sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
e1f3343575c40b926717098aa1905b989f37a522705d86cd0272ebd724c54e19 | R | 11,048 | 272 | #!/usr/bin/env Rscript
#### Fine cell type annotation of myeloid cell subset for MBM_sc
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
library(viridis)
'%notin%' <- Negate('%in%')
path.ct <- 'data/cell_type_DEG/MBM_sc/myeloid/'
filename <- 'MBM_sc_myeloid'
seu <- rea... |
ea58a05658a998fba3c044cb37fa1640f664c373b6f9fe8df8a5c1f28d03044b | R | 11,088 | 268 | # CIRCUITS Multiregion Single Cell RNA-seq - single cell resilience
# Cell proportions using DirichletReg (cell subtypes - subclusters) BY MAJOR CELL TYPE
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# September 2022
# activate conda environment in ITHACA
# conda activate use_seurat_r4
# Open R
# R
setwd("/home/D... |
40b7722f5417479f2a77546f039b2a430052755dea716cba5435f3a71c9fe3f0 | R | 11,100 | 222 | # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
# Main pipeline.
# Study title: "Characterising distinct subgroups of very preterm born children and
# exploring differences in neonatal structural and functional brain patterns"
# Date: 19/11/2021
# Author... |
57a6be0c81a1b2745b9726ba7a671f22d2b78ad50626fed3011625791a8f33ab | R | 11,100 | 227 | # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
# Bootstrap code - to run on an HPC cluster (we used the King's College London Rosalind computing interface: https://rosalind.kcl.ac.uk/)
# Study title: "Characterising distinct subgroups of very preterm born ... |
3bfc53bac261c2e229024b2fa9e73b2b38b2cd0e7887211383676eb2bb16f5a4 | R | 11,103 | 243 | # AIM ---------------------------------------------------------------------
# this is the initial step for the counting of senescent cells. In this step I generate the signatures score.
# libraries ---------------------------------------------------------------
library(tidyverse)
library(ggrepel)
library(lemon)
# rea... |
d563045437362fb592b81413a62ab18172c4265dafc80b5b03e09ff4dce926b5 | R | 11,104 | 270 | suppressPackageStartupMessages(library(tximport))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(stringr))
suppressPackageStartupMessages(library(tidyr))
suppressPackageStartupMessages(library(glue))
# option_list <- list(make_option(c("-s", "--sample-table"),
# ... |
ff74d2d914c258e11bf35aff6485428b9928e08b7e2ed0eb1a35c0aa625d09bc | R | 11,104 | 245 | # AIM ---------------------------------------------------------------------
# this is the initial step for the counting of senescent cells. In this step I generate the signatures score.
# libraries ---------------------------------------------------------------
library(tidyverse)
library(ggrepel)
library(lemon)
# rea... |
df86edad8320b8309adb43f530017c11ee802346706fd9862191ac09240774a9 | R | 11,108 | 202 | # this script runs weighted GAMMS on the imputed datasets producing one partial effect of LSNS per gender
# load required packages
library("mgcv") # version 1.9-1
library("mgcv.helper") # version 0.1.9
library("gamm4") # version 0.2-6
library("mice") # version 3.17-44
library("tidyverse") # version 2.0.0
# define whe... |
f2011d027ae503ad46524f08e76fb982f109b81a182472a26b1d6a0d248d8307 | R | 11,121 | 261 | # libraries ---------------------------------------------------------------
library(Seurat)
library(tidyverse)
library(scales)
library(ggrepel)
library(cowplot)
# read the data -----------------------------------------------------------
# read in the dataset
data.combined <- readRDS("../../out/object/revision/120_WMCX... |
45d707d482ec035d0edaa8287429925b4333c10260c7dc91b9dc3305f54caa4c | R | 11,126 | 223 | ---
title: "Hypothesis testing H&E"
author: "Gerard Baquer"
date: "2024-09-09"
output: html_document
---
```{r}
# Libraries
library(rhdf5)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)
# Remove non-biological pixels
he.rename_labels<-function(img,labels=c("background","CMC","viable tumor","infiltrat... |
9068c89a928c8b26ad414b1e4a9bf8137aef259213b5f394266cf1a51baf34e6 | R | 11,192 | 363 | ### Integrate stages' data from Four data-sets where consider gene names in combination
setwd("/home/Fatemeh/0--ThirdProject")
##### Load Data #####
##### Integrate NAT Data #####
##### NAT CD4 #####
MAD_NAT_CD4T <- read.table("DATA/Gastric/CellTypeStage/MAD_nat_CD4T_matrix.txt", header = T)
dim(MAD_NAT_CD4T)
... |
42f170457354cb7d29555b6251c73733ce9ecb6ff4d3202074434242a9e2b6ab | R | 11,238 | 250 | ---
output:
html_document: default
pdf_document:
latex_engine: xelatex
editor_options:
markdown:
wrap: sentence
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
########################################################################################################
# Integratin... |
4bd68eb3f7cc170742babf72433a3b745defb1ef9c85beb3a5525c6c6c577067 | R | 11,265 | 285 | #!/usr/bin/env Rscript
