sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
c9525328cb9df6b88091ceb3ee7e670f533745de2f4bdda3ab602f2b72bc9d61 | R | 19,352 | 454 | library(ggplot2)
library(ggrepel)
library(ggnewscale)
library(patchwork)
library(scales)
library(dplyr)
library(tidyr)
library(forcats)
library(stringr)
library(tibble)
library(readr)
library(purrr)
library(broom)
library(broom.mixed)
library(lme4)
library(ineq)
library(pheatmap)
library(RColorBrewer)
library(Matrix)
l... |
562702a4421159798de3bcfd8f5e74d9c51eb5facc24ffd82f7dcc43256fe1fe | R | 19,408 | 445 | ################################################################################
### Drokhlyansky et al., 2020 Mouse Adult Distal Colon (MADC) scRNA-seq
### Reprocessing Via Seuratv5
### 11-104 weeks-old
### Sox10-Cre;INTACT, Wnt1-Cre2;INTACT, Uchl1-H2BmCherry:GFP-gpi
############################################... |
e578f5929cc9434f1f51c9f2129f3ad4c2f314a10aed03d004358ecbbf107973 | R | 19,444 | 505 | ---
title: "Create a minimal palette for displaying multiple disease labels"
output:
html_notebook:
toc: true
toc_float: true
author: Candace Savonen, Krutika Gaonkar, Jaclyn Taroni, and Stephanie Spielman
params:
release: "release-v23-20230115"
date: 2022
---
## Purpose
There are multiple "disease la... |
70c39b57fd7e975ff6e603983c5e425f9f79a88e5c55f856c7594975ac792956 | R | 19,533 | 523 | # --------------------
# title: Figure5 Code
# author: Hu Zheng
# date: 2026-01-01
# --------------------
library(Seurat)
library(tidyverse)
library(hdWGCNA)
library(cowplot)
library(patchwork)
library(enrichR)
library(GeneOverlap)
library(ggpointdensity)
library(viridis)
library(ggrastr)
library(RColorBrewer)
librar... |
0c8085b6d8c64ed6c87f3d41ef6e6832a07039e8abd9a67124180a039f0cb6a5 | R | 19,571 | 522 | ## Ependymal 1 script: GSM_2677817
## Run until log normalization
## Save seuratobject
library('Seurat')
library('dplyr')
library('gridExtra')
library('scater')
source('/home/clintdn/VIB/DATA/Sophie/RNA-seq_Sandra/CITEseq_Test/RAW_DATA/script_functions_COVID.R') #KEVIN
###############################################... |
a98901e2aa538fa8a172b33dc97abf11d7a82fa580f10f24bf00801631f5da31 | R | 19,571 | 522 | ## Ependymal 2 script: GSM_2677818
## Run until log normalization
## Save seuratobject
library('Seurat')
library('dplyr')
library('gridExtra')
library('scater')
source('/home/clintdn/VIB/DATA/Sophie/RNA-seq_Sandra/CITEseq_Test/RAW_DATA/script_functions_COVID.R') #KEVIN
###############################################... |
e2488b85ae933b12fb81dbdfaef46a8e5248a5118216b1576144fd944f066fb0 | R | 19,573 | 522 | ## Ependymal 3 script: GSM_2677819
## Run until log normalization
## Save seuratobject
library('Seurat')
library('dplyr')
library('gridExtra')
library('scater')
source('/home/clintdn/VIB/DATA/Sophie/RNA-seq_Sandra/CITEseq_Test/RAW_DATA/script_functions_COVID.R') #KEVIN
###############################################... |
6a8dd974e7cf3c0ee2bca74c726e93bf2b001b2ea5827db29b87db70a87ffb7f | R | 19,579 | 472 | library(tidyverse)
library(ggplot2)
library(cowplot)
library(patchwork)
library(extrafont)
library(officer)
library(rvg)
library(ggnewscale)
library(afex)
library(broom)
library(broom.mixed)
library(flextable)
theme_set(theme_cowplot() +
theme(text = element_text(family = "sans", size=9),
axis... |
f7e49e19a1b4c8c687b611701d4db800105bf872760697ceefe8a9caa5b43c1b | R | 19,599 | 406 | ---
title: "Compile molecular subtyping results"
output:
html_notebook:
toc: true
toc_float: true
author: Jaclyn Taroni for CCDL, Jo Lynne Rokita for D3b, Zhuangzhuang Geng for D3b
date: 2020
params:
is_ci: FALSE
---
The purpose of this notebook is to aggregate molecular subtyping results from the follow... |
f0be0512087bc24aaf5c05775068d4e68f083a5547acb9d670a005928c3746dc | R | 19,610 | 555 | suppressMessages(library(ggplot2))
suppressMessages(library(RColorBrewer))
suppressMessages(library(showtext))
suppressMessages(library(ggtext))
suppressMessages(library(VennDiagram))
suppressMessages(library(Cairo))
suppressMessages(library(dplyr))
suppressMessages(library(gridExtra))
suppressMessages(library(magick))... |
d6df0381cd83eb243d495a25be8d17387a9b5c5d1af5cc3b05de601d7927a583 | R | 19,621 | 559 | suppressMessages(library(ggplot2))
suppressMessages(library(RColorBrewer))
suppressMessages(library(showtext))
suppressMessages(library(ggtext))
suppressMessages(library(VennDiagram))
suppressMessages(library(Cairo))
suppressMessages(library(dplyr))
suppressMessages(library(gridExtra))
suppressMessages(library(... |
c9ab8e865ee161d16b6181c0a7b6400f215ab679a96ba1d1933534e8bd27487a | R | 19,698 | 499 | ## Rebuttal: Processing new Betsholtz lab data (10/2023)
