sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
24d91c08dd9f72a79d4331131ee7437f4bf7cb90474e876c284f955d64b6dc6e | R | 12,903 | 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... |
816be965f6655823d04d373c847f256a77b51d2b9a2b676831fa9b6caa7d3011 | R | 12,915 | 305 | # Authors: Lauren Rylaarsdam, PhD; Ben Skubi (Facet)
# 2024-2025
######################################################################
#' @title loadWindows
#' @description Load pre-computed c and t observations aggregated with Facet
#'
#' @param obj Amethyst object containing the h5paths to the pre-aggregated window... |
177abb5041aa8f3e7cdf530019c92d213a2f31bd45c3af5bf7b9ec06ceea5fbf | R | 12,927 | 318 | ---
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$set(echo = TRUE, fig.width = 12, fig.height = 12)
library(dplyr)
library(latex2exp)
library(tidyverse)
library(ggplot2)
library(readxl)
lib... |
4646edca760952a87e3c1568521733dc5ca3a151f9370438ac846ce4804a69cc | R | 12,945 | 323 | ---
title: "R Notebook of Figure 5"
output:
null
---
```{r Packages}
library(tidyverse)
library(Seurat)
library(Signac)
library(qs)
library(readxl)
library(hash)
library(fgsea)
library(DBI)
library(hash)
library(biomaRt)
library(presto)
library(ggpubr)
library(purrr)
library(patchwork)
library(parallel)
library(uma... |
441fb4fc3d586187e4d8b3f2d8777d51174221a62917672bfd1510fd2fff7aa0 | R | 12,959 | 348 | library(tidyverse)
library(ggrepel)
library(ggrastr)
set.seed(123)
#' Tidy up DESeq2 results table for analyses of enrichment
#' Removes low count genes (those filtered out by DESeq's independent filtering) & ensures 1 row per gene
clean_deseq_df <- function(df, padj_col = "padj", id_col = "gene_name") {
# Remov... |
a41a893bdcd249aa115b40a2411a36149b04d63c0190daf38521e141898ed496 | R | 12,959 | 234 | #Data from Wong et al 1993 and Demas et al 2003 assumed to be in the same directory
library(fields)
dt=0.05
source("processing_functions.R")
D_P9_files=c("Demas2003P9_CTRL_MY1_1A","Demas2003P9_CTRL_MY1_2A")
D_P15_files=c("Demas2003P15_CTRL_MW2_2A","Demas2003P15_CTRL_MS5_1A","Demas2003P15_CTRL_MI1_2B","Demas2003P15... |
062fa86a45cfeb913474445a2be7d22ff0633954b94ae69af1fcc25f88e429d0 | R | 12,962 | 320 | ---
title: "OD histogram analysis"
author: "C-M Svensson"
date: "2023-02-23"
output: pdf_document
---
```{r setup, include=FALSE}
rm(list = ls())
knitr::opts_chunk$set(echo = TRUE, fig.width = 12, fig.height = 12)
library(dplyr)
library(latex2exp)
library(tidyverse)
library(ggplot2)
library(readxl)
lib... |
7b46c4d10154716e6e9d32ecaf9f5e0f1fdb3ff84e23a0eb3e053fb9cf5145d5 | R | 12,969 | 319 | ---
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$set(echo = TRUE, fig.width = 12, fig.height = 12)
library(dplyr)
library(latex2exp)
library(tidyverse)
library(ggplot2)
library(readxl)
lib... |
489a1dd2c84825aed4cddf9cb9510869ef0eeffe32c072454df11844447bd052 | R | 12,989 | 320 | ---
title: "OD histogram analysis"
author: "C-M Svensson"
date: "2023-02-23"
output: pdf_document
---
```{r setup, include=FALSE}
rm(list = ls())
knitr::opts_chunk$set(echo = TRUE, fig.width = 12, fig.height = 12)
library(dplyr)
library(latex2exp)
library(tidyverse)
library(ggplot2)
library(readxl)
lib... |
d26fda182b0c567ae9a7b0e4a031c5277e93abc6937861beaf665fdb7cb3b299 | R | 12,989 | 252 | # Run EWCE to explore directional unique DEGs for ADvsRES - Prefrontal cortex (PFC)
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# Aug 2023
## Genes down or up in AD vs RES only (not in ADvsCTRL or RESvsCTRL)
## The EWCE R package... |
9bc01a8dcfe30069e8c3e8356b343e30bc7ae2768139687459c321b3d1fb7b28 | R | 12,991 | 351 | ---
title: Apply combined atlas from snRNA-seq and scRNA-seq to analyzed query datasets
author: "M. Andreatta <massimo.andreatta at unil.ch> and S. Carmona <santiago.carmona at unil.ch>"
knit: (function(input_file, encoding) {
out_dir <- 'docs';
rmarkdown::render(input_file,
encoding=encoding,
output_file=file.pa... |
80e3f2cafcd6f5b0a901d545926a2d2ef085fcd14903d08027b257e37f99109a | R | 13,039 | 303 | library(dplyr)
library(scales)
library(gridExtra)
library(ggplot2)
library(pvclust)
library(grid) # for viewport() for setting margins in grid.arrange
library(ComplexHeatmap)
library(ggplotify) # for as.grob()
library(circlize) # for colorRamp2
library(png)
dataPath = "data/"
figurePath = "Figures-and-Tables/"
aggrDat... |