#### Fine cell type annotation of myeloid cell subset for MBM_sn
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
library(viridis)
'%notin%' <- Negate('%in%')
path.ct <- 'data/cell_type_DEG/MBM_sn/myeloid/'
filename <- 'MBM_sn_myeloid'
seu <- rea... |
df74cc2c3e4cd9c42553f9ccc3000c2fa8e20273a4f862bef1a99a37cbfc39d2 | R | 11,304 | 260 | library(qs)
library(DEP)
library(limma)
library(readxl)
library(dplyr)
library(SummarizedExperiment)
library(ggplot2)
library(pheatmap)
library(stringr)
library(ComplexHeatmap)
library(GOSemSim)
library(simplifyEnrichment)
library(rrvgo)
library(wordcloud)
library(pathfindR)
library(tidyr)
library(radia... |
1913ffeaa7e654b9fdce1f11f2be4c7394b9ae6e2b4e7c6c8e4e5dada78e5d59 | R | 11,309 | 403 | # make figures for Alena PICALM paper, additional data
# Siwei 23 Apr 2024
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
}
## Fig_5I ####
df_raw <-
read_excel("tables_4_plot_v4.xlsx",
sheet ... |
5ebdecb01358616789079b78be2192785209950ba208371861c5bdb4182ed394 | R | 11,355 | 259 |
require(ggplot2); require(scales); require(reshape2);
#install.packages("dplyr")
require(dplyr)
#require(Hmisc)
library("readxl")
library(RColorBrewer)
library("ggsci")
#install.packages("ggrepel")
library("ggrepel")
library(ggpubr)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
#setwd('/Users/admin/D... |
4199943add14eb23efdcf235c449b148c143bc944065caee3477cb6e29ad4a80 | R | 11,393 | 173 | #prepare files for scDRS/FUMA/MAGMA
##### 1. load packages and data#######
###load packages
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if (!require("tidyverse")) {
install.packages("tidyverse")
library(... |
984594cb0182460c080828377a136a2b20cb86119d0dfc94f8818b82e1a35523 | R | 11,399 | 199 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
library(goseq)
#### GO enrichment ####
##Adapted from (https://gitlab.aleelab.net/august/ad-... |
ce796630523d44b032e826633cd90f3db315b34f71c129c27f8d35cbeb616f5b | R | 11,399 | 385 | ### MNT 10x snRNA-seq workflow: step 02b
### **Region-specific analyses**
### - (3x) DLPFC samples from: Br5161, Br5212, Br207
### **Comparing to Spatial Transcriptomics data
### Taken/adapted from /dcl02/lieber/ajaffe/SpatialTranscriptomics/HumanPilot/Analysis/Layer_Guesses
### Initiated MNT 17Feb2020
### Upda... |
9c8b1db5cdc9783fe823d48bfe0a00941da6a4227cbd5c29c61d282356f93ca9 | R | 11,467 | 256 | # coding UCRÏà¹Ø»ùÒòÔÚbrainµ±ÖÐÓÐÌØÊâpattern
# ÀûÓÃÊý¾Ý¿âÊý¾Ý²é¿´coding UCRÏà¹Ø»ùÒòÔÚcerebellum¡¢heart¡¢kidney¡¢liver¡¢ovary¡¢testisµ±Öеıí´ï
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(dplyr)
library(pheatmap)
library(readxl)
library(stringr)
library(tidyverse)... |
c02712ea6c6336e8ffb9d81d550536ef67cdbfe0d26ac5ef1b7eeab6f4dbd6f6 | R | 11,490 | 263 | library(KFAS)
# library(rucm)
options(digits=10)
dta <- read.csv('datasets/macrodata/macrodata.csv')
# Irregular (ntrend)
mod_ntrend <- SSModel(
dta$unemp ~ -1 + SSMcustom(Z=matrix(0), matrix(0), matrix(0),
matrix(0)),
H=matrix(NA))
res_ntrend <- fitSSM(inits=c(log(var(dta$unemp))), m... |
2c254f23613225ede59a50de56b77f68e73e2d55b4b5ad050368795c86225e9a | R | 11,493 | 250 | #plot for the simulation results
##### load packages and results ####
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("tidyverse")) {
install.packages("tidyverse")
library("tidyverse")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if (!requi... |
7309bf1b4008ffe4b646c560a11dbe700c1d3e58b3a9af8e28d4f5c2da6fedc6 | R | 11,510 | 273 | # comparison to differentially expressed gene candidates of former bulk RNA-Seq studies
#
# Chen et al. 2015
# Molecular Profiling of Patient-Matched Brain and Extracranial Melanoma Metastases Implicates the PI3K Pathway as a Therapeutic Target
# https://pmc.ncbi.nlm.nih.gov/articles/instance/4216765/
# Suppl. Table S... |
262a684d6526ba351f6be6fa9a1df726ba1e3a973a0a2e0b7e5546e8297d0cba | R | 11,535 | 339 | ```{r}
library(Seurat)
library(dplyr)
library(biomaRt)
library(svglite)
```
```{r}
# Download Human NAC data from CELLxGENE: https://cellxgene.cziscience.com/collections/283d65eb-dd53-496d-adb7-7570c7caa443
obj <- readRDS('Human_NAC.rds')
obj <- CreateSeuratObject(obj[["RNA"]]@data,meta.data=obj@meta.data)
```
```{r... |
c25e19e356d20e8a3eefa690b3982cf15186b65c186ab60882908e90a8f54185 | R | 11,583 | 255 | # AIM ---------------------------------------------------------------------
# this is the initial step for the counting of senescent cells. In this step I generate the signatures score.
# libraries ---------------------------------------------------------------
library(tidyverse)
library(ggrepel)
library(lemon)
# rea... |
830018d19f52ab60a1edc25babbbbc467536deec59f12f05b096dd46e6c3c46d | R | 11,592 | 256 |
## Functions to calculate PCA and plot ##
#1. screePlot() = calculates variance explained by principle components of pca results. Option to plot barplot.