## https://betsholtzlab.org/Publications/BrainFB/Data/BFBdata.html
## Three datasets to read in, normalize and annotate according to metadata Betsholtz lab
## No extra filtering! Just like their count table!
## Raw read counts!!
## Run until log normalization
##... |
bdcb143530b3f5156e1cdf220b0000a9493d3c99d695fb88fcbab7bbe492e46d | R | 19,723 | 873 | ---
title: "Immune Cell Profiling in Anti-GAD65 - Data analysis and Visualization"
author: "Sumanta Barman"
---
# Overview
This document contains the analysis pipeline for immune cell profiling in anti-GAD65 encephalitis. The analysis includes:
- Single-cell RNA-seq data processing and visualization
- Cell type ann... |
460ff4d82a8b021290d6fa38c9ed379d7289eac22a51b75d97c7531a34b7005f | R | 19,729 | 346 | %\VignetteEngine{knitr::rmarkdown}
---
title: "MuSiC: Sample Analysis"
output: html_document
---
Installation
------------
```{r}
# install devtools if necessary
if (!"devtools" %in% rownames(installed.packages())) {
install.packages('devtools')
}
# install the MuSiC package
if (!"MuSiC" %in% rowname... |
f3aee7bb1b71c4ecd944907f0aa8c65ec99167c565c31f80c3780794356b4f05 | R | 19,747 | 446 |
###########################################
## Functions to visualise latent factors ##
###########################################
#' @title Beeswarm plot of factor values
#' @name plot_factor
#' @description Beeswarm plot of the latent factor values.
#' @param object a trained \code{\link{MOFA}} object.
#' @param f... |
d209ce3846e19d0a31346f1224472799dc97876c366ea514d0ab90bf0d306a00 | R | 19,762 | 524 | ## Vanlandewijck script: GSE98816 -> raw read counts!!
## Run until log normalization
## Save seuratobject
library('Seurat')
library('dplyr')
library('gridExtra')
library('scater')
source('/home/clintdn/VIB/DATA/Sophie/RNA-seq_Sandra/CITEseq_Test/RAW_DATA/script_functions_COVID.R') #KEVIN
###########################... |
4c825f65d0f2ef7e19ce48d10a6f76247fd060c6b77972c7ac326effe4199466 | R | 19,769 | 491 | #Set up environment----
setwd("/Users/elizabethmallott/Dropbox/Projects/Gut_microbiome/mouse_inoculation/shotgun/humann2_output")
#Import data----
bray_gene = as.dist(read.table("bray-genefamilies-unstrat-noinfant.tsv", header = T))
jaccard_gene = as.dist(read.table("jaccard_genefamilies_unstrat_noinfant.tsv", header... |
2c81e8345345efdfdcec8741d87c9d89245abe02655f046585772007ea5deced | R | 19,782 | 462 | #' Generate data files required for shiny app
#'
#' Generate data files required for shiny app. Five files will be generated,
#' namely (i) the shinycell config \code{prefix_conf.rds}, (ii) the gene
#' mapping object config \code{prefix_gene.rds}, (iii) the single-cell gene
#' expression \code{prefix_gexpr.h5}, (iv)... |
cc19e151500e4c1cca256cb6ff971625258474f5ece986c534cbf4f3aaee913e | R | 19,801 | 477 | #' @title Calculate variance explained by the model
#' @description This function takes a trained MOFA model as input and calculates the proportion of variance explained
#' (i.e. the coefficient of determinations (R^2)) by the MOFA factors across the different views.
#' @name calculate_variance_explained
#' @param ob... |
4f6e1bbb3a41746a5308a698617ba5c77a4b0f137603a5d5e6a77a24aed62d78 | R | 19,852 | 502 | # 4. Neuroimmune BPs -----------------------------------------------------------
## 4.1 Load packages and functions ---------------------------------------------
source("./codes/my_packages.R")
source("./codes/my_functions.R")
# Load classification lists:
# neuroimmune classification - genes:
neuro = scan(f... |
08bc7066788f2391a4338f8ff4ccc7b2d882b91e514b74b8423cadc685a9a588 | R | 19,884 | 779 | ---
title: "MotiMus Questionnaire Data"
Me: Ségolène M. R. Guérin
output:
html_notebook:
code_folding: hide
toc: yes
pdf_document:
toc: yes
html_document:
toc: yes
word_document:
toc: yes
editor_options:
markdown:
wrap: sentence
---
# Preamble
```{r preamble, warning=FALSE, message=F... |
1f0581ca17c47448e632ce4ca63a0a71336c9a6a9101776e8a3ad8eddebf4a52 | R | 19,903 | 554 | #!/usr/bin/env Rscript
prompt_for_install <- function(pkg) {
cat(paste0(pkg, " is not installed. Would you like to install it? (y/n) "))
response <- tolower(readLines("stdin", n = 1))
if (response == "y") {
if (pkg == "ShinyCell2") {
remotes::install_github("OpenOmics/ShinyCell2", quiet = TRUE)
} el... |
7300c3cc3c2829c9b12fef20b9ec96bd6d85d4d3f203380d8af85cd32076cd40 | R | 19,939 | 637 | # rm(list=ls(all=TRUE))
# library(mvnfast);library(matrixNormal)
#
# # Gamma likelihood
# S=function(x,lambda) sapply(x,function(h) sign(h)*max(c(0,abs(h)-lambda)))
# penmu=function(z,LD) {
# lams=seq(0,qnorm(0.975),0.05)
# pens=likes=regs=c()
# for(i in 1:length(lams)) {
# regz=S(z,lams[i])
# like=dmvn(... |
3c587a346fb612fd9ccc958713eff64ef1ed1d1924bad75f557089450a347d36 | R | 19,952 | 587 | ---
title: "Filter MTP Tables"
output:
html_notebook:
toc: TRUE
toc_float: TRUE
toc_depth: 4
author: Eric Wafula, Sangeeta Shukla for Pediatric OpenTargets
date: 01/10/2021
---
Purpose: Remove Ensembl (ESNG) gene identifier in the mutation frequency tables, including SNV, CNV and fusion, TPM summary sta... |
73f2405f1b320d9b6fdd1f1cb1fcce6f42b70e80f151abfb5b73dec0994e7253 | R | 19,956 | 424 | #____________________________________________________________________________________________
# R (version 4.2.1) code for building of mechine learning models in the following manuscript:
# "Predicting dominant terrestrial biomes at a global scale:
# Assessments of machine learning algorithms, climate variables ind... |
9faad3e02feebe7c55b58bc65379684d28a47f8baf82b21a0178e7a635e7085d | R | 20,019 | 469 | #---------------------------------------------------------------------------------------------
