sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
6ee549e5ca27bf354c71135a2aee0be791db4466550c8ee5c06df0c9a4538785 | R | 13,431 | 297 | #' @title Heatmap
#' @description Generates a heatmap plot.
#' @param score A matrix for input, for instance, one generated using the CalcStats function.
#' @param color_scheme Specifies the color gradient for the heatmap visualization.
#' This parameter accepts multiple input formats to provide flexibility in defini... |
15a54e43cfd4a28607ebc7509cca302cd7304994724df8329bb72f469791b210 | R | 13,434 | 343 | ##########################################################################
# Sex Analysis of CCA GMV Weights for CMI-HBN and Stanford Cohorts
# Author: Yuan Zhang
# Date: 2026-03-24
#
# Description:
# 1. Loads math and reading CCA results for both CMI-HBN and Stanford.
# 2. Extracts the brain-side CCA scores (U) an... |
8798a1d1c00813bafb399c53453669e3b05e50b547d745b20e6fe98849cf351b | R | 13,447 | 400 | # ===================================================================
# Cluster Identification Markers - Publication Figures
# ===================================================================
library(Seurat)
library(ggplot2)
library(patchwork)
library(tidyverse)
# Load your Seurat object if needed
# retina <- read... |
b3efe36e2b621a10db99aaed0d4684514c79d68c72d08ca5369c2d97681de109 | R | 13,536 | 436 | library('SingleCellExperiment')
library('here')
library('jaffelab')
library('scater')
library('scran')
library('pheatmap')
library('readxl')
library('Polychrome')
library('cluster')
library('limma')
library('sessioninfo')
library('limma')
load("rda/sce_layer.Rdata")
source("layer_specificity_functions.R")
###########... |
bbc2810eaafdedb5b28dc8f0aeaffbefbcf0f0669c65507829bbece1a02073b5 | R | 13,549 | 397 |
####################
# load spot level ##
####################
library('SingleCellExperiment')
library('here')
library('jaffelab')
library('scater')
library('scran')
library('pheatmap')
library('readxl')
library('Polychrome')
library('cluster')
library('limma')
library('sessioninfo')
library('janitor')
library('org.H... |
d0749ef372ff4f19daf2ccb018414c8f411c7e91ec35f2583f7927e490bd87da | R | 13,552 | 376 | # ===================================================================
# FINAL Comprehensive GO Enrichment + GSEA Analysis
# Maximum sensitivity for Rods, Cones, Muller Glia
# ===================================================================
library(clusterProfiler)
library(enrichplot)
library(org.Hs.eg.db)
library(g... |
f59129b8047ad0a4c5877296d7027ef0fd265049561a3d2d0ba92b568acf956f | R | 13,630 | 320 | library(readr)
library(circular)
library(plotrix)
library(ggplot2)
library(gsignal)
library(dplyr)
# Replace "your_folder_path" with the actual path to your folder
age="12m"
folder_path <- paste0("//clnsd009/Users/fbigand/OneDrive - Fondazione Istituto Italiano Tecnologia/WORK/COLLABS/Trinh/Tiny-dancers/tinyD... |
68080e70ad1bff2cf4da05aa5ea85cf5cb2b6a21e8a0578f35308d3023b4c93d | R | 13,690 | 347 | # ===================================================================
# Volcano Plots - Müller Glia and Cones with Specific Gene Highlighting
# ===================================================================
library(ggplot2)
library(ggrepel)
library(tidyverse)
# ===================================================... |
281804e574c8b165a929b9d7f57f784c86e4b65471cedadcc555f6b797200004 | R | 13,693 | 397 | ---
title: "ED Fig 3e, 3f - Arp3 patch velocity (MSD) and lifetime"
author: "Lin et al., Nature 2026 (Bradke lab, DZNE)"
output: html_notebook
---
<!--
================================================================================
PANEL TARGETS (Lin et al., Nature 2026)
============================================... |
ba0cbc784c1ee721468003dcb89febad50da9829b5ee8cd2a570ddb0ee4418a4 | R | 13,712 | 352 | # Load required libraries
library(Seurat)
library(ggplot2)
library(dplyr)
library(patchwork)
# Set working directory
setwd("/home/doyang/turbo/CLRN1 WT VS KO 10M SnRNAseq/")
# Load the Seurat object with donor IDs
retina <- readRDS("CLRN1_Retina_with_DonorIDs.rds")
# Check it loaded
retina
# Define a nice color pal... |
d848fcc3e1db80f366559c7694fce7439f062655d1ceb9b800e9880931cea663 | R | 13,738 | 321 | ---
title: "Utility Tools and Functions"
author: "Yichao Hua"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
toc_depth: 3
theme: default
vignette: >
%\VignetteIndexEntry{Utility Tools and Functions}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
## ... |
abd869d6db7056de7bfe23049439eedb3701c4bfc0861078fa50b5391c810e55 | R | 13,791 | 337 | # CSF_plasma_cfDNA_size_comparision.R
# this file is meant to be used inside Rstudio
# this file compares the cfDNA nucleosome ratios and size distribution between the plasma and CSF samples
library(data.table)
library(dplyr)
library(ggplot2)
library(tidyverse)
# read in CSF length files
files_CSF <- Sys.glob('/mnt... |
d833964d59a340d22b90c9fa0112de7ded4d92823fe2e999791279e0e526a8fb | R | 13,838 | 392 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# ED Fig 7b — Length difference induced by PA-Rac1 activation at the
