sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
05307a990353a7d28ebf323d88b288f4150bfa6d07b4020e60535785f33e2f61 | R | 11,876 | 182 | library(SeuratDisk); library(Seurat); library(dplyr); library(ggplot2); library(patchwork);library(RColorBrewer)
library(ktplots); library(pheatmap); library(viridis); library(tidyverse)
library(clusterProfiler); library(org.Hs.eg.db); library(AnnotationHub) ;library(enrichplot); library(DOSE); library(ReactomePA)
... |
f9c228207259953a4f0f9e5f936ea4705ee8c1d746bc2653be3ed17f3e1b214c | R | 11,886 | 291 | #############################################################
# Generate Figure 3A, 3B, 3D
#############################################################
library(ggplot2)
library(scales)
library(ggpubr)
library(clusterProfiler)
library(openxlsx)
library(org.Hs.eg.db)
library(org.Mm.eg.db)
library(ReactomePA... |
609685ea354981abe5a60f6b01b7d7bd4874e876b9d36b479bef80c16b648ba1 | R | 11,893 | 201 | source('scripts/config.R')
source('scripts/aux_functions.R')
source('scripts/plot_functions.R')
source(paste0(main_dir,'results/hg38/hic/config.R'))
source(paste0(main_f,'scripts/main_functions.R'))
source(paste0(main_f,'scripts/aux_functions.R'))
source(paste0(main_f,'scripts/plot_functions.R'))
source(paste0(main_f,... |
8e22eca84477deee34ccd2ffa6fc614a8d096b10371980a3d5ec88b00343546a | R | 11,895 | 269 | # cell interaction
merFISH_integrated_L3 = readRDS(file = "./RDS/merFISH_integrated_L3_final_0719.seurat.rds")
merFISH_integrated = readRDS(file = "./RDS/merFISH_final.0827.RDS")
cal_CCI = function(obj, s){
merFISH_integrated_test = suppressWarnings(subset(obj, slice == s))
embeddings = data.matrix(merFISH... |
e37d8e8b6a2cb1df5f0017ec63859bd1279ca6f0079522bc0357313b00c456ea | R | 11,898 | 225 | ---
title: "Calling Hits on Vglut 410 Data"
author: "Stephen P Plassmeyer"
date: "7/17/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
```
# Load and Format Data
```{r}
compiled_counts <- fread('../../../data/compiled_cou... |
84272cfb8f6c05f69b3c6b9b988f71e52f701f66c68ed09463e52bd6335a68a6 | R | 11,900 | 343 | ###################################
# LC Visium analyses: preprocessing
# Lukas Weber, Oct 2022
###################################
# note: using R 4.2, Bioconductor 3.15, SpatialExperiment 1.6.1
# module load conda_R/4.2
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
li... |
adf8ca7b134356831a25af33c43462f18b6f5f456c4b94cf2e8f2fbb2fff055d | R | 11,919 | 374 | source("paper.R")
# * configs
atacMeta <- readRDS(cembav2env$sa2metaFile)
peakBed <- Sa2PeakCalling$loadFinalPeakBed()
nds <- 10000
# * load atac and mCG signals.
logCPM.scbyp <- Sa2PeakCalling$read.sa2CPM.pbysc(
cap = 0.99)
mCG.all <- readRDS(Sa2PeakCalling$sa2.mCG.rds.File)
scnmMap <- cembav2env$load.sa2.subclass... |
5760d6b74ca728f2a629074582339d7c13efd1eb2b5b22fbae291f072d34cde7 | R | 11,923 | 234 | #' Transform 'QIIME2' results to 'microtable' object.
#'
#' @description
#' Transform 'QIIME2' qza results to microtable object.
#' @param feature_table the ASV abundance data with qza format, such as the \code{'data2_table.qza'} in the example.
#' @param sample_table default NULL; the sample metadata table; four types... |
303b2e60459a34b854490dda5fded82b39325cf7fb94bd6236d040ac5eeaa524 | R | 11,993 | 234 | ---
title: "Calling Hits on Vglut 410 Data"
author: "Stephen P Plassmeyer"
date: "7/17/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
```
Updates:
07-07-2021
* Re-ran with LMM for calling allelic effects... |
35f8952f306be7bdfa963af14887d03eaa140bdf8f000dcd03f471917594ae86 | R | 12,009 | 304 | ---
title: "scapGNN: Graph Neural Network-Based Framework for Single Cell Active Pathways and Gene Modules Analysis"
author: "Xudong Han and Xuejiang Guo"
date: "`r Sys.Date()`"
output:
rmarkdown::html_vignette:
toc: true
vignette: >
%\VignetteIndexEntry{Graph Neural Network-Based Framework for Single ... |
b506c499dbba5cd5c0c418a8d1e0ac965c1d6c875616d048ab5735b7c0344662 | R | 12,029 | 402 | ---
title: "LC analyses: features per spot (MiSeq reads)"
author: "Lukas Weber"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
toc_depth: 3
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
Analyse... |
0b8f4a03c29c63fd46188aaf219dcaa14544bfdcc53df1d328dd319078c4e3b1 | R | 12,126 | 303 | ---
title: "Quickstart Tutorial"
output: rmarkdown::html_vignette
fig_width: 6
fig_height: 4
vignette: >
%\VignetteIndexEntry{Quickstart PBMCs Tutorial}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
In this tutorial, we will construct a Symphony reference from two PBMC datasets from 2 technolo... |
bb4fb3a90fb396157de75e1bdafaac2a58b2bf445039ddf44739a17bc286c395 | R | 12,141 | 464 | ################################################################
# LC snRNA-seq analyses: DE testing (vs other neuronal clusters)
# Lukas Weber, Apr 2023
################################################################
library(here)
library(SingleCellExperiment)
library(scater)
library(scran)
library(dplyr)
library(t... |