# R code for generating VCE (Visualize Climate IMage), which is used for traininig CNN models.
# This code draw VCE for each grid and store it
# in the folder corresponding to the potential vegetation number
#
# This c... |
3d265ce7812893d6190e951fbc75684d4a5f2a448bff714706abb93a83a074e0 | R | 20,025 | 633 | ```{r}
library(Seurat)
library(readxl)
library(ggplot2)
library(ggrepel)
library(ggpubr)
library(dplyr)
```
```{r}
# Read the results for the specified comparisons
d1_spn <- read_excel("D1-SPN DEGs between 16p males vs wt females subset_fold_changes.xlsx")
d2_spn <- read_excel("D2-SPN DEGs between 16p males vs wt fema... |
c7ab2d7d1ced46aa424cc1e6603e9da30067dd55b676f93324d683479c423577 | R | 20,026 | 611 | ---
title: Visual Search, all pairs, behavior analysis
author:
- Mathias Sablé-Meyer
- Lucas Benjamin
- Cassandra Potier Watkins
- Chenxi He
- Maxence Pajot
- Théo Morfoisse
- Fosca Al Roumi
- Stanislas Dehaene
lang: en
output: rmdformats::readthedown
---
```{r settings, echo = FALSE, message=FALSE}
kn... |
d971afc0e15158e87cd259b9e259aa960722d33e6cb7a261d434e78d035da562 | R | 20,058 | 541 | # pROC: Tools Receiver operating characteristic (ROC curves) with
# (partial) area under the curve, confidence intervals and comparison.
# Copyright (C) 2011-2014 Xavier Robin, Alexandre Hainard, Natacha Turck,
# Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez
# and Markus Müller
#
# This program is free soft... |
60297c6b6fe6bf2af365a2c634e7d73127639515dd20eb8009b8e1af1f73c5dd | R | 20,152 | 548 | ---
title: "Analysis Training"
author: "Marcos Moreno Verdú"
date: "2024-04-05"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Load packages and data
Packages
```{r include = F}
library(tidyverse)
library(modelsummary)
library(ggdist)
library(Hmisc)
... |
0c44d0945787316c780947b7f9695a14eae7cc06adc2286daeab151c7710da3a | R | 20,221 | 550 | ---
title: "Plots_Brain_Thresholds"
author: "HannahSavage"
date: "2023-05-18"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#Set env
```{r, include = FALSE}
library(readxl)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(grid)
library(reshape)
library(scales)
... |
1d6dd3abebc19bd659368703b0dabc8f8f281940b88835a027c530b7e2867879 | R | 20,246 | 590 | # --------------------
# title: Figure4 Code
# author: Hu Zheng
# date: 2026-01-01
# --------------------
library(Seurat)
library(tidyverse)
library(cowplot)
library(aplot)
library(pheatmap)
library(ggstatsplot)
library(ggsci)
library(circlize)
library(RColorBrewer)
library(ggrepel)
library(clusterProfiler)
library(or... |
ac8a8f1869dc119b5325fdf89bee3a219bf77ec0af1d9a84bca16478d204fe5e | R | 20,369 | 608 | ############################################################
## Cross-ancestry replication analysis
############################################################
## Packages
library(data.table)
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)
library(ggpubr)
library(broom)
library(pwr)
library(openxlsx)
s... |
f3d36daa67372b97233bae60031761b0e6530552a4bad7f672958ce90674bc93 | R | 20,403 | 724 | ## Utility functions for mrf3.R
# Get tree net from random forest
get_tree_net <- function(mod, tree.id){
xvar.names <- mod$xvar.names
xvar.factor <- mod$xvar.factor
native.array <- mod$forest$nativeArray
native.f.array <- mod$forest$nativeFactorArray
node.stat <- mod$node.stats
native.array <- cbind(nat... |
b5c9ce0c341f56eff955bd41a90377abefbd583c25115777fcfb23d4a6751ad3 | R | 20,428 | 665 | data(agaricus.train, package = "lightgbm")
train_data <- agaricus.train$data[seq_len(1000L), ]
train_label <- agaricus.train$label[seq_len(1000L)]
data(agaricus.test, package = "lightgbm")
test_data <- agaricus.test$data[1L:100L, ]
test_label <- agaricus.test$label[1L:100L]
test_that("lgb.Dataset: basic construction,... |
e36ed37439cbdba6740ac731423f1ec8f10abe13ead4ca376b34002a03c61709 | R | 20,447 | 432 | # recover ciliary genes with pipeline for candidate gene selection
library(igraph)
library(doParallel)
library(foreach)
library(tidyverse)
traitAnnotation = read.csv('data/traitOverview.csv')
variantsWithHPOandMP = read.csv('data/variantsCiliopathyMP.csv')
pageRankScores = readRDS('data/pagerankScores.rds')
variantsC... |
bfa4199873a1fcc5de1a8842984dbd704dad9676f35be456bf79f037de4a6dba | R | 20,492 | 444 | #' Plot all-versus-all alignments stored in PAf format.
