sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
55fa5195e5a7488c0fc5fcf9ff9363cbe97ae2e08d798a6130f4826491ac27c6 | R | 10,251 | 283 | ############# Baseline by non-parametric bootstrap #########
# Import portrait divergence functions from github: https://github.com/bagrow/network-portrait-divergence
# Packages
library(abind)
library(tidyverse)
library(foreach)
library(doParallel)
library(reticulate)
# Working directory
#setwd("~/FC-Network-Replicab... |
525014c8d703b9abcdac7c5cc5eb920ffd43d247767d3b8b2149477d946fec10 | R | 10,266 | 238 | library(ggsegGlasser)
library(ggsegSchaefer)
library(ggseg)
library(dplyr)
library(ggseg3d)
library(R.matlab)
library(ggplot2)
library(cifti)
library(stringr)
library(factoextra)
library(matrixStats)
library(scales)
library(Hmisc)
library(tidyr)
library(cocor)
# atlas data.
schaefer200x17_label <- read.csv("G:/datase... |
a55a990d4ab1d3e871a541254541348ea6c9615655accd03f9d34152fd34558e | R | 10,268 | 261 | library(SCENIC)
packageVersion("SCENIC")
library(AUCell)
library(RcisTarget)
library(GENIE3)
library(zoo)
library(mixtools)
library(rbokeh)
library(DT)
library(NMF)
library(pheatmap)
library(R2HTML)
library(Rtsne)
library(doMC)
library(doRNG)
library(scRNAseq)
library(SCopeLoomR)
library(SCENIC)
library(Seurat)
###数据准备... |
b74aa7e4d7c8c3455ff6ffccb830d426611e84f6b4f7f8a76de69dd4d5d2b98e | R | 10,269 | 249 | library(readxl)
library(dplyr)
library(ComplexHeatmap)
library(circlize)
library(tidyr)
library(ggplot2)
library(outliers)
aMgNp7_raw <- read_excel("AtlasV7/Axons/MgNp7_AtlasV7Axons.xls",
sheet = "Sheet1")
aMgNp8_raw <- read_excel("AtlasV7/Axons/MgNp8_AtlasV7Axons.xls",
... |
a2294bc182f40591dd80698a0ccae7800c71749615e40cdc73c99b8983c30791 | R | 10,283 | 273 | #####################################################################
# MODULE testing data Prediction Pipeline - Network Averaging
#####################################################################
# This script implements the final step of the MODULE pipeline where
# predictions from multiple networks are comb... |
c2346576ff42ab1bffc1f48f2599775a33944a9128d0a8427d25e2e599e5f661 | R | 10,289 | 288 | ################################################################################
# Figure 3C with Individual Points
# Creates multi-panel grid of all tissue scatter plots (AGE vs Factor1)
# with individual sample points colored by DTHPLCE and copies individual
# tissue plots to the journal guidelines folder
###########... |
0637a4fe9ea67b2e87591c7027a9f16ca33de96c3a00c7f0203b8fefb1d8ff6c | R | 10,300 | 307 | #!/usr/bin/env Rscript
# Independently decode VCF GT calls and calculate signed reference-dosage
# correlations using base R only.
decode_reference_dosage <- function(sample_field) {
genotype <- sub(":.*$", "", sample_field)
alleles <- strsplit(genotype, "[/|]")[[1]]
if (length(alleles) != 2L || any(allel... |
1ccd0502128c8c50d95af7ebc4e286eed2bbabf203d87d6079c9d8de56674cf6 | R | 10,310 | 251 | # Phase 6: GEO Dual-Disease Comparison
# UC (GI bleeding proxy) vs Ischemic Stroke — target expression direction comparison
#
# GI: GSE66407 (gut biopsies, UC/CD/controls, 368 samples, Ensembl IDs)
# Stroke: GSE16561 (whole blood, ischemic stroke vs controls, 63 samples, Illumina)
library(GEOquery)
library(limma)
libr... |
9be67bc52aae5225a9213abb6c735378af18d8098044f81f81a92230d11f9bcb | R | 10,319 | 292 | rm(list = ls())
library(Seurat)
library(Matrix)
library(ggplot2)
library(plyr)
library(tidyverse)
library(tidyr)
library(dplyr)
library(ggpubr)
library(reshape2)
library(rio)
library(data.table)
library(harmony)
library(GeneOverlap)
library(readr)
source('~/SCRIPTS/utility_functions.R')
set.seed(1234)
# Subset non-spn... |
e6cdd5c26b32535b0d8389ea8a31e44d048492089798fe4e066c45e2de6d7846 | R | 10,321 | 287 | #' Find the root identifier of a FANC neuron
#'
#' @inheritParams fafbseg::flywire_rootid
#' @param ... Additional arguments passed to \code{pbapply::pbsapply} and
#' eventually to Python \code{cv$CloudVolume} object.