b3bb6bb3f3673141f476d75b6ef4342ccf551a68d277e92b7990eb6832dc80b5 | R | 13,058 | 285 | # Feb 5 2019 Siwei
# plot the pileup figures of
# cell-type-specific genes
# ATAC-Seq
# init
library(Gviz)
# data("cpgIslands")
library(rtracklayer)
library(BSgenome)
library(BSgenome.Hsapiens.UCSC.hg38)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(ensembldb)
library(org.Hs.eg.db)
##########
marker_gene_list <- ... |
ead8a595dbd3d608e8323e66c2a93c58f5cded96a871eae1380761eb69bc8de0 | R | 13,061 | 207 | ---
title: "Tutorial"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Tutorial}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup}
library(PAWS)
```
# Pre-processing data
## Lo... |
a54991b18e5a607886d0d9bb828cd35ad4d23cbb25ff63e354613c9bd8d27876 | R | 13,065 | 269 | # Pathogenicity evaluation of SNPs within UCRs and control fragments
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(stringr)
# obtainAllSNPsWithinUCRs -------------------------------------------------
# UCR
load("D:/R_project/UC... |
fb1147782f8a2cd99bfa933c3f7a437b48f643fc4b3ee9acef33301eb173434e | R | 13,080 | 276 | #!/usr/bin/env Rscript
### title: T cell analysis (UMAPs, violins, diffusion maps)
### author: Jana Biermann, PhD
library(Seurat)
library(destiny)
library(dplyr)
library(ggplot2)
library(gplots)
library(viridis)
library(scales)
library(patchwork)
library(reshape2)
'%notin%' <- Negate('%in%')
colBP <- c('#A80D11', '... |
a5f702589a55ab9102a79c2b4bd2c78bad078ef778756d08b38f58dfecbbc4ce | R | 13,104 | 314 | # Authors: Andrew Adey, PhD; Lauren Rylaarsdam, PhD
# 2024-2025
############################################################################################################################
#' @title makeDoubletObject
#' @description This function makes the new object with true cells as well as artificial doublets
#'
#'... |
1f6eff1c97eda07636a9e290befcb5a889f02c79bdb7bd57470e19154ec1fa4f | R | 13,128 | 279 |
library(Seurat)
blacklist <- c("BTRC_17_BTRC_17",
"BTRC_26_BTRC_26",
"BTRC_15_BTRC_15",
"BTRC_34_BTRC_34",
"BTRC_44_BTRC_44",
"BTRC_39_BTRC_39",
"BTRC_87_BTRC_87",
"BTRC_41_BTRC_41",
"BTR... |
d87130d90557c8718918630f3508b08497872eb6c9f70a6b2a4ce4580b2d6e58 | R | 13,133 | 227 |
## Analysis of adult (from the bulk lifecourse dataset) age regression results run on bulk fetal DMPs.
library(ggplot2)
library(data.table)
library(scales)
library(plyr) # for ddply
library(dplyr)
library(viridis)
#1. Load data ======================================================================================... |
9470a74d79c97061e3ff14fe589646eb46558124489154f276342b2999a68162 | R | 13,150 | 261 | # Compare the social perceptual evaluations between GPT4 Vision and humans in the video data
# Severi Santavirta 28.5.2025
library(corrplot)
library(stringr)
library(lessR)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(psych)
library(ape)
##----------------------------------------------------------------... |
95cefbb10e4d11a786ef89731086ae9a5b95d58d4be5a8cc0abce76e56612991 | R | 13,161 | 277 | ---
title: "GWAS preprocessing for _seismic_ input"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{GWAS preprocessing for seismic input}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
##... |
af1ec7058aaf1e20dfe4ea710f4d504633f28e26a47a0ce439a66471ce8ebd45 | R | 13,170 | 252 | #!/usr/bin/env Rscript
### title: Print out heatmaps of IG combination usage in B cells
### authors: Yiping Wang, Jana Biermann
library(Seurat)
library(dplyr)
library(ggplot2)
library(gplots)
library(viridis)
seu <- readRDS("data/MBPM/data_MBPM_scn.rds")
seu <- subset(seu, cell_type_int %in%c('Plasma cells','B cells... |
61ae3cef11af4bdd5affa2f52ae2954cbdcae5a45b28f1190f6b59ff91db384a | R | 13,195 | 389 | # Siwei 19 Feb 2024
# make peak file contains ASoC SNPs
# init ####
{
library(Seurat)
library(Signac)
library(EnsDb.Hsapiens.v86)
library(GenomicFeatures)
library(BSgenome.Hsapiens.UCSC.hg38)
library(GenomicRanges)
library(org.Hs.eg.db)
library(stringr)
library(future)
library(readr)
# libra... |
97178876c6cc78702e632c467c89914428ebf728bf501f55c314d91eec40fdbc | R | 13,212 | 318 | #' amulet
#'
#- A reimplementation of the Amulet doublet detection method for single-cell
#' ATACseq (Thibodeau, Eroglu, et al., Genome Biology 2021). The rationale is
#' that cells with unexpectedly many loci covered by more than two reads are
#' more likely to be doublets.