da82c70884c47ca9cd3c5cf82fd71a45b357fa18dbe9b3ec0ceebc126861b4b4 | R | 12,154 | 465 | # This code is to plot the Fig S9 and Fig. S11 S12
# WANG. 04-April-2024
# Remove all objects created before to prevent clash
rm(list = ls())
library(dplyr)
library(ggseg)
library(ggplot2)
library(tidyr)
library(patchwork)
library(readr)
library(reshape2)
library(stringr)
# Set the working directory to the path wh... |
95ece66aa82e4a6ae61394358d58151ea184102d96bf34c3e24358bb8efffef3 | R | 12,214 | 229 | ---
title: "Re-evaluation of Cross Experiment Reproducibility, new N2a DNA Counts"
author: "Stephen P Plassmeyer"
date: "12/13/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../code/mpra_tools/mpra_tools.R')
library(ggpubr)
library(lawstat)
```
Here, I revisit ... |
7dc32157550da9a0802fd8aa31b341593f870ea1bd383f358abd384b2e5c5186 | R | 12,235 | 292 | plot.power.htest <- function (x, ...){
# initial checks
if (class(x) !=
"power.htest")
stop("argument must be of class power.htest")
pwr.methods <- c("One-sample t test power calculation", "Two-sample t test power calculation", "Paired t test power calculation", "t test power calculation", "Difference... |
fa044037baf762088e59d9f64dfd10fd713ce123c8b5a7d4898a0d66af0ccdf1 | R | 12,260 | 352 | # Figure 2 neuroPN
# SP | 2022 | sebastian@pechmannlab.net
library(ggplot2)
library(reshape2)
library(ggdendro)
library(cowplot)
library(viridis)
setwd("M2/neuro/")
# Figure 2A ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
c1 <- colorRampPalette(c("#29ABE2", "white"))(7)
c2 <- colorRampPalette(c("w... |
448abc0148d9e37d12ab5daaf31c634825a4b7d8934ca4f1a58a25c824165f77 | R | 12,269 | 392 | # Figure 3 neuroPN
# SP | 2022 | sebastian@pechmannlab.net
library(ggplot2)
library(reshape2)
library(ggdendro)
library(cowplot)
library(igraph)
library(ggraph)
setwd("M2/neuro/")
# Figure 3A ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
degree5 <- data.frame(read.table("data/processed/coexp_5_degre... |
8cf6a11f3c3b5c8a79113156862d7e757207af8db91ff12dd4323597013ff7e3 | R | 12,280 | 215 | source('scripts/config.R')
source('scripts/aux_functions.R')
source('scripts/plot_functions.R')
source(paste0(main_dir,'results/mm10/hic/config.R'))
source(paste0(main_f,'scripts/main_functions.R'))
source(paste0(main_f,'scripts/aux_functions.R'))
source(paste0(main_f,'scripts/plot_functions.R'))
source(paste0(main_f,... |
1322e9a5c8b7538e07c254c38aee47af0b8efb784839c3edb1027518e6b71fc7 | R | 12,289 | 316 | #expr vector
#diagnosis vector
#coefficient dataframe
#need cpm transformed
Run_LM_CovSelect = function(x = list(expressionMatrix = dat,
Main = covariables.main,
Covariables = covariables.potential,
MainContr... |
ed15d3f12ff5b969e29a70b010dd67dc93af9a664ea93fe692e8d1a060e2c176 | R | 12,354 | 189 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
3aed372f6abb71ca0766eafa1e7ec40b4c7ec306ef077f7fa57b0d96faadee62 | R | 12,359 | 286 | ---
title: "R Notebook"
output: html_notebook
---
---
```{r}
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(dplyr))
library(pheatmap)
library(RColorBrewer)
library(ggpubr)
library(ggsignif)
library(openxlsx)
library(viridis)
li... |
40a281fd6a8e18f13f85f04de18670421a9458398935c0b84b61d22dd7c56406 | R | 12,390 | 307 | ---
title: Calculation of microbiome variance explained by phenotypes
author: Cas Swarte
output: html_notebook
# ==========================================================================================
# By: Weersma Group, TransplantLines (UMCG)
#
# UMCG Transplant... |
6bb06cbe592ca34d31e96dbbd1dc05f64ded692efdccd0bad72be6f8b6bbc110 | R | 12,439 | 238 | library(lme4)
library(car)
library(emmeans)
library(Rmisc)
library(tidyverse)
library(ggplot2)
sink('../../output/burst_stats_out.txt')
df<-read.csv('../../output/bursts.csv')
df$new_subject<-as.character(df$subject)
subjs_12m<-unique(df$subject[df$age=='12m'])
subj_idx<-100
for(subj in subjs_12m) {
df$new_subject[... |
dd9e60137b0b46b37d0c66a9abde7b8c39f9c6ea298a073f636d35dea91bd384 | R | 12,460 | 210 | # Load library
library(shinythemes)
library(shinydashboard)
library(shiny)
ui <- dashboardPage(skin = "black",
dashboardHeader(title = "Lymphedema Prediction", titleWidth = 300),
dashboardSidebar(
width = 300,
sidebarMenu(
... |
4458500ced827923057b3dc5e8d710c2266169ab8c5a38dc765ea389b8c4b70c | R | 12,493 | 337 | # kpmp snRNA aggregated analysis loy
#########################
# figure umap
library(Seurat)
library(here)
library(ggplot2)
# create a plot dir
plots <- here("ckd","figures")
dir.create(here(plots))
kpmp <- readRDS(here("ckd","rna_aggr_prep","kpmp","kpmp_prep.rds"))
plotA <- DimPlot(kpmp, label=T, repel=TRUE, raster=... |
08b773c09817b49b0082153f3b8c9f75d96b59a696b2ed45f90ba816873de6e5 | R | 12,506 | 189 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
da1712da796cd6af324012dd6e4240909bf0589f76de3755e15359e16f147a4c | R | 12,519 | 285 | #' Comorbidity Analysis \code{cAnalysis}
#'
#' Given an object of type \code{comoRbidity}, a comorbidity analysis is perform,
#' for the subset of population under specific conditions of age and sex. It
#' generates a \code{cAnalysis} object.