#' @return A vector of root ids (by default character)
#' @export
#' @family fanc-ids
#' @seealso \c... |
1f6cd72e892a3e524db458eb420a49c539775c033a1587bb496940a92c97f4f9 | R | 10,322 | 401 | l0norm <- function(x) {
return(sum(x > 1e-10))
}
l1norm <- function(x) {
return(sum(abs(x)))
}
l2norm <- function(x) {
return(sqrt(sum(x*x)))
}
l2norm_squared <- function(x) {
return(sum(x*x))
}
# this function accepts missing values
#
# in daisy from cluster:
# If the metric is "euclidean",
# and n_g is t... |
e1c093ad44ddbfcb67c34708e1c70f0f09083b2e3fa3afb65a50f65e636f39a5 | R | 10,327 | 283 | #####################################################################
# Genotype Data Processing Pipeline - Optimized for cis-eQTL analysis
# Brain Region Specific Version
#####################################################################
# This script loads and processes genotype data from PLINK format,
# conv... |
aa1505b1ed73e5b67d8c494ecfa09fc404d55658a6690d501601decfe68d085d | R | 10,328 | 255 | #!/usr/bin/env Rscript
# Read in quietly to avoid outputting "Loading required package: Rcpp" to stderr.
library(castor, quietly = TRUE)
Args <- commandArgs(TRUE)
# Read in command-line arguments.
full_tree <- read_tree(file=Args[1], check_label_uniqueness = TRUE)
full_tree$edge.length[which(full_tree$edge.length ==... |
f218cc828b89b343d8ade011a16171b3f7eb8f6102d3e5e294c7b62b5fc85b55 | R | 10,329 | 242 | #' banc-nblast-native — BANC self-NBLAST in native space (no mirroring).
#'
#' Surfaces ipsilateral sister cells, partial duplicates, and within-side
#' morphology clusters that `banc-nblast-lr.R` (mirror) cannot find.
#' Resumable.
#'
#' @section Reads:
#' - SeaTable `banc_meta`, BANC L2 skeletons
#'
#' @section Wri... |
8dacb5a651734e906ae2554cedcc7652743a264aacfa1294736da728299ac8b2 | R | 10,340 | 213 | #########################################################################################################################
### This function is used for predicting DNA methylation age in cortical samples ###
### It was built using cortical DNA methylation array data (Illumina 450K)... |
f4712a6f72e4f5859338af058101b6062cf98455ff3e5efcaff9f74e0c2169a9 | R | 10,342 | 291 | ---
title: "GAM Model partial effect graphs + heatmaps"
author: "Neslihan Yildiz Ozhan"
date: "2024-01-16"
output: html_document
---
```{r}
#NIH Toolbox visualization
library(ggplot2)
library(mgcv)
library(dplyr)
library(gratia)
data <- read.csv("~/Desktop/ABCD/abcd_coi.csv")
# z-score scale
data <- data %>%
mutat... |
ee53e6ad81a2f211a4173a923d767ef976926292d382c6d2ea2831f3eaf26090 | R | 10,360 | 218 | setwd(choose.dir()) #set the environment
library(tidyverse)
library(dplyr)
library(pracma)
library(ggforce)
library(purrr)
#### Load file (Spike List) - only set to do one file at a time ####
MEAdata <- read.csv(choose.files(default = "", caption = "Select files",
multi ... |
630c2506f0de4c07555b59e66cda58ec6a11702f6288b0a641aaf9607f9ef50d | R | 10,382 | 280 | #!/usr/bin/env Rscript
# ==============================================================================
# Benchmark Script for tsImpute on Simulated Data
# ==============================================================================
# Runs tsImpute + KMeans clustering on simulated scRNA-seq data.
# Calculates all 5 m... |
ebe3d5cf8e778833bc0afeae0b72fbacc4cf040110e9a56cbd0cdeffda022b3e | R | 10,390 | 196 | ##################################
# Analyze malignant cell abundance across tumor types and grade
# Author: Kevin Johnson
##################################
library(tidyverse)
library(RColorBrewer)
library(viridis)
library(ggpubr)
library(openxlsx)
library(EnvStats)
library(ggrepel)
library(cowplot)
proj_dir <- "... |
9b6aa5a6957949fc9d98bb6f0f0963634ada88be548d93a7b74b0c0095f481b8 | R | 10,393 | 263 | ---
title: "Effect Estimation Statistical Analyses"
output: html_document
---
```{r, echo=TRUE}
knitr::opts_chunk$set(echo = TRUE)
library(lmerTest)
library(dplyr)
library(readr)
library(emmeans)
library(multcomp)
library(tidyr)
```
```{r}
extract_stats <- function(m, coef_names) {
model.df = data.frame(coef_names,... |
ba238d22a5581bfc74a5dd0903e33f4f3e8e7b34fe65e28707e964d40133c46f | R | 10,398 | 287 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
######################
### load libraries ###
###... |
f7b614ee1bd5f6050825c0387e93c44a7b6b325c1af32dc109f9e046f9951d49 | R | 10,409 | 275 | # Summarized topics with SNPs info (to one table or visualization)
library(ComplexHeatmap)
library(dplyr)
library(ggplot2)
library(data.table)
library(colorRamp2)
library(ggplotify)
library(patchwork)
library(gridExtra)
outp <- "/geschwindlabshares/RexachGroup/Xia_Data/atac_cellrangerOut/project0318/cisTopic"
add <- ... |
ac738f1ad343ec9632ca5508eb9f5aba48ca7f2fd1136ffbdb9d5ea46eb42077 | R | 10,417 | 346 | # Environment ------------------------------------------------------------------
library(tidyverse)
library(lme4)
library(ggseg)
library(patchwork)
library(here)
library(glue)
library(ggraph)
library(igraph)
source(here("src", "R", "plot_ggseg_brain.R"))
source(here("src", "R", "run_lda_workflow.R"))
# Data I/O ----... |
3db44c69afb9189640470bb2740915775422a9ef3c57117c410a2eff2323e4e0 | R | 10,419 | 128 | #!/usr/bin/env Rscript
library(Matrix)
library(Seurat)
library(tidyverse)
library(readxl)
library(Augur)
source("scRNAseq_funs.R")
RowSD <- function(x, rmNAs = TRUE) {
if(isTRUE(rmNAs)) {
sqrt(rowSums((x - rowMeans(x, na.rm = rmNAs))^2, na.rm = rmNAs)/(sum(!is.na(x)) - 1))
} else (
sqrt(rowS... |
cbdb02f0aec8c296e1eaa4895f1c93761acd02c7637453d1c89b82882061bc13 | R | 10,433 | 287 | #' banc-cx-review-images — Generate comparison PNGs for CX connectivity mismatches.