# growth cone, with 40 µM para-aminoblebbistatin vs control.
## What this file does
1. Reads per-cell Pre/Act/Post neurite length CSVs for two conditions:
- paraBlebb GC (line 99: ``rep(1:10... |
8554fbafef50bab47671cb86769d3a20b4ea1eebf3ebe9a57d196f5df38346af | R | 13,850 | 352 |
# function to create polar dendrogram
circlize <- function(dend_list,heatmap,heatmap_factor,clusters,cluster_label_size,split,col_fun1,labels_size,group_colors,group_colors_vec,max_height,track_height,highlight_index,highlight_color,height,width) {
# clear circos
circos.clear()
# setting up global param... |
d4f5ca2c21e44b6867022f1cb4db62022e7250b8e067d8eedc686d5834af242f | R | 13,890 | 401 | calc_entropy <- function(u) {
p <- u[u>0]
p <- p/sum(p)
-sum(p*log(p))
}
# given a matrix of labels, calculate all pairwise ARIs
calc_aris <- function(m, flavour="ARI") {
a <- diag(ncol(m))
for(i in 1:(ncol(m)-1))
for(j in 2:ncol(m)) {
if(flavour=="ARI") {
require(mclust)
a[i,j] <-... |
f75e8c58b83a0a3f6e61bc2546a832c5bbee96bfea630e6ef4a7eae9907242d9 | R | 13,975 | 272 | #How does neural efficiency relate to EX-CBT?
#########################################################
### (A) Installing and loading required packAGEs
#########################################################
if (!require("dplyr")) install.packages("dplyr", dependencies = TRUE)
if (!require("lme4")) install.packages(... |
a75fe3146281d5a9997c1cecc791755f237f5847b78eba949c1ec247e37de731 | R | 14,021 | 319 | library(readr)
library(circular)
library(plotrix)
library(ggplot2)
library(gsignal)
library(dplyr)
# Replace "your_folder_path" with the actual path to your folder
folder_path <- "tinyDancers_commonAges/csv_for_trinh/age6m"
# Create an empty data frame to store results
baby_results_df <- data.frame()
baby_results <- ... |
32f484fcc4f73bf1da8d222b89b975ad51e2dab80597446c6d7620e27e6fe2fc | R | 14,032 | 288 | #' @title Enhanced Violin Plot
#' @description Generates advanced violin plots distinct from Seurat's VlnPlot. This improved version offers a more compact design for efficient space utilization, the ability to overlay a boxplot, and convenient inclusion of statistical annotations. The function accommodates input in the... |
c79e2ca9068715a00ca8ca28085bab02a578cbd4d4761b547bbe559deb4e2a8e | R | 14,067 | 453 | ## module load conda_R/3.6.x # devel
## ----Libraries ------------------
library(tidyverse)
library(ggplot2)
library(Matrix)
library(Rmisc)
library(ggforce)
library(rjson)
library(cowplot)
library(RColorBrewer)
library(grid)
library(readbitmap)
library(Seurat)
library(SummarizedExperiment)
library(rtracklayer)
## Fu... |
ffb378d92ff39876b8757ca5046cc5bc6eea57a06cbf633c16f291d79929a5e2 | R | 14,251 | 303 | ---
title: "Geneset Enrichment Analysis (GSEA)"
author: "Yichao Hua"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Geneset Enrichment Analysis (GSEA)}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
## Table of Contents
1. [Conduct GSEA using the GO ... |
6656ca18f69953462e8a93aecacba2c8cc00b99350d6cedb95963c58805c6671 | R | 14,256 | 404 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# Actin-filament orientation distribution from cryo-EM tomograms (WT vs Arp3 KO)
## What this file does
Reads per-tomogram actin-filament orientation tables (angles relative to the
leading edge, exported from the segmentation + tracing pipeline; data pasted
into... |
3fdae96d0efaee2174ec924bf7c0d7d66e0d204436e0c2d9fdf0be591c5c2c46 | R | 14,266 | 426 | #' @title Default discrete color presets by 'I want hue'
#' @description Generate color presets from 'I want hue' online tool
#' @param n How many colors to generate
#' @param col.space Color space, Options: "default", "intense", "pastel",
#' "all" (k-Means) or "all_hard" (force vector)
#' @param set Several random pre... |
d0d8790225d01dba67b5bdf019d9b178a0ec9e595b6f0657bf53588ee0b82789 | R | 14,277 | 402 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# Actin-wave frequency: per-neurite and per-cell across polarization stages
## What this file does
Reads in-line per-cell wave-event count tables (segregated by polarization
stage: unpolarized, polarizing, polarized, and by neurite type: axon, minor
neurite) and... |
6df216ba82760b60baba2eca91dc5c77f800ed3babdc405ab5203f054d5f4221 | R | 14,424 | 320 | #!/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.