#'
#' This function takes PAF output file from minimap2 reporting all-versus-all alignments of multiple FASTA sequences
#' and visualize the alignments in a miropeat style.
#'
#' @param seqnames.order A user defined order sequence names to be plotted from top to t... |
f73d1ded1a38b85a0dfd30e87e152a74c676eec7a2b7225cd2dae0eada653a7a | R | 20,514 | 664 | # Test olink_lmer ----
test_that(
"olink_lmer - works - reference",
{
skip_if_not_installed(pkg = "lme4") |> suppressPackageStartupMessages()
skip_if_not_installed(pkg = "lmerTest")
skip_if_not_installed(pkg = "broom")
skip_on_cran()
# Load reference results
# tests are skipped if files ar... |
930d5f0afcb95ba5cf1e481becba60c43f016d7b09be435626886f15df31354c | R | 20,601 | 456 | ################################################################################
### Zeisel et al., 2018
### Mouse Adult Small Intestine scRNA-seq Reprocessing Via Seurat
### P21 (1Male+1Female x 3), P23 (1Male+1Female), and 8 weeks (1Male+1Female)
### Wnt1-Cre;R26Tomato mice
### Downloaded from https://storage.... |
839e1d75964791d237b2c01b3ac7408f00e8160ed87d01369cf0a9ea873a4059 | R | 20,666 | 502 |
################################################
## Get functions to fetch data from the model ##
################################################
#' @title Get dimensions
#' @name get_dimensions
#' @description Extract dimensionalities from the model.
#' @details K indicates the number of factors, D indicates the n... |
21b5d72266526253ae42b69608f0b4443697ab23ca8346e9a964b3d4c1feaa78 | R | 20,798 | 435 | ################################################################################
### May-Zhang et al., 2021
### Mouse Colon, Duodenum, and Ileum 6wks snRNA-seq Reprocessing Via Seurat
### 6Wks Phox2b H2B-CFP+ high intensity nuclei
### 10x Genomics Runs
#############################################################... |
9c73a1da79186bcfe6a8cc7df15cfbcc4504e38706eb7e8ddeeec4bb58b73dbd | R | 20,803 | 419 | #------------------------------------------------------------------------------#
# #
# #
# ... |
f8515f11e7dc13c3b2330f06540e99be04542ef0ffdba1eb7ca9da20b2a2731d | R | 20,818 | 507 | # Load general parameters for calibration
load_calib_params <- function(l_params_model, # Model parameters to update
l_params_outcome, # List of outcome parameters
l_censor_vars, # List of variables to combine for censor variables
... |
5be4a7d87c43658316f4f6fd1730555fadcdbcd1301f896f8fa675b005540cca | R | 20,819 | 632 | library(ggplot2)
library(ggrepel)
library(ggnewscale)
library(patchwork)
library(scales)
library(dplyr)
library(tidyr)
library(forcats)
library(stringr)
library(tibble)
library(readr)
library(purrr)
library(broom)
library(broom.mixed)
library(lme4)
library(ineq)
library(pheatmap)
library(RColorBrewer)
library(Matrix)
l... |
908c0f0bf58e25182c952e53cd3d138adaeb620a88b54de7a30e995b0b01d2cc | R | 20,820 | 476 |
###########################################
## Functions to visualise the input data ##
###########################################
#' @title Plot heatmap of relevant features
#' @name plot_data_heatmap
#' @description Function to plot a heatmap of the data for relevant features, typically the ones with high weight... |
f93a100628efa607a8b5a38e60b9492fcfb898154427b404b4c9ce479b0f1a60 | R | 20,864 | 577 | # --------------------
# title: Figure6 Code
# author: Hu Zheng
# date: 2026-01-01
# --------------------
library(Seurat)
library(tidyverse)
library(hdWGCNA)
library(cowplot)
library(patchwork)
library(enrichR)
library(GeneOverlap)
library(umap)
library(scCustomize)
library(ggpointdensity)
library(Biorplot)
source('b... |
81a5de93748335fc5394b7f2b369b2666ab94446fbfdd5caa0ceca6265151ae2 | R | 20,920 | 611 | #' @export export_to_gnn
export_to_gnn <- function(data, name, which = "tas", undirected = FALSE) {
path <- file.path(paste0("set_", name), "GNN/tree/")
path_EL <- file.path(paste0("set_", name), "GNN/tree/EL/")
eve:::check_path(path)
eve:::check_path(path_EL)
if (which == "tas") {
for (i in seq_along(da... |
7d4c6d4c1eeb67ae09b61730a03c70c5d395ebf64d6b7e253bb52a311268c34a | R | 20,935 | 610 | #' Calculate Cluster Stats
#'
#' Calculates both overall and per sample cell number and percentages per cluster based on orig.ident.
#'
#' @param seurat_object Seurat object name.
#' @param group.by meta data column to classify samples (default = "orig.ident").
#' @param order_by_freq logical, whether the data.frame sh... |
3f8d97703d21a0cf7dc698293c5515e1d54ca42169a6c993677653c4abb98ab8 | R | 20,945 | 437 | #' @title Plot subject-level summaries
#' @description This function constructs subject-level visualizations of lesion-based damage and disconnection
#' @param cfg a pre-made cfg structure (as list object).
#' @param subject either a string giving a subject ID, or an integer giving the subject index.