#'
#' @param input A comorbidity object, obtained with the query function.... |
cc63ef8da76503f5c3030508e068fff1583728af5e53ed3180f39410defdad3b | R | 12,567 | 289 | ## to replicate conda environment used in paper, install conda, and then call the following in terminal: conda env create -f environment_droplet.yml - yml file here: https://github.com/brianherb/HumanHypothalamusDev/blob/main/environment_droplet.yml
library(Seurat)
library(limma)
devtools::source_url('https://github.... |
eacab85968ab18eed05379a95e9d378549047eb0d7a6ea64cf7873cac05959f9 | R | 12,685 | 471 | ---
title: "LC analyses: preprocessing distributions"
author: "Lukas Weber"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
toc_depth: 2
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
```
# Introductio... |
a80afed732710555284f201706b4cb6340b289c679213f5ae1af43d28dd67d44 | R | 12,752 | 283 | library(tidyverse)
library(reshape2)
library(gg3D)
library(ggVennDiagram)
library(ggsignif)
library(ggpubr)
library(caret)
library(irlba)
library(factoextra)
library(plotly)
# Load all human and mouse data and preprocess/scale them------
homologues_summary <- data.table::fread('results/HumanMouseHomologuesMap.csv') %>... |
918b1b5d5b2b73acd62a407c4c3b68b2cd5893850ac33203f1cf0e17b2fe1b84 | R | 12,757 | 225 | library(rtracklayer)
library(reshape2)
library(data.table)
library(ggplot2)
library(matrixStats)
options(stringsAsFactors = F)
source('/path/to/hicchip/scripts/hic_helper.R')
root='/path/to/fetal_hic/hicpro/loop/self_contact_loop_gene'
dir.create(root,recursive = T)
setwd(root)
cells=c('glia','neuron','fetal')
lp_path=... |
e3765cb0a8be6cbe858227ad79f635d5025c232a502f26898341583ed66c0cef | R | 12,787 | 265 | ---
title: "N2a 410 MPRA QC"
author: "Stephen P Plassmeyer"
date: "7/17/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
```
# Load and Format Data
```{r}
compiled_counts <- fread('../../../data/compiled_counts/asd3utr_com... |
e18dd1da7495adaa8a6aca6a5d6a283fba8bd7b273ef81a875e626a2b8d1bd5b | R | 12,830 | 424 | ###############################################################
# LC snRNA-seq analyses: clustering and supervised thresholding
# Lukas Weber, Oct 2022
###############################################################
library(here)
library(SingleCellExperiment)
library(scater)
library(scran)
library(bluster)
library(gg... |
31b4c6a5e8c6cd2d4239e24030c3c92feb915b21d146d2f8a539f8bc996f9547 | R | 12,917 | 488 | ################################################################
# LC analyses: pseudobulked differential expression (DE) testing
# Lukas Weber, Oct 2022
################################################################
# adapting code by Leonardo Collado-Torres, Abby Spangler, and Sowmya Parthiban from:
# https://gith... |
d802bf04bb93f7e17b012b88ff9f1af3e2887994b30466c7057460abf89c011a | R | 12,933 | 408 | #Integrate:
#This script takes all the processed samples and merges them together. We check the name of the file for tags indciating well
#We then filter and cluster the data
## ----receive input from nextflow----------------------------------------------
#Get parameters
args <- commandArgs(TRUE)
path<-args[1]
timep... |
28d57b75b9901d6c41fae084b23ab597012670930fd61814d600143b9fba3dbf | R | 12,976 | 266 | ---
title: "Figure 4"
author: "Stephen P. Plassmeyer"
date: "5/03/2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../../../code/mpra_tools/mpra_tools.r')
library(ggpubr)
library(MASS)
library(ggrepel)
theme_set(theme_bw(base_size = 6))
```
... |
9f8b3248386a842b910b5d6ed7ed506a95a7f3d41789080a9e324b15620fa7d9 | R | 12,986 | 415 | #' @importFrom sparseMatrixStats rowSds
.scale_uv <- function(x) {
sds <- rowSds(x, na.rm = TRUE)
# TODO find a more efficient way of scaling the matrix
# t1 <- t(scale(t(x), center = FALSE, scale = sds))
# Scale by gene (each row by its sd) for unit variance
t1 <- x / sds
t1
}
#' @importFrom M... |
155fb7295df48c4acdc932bb420eba0861efa7e9cf1a39ef82bb6841e5203bae | R | 13,036 | 265 | ### -------------------------------------- ###
# Evolutionary model selection
# Roberto Toro, Katja Heuer 2018
# Script adapted by N. Magielse
# for inclusion of cerebellar data - 2022
### -------------------------------------- ###
## SET-UP ##
library(ape)
library(Rphylopars)
library(phytools)
library(magrittr)... |
25c0c9dc7fc0ade38c5fedcdace41596aebbd1c557ff1b9413f60ab27a8ba1fc | R | 13,051 | 274 | ---
title: "nalysis of MEA data and plotting - using output from matlab"
output: html_notebook
---
```{r}
library(ggplot2)
# library(RColorBrewer)
library(tidyverse)
library(ggpubr)
library(jcolors)
library(dplyr)
library(janitor)
library(plotrix)
```
```{r}