# tximport::summarizeToGene() reorders gene-level output rows via base R's
# rowsum(reorder=TRUE), which sorts... |
1d50efc9eff03f8485d04d4903013a4591c3289a4d2277ba8e45a6c86aa1fee3 | R | 14,578 | 333 | library(readr)
library(circular)
library(plotrix)
library(ggplot2)
library(gsignal)
library(dplyr)
# Replace "your_folder_path" with the actual path to your folder
age="3m"
folder_path <- paste0("//clnsd009/Users/fbigand/OneDrive - Fondazione Istituto Italiano Tecnologia/WORK/COLLABS/Trinh/Tiny-dancers/tinyDa... |
8083ade1a62a218fd8f600c2f78ad058284b7adeec25b260f5324154a65b165b | R | 14,603 | 428 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# Arp3 patch moving velocity + lifetime from TrackMate trajectories
## What this file does
Reads TrackMate-exported per-patch trajectory CSVs (`Tra_file`) and MSD CSVs
(`MSD_file`) per cell from `Path_1` (lines 45, 51, 64-65; subfolder
enumeration at line 329). ... |
7c3d238a23ab22a971dc7249d4ff4ec4510cc83d6f56db68bde525084f7f5b50 | R | 14,674 | 458 | ### CORRELATION EVENTS - FISHER'S EXACT TEST APPROACH ####
library(arrow)
library(dplyr)
library(ggplot2)
library(tidyr)
library(purrr)
library(tibble)
setwd("")
###FOR ATSS-AS CORRELATION
#Read in data:
ES_events <- read.table("./code/AS_APA/output/output_APA_AS_corr/ORFanage_events_SE_strict.ioe",
... |
5f9e9a1df8ac9ce1b49fc7bbbc1cd34493fc548df7c095be372bdef1a6d45603 | R | 14,799 | 327 | #' @title Run Palantir Diffusion Map and Calculate Pseudotime
#' @description This function suite uses the Palantir algorithm to first calculate the diffusion map based on pre-calculated dimension reductions in Seurat (e.g., PCA, harmony), adding the diffusion map (dm) and multiscale space (ms) embeddings back to the o... |
0d48d3af559091083283e0e92524731293c0af6474de67aeb6daebb799f8f053 | R | 14,809 | 402 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# CONSOLIDATOR (STAGE-2) for Fig 3n + ED Fig 5b — multi-condition
# comparison of PA-Rac1 activation effects across 5 conditions
# (WT GC, WT Soma, Arp3 KO, PA-Rac1-C450M, PA-Rac1-T17N).
## What this file does
This is the **STAGE-2 CONSOLIDATOR**. It loads its o... |
1b31bbadee0623624dba418e50550b6e5ed06ea1a929a4077522495e80649a77 | R | 14,850 | 400 | setwd("/Users/zhangyuan/Google Drive/2023_math_reading_neurotransmitter/GitHub")
library(CCA)
library(CCP) # for cca statistical test
library(permute)
library(readxl)
library(R.matlab)
library(psych) # for pca
library(reshape2)
library(ggplot2)
library(ggrepel)
library(ggseg)
#########################################... |
9b1919e830363ff160a2d856efd88f3934b0379c1fd4328889f7b2b375dd8a28 | R | 14,853 | 452 | ---
title: "biomotion"
author: "Mirko Zanon"
date: '2024-11-05'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r cars}
# ==== LOAD PACKAGES ====
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
library(emmeans)
library(ez)
library(afex)
library(ggpubr)
l... |
33072cff18da877eecd58554d268a4fbc722fd9f974924d1584b281a85382c90 | R | 14,912 | 222 | ###
library(jaffelab)
metricFiles = list.files(
"/dcs04/lieber/lcolladotor/with10x_LIBD001/HumanPilot/10X",
pattern = "metrics_summary_csv.csv",
full = TRUE,
recur = TRUE
)
names(metricFiles) = ss(metricFiles, "/", 8)
metrics = sapply(metricFiles, read.csv, as.is = TRUE)
## with high mean rates of ex... |
a633b8f19e8589bf0fbce292ba96bb6a0f275bf3f723f7c122d3fa32f1c3f5ec | R | 14,969 | 333 | install.packages("rstatix")
install.packages("tidyverse")
install.packages("dplyr")
install.packages("ggplot2")
install.packages("effectsize")
install.packages("vcd")
install.packages("rcompanion")
install.packages("survival")
install.packages("survminer")
install.packages("car")
install.packages("glmtoolbox")
install.... |
74a900956d8d58c80d47b73bd24b7bafc90c530e764ceefe6d34a15acd6d8a95 | R | 15,005 | 382 | ---
title: "What's New in v1.2.0"
author: "Yichao Hua"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{What's New in v1.2.0}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
## New Features and Enhancements in v1.2.0
### Dark Theme Support for Feature P... |
a16a8ccdcfb9c82fa61e7ee71bd29c6e244b6a665aad987c8186fa4a242fa3ed | R | 15,024 | 435 | ################################################################################
