494ebebb0262c377700e258c28f1250e9fd90d94 | R | 14,814 | 313 | ---
title: "N-back Preprocessing"
date:"24/02/2022"
output: html_document
---
## N-back RMarkdown (February 24, 2022; Version 0.1)
This is an RMarkdown to allow for reproducible and accurate preprocessing of the N-back Psychopy
task data created by Michael Colwell (michael.colwell@psych.ox.ac.uk / ORCID 0000-... |
5b9e9a1b6a21677f4d3775dc51c12ed1edad5a9a617e33147902062364466b99 | R | 14,818 | 380 | Libraries
```{r}
library(data.table)
library(psych)
library(GPArotation)
library(corpcor)
library(EFAtools)
library(lavaan)
library(lavaanPlot)
library(esemComp)
library(ggplot2)
```
Set working directory
```{r}
setwd('/UK_BB/brainbody/cognition/folds/')
```
Upload train and test sets generated and s... |
88f8a129ad204c0ccc5024e2f2a9ee15413a60918923265b355b715cc69352e7 | R | 14,818 | 418 | # Global RNA-seq differential expression analysis
# Requirements:
# - Working directory contains:
# quants/ # per-sample Salmon quant directories
# tx_id.txt # transcript IDs from reference
# gene_id.txt # corresponding gene IDs
# sampleInfo.csv # or sampleInfo_2.csv, with... |
befa851a98450a162746ba5cf7b6d6e10cb978690c9c3d99b10c05614a2083c4 | R | 14,828 | 329 | # ==============================================================================
# Script 01: Master Data Loading Orchestrator
# ==============================================================================
# This script orchestrates all data loading in the correct order
# as defined in Main_Data_Analysis.Rmd
#
# Exec... |
0481379fd0ff2650665e74b7952406dd5c869396341d9c99f558593dd6692143 | R | 14,833 | 442 | #### Section: Lineplot Construction ####
plot_anova_lineplot_meanse <- function(data,
info,
layout_values,
line_colors = NULL,
base_size = 14,
... |
067fc421dda2a9ea458c382a0adab9de93fc5ca2e4d82ea01132bbb8d45c30b2 | R | 14,841 | 313 | #' banc-tracing-status — Status updates + roughly-proofread sensory marking (curation).
#'
#' Per-task curation script split out from the old
#' `banc/utilities/banc-tracing.R`. Updates status from external tracing
#' sheets, applies super_class corrections, cleans up orphans, marks
#' sensory neurons roughly-proofread... |
44e373714a07ad09e3ba7d0c46b0978b711a4d96fd5c07165a323875eb556da2 | R | 14,842 | 246 | here::i_am("scripts/1.neural_differentiation_dataset/1.3.CroCoNet_analysis/1.3.5.cor_kIM_VS_cor_adj.R")
library(tidyverse)
library(CroCoNet)
library(ggsignif)
library(patchwork)
library(cowplot)
library(here)
dir_cor.kIM <- here("data/neural_differentiation_dataset/CroCoNet_analysis/")
dir_cor.adj <- here("data/neura... |
34396928f2fa6d229f79522f8080a5199bdf9b93feb2a58b8f3f059c15e910ca | R | 14,873 | 391 | #############################################################
###### File Name: ROSMAPMARS_Freeze2_Harmonization_Script_20231020.R
###### DATE: 2023-10-20
###### Authors: Jeremy Pasteris (Gary Beecham, PI)
###### Purpose: This script is intended to transform data received from ROSMAPMARS and delineate the exact way... |
ea34f8bd72a0d4b6d51e8cf3309551f86c030e9f86668d5444a6e985e57c59d0 | R | 14,909 | 412 | # =============================================================================