#'
#' For each row in `cx_connectivity_mismatches.csv`, render a three-mesh
#' comparison PNG: BANC neuron (blue), curated SeaTable-type FAFB neurons
#' (red), connectivity-matched FAFB neuron (green). Uses `fafb_match` where
#' availab... |
1574fad671b880ec130a781116ca0b05f2e633a2f2a4b903b2412883ef4ac57c | R | 10,435 | 223 | #' @import ggplot2
plotSimilarityStat <- function(plot.df, y.lims, fill.color, text.angle=60, size=0.3, width=0.25) {
ggplot(dplyr::filter(plot.df, !SameCondition), aes(x=Type, y=DiffStat)) +
geom_boxplot(fill=fill.color, outlier.alpha=0, show.legend=F) +
geom_hline(aes(yintercept=split(DiffStat, Type) %>% s... |
21aff9a467f3af981ef204f4a2778622f5bfefc28ef9dc39dcfb584c3bc43ae5 | R | 10,435 | 205 | library(tidyr)
library(mgcv)
library(gratia)
library(tidyverse)
library(dplyr)
library(numDeriv)
# colnames(gam.data)[202] <- "sex"
# colnames(gam.data)[203] <- "subj_id"
# colnames(gam.data)[204] <- "mean_fd"
# colnames(gam.data)[205] <- "mean_gs"
# colnames(gam.data)[206] <- "mean_br"
# colnames(gam.data)[207] <- "m... |
52169462c88f65b52e048c0002a909ff3e11e132cad526f7205d5c3bafd29279 | R | 10,452 | 325 | library(dplyr)
library(plyr)
library(Seurat)
library(ggpubr)
library(reshape2)
library(tidyr)
library(tidyverse)
library(DropletQC)
source('~/SCRIPTS/utility_functions.R')
set.seed(1234)
#####
## LOAD COUNT MATRIX
#####
# Set the samples and read them with QC
samplePath = '/home2/gkonop/project/03_MATRIX_FROM_CELLBE... |
d6bfe112832a020fda336babcea4d09f120ab7010b63755b9c75132049a6d989 | R | 10,459 | 342 | # ===============================================================
# 🧪 Visualization Module — One-way ANOVA (Apply-button version)
# ===============================================================
visualize_oneway_ui <- function(id) {
ns <- NS(id)
sidebarLayout(
sidebarPanel(
class = "ta-sidebar",
... |
209a071024ad7206be6dd666d39a99bf8feca4572aae284c3010ddf23004c485 | R | 10,469 | 203 | #' Run Python implementation PANDA in R
#'
#' \strong{PANDA}(Passing Attributes between Networks for Data Assimilation) is a message-passing model
#' to reconstruct gene regulatory network, which integrates multiple sources of biological data-including protein-protein interaction data,
#' gene expression data, and tr... |
fff3cc17460b277e65429d229093fe5c6dbd852e529ae9a695683109492a4495 | R | 10,477 | 231 | #' @import dplyr
NULL
fTest <- function(de.genes, n.all.genes, gene.set, ...) { # Where k is randomly drawn sample
# See https://www.pathwaycommons.org/guide/primers/statistics/fishers_exact_test/
n <- length(intersect(de.genes, gene.set))
n.de.in.gs <- length(intersect(de.genes, gene.set))
c.table <- matrix(... |
0a546183a3a6e6f6c2ecec7dc76f49883ce2f83a87b05ef337243129433d0f28 | R | 10,483 | 243 |
require(igraph)
require(tidygenomics)
require(fuzzyjoin)
require(data.table)
make_read_plot_data <- function(DT,
prime3_cluster_window = 25, cluster.TR.overlaps=T, cluster_neighbours = T, optimal_overlap = T,
is.transcript.pos=T,
add.intr... |
2ec9ff8e547bb2d813dcb50a8bd30947c9a1fb90029685f044b74045308857ad | R | 10,484 | 275 | #####################################################################