### Pre-GEMMA Genome-Wide Scan Data Organization from Old Files from 2011
### Data import, fixing data formatting, UCSC LiftOver from mm6 to mm10,
### writing files for covariates, phenotypes, making ped files.
### Excluding Chromosom... |
ede49af1473e4a18de112a0b19778e3fb4ba1121eb9cf08b7efab9000de4fd94 | R | 15,039 | 268 |
---
title: "CellChat analysis of multiple spatial transcriptomics datasets"
author: "Suoqin Jin"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: true
theme: united
mainfont: Arial
vignette: >
%\VignetteIndexEntry{CellChat analysis of multiple spatial transcriptomics datasets}
%\V... |
ac5066fc8c415924cc9546aff1c29696b9e3931abc691630affebdf6e366f7e5 | R | 15,127 | 393 | PredictLabels2 = function(test, model, test_ident = NULL, scale = TRUE, scale.by.model = FALSE, assay = "RNA", slot = "data", verbose = FALSE,
return.prob.matrix = FALSE){
genes.use <- model$bst_model$feature_names
train_id = factor(colnames(model$test_mat), levels = unique(colnames(model... |
c11083f9ebf5a63fc1d63e32601330a2e4e2fba358c50519a5df58322fc7a4e1 | R | 15,157 | 416 | # ===================================================================
# Quality Control (QC) Metrics Analysis - snRNA-seq
# ===================================================================
library(Seurat)
library(ggplot2)
library(patchwork)
library(tidyverse)
# Load your Seurat object if needed
# retina <- readRDS... |
2667fd019aeba6741a31acff7f1ec42ac2851116802c67ed368f3a370f057a76 | R | 15,213 | 209 |
---
title: "CellChat inference and analysis of spatial-informed cell-cell communication from spatial imaging data"
author: "Suoqin Jin and Jingren Niu"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: true
theme: united
mainfont: Arial
vignette: >
%\VignetteIndexEntry{CellChat infere... |
3f2464e940c1e9e1d62650189c16bf5928a9859872718c5ac3649c2d9edceb5b | R | 15,216 | 371 | # QoM script
library(readr)
library(dplyr)
library(pracma)
# Replace "your_folder_path" with the actual path to your folder
ages<- c('3m', "6m", "12m")
# conditions <- c("HighVoice", "LowBass")
conditions <- c("Baseline", "Control","HighVoice", "LowBass")
pm_frame_qom_all <- list() # Assuming the third dimension is... |
d8677203145e561c47acbc507346bcbd47b4d04e119f561be6a6333916388e6e | R | 15,218 | 393 | #Load the necessary R packages
rm(list = ls())
library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(cowplot)
library(presto)
library(bluster)
library(scran)
library(ape)
library(ggtree)
library(tidyr)
library(biomaRt)
library(scDblFinder)
library(plotly)
library(viridis)
library(s... |
99beada8d5701d9c3c65aaa3ba019be0441b44ca7bdad4a22d3abf8dfef531a4 | R | 15,231 | 315 | ---
title: "Canonical template: Fig 3n + ED Fig 5b PA-Rac1 precursor (Pre/Act/Post)"
output: html_notebook
---
<!--
================================================================================
CANONICAL TEMPLATE - DO NOT RENDER DIRECTLY
============================================================================... |
ffe8af78ff502bb3ab0dd415dba638219c6c8bb614669c845afe53914ad57451 | R | 15,256 | 512 | # Preparation
```{r}
#| label: import-r
library(IsoformSwitchAnalyzeR)
library(tidyverse)
library(ggpubr)
library(pheatmap)
library(RColorBrewer)
library(stringr)
library(scales)
library(arrow)
library(dplyr)
```
# Build IsoformSwitchList object
Get filtered isoform count matrix
```{r}
#| label: get-Isoseq_Expressi... |
0f39cd9e2f53929a339cf7c8e9ed655c1fdc7c2017bdaa3ef21dc0023d9abd5c | R | 15,387 | 423 | ---
title: "Lipidomics_HeLa_iN-diff133_ASAH1-WC-OrganellIeIP"
output: html_document
date: "`r Sys.Date()`"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# I. load packackes
```{r}
# Core Data Manipulation & Tidyverse
library(tidyverse) # Includes ggplot2, dplyr, tidyr, readr, tibble, purr... |
5c191082ffe8003b66881962e7da82e48db34414cf0836ffc4de87883818044f | R | 15,526 | 362 | # SCRIPT: APA Alternative Splicing Analysis
# AUTHOR: Shreejoy / Gemini
# DATE: 2023-10-27
#
# DESCRIPTION:
# This script analyzes the alternative splicing of the AGO1 gene in relation to
# different polyadenylation (PolyA) sites and cell differentiation timepoints
# (iPSC, NPC, CN). It uses a beta-binomial regression ... |
c6b2a255b65f80cde0f070cbeb11bc4c594924fb33daa37dbb1192167e3b55a0 | R | 15,530 | 401 | library('SingleCellExperiment')
library('here')
library('dplyr')
library('sessioninfo')
## Load data
load(here(
'Analysis',
'Human_DLPFC_Visium_processedData_sce_scran.Rdata'
))
## For plotting
source(here('Analysis', 'spatialLIBD_global_plot_code.R'))
genes <- paste0(rowData(sce)$gene_name, '; ', rowData(sce... |
df9644b3453faae3821e0f51fbf9b85f294a4871e373eff080ae44136120254e | R | 15,591 | 296 | library(stringr)
library(lme4)
library(lmerTest)
library(ggplot2)
library(ggthemes)
#library(readr)
if (requireNamespace("rstudioapi", quietly = TRUE) && rstudioapi::isAvailable()) {
exdir = dirname(rstudioapi::getSourceEditorContext()$path)
setwd(exdir)
}
dirPath = file.path("data", "r_inputs")
figurePath = file.... |
2ec468bb07aa9628a9c8890841e5237ae2b7d0e06bcd811746218f5a9edea55b | R | 15,617 | 411 | # module load conda_R/3.6.x
library('SingleCellExperiment')
library('ggplot2')
library('sessioninfo')
## load rse list
load("Human_DLPFC_Visium_processedData_rseList.rda", verbose = TRUE)
sceList <- lapply(rseList, function(rse) {
SingleCellExperiment(
assays = list(counts = assays(rse)$umis),
row... |
76f999d69b5b83e0c33bd0acda3c4ccba365c7d7bd906314a64c89651123b4c3 | R | 15,778 | 454 | ################################################################################
