sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
15a2d9d996af03104682ecc19cd5374a003f0b6cdab69447c420dc8ee8421b01 | R | 22,747 | 362 | ####################################################################
####### paper data import #############################################
####################################################################
Paper_data = read.delim('Paper_data.csv', sep=';', header = TRUE) # data import
head(Paper_data, n = 8) ... |
5734137d13e02274a395cb1e2fbc37670dde4019d24d8e8bfad20e7813fe99b1 | R | 22,779 | 474 | # Analysis Script for the taste_test TUE008 Depression Study
# Author: Corinna Schulz, corinna.schulz96@googlemail.com
# Created: March 2023
# Edited: July 2024
# Code content:
# (1) SET UP
# (2) LOAD DATA
# (3) PREPARE VARIABLESf
# (4) DESCRIPTIVES TABLE
#############################################################... |
710f3b0ac28727261b136d56d4636cb79cd6f3c3e40d2663d444042fff48d89c | R | 22,968 | 581 | # # DES in function
# DESin <- function(x, recent, bin.size, reps = 1, verbose = T) {
#
# # load data
# if (is.data.frame(x)) {
# dat <- x
# }else{
# dat <- read.table(x, sep = "\t", header = T, row.names = NULL)
# }
#
# nes <- c("scientificName", "earliestAge", "latestAge", "higherGeography")
# if(... |
c6473edf3e4d5625faf47ce4217ab6ac1fb27989aea6894dd9475e1780b14c7a | R | 22,992 | 894 | ---
title: "Run line by line preprocessing of DNA methylation data"
subtitle: "Preprocess Rosmap 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
them... |
93364e7506ecf08a732a866a2777c0fda76950df2ad751dc3173d992ecb169fb | R | 23,027 | 675 | #' @include tracking-globals.R
NULL
# Translate metric to value to safe format for REST.
metric_value_to_rest <- function(value) {
if (is.nan(value)) {
as.character(NaN)
} else if (is.infinite(value) && value > 0) {
"Infinity"
} else if (is.infinite(value) && value < 0) {
"-Infinity"
} else {
a... |
0df72d22d4e17075e362f2eef946d6145034636582709aad549d9f20e97c14d3 | R | 23,037 | 636 | #' Check if sample identifiers are stored in a BGEN file (for BGEN v1.2)
#'
#' Check if sample identifiers are stored in a BGEN file (for BGEN v1.2)
#'
#' @param bgenFile a character of BGEN file. Sometimes, BGEN file does not include sample IDs. This information can be extracted from BGEN file. Please refer to https:/... |
f0bb96dbb30f70de252bdbef6d0b15f48c4616f8768d017b4c973733cfb9b545 | R | 23,065 | 894 | ---
title: "Run line by line preprocessing of DNA methylation data"
subtitle: "Preprocess 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: ... |
e4a1fac8c004d5dd5af22c3f29148bcce43420177f815fca985bb8a93eb0607b | R | 23,094 | 516 | here::i_am("scripts/1.neural_differentiation_dataset/1.3.CroCoNet_analysis/1.3.1.CroCoNet_analysis.R")
library(CroCoNet)
library(tidyverse)
library(ape)
library(SingleCellExperiment)
library(foreach)
library(here)
library(ggbeeswarm)
library(ggrepel)
wd <- here("data/neural_differentiation_dataset/CroCoNet_analysis/"... |
7bcabb71cd9d73383f24380d02b79091e28336b01e4bb5063abce3b8e35738cd | R | 23,139 | 366 | #' Plot the distributions of preservation statistics
#'
#' Plots the distribution(s) of the chosen preservation statistic(s) for the actual and random modules and for within-species and cross-species replicate pairs. If a statistic is a good measure of module preservation, the preservation scores are expected to be 1) ... |
8cfb0be4f3ead788786cb05d15418cb46b6cd73fcb37bd03b066b835ec524cb0 | R | 23,246 | 621 | #' Decode and manipulate Neuroglancer scenes (from URLs or JSON blocks)
#'
#' @description \code{ngl_decode_scene} takes a Neuroglancer scene from your web
#' browser and turns it into an R \code{list} object that can be
#' programmatically manipulated e.g. to add/remove segments. Manipulate these
#' scenes with ... |
34be9cc0ea5586d36bc7c2152eedb0f2afbc8d1713ce0b0acb94dc0c2c16da0a | R | 23,252 | 556 | # ============================================================================
# Figure 1E & 1F: Factor Analysis Plots + Source Data CSVs
# ============================================================================
# Standalone script extracted from Figure_1E_1F_Factor_Analysis.Rmd
#
# Figure 1E: (a) Heatmap of facto... |
54d9626e9f8e75ca5e7098ef695fd633d4fb4cbbd0d602ba9a7b0dbc8d6b14ee | R | 23,262 | 638 | # =============================================================================
# Electrophysiology Estimated Marginal Means Analysis
# Model-based treatment comparisons at each voltage using emmeans
# =============================================================================
# Load required packages
required_packa... |
c4219d35650ed18adb1c96c2c88ab38f6426fd343123baaaee7d2db610b74778 | R | 23,312 | 609 | ---
title: "Tutorial 01: Data Access"
author: "Alexander Bates"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
code_folding: show