#' @param type a s... |
6ade782105cb77985f06630c4565a99bd3ad91e3cf9ad8396d6e785dfb37b577 | R | 20,997 | 484 | # pROC: Tools Receiver operating characteristic (ROC curves) with
# (partial) area under the curve, confidence intervals and comparison.
# Copyright (C) 2010-2014 Xavier Robin, Alexandre Hainard, Natacha Turck,
# Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez
# and Markus Müller
#
# This program is free soft... |
5d19bf35bb296ce3204f351c29c58ffbe747236000c8e4c33bb02894c09c92a1 | R | 21,074 | 526 | ######################################################
## Set the current working directory
######################################################
library(rstudioapi) # make sure you have it installed
current_path <- getActiveDocumentContext()$path
setwd(dirname(current_path ))
base_dir = dirname(current_path)
######... |
3832f8f0a08ca98909501ca8fe805099a1935d51a79123905c3652a2cb2ef715 | R | 21,133 | 592 | # --------------------
# title: FigureS8 Code
# author: Hu Zheng
# date: 2026-01-01
# --------------------
library(Seurat)
library(scCustomize)
library(tidyverse)
library(umap)
library(tidydr)
library(cowplot)
library(ggrepel)
library(pheatmap)
library(viridis)
library(sciRcolor)
library(scRNAtoolVis)
library(networkD... |
1ae3a71047d1e79a795265eaba64122bb3b14046a9de74e3e3d859e0d63bdebd | R | 21,188 | 540 | # Documentation
#' Lasy linear regression function
#'
#' @description This function performs linear regression and print results in tibble output.
#' This function aims to provide the results of the regression analysis in the format, which is frequently
#' desired in academic journals.
#'
#' @param data data frame or t... |
5741d1885ad6f8412bb07100750a21540ae9a0c42590400fc334687be3ef7ae8 | R | 21,195 | 596 | ---
title: "Age_Sex_Sample_Size"
author: "HannahSavage"
date: "2022-11-07"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## SET ENV
```{r, include=FALSE}
library(readxl)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(grid)
library(reshape)
library(scales)
lib... |
1e9c570bc098ee857b68fa59b7735fc6ad4ed5eb096d6bc016e1e7e070e439c5 | R | 21,217 | 720 | #' Performs sparse canonical correlation analysis.
#'
#' @param X An n by p numeric matrix, or a character string pointing to a
#' PLINK dataset.
#'
#' @param Y An n by k numeric matrix
#'
#' @param lambda1 Numeric. Non-negative L1 penalty on canonical vectors of X.
#'
#' @param lambda2 Numeric. Non-negative L1 penalt... |
1c7121a7480f3df609435409997faf2f44cab1e28df74a8828a01901a20d702c | R | 21,319 | 561 | ###Figure 2 plotting######
library(ggplot2)
library(ggrepel)
library(ggnewscale)
library(patchwork)
library(scales)
library(dplyr)
library(tidyr)
library(forcats)
library(stringr)
library(tibble)
library(readr)
library(purrr)
library(broom)
library(broom.mixed)
library(lme4)
library(ineq)
library(pheatmap)
library(RCo... |
6efdbf73c21e549caa750c5847835247211c2f301e581897f6095c4be0aa54b7 | R | 21,341 | 592 | # --------------------
# title: Figure2 Code
# author: Hu Zheng
# date: 2026-01-01
# --------------------
library(Seurat)
library(scCustomize)
library(tidyverse)
library(tidydr)
library(cowplot)
library(umap)
library(pheatmap)
library(ggblur)
library(ggrepel)
library(viridis)
library(networkD3)
library(Biorplot)
sour... |
8682044c1e9a3adbc14c8b7fead9bddf6c79af902ccb30cf532471eb5d31d0e7 | R | 21,378 | 472 |
#######################################################
## Functions to prepare a MOFA object for training ##
#######################################################
#' @title Prepare a MOFA for training
#' @name prepare_mofa
#' @description Function to prepare a \code{\link{MOFA}} object for training.
#' It require... |
6d190381063086391d4330481e26a327d7d8e261ce49bbb82d87c3529ace27c6 | R | 21,399 | 448 | # Food Intake Analysis Suite
# Author: Laura Kaiser
# Description: This script processes feeding event data from Promethion systems,
# groups feeding bouts into meals, generates heatmaps, and summarizes intake metrics.
# Required input:
# - Animal info table (with columns: Cage, Animal No, Date, Time, etc.)
# - Fee... |
c4ff65442f6eac075ebff9ecf4edd47b18eae1fc0bc32a7498a23a5fde60b391 | R | 21,409 | 556 | ##############################################################################
### 3. continental scale competitive strength --------------------------------
### in this script we calculate the CSI and plot results ---------------------
##############################################################################
##... |
7444cd34ed03b15645b073feaab6adf341e273f21539385cf5bf0e3f57455d59 | R | 21,446 | 455 | # Meta-analysis of scRNA-seq data of Neocortex developmental time points - Part 2 : Clustering ---------------------
# E10-P4
# Rahul Jose
# SCB, RGCB
# October 2024
# Primary Aim :
# For the identification of NIHes1 and NDHes1 cells across developmental time points
# Data ---------------------------------... |
b54d365eb565f8e576a759538120290e2d96e200c12137f50282bbdd5fb90e2b | R | 21,463 | 610 | # pROC: Tools Receiver operating characteristic (ROC curves) with
# (partial) area under the curve, confidence intervals and comparison.
# Copyright (C) 2010-2014 Xavier Robin, Alexandre Hainard, Natacha Turck,
# Natalia Tiberti, Frédérique Lisacek, Jean-Charles Sanchez
# and Markus Müller
#
# This program is free soft... |
2db47c6d34f5b373d170647669e2d29953427cc108b6b5c4d7261885663e6c4e | R | 21,594 | 531 | # Meta-analysis of scRNA-seq data of Neocortex developmental time points - Part 2 : Clustering ---------------------
# E10-P4
# Rahul Jose
# SCB, RGCB
# October 2024
# Primary Aim :
# For the identification of NIHes1 and NDHes1 cells across developmental time points
# Data ---------------------------------... |
a067fca359265d142d6eb29c7a461996c0b642d6bb195fede693ad9a37e38ddc | R | 21,678 | 575 | ---
title: "Figure 4"
author: "Maksym Zarodniuk"
date: "Compiled on `r format(Sys.time(), '%d %B, %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```
```{r, include=FALSE}
library(DESeq2)
library(msigdbr)
source("../Figure_3/util.R")
library(ggrep... |
fcfe6125f17a29a32de5e5f7b6af15e5fee6a32a4d6f1928335d161acd132c46 | R | 21,734 | 500 | ## mylevels() returns levels if given a factor, otherwise 0.