#'
#' @param x The path to a fragments fi... |
e3c97516b2f0f4c98bdb34db7c41908a113176e30379853b0974af2c2f7b226d | R | 13,251 | 378 | # construct ANN model
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(neuralnet)
library(pROC)
library(caret)
library(readxl)
library(ggview)
library(data.table)
lwd_pt <- .pt*72.27/96
set.seed(123)
n <- 200
data <- data.frame(
gene1 = rnorm(n)... |
d1e72c82b3048b4cc60e6ef39f104f1ce2011e9aa22fcdeb8cb2e35b61e93bf7 | R | 13,299 | 361 | ---
title: '1. xQTLbiolinks: query and download'
author: "RuoFan Daing"
date: "2023-05-03"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{query_download}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
lang: en-US
---
```{r, include = FALSE}
knitr::opts_chunk$set(
echo=TRUE,
p... |
6bce2a786f30fc4205eab74e48eabcd4ec68ef9abb4030e11989dc142ef1849c | R | 13,321 | 282 | ### MNT 10x snRNA-seq workflow: step 04
### **Region-specific analyses**
### - (5x) sACC samples (M & F donors)
### - Comparison to Velmeshev, et al (Science 2019)
#####################################################################
library(SingleCellExperiment)
library(EnsDb.Hsapiens.v86)
library(scater)
l... |
cdf9a336f62f932963e9f26b94c4c79734da34b09741353934331b40127a3ad8 | R | 13,348 | 326 | require(optparse)
require(tidyverse)
require(ggpubr)
require(cowplot)
require(scattermore)
require(extrafont)
# variables
FIBROBLASTS = c("BJ_PRIMARY","BJ_IMMORTALIZED","BJ_TRANSFORMED","BJ_METASTATIC")
LAB_ORDER = c('WT','C','CB','CBT_228','CBT3','CBTA','CBTP','CBTP3','CBTPA')
# formatting
LINE_SIZE = 0.25
FONT_SIZ... |
b81783748aa89336fcb15e172886157fb43e69b23771dc0fb2250114b171b698 | R | 13,359 | 306 | ##########################################################
############## SENESCENCE INDEX TOOL (SIT) ###############
##########################################################
# libraries ---------------------------------------------------------------
library(tidyverse)
library(Seurat)
library(homologene)
library(RCo... |
e0de42dd0213b6960d28df5084ac4f0e370ada35cdfaa3fd6b968e05131fe06b | R | 13,403 | 319 | #' Version 1.0
#' This script was last modified on 11/02/2016
#' Script: Taxonomic Binning
#'
#' Provides an overview of sample-specific relative abundances for all taxonomic levels
#'
#' Input:
#' 1. Set the path to the directory where the file is stored
#' 2. Write the name of the examined OTU file
#'
#' Output: The ... |
e56ebefec5172ecf464ca0c5512733c043f4bde357ff475fb32227fb313e9736 | R | 13,422 | 269 | # Compare the social perceptual evaluations between GPT4 and humans in the frame data
# Severi Santavirta 22.5.2025
library(corrplot)
library(stringr)
library(lessR)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(psych)
library(ape)
##-----------------------------------------------------------------------... |
fc93f733bedff39618d30b533d31b90f9ec07c806b3a59296c7382e2418eb090 | R | 13,426 | 335 | # Load packages
library(tidyverse)
# Clear global environment
rm(list=ls())
setwd("/home/emba/Documents/EMBA/CentraXX")
# load raw data
# columns of Interest: internalStudyMemberID, name2, code, value, section, (valueIndex), numericValue
df = read_csv("EMOPRED_20250127.csv", show_col_types = F) %>%
select(internal... |
0829880777cfd2dfee1821dba57b60b77055944156a2ab9e644122e4df655e65 | R | 13,447 | 293 | # install.packages("Seurat")
# install.packages("harmony")
# install.packages("SingleCellExperiment")
library(Seurat)
library(dplyr)
library(harmony)
library(ggplot2)
library(SingleCellExperiment)
setwd("E:/005---ThirdProject/ThirdObject/0.RealData/")
# 1. Load raw matrix
raw_matrix <- read.table("GSE13... |
1d2c5073a6927a3ec79a06de7ec5b35211b774dcdce18f0619a50e9f820d2a0c | R | 13,465 | 314 |
library("tidyverse")
library("sessioninfo")
library("DeconvoBuddies")
library("here")
library("viridis")
library("GGally")
library("patchwork")
plot_dir <- here("plots", "12_other_input_deconvolution", "05_deconvo_input_plots")
if (!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
## load colors & shapes... |
2fcb365cf23722f59b3ab5e8e638e1161586000272a4fda5fdfaf8b74c161f66 | R | 13,509 | 265 | # Compare the social perceptual evaluations between GPT4.1 Vision and humans in the video data
# Severi Santavirta 28.5.2025
library(corrplot)
library(stringr)
library(lessR)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(psych)
library(ape)
##--------------------------------------------------------------... |