#2. pca.Gene() = runs PCA on betas. [...] = additional arguments such as scale=TRUE to be supplied to prcomp.
#3. plotPCA() = scatter plots of 2 PCs. Uses screePlo... |
551245d13bfc2abf7482c320b2a9b0627128bae12ca41a9794548553f3aadc3e | R | 11,593 | 240 | ####################################################################################################
## Package : CARD
## Version : 1.0.1
## Date : 2021-1-7 09:10:08
## Modified: 2021-5-20 15:25:07
## Title : Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics by CARD.
## Authors : Ying Ma
## Co... |
58e89a2fb168994719d9cd842f39bff24b55ac1a6e8afd569482a580c17fd863 | R | 11,594 | 420 | # Siwei 19 Jun 2024
# match RNASeq count matrices to individual and genotype data
# install.packages("LDlinkR")
# init ####
{
library(readr)
library(edgeR)
library(ggplot2)
library(RColorBrewer)
library(stringr)
}
# load data ####
df_gene_names <-
read_csv("freshmicro_counts/RNAseq_GeneInfo.csv")
df_r... |
5d76cad28a15c73c8412bb961887fe55e795b2e3a0206277b98a31630b89179d | R | 11,622 | 282 | #!/usr/bin/env Rscript
# Author: Tim Sterne-Weiler, Ulrich Braunschweig 2014-2024
# u.braunschweig@utoronto.ca
# Copyright (C) 2014 Tim Sterne-Weiler, Ulrich Braunschweig
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the "Softwa... |
d4090c6140970fc56373ffc9bf67209dc7537bd05956e5db6ac5449f62e5dfba | R | 11,629 | 263 | #' doubletThresholding
#'
#' Sets the doublet scores threshold; typically called by
#' \code{\link[scDblFinder]{scDblFinder}}.
#'
#' @param d A data.frame of cell properties, with each row representing a cell, as
#' produced by `scDblFinder(..., returnType="table")`, or minimally containing a `score`
#' column.
#' @par... |
ae1419fd12d1af40decb2f8af6941bd2513a22e6c3a0aecbc07170e648e91975 | R | 11,643 | 221 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
library(parallel)
library(MutationalPatterns)
ref_genome="BSgenome.Hsapiens.UCSC.hg19"
chr_orders=c(paste("chr",1:22,sep=""),"chrX","chrY","chrM")
li... |
6ccbb8a38caa35826918dbc0b3948dc3e48066d1f1aaad41e6b80710d7d64026 | R | 11,651 | 195 | library(RColorBrewer)
library(Matrix)
col2<-brewer.pal(n=12, name="Paired")
col1<-brewer.pal(n=9, name="Set1")
col<-c(col2,col1)
plot_cell_ID<-function(data, cutoff=100, sample="NA", ...){
plot(rev(sort(data)), pch=19, cex=0.5, col="grey", xlab="# of Cellular Barcodes", ylab="# of Reads", log="xy", main=sample)
fi... |
a979c26342bf6e0114a4483594979da7825ef9bb896cf651e551efccb24e38e6 | R | 11,657 | 243 | # libraries ---------------------------------------------------------------
library(harmony)
library(Seurat)
library(dplyr)
library(cowplot)
library(tidyverse)
library(ggrepel)
library(scales)
library(RColorBrewer)
library(SeuratWrappers)
# # read in the data --------------------------------------------------------
da... |
e5a083a95d9bdf4bc3ee9b65d2ed49b162eb839ab9bccd3891dff043e8e93d6b | R | 11,657 | 271 | library(GenomicRanges)
library(readxl)
setwd("ATAC")
astro <- read.csv("Astrocytes_classification_AUD_vs_Control_all_peaks_anno.csv")
oligo <- read.csv("Oligodendrocytes_classification_AUD_vs_Control_all_peaks_anno.csv")
micro <- read.csv("Microglia_classification_AUD_vs_Control_all_peaks_anno.csv")
opcs <- rea... |
39405ed3af68c61c81ada91538bb84596d1918df5845fca5cf74350945217c68 | R | 11,661 | 249 | #generate 4 basic functions showing possible gene expression shifts
low <- function(x) {
0 + runif(1, 0, 0.7)
}
high <- function(x) {
2.3 + runif(1, 0, 0.7)
}
up <- function(x) {
exp(x/2)-1 + runif(1, -0.4, 0.4)
}
down <- function(x) {
1/x + runif(1, 0, 0.4)
}
library(ggplot2)
library(patchwork)
#m... |
f6913d0474d4e8c05b4aa383c6ef081c49d6d115e55297088bcb5934df3ee528 | R | 11,663 | 293 | # Load required packages
library(TwoSampleMR)
library(ieugwasr)
library(data.table)
library(dplyr)
library(stringr)
library(tidyverse)
# Custom function for string concatenation
'%+%' <- function(x, y) paste0(x, y)
# -------------------------- 1. Set Up Directories --------------------------
# Replace wit... |
33306873d2f221053d785258e322635a8016e95d1bb1fb672b3883f3389937a1 | R | 11,670 | 287 | library(data.table)
library(ggplot2)
library(arrow)
setwd("[Please replace with your working directory path]")
# -------------------------- 1. Initialize Parameters & Load GWAS Data --------------------------
# Load GWAS data and generate standardized variant IDs
gwas_path <- "[Please replace with path to novel... |