### Pre-GEMMA Genome-Wide Scan Data Organization from Old Files from 2011
### Data import, fixing data formatting, UCSC LiftOver from mm6 to mm10,
### writing files for covariates, phenotypes, making ped files.
### Not excluding any c... |
524d3cb9d01f3732def967c5ceb8cf0a7bbcea8111a6ca89c2e43c4b31feba77 | R | 15,796 | 472 | ---
title: "Visium Lieber Example Analysis Notebook"
author: '[Stephen Williams, PhD.](mailto:stephen.williams@10xgenomics.com) 10x Genomics
Senior Scientist - Computational Biology'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
html_notebook:
code_folding: none
theme: journal
toc: yes
... |
c3554a9eb841155751dfab49f8bf69d8d5a56fb23ceec2f8dbcaf57f9a082021 | R | 16,039 | 423 | #' @title Simultaneous Visualization of Three Features in a Single Plot
#' @description This function visualizes three distinct features on a single dimension reduction plot using a color blending system. It allows for the quantitative display of gene expressions or other continuous variables by mixing colors according... |
e630a3482ffa81d0bbf9a7b30c99e028dfd26a8c96009bafc96758b8d829da44 | R | 16,040 | 336 | suppressMessages(library(Seurat))
suppressMessages(library(ggplot2))
suppressMessages(library(patchwork))
suppressMessages(library(cowplot))
library(tidyverse)
library(clustree)
setwd('~/Desktop/project/Ciona_ST/')
set.seed(123)
result_dir <- 'result/result1/clustering/'
nc_merge <- readRDS('result/result1/preprocessi... |
55cf36bd5893c823f8c7094daa1458e3bb09e94cdfb71928291dcaff35522643 | R | 16,123 | 514 | ---
title: "Visium Lieber Example Analysis Notebook"
author: '[Stephen Williams, PhD.](mailto:stephen.williams@10xgenomics.com) 10x Genomics
Senior Scientist - Computational Biology'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
html_notebook:
code_folding: none
theme: journal
toc: yes
... |
e3582bdbbb44be3cf0b06f3e0cef9038bd593b9d3aacf5cca5c9c74e05cf3ad2 | R | 16,490 | 487 | # Extended_data_Fig.8
#
# -----------------------------------------------------------------------------
# Title: Lepr / Adrb2 coexpression across public murine single-cell datasets
# -----------------------------------------------------------------------------
#
# For every dataset below, each cell's log-normalized exp... |
5597e1aef8ee7bce84500b631ba04105dcfc6583b9a3d601e575ac1943fdbb98 | R | 16,558 | 318 | install.packages("rstatix")
install.packages("tidyverse")
install.packages("effectsize")
install.packages("vcd")
install.packages("rcompanion")
install.packages("survival")
install.packages("survminer")
install.packages("car")
library(rstatix)
library(dplyr)
library(ggplot2)
library(effectsize)
library(vcd)
library(rco... |
e6ee03c9a05e13bc9b6a7570e7bb1788fe3d9bfca5b940a5f4f39556a1b9b1be | R | 16,558 | 501 | #' @rdname geom_text_repel
#' @export
geom_marquee_repel <- function(
mapping = NULL, data = NULL, stat = "identity", position = "identity",
...,
box.padding = 0.25, point.padding = 1e-6,
min.segment.length = 0.5,
arrow = NULL,
force = 1,
force_pull = 1,
max.time = 0.5,
max.iter = 10... |
3c923b91e8db4c42c4e88f92aa71e26e9f750eec17fbce18c5e77ec533053584 | R | 16,754 | 448 | # =================================================================
# Laconic Fluorescence Analysis Script
# =================================================================
# This script analyzes fluorescence microscopy data from dual-channel imaging
# It processes both Laconic sensor and pHrodo measurements
# Perfor... |
ffe6805eb38d30f2f7a404e4ac5b8801e3fb9ec34560c5dd64745c8c4ca2353d | R | 16,878 | 455 | library(readr)
library(tidyr)
library(lme4)
library(effects)
library(car)
library(ggplot2)
library(readxl)
library(emmeans)
library(dplyr)
setwd("~/OneDrive - Fondazione Istituto Italiano Tecnologia/IIT_Postdoc/WP4/MATLAB/MUSICOM_R")
ERP_output <- read_excel("ERP_all_peak.xls")
data_long_pc <- gather(ERP_output, elec... |
f86b7432da12982367672ab5e084171e1b40042b86fb0c904dade0b6547808fb | R | 16,892 | 563 | ---
title: "Alzheimer PRS"
author: "Nuzulul Kurniansyah"
date: "09/20/2024"
output: md_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Introduction
This repository provides information regarding the construction of a polygenic risk score (PRS) for Alzheimer Dieseses (AD) that we de... |
2b48d5bd044b79f3e7637ac061d45209bbc1f6cdd36ddd9e02d8655940f6f771 | R | 16,910 | 403 | .userGWAS_main <- function(i, cores, k, n, I_LD, V_LD, S_LD, std.lv, varSNPSE2, order, SNPs, beta_SNP, SE_SNP,
varSNP, GC, coords, smooth_check, TWAS, printwarn, toler, estimation, sub, Model1,
df, npar, utilfuncs=NULL, basemodel=NULL, returnlavmodel=FALSE,Q_SNP,mod... |
a29380846f15111955ad72188e427523b28c70a6c149c14d4c677641dd224fa6 | R | 17,074 | 388 | #' userGWASa: Ultra-fast multivariate GWAS with flexible analytic estimation
#'
#' Runs a multivariate GWAS across a set of
#' GWAS summary statistics and a user-specified factor model. Factor-specific
#' SNP effects (betas, SEs, Z-statistics, p-values) and an omnibus
#' heterogeneity statistic (Q_omnibus) are computed... |
2a6140f181f56f4c0490f267ba3a666f92a3ffedcd048d528e37d7dc7d0c7de2 | R | 17,094 | 483 | ---
title: "R Notebook"
output: html_notebook
---
<!--
# Tau-positive axon polarization level vs neurite length (blebbistatin / cytoD)
## What this file does
Reads in-line per-neuron Tau-intensity and neurite-length tables for WT and
Arp3 KO neurons treated with DMSO or 20 uM blebbistatin. Neurons with at
least one ... |
eac2e41b5439094222b18cc70053b0d80e313095f3fd2e3e515629f86c346194 | R | 17,096 | 297 | #' Run individual-level SuSiE fine-mapping from explicit tensorQTL-derived files
#'
#' For every gene whose tensorQTL index association has \code{qval <= qvalue_threshold}
#' in \code{index_eqtl_file}, this function identifies all cis variants tested for that
#' gene (from \code{cis_pairs_file}), loads the gene's norma... |
2c94a1218665f771bdc2a5153a529ded6a6e56e5b8c31712e44a0d848c0138cf | R | 17,287 | 431 |
#This is for R CMD CHECK
if(getRversion() >= "2.15.1") utils::globalVariables(c("."))