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, fig.width = 10, fig.height = 6)
# Dataset select... |
a039fc1016d3e23f158e27f6e9e55f802b420323ec5f52504335f94c1efdd3d1 | R | 23,314 | 567 | #' Annotate an XYZ location in Clio as a soma or root
#'
#' @details You should use the tags in the following order of preference
#' \describe{
#'
#' \item{soma}{ If there is a visible soma, use this tag.}
#'
#' \item{tosoma}{ If the soma is not in the dataset, but you know where it is,
#' use this tag.}
#'
#' ... |
42b8423db4b99f77ebcc90886fddddd0d0d1b05114eb3b062203e67bd1d6da27 | R | 23,324 | 628 | # ===== Run Script from Terminal ===== #
# Rscript "/Volumes/PROJECTS/Ongoing/Uncertainty/fMRI/Analysis/data/scr/get data_list.R"
# ==================================== #
# --------- Load libraries --------- #
library(tidyverse)
library(rjson)
library(gtools)
scr_path = '/Volumes/PROJECTS/Ongoing/DPhil-Human-Study/D... |
0e9b63612e6c03a84f37558a7eb1fafc304ea82e9e9db28d79819f2095a0c1d3 | R | 23,326 | 629 | ################################################################################
# NicheNet pipeline (Receiver: ChAT; Sender: Microglia)
#
# Comparisons (default):
# 1) KO-axotomy vs KO-sham
# 2) WT-axotomy vs WT-sham
#
# Main outputs (per comparison):
# - DEG tables (receiver)
# - Ligand activity (AUPR correc... |
3d73ed96f55a18425a5abc405db41e0946f48eaceb67598ef014feff7306949b | R | 23,395 | 608 | ###############################################################################
## Comprehensive GSVA Analysis - All Databases ##
###############################################################################
## PURPOSE: Run GSVA on ALL pathways tested in GSEA analysis to create ##
... |
ee34bfe8e645e37c7c1ebbd0dc3215e0881c2ea8d90a036674ffc57460d6dec7 | R | 23,398 | 508 | #!/usr/bin/env Rscript
# ============================================================================ #
## 01_mgngtus_regression.R
## MGNG-TUS study: Fit mixed-effects logistic/linear regression models to behaviour (responses, repetitions, RTs).
## Copyright (C) Johannes Algermissen, University of Oxford, Oxford, UK, 2... |
55685d9ef09e3960b63683033811655c799070a191c47ff06fef0c1f01d582ea | R | 23,401 | 636 | 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)
# Read annotated... |
6186ca251048127b875f6c9e36015687063e5d0d0ffbdef69dc9563b83f94cab | R | 23,553 | 631 | ##########################################################################################
###############--------------- MODEL AGNOSTIC TASK ANALYSES ---------------###############
##########################################################################################
### In this script:
# (1) Mixed-effects ANOVA... |
71cc6675d3e04fbe15d879b9e434ddcb33864d2572beabcd272d7fbde728f6aa | R | 23,631 | 606 | checkArgsBool = function(arg0, arg0name){
if(!arg0 %in% c(TRUE, FALSE))
stop(arg0name, " should be TRUE or FALSE.")
}
checkArgsNumeric = function(arg0, arg0name, minVal, maxVal){
if(!is.numeric(arg0) | arg0 < minVal | arg0 > maxVal)
stop(arg0name, " should be a numeric value ranging from 0 to 0.5.")
}
C... |
7653ff433cc734579193036179cfba9de1e5be2f4fd79b61d18607b72f5140f1 | R | 23,644 | 694 | ---
title: "Presentation PILT Script"
author: "Michael Colwell"
date: '2024-04-10'
output: html_document
---
##PILT pre-processing script
This is the behavioural processing script for the Probabilistic Instrumental Learning Task (PILT), modified for the PEACE study by Michael Colwell (michael.colwell@psych.ox.ac.uk).... |
de06dc79d64275fe7cae6755a76c3abe3e161fb643da20e2cf0381aaea0ee599 | R | 23,701 | 426 | ##################################
# Analyze infercnv metadata to revise uncertain malignant classification for CARE IDH-mutant astrocytomas
# Author: Kevin Johnson
# Date Updated: 2026.03.20
##################################
library(tidyverse)
library(purrr)
library(ggdist)
library(viridis)
library(Seurat)
library(h... |
140246988a7d24c7791d073b088ec7a44b2072e234babaac41e15de643655acb | R | 23,721 | 561 | plotCorrDensity.SOSet <- function(object,
filename = "pairwiseCorrDensityPlot.pdf",
breaks = NULL,
width = 15, height = 15) {
mat <- object@corMat
rank <- object@NMFrankL
if (is.null(mat)... |
e5f011824b37bdfcc53b810015af9168c26dad6f95963e00f64ebf03fd3b3f75 | R | 23,756 | 547 | # load packages
library(ggplot2)
library(Seurat)
library(dplyr)
library(Matrix)
library(plyr)
library(tidyr)
library(reshape2)
library(data.table)
library(ggrepel)
library(ggpubr)
library(WGCNA)
library(DESeq2)
library(fastDummies)
set.seed(1234)
source("/bmapfs/archive/konopkalab1/SCRIPTS_pr/SCRIPTS/utility_functions.... |
39a4da5341da5772285d75c962645b7eb8aa4f3f646374f3ce90a7d62aacbf78 | R | 23,880 | 926 | ---
title: "Run line by line preprocessing of ROSMAP 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: ... |