5c51505f073e38233bdc1c9c7d1f276000c587ed42fb428a2a9d8756e342afdd | R | 11,692 | 365 | ---
title: "R Notebook"
output:
html_document:
df_print: paged
editor_options:
chunk_output_type: console
---
View DE results for neutrophils and the neutrophil subclusters
```{r}
setwd("/Users/mary/non_dropbox/exps/exp042_meninges_stress_dropseq/")
library(ggplot2)
library(kableExtra)
library(RColorBrewer)
l... |
d3b5a6f393cfa58f501758abfb54c4028a99369789ae9a08c4af372a17feba22 | R | 11,713 | 278 | # ============================================================================ #
# Script: CCA and Correlation Analysis for Imaging Data (IDoct Version)
#
# Description:
# This script performs a canonical correlation analysis (CCA) between modelled
# cognitive data (IDoct version) and brain imaging metrics. It begi... |
c63c0f1419609d78d05a1c6fb901603bdd79c2d4cbf047cd679b6ed862cb7416 | R | 11,732 | 252 |
## Enrichment of chromosomes within EWAS results ##
library(data.table)
library(dplyr)
resultsFile <- paste0(AnalysisPath, "ageReg_fetalBrain_EX3_23pcw_annotAllCols_filtered.rds")
res <- readRDS(resultsFile)
colP <- 'P.Age'
colBeta <- 'Beta.Age'
#1. Load results & annotate =======================================... |
403bdbb53586d1f540acd36321b3614a24da8386bb451dbafa86e0e94c3d9616 | R | 11,770 | 259 | #!/usr/bin/env Rscript
### title: Compare copy number alterations among TCGA, Davies, and MBPM datasets
### author: Yiping Wang date: 09/27/2021
library(copynumber)
library(stringr)
library(infercnv)
library(grid)
library(rlist)
library(matrixStats)
library(dplyr)
tcga_clinical = read.table("gdc_download_skcm_clinic... |
eb7a0df49fffe3b6686c6e7529fed12f10d78093c1a9b0a1b5a858308d32397e | R | 11,847 | 234 | library(dplyr)
library(Seurat)
library(monocle)
library(ggplot2)
library(RColorBrewer)
library(cowplot)
library("viridis")
library(reshape2)
MTN.color = brewer.pal(8,"Dark2")[1:3]
names(MTN.color) = c("E","T","M")
load("../CNVfiltered_11561cells_combine_BGI500DipseqSeed30res017.updatemeta.RData")
EPI = subset(embryo.i... |
1fa907ffd583d558db1b10928eddc3a99aeb3eaa26a14d5980a313c3708d6749 | R | 11,855 | 200 | # this script produces composite plots of the partial effects of LSNS on our outcomes comparing them to the effect of age and between imputations
library(tidyverse) # version 2.0.0
library(gratia) # version 0.10.0
library(patchwork) # version 1.3.0
library(MetBrewer) # version 0.2.0
library(waffle) # version 1.0.2
#... |
a48937bfee13ba0384066252d1ee32295588acf6027ac4fbdebe4634342b9a39 | R | 11,864 | 263 | # protein-protein/protein-link community/protein complex
# condensates
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(dbplyr)
library(tidyverse)
library(biomaRt) # ÔÚʹÓÃbiomaRtµÄʱºòdbplyrµÄ°æ±¾²»ÄÜÌ«¸ß
library(curl)
rf_neighboring_protein_coding <- read.table(file = "02-analysis/13-Network_analys... |
1447ae37dd7958c8623b0cbd6c562febb7fc6bfd422f6c7872bb69f90c5c9b5f | R | 11,869 | 255 | ---
title: "OD histogram analysis"
author: "C-M Svensson"
date: "2023-02-23"
output: pdf_document
---
```{r setup, include=FALSE}
rm(list = ls())
knitr::opts_chunk$set(echo = TRUE, fig.width = 12, fig.height = 12)
library(dplyr)
library(latex2exp)
library(tidyverse)
library(ggplot2)
library(readxl)
lib... |
e056b1904b924c155326f40f471a64803e109146bebfe3cdbfa48c058ebc76a9 | R | 11,870 | 310 | rm(list = ls())
options(StringAsFactors = F)
library(limma)
library(dplyr)
library(psych)
library(tidyverse)
library(CovariateAnalysis)
library(clusterProfiler)
library(org.Hs.eg.db)
library(broom)
library(VGAM)
library(foreach)
doParallel::registerDoParallel(10)
source("codes/00-02-functions2.R")
# Loading covar... |
7e4936424f9f76e914768cb617a730a2889a141bfee81ac582aef7c919a8465e | R | 11,881 | 295 | #!/usr/bin/env Rscript
#
# Author: Tim Sterne-Weiler, Ulrich Braunschweig 2014-2024
# u.braunschweig@utoronto.ca
## Check that columns of INCLUSION... table are what we think they are
checkHeader <- function(x, replicateA, replicateB) {
reps <- c(replicateA, replicateB)
sampInd <- unlist(sapply(reps, FUN=funct... |
c78ae879977603a4fd75ab766313ed58d787652d1d0562ea17be54dc8c9987de | R | 11,915 | 295 | ## This script inputs the raw and mapped microbiome data and produces tables on