# load data
# in excel pre generate a file with only in... |
88a19860c469aea3d16ffe7712db11ef5e54f84e28eb7ac5c31b5a541b7c5af9 | R | 13,097 | 293 | ---
title: "Figure 5"
author: "Joe Raymond"
date: "August 5, 2021"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(tidy = TRUE)
knitr::opts_chunk$set(eval = TRUE)
knitr::opts_chunk$set(fig.width = 8)
knitr::opts_chunk$set(fig.height = 4)
knitr::opts_chunk$s... |
73c2ea9898041305159e7874503e633b6a1b561c60cf0c6367d9b185f899fabe | R | 13,168 | 432 | # install and load required packages
for (package.name in c('MKinfer', 'afex', 'ggplot2')) {
if (!require(package.name, character.only=TRUE)){
install.packages(package.name, dependencies=TRUE)
require(package.name, character.only=TRUE)
}
}
# define global variables
plot_dir = './output... |
e297c94278141aa36d48e708b8ddaca6f142eb246d63a5ffa1528eb0747f9b53 | R | 13,224 | 389 | ## ---------------------------
##
## Script Purpose: Figure 3 demonstrating single mediation modelling
## Geom_point plot comparing the estimated indirect effects of the mediators
##
##
## (1) Assign new column describing which latents are in which group (Heart v Brain)... |
26a9f05915f9d4ba65e9f6799c5a0905f4ef10fae0d052d7c5838a8c616c72b8 | R | 13,235 | 379 | library(dplyr)
library(Seurat)
library(patchwork)
# load the mice scRNAseq data
DA_mock1.data <- Read10X(data.dir = "mock_1/filtered_feature_bc_matrix/")
DA_mock1 <- CreateSeuratObject(counts = DA_mock1.data, project = "DA_mock1", min.cells = 3, min.features = 100)
DA_mock1
# load the mice scRNAseq data
DA_infect.... |
25770dc4987d685025e73cd0cfe6ff782cc65f83e85c81005889b3adf9ee869f | R | 13,238 | 332 | ### ------------------------------------- ###
### Robustness of results,
# replication in Stephan et al. (1981) data.
### Neville Magielse, 2023
### ------------------------------------- ###
### SET UP ###
getwd()
setwd("~/Documents/PhD/EvolutionPrimates/Final_code/")
outputdir <- "7.robustness"
dir.create(o... |
b1d554a307911aa12a2410bd9ae45ec89d8de4f6dabb0585355e1bc75c52102e | R | 13,246 | 361 | ---
title: "Spatial Proteomics of a Human Brain Tumour"
output:
html_document:
df_print: paged
---
### Load/install required libraries
```{r}
if (!require("pacman")) install.packages("pacman")
library(pacman)
p_load(knitr, dplyr, reshape2, spdep, raster, doParallel, scales, viridis, plot.matrix,
... |
a4aadfae7d07b19be57104bac87f52f82b3d986f3fadec794d860a8b07346ebd | R | 13,324 | 419 | # Individual level statistics and plots
library(tidyverse)
library(hrbrthemes)
library(sjmisc)
library(qvalue)
library(glue)
library(viridisLite)
library(see)
library(texreg)
library(patchwork)
library(lemon)
mappings <- read_csv("target_cat_mappings.csv") %>%
rename(
"target" = index,
"item_cat" = category... |
d397c270d5d837e29c40eec1abd6af75d7a96aa8001e8ceb2275d63354c6156d | R | 13,360 | 336 | ### WGCNA script
## Required libraries
library(WGCNA)
library(DESeq2)
library(ggplot2)
library(clusterProfiler)
library(dplyr)
library(ggpubr)
#EXPRESSION MATRIX INPUT---------------------------------
data <- read.table("merged_sumrawcounts.txt",sep=",",header=TRUE)
condition <- factor(c("C","C","C","C","C","C","C","C... |
380abb39bcb60565d4abb2a0eb48ab42c13c28a01258597717f606b8863e0ad2 | R | 13,367 | 300 | ---
title: "DCN - Aggregating NDA"
author: "<h3>by Michael Demidenko</h3>"
date: "`r format(Sys.time(), '%B %Y')`"
output:
html_document:
theme: united
highlight: tango
toc: yes
number_sections: yes
toc_depth: 2
toc_float:
collapsed: yes
smooth_scroll: yes
code_folding: hide
... |
2234433af05b67a2b7ca1d015cf4107f6c391821a52a01abc50445d6e884bda4 | R | 13,369 | 373 | #!/usr/bin/env Rscript
#load required packages
library(Seurat)
library(dplyr)
library(ggplot2)
#set all directory paths
indir <- paste0("/gladstone/bioinformatics/projects/",
"mn-1318-maxine-nelson-yadong-huang-snrnaseq-mm10-aug-2022/",
"data")
outdir <- paste0("/gladstone/bioinformat... |
1c1732ddac1ad1e6d0e8c31e1fb213a5f6ff3700cb2e78adc6731f1a78f5dd92 | R | 13,373 | 399 | ###loop analysis function
library('Matrix')
library('data.table')
library('GenomicRanges')
library('rtracklayer')
library('foreach')
library('doParallel')
read.hic <- function(fh,ch,resol,norm_method='NONE'){
ch_s <- gsub('chr','',ch)
cmds <- c('straw',norm_method,fh,ch_s,ch_s,'BP',resol)
dat <- fread(c... |
d1ecc9383b60a4b0b5d22a3dff225601fae757d3f46480bb9586fa8c605b3841 | R | 13,397 | 432 | ---
title: "LC analyses: features per spot"
author: "Lukas Weber"
date: "19 Nov 2020"
output:
html_document:
toc: true
number_sections: true
toc_depth: 3
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
Analyses for LC project:... |