1197714732fd48a08a46a47c05a7b60bba15d554a943ece3368daed256e68af5 | R | 23,933 | 626 | getuw_lasso <- function(ds,
wbounds = NULL,
nwbound = 100,
max.iter = 15,
iseed = NULL,
silent = FALSE,
warm_start = TRUE) {
# dim
n2 <- nrow(ds)
p <- ncol(ds)
n <- cei... |
ab9e2d3d76d3721b80bcc5b92191c54ee6bddd13cd6d6e5ea9e3a25a8b547600 | R | 23,951 | 595 | ####################################################################
# Gene Activity Score Methods
####################################################################
#' Add GeneScoreMatrix to ArrowFiles or an ArchRProject
#'
#' This function, for each sample, will independently compute counts for each tile
#' per c... |
8daa506f3ea8973cccbafcf986b7d75844f01c1a948fa05f435b34896c09bb45 | R | 23,957 | 533 | ##########################################################################################
# Clustering Methods
##########################################################################################
#' Add cluster information to an ArchRProject
#'
#' This function will identify clusters from a reduced dimensions ... |
9fafd77a8a65f1ffb17d78a33ab978a589d8a71943690cb60e2f44ec32a1603e | R | 23,971 | 702 | library(shiny)
library(ggplot2)
library(purrr)
library(shinythemes)
library(shinyWidgets)
library(MHCbinding)
library(DT)
library(dplyr)
library(ComplexHeatmap)
library(circlize)
library(cowplot)
library(shinyFeedback)
library(waiter)
library(heatmaply)
library(slickR)
library(shinymaterial)
library(Biostrings)
#dyn.lo... |
c3ddccfaa70cbfb20fb6b2ecb508757e77c74f2e5900eed2644371ac7a8b95c5 | R | 24,002 | 632 | ##BULK RNA------------
# 读取数据
#GSE256068--------------
library(data.table)
GSE256068_raw_data <- fread("GSE256068_raw_data.csv.gz")
class(GSE256068_raw_data)
GSE256068_raw_data <- as.data.frame(GSE256068_raw_data)
library(stringr)
GSE256068_raw_data[1:4,1:4]
library(org.Hs.eg.db)
gene_symbols <- mapIds(
org.Hs.eg.db... |
f824dcecbbc7fe73543fa331d5df2a9ae6ee09fd36cc7b8caa14dce887fb7b92 | R | 24,181 | 682 | # # 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)
##################################################... |
0bfa6841badff5d00f0207a6fcdce378c81cbd1ff97c87ce9f909e3a00d6159f | R | 24,275 | 635 | ---
title: "SSRI – Differential Metabolomics Analysis"
author: "Abishek Arora"
date: "10/06/2025"
---
# Differential Metabolomics Analysis
Mass spectrometry was performed on samples for cell lines exposed to fluoxetine hydrochloride (FH), citalopram hydrobromide (CH), sertraline hydrochloride (SH) and paroxetine hydr... |
01b24abf5f3257d4a33486cb010670feb9e760a69cf0360e8d9f6368fe44c4cc | R | 24,297 | 580 | ---
title: "sustain_cogdata"
author: "Marlene Lin"
date: "2/10/2025"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# import
```{r}
library(car)
library(gridExtra) # For handling grid-based plots
library(effects)
library(patchwork)
library(jsonlite) # for saving to J... |
a2db3b9508bc129f805a4e48fb01b3ffab0e6dd60f7acd1bf86f1234e17de17a | R | 24,338 | 568 | #' banc-meta — Compile `banc_meta.csv` from L2 metrics + NBLAST CSVs + IDs.
#'
#' Builds `banc_meta.csv` by combining: (1) L2 skeleton metrics from
#' per-neuron CSVs, (2) `banc_ids.csv` + root positions, (3) compiled
#' NBLAST CSVs to derive `auto:*` cell types above score thresholds.
#' Plots have moved to `banc/nbla... |
804b5701efaf5125ac344fe1a84a87899d1b4ba68075f060d2093b0f2af9ecde | R | 24,369 | 502 | # Load libraries
library(fafbseg)
library(tidyverse)
library(readr)
library(catmaid)
# Get the data we have already built
gt.nt.orig <- data.frame() #readr::read_csv(file = "gt_data.csv")
# Transmitters we care about
fast.nts <- c("acetylcholine", "glutamate", "gaba", "glycine",
"dopamine", "serotonin", "o... |
4e2278d5861e21035dbdda560ae67427c1e073dee2c00217b6c25647e043b899 | R | 24,531 | 622 | getuw_lasso_lagrange <- function(ds,
lambdas = NULL,
nlambda = 100,
max.iter = 500,
iseed = NULL,
silent = FALSE,
warm_sta... |
0ffce93bb4b96ff52e32b8c9449c83d2cccbbc9b4ab88fc4ec002c7aa3b1d5b6 | R | 24,582 | 651 | #####################################################################
# Module Training Core Functions
#####################################################################
# This file contains the core functions for:
# 1. Data preparation and processing
# 2. Model training and cross-validation
# 3. Performance e... |
0974299aa445f24690c0838a8f3240fe713168b61f1e792297d3714bd80a0ae6 | R | 24,644 | 778 | ---
title: "SpNeigh analysis of mouse brain Xenium 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 format(Sys... |
ce6cc4115c2737027746260b16471afd1f777de1eee3c1d357c8e3c43db3e583 | R | 24,648 | 592 | library(ordinal)
library(tidyr)
library(dplyr)
library(lmerTest)
library(BayesFactor)
library(bayestestR)
library(emmeans)
library(ggplot2)
library(ggeffects)
library(rlang)
library(afex)
"
The following are the behavioral data analyses for experiment 1 of the Cogitate Consortium.