# 1) A table with all taxonomies and mapping information
# 2) A table with all species level reads.
# 3) A table with
# a) mean+sd of relative abundances for each species
# b) mean+sd of relative abundances after filtering on spe... |
76e6d3e5c6822d348cfc762db56eedecfc40da00f6ab206973a2ad4e745a9646 | R | 11,920 | 288 | library(tidyverse)
library(fgsea)
library(dorothea)
library(decoupleR)
source("scripts/helpers.R")
set.seed(123)
# target genes with an ELK1 chIP seq peak within 1kb of TSS
chipatlas_elk1 <- read_tsv("data/chip_atlas/2023-11-15_chipatlas_tss_1kb_ELK1.tsv")
ferguson_deseq <- read_csv("data/tdp43_kd_collection/deseq2_ou... |
d99dfc7b5861a3c8560873378664ebb2d053e4291c7b52bd728e4c8bb407a9a6 | R | 11,925 | 293 | # libraries ---------------------------------------------------------------
library(tidyverse)
library(ggrepel)
# read in the data --------------------------------------------------------
folder <- "../../out/table/modules_SENESCENCE/"
file <- dir(folder) %>%
str_subset(pattern = "^Module_score_")
df_modules <- l... |
30e592d256c3da7c98bd0907d1d101f600b91b936646561f3aad5dba036f68f2 | R | 11,926 | 304 | ---
title: "Smoothed heatmaps for Figure S5"
output: html_document
date: '2023-06-20'
---
```{r}
library(Signac)
library(Seurat)
library(dplyr)
library(tidyverse)
library(RColorBrewer)
library(ComplexHeatmap)
library(GenomicRanges)
source("AuxFunctions.R")
```
Continue here after VIA pseudotime (associated jupyter n... |
39e56913a6d16fd9cc8f80e5982604d914e1f979574c46edce3a991182a0f1b9 | R | 11,930 | 355 | if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("edgeR")
library(edgeR)
setwd("E:/005---ThirdProject/ThirdObject/0.RealData/")
##### Read Data #####
ESCC_UMI<- read.table("ESCC/GSE199654/GSE199654_scTDN_UMI_matrix_epithelial_cells.txt/scTDN_UM... |
3f47baa4f9126f9748d1811a9cc9ba616f96f66364f34797cadeb3bf67244c24 | R | 11,947 | 334 | # Siwei 09 Oct 2023
# Make bubble plot of Alena's PICALM MAGMA result
# init #####
{
library(readr)
library(ggplot2)
library(RColorBrewer)
library(stringr)
library(scales)
}
# load data #####
df_raw <-
read_table("assembled_MAGMA_results.txt")
df_to_plot <-
df_raw
df_to_plot$`-logP` <-
0 - log10(df_... |
5225a6ccbc75ff2eb06a224ed6ed20ba7db47ca7d5f105b6bb858d3b9e219ad0 | R | 11,954 | 172 | # this script's purpose is to summarise the results of our models
library(tidyverse) # version 2.0.0
library(gratia) # version 0.10.0
library(qvalue) # version 2.38.0
"%not_in%" <- Negate("%in%") # define not in operator
# define whether SES-weighted or unweighted results should be used
weighted <- 1 # set to 0 for u... |
d55b9c1c2e0b6464f3be300f7a79b9691f6778606f9a6908580aa84371c13b61 | R | 11,959 | 406 | # ====UI====
output$tendencyConfirmedRegionPicker <- renderUI({
if (input$selectTendencyConfirmedMode == "一般") {
return(
pickerInput(
inputId = "regionPicker",
label = i18n$t("地域選択"),
choices = regionName,
selected = defaultSelectedRegionName,
options = list(
... |
df83a04511260476567e0c4e4676751fe9a01a37a2e3afe16bbe64a69bab024e | R | 11,963 | 315 | ## Based on:
# https://github.com/LieberInstitute/brainseq_phase2/blob/master/twas/read_twas.R
# https://github.com/LieberInstitute/brainseq_phase2/blob/master/twas/explore_twas.R
# https://github.com/LieberInstitute/brainseq_phase2/blob/master/twas/explore_twas_psycm.R
library("readr")
library("purrr")
library("dply... |
55fcbb33f970abbc16f4d08715e6f34ca4502aa2a5dcffd00b239973306d3ccc | R | 12,034 | 253 | #' Version 2.0
#' This script was last modified on 20/01/2020
#' Script Task: Normalize OTU-tables
#' Author: Ilias Lagkouvardos
#' Contributions by: Thomas Clavel, Sandra Reitmeier
#'
#' Normalize abundance values of the input OTU table
#' Calculate relative abundances for all OTUs based on normalized values
#' Calcul... |
158721bdbfc4b912a8af5c76988cf6300feba4f0fe0172083e01885254886a53 | R | 12,058 | 293 | # Run CellChat to explore cell-cell communication - major cell types (continued)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github... |
228fa586c3e53d49685b4ff818aeaaa19f37447da3c02295077bf3388926dfa0 | R | 12,058 | 293 | # Run CellChat to explore cell-cell communication - major cell types (continued)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github... |
9ab8b098444d9945582dc4f05c9080f846f4736595d1a9954196d4eeb0f33d8f | R | 12,068 | 293 | # Run CellChat to explore cell-cell communication - major cell types (continued)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github... |
cd5cbf7e31241c6ec9245165306642104a055ddfc4ba8ba05f62082c4345e77b | R | 12,068 | 293 | # Run CellChat to explore cell-cell communication - major cell types (continued)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github... |
0d1bec53a9b16f1454d1dd7c287dcac5127d8daeef780826aff472640ab64de3 | R | 12,072 | 293 | # Run CellChat to explore cell-cell communication - major cell types (continued)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github... |