#' Run standard analysis for census, roll call, and csi.
#'
#' Run standard analysis for census, roll call, and csi.
#' All input files are optional, plots will be generated based on the files that are not null.
#'
#' While ma... |
520dd49ce5c68050e6d9c6dfad22a4031ebb070f4f0353931e47dad9ec3dc09f | R | 17,467 | 368 | #!/usr/bin/env Rscript
# =============================================================================
# normalize_beta_only.R
# =============================================================================
# Normalizes datasets that only provide beta-value matrices (no IDAT files)
# following the NBIS Array Tutorial a... |
1455328016ef4f2a81a59db60343ad58307a2f629ab9ec0fca1cfbc7d0a97140 | R | 17,490 | 608 | library(dplyr)
library(tidyr)
library(ggplot2)
library(rstatix)
library(patchwork)
library(purrr)
library(ggpubr)
library(stringr)
set.seed(42)
# ==============================================================================
# Read data
# ==============================================================================
... |
c9529375bf253e289061e79db1003f5a0861b8cdd48dc5a57b940e89c58ffd02 | R | 17,567 | 410 | # ==============================================================================
# Script: 9_outcome.R
# Manuscript relevance: 3.6, Fig. 6
# ==============================================================================
# PURPOSE:
# Test whether segment-level LP dynamics parameters differ systematically
# between p... |
20d585cb73e79867cc09a3220dcfd9c0a10872c475c7552ea107b18043f17098 | R | 17,585 | 478 | #========================================================================================#
# Author: James M Roe, Ph.D.
# Center for Lifespan Changes in Brain and Cognition, University of Oslo
#
# Purpose: Reproduce Extended Data Fig. 4 (Sensitivity analyses correcting for additional covariates)
# Script is fu... |
6781470ba6812a2ed9eead0a6230e1ba74c2bf75a0c7e1d1d671ff05bde35e2f | R | 17,638 | 552 | # Packages
library(xgboost)
#library(philentropy)
#' A function to compute the relative tightness of clusters by comparing within cluster diameter to cross-cluster distances
#'
#' @param coord_ids character vector corresponding to the factors of the reduced dimensional space.
#' @param df data frame with columns corr... |
5b9f44116fdc8b1cde968059cd9958343d913b12f54fd40974babd34602adc35 | R | 17,675 | 517 | ---
title: "ED Fig 6e - MRLC and Arp3 patch lifetime"
author: "Lin et al., Nature 2026 (Bradke lab, DZNE)"
output: html_notebook
---
<!--
# Arp3 / MRLC patch lifetime + diffusion velocity (alternative-cohort variant)
## What this file does
Alternative-cohort variant sourced from `D:\DVElite`. Reads ComDet trajectory... |
289fd37ff757d6399a099724fc9edf6df8cb9b3b06ed5bbb007fbbbb82b82c2d | R | 17,906 | 575 | ---
title: "01 Load and QC"
output: html_document
---
```{r setup, include=FALSE}
# If running interactively from within /scripts, move up to project root
if (basename(getwd()) == "scripts") setwd("..")
# Now we are in the project root
knitr::opts_knit$set(root.dir = normalizePath("."))
knitr::opts_chunk$set(echo = ... |
fb70e0a2144bc9777041ccb61a160276ce848b7f6295cb92ff52e5b63d48945a | R | 18,199 | 505 | ---
title: "R Notebook"
output: html_notebook
---
<!--
## What this file does
Same Pre/During/Post microperfusion analysis structure as
``fig03r_microperfusion_actin_response.Rmd`` (which is currently
in the same Fig03_Arp3 folder), but restricted to the **DMSO control**
condition only:
1. Reads per-cell intensity C... |
a76a855bdb82f5be4354e2a6c5d8f027f759a2095186d7f7df8fbfff1af2f980 | R | 18,294 | 403 | commonfactor <-function(covstruc,estimation="DWLS"){
time<-proc.time()
#function to create lavaan syntax for a 1 factor model given k phenotypes
write.Model1 <- function(k) {
Model1 <- ""
for (i in 1) {
linestart <- paste("F1"," =~ NA*",colnames(S_LD)[i], sep = "")
if (k-i > 0) {
... |
8c4012a44cecbbe7f3647efc6a66dce3cebfe5fd75fafccd252a3c58b14e567a | R | 18,324 | 512 | ###
library('readxl')
library('limma')
library('sessioninfo')
library('parallel')
library('jaffelab')
library('janitor')
library('lattice')
library('org.Hs.eg.db')
library('GenomicFeatures')
library('scran')
library('here')
library('RColorBrewer')
library('ggplot2')
library('fields')
## load sce object
sce_layer_file ... |
e5a58bdd6a509a54ee3b35a0cb7fc20723aaa421de61f12aaf38a5d8571928a6 | R | 18,389 | 457 |
#' Converts gene names within a Seurat object to lowercase
#'
#' @param object A Seurat object.
#' @param integration Boolean value indicating whether the object has an integrated assay.
#'
#' @return Returns a Seurat object with genes in lowercase.