The models below follow pre-registr... |
7e266620f2b27cc5dd8ca6264b21b8446186d4b0b1b0a7749866f5f75f51bc92 | R | 24,699 | 419 | here::i_am("scripts/4.paper_figures_and_tables/suppl.tables.R")
library(openxlsx)
library(tidyverse)
library(CroCoNet)
library(igraph)
library(here)
wd <- here("data/paper_figures_and_tables")
## Helper function to handle numeric variables --------------------------
round_numeric_columns <- function(df, signif_di... |
3ebcfa6d7c00a9fabe6090fc51543dfe3599bf88129c693befdc78d4a9afbc16 | R | 24,770 | 598 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
######################
### load libraries ###
###... |
54b812a996c0163c8588a8467324faf3126dd36bcdd4b0bbc58498ed124aa3af | R | 24,785 | 507 | #' Given a set of genes of interest, full unipartite networks with scores (one network for each sample), a significance
#' cutoff for statistical testing, and a hop constraint, UNAGI finds a subnetwork of
#' significant edges connecting the genes.
#' @param nodeSet A character vector of genes comprising the targets of ... |
9bc654368313edab3cb4b56834f020ea6a5902452bf83a9431abaeb38f14d5ae | R | 24,794 | 428 | # ----------------------------------------------------------------------------------------------------
# Load packages
# ----------------------------------------------------------------------------------------------------
library(NMF)
library(ggplot2)
library(scales)
# upload this function
source("/vast/palmer/pi/ve... |
c63747c87bc2cf3935eec477e1f07f538331e5768ac7755a460b7bd8ada8c5f3 | R | 24,803 | 723 | #' Removes spatial outlier cells based on local k-nearest neighbor distances
#'
#' Identifies and removes spatial outliers based on local density.
#' For each cell, the average distance to its k nearest neighbors is computed.
#' Cells with a mean k-NN distance greater than a specified cutoff are
#' considered outliers ... |
a10487beb08d346b94da01a8a6ba684efd19107fe98fbf4cc2e61a2dd46dc67a | R | 24,867 | 680 | ---
title: "ICLE_Data_Analysis"
author: "Osama Shiraz Shah"
date: "`r Sys.Date()`"
output:
github_document:
toc: true
toc_depth: 3
editor_options:
chunk_output_type: inline
---
# Publication and Data Availability
This analysis accompanies the ICLE manuscript (PubMed: )
Raw sequence files are deposited ... |
da65fa9ae690ee8425f6540e987241ccc2fd57eb136e352c8b722e7a55960999 | R | 24,873 | 371 | #!/usr/bin/env -S pixi run --manifest-path /app/pixi.toml Rscript
#options(stringsAsFactors=F, scipen = 999)
options(stringsAsFactors=F)
library(SAIGE)
BLASctl_installed <- require(RhpcBLASctl)
library(optparse)
library(data.table)
library(methods)
library(parallel)
print(sessionInfo())
option_list <- list(
make_op... |
04c19e229a2cb757097eb46ff4202ff9bbdb19706fbf6258f318329a5dfbf03d | R | 24,919 | 596 | # Supplementary Material for "Safety analysis of Off-Label Intravitreal Bevacizumab
# in the FDA Adverse Event Reporting System (FAERS) Database"
# Homero Contreras-Salinas, Janet Cristina Vázquez-Beltrán,
# Gisela García Sánchez, Sebastian Quirarte-Justo,
# Sarahí del Carmen Gómez Macías, Lourdes Yolotzin Rodríg... |
13ff50612ea1b4d3a5354c00fe7094cd37a60ddba2dccee64f908e03bef019fd | R | 24,940 | 831 | #' @title QQ plot
#'
#' @description `fig_qq` creates a quantile-quantile (QQ) plot.
#'
#' @details This plotting function plots a quantile-quantile plot of
#' -log10(p-values). Observations can be divided into groups and can include
#' corresponding confidence intervals. This plot is based on the
#' [QQ plot](ht... |
8ef81a15d4a5b466fd8c27bd724e55e5676d3937e4e220e4edfde36d025b8014 | R | 25,031 | 702 | ---
title: "Unified Sleep Analysis: Diary & Actigraphy"
author: "Emily Stanyer"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
df_print: paged
params:
diary_dir: "Data/raw/sleep_diary/"
actig_dir: "Data/raw/actigraphy/"
eeg_path: "Data/processed/EEG/clean_EEG_data.xlsx"
out_dir: "Output/comb... |
bdf235fe7eb76b6793e3099311126443359b41df0fe33650622e456f27633797 | R | 25,041 | 666 | #' Annotate your Expression Set with biomaRt
#'
#' @param obj ExpressionSet object.
#' @param genes Genes or rownames of the ExpressionSet.
#' @param filters getBM filter value, see getBM help file.
#' @param attributes getBM attributes value, see getBM help file.
#' @param biomart BioMart database name you want to con... |
1ddce649b5f451d37a01bea2ac7ea3c0f4d979ad6a6d1802ad3bfac218f7d32b | R | 25,315 | 764 | ---
title: "Folate_ConBP_Male"
output: html_document
date: "2024-01-11"
---
Load RNA-sequencing data, Consensus between Brain and Placenta for males
```{r}
library(WGCNA)
library(readxl)
#only use samples that have
#No PCB ; No Folic Acid (0 / No)
#No PCB ; Yes Folic Acid (0 / Yes)
#Yes PCB ; No Folic Acid (0.1 / No... |
a9746c8fa35f3cc8bdb9110f14ae6208e1b6c3794c3d2322a1da7caec7d9d527 | R | 25,333 | 791 | ---
title: "Supplementary Figures — 1, 2, 3, 7, 8"
---
This script produces all supplementary figure panels.