# INGENE Training Pipeline
#####################################################################
# This script implements the INGENE (Inter-Network Gene Expression) approach
# for training gene expression prediction models.
#
# Key Features:
#... |
3d87421e26d814c29b5ceedf30ac7cf752d55e1a25542133438a07ea2e2df4b5 | R | 10,506 | 413 | #!/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("--pat... |
7325960c4d8f4195240be84da86bc2af4e3237444b85231644e3d8cf20e39afc | R | 10,511 | 301 | # # Step.00 ----------------------------------------------------------------------
rm(list = ls())
gc(reset = T)
source('script/Functions/Functions.R')
# unload packages
pkgs[which(load.packages == F)]
rpid = Sys.getpid()
options(future.globals.maxSize = 1000 * 1024^3)
##################################################... |
d09a9932e8b51b5be288db510d9a51cfff5584d0cc03bdd369a73b9927fe4545 | R | 10,520 | 222 | ---
title: "Code of runSCENIC_3_scoreCells()"
subtitle: Workflow run by runSCENIC_3_scoreCells()
output:
BiocStyle::html_document:
number_sections: no
toc: yes
toc_float: false
css: corrected.css
html_notebook:
toc: yes
pdf_document:
toc: yes
package: '`r pkg_ver("SCENIC")`'
vignette: >
... |
0f2a536ca66b0016ce7b11a58e643770bdb6663064bc32008b44c56a4dffd35b | R | 10,590 | 33 | cran_mirror <- Sys.getenv("R_CRAN_MIRROR", "http://cran.rstudio.com/")
options(repos = cran_mirror)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("methylKit")
library(BiocManager)
library(stringi)
library(pacman)
library(methylKit)
library(ade4)
library(FactoMi... |
c2712dd0d3138583f96cca3cfef9a7f6edcb41c2cecd54f653a2c6188772f611 | R | 10,655 | 296 | # ==============================================================================
# Script 08: Figure 2C - CDH1 Exonic Deletions Analysis
# ==============================================================================
# Description: Analyzes CDH1 exonic deletions from WES data and generates
# a heatmap vis... |
be1a20e9abf7eb48011507380ad7a2e961e18c4fa231edcd9c839ef6e1f86465 | R | 10,660 | 250 | #Data preparation
library(haven)
library(readxl)
library(writexl)
library(stringr)
library(dplyr)
#load spss file with the actigraphy inclusion criteria
inclusion <- read_sav('~/Life-ADULT/pv801/actigraphy data/PV0801_T01408_MRT_15days_before.sav')
colnames(inclusion)[1] <- c("SIC")
#load full file with LIFE data
li... |
0022cc1d9404b49d13b619d06061de78c455d0894b637df88cccae0aef7979d3 | R | 10,673 | 216 | #' Return neuron metadata from Cambridge seatables
#'
#' @description This function is a generic building block for access to
#' experimental/in progress neuron metadata. It is intended for internal use
#' and the end user or developer is responsible for choosing the active CAVE
#' dataset (see \code{\link{choose... |
37f6586eaa11ced0fc08d4909e8b1c2c6a220a817f2d1c7fda5db97dd202a3de | R | 10,679 | 232 | # required packages
packages = c('rmatio', 'circlize', 'RColorBrewer', 'BiocManager', 'optparse', 'ComplexHeatmap')
# load required packages
invisible(lapply(packages, library, character.only = TRUE))
# options for the R script
# Args :
# output_directory : path to the output folder where the disconnectom... |
db2cbf2411b7e00e5cca7da69a23f79afe5200efe3c7654def5b6009c08a2483 | R | 10,679 | 285 | library(shiny)
library(ggplot2)
library(purrr)
library(shinythemes)
library(shinyWidgets)
library(MHCbinding)
library(DT)
library(dplyr)
library(waiter)
waiter_set_theme(html = spin_rotating_plane(), color = "#333e48")
input_tabs <- tabsetPanel(
id = "upload",
type = "hidden",
tabPanel("Peptides (paste)",
... |
4a5c02ae36a2074ebe53551e96826ef9e9f27f9e4bc5e2ea1b43b15e56c70a0e | R | 10,697 | 225 | # Load necessary libraries
library(dplyr)
library(ggplot2)
library(ggrepel)
#### Get H_gene_set_W from 00.Geneset.R ####
if (!exists("H_gene_set_W")) {
source("/home/joonho345/1_Epilepsy_RNA/00.Geneset.R")
if (!exists("H_gene_set_W")) {
stop("ERROR: H_gene_set_W not found. Please run 00.Geneset.R first or ensu... |
b81b44f13f8359bd3673258a4fb9ab4d7f1f5e25d6465f3ea829b000ecd1fa71 | R | 10,701 | 199 | library(tibble)
library(readxl)
library(dplyr)
library(reshape2)
library(tidyr)
library(ggplot2)
library(readr)
library(here)
library(magrittr)
library(conflicted)
library(eulerr)
conflicts_prefer(base::intersect)
conflicts_prefer(dplyr::filter)
#for writing files out
base_folder <- here("results","full_run_ZengAWS.16... |
bf6c0a66bce61d41511732e942f97fd7b5af62d63194b727246aa831ad364aa8 | R | 10,711 | 287 | library(rhdf5)
library(data.table)
library(dplyr)
library(ArchR)
library(parallel)
library(preprocessCore)
addArchRThreads(threads = 20)
addArchRGenome("hg38")
library(BSgenome.Hsapiens.UCSC.hg38)
projdir <- "projrmSubset_subPeak"
projmeta <- readRDS("projATAC_50subC_meta_584904.rds")
finalSubCs <- sort(unique(projmet... |
7553b742c1d89b5159985dafc807e169557f72504d6235ba723b3abf07dfbac9 | R | 10,712 | 191 | library("here")
library("sessioninfo")
outdir <- "spatialLIBD_files"
pkgname <- "spatialLIBD"
meta <- data.frame(
Title = c(
"Human_Pilot_DLPFC_Visium_spatialLIBD_spot_level_SCE",
"Human_Pilot_DLPFC_Visium_spatialLIBD_layer_level_SCE",
"Human_Pilot_DLPFC_Visium_spatialLIBD_modeling_resul... |
888815bf6d4e9e141c568b08ee79ef8bb731c50ec213a9b6a94343dda02909c7 | R | 10,723 | 264 | #' banc-alignment-fill-super-class — Fill empty `super_class` from NTAC/alignment consensus.