b49b6ad0f2396dd334e2bf34c18467606f40d7788ba0f5ce416951f64d8ba1b7 | R | 13,540 | 695 |
################################## LIBRARY ############################
library(irr)
library(psych)
############################# OFT ########################
# Expl dur ---------------------------------------------------
OFT_expl_dur <- matrix(c(
67.012,92.727,
51.496,57.872,
37.733,21.086,
... |
b9ecfb1e1833f47bb177cc9d8b8280a771574806dce4d5bbba3d2e2b506bdfdc | R | 13,551 | 263 | #rm(list=ls())
library(ggplot2)
library(dplyr)
library(Seurat)
library(patchwork)
library(Nebulosa)
library(FigR)
library(BuenColors)
library(GenomicRanges)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
library(GenomicFeatures)
library(org.Mm.eg.db)
setwd("/mnt/nas1/Users/Yanxiang/Processed_data/2024/04032024_SpMETRTE14R... |
9698052abf3c309f5ce6eba3c29b6a7797e524e5268b65217560475929bb7532 | R | 13,576 | 403 | # Siwei 19 Feb 2024
# make peak file contains ASoC SNPs
# init ####
{
library(Seurat)
library(Signac)
library(EnsDb.Hsapiens.v86)
library(GenomicFeatures)
library(BSgenome.Hsapiens.UCSC.hg38)
library(GenomicRanges)
library(org.Hs.eg.db)
library(stringr)
library(future)
library(readr)
# libra... |
0ac23017abfba94dd098f0dc26a4c1d9722f95b78020cbf2421c7041e05486ee | R | 13,624 | 298 | # 18 Dec 2023 Siwei
# Relabel Velmeshev 2023 cells and convert to Assay5
# project 20000 cells, do not integrate
# init ####
{
library(Seurat)
# library(Signac)
library(readr)
library(future)
library(parallel)
library(ggplot2)
library(RColorBrewer)
library(stringr)
library(viridis)
library(gridExtr... |
e8f7af27acf89bc7dbfaa270e2c2dee51d38a2f3d37b6dd12affe31d761c2263 | R | 13,653 | 307 | library(tidyverse)
source("scripts/fncs_plot_iclip.R")
#' read in coverages files - assumes a named vector
get_combined_coverages <- function(files, flank_interval) {
files %>%
map(~ parse_coverage(.x, 500)) %>%
bind_rows(.id = "origin") %>%
# pull out site type & status
separate(origin, into = c... |
f1973c4fe2c2fd58eb2967200fd8c9c7f004a365480e303be13ab736f1b4ec04 | R | 13,654 | 363 | # Load packages
library(tidyverse)
# clear global environment
rm(list=ls())
setwd("/home/emba/Documents/EMBA/VMM_analysis/00_input")
dir.in = '/home/emba/Documents/EMBA/BVET'
dir.post = '/home/emba/Documents/EMBA/BVET-Nacherhebung'
dir.out = '/home/emba/Documents/EMBA/log-check'
tr = 2.451
# read in HGF data ----... |
f86bd8c2d169885bbcf6e59d988ba4efcf6744364ad893e6b09800be38253223 | R | 13,671 | 367 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == "hokkaido" && is.null(GLOBAL_VALUE$hokkaidoData)) {
# GLOBAL_VALUE <- list(hokkaidoData = NULL, hokkaidoPatients = NULL) # TEST
GLOBAL_VALUE$hokkaidoData <- fread(file = paste0(DATA_PATH, "Pref/Hokkaido/covid19_data.csv"))
GLOBAL_VALUE$hokkaidoDataU... |
babb31cd26a917bb555e7677a85477340de8ca39d26264f083e883e559633da7 | R | 13,691 | 396 | library(DESeq2)
library(sqldf)
library(stringr)
library(gplots)
library(RColorBrewer)
library(ggVennDiagram)
library(ggplot2)
library(reshape2)
library(cowplot)
library(patchwork)
library(enrichR)
library(gridExtra)
################################################################################
#####################... |
5ca9a0885e17d05fad7c2916fb642f8690d92dd653a1ccc80f75486d75cc7cdb | R | 13,724 | 254 | # Run EWCE to explore directional genes associated with cognition identified by Pourya Naderi - Prefrontal cortex (PFC) - all cells
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# Aug 2023
## Genes negatively or positively associated ... |
01e051625b11cda945b8347210bd11aecd76a7764ff28b4bfeee4230a139a9b9 | R | 13,743 | 273 | # Compare the social perceptual evaluations between GPT4.1 and humans in the frame data
# Severi Santavirta 22.5.2025
library(corrplot)
library(stringr)
library(lessR)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(psych)
library(ape)
##---------------------------------------------------------------------... |
64608041960278dd93809470782a36acacc2ddc9c0e4aefe760ac63ab2b3bbad | R | 13,763 | 358 | library(tidyverse)
library(data.table)
#----------{01 MRBMA for exposures}--------------------
#-------------{00.01 prepare exposures}---------------
##-------------{00.01.01 LOAD}---------------------
local_exposure_df <- openxlsx::read.xlsx("/mnt/data/lijincheng/mGWAS/result/02MRBMA/exposures_mediators_paths.xlsx",s... |
65ef4de29601b7f1e4ef3b931c095d29abe8a8f70544b1e910d671c5dd22f13f | R | 13,798 | 424 |