Inputs (processed Seurat objects from analysis scripts):
- `output/01_basak/basak_snRNA_seurat.rds`
- `output/02_meta/meta_snRNA_allcells_seurat.rds`
- `output/02_meta/meta_snRNA_neurons_seurat.rds`
- ... |
42d4988a9a9a688a38eb34147cac4a7ef027d8af62a13d55c0dc5113a002df77 | R | 25,340 | 679 | ##########################################################################################
# Transcription Factor Footprinting Methods
##########################################################################################
#' Calculate footprints from an ArchRProject
#'
#' This function will get footprints for all... |
968cb3d3470d16242982bef5f02f3f8689c201ad5ca7848abc9c1d286e2f9504 | R | 25,369 | 589 | # Evaluates and plots the results from the machine learning analyses.
# Also performs follow-up analyses on the final pattern and theory-driven analyses. If analyses are to be conducted from scratch,
# scripts 3-5 have to be ran first. If not calculated from scratch, this script uses files from the Results folder and ... |
56753bbe8776c861f933650b27294d6c144e0bc35b3d95bf2fc2a2543bb36514 | R | 25,413 | 486 | ---
title: "acesinabcd"
author: "FB"
date: "2024-03-11"
output:
word_document: default
html_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Youth-deferred ACEs proxy scores for ABCD data
Running this code will create an ACEs proxy score for Baseline, Yr1, Yr2, and Yr3 ... |
71b8eb3f1b00b97a05c7ec6e99074f7748d34ce4fbccc49a781ccf808df823e8 | R | 25,498 | 815 | library(dplyr)
library(tidyr)
library(cluster)
library(ggrepel)
library(FactoMineR)
library(ggplot2)
library(factoextra)
library(ggforce)
library(psych)
library(plotly)
library(purrr)
library(stringr)
library(lmerTest)
library(reshape2)
library(tidyverse)
library(pls)
library(corrplot)
library(glmnet)
library(vip)... |
9bd4c499ed7aca3a4e88eb4c43ae414720418df03fde68e73751c9d0170c9eb5 | R | 25,552 | 558 | ---
title: "MetaNeighbor : a method to rapidly assess cell type identity using both functional and random gene sets"
author: "Megan Crow, Stephan Fischer, Sara Ballouz, Manthan Shah, Jesse Gillis"
date: '`r Sys.Date()`'
output:
pdf_document:
fig_caption: yes
number_sections: yes
toc: yes
toc_depth: 6
... |
903218ee552bfe6ababb93a5eadaa318cf5d7727a19394556a207ea09759f52e | R | 25,557 | 403 | conda activate R4.3
# /histor/sun/wangyan/anaconda3/envs/R4.3/bin/R
library(CellChat)
library(patchwork)
library(Seurat)
#library(SeuratData)
#library(tidyverse)
library(NMF)
library(ggalluvial)
library(ggplot2)
library(svglite)
library(ComplexHeatmap)
options(stringsAsFactors = FALSE)
setwd("/histor/sun/wangyan/shi... |
af5fa087d4c0ee8fa3cb01f05147def9cccb5e094904c3b6717ea4ce88fb4298 | R | 25,573 | 1,091 | ---
title: "Pathway analysis of CpGs significant by coMethDMR and comb-p"
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... |
0999573191e72cf19fded652bbc9bd7749fc9f4692fc8093a1e5cbfd0c53816d | R | 25,649 | 639 | ################################################################################
# Microglia analysis pipeline (PUBLIC-SAFE, anonymized)
#
# What this script does:
# 1) Load 10x Genomics HDF5 (raw_feature_bc_matrix.h5)
# 2) Subset cells using a barcode→Group CSV
# 3) Preprocess: SCTransform → fallback to LogNorma... |
6979fbcd5fb92b48c4a7e34b7b248bb5dbdd4f62c71fc726f2bc20949fbc565c | R | 25,687 | 595 | # https://bioinformatics.ucalgary.ca/publications/MM-in-PVIN/GeoMx/
# GeoMx Data Preprocessing/Analysis
baseDir <- getwd()
setwd(baseDir)
library(NanoStringNCTools)
library(GeoMxWorkflows)
library(GeomxTools)
library(stringr)
library(dplyr)
library(scales)
library(ggplot2)
library(ggpubr)
library(ggsankey)
library(pl... |
1194f0228fd599e48a5ae45e91e8cd1f1f902c944ea5f27768062d5a01398449 | R | 25,761 | 489 | #####################################################################
############################ Aims ####################################
#####################################################################
#### Processing of joined data assembled from datasets retrieved from individual #papers and standardize... |
6651250e646e349663e0ec422750fa54f23bb516a8f6bffb3902fca392116fbe | R | 25,864 | 679 |