mylevels <- function(x) if (is.factor(x)) levels(x) else 0
"randomForest.default" <-
function(x, y=NULL, xtest=NULL, ytest=NULL, ntree=500,
mtry=if (!is.null(y) && !is.factor(y))
max(floor(ncol(x)/3), 1) else floor(sqrt(ncol(x)... |
d8fcc0dfb6e381b67aa63e0f0d19e71d7d8f9243ccb1f26f5778cccfe76f1a19 | R | 21,842 | 500 |
###########################################
## Functions to visualise latent factors ##
###########################################
#' @title Beeswarm plot of factor values
#' @name plot_factor
#' @description Beeswarm plot of the latent factor values.
#' @param object a trained \code{\link{MOFA}} object.
#' @param f... |
dfbe186d7a5c89d27c568ce4e323e6667a8aecb4c23e83d299dd755010d76f8e | R | 21,960 | 767 | ---
title: "script03_analysis"
author: "Shamini Ayyadhury"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more ... |
d0391296b87811451e3eb4531d78881b8914c4bb9c700f2bb66c0f3c1919fdbd | R | 21,988 | 489 | # Function for detecting peaks ++++++++++++++++++++++
# Author: Meike Bielfeldt, Kai Budde-Sagert
# Created: 2025/04/29
# Last changed: 2025/05/06
detect_peaks <- function(x_values = df_dummy$Time_in_min,
y_values = df_dummy[[MFI_values]],
... |
464b9a6d60c5da3158eef3de87f4da3c2ba20df8c8990fae889880bc27eb19f4 | R | 22,004 | 846 | ################################################################################
# Immune Cell Profiling in Anti-GAD65 - Data Analysis and Visualization
################################################################################
#
# Author: Sumanta Barman
# Description: Complete R analysis pipeline for immune cell... |
cc27fccf5007ac713033b297a431b5d023a35d24e99c42557e945763a6e56a01 | R | 22,101 | 507 | # Add gene and cancer_group annotations to a long-format table
#
# Args:
# - long_format_table: A tibble that has zero or more of the following character
# columns that are required for adding their corresponding annotation columns:
# - Gene_symbol: HUGO symbols, e.g. PHLPP1, TM6SF1, and DNAH5. The Gene_symbol
# ... |
8d55bc9de364d22ca58211a11ae0e1bc6cb08a9740ccaf7df2d9ce370840cf62 | R | 22,247 | 477 | library(Seurat)
library(ggplot2)
library(DESeq2)
library(fdrtool)
library(tidyverse)
library(gridExtra)
library(pheatmap)
library(ComplexHeatmap)
library(dplyr)
library(maditr)
library(igraph)
library(purrr)
library(stringr)
library(circlize)
source("~/PD_project_analysis/manuscript_scripts/MV_utils.R")
color_palette_... |
53b46f87acc211dbec7c127e2e4c1fa642e28194725c386ad895253e6da95c1f | R | 22,255 | 479 | ################
### Monocle3 ###
################
library(Seurat)
library(monocle3)
rootMain <- "figures/main/"
rootSupp <- "figures/supp/"
rootDir <- "otherPlots/monocle3/"
obj <- readRDS("saved/toZenodo/midBrainIntegration.RDS")
obj$Dataset2 <- obj$Dataset
obj$Dataset2[grepl("Agarwal", obj$Dataset2 )] <- "Agar... |
feef07b284841f006eb90f908761e944a20bd89fa0256e6a7e6c146a16c3492c | R | 22,314 | 518 |
# Bulk RNA-seq Analysis of Plp1-eGFP+ and Plp1-eGFP- Cells
#
# Author: [Anoohya Muppirala]
# Date: [11-09-2025]
#
# Description:
# This script performs a comprehensive differential gene expression (DGE) analysis
# of bulk RNA-sequencing data from Plp1-eGFP positive (glia) and negative
# (non-glial) cells. Samples wer... |
4126d95357b0a59f3eb61ac7a28c5644de2df2a48f52929fd20aba3e8d1a8f6f | R | 22,328 | 639 | ############################
## iTReX plot functions ##
## Author: Dina ELHarouni ##
############################
## color palette for QC plotting
cbPalette <- c("gray", "#21698D", "#E8B693", "#CA670D", "#CAD3DC", "darkolivegreen4", "#003152")
## plotting QC controls
platePlotControl <- function(screenData, plotPla... |
e05f056f65c412d2258bf4d1573ba845475aeca5a47c0a8dadb9c5759851adf6 | R | 22,376 | 543 | #' @title Calculate variance explained by the model
#' @description This function takes a trained MOFA model as input and calculates the proportion of variance explained
#' (i.e. the coefficient of determinations (R^2)) by the MOFA factors across the different views.