#'
#' For BANC neurons where SeaTable `super_class` is blank, the alignment-implied
#' and NTAC-implied super_classes agree, and the predicted super_class lies in
#' a safe whitelist (optic_lobe_intrinsic / central_brain_intrin... |
dc6b1d6efafbc5589884637e89df36ada0986c2450624bb809a113c5dd4dabf1 | R | 10,725 | 482 | ---
title: "Get correlation between PHC_MEM and DNAm_residual or PHC_pTau"
author:
- David lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: pygments
theme: lumen
toc: true
number_sections: true
df_print: pa... |
ab3e8fbe292cd901b0b6c4d1843bdbb8ca9719b3f9f38be11241e6d039c900b4 | R | 10,743 | 281 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# startup code
#################
##### PATHS ###... |
71b2c74ddd042ea258a4b9b9f580135b0ae22f252a2a620ee0f7db54e4e5e1d4 | R | 10,759 | 309 | #Figure 2d and Supplementary Figure 3d
# Load necessary libraries
library(tidyverse)
library(ggplot2)
expr_data <- npc_counts_tpm #input tpm
gene_row <- expr_data %>% filter(X == "ENSG00000204175") #input gene of interest
# Create long format data for plotting
plot_data <- data.frame(
Expression = as.numeric(gene_... |
4df668f002fc88dfb6034e840df90ddd7c167efa7559cbe993fb23e06bcd1a85 | R | 10,768 | 274 | library(lightgbm)
library(dplyr)
library(stringr)
library(ROCR)
library(pROC)
library(ggplot2)
library(treeshap)
library(shapviz)
library(tidyverse)
library(patchwork)
library(segmented)
PIDgroup <- "D:\\work2\\t2dAndComplicationsUkb\\data\\processed\\grouping\\PIDgroup.rds" %>%
readRDS(.)
trait3 <- ... |
79fccf161c3aa24e5fae709090e5f2ea94a29ac8dcfa21c048de3bf251a9a754 | R | 10,769 | 316 |
##############################################################
# Script: 05_visualization.R
# Purpose: Generate heatmap
##############################################################
# ========================
# Load libraries
# ========================
library(tidyverse)
library(ComplexHeatmap)
librar... |
cf31d92fe202a317cb081011b616dada34e7276c7754eea63d653245cd1752b7 | R | 10,772 | 303 | # ===============================================================
# 🧪 Pairwise Correlation — GGPairs Visualization Module
# ===============================================================
pairwise_correlation_visualize_ggpairs_ui <- function(id, base_size_value = NULL) {
ns <- NS(id)
base_size_value <- suppre... |
e90e23ddf7512e90c99e114cfe8ab6220511aef26162a63e568671bbf65f6022 | R | 10,773 | 355 | # ===============================================================
# 🧪 Table Analyzer — PCA Module
# ===============================================================
pca_ui <- function(id) {
ns <- NS(id)
list(
config = tagList(
uiOutput(ns("vars_ui")),
tags$details(
tags$summary(strong("Adva... |
5632717ecffb889b7e3da0fb5bc29e07c464ea9591ec9bff49c524217df7f8d1 | R | 10,807 | 232 | #' @rdname SOSet
#' @export
#'
setClass(
"SOSet", # Spatial Omics Set
representation = representation(
NMFobjL = "list", # nmf objects/results, e.g., list(dataA = nmfObjResA, dataB = nmfObjResB)
NMFrankL = "list", # select rank(s) to include in the correlation network, e.... |
6d3562771c775beb39b2240aba0ab43bfc01d7ab759b2a4448bb3cfd701d1e8b | R | 10,810 | 273 | #' banc-alignment-fill-cell-type — Fill empty cell_type from NTAC/alignment consensus.
#'
#' For BANC neurons where alignment and NTAC agree on a cell_type and
#' (a) the existing cell_type is blank or `auto:*`, (b) the super_class is
#' populated and matches the alignment-predicted super_class, and (c) the
#' super_cl... |
cde3ab041daa3410b0d12127081ffb7bb43c2ba4a3d7b4850e99d3095f088655 | R | 10,810 | 486 | ---
title: "Get correlation between PHC_MEM and DNAm_residual or PHC_pTau"
author:
- David lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: pygments
theme: lumen
toc: true
number_sections: true
df_print: pa... |
56894ffab311bbfdb1608cbb3c1e7ecdd7cbdad5039f50d610f716fbc5c5c86f | R | 10,818 | 486 | ---
title: "Get correlation between PHC_MEM and DNAm_residual or PHC_pTau"
author:
- David lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: pygments
theme: lumen
toc: true
number_sections: true
df_print: pa... |
d9409173e7e9cd12313f1681b36390f5fae37ca5722e013b98501d9d4d3729eb | R | 10,830 | 264 | #Rscript /media/StorageOne/HTS/VirusMeta/SAM_BAM/virus_positivity_OR.R '/media/StorageOne/HTS/VirusMeta' '/media/StorageOne/HTS/Projects/2011_G5_96ALL-HiSeq' '/media/StorageOne/HTS/Projects/2011_G5_96ALL-HiSeq/PB/virus_final_index.csv' '/media/StorageOne/HTS/Projects/2011_G5_96ALL-HiSeq/case_control_id.txt' '/media/Sto... |
9f577cec2790eb33a5235def717aee05bda844ed71e6401d0a7026a762fe461d | R | 10,831 | 307 | # Code for E-BLUP model adapted from Jarquin et al. 2014 and used in the following manuscript:
# "Tailoring AI and ML Models for Genotype-by-Environment Prediction Leveraging Environmental Covariates: A European Rye Example"
# Code written by:
# Wera Eckhoff, University of Hohenheim, PhD student, wera.eckhoff@gmail.com... |
c51c33d46ec59b50846d33e8cfa9e0b92d15a2222a9851824b1d2944fb981de0 | R | 10,847 | 240 | #' Plot the tree representations of modules
#'
#' Plots the tree representations of one or more modules.