ddba95ee7795597bcbeff662bdc20008b99f1db4e562eff01be5f52f3a07fad7 | R | 13,414 | 302 | ---
title: "MG A549 experiments - DEA and exploration"
author: "Pierre-Luc Germain"
date: "3/1/2022"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
---
```{r}
suppressPackageStartupMessages({
library(SummarizedExperiment)
library(SEtools)
library(sechm)
library(edgeR)
libra... |
3d093a2dc499336a490dfe5de8480bfaf952b824af9ae1275ed61408dedd857b | R | 13,443 | 411 | ---
title: "6mo_MENS_followup.Rmd"
author: "Jared Slosberg"
date: "11/25/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r initialize, echo = F, message = F, results = 'hide'}
library(monocle3)
library(xtable)
library(scales)
library(pheatmap)
library(dplyr)
lib... |
ef4977e6a16d7f380316cd8b08d5d40a0f108d4a0431056959f9b4b12e2da388 | R | 13,468 | 309 | require(R.matlab)
rm(list = ls())
ecgpath='/data/p_02186/TMS_ECG2/analyses/EKGdata/'
setwd(ecgpath)
fdipath='/data/p_02186/TMS_ECG2/analyses/EEGtms/FDIdata/'
bigdata=c()
subids=c('VP01', 'VP02', 'VP03', 'VP04', 'VP05','VP06', 'VP07', 'VP08', 'VP09', 'VP10', 'VP11',
'VP12', 'VP13', 'VP14', 'VP15', 'VP16', 'VP17... |
1938dd6c227ca0a174c6aa0c85c0b6de67d8722cd28676a8d51dcedefa8b662c | R | 13,472 | 229 | source('scripts/config.R')
source('scripts/aux_functions.R')
source('scripts/plot_functions.R')
source(paste0(main_dir,'results/mm10/hic/config.R'))
theme_set(theme_cowplot())
theme_border <- theme(axis.title.y = element_blank(),axis.ticks.y = element_blank(),axis.text.y = element_blank(),axis.title.x = element_blank... |
0ec388534feb4bd7b8c2f517fd26ed2884c4314012228eae293fbb82ac81d7bf | R | 13,514 | 357 | ## LOAD PACKAGES ----------
# Data manipulation
library(tidyverse)
# Plots and visualizations
library(grid)
library(viridis)
library(ggrepel)
library(ggpubr)
library(svglite)
# Gene annotations
library(biomaRt)
library(org.Mm.eg.db)
library(org.Rn.eg.db)
library(limma)
# Other
library(clipr)
## BEFORE USING TH... |
facc584f78236bfeb00a099b7d1c275ceb12cf03332980eac8f8dea684f22619 | R | 13,544 | 304 | ---
title: "Gene_Enrichment_Analysis"
author: "F Mastropasqua"
date: "10/5/2022"
output: html_document
---
```{r}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library("dplyr")
```
```{r}
SFARI_sel_list <- read.csv("./SFARI_release_072022_list.txt", header = TRUE)
DD_list <- read.csv("./NDD_list.txt", header = FALSE... |
23075306cb0ab8e4d60a9fe6bc4d9714558c554f127eb544a26e901b95cadc5c | R | 13,558 | 352 | #' Internal function:
#'
#'
#' @export
FilterInx_step1 <- function(INX,cellTypeA,cellTypeB,proteinTypeA,proteinTypeB) {
nodesA <- INX$nodes[INX$nodes$cellType == cellTypeA &
grepl(proteinTypeA,INX$nodes$proteinType),]
if (nrow(nodesA) < 1) {
stop(paste0("No ",proteinTypeA," in ",cellTyp... |
efee5d839824ea58fd5b42f42221eb93e7149f1ff2dcf29beb12bcbf10c636e9 | R | 13,577 | 305 | library(tidyverse)
library(cmapR)
library(ggplot2)
library(ggpubr)
library(doFuture)
# parallel: set number of workers
cores <- 11
registerDoFuture()
plan(multisession,workers = cores)
library(doRNG)
########## The whole pre-processing analysis is in the L1000 folder of the new data ###############
### Load data and ... |
e845cf244a80f65c55b1e8472682beb1756c7ce714a41ad14f59506dc27ccbd2 | R | 13,588 | 400 | ################################
# LC analyses: exploratory plots
# Lukas Weber, Jun 2023
################################
# module load conda_R/4.2
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
library(SpatialExperiment)
library(here)
library(dplyr)
library(tidyr)
libra... |
a2e15e2d1566198d1bd28a8c7baf8db83467ebb91ab0cc417e3ac21e6733f2e5 | R | 13,596 | 443 | ---
title: "LC analyses: features per spot (LC samples) - all spots"
author: "Lukas Weber"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
toc_depth: 3
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introducti... |
fc9205ea689acdcf0cdccfbed5cbb913e19c1abe54663e937b890e3d92bcbee4 | R | 13,603 | 424 | ## This is for preprocess seurat v5 data
library(tidyverse)
library(Seurat)
options(Seurat.object.assay.version = "v5")
library(SeuratObject)
# Not sure if needed
library(SeuratData)
# seurat5 version wrappers
# needed for FastMNNIntegration
library(SeuratWrappers)
# [FIXED] Seurat5 use future inside, this is a must
op... |
bc6ab67d4690d42761d02d2ae9a5dbf8ed708d5ec1ba9ed7a9341b18b5885414 | R | 13,654 | 312 | ---
title: A Python Package for Density Ratio Estimation
author: Koji Makiyama (@hoxo-m), Ameya Daigavane (@ameya98), and Krzysztof Mierzejewski (@mierzejk)
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include=FALSE}
knitr::opts_chunk$set(
collapse ... |
6d4d5e9711e4c087949d3dca5cb9c82fcc7b067303be129063cd6a1e9a9a0333 | R | 13,729 | 519 | # =========================== #