# =======================
# Script for generating Figure 3, and Extended Figures 8 to S11
# =======================
## this code takes trait data to generate subsets, needed for running rates through time for subsets in PyRate
## as weel as partial dependence rates-through-time.
## then it will read these RTTs to cre... |
5014b0c93a07a2961f7dffcdbaa7add9996d2aa1dad88d4e7e2ee59add2962cf | R | 25,931 | 543 |
#install.packages
library(Seurat)
library(ggplot2)
library(DoubletFinder)
library(dplyr)
library(cowplot)
library(reshape2)
library(MAST)
setwd("/athena/ganlab/scratch/lif4001/LG815_TDI/integration_mt5")
LG815_TDI_integrated <- readRDS("LG815_TDI_integrated_PCA_0.1.rds")
Idents(LG815_TDI_integrated) <- "seurat_cluster... |
839fd585a93fdecf7ee0eb72ce9a046360198c02a3e970a92097806e45976e75 | R | 26,045 | 793 | rm(list = ls())
library(dplyr)
library(plyr)
library(Seurat)
library(ggpubr)
library(reshape2)
library(tidyr)
library(tidyverse)
library(DropletQC)
source('~/SCRIPTS/utility_functions.R')
#####
## LOAD COUNT MATRIX
#####
# Set the samples and read them with QC
samplePath = '/home2/gkonop/project/03_MATRIX_FROM_CELLBE... |
c306a5bdf605091e72ccb22a15c13c9d4d637fd127d02039ccf96214513c9d82 | R | 26,111 | 575 | #########################################
# SCRIPT TO DISPLAY SAMPLES CLUSTERING #
########################################
library(MOFA2)
library(reshape2)
library(stringr)
library(circlize)
library(RColorBrewer)
library(ComplexHeatmap)
library(tidyverse)
library(data.table)
library(doParallel)
library(for... |
11c55debd7cac36348424f6ed0aa05ec11cee439bf63c7ac18fe26dfe233f5c7 | R | 26,181 | 442 | library(tidyr)
library(dplyr)
library(ggplot2)
library(readr)
library(here)
library(magrittr)
library(conflicted)
conflict_prefer("first", "dplyr")
conflict_prefer("rename", "dplyr")
conflict_prefer("select", "dplyr")
conflict_prefer("filter", "dplyr")
conflict_prefer("count", "dplyr")
conflicts_prefer(base::union)
ba... |
55ec73cd74d488f7313aa999efce626bf372cd36a84415a4cc0507357a0cf00b | R | 26,197 | 676 | #' banc-synapses-v3 — Process v3 synapse predictions and compare capture vs v2.
#'
#' Resumable: download v3 shards → region/neuropil via alpha-shape →
#' svid→root_id (cache + CAVE) → capture rates → v2↔v3 comparison.
#'
#' @section Reads:
#' - GCS v3 raw synapse parquet shards
#' - SeaTable `banc_meta`, CAVE (svi... |
187758c27959d6ea7510f64b3c7f511e277643c6fbf93754dc56fff038c5640a | R | 26,302 | 759 | ### -----------------------------------------------------------------
### Legacy exploratory analysis by a member of the Wilson Lab.
### Historical record only — not part of the bancpipeline release.
### -----------------------------------------------------------------
# Script to explore oviDNs cluster
#############... |
9035ef7c19c4b8e0aab0118e97381c4365e6bfe454f35a7fd0f24db4bf5b17a6 | R | 26,541 | 558 | #' @importFrom stats qnorm
NULL
#' Get a vector of the names of an object named by the names themselves.
#' This is useful with lapply when passing names of objects as it ensures that the output list
#' is also named
#'
#' @param g an objects on which we can call names()
#' @keywords internal
namedNames <- function(g)... |
7fd4c0920a093cce33bf79ed054c6b7c1329165b87c98cad1db1e425a83d0fb0 | R | 26,579 | 616 | here::i_am("scripts/2.validations/2.8.POU5F1_CRISPRi/2.8.8.QC_and_filtering.R")
library(tidyverse)
library(BPCells)
library(Seurat)
library(SeuratObject)
library(SeuratDisk)
library(foreach)
library(doParallel)
library(Matrix)
library(ggh4x)
library(plyranges)
library(Biostrings)
library(patchwork)
library(fuzzyjoin)
... |
cc26fbec43fb20f68eb2b388ee41fa801392607cad1a842c9b42356c9c1d5aed | R | 26,627 | 583 | library(trackViewer)
library(dplyr)
library(RColorBrewer)
#practice stuff
set.seed(123) ## set seed for random sampling to make sure it can be repeat.
## Here we use SNP sample data
SNP <- c(10, 100, 105, 108, 400, 410, 420, 600, 700, 805, 840, 1400, 1402)
## use GenomicRanges::GRanges function to create a GRa... |
07f1840f57f108c54d740b9f95fc4cbddb99ae333e635dab953234e6b1e6dcdc | R | 26,683 | 541 | #' Download the Human DLPFC Visium data from LIBD
#'
#' This function downloads from `ExperimentHub` Visium, Visium Spatial
#' Proteogenomics (Visium-SPG), or single nucleus RNA-seq (snRNA-seq) data
#' and results analyzed by LIBD from multiple projects.