# CHCHD10 Interactome – Step 1 (v5)
#
# PURPOSE
# Read Table 3 MaxLFQ, clean column headers, apply CHCHD10 IP presence rules,
# and generate candidate interactor tables.
#
# INPUT
# D:/Case_project/CHCHD10/CHCHD10_interactome/... |
74c99e49c4ea2acdd00beba91c3f495e55bacf3658f5a3484f60f54026e3cce4 | R | 14,913 | 441 | ---
title: "Fig5"
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE)
```
## Fig. 5: statistics and panel figures
Panel A) the variance explained by the first principal component (PC1) for neurodevelopmental and psychiatric disorders (NPDs), their associated c... |
cf1c5eff285352a8579a860c030d98c4d94bbbd40b80d5609ac231244f8dd887 | R | 14,926 | 371 |
# R4.4
# Load required libraries
pacman::p_load(data.table, dplyr, biomaRt, GenomicRanges, Biostrings, BSgenome, stringr)
# BiocManager::install(c(
# "BSgenome.Hsapiens.UCSC.hg38",
# "BSgenome.Mmusculus.UCSC.mm39"
# ))
# pacman::p_load(BSgenome.Hsapiens.UCSC.hg38... |
675282c44308afe8b079b944a7b92b06fa0f439d275bc6cd98be8a8625c7cc0b | R | 14,935 | 352 | # ========================= Excitatory sets 1/2/3: normalize-if-needed + DEG (step-logged) =========================
# MODE: FILTERED WILCOXON DEG
# This script runs FindMarkers with Seurat-style thresholds to focus on robust DEGs:
# • logfc.threshold = 0.25 → keep only genes with ≥0.25 log2 fold-change
# • min.... |
07ba91d3ca5a417eecae2789114cd29e9d84d1d15b35fd523e974d9216bd6781 | R | 14,940 | 299 | library(dplyr)
library(ggplot2)
human_l2fc_df <- read.table("/home/joonho345/1_Epilepsy_RNA/RNA_Human/11.Sample_variables/02.deseq/DESeq_ALL.txt",
sep = "\t", header = TRUE, row.names = 1, fill = TRUE)
#Human_groups <- c('HS', 'withoutHS', 'childhood', 'adolescence', 'adult', 'X20', 'X40',... |
fc1e079803bd99abad80990150a6bb54b84fcef86dc138b98ecc8dd269134b5c | R | 14,957 | 401 | root="C:/Users/Jun/Documents/R/AA_wSignal/"
pre=2 #for peri-event histogram in seconds
post=2 #for peri-event histogram in seconds
preheat=10 #for heatmap in seconds
postheat=30 #for heatmap in seconds
sps=0.1258291 #signals/second
tdir=paste(root,"rtrack",sep="")
tfiles=list.files(tdir)
outpath=paste... |
9e5b4dc99f6d55066adbd9fb48c776dda622c121c3b78105834d1cb51c77868d | R | 14,973 | 309 | setOldClass("igraph")
#' @include class-SOSet.R
#' @import igraph
#' @rdname SOTK
#' @export
#'
setClass(
"SOTK",
slots = c(
SOSet = "SOSet", # NMF objects/results and correlation matrix in SOSet class
sample2metagene = "list", # assign samples to metagene based on thei... |
d7c270dba9557b0211e8629e2d1a57aa02de10cfa687b900a995efa0b84b9180 | R | 14,978 | 336 | #' Plots symmetric AUROC heatmap, clustering cell types by similarity.
#'
#' @param aurocs A square AUROC matrix as returned by MetaNeighborUS.
#' @param cex Size factor for row and column labels.
#' @param margins Size of margins (for row and column labels).