library("tidyverse")
library("scales")
library("here")
library("sessioninfo")
library("DeconvoBuddies")
library("readxl")
#### Set-up ####
# plot_dir <- here("plots", "03_HALO", "01_import_HALO_data")
# if (!dir.exists(plot_dir)) dir.create(plot_dir)
data_dir <- here("processed-data", "03_HALO", "01_import_HALO_data... |
df4188d667efcdefa9f9b94c266dbbb4e6e27d4dfe4b288a2885f05582288dbe | R | 13,814 | 416 | # Siwei 26 Jul 2024
# Import scRNA-seq data of GSE254025
# Check the expression of PICALM in risk vs non-risk cells
# also compare Alena's iMG snRNA-seq with GSE254025's HOMEO, LDAM, DAM populations
# run hierachical clustering
# init ####
{
library(Seurat)
library(Signac)
# library(remotes)
# library(Seurat... |
e70d0c07795d83adcb398ddc6c4fa755b3bd86913ec3ecea54741028bde69772 | R | 13,835 | 257 | ```{r}
# load libraries
suppressPackageStartupMessages(library(xfun))
pkgs = c("SingleCellExperiment","tidyverse","data.table","dendextend","fossil","gridExtra","gplots","metaSEM","foreach","Matrix","grid","spdep","diptest","ggbeeswarm","Signac","metafor","ggforce","anndata","reticulate","pryr",
"matrixStats"... |
d9e2005c6b2cbaf4627bd3c34a89b25d3e3791f78aa31f75e1132926c8300536 | R | 13,838 | 261 | #!/usr/bin/env Rscript
### title: Integration of MBM05_sc and MBM05_sn for direct comparison
### author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(gplots)
library(ggplot2)
library(scales)
library(viridis)
library(ggpubr)
library(DropletUtils)
library(SingleR)
'%notin%' <- Negate('%in%')
colSCSN <- c... |
e9aaa9146922b01ac6b5f8503071b96ee107c89b45bb08f8b14ba41f908cccb8 | R | 13,839 | 391 | ####
library(readxl)
library(jaffelab)
library(readr)
library(pracma)
library(RColorBrewer)
dir.create("square_pdfs")
## read in reference data
ref = read_excel("raw_data/Square_expected_expression_revisionMNT.xlsx")
ref = as.data.frame(ref[,1:5])
ref_mat = as.matrix(ref[,2:5])
rownames(ref_mat) = ref$Population
## ... |
7708dedabaf2757a62cb0cd17e218cedba3133dab6ab959e74dca16347422d27 | R | 13,868 | 463 | rm(list = ls())
library(stringr)
library(ggplot2)
library(RColorBrewer)
library(patchwork)
library(dplyr)
library(DirichletReg)
library(tibble)
cell_counts <- read.csv("Qupath_EC/annotations_processed_V2.csv",)
cell_counts[is.na(cell_counts)] <- 0
temp <- as.data.frame.matrix(cell_counts)
#temp <- temp[temp$Group !=... |
a24aa2db801727041738677edfa5aa46df0e1bd60791623902faecba31bffc03 | R | 13,928 | 305 | library(dplyr)
library(Seurat)
library(patchwork)
library(readxl)
library(edgeR)
##Expression profile within each cell type
#Separate cell types in each tissue and transfer count to CPM
str <- "~/dat/cattle_scdata/Global atlas/All_rds/annotation_rds/CellType/CPM/"
setwd("~/dat/cattle_scdata/Global atlas/All_r... |
51bc0373381112196bf63b756b267db494b6db9161b758de3c0179b16d808d13 | R | 13,986 | 286 | ---
title: "Dunnart ChIP-seq data pre-processing"
author: "lecook"
date: "2022-02-23"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
# Snakemake pipeline
For full snakemake script see code/dunnart_peak_calling/dunnart_snakefile.
## Create conda environment
```{bash eval=FALSE}
# crea... |
a08bf42a3bcfae8414c4f2cad344df16695811b2de7d3a82d0cbdb543df762b5 | R | 13,986 | 264 | ####################################################################################################
## 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... |
ccfa2a72be40a9ef2fa8f2eeba20da57a60390ef0ab301e0f1fe2d8cba2c6b61 | R | 14,067 | 413 | # Siwei 19 Feb 2024
# make peak file contains ASoC SNPs
# init ####
{
library(Seurat)
library(Signac)
library(EnsDb.Hsapiens.v86)
library(GenomicFeatures)
library(BSgenome.Hsapiens.UCSC.hg38)
library(GenomicRanges)
library(org.Hs.eg.db)
library(stringr)
library(future)
library(readr)
# libra... |
b11b1f46e8897b532ce5190238ee790f801df91bf411df4a29aa4d7ee9c231d5 | R | 14,109 | 321 | # --------------------------------------
# Analysis of Saunders et al dataset
# --------------------------------------
# load packages
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if (!require("tidyverse")) {... |
5a6cb3004cfa80a277b76ad8d9fc670c8a414c4613b2c026ca8250cb8747cba6 | R | 14,139 | 260 | ---
title: "Batch Correction"
output:
html_document: default
pdf_document:
latex_engine: xelatex
date: "2024-11-11"
author: "Lauren Rylaarsdam"
---
Single-cell analysis often involves integrating data from multiple contexts. Sometimes, artifacts can be