#' If `ExperimentHub` is not available, this function will
#' dow... |
08a56305a2862b0a769156b5b5eeb0a399be5e833ca40ac55e20f79914ce7805 | R | 26,690 | 798 | ---
title: "Female_ConsensusBrainPlacenta_PCBonly"
output: html_document
date: "2023-10-05"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#Conserved modules between female brains and placenta
```{r}
# Display the current working directory
getwd();
# If necessary, change the path below to the... |
011adf59b73392a80da7b1af25b127cf3c94191dad129a885f1b42dd1fc6fab5 | R | 26,693 | 568 | ---
title: "DE analysis through MultiNicheNet"
author: "Robin Browaeys"
package: "`r BiocStyle::pkg_ver('multinichenetr')`"
output:
BiocStyle::html_document
output_dir: "/Users/robinb/Work/multinichenetr/vignettes"
vignette: >
%\VignetteIndexEntry{DE analysis through MultiNicheNet}
%\VignetteEngine{knitr::rmarkd... |
f70303df3e5bd35f66d4cfabd0e1b74bf81eb1258c2937dc9fb4a907d65074fb | R | 26,803 | 442 | #set working directory ====
setwd("/athena/ganlab/scratch/lif4001/DVPV/DF_1stRound")
#install.packages
library(Seurat)
library(ggplot2)
library(DoubletFinder)
library(SoupX)
#for loading Cell Ranger counts:
sc = load10X('/athena/ganlab/scratch/lif4001/DVPV/cellranger/DVPV_CTX_311/outs')
sc = autoEstCont(sc)
DVPV_CTX_3... |
cc405c9d8763a6ea7ef0ccc374acfe8ef25c2a02a388e4a0fc97763b3a4ff810 | R | 26,885 | 424 | #' Plot the networks of modules
#'
#' Plots the top \code{N} strongest connections of the chosen module(s) based on the consensus network. The edge width indicates the consensus edge weights, while the edge color indicates either the degree of edge divergence across species or the direction of interaction between the 2... |
4f0011b9e6136b43530ab305bc14a473ad31814a5d822486f9d6d63ba3745d0a | R | 26,892 | 504 | #' Estimate the effectize sample size
#'
#' @param plinkFile character. Path to plink file to be used for calculating elements of the genetic relationship matrix (GRM). minMAFforGRM can be used to specify the minimum MAF of markers in he plink file to be used for constructing GRM. Genetic markers are also randomly sel... |
1d5d5c7177012cbdc9b1be9d303bb7779dbf4306bc5efadfccdb7d39b23227af | R | 26,948 | 576 | here::i_am("scripts/4.paper_figures_and_tables/figure2.R")
library(tidyverse)
library(SCORPIUS)
library(patchwork)
library(Matrix)
library(SingleCellExperiment)
library(igraph)
library(inflection)
library(ggnewscale)
library(ggsignif)
library(ggraph)
library(ReactomePA)
library(here)
library(ggtext)
library(cowplot)
l... |
f22a92797c81aef2a0b03503562e41f8881afd5542dcf376e6c3404512abbeba | R | 26,972 | 686 | #'
#' Specify Priors for the Chosen Model
#'
#' These values are entered manually by default but can be recycled from another prior (given in the `update` argument).
#'
#' Where a value is not supplied, the user is prompted to enter numeric values (or functions that evaluate to numbers).
#'
#' To get the prior help use... |
d95437218fb7747fd11decda957b12ce2c16812cc209f478dbcaae1fd2a0fd8b | R | 26,979 | 512 | # MODEL FITTING: whole-brain co-fluc amplitude DEPENDENT CHANGES IN regional co-fluctuation score.
library(dplyr)
library(R.matlab)
library(ggsegGlasser)
library(ggsegSchaefer)
library(ggseg)
library(ggplot2)
library(ggdensity)
library(ggseg3d)
library(cifti)
library(stringr)
library(factoextra)
library(matrixStats)
li... |
cec8826d5faa1edfa26dbbe3cff44908885bbd0574f87fef7008dd7db5820e7c | R | 27,039 | 729 | ###############################################################################
## Master GSVA Table Generator ##
###############################################################################
## PURPOSE: Create a comprehensive master table of GSVA trajectory analysis ##
... |
7abe41cc98c5c0931f72232ed4cbd445fab706c303ab18a29a944cfb5aaf9b4f | R | 27,044 | 670 | runCIE <- function(databaseType = c("TRED", "string", "ChIP", "TRRUST"),
filter = FALSE,
DGEs, p.thresh = 0.05, fc.thresh=log(1.5), logFC=TRUE,
methods,
filteredDataName=NA, ents=NA, rels=NA, useFile=TRUE,
useMart=FALSE, useB... |
539a1afb49e7f135953c8f8718bdf996a908cdd25d4b6e738cb8a03f3bff7daf | R | 27,078 | 880 | #' @useDynLib ArchR
#' @importFrom Rcpp sourceCpp
#' @importClassesFrom GenomicRanges GRanges
#' @importFrom GenomicRanges GRanges
#' @import data.table
NULL
setClassUnion("characterOrNull", c("character", "NULL"))
setClassUnion("GRangesOrNull", c("GRanges", "NULL"))
setClass("ArchRProject",
representation(
pr... |
f62344c6488131d89fe524f5ee957acb2ce69cf2fd53d9ebef83f02177cebff1 | R | 27,146 | 877 | # map_seurat
#
#
# Seurat 5.0.1的标签转移和UMAP坐标映射
#
# `Map_Seurat_normalize`和`Map_Seurat_cluster` implement the standard process for scRNA
#
#
# + Seurat_to_mtx
# + Map_Seurat_normalize
# + Map_Seurat_cluster
# + Map_Seurat_cluster_run_harmony
# + Map_Seurat_mapquery
# + Map_Seurat_example
# + precess_after_Seurat
# ... |
98eeacaf8b12051acb305681ddc2a611ecb6a017eb555cb62de20c2de6668d5d | R | 27,148 | 697 | ---
title: "Conos Walkthrough"
output:
rmarkdown::html_vignette:
vignette: >
%\VignetteIndexEntry{"Conos Walkthrough"}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
- [Loading the Data](#loading-the-data)
* [Pre-processing with pagoda2](#pre-processing-with-pagoda2)
* [Pre-processing wi... |
bd1f325fc5d32fad09998bed5cb9aa455f91532dab8623df57467298acd68694 | R | 27,191 | 711 | # DE test each control gRNA against all others
de_testing_cntrl <- function(sce_cntrl, cntrl_ids) {
sce_cntrl_filt <- sce_cntrl[, sce_cntrl$gRNA %in% cntrl_ids]
metadata <- colData(sce_cntrl_filt) %>%
as.data.frame()
# design
design <- model.matrix(~ 0 + gRNA + individual + batch, data = metadata)... |
502c1c27051985a302f613fba306fa89448eb7e4a8cd9bc3041539a6bc9465c8 | R | 27,233 | 557 | # MODEL FITTING: whole-brain co-fluc amplitude DEPENDENT CHANGES IN regional co-fluctuation score.