#' @param ... Additional graphical parameters that are passe... |
c4291dd2f0b40f4c09dec77966c3811c41af5a3a8d99306d80f0fb0213f9aea7 | R | 14,982 | 479 | library(dplyr)
library(tibble)
library(readr)
library(lme4)
library(rstatix)
library(ggplot2)
library(ggpubr)
library(tidyverse)
library(patchwork)
df <- read_csv("data/TREC_KREC_combine_Baby_4.csv")
df$SNP <- factor(df$SNP)
df$SNP_merge <- factor(df$SNP_merge)
df$group <- factor(df$group)
df$sex <- factor(df$sex)
df... |
e9449c15e33a1dfadf5cb21f05baa24611aeb2c87890f5381bec02835dc10b9d | R | 14,987 | 290 | ##############################################
# SCRIPT TO DISPLAY THE TOP FEATURE WEIGHTS #
##############################################
library(MOFA2)
library(readxl)
library(dplyr)
library(patchwork)
library(ggplot2)
library(stringr)
# Retrieving MOFA
load("C:/Users/User/Documents/Drive_PC/Lucas/GitHub... |
1068bf90e027b157be11abcfb505bf15cc9af0d3f7cbfd694d10c36e60b765c6 | R | 14,991 | 334 | here::i_am("scripts/2.validations/2.7.POU5F1_LTR7_enrichment/2.7.8.plot_regions_near_SPP1_and_SCGB3A2.R")
library(tidyverse)
library(plyranges)
library(data.table)
library(Gviz)
library(cowplot)
library(patchwork)
library(rtracklayer)
library(scales)
wd <- here("data/validations/POU5F1_LTR7_enrichment/")
fig_dir <- h... |
5709457098a3a435cb1ac1dabf7a0b6c8819575ba7b5ed15c3016f23666d4b2f | R | 14,996 | 390 | #' Create a genome annotation object for ArchR
#'
#' This function will create a genome annotation object that can be used for creating ArrowFiles or an ArchRProject, etc.
#'
#' @param genome Either (i) a string that is a valid `BSgenome` or (ii) a `BSgenome` object (ie "hg38" or "BSgenome.Hsapiens.UCSC.hg38").
#' @p... |
d0963611ca7a317a4c1c0e7cba9fcf33304366be13765a406176984ab7d5c122 | R | 14,997 | 161 | #' Choose or (temporarily) use a FAFB autosegmentation
#'
#' @details Each released segmentation implies a number of global options. This
#' package comes with multiple different default scene urls specified via
#' \code{\link{choose_segmentation}} or \code{\link{with_segmentation}}. This
#' is the easiest way to... |
838f14a84586cb1309c0c4797b0b17183715d83b48d72d6586aaf4e537ef35f1 | R | 15,008 | 383 | # This script performs WGCNA on vcar
# Get a vector of genes recognised previously as expressed in species
vcar_dds <- read_rds("~/2023-species_comparisons/DGE_pipelines/vcar_dds_filtered_052024.rds")
vcar_anno <- read.csv("~/2023-species_comparisons/tables/annotation/vcar_anno.csv")
vcar_all_genes <- rownames(vca... |
b99379b312b9612c72bd79d8f25c09a5a73fa31da38565c7c67a418d61e28203 | R | 15,009 | 305 | # Load required libraries
library(ggplot2)
library(ggrepel)
library(dplyr)
library(tibble) # To use rownames_to_column
########## Human gene count ##########
# Define Target Comparison
Target_comparison <- 'FILTERED_1_MTLEALL_NL'
Target_group_name <- '1'
# File path for the DESeq results
file_path <- paste0("/home/... |
1570a97fc9d9d74c9b35b19d567ac48513cae12d2979e472e8e95be7265d9ac1 | R | 15,019 | 337 | #' banc-v3-synapse-sample — Regenerate the v3 synapse spatial sample CSV.
#'
#' Standalone replacement for the v3 sample block in
#' `banc-calculate-completion.R`, which hit the SLURM-only arrow C++ URI bug
#' ("Invalid: Unrecognized filesystem type in URI: file:///_") on the 15 GB
#' spatial parquet. Uses narrow parqu... |
34a54b15eec806a87ac651d2d13f9b5f9dd6b2572e6ee586fec9a36f09dcf13d | R | 15,068 | 363 | #' banc-calculate-connectivity — Build the versioned BANC synapse parquet + simple edgelist.
#'
#' v3 pulls CAVE-ingested synapses from GCS (not the local v3 parquet — see
#' Notes); v2 pulls the human-readable CSV.gz. Both branches dedupe autapses,
#' filter by size and SeaTable inclusion set.