# 5. APOE2 stratified: SABRE #
# =========================== #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(data.table)
library(dplyr)
library(magrittr)
library(mice)
library(purrr)
library(tidyr)
## Read in data ----
# Principal components (EUR)
eur_p... |
9c64df1d3be5343efa397edad0c49ea58999287593235f8c27f6c3814b6b54dd | R | 13,745 | 283 | # MLAging project DY@WebbLabBrown
# data: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE188646
# from paper: https://doi.org/10.1038/s43587-022-00246-4
library(tidyverse)
library(Seurat)
##--1.read data from 10x and perform preprocessing----
Aged_1 <- Read10X(data.dir = '../filtered_feature_bc_matrix/Aged_1/f... |
2ed92fffe3b384124c0118a2041ca2069f438fe96d496adda127ea64d17f2f30 | R | 13,849 | 249 | theme_darker <- function(base_size = 11, base_family = "", base_line_size = base_size/22, base_rect_size = base_size/22)
{
half_line <- base_size/2
theme_light() %+replace%
theme(line = element_line(colour = "grey100", size = 0.5, linetype = 1, lineend = "butt"),
rect = element_rect(fill... |
26540e31bdb94193adac7e8664d40c2f0e0d208b7c0581f35b2d3c29ab435f2e | R | 13,861 | 277 | ## LOAD PACKAGES ----------
# Data manipulation
library(tidyverse)
library(rlist)
library(readxl)
# Plots and visualizations
library(grid)
library(viridis)
library(ggrepel)
library(ggpubr)
library(svglite)
# Statistics
library(rstatix)
# Other
library(clipr)
## SET PARAMETERS ----
# Change the tidyverse functio... |
fd06d12b58b0d60c5956719afc0554e56734703d1f23c74e7a6ad71213644b1a | R | 13,866 | 349 | # call LOY barcodes for the atac samples and write an annotated seurat obj
# RUN local interactive
# SCRATCH1=/mnt/g/scratch
# docker run -it --rm \
# --workdir $HOME \
# -v /mnt/c/reference:$HOME/reference \
# -v /mnt/g/cellranger_atac_counts:$HOME/cellranger_atac_counts \
# -v /mnt/g/ckd:$HOME/ckd \
# -v $HOME:$HOME... |
67b8c84121ed372ee5c3eb1b27cfcd573cf386dac7739a01a4685cdf194d8d50 | R | 13,913 | 300 | library(SeuratDisk); library(Seurat); library(dplyr); library(ggplot2); library(patchwork);library(RColorBrewer)
library(ktplots); library(pheatmap); library(viridis); library(tidyverse)
readRDS("pk_all.rds") -> pk_all
########################
### DEG of TCGA-PAAD ###
########################
# Get DEG from... |
8ee42f51f3b91ddcd63736c6abc8b23ae66593c354d591f136f4200c1895b0cc | R | 13,942 | 282 | #' Query your data and generates a \code{comoRbidity}
#'
#' Given 4 files (patientData.txt, diagnosisData.txt, admissionData.txt and
#' indexDiseaseCodes.txt), generates some RData and create an object of type
#' \code{comoRbidity}.
#'
#' @param databasePth Determines the path where the three required input files
#'... |
3d65300b0d8ce323dc2d68912f377ff9fd07f82dec854fdc6d21c286ef81d4f7 | R | 13,946 | 417 | #' @rdname utils
#' @name matrix_distance
#' @title Calculate the distance between two matrices
#' @param matrix_x matrix x
#' @param matrix_y matrix y
#' @param metric metric used to calculate distance. Defaults to manhattan
#' @return the distance between two matrices
#' @export
matrix_distance <- function(matrix_x, ... |
d927e633d86f1cdddbd68cd0ab112a57c80353f40b412c79638d7f708dd20ef6 | R | 13,984 | 310 | ---
title: "Figure 2"
author: "Joe Raymond"
date: "April 1, 2021"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(tidy = TRUE)
knitr::opts_chunk$set(eval = TRUE)
knitr::opts_chunk$set(fig.width = 8)
knitr::opts_chunk$set(fig.height = 4)
knitr::opts_chunk$se... |
24e850b34793aeb4dd230e9816377b7b7c9ceb7d409968872ed9ffcf90751b90 | R | 14,015 | 227 | library(tidyverse)
library(reshape2)
library(gg3D)
library(ggsignif)
library(ggpubr)
library(caret)
### Load performance results from different approaches---------
CPAbased_model <- data.table::fread('results/10foldvalidationResults_lungs.csv') %>%
select(-V1) %>%
rownames_to_column('fold')
autoencoders_model <- ... |
89bf822472c6360bbd72d62eb5f41b78b9080359836cb9d42c187eb8faae7969 | R | 14,075 | 471 | # this script will analyze degs for pathway enrichment
# to run locally:
# SCRATCH1=/mnt/g/scratch
# docker run -it --rm \
# --workdir $HOME \
# -v /mnt/g/reference:$HOME/reference \
# -v /mnt/g/cellranger_atac_counts:$HOME/cellranger_atac_counts \
# -v /mnt/g/cellranger_multi_counts:$HOME/cellranger_multi_counts \
# -... |
778b7f8e9376205afaa601894929c90303e0baf644aaf6cb7cd645c7c628af2e | R | 14,078 | 397 |
.limit <- function(v) {
m = 10
Sign = sign(v)
v = abs(v)
v.lim = Sign * (floor(m * v) / m)
return(v.lim)
}
.limits <- function(v, symmetric = TRUE) {
stopifnot(length(v) == 2, all(sapply(v, class) == 'numeric'))
if (!symmetric) {
return(sapply(v, .limit))
}
limit... |