04c7b789f2f6a169ba024b26b5198ffc28c3ce3585a84585579d5da021a8dc2b | R | 12,078 | 293 | # Run CellChat to explore cell-cell communication - major cell types (continued)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github... |
d31d106f3469f96300bc2db2d9caf5bf2af4bc1bcdc74905748b746fa70d9588 | R | 12,078 | 293 | # Run CellChat to explore cell-cell communication - major cell types (continued)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github... |
393fa2bce7181c1f80753c37d5369dfdd01f3fba4fa92f9a064d7ba2b01423d6 | R | 12,082 | 293 | # Run CellChat to explore cell-cell communication - major cell types (continued)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github... |
3e7fe16ec139762b1f8326c5b468514efa296a85f152635ced6f8c9bf9f81f74 | R | 12,092 | 293 | # Run CellChat to explore cell-cell communication - major cell types (continued)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# March 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github... |
868190c0a0c2ea3f7d1db8fb168829cb9866d3ca0c8ba903b220d089afb4d35d | R | 12,124 | 432 | # Siwei 02 Feb 2025
# plot new Fig 6f
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
library(dplyr)
library(data.table)
library(DescTools)
library(multcomp)
library(gridExtra)
}
# load raw data ####
... |
69e64a70eed7c2c935f49f9dbae991e840b6d8e063ec3a3744b06e7d2d5f2c94 | R | 12,183 | 293 | #!/usr/bin/env Rscript
# Author: Kevin Ha, 2014
# k.ha@mail.utoronto.ca
# Copyright (C) 2014-2017 Kevin Ha
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including w... |
59d56d7a341c0975563d366fe61e89b1052be80cbec0d22758707aca390912fc | R | 12,224 | 411 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == "world" && is.null(GLOBAL_VALUE$World$PositiveAndDeath)) {
# GLOBAL_VALUE <- list(
# World = list(
# Summary = NULL,
# SummaryTable = NULL
# )
# ) # TEST
GLOBAL_VALUE$World$Summary <- fread(paste0(DATA_PATH, "FIND/worldSu... |
f03a6bdf1f614ecf406ee84877972f36f079ee8e4e14b6cd3179f526292c7710 | R | 12,243 | 341 | ####
library(readxl)
library(jaffelab)
library(readr)
library(pracma)
library(RColorBrewer)
dir.create("circle_pdfs")
## read in reference data
ref = read_excel("raw_data/Circle_expected_expression_revisionMNT.xlsx")
ref = as.data.frame(ref[,1:5])
ref_mat = as.matrix(ref[,2:5])
rownames(ref_mat) = ref$Population
## ... |
870c53cdff63eaa4656f92d182bad8e06d4ac624d9b46f2dd845e27d97bcb315 | R | 12,267 | 218 | libs <- c('dplyr', 'readr', 'ggplot2', 'tidyr', 'brms')
sapply(libs, require, character.only = TRUE)
source("Scripts/utils.R")
# read in the data
starts <- read_csv("Data/starts.csv") |> mutate(MouseID = stringr::str_extract(NetworkFilename, "[0-9]{6}")) |> filter(!NetworkFilename %in% omit_videos)
bins <- read_csv("... |
bdb7e9bfc7b376ebde9e8e0a9644791735507b01e256a68629b1f9769bc1ef85 | R | 12,325 | 312 | ---
title: "OD histogram analysis"
author: "C-M Svensson"
date: "2023-02-23"
output: pdf_document
---
```{r setup, include=FALSE}
rm(list = ls())
knitr::opts_chunk$set(echo = TRUE, fig.width = 12, fig.height = 12)
library(dplyr)
library(latex2exp)
library(tidyverse)
library(ggplot2)
library(readxl)
lib... |
b5f89e4ff9aa6a08965373d2371977daaba5851bb7a9b9bc994e9da705be08ce | R | 12,335 | 351 | library(tidyverse)
library(data.table)
plot_junction = function(junc,plotin_table = spliced_counts_ale){
vals = c(`ALS-TDP` = "#E1BE6A", Control = "#40B0A6", `FTD-TDP` = "#E1BE6A",
`ALS\nnon-TDP` = "#408A3E", `FTD\nnon-TDP` = "#408A3E")
gene_name = plotin_table[paste_into_igv_junction == junc,uni... |
c02a632d5918eaea7a2e496b889fc032c3f7f9a6a02ce5bab824a6fad5e71eb5 | R | 12,335 | 414 | # 20 Dec 2023 Siwei
# Will integrate as instructed in their github code
# https://github.com/velmeshevlab/dev_hum_cortex/blob/main/snRNAseq_integration
# Subset the reconstructed Velmeshev object into Ex and IN subsets
# Need to make the plots separately
# init ####
{
library(Seurat)
library(Signac)
library(re... |
f05ca55d04611af673eaa4ac888b9381aa1b7696d6f55b6aebe8c281d6966ef5 | R | 12,347 | 227 | #generate plots for results of the Saunders data set
##### load packages and results from scdrs/ours/fuma/magma #####
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("tidyverse")) {
install.packages("tidyverse")
library("tidyverse")
}
if (!require("magrittr")) {
install.package... |
3c80646c3f08116bb531632effd52d4262cdc9a31d2dafe6ee5df372f54c4010 | R | 12,348 | 250 | ## Dissociation of Clinical Outcomes and CSF Proteinopathy Biomarkers in Parkinson’s Disease: ##
## Cognitive–Affective Dissociation with Specificity for Tau ##
#Disclaimer: As the PPMI database is always evolving, it is possible that the code may not work if the database has changed since the date the code was create... |
3f53ce765f9436e2ea4e6538494e4b9f6dba8402ba4c777b2d000a27328e62be | R | 12,375 | 293 | # simulate_data.R