#'
#' The module trees are reconstructed based on pairwise preservation scores between replicates, both within and across species (see \code{\link{reconstructTrees}}). The tips of the resulting tree represent the re... |
40af84be92d5841926b85acb7f0fb3a9918b3cd2ebc0ac49aa05faa218e19f49 | R | 10,854 | 203 | ---
title: "Building PANDA and LIONESS Regulatory Networks from GTEx Gene Expression Data in R"
author: Deborah Weighill
date: "2019-11-06"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Building PANDA and LIONESS Regulatory Networks from GTEx Gene Expression Data in R}
%\VignetteEngine{knitr::rm... |
7bd27bd3ee305072250e204b041a11779afd897c7b41da69a074d5ed47e73382 | R | 10,856 | 272 | flywire_sirepo_url <- function(repo) {
paste0("https://github.com/", repo)
}
flywire_sirepo_dir <- function(..., repo='flyconnectome/flywire_annotations',
create_basedir=FALSE) {
udd=fafbseg_userdir()
if(create_basedir && !file.exists(udd))
dir.create(udd, showWarnings = FALSE,... |
298547627ad2249c9c4b4be4979f2699df48f7513c9dcebda5c5dce5e16d73a3 | R | 10,862 | 309 | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
if(!require(data.table)){ install.packages("data.table"); library(data.table) }
if(!require(tidyverse)){ install.packages("tidyverse"); library(tidyverse) }
if(!require(optparse)){ install.packages("optparse"); library(optparse) }
})
#######################... |
ae916c70c25bfda400384fb065ccd2f26c10b85f8c22d838de49fd59d2fb8af1 | R | 10,866 | 247 | ################################
### Make BANC NT predictions ###
################################
source("banc/banc-startup.R")
poss.nts <- c("acetylcholine","gaba","glutamate","dopamine","histamine","octopamine","serotonin","tyramine")
bancsynapses.nts <- "/n/data3_vast/hms/neurobio/htem2/users/kd193/banc_neurotransm... |
8317e9aeb01f2524bcdf4fd95f1b2cfe2d1e5e3b33d377e7b02132b2e1a8fb80 | R | 10,878 | 165 | # =============================================================================
# Figure 6 — Output Log Generator
# =============================================================================
# Run this AFTER executing fig6_models6and7_behavior.R (sections 1–6).
# It captures all model summaries, emtrends, and Holm-c... |
e164221a2af4153f4f3e526b0b426c0c4992511b57372624bfdfb69d62a760b1 | R | 10,880 | 288 | ---
title: "Spatial Correlation Statistical Analyses"
output: html_document
---
```{r, echo=TRUE}
knitr::opts_chunk$set(echo = TRUE)
library(lmerTest)
library(dplyr)
library(readr)
library(emmeans)
library(multcomp)
library(tidyr)
```
```{r}
extract_stats <- function(m, coef_names) {
model.df = data.frame(coef_name... |
b23cc928de4f0940abef5e36cecbd585f7a8acabc93141d85b6d73f9103d2f36 | R | 10,885 | 314 | #!/usr/bin/env Rscript
# ============================================================================ #
## 06_mgngtus_power.R
## MGNGTUS Nomiki's data: compute post-hoc sensitivity/power.
## Copyright (C) Johannes Algermissen, University of Oxford, Oxford, UK, 2024-2025.
rm(list = ls())
# ============================... |
fa2f2262a1ac3023b3c473a99ea9dcb8a6911df2894456e2acf8a6637fbd6964 | R | 10,917 | 328 | #' Differential expression analysis between two groups of cells using limma
#'
#' Performs differential expression analysis between two groups of cells
#' using the `limma` linear modeling framework.
#' Supports optional observation-level weights (e.g., spatial weights)
#' and filters genes by minimum expression thresh... |
9d5b3315e0b4bdf98c6e7667c1153c50475348b6b11dd38bb87989ec9d7f5814 | R | 10,931 | 272 | ---
title: "Sptial Omics Toolkit"
author: Heewon Seo
date: "`r format(Sys.Date(), '%m/%d/%Y')`"
abstract: >
The Spatial Omics Toolkit (SOTK) is an R package containing a comprehensive set of functions to identify biological modules from spatial transcriptomics profiles. Multiple profiles across different cohorts or p... |
f990c477573c33b51d375a61e52a75c10717e61e5b2bf89197751ba9e4a10aff | R | 10,932 | 286 | #' banc-sjcabs — Export per-neuron SWCs, region cut-outs, neuropil meshes for collaborator share.
#'
#' @section Reads:
#' - `banc_<ver>_meta.feather`, `banc_<ver>_synapses_enriched.parquet`,
#' `banc_<ver>_simple_edgelist.feather`, `banc_<ver>_edgelist_split.feather`
#'
#' @section Writes (under versioned save p... |
b6763569cbc0066468592dc7b12c2f04ff34348615358a8b868a397c216d7c44 | R | 10,935 | 328 | #' Differential expression analysis between two groups of cells using limma
#'
#' Performs differential expression analysis between two groups of cells
#' using the `limma` linear modeling framework.
#' Supports optional observation-level weights (e.g., spatial weights)
#' and filters genes by minimum expression thresh... |
10f5ae1753d4b97d57f2b3c0e5ff5a307d190d41f1b64f535fee6dee720b13aa | R | 10,949 | 297 | #!/usr/bin/Rscript
#' Combine Association Results and Calculate Genome-Wide FDR
#'
#' This script combines association results across all chromosomes and parts,
#' then calculates genome-wide False Discovery Rate (FDR).
#'
#' CRITICAL: FDR must be calculated on ALL tests genome-wide to be valid.