eb51389f30d737727039f3dbbda1f694af90c987e045cee3146476dcce1f49fb | R | 14,093 | 380 |
# PACKAGES ----------
# Data Manipulation
library(tidyverse)
library(reshape2)
# Modeling & Differential Expression
library(edgeR)
library(limma)
library(variancePartition)
# Plots
library(ggpubr)
library(pals)
library(RColorBrewer)
library(wesanderson)
library(svglite)
# BEFORE USING THIS SCRIPT ----------
# Use... |
e75e0fc25f7f8642979af727d6e099cdcc9cd0ced31baff6e7305e543340993d | R | 14,117 | 486 | ---
title: "DEWSeq Analysis"
author: "Thomas Schwarzl and Sudeep Sahadevan"
date: "07/10/2020"
output:
BiocStyle::html_document:
toc: true
toc_float: true
toc_depth: 5
params:
protein: ""
sampleinfo_file: ""
countmatrix_file: ""
annotation_file: ""
output_windows_file: ""
output_regions_file: ... |
53383b9968b23fd78ed3ea367d5437b4065e28618e286888eae5e377ea3db4d8 | R | 14,268 | 463 | ---
title: "LC analyses: features per spot (LC samples)"
author: "Lukas Weber"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
toc_depth: 3
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
Analyses... |
9195e00ef206114d90c9a82a4d25a5df9022cd2722f31a8b879280c0b95cf9db | R | 14,298 | 302 | #' Draw two oncoplots side by side for cohort comparision.
#' @details Draws two oncoplots side by side to display difference between two cohorts.
#'
#' @param m1 first \code{\link{MAF}} object
#' @param m2 second \code{\link{MAF}} object
#' @param genes draw these genes. Default plots top 5 mutated genes from two coho... |
2e76370f5ab77e5c46fffb503d74d8ea0ea98ec234f7f16ac2d53d325107d541 | R | 14,367 | 360 |
library(dplyr)
library(monocle3)
library(here)
library(ggplot2)
library(scales)
library(pals)
library(grid)
library(gridExtra)
library(viridis)
library(tidyverse)
library(tricycle)
library(ggrastr)
library(scattermore)
library(ggpubr)
library(cowplot)
source(here("scripts/accessory_functions/monocle_mods.R"))
source... |
9162cb07e4f26518374c1363bbaf0b48fec84a81f42544f49d5c756f3f8e87ae | R | 14,417 | 422 | library(ggplot2)
library(tidyverse)
# continous color function (from white to red)
colfn <- circlize::colorRamp2(breaks = c(0,1), c("white", "red"))
#' @param group.order: order the group
#' @export
drawReplicateRatio <- function(g2r, group.order = NULL) {
if (!is.null(group.order)) {
g2r <- g2r[g2r[, 1] %in% g... |
02b9c7e5404da811fe8baf804cd0da2bdb9d0905bb6fc802ca8d00ec8480c082 | R | 14,424 | 284 | ### load required libraries
require(rtracklayer)
library(chipenrich)
library(GenomicRanges)
library(IRanges)
library(AnnotationHub)
library(eulerr)
library(UpSetR)
### Select working directory and destination folder
setwd(choose.dir())
path_res=paste0(choose.dir(), "/")
### Import MACS2 broadpeak files... |
ad83d0ddf7add027bcb3e31461ba60b6c842ea0520846dd57ccc21615359f281 | R | 14,446 | 146 | load("duplications.RData")
library(dplyr)
duplication=filter(duplication_separated,Support >= 0.5)
domain=read.csv("Domain_11_species.csv",header=T)
library(dplyr)
#elegans
duplication_elegans=filter(duplication,Species=="elegans")
duplication_N0=duplication_elegans[grep("N0",duplication_elegans$Species.Tree.Nod... |
87b9b5d13ea899c6a7fefbe8ebcb03ed90974617932d95acf8a945905589227c | R | 14,464 | 316 | ---
title: "Format and Compile Counts"
author: "Stephen P Plassmeyer"
date: "Updated 03/31/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../code/mpra_tools/mpra_tools.r')
```
```{r}
data<-c()
```
```{r}
reformat_element_name <- function(counts.data){
counts.... |
a17a8feb3bdd2c03bcbfa7cb20c8ee8da9e49c7bdae285afac187d3ea32a3c2a | R | 14,469 | 349 |
#Function to iterate through data and partition into cell types
RecursiveClustering <- function(seuratObject,masterDendrogram=NULL,masterClassification=NULL,indexRecord=NULL,clusterCounter=1,minSize=50,depth=1,giveUp=FALSE,isIntegrated=TRUE, DEscore.thresh=60,logFC.thresh=log(2)){
stop<-FALSE
if(isIntegrated)... |
e5a8c93917ec63236f44a5fb5b5f591e5d61372dbbdeb1ea016e9ffefb56da5a | R | 14,519 | 323 | # Heatmap of brain response correlation
# libraries needed
library(tidyverse)
library(R.matlab)
library(glue)
library(lsa)
library(viridis)
library(patchwork)
# load data
path <- "bids/derivatives/meg-derivatives/grand_ave/matricies/modality-picture.mat"
m <- readMat(path)
megdata <- m$megdata
# calculate correlation... |
1c17e8c23352ea897aeb04adcadcd583b880fe7009927e28292bb40e30a8b840 | R | 14,545 | 398 | ###############################################################################
##
## Single Cell Temra analysis
##
## This Workflow is based on:
## https://nbisweden.github.io/workshop-scRNAseq/labs/compiled/seurat/seurat_01_qc.html
##
## Oliver Hunewald
## 10.05.2023
##
###############################################... |