introduced that reflect technical biases instead of true... |
e2075f7da1994599cd087c7ba19dffd1fc019809e9cfe7bdfe9e3927d090a82b | R | 14,170 | 413 | # fig 3E
# visualize human network in primary and secondary cortices & limbic
# and association cortices
library(dplyr)
library(RCy3)
library(netZooR)
library(ggplot2)
library(tidyr)
library(ComplexHeatmap)
library(circlize)
library(twice)
data("hg19rmsk_info")
hm_c1_sig <- read.csv("tables/hmc1_sig_forNetwork.csv")... |
453d38692a52660f3ac66e108ec8123b327fb550193d0fc1ef93c02ffaf7eb82 | R | 14,197 | 326 | ####################################################################################################
## 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... |
60b584e264f81cce227ccbabb289b0e0a232e71658473d9718c1b396d7411035 | R | 14,204 | 408 | ```{r}
# load libraries
library(tidyverse)
library(data.table)
library(Matrix)
library(Rfast)
library(matrixStats)
library(ggridges)
library(reticulate)
library(anndata)
library(scales)
library(ComplexHeatmap)
library(forcats)
library(igraph)
library(mclust)
library(future.apply)
library(UpSetR)
library(gtools)
# sou... |
18934c9aa4f922d258ca6dd42d553ee568ccca4dec5acf742c134cc0cb3c72b0 | R | 14,230 | 414 | # 08 Nov 2023 Siwei
# sample GABA, nmglut, and npglut neurons
# init ####
{
library(Seurat)
library(Signac)
library(readr)
library(future)
library(parallel)
library(ggplot2)
library(RColorBrewer)
library(stringr)
}
plan("multisession", workers = 2)
options(expressions = 20000)
options(future.globals.... |
44fd2ccc96f4d73f47d55b0189d0f59fd12260a72a1ca950e2acee61e2bc10f2 | R | 14,253 | 263 | #----08_period_analysis_v01-----------------------------------------------------
#-------------------------------------------------------------------------------
# Locomotor activity analysis for Reinhard et al. 2025 (10.1073/pnas.2506164122)
# Requirements:
# 1)scripts:
# 01_setup_v01
# 02_variables_an... |
036de7352c0522a02533d4d933f2f63e0d83e56c5d76bed6474eadd60e2cbb8c | R | 14,373 | 350 |
library("SingleCellExperiment")
library("ComplexHeatmap")
library("tidyverse")
library("here")
library("sessioninfo")
library("circlize")
## prep plot_dir
plot_dir <- here("plots", "06_marker_genes", "06_marker_gene_heatmap")
if (!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
## load colors
load(here... |
e9ac62041abcaf2ba06177fdb1447d18ca558c0f5fe630c21a1c9208f1e1b64b | R | 14,384 | 453 | #!/usr/bin/env Rscript
print("##################################################")
print("# Calculating Optimal Number of PCs using PCA CV #")
print("##################################################")
# This a R implementation of the algorithm described at:
# - https://stats.stackexchange.com/questions/93845/how-to... |
e7923d53e9b468ec150ee87e5e7ac7e19139faa8ed9759f82e2da0471b79acd3 | R | 14,394 | 591 | ---
title: "enhanced model"
author: "KSA"
date: "`r format(Sys.time(), '%Y-%m-%d')`"
output: md_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = TRUE,
message = FALSE,
warning = FALSE,
error = FALSE,
fig.width = 12,
fig.height = 10,
cache = FALSE
)
#when knitting will always be don... |
8674cde2cd4f6d2c5ee0b09b88d68219fdde66760f15bb0610aa46600e33e483 | R | 14,416 | 320 | # AIM ---------------------------------------------------------------------
# define both the automatic and the manual annotation of the dataset
# SCType annotation -------------------------------------------------------
# libraries ---------------------------------------------------------------
library(Seurat)
libra... |
78e311ba16e944f68e578fd4c18fc02b3bb6da209a1f3dc75c640fa9ef878260 | R | 14,467 | 383 |
pacman::p_load(R.matlab,
reshape2,
tidyverse,
dichromat,
ggsci,
viridis,
nlme,
emmeans,
flextable,
showtext,
rlang)
source(file = "_Common.R")
target_file <- "output/c... |
70c393d4424d7aa0001ced638faee70fb56d28a2d31cd165d9abf2ec29736e83 | R | 14,515 | 292 | ##########################################################
############## SENESCENCE INDEX TOOL (SIT) ###############
##########################################################
# libraries ---------------------------------------------------------------
library(Seurat)
library(ggplot2)
library(homologene)
library(RColo... |
215ef7c0ba779e621472942b39f441ee69da3f39eb35fe1b9229335029b002d6 | R | 14,535 | 404 | #' @title Tissue name and tissue id mapping of GTEx V8.
#' @description
#' A dataset containing the 54 tissues' name and corresponding ID of GTEx V8.