# -----------------------------------------------------------------------
# Simulates heritable gene expression and GWAS summary statistics for
# TWAS validation.
#
# Model:
# expr_i = G_causal_i %*% w + eps_expr (h² = hsq)
# y_i = sum_g( beta_g * expr_g_i ) + eps_y (for causal gene... |
c36d43c0dcf2f4b57b98c8dcc24dbbbefd38e9303460224d742c5e6ebc406c85 | R | 12,390 | 349 | library("tidyverse")
library("SingleCellExperiment")
library("here")
library("sessioninfo")
library("here")
library("broom")
#### Set-up ####
plot_dir <- here("plots", "03_HALO", "07_TREG_boxplot")
if (!dir.exists(plot_dir)) dir.create(plot_dir)
data_dir <- here("processed-data", "03_HALO", "07_TREG_boxplot")
if (!d... |
5a51dc4bea497bdc36298150379b2390f6dae658adc0653e471fab3fba733ceb | R | 12,511 | 428 | library(Seurat)
library(Matrix)
library(dplyr)
library(ggplot2)
setwd("E:/005---ThirdProject/ThirdObject/0.RealData/")
# Load full matrix and annotation
mtx <- readMM(gzfile("GSE234129/GSE234129_count_matrix.mtx.gz"))
features <- read.delim(gzfile("GSE234129/GSE234129_features.tsv.gz"), header = FALSE)
ba... |
3a067d446d33160fc0adce76f540e6f016d2764a5315bcff766e4b91d34d8195 | R | 12,512 | 286 | # AIM ---------------------------------------------------------------------
# correlate the autophagy scores with the senescence scores.
# libraries ---------------------------------------------------------------
library(Seurat)
library(tidyverse)
library(scales)
library(ggrepel)
library(cowplot)
library(ComplexHeatma... |
5fd33d6bc0c4723c25449933457e62c98395357fd4e872b253b77d53995a5443 | R | 12,545 | 464 | # Siwei 26 Jan 2025
# plot new Fig 5c-f
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
library(dplyr)
library(data.table)
library(DescTools)
library(multcomp)
library(gridExtra)
}
# load raw data ###... |
f9344a84bdd8478810842793854f66dc27423beaf2bf8690542a12d30b35ccad | R | 12,556 | 428 | # Siwei 02 Jun 2024
# plot 5 western blot-confirmed genes at transcript level
# and their epitope sites;
# init #####
{
library(Gviz)
library(rtracklayer)
library(GenomicFeatures)
library(BSgenome)
# library(BSgenome.Hsapiens.UCSC.hg38)
# library(TxDb.Hsapiens.UCSC.hg38.knownGene)
# library(ensembldb)
... |
9ccbcadf47b4104e1e9a8ccac20a2506910c2e6f39949e3b44fb15eb847c7e0e | R | 12,558 | 341 | library(tidyverse)
long <- read_tsv("/Volumes/LaCie/Drosophila/drosophila_nanopore/DRS_compairison/results/squanti_short_long/corr_filtered_control_pooled_Nanopore_stg_classification.txt")
#long_counts <- read_tsv("/Volumes/LaCie/Drosophila/drosophila_nanopore/DRS_compairison/results/squanti_short_long/filtered_contro... |
b0be6348f1107d83015119a1fb6c6fd774c91d64fc54f0a89869d3ac747fc7a4 | R | 12,558 | 370 | #!/usr/bin/env Rscript
##
## Parse a VCF file and generate a table compatible with ANNOVAR output.
##
## usage: Rscript --vanilla vcf-table.R sample_name in.vcf out.txt
##
# increase output width
options(width = 120)
# print warnings as they occur
options(warn = 1)
# get scripts directory (directory of this file) ... |
4c84bd9c3828e5e58cb91cc2e8c0ef6c6294a673a6326b3ed278bb364e35f863 | R | 12,559 | 288 | # Authors: Lauren Rylaarsdam, PhD; Stephen Coleman, PhD
# 2024-2025
############################################################################################################################
#' @title clusterCompare
#' NEEDS UPDATING
#' @description Correlates average percent methylation over 100kb windows aggregate... |
8c47d78ba1ec8d9785ea4400cfda8a24f5116f04c9af7b50b23cf570ce1d6bd6 | R | 12,569 | 218 | library("SingleCellExperiment")
library("rafalib")
library("iSEE")
library("lobstr")
library("here")
library("whisker")
library("usethis")
library("withr")
library("rsconnect")
library("sessioninfo")
load(here("rdas", "revision", "regionSpecific_NAc-n8_cleaned-combined_MNT2021.rda"), verbose = TRUE)
source(here("shin... |
95429fa665682155eb7de9d78992981de4f0acf15d103658246440eaec1e7cba | R | 12,575 | 373 | library(reticulate)
library(ggplot2)
library(Seurat)
library(bulkAnalyseR)
library(ComplexHeatmap)
library(dplyr)
library(plotly)
library(MASS)
library(rgl)
library(processx)
source_folder <-
expr_folder <- file.path(source_folder, "07.Expr_matrix")
bulk_folder <- file.path(source_folder, "08.Bulkanalyser")
output_fo... |
a7d9643ae5d19646df62f6d8351e3a58cb439f605efd8cc2f6a646f82e142de6 | R | 12,592 | 280 | suppressPackageStartupMessages(library(optparse))
option_list = list(
make_option(c("-c", "--circRNA.targets"), type='character',
help="circRNA targets (ciri2)"),
make_option(c("-g", "--genes"), type='character',
help="Prepared gene database (bed)"),
make_option(c("-e", "--exons"), t... |
2cfa929869bf9e94c79dc08b42e918a2a26e4ce24a8a6cd8d3d78cfb965832cf | R | 12,629 | 422 | rm(list = ls())