LowerCase_genes = function(object, integration = FALSE){
rowname... |
0fa4a44c31c1f095d687b2101746c28722d28859452b676eee95ac925dbcd136 | R | 18,473 | 431 | ---
title: "Fig6"
author: "Sayeh Kazem"
output: github_document
---
## Fig. 6: Gene dosage responses across traits and functional gene sets.
#### -- Figure legend -- ####
**Legend:** (A) Proportion of gene dosage responses significant for deletions-only, duplication-only, and both deletion-duplication, across brain... |
3cc295461c525e5fbced915c60082038f1f85e62a1952c1f79ec6962e23dfc00 | R | 18,475 | 733 | ---
title: "beat-side GAL4 in PNs"
output: html_notebook
---
This analysis was based on the GAL4 expression pattern data updated in May 2026.
# Load packages.
```{r}
library(tidyverse)
library(magrittr)
library(gplots)
library(RColorBrewer)
library(ggpubr)
library(pheatmap)
```
# Fig 3. Load the dataset and run ... |
82ae08eda86c6c18314db4c7b9f916ce12b97dbfe32784e697b6c573a194a1e9 | R | 18,600 | 575 | #' @rdname geom_text_repel
#' @param label.padding Amount of padding around label, as unit or number.
#' Defaults to 0.25. (Default unit is lines, but other units can be specified
#' by passing \code{unit(x, "units")}).
#' @param label.r Radius of rounded corners, as unit or number. Defaults
#' to 0.15. (Default ... |
bf90288d95853416c773273186110a92e194837c586c49f19f3a0601fdcdf0ec | R | 18,746 | 377 | ---
title: "Fig4"
author: "Kuldeep Kumar"
output: github_document
---
## Fig. 4: Dissecting pleiotropy, gene function, and genetic constraint.
#### -- Figure legend -- ####
**Legend:** (A) Correlation between the fraction of constraint genes (LOEUF top-decile) within a gene set and the number of traits showing signif... |
f786ec73439aefb3af5c113a1c4a5f7cab7ca85d37bc462d9cbdaf73d9f314dc | R | 19,062 | 460 | #!/usr/bin/env Rscript
# =================== CLI ===================
.parse_cli <- function() {
args <- commandArgs(trailingOnly = TRUE)
kv <- list()
for (a in args) {
if (startsWith(a, "--")) {
a2 <- sub("^--", "", a)
if (grepl("=", a2, fixed = TRUE)) {
parts <- strsplit(a2, "=", fixed =... |
24a6b2170fbb57d3aa11c71c26372c3218379b826c539743704e1a079ed4878f | R | 19,076 | 454 | ---
title: "Canonical template: ED Fig 11d-f EB3 length under CK-666"
output: html_notebook
---
<!--
================================================================================
CANONICAL TEMPLATE — DO NOT RENDER DIRECTLY
================================================================================
This Rmd... |
5e89ae6924263477e0e33ae02d76335b57ce3a8ecdcf880d7f7bb57ec58ce4c5 | R | 19,186 | 380 | #' Parallel Analysis Based on Multivariate LDSC
#'
#' \code{paLDSC} performs parallel analysis using LDSC-derived genetic (co)variance matrices to determine the number of non-spurious latent dimensions in genomic data. The function compares the eigenvalues from the LDSC matrix to those derived from null matrices gene... |
e5621ab07dc72191ef20647e31410bd4a57b6b439afa84bef8efc751642b64a7 | R | 19,524 | 552 | #' @include generics.R
#'
NULL
#' @param seu A Seurat object. Only applicable when using the Seurat method.
#' @param features Features to be plotted, which can include gene expression, metrics, PC scores, or any other data that can be retrieved using the `FetchData()` function. Only applicable for the Seurat method.
... |
7dfab108f107c56f97b64065b8820624aee95471bf68670503276c24a20ca3fe | R | 19,800 | 468 | # Internal file-reading and validation helpers for run_eqtl_finemapping_files().
#
# All functions in this file are internal (not exported). They read and
# validate the five tensorQTL-derived input files described in
# susie_eqtl_finemapping_package_plan.md.
#' @keywords internal
#' @noRd
construct_dataset_paths <- f... |
12f9995006bcfbac5021955ce451cbea9f42d610ad5d7ad0111be19d40d526b3 | R | 19,804 | 428 | ### IsoformSwitchAnalyzeR ###
library(arrow)
library(IsoformSwitchAnalyzeR)
library(rtracklayer)
final_pb_ids <- import("nextflow_results/V47/orfanage/orfanage.gtf") %>%
as.data.frame() %>%
distinct(transcript_id) %>%
pull(transcript_id)
#Subset the transcript file to these PB IDs.