5777db1f9a3e8cd012d3a99a997e9cf568a719fdd3e6f6e91e3331cf70937079 | R | 14,566 | 351 | args = commandArgs(trailingOnly=TRUE)
if (length(args) < 10) {
stop("Usage: generate_feature_grid <trackdb> <track> <source track> <score filter> <feature1 data> <feature2 data>
<output_data> <range> <resolution> <output_image> <domains>")
}
construct.grid = function(interv1,interv2,min_dist,max_dist){
return(ddply... |
7d59b085e86e223554cb3f56633c62344eb86f23f6b3f2ea9f1d95e29458905f | R | 14,583 | 289 | ---
title: "455 IVT Pilot Transfection Analysis"
author: "Stephen P Plassmeyer"
date: "6/10/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../code/mpra_tools/mpra_tools.R')
library(patchwork)
```
```{r}
compiled_counts <- fread('../../data/compiled_counts/as... |
f20936ad50877061ccbd45eaab8241dc5a979935d95d8aa085d02299ff42dfbb | R | 14,613 | 419 | #!/usr/bin/env Rscript
#load required packages
library(Seurat)
library(dplyr)
library(ggplot2)
#set all directory paths
basedir <- paste0("/gladstone/bioinformatics/adnetworksppg/Project_1_Huang/NK01/",
"results/HMGB1_snRNA_mm10/06_seurat_analysis_batch1_batch2_merged")
indir <- paste0(basedir,
... |
a8ddd362d364eacf924df67de45a365904060e6104dc27a1f9ff1af869ee6d79 | R | 14,644 | 252 | ---
title: "Model Evaluation for 410 N2a TRAP Data, with Updated DNA Counts"
author: "Stephen P Plassmeyer"
date: "Last Updated, November 18, 2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
```
# Overview
This notebook e... |
8ec82aed035826db5ef7d2462eefec2555a32df7ad572bb3e27382026d5d3dc8 | R | 14,655 | 329 | ##### load libraries #####
library(data.table)
library(GenomicRanges)
library(kimisc)
start_time <- Sys.time()
##### get commnad line arguments #####
args <- commandArgs(TRUE)
filedir <- args[1]
if(grepl("\\/$", filedir)==F){
filedir <- paste0(filedir, '/')
}
##### get config parameters #####
curfile <- thisfile()
... |
796ff882d6ee93b05aeb9944b767c654b0aedabe6ca38e2d672dccef691283cb | R | 14,682 | 350 | ### Between -cluster comparison
#For this analyses, we will use the **lme4** package in R to fit generalized
#linear mixed effects models. We are going the model the change in the chance
#(or more formally the odds) of cells from a given mouse belonging to a given
#cluster from the 3 genotypes to the PS19-fE4_Saline... |
23dbe73190d6a2b840ba458eb180881e454a5688a86596c51d27d5ab297a02a8 | R | 14,688 | 341 | ## conda activate r4
## packages for data table processing
library(here)
library(tidyverse)
library(RColorBrewer)
library(rcartocolor)
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(future)
## gene expression heatmap plots
library(Nebulosa)
ss <- function(x, pattern, slot = 1,... |
d8966e2909084bacad4c22020dbe993231087aa3255df1f2a2325ce03264d8ce | R | 14,700 | 388 | #!/usr/bin/env Rscript
#load required packages
library(Seurat)
library(dplyr)
library(ggplot2)
library(gridExtra)
#set all directory paths
outdir <- paste0("/gladstone/bioinformatics/projects/",
"mn-1318-maxine-nelson-yadong-huang-snrnaseq-mm10-aug-2022/",
"results")
indir <- paste0(... |
b7a65d9f549c9bfe182fdca0a98a942459c956c2e6be6df89634d7b22fc75da5 | R | 14,714 | 333 | ---
title: "Alpha- and beta-diversity analysis for HRQoL - Gut microbiome TransplantLines"
author: J.C. Swarte
output: html_notebook
# ==========================================================================================
# By: Weersma Group, TransplantLines (UMCG)
#
# ... |
8ff83f9c6f9119a3033fb96355f0422066851d92d7879798a9b066d1ea61ac56 | R | 14,761 | 377 | #thresholding analysis - FSQC cut offs
library(effectsize)
library(parameters)
library(plyr)
library(ggplot2)
library(ggseg)
library(ggsegGlasser)
library(dplyr)
#lmer including partial R
######CORTICAL THICKNESS
#cut offs at 3, 2.5, 2, 1.5
# CT with HCP atlas
#cut off below 3
output_CT_HCP_FSQC3<- data.frame()
for... |
bff9581dbd2235a1c959dd7e90801b2b0160b9ee3d337760034004b6d61759b7 | R | 14,801 | 537 | # ============= #
# 7. Figure 2 #
# ============= #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(cowplot)
library(ggstance)
library(grid)
library(tidyverse)
## Figure 2a ----
### Format data ----
# List of pathogens
pathogens <- c(
"HSV1",
"HSV2",
"VZV",
"EBV",
"CMV",
"H... |
56004d4aa886702f77ebfdc1d53c7142ef4addbb2d5b2ee404ea703dfb0f478e | R | 14,807 | 346 | # integrate scRNA and merFISH data
# 3/28/2022
library(pals)
# load data
load("../scRNA/all_cell_inDD_sobj.RData")
DimPlot(all_cell_inDD_sobj, reduction = "tsne", group.by= "CellType",
label = F) + NoAxes() +
scale_color_manual(values = as.vector(alphabet()))
merFISH_integrated = readRDS(file = "./RDS/merFI... |
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