#' @docType data
#' @keywords internal
#' @name tissueSiteDetailGTExv8
#' @format A data frame with 54 rows and 2 variables
#' \describe{
#' \item{tissueSiteDetail}{c... |
b16fbd43c3b6bc4318ddc543f0ca6cf855a7044fc2a55bfc8a52aca45a4a9a86 | R | 14,556 | 313 | #dogs Levi late dataset
levi.data <- Read10X_h5("G://Levi_Jon/late/Ascl1_Atoh1_late_timepoint/outs/filtered_feature_bc_matrix.h5", use.names = TRUE, unique.features = TRUE)
levi <- CreateSeuratObject(counts = levi.data, project = "late", min.cells = 3, min.features = 200)
s.genes <- cc.genes.updated.2019$s.genes
g2m.ge... |
7470be047e102ebf83784529a0d12ef953abbbaa904548066ab7a5e523de1716 | R | 14,560 | 357 | # ÀûÓÃggtree»æÖƽø»¯Ê÷
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggtree)
library(ggplot2)
library(ggsci)
library(patchwork)
# ½ø»¯Ê÷ ---------------------------------------------------------------------
tree <- read.tree(file = "01-data/26-Evolution_newly_emerging_UCR_distr... |
4a471bd2b5b302a5f0f6470f22d77b653a12b052e7b07fc19ec5970a558ea1e8 | R | 14,641 | 377 | #' Export diagnosis files to RAP persistent storage
#'
#' @description In the UK Biobank RAP export tables for HES, GP, death, and cancer registry data, plus self-reported illness fields, using the table-exporter. This is essentially a wrapper function to submit jobs to the table exporter.
#'
#' Suggest executing in an... |
0e97a080ed9ced18a3fdc3a210f6eb0a43d9921a64b88ed63f10bccc13b2afdc | R | 14,656 | 373 | library(shiny)
library(shinydashboard)
library(data.table)
library(DT)
library(ggplot2)
library(shinycssloaders)
library(shinydashboardPlus)
library(shinyWidgets)
library(leaflet)
library(rjson)
library(htmltools)
library(leaflet.minicharts)
library(echarts4r)
library(sparkline)
library(shinyBS)
library(shiny.i18n)
lib... |
262902e54973e3c5d85e7eb4156ac55f6e3575ab147f41cc18b0ea44ea39c104 | R | 14,678 | 454 | library("tidyverse")
library("scales")
library("here")
library("sessioninfo")
library("DeconvoBuddies")
#### Plot Set-up ####
plot_dir <- here("plots", "03_HALO", "01_explore_data")
if (!dir.exists(plot_dir)) dir.create(plot_dir)
load(here("processed-data", "00_data_prep", "cell_colors.Rdata"), verbose = TRUE)
# cell... |
632fd870b867dba37feee7007c474cc1923469af997bcc120a806204069181ba | R | 14,692 | 352 | #!/usr/bin/env Rscript
### title: Microglia (MG) analysis (DEG, diffusion component analysis, pathway analysis)
### author: Jana Biermann, PhD
print(Sys.time())
library(Seurat)
library(destiny)
library(SingleCellExperiment)
library(dplyr)
library(ggplot2)
library(gplots)
library(ggpubr)
library(scales)
library(viri... |
35d9f41b2b300d98f04cadf7900bf00d5f5be4541c7e200824ecf0d9ef8112e9 | R | 14,701 | 387 | suppressMessages(library("here"))
suppressMessages(library("optparse"))
suppressMessages(library('glmnet'))
source(here("utils","plink_utils.R"))
option_list = list(
make_option("--bfile", action="store", default=NA, type='character',
help="Path to PLINK binary input file prefix (minus bed/bim/fam) [re... |
26f676b82a756c49de378ead30cc01b8fcc1eb8155cbe1b77efedff1f1490028 | R | 14,742 | 214 | fig_5_markers <- function(){
return(
list(Activation =
c("HLA-DQA1", "HLA-DQB1", "HLA-DRA",
"HLA-DRB1", "HLA-DRB5", "IL2RB", "CD69",
"CD74", "TFRC", "CD44", "TNFRSF9", "VCAM1", "ICAM1"),
`Cytotox-cytokines` =
c("TNF", "IFNG", "PRF1", "GZMA",... |
b9ff83d44a64220ee90ce13661b5bb4553fe21a27d30be304b0c4599f45c20a5 | R | 14,767 | 333 | # Run DEXSeq differential usage analysis on count matrix of alternative last exon usage
# Copyright (C) 2024 Sam Bryce-Smith samuel.bryce-smith.19@ucl.ac.uk
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# ... |
4513e59b2c6ff4f4440d0c471db4ee23956bdd0b9cd76ebdea141b35d99d05f8 | R | 14,786 | 348 | #!/usr/bin/env Rscript
# Pathway analysis from differentially expressed gene lists using DESeq2
# Author: Gisela Gabernet
# QBiC 2019; MIT License
library(gprofiler2)
library(ggplot2)
library(reshape2)
library(pheatmap)
library(pathview)
library(AnnotationDbi)
library(optparse)
# Need to load library for your specie... |
2e4eb72fe3ed8ba17a1493a3da814854c008b165f2534ec41f23324cb408cbca | R | 14,788 | 380 |
pacman::p_load(R.matlab,
reshape2,
tidyverse,
dichromat,
ggsci,
viridis,
nlme,
emmeans,
flextable)
source(file = "_Common.R")
target_file <- "output/commbio_powFig3_.RData"
# Load D... |
49fb1daa8595c25fd3ddd3bddf9832600c8ec518354ca1eeb0405f8d8aef6e70 | R | 14,795 | 390 |