#' Per-chromosome or... |
4955340ed6b37a7b2f73b2c9f2add91e5461f1dff9c9156375293574c16cc0e8 | R | 10,967 | 225 | # ----------------------------------------------------------------
# The following script loads and processes data from:
#
# Erawijantari et al. Influence of gastrectomy for gastric cancer
# treatment on faecal microbiome and metabolome profiles.
# Gut. 2020 Aug;69(8):1404-1415. doi: 10.1136/gutjnl-2019-319188.
#... |
f3beb7f236e573ff3ae6708959ca83815ac8ccc319412beec5b4eb749bf38a0c | R | 10,972 | 313 | ####################################################################
# Tile Matrix Methods
####################################################################
#' Add TileMatrix to ArrowFiles or an ArchRProject
#'
#' This function, for each sample, will independently compute counts for each tile
#'
#' @param input An... |
ec548baf148f5cde04e9eb312dbe2c946132ff21f4a1517c6de882a3b13ea4ae | R | 10,977 | 293 |
#' Get predicted dense core vesicle locations in the flywire dataset
#'
#' @description Preliminary dense core vesicle (DCV) detection results from the Lee group, Stephan Gerhard and Minsu Kim.
#'
#' @importFrom httr POST
#' @inheritParams flywire_fetch
#'
#' @param rootid flywire rootid/rootids
#' @param dataset whic... |
d4f1e878527a0d268f3640f123f18bca2b70c231fae17133e8253ceea42831f2 | R | 10,983 | 245 | # fake root ids at layer 10
fake_rootids <- function(n) as.character(bit64::as.integer64("720575940600000000") + seq_len(n))
# fake fetcher returning two leaves per id, except the last id which has none
fake_fetcher <- function(env, empty=NULL) {
function(ids) {
env$calls[[length(env$calls) + 1]] <- ids
sapp... |
ab8eee662106796fb083ada69120d3230919fbfa8139addb0ac37c2becd20ab4 | R | 10,995 | 228 | library(dplyr)
library(R.matlab)
library(ggsegGlasser)
library(ggsegSchaefer)
library(ggseg)
library(ggplot2)
library(ggseg3d)
library(cifti)
library(stringr)
library(factoextra)
library(matrixStats)
library(scales)
library(Hmisc)
library(tidyr)
library(cocor)
library(mgcv)
library(gratia)
library(tidyverse)
library(nu... |
8c4cde45e3cbafdfa9500c9327eb61d1d3798001d245436d07b0f6da81b470cb | R | 11,003 | 466 | ---
title: "Run meta analysis comparing Rosmap and BDR coMethDMR results"
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: t... |
0442a838f87f85667e5b79ae4a45388dc118a397cea98c17e8be4d284389bea2 | R | 11,005 | 280 | #' banc-alignment-validate — Validate optic-lobe alignment results.
#'
#' Read-only validation: (1) holdout accuracy on intrinsic neurons with known
#' types, (2) neurotransmitter consistency vs FAFB type consensus, (3)
#' mismatch detection vs existing SeaTable annotations. Does not push to
#' SeaTable.
#'
#' @section... |
45d92eee3a4e6546fb04d3a603a6611c27a04dd94dc0e22809117c8c94520a53 | R | 11,032 | 335 | # ==============================================================================
# Script 03 (Fig 1C): SET Signature Analysis
# ==============================================================================
# Description: Calculates SET scores and generates visualization heatmaps
# showing ER-positive and ... |
6a2d5fa8c202e41284bf65e01dd443e6c37873811593ed87301ddebeb592daad | R | 11,045 | 298 | #####################################################################
# Genotype Data Processing Pipeline - Optimized for cis-eQTL analysis
# Brain Region Specific Version
#####################################################################
# This script loads and processes genotype data from PLINK format,
# conv... |
d51016635b21027200cb6e1fed65cab56619c9a312985a16cc63c9e778c3bd24 | R | 11,067 | 359 | # ===============================================================
# 🧪 Table Analyzer — Pairwise Correlation Module
# ===============================================================
ggpairs_ui <- function(id) {
ns <- NS(id)
list(
config = tagList(
uiOutput(ns("vars_ui")),
tags$details(
tag... |
64745669361f81a69dbe23f502233dce5545767822ac8aefafc05634bd531a4e | R | 11,078 | 447 | ---
title: "Run Bacon Correction"
author:
- David lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: pygments
theme: lumen
toc: true
number_sections: true
df_print: paged
code_download: false
toc_floa... |
21e433a3c34fdcce153fce6a094feb418abb475453658334b3421d2a57c1d092 | R | 11,087 | 358 | ---
title: "S2: analysis of pupil sizes with brms"
author: "I. S. Plank"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
fontsize: 10pt
#geometry: margin=0.5in
---
```{r settings, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, fig.align = 'center', f... |
07f6c8a33200c7980da5002e23d5054e3575f397168260d2cf7abc1caeb8b124 | R | 11,088 | 276 | #' banc-calculate-root-positions — Compute root / soma position per BANC neuron.
#'
#' Prefers nucleus position if available; otherwise picks the furthest L2
#' skeleton leaf outside neuropil + neck connective. Incremental.