library(dplyr)
library(R.matlab)
library(ggsegGlasser)
library(ggsegSchaefer)
library(ggseg)
library(ggplot2)
library(ggdensity)
library(ggseg3d)
library(cifti)
library(stringr)
library(factoextra)
library(matrixStats)
li... |
87ce44de97a9f168b5cf95b5c1e1bd06073bf3ec0e90291f96a4ca3c574a0f1c | R | 27,382 | 847 | #' Removes spatial outlier cells based on local k-nearest neighbor distances
#'
#' Identifies and removes spatial outliers based on local density.
#' For each cell, the average distance to its k nearest neighbors is computed.
#' Cells with a mean k-NN distance greater than a specified cutoff are
#' considered outliers ... |
477c08ec0835babdc819ef442df353c46e691cd313ea792dd44acc1894272cd1 | R | 27,624 | 702 | get_objects <- function(type, selection = NULL, sample_prior = F, design = NULL, sampler = NULL,
prior = NULL, stage = "sample", N = 1e5, map = FALSE, ...) {
selection <- map_selecter(map, selection)
return_prior <- ifelse(is.null(sampler), TRUE, FALSE)
if (type %in% c("standard", "blocked... |
45e1a7a84030fd584cdacaa7cbec397960b5d640b68c33bb58e8b57aa78d1e33 | R | 27,648 | 776 | ---
title: "Figure 3: MIC-Drop-seq Analysis - transcriptional and cellular phenotype validation"
author: "Clay Carey"
output: html_document
---
```{r setup}
# Set working directory for analysis- you MUST download the input dat aand set this directory manually for the code to work
knitr::opts_knit$set(root.dir = "/Use... |
b5381db2024e5b9c0ef3db8ede882b798f0d509165b06394c7992650c0e2ac53 | R | 27,723 | 546 | #colocalization analysis
library(GenomicRanges)
library(dplyr)
library(coloc)
library(stringr)
library(locuscomparer)
library(eQTpLot)
library(ggplot2)
GWAS.reg <- read.delim("D:/35379992-GCST90027158-MONDO_0004975-Build38.tsv", header = TRUE, sep = "\t", quote = "\"", dec = ".", fill = TRUE, comment.char... |
0de52cc1fe36ec60abd4046d06bd71560d04295f8665130b66c9ae371fe4b97f | R | 27,807 | 619 | #######################################################################################
###############--------------- MODEL BASED TASK ANALYSES ---------------###############
#######################################################################################
### In this script:
# (1) Preprocess data
# (2) Model ... |
763686dfa934934d0f4c0492aad3e6a21eb9a818728d0e2a34ee77fac38ea6c4 | R | 27,985 | 220 | generate_prioritization_tables_OLINK = function(sender_receiver_info, sender_receiver_de, ligand_activities_targets_DEgenes, ligand_activities_targets_OLINK, contrast_tbl, sender_receiver_tbl, grouping_tbl, prioritizing_weights = c("de_ligand" = 1,"de_receptor" = 1,"de_ligand_OLINK" = 2, "de_receptor_OLINK" = 2, "activ... |
0ec75841ff9a465cdf205e62d59836a777cd50b4b77a80d25dff90fd18f5d78a | R | 28,098 | 602 | #' banc-ngl-upload — Upload Neuroglancer-compatible meshes + segment_properties to GCS.
#'
#' For each cross-dataset target (BANC, FAFB, MANC, hemibrain, FANC,
#' maleCNS, mirror) uploads precomputed-mesh fragments to the public GCS
#' neuroglancer layer and refreshes the per-dataset `segment_properties`
#' JSON. Per-d... |
dc94a3bacb1fd7c0a65e1a59a0ba811e761c244c87ba594f402396da792b08b9 | R | 28,118 | 703 | ###############################################################################
## Critical Period Trajectories: D35 → D65 Maturation Dynamics ##
## Extended with MitoCarta Compensation Pathways ##
###############################################################################
#... |
5ca4a1907f3889ac96a708570b868710ceeaf69c0862a80c0f86b43f3464748f | R | 28,159 | 592 | # ------------------------------------------------------------------------------
# NeuroConnectivity - Functional calcium analysis
#
# Author: Winnok H. De Vos
# Modified by: Marlies Verschuuren
# Creation date: 2019-12-13
# Last Modified: 2023-12-20
# -------------------------------------------------------------------... |
ff63410aa232cd20483cbece04ce9e206cf6c9c2069d4c55bf0f837b05c9f374 | R | 28,160 | 658 | ###############################################################################
## Critical Period Trajectories: D35 → D65 Maturation Dynamics ##
## Show how Ctrl vs mutants diverge during critical period ##
###############################################################################
... |
2137ed3f0e2428b52e2d3fa1e8e31200cf6b673633bd50c0c0e36d3c3378d647 | R | 28,197 | 800 | ###############################################################################
## Developmental Framework Visualization ##
## Early (baseline) → TrajDev (trajectory) → Late (outcome) ##
###############################################################################
... |
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