#' @name calculate_variance_explained
#' @param ob... |
25fe21d1b2db7485cf1c9e19b3278dd6e3129ce734ae234f9d9bffdcbc51cbb0 | R | 22,412 | 573 | # 1. Data processing -----------------------------------------------------------
## 1.1 Load packages and functions ---------------------------------------------
source("./codes/my_packages.R")
source("./codes/my_functions.R")
## 1.2 sample selection and metadata ------------------------------------------
# Nagele ... |
21e29b9135faf75eeb1afe97c1d0f0f9040cdd5411be4c0614e8ac7ccb54d23f | R | 22,439 | 732 | ---
title: "Schmidt et al. - Supplementary Figure 3"
author: "Anne Hoffrichter"
date: "2024/03/21"
output:
bookdown::html_document2:
code_folding: hide
fig_caption: true
toc: yes
toc_depth: 4
toc_float:
collapsed: yes
link-citations: yes
---
```{r loadLibraries, message=FALSE, warning=FALS... |
3b3adec8131b237917a1858deb371401e257ada7da93ce5eada85210f1905822 | R | 22,496 | 427 | WGCNAblock <- function(){
##### Loading #####
setwd("D:/valentin/main/")
source("./scripts/utils.R")
source("./scripts/WGCNA/WGCNA_functions.R")
#Load WGCNA model (per cohort)
load("./WGCNA/PITT/PITT_subtyping.wgcna.network.rdat")
load("WGCNA/PITT/PITT_subtyping.wgcna.softThreshold.rdat")
load("WGCNA/PITT... |
e63838716c9a0d55985e74e9d4b62f04ea383bab8f29b3a820859f29beccd573 | R | 22,530 | 613 | # ==============================================================================
# S10_visualization.R
# Server logic for Step 6: Data Visualization
# Handles visualization of single features and their correlated partners.
# ==============================================================================
# -------... |
711dec09360f0428d2e23d1081971af6454496ecffcf5fd8795aac952b5e2383 | R | 22,535 | 615 | #INFORMATION-----------------------------
#LOAD LIBRARIES ------------------------
library(data.table)
library(dplyr)
library(DT)
library(Matrix)
library(matrixStats)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(gridExtra)
library(gplots)
library(limma)
library(plotly)
library(scater)
library(scran)
libra... |
1dbd90b0fe4cff1fdffc3f5158728bced9eeb16912dace0ac2c5c68097d4477b | R | 22,577 | 495 | rm(list=ls())
## COMMON LIBRARIES AND FUNCTIONS
source("100.common-variables.r")
source("101.common-functions.r")
source("200.variables.r")
source("201.functions.r")
## SCRIPT SPECIFIC LIBRARIES
## SCRIPT SPECIFIC FUNCTIONS
## SCRIPT CODE
##
##
if( 1 ) {
Print.Disclaimer( )
##
## Set random seed for co... |
935dd85eef1516a6bcfd695bbf0782c346fdd0bf269b73759005bc9b87c8c193 | R | 22,608 | 833 | ---
title: "Immune cell profiling anti-GAD65 – QC and preprocessing"
authors: "Sumanta Barman"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = TRUE,
warning = FALSE,
message = FALSE,
fig.width = 10,
fig.height = 8
)
# Load required libraries
suppressPackageStartupMessages({
library(Seurat)
... |
2f44fd426694b22fc5be35f6706884c8499148a0680691a1410aab1229e6c2f9 | R | 22,646 | 579 | library(gprofiler2)
library(dplyr)
library(ggplot2)
rootMain <- "figures/main/"
rootSupp <- "figures/supp/"
rootDir <- "otherPlots/monocle3/"
##ctrl vs patients
geneUniverse <- read.table("saved/seurat/DEgeneUniverse.txt")
resDE <- read.table("saved/suppTables/TableS11.txt", header=TRUE)
stopifnot(all(resDE$geneSymb... |
051ed0aac7f3d53f878dbfd58315e4442d1c8d6497d2dfbbdd96204fa2553fd1 | R | 22,786 | 596 | #' @export parse_filename
parse_filename <- function(filename) {
filename <- str_remove(filename, "\\.rds")
# Split the filename into parts separated by underscores
parts <- str_split(filename, "_")[[1]]
# Determine the group (EVE or DDD)
group <- parts[1]
# Initialize an empty list to store the extracted... |
96f5aa0d2513a4108a48b4ebe261c73a2451307a6442782bec9f7a99df197141 | R | 22,843 | 731 | #' Function to plot a PCA of the data
#'
#' @description
#' Generates a PCA projection of all samples from NPX data along two
#' principal components (default PC2 vs. PC1) including the explained
#' variance and dots colored by QC_Warning using
#' \code{stats::prcomp} and \code{ggplot2::ggplot}.
#'
#' The values are by... |
3817cc1a9d9b4fada4cac8c113ca2bd7aa466eb2af6c84d952f432c009dcd7fa | R | 22,859 | 486 | library(Seurat)
library(scrattch.vis)
library(scrattch.hicat)
library(scrattch.io)
library(tibble)
library(dplyr)
library(gplots)
library(data.table)
library(SeuratDisk)
setwd("/mnt/DD/Sc RNA-Seq/LR")
source("/mnt/DD/Sc RNA-Seq/Cortex/Cortex/Function created or adapted/function utils.R")
###########################... |
5819eb8577d912704768dfe003753f7c5bf056594da97af0208304f8780a8146 | R | 22,937 | 372 | ---
title: "README"
author: "Rasmus Kirkegaard"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
options(scipen = 10)
```
# R10.4.1 Zymo HMW basecalling
With the release of R10.4.1 I wanted to check the quality of the... |
5f3f19e1fba27e5dafee4759f288d9a72cb24aac4dc5a3f41dc8a406bf4cef6d | R | 23,008 | 413 | #' Evaluate Discovered Biomarkers Across Multiple Datasets and Groups
#'
#' Reads biomarker definitions from a discovery results file and evaluates their
#' performance (Repeatability, Separation, Sample Size Estimate) on specified
#' datasets and diagnostic groups (CU/CI), optionally calculating confidence intervals.