#'
#' @section Reads:
#'... |
59f316995fa1ad15143ef1d0bd63b8274ea1fd156d071c4b1bd27b0b06cca92a | R | 15,068 | 375 | # ===============================================================
# 🧪 Visualization Module — LM / LMM
# ===============================================================
visualize_lm_ui <- function(id) {
ns <- NS(id)
sidebarLayout(
sidebarPanel(
class = "ta-sidebar",
width = 4,
h4("Step 4 — Vi... |
4414663a1e155b10c1d54453446c9830883247cb5fff72ab638ba43c23190ec4 | R | 15,080 | 352 | library(Matrix)
library(data.table)
library(Rfast)
# pyconf = reticulate::py_discover_config()
# if(pyconf$python != "C:/Users/tysheff/.conda/envs/R_keras/python.exe") reticulate::use_condaenv("R_keras")
library(keras)
source("R/combined_features.R")
source("R/ml_tuner.R")
source("R/combined_preprocess.R")
source("R/co... |
980685786f481b8f2fb8e0c46e56c8aac5c11e5f18c48f6ff231c3c33dc96151 | R | 15,088 | 397 |
# n-choice uniformly varying start point (0-A) Wald
# race, with t0, v, A, b (boundary) parameterixaiton
# pigt, digt, rwaldt Copyright (C) 2013 Trisha Van Zandt distributed with:
# Logan, Van Zandt, Verbruggen, and Wagenmakers (2014). On the ability to
# inhibit thought and action: General and special theories... |
d592e8af4b9425db7837bbb1ad2b4e9735ff4929ddb145d40370bd6eb8544978 | R | 15,093 | 390 | library(ggplot2)
library(dplyr)
library(svglite)
#### Read outlier files ####
outlier_dir <- "/home/joonho345/1_Epilepsy_RNA/RNA_Human/06.Selection/01.TPM_Outliers"
# Try to read the combined all outliers file first (most efficient)
all_outliers_file <- paste0(outlier_dir, "/01.ALL_Outliers_All_Genes.txt")
if (file.e... |
b380bb7e31841300cc58a98a0ddb8ea37d010bd0fd1e28be38c7493143c8be3a | R | 15,109 | 445 | ---
title: "PILT_Comp_Analysis_FENCOG"
author: "Michael Colwell"
date: '2023-04-09'
output: html_document
---
##PILT pre-processing script
This is the computational modelling analysis for the Probabilistic Instrumental Learning Task (PILT), modified for the PEACE study by Michael Colwell (michael.colwell@psych.ox.ac.... |
80bcdbdbcdcae3e28dec508cc1ea9c2a939d088e946cd4514290c929266f7c78 | R | 15,158 | 362 | #####################################################################
# Helper Functions for cis-eQTL Analysis
#####################################################################
#' Generate stratified fold IDs based on diagnosis
#' @description Creates cross-validation fold assignments while maintaining balanc... |
e94943739b63ab53acbc9b3e11e27da6f55004608180e457be3ee04acdd6aa0d | R | 15,196 | 388 | here::i_am("scripts/2.validations/2.8.POU5F1_CRISPRi/2.8.9.stemness_scores_and_knockdown_efficiencies.R")
library(tidyverse)
library(foreach)
library(doParallel)
library(here)
wd <- here("data/validations/POU5F1_CRISPRi_processed_data/")
## Subsetting POU5F1-perturbed and control cells -----------------------------... |
49b7174c8ae7d8787de3419d04ee19c07b6c38fdc5303983d5e16c797f4c3274 | R | 15,199 | 344 | library(CroCoNet)
library(foreach)
library(tidyverse)
library(scran)
library(transformGamPoi)
library(ape)
library(SingleCellExperiment)
library(foreach)
## Genes ----------------------------------------------------------
# list of regulators
regulators <- sort(c("SOX2", "POU5F1", "ONECUT2", "TCF4", "BNC2", "TEAD1"... |
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