library(stringr)
library(ggplot2)
library(RColorBrewer)
library(patchwork)
library(dplyr)
library(DirichletReg)
library(tibble)
cell_counts <- read.csv("Qupath_EC/annotations_processed_V2.csv",)
cell_counts[is.na(cell_counts)] <- 0
temp <- as.data.frame.matrix(cell_counts)
#temp <- temp[temp$Group !=... |
969f37ebfb8bd0d5b93d6a47890f011f9bb0154197070fb3273255120067d98b | R | 12,634 | 117 | libs <- c("dplyr", "readr", "ggplot2", "tidyr", "stringr", "ordinal", "patchwork")
sapply(libs, require, character.only = TRUE)
source("Scripts/utils.R")
# read in the data
starts <- read_csv("Data/starts.csv") |> mutate(MouseID = stringr::str_extract(NetworkFilename, "[0-9]{6}")) |> filter(!NetworkFilename %in% omi... |
06db57f7a2d9be1676f9f52c5840ef233f86a51db056248d7bffd6655fb7a939 | R | 12,680 | 294 | # libraries ---------------------------------------------------------------
library(Matrix)
library(data.table)
library(Seurat)
library(SeuratData)
library(dplyr)
library(gt)
library(SPOTlight)
library(igraph)
library(RColorBrewer)
# old function spotlight --------------------------------------------------
SPOTlight_o... |
fd1293d32522e0e66c276e5a9e569d4abae7833af4c5a3f7299d9b8cd1d664d9 | R | 12,683 | 229 | #### Oligodendrocytes analysis ####
### Pre work ####
library(tidyverse)
library(Seurat)
library(SeuratObject)
library(ggplot2)
library(doParallel)
library(future)
library(cowplot)
library(patchwork)
library(monocle3)
library(monocle)
library(SeuratWrappers)
library(Nebulosa)
library(dplyr)
library(edgeR... |
7139e7e4f18edeed4035d720c49bf825c7ec0eaea78dab98f995fd3b816afd74 | R | 12,700 | 319 | #!/usr/bin/env Rscript
### title: Integrating TCR output from Cell Ranger vdj (v6.1.1) and
### visualization of TCR distribution
### author: Jana Biermann, PhD
print(Sys.time())
library(Seurat)
library(dplyr)
library(ggplot2)
library(gplots)
library(viridis)
library(scales)
library(stringr)
library(reshape2)
librar... |
d371bfaa88bdb1a5139c69a58d725b7dbacc7aa9d622b49a84b8c26d20a83905 | R | 12,734 | 308 | ---
title: "OD histogram analysis"
author: "C-M Svensson"
date: "2023-02-23"
output: pdf_document
---
```{r setup, include=FALSE}
rm(list = ls())
knitr::opts_chunk$set(echo = TRUE, fig.width = 12, fig.height = 12)
library(dplyr)
library(latex2exp)
library(tidyverse)
library(ggplot2)
library(readxl)
lib... |
6ba7d8122cdc936214efd00d6e679f28432ea195a7f4b28b5511c682ffbb444e | R | 12,815 | 268 | # Run EWCE to explore common variants reported in Bellenguez et al 2022 - prefrontal cortex (PFC)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# Feb 2024
# The list of genes from Bellenguez et al. 2022 was produced as follows:
## All... |
7b7d805498072e3cf2db0e7fa3f4edd37bf3969e5fe5a47cf6aa2e7570c039b5 | R | 12,816 | 268 | # Run EWCE to explore common variants reported in Bellenguez et al 2022 - prefrontal cortex (PFC)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# Feb 2024
# The list of genes from Bellenguez et al. 2022 was produced as follows:
## All... |
84d7afce1879428eb77cb75deb60e2bf1f0f32187126b1db4dc38297de77f8e1 | R | 12,830 | 440 | library(dplyr)
library(twice)
library(TEKRABber)
library(ggplot2)
library(viridis)
library(ComplexHeatmap)
data("hmKZNFs337") #337 KRAB-ZNFs in human
data("hg19rmsk_info")
# 1. create meta data
df_meta <- metadata %>%
left_join(brain_meta, join_by(brain_region == region))
# 2. do TPM conversion in human and NHP... |
a6d09291ebb887d997a496b3176031dfbbcc1b4ede56d4fc84f701d7ea8fef11 | R | 12,848 | 245 | ---
title: "Gene Expression Analysis_TM integrated data_Cluster Markers & Species-Enriched Genes"
author: "Yuanming Liu"
date: "2024/8"
output: nl_document
---
# Load packages
library(dplyr)
library(Seurat)
library(ggplot2)
library(readr)
library(clusterProfiler)
library(org.Mm.eg.db)
# Load homologous gene conve... |
77813e00838e879af3c78d61c6a8700e6cefadf551b2445c7176e3b05abf88c0 | R | 12,853 | 280 | ---
title: "de view"
output: html_document
---
Code for scRNAseq pathway analysis related to neutrophils
```{r}
setwd("/Users/mary/non_dropbox/exps/exp042_meninges_stress_dropseq/")
library(Seurat)
library(ggplot2)
library(tidyverse)
library(magrittr)
library(here)
```
Get out_preneut, out_neut, out_A .... F (neut ... |
78d80f44e108b13056abd4c3ef9690df9ffc9ebe1e058864aa80a45c7075c8f5 | R | 12,895 | 294 | #' Detect doublet clusters
#'
#' Identify potential clusters of doublet cells based on whether they have intermediate expression profiles,
#' i.e., their profiles lie between two other \dQuote{source} clusters.
#'
#' @param x A numeric matrix-like object of count values,
#' where each column corresponds to a cell and e... |
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