tr_count <- ... |
13e9d0659455dab5e58a123ea231d9fdd17cd2d5c2e69d699389e02f895d1104 | R | 19,876 | 492 | ---
title: "Frequently Asked Questions (FAQ)"
author: "Yichao Hua"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Frequently Asked Questions (FAQ)}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
## Table of Contents
1. [Running scVelo Functions in RS... |
fd2388b7dd62268ab780545503364e78b1e19cc2a6a6e2444a3ba73a1c245b24 | R | 19,931 | 477 | ---
title: "Canonical template: Fig 4m / Fig 4m-extension line-profile colocalization"
output: html_notebook
---
<!--
================================================================================
CANONICAL TEMPLATE — DO NOT RENDER DIRECTLY
==========================================================================... |
673f0d7fcc8a873fe85a6c11078e6a16d68d65c08105d2828a6240c7f564d4d3 | R | 19,957 | 436 | #!/usr/bin/env Rscript
# =============================================================================
# preprocess_methylation_arrays.R
# =============================================================================
# IDAT-based methylation preprocessing using minfi, following NBIS tutorial:
# https://nbis-workshop-... |
3619185dc96412fc1fc7c82d23814b8d5fdb57009b4e900cddfa1c874cbcf598 | R | 20,039 | 524 | #### GenomicSEM multivariable HDL function, based on the amazing work by Ning, Pawitan and Shen, Nature Genetics (2020)
hdl <- function(traits,sample.prev=NA,population.prev=NA,trait.names=NULL,LD.path,Nref = 335265,method="piecewise"){
### Do some data wrangling for the LD files:
cat("GenomicSEM multivariable... |
c7e574903b918bc1e5c9cfb607938d4047708c4796e573ccba399e013fac7d65 | R | 20,194 | 498 | ---
author: "Sayeh Kazem"
title: "Fig5"
date: "`r Sys.Date()`"
output: github_document
---
## Fig.5 : Gene dosage responses across traits (+ S22, S23, S24 & ST6).
#### -- Figure legend -- ####
A)Illustration of gene dosage responses across 3 brain and non-brain traits. Each line connects the burden correlations ... |
aef3a0b7c314befe2197cdfe4036f364ada26f8763a52df9a0f4a0415540750d | R | 20,236 | 507 | ---
title: "neuron_endo_syn_eval"
output: html_document
date: "2025-08-15"
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# I. Load packackes
```{r}
library(devtools)
# Core data manipulation
library(tidyverse)
library(readr)
library(dplyr)
library(stringr)
librar... |
950c478ad8217c7145ce000f1fdf815c4f437bf961b747cb3e509d1389eb7c1c | R | 20,306 | 572 | ---
title: "Comparison SpatialDE genes"
author: "Lukas Weber"
date: "`r format(Sys.time(), '%Y-%m-%d')`"
output:
html_document:
toc: true
toc_depth: 2
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
```
# Comparison SpatialDE genes
Comparison of top significant ... |
8fb9feb4ce7744581fef7f04bd9c439b7397d657dd801f0a3d3c14e306f2f088 | R | 20,414 | 389 | ################################################################################
### Modifier Interval Candidate Gene Pipeline Part 6:
### Conserved Sox10 binding motifs in modifier intervals: Gopinath et al., 2016
### set of conserved Sox10 binding motifs across mouse, chick, human.
##############################... |
f87a8159c48d2fbccb8d3019d211538a67956167d3afa656987c94f9c3b39f07 | R | 20,469 | 561 | #' Load LOLA Database for RegRegSEA
#'
#' This function loads and processes LOLA databases. It always loads the core database
#' and optionally combines it with the ext database if a path is provided.
#' By default, it keeps all collections, but can filter for specific ones if requested.
#'
#' @param lola_core_path Pat... |
a4d46c318342640403a39fa241f608a3bb3212b9758080215096b7cf8beb69e5 | R | 20,494 | 385 | ################################################################################
### Processing of Zhao et al., 2022 Developmental Cell scRNA-seq data via
### Seurat V5 SCTv2 Integration.
################################################################################
library(Seurat)
library(dplyr)
library(pat... |
3e5d4f673ca500889389e1c200cd16758f656139e264e75391bbc67d615dfbd2 | R | 20,660 | 534 |
# Check cell attributes; add missing ones
make_cell_attr <- function(umi, cell_attr, latent_var, batch_var, latent_var_nonreg, verbosity) {
if (is.null(cell_attr)) {
cell_attr <- data.frame(row.names = colnames(umi))
}
# Make sure count matrix has row and column names
if (is.null(rownames(umi)) || is.null... |
cc42bacadd139c6e4f2972bea194845506ad0cb3309b56beaf2d8f3bb0f223ab | R | 20,933 | 385 | parameters = data.frame(species = phylogenetic_order,
group.by = c("animal", "animal", "animal", "animal", "animal", "batch", "animal", "animal", "animal", "animal", "animal", "animal", "animal", "animal", "animal", "animal", "animal", "orig.file", "animal", "animal"),
... |
6ee373c81e2cbe5161544d1f7b973e9418e0f8ff50faa63c38194619d4813cc0 | R | 21,014 | 397 | ---
title: "Aurelia Atlas | Supplementary Data Figures"
output: html_document
---
```{r, setup, include=FALSE}
# Global chunk options
knitr::opts_chunk$set(
warning = FALSE,
message = FALSE,
echo = FALSE,
fig.width = 12
)
setwd("/lisc/data/scratch/molevo/agcole/R/Aurelia_51k/Ac_manuscript_revision_ACOE")
#load... |
6a5972978e3b685bacf4901660bec5a7f9f1a6fbad0ade6d0442c8348e6136d4 | R | 21,019 | 705 | ---
title: "beat-side GAL4 in ORNs"
output: html_notebook
---
This analysis was based on the GAL4 expression pattern data updated in April 2026.
## Load packages.
```{r}
library(tidyverse)
library(magrittr)
library(gplots)
library(RColorBrewer)
library(ggpubr)
library(showtext)
library(pheatmap)
```
## Load the d... |
3e9699eab421bc13f80c9fe84787132157ae8db14f633086ae09f56cc5620416 | R | 21,285 | 580 | plotCellTypePhylo2 <- function(tree){
admittedModules=unique(c(filteredOverlapList$mod1, filteredOverlapList$mod2))
# generate the multiWGCNA layout
if(is.null(layout)){
myCoords=list()
for(level in 1:3){
WGCNAs=getLevel(level, design)
from=0-width*length(WGCNAs)/2
to=0+width*le... |
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