#'
#' @section Reads:
#' - SeaTable `banc_meta`, L2 SWCs
#'
#' @section Writes:
#' - `banc_r... |
38fbd553a4d2344141f3d15697fd939af3f4f76f9207b014c5403eddad291edc | R | 11,090 | 258 | #### Standard LBA ----
# # Moved to C+_ in model_LBA.cpp
#
# pnormP <- function (x, mean = 0, sd = 1, lower.tail = TRUE)
# ifelse(abs(x) < 7, pnorm(x, mean = mean, sd = sd, lower.tail = lower.tail),
# ifelse(x < 0, 0, 1))
#
# dnormP <- function (x, mean = 0, sd = 1)
# ifelse(abs(x) < 7, dnorm(x... |
9685ab7a7bece2f95c40a54eb187a9dc88228c7e9124efc4d86547be9f0efb36 | R | 11,090 | 447 | ---
title: "Run Bacon Correction"
author:
- David lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: pygments
theme: lumen
toc: true
number_sections: true
df_print: paged
code_download: false
toc_floa... |
2ebda32edc8aea6504caea073917ba450530f60673721f4fdfa07bff82d9f083 | R | 11,092 | 447 | ---
title: "Run Bacon Correction"
author:
- David lukacsovich^[University of Miami]
- Lily Wang^[University of Miami]
date: "`r Sys.Date()`"
output:
rmarkdown::html_document:
highlight: pygments
theme: lumen
toc: true
number_sections: true
df_print: paged
code_download: false
toc_floa... |
f3cc980d5142afbcd4a21a6374aacf0f6d2cd23b394c0c01be16dc1163da9091 | R | 11,114 | 162 | library(neuprintr)
library(malevnc)
#library(gplots)
#library(viridis)
library(ggplot2)
library(dendsort)
library(nat)
#change path to this file as necessary
source(file.path(getwd(), "get_partners_by_multistep_conn_fun.R"))
simple_row_hierarchical_clustering <- function(mat, Rowv = NULL) {
d=dist(mat,method="eucli... |
eb0e5127c4a5c82db54e52a8c7183524f69d89c097071d81069de76e4ad54b92 | R | 11,116 | 232 | # ============================================================================
# MASTER SCRIPT: Generate All Main Figures for ISRGDF15 Paper
# ============================================================================
#
# This script orchestrates the full pipeline to reproduce all main figures:
# Step 1: Process ra... |
e469f049d19d0e3a9642847704879d02cddb986984026a72437c1d4510ff6375 | R | 11,119 | 650 | # Reproducible workflow for spatial autocorrelation,
#Global Moran's I, and LISA cluster analysis
# --------------------------------------------------
# 1. Required Packages
# --------------------------------------------------
packages <- c(
"sf",
"dplyr",
"spdep",
"tmap",
"readr",
"tibble"
... |
596767eafe3ce884b2ec3b1c0423c0dc1ebcb13e780a249f9bff495da2ae4409 | R | 11,122 | 310 | ### to check each terms which category they fit well
# Define list of data names to process
data_names <- c(
# '02.' prefix - intersection and unique between M_KAI_IH and R_PPS
'intersection_M_KAI_IH_IPSI_R_PPS_A_up',
'unique_M_KAI_IH_IPSI_A_up',
'unique_R_PPS_A_up',
'intersection_M_KAI_IH_IPSI_R_PPS_A_down'... |
74efc1212efed5a529424b63394ec57902a47d2fcb32548bf092fa632732b4f6 | R | 11,142 | 216 | # Supplementary plots TAU per region
library(readr)
library(tidyverse)
library(ggplot2)
library(reshape2)
library(ggrain)
library(rempsyc)
# Set the plot theme
settheme = theme(strip.text.x = element_text(size = 10, face = "bold"), strip.text.y = element_text(size = 10, face = "bold")) +
theme(axis.text.x = elemen... |
aeec10c0684a1ffe46605be628284f765c541131c53dd8a328f70866d1ef6867 | R | 11,143 | 320 | library(ggplot2)
library(tidyr)
library(dplyr)
library(stringr)
library(readxl)
library(reshape2)
library(tidyverse)
setwd("")
############################
####### ALFF ##############
# Upload data
data_results_ALFF <- read.csv("ALFF_results_noOUT.csv")
data_subjects_ALFF <- read.csv("dataALFF_long_noOutliers.csv")
... |
28a55e97f8e83a829b56d67bb3e76a6926f9079e8b444afe494792ad5344a6c7 | R | 11,161 | 344 | # ===============================================================
# ?? Regression shared modeling utilities
# ===============================================================
reg_fit_model <- function(dep, rhs, data, engine = c("lm","lmm")) {
engine <- match.arg(engine)
form <- as.formula(reg_formula_text(dep, rhs)... |
2a51fac75dc0046262422cf1d1bbd1f280c484f4be40daa94642c0f9a7935492 | R | 11,175 | 218 | ##############################
### Run ArchR peak calling analysis on CAREmut multiome ATAC data longitudinal analysis of TME cell type
### Author: Kevin Johnson
##############################
## Longitudinal per-patient analysis of differential chromatin accessibility per cell type
# Specify directories
workdir <- "... |
8886f768998c7f82c300d3d5e5dc01252344b7d1ce90e9e379bc2af2a2b17d3f | R | 11,181 | 296 | # ============================================
# Script: Nodomap_cellchat_analysis.R
# Purpose: Run cellchat analysis on combined nodomap and mouse hindbrain dataset to identify possible communication between nodose ganglion neurons and hindbrain neurons.
# Inputs: - mouse-hindbrain-fedfast-2023.RDS (mouse hindbrain ... |
c08f2b6c304487f1846a36ae05caf4e74c4eeafcc1d16afc97eced19f567963a | R | 11,186 | 230 | library(ggExtra)
library(ggplot2)
library(tidyr)
library(MetaNeighbor)
library(conflicted)
library(magrittr)
library(reshape2)
library(dplyr)
library(here)
library(readr)
library(tmod)
conflict_prefer("count", "dplyr")
conflict_prefer("select", "dplyr")
conflict_prefer("filter", "dplyr")
Sys.setenv("VROOM_CONNECTION_S... |
483507cad7df4d362f09ebc15e505d412b13bb79b0244df0924e8455f4c04035 | R | 11,211 | 391 | #########################################################
# R coloc baseline:
#########################################################
# Creating the R coloc baseline output we need to compare
# our python implementation against
#########################################################
# loading data
library(coloc)
l... |
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