... |
5398cafcfd2fa69e57ba4cf47d2d33d2905f5ad0543c4a68784f541e9a1766d0 | R | 23,016 | 644 | #INFORMATION-----------------------------
#LOAD LIBRARIES ------------------------
library(data.table)
library(DT)
library(dplyr)
library(ff)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(gplots)
library(gridExtra)
library(Matrix)
library(matrixStats)
library(magrittr)
library(plotly)
library(reshape2)
lib... |
097a32756474b38804e694573dc03d4d652cf29c4bf822a8d69d20a9d9aa95cb | R | 23,301 | 649 | ---
title: "Figure 5"
author: "Maksym Zarodniuk"
date: "Compiled on `r format(Sys.time(), '%d %B, %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```
```{r, include=FALSE}
library(tidyverse)
library(ggpubr)
library(cowplot)
library(rstatix)
librar... |
3b0c472fb9bad9cd94b63e0c20c3145acfb4bf1f4e35899291b0fe80f3f2edac | R | 23,302 | 520 | library(Seurat)
library(ggplot2)
library(stringr)
library(gridExtra)
library(cowplot)
library(MASS)
library(viridis)
library(rhdf5)
source("~/PD_project_analysis/manuscript_scripts/MV_utils.R")
color_palette_cluster <- c("DaN1" = "#0072B2",
"DaN2" = "#56B4E9",
"Ga... |
684f9a457ce4fe8f2b7ffebc4ec4eb06b1ae0c6c90caed63e5825abb03856c7f | R | 23,328 | 457 | library(Seurat)
library(readxl)
library(ggplot2)
library(ggrepel)
library(ggpubr)
library(writexl)
library(DESeq2)
#Load the CellRanger outputs of individual sample
WTmale1.data <- Read10X(data.dir = "/Shared/NEURO/AbelLab/Yann/scRNA-seq_16p11.2_males/Sample795/outs/filtered_feature_bc_matrix")
WTmale2.data ... |
98174d1d78449a91b4a17fa3242d7e5e48bf9da40f13a56be493848debc35edb | R | 23,339 | 516 | start_time = Sys.time()
cat(file=stderr(), 'Loading dependencies...')
options(stringsAsFactors=F)
suppressMessages(library(tidyverse))
suppressMessages(library(janitor))
suppressMessages(library(openxlsx))
suppressMessages(library(reshape2))
if(interactive()) {
setwd('~/d/sci/src/genetic_support')
}
cat(file=stderr... |
bae72e0be0c908b36fb1003ad2cae36d0b0faf53bc429ee54e3467f5bb41ac27 | R | 23,368 | 658 | rm(list = ls())
library(dplyr)
library(ggplot2)
library(caret)
library(shapviz)
library(ranger)
library(fastshap)
library(randomForest) # or ranger, depending on your model
library(ggplot2)
# Load data ----
if (TRUE) {
tmp <- base::sort(list.files(pattern = "datalists_",
... |
8d0cd9486ff7c7a12445233701298c890ad8e39d4b48d6ba8b6318376d109610 | R | 23,462 | 680 | # Code to generate Figure 2 of the Jokura et al 2024 Ctenophore apical organ connectome paper
# source packages and functions ------------------------------------------------
source("analysis/scripts/packages_and_functions.R")
# note: SSN were renamed to ANN, but a lot tof the code still uses SSN
# load cells ------... |
41918b0107a24eccf2f4357e05501373ec3e548ecb2d2afba6b49011c98f98b2 | R | 23,477 | 489 | library(slingshot)
library(uwot)
library(Seurat)
library(SingleCellExperiment)
library(RColorBrewer)
library(ggplot2)
library(ggbeeswarm)
library(ggpubr)
obj <- readRDS("saved/toZenodo/midBrainIntegration.RDS")
# for "midBrainDatasets_ccaIntegration_v3.RDS"
allctypes <- names(table(obj$annotation_mixed))
namesUnifie... |
0f105d3d67250fc102f5898cdf4f8c3244386871bcf92683bd33508ace594c69 | R | 23,504 | 519 | ---
title: "General Helpers & Utilities"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output: rmarkdown::html_vignette
theme: united
df_print: kable
vignette: >
%\VignetteIndexEntry{General Helpers & Utilities}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
***
<style>
p.caption {
fo... |
da21d526856ee2c1f815fb566b682411256b87063f1d37624b3bd329bd149ea7 | R | 23,504 | 656 | #' Perform clustering in BANKSY's neighborhood-augmented feature space.
#'
#' @details
#' This function performs clustering on the principal components computed on
#' the BANKSY matrix, i.e., the BANKSY embedding. The PCA corresponding to the
#' parameters \code{use_agf} and \code{lambda} must have been computed wit... |
4f867553747ffbba08538e22e500d366ad660976013d48d6bd2d18ff5011b6a7 | R | 23,606 | 921 | #!/usr/bin/env Rscript
library(Signac)
library(Seurat)
library(ggplot2)
library(gridExtra)
library(cluster)
library(GenomicRanges)
library(GenomeInfoDb)
library(rtracklayer)
library(optparse)
library(dplyr)
library(stringr)
options(error = function() traceback(2))
option_list <- list(
###### Sample ID ######
make... |
2e90b80f2ef05c055fa59b683a48028770893167f92ad802c373f3a39b6393f5 | R | 23,614 | 452 | ## Creating Fibroblast species object (Fig7) with our Fibroblast scRNA-Seq data (7/22/82 wo ChP 4V&LV) and human snRNA-seq data (Yang et al.)
## Script performs the BBKNN workflow on the object following the attempted CCA workflow
## Script continues with processing and exploration of the Fibroblast species object for ... |
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