library("tidyverse")
library("SpatialExperiment")
library("ggrepel")
library("ggExtra")
library("GGally")
library("here")
library("sessioninfo")
library("broom")
#### Set-up ####
plot_dir <- here("plots", "03_HALO", "09_compare_proportions")
if (!dir.exists(plot_dir)) dir.create(plot_dir)
# data_dir <- here("process... |
86d9b1507b616302283e59ea378470ef09d71f88164f8d71d5d02cc97f33102e | R | 14,809 | 373 | packages <- c("valr", "DESeq2", "biomaRt", "tximport", "vroom", "dplyr", "tibble")
install.packages(setdiff(packages, rownames(installed.packages())), repos = "https://cran.ma.imperial.ac.uk/")
library(valr)
library(DESeq2)
library(biomaRt)
library(tximport)
library(vroom)
library(dplyr)
#library(tidyr)
library(tibble)... |
2ab6857b002cafa589a938cae440700840468cee957691d44b5d75124f994834 | R | 14,814 | 398 | library(data.table)
library(sparkline)
# ====準備部分====
source(file = "01_Settings/Path.R", local = T, encoding = "UTF-8")
source(file = "01_Settings/color.R", local = TRUE, encoding = "UTF-8")
# 感染者ソーステーブルを取得
byDate <- fread(paste0(DATA_PATH, "byDate.csv"), header = T)
byDate[is.na(byDate)] <- 0
byDate$date <- lapply(... |
d07fa4e7bc2dc942820844a420aff21f52d59a64b7e5676ec2263932532ea8f3 | R | 14,869 | 303 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
library(readr)
library(plotrix)
library(GenomicRanges)
library(scales)
### Mobile elements
####################
#### mobile elements
mle <- fread("../UCSC_hg38_repeatMasker.tsv", data.table = F)# 5,633,664
mle <- mle[mle$repFamily == "Alu" | mle$repClass == ... |
a02b0503634b62faf267fd8788dd50ebde2d9c94b770ce4117614c217ec3567f | R | 14,883 | 264 | # Explore the results of drug signature analysis
library(ggplot2)
library(forcats)
library(data.table)
#### Mild ####
mi_query_result <- fread("/path/to/drug_repurposing/CMap/Mild/query_result.gct",skip=2)
mi_query_result <-mi_query_result[-1,]
#-log10(0.05) -> 1.30103
mi_query_result_sig <-mi_query_result[abs(as.n... |
ed19046c0c861f7f863ea625700582e88399ab63ed0a0ccfc8e66edb5301b842 | R | 14,888 | 374 | packages <- c("valr", "DESeq2", "biomaRt", "tximport", "vroom", "dplyr", "tibble")
install.packages(setdiff(packages, rownames(installed.packages())), repos = "https://cran.ma.imperial.ac.uk/")
library(valr)
library(DESeq2)
library(biomaRt)
library(tximport)
library(vroom)
library(dplyr)
#library(tidyr)
library(tibble)... |
6767733da9ba895afc5a82652f23b0b6e1ab1c4e4fd54df16e8b0bb8f36da9a1 | R | 14,966 | 201 | # ***************************************************
# Preprocessing steps for Saunders et al dataset
# ***************************************************
if (!require("here")) {
install.packages("here")
library("here")
}
if (!require("magrittr")) {
install.packages("magrittr")
library("magrittr")
}
if(!requi... |
76cc127c922edfdd54ffe9d5b3afb5a53a8e8d05848e7b84c38a54f35fe7b3df | R | 14,985 | 360 | #' getArtificialDoublets
#'
#' Create expression profiles of random artificial doublets.
#'
#' @param x A count matrix, with features as rows and cells as columns.
#' @param n The approximate number of doublet to generate (default 3000).
#' @param clusters The optional clusters labels to use to build cross-cluster
#' d... |
a3225ccdd052eeb943399eae3b701ff2a674b09973ad0862e06743eebaf2c315 | R | 15,034 | 281 | # 13 Sept 2019
# function for plotting rs78710909 site (BIN1)
# revised from plot_anywhere and plot_ASoC_composite
# Use OverlayTrack to combine data tracks
# init
library(Gviz)
# data("cpgIslands")
library(rtracklayer)
library(BSgenome)
library(BSgenome.Hsapiens.UCSC.hg38)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
l... |
e4beb7793199e485f2be66530b3613ac03ea88a8302a927adf8dfdd33a905379 | R | 15,053 | 467 | # # Installation
# install.packages("devtools")
# library(devtools)
# install_github("pcahan1/CellNet", ref="master")
# install_github("pcahan1/cancerCellNet@v0.1.1", ref="master")
# ! It seems the script accepts input matris in FPKM/TPM format (need log1p
# transform) rather than raw counts !
# Siwei 20 Sept 2023
# ... |
721a2970acb4c7c9f6d110940a56a7c0a7f622ff8d04bb2df4bf27bcf838e2f5 | R | 15,083 | 398 | # I'm a bad person
setwd("~/mount/hpc_uni/mammary_gland_transcriptomes/test_pipeline/")
library(tximport)
library(tidyverse)
theme_set(theme_minimal(base_size = 16))
# transcript-to-gene
tx2gene <- read_tsv("data/external/reference/transcript2gene.tsv") %>%
# ensure columns are in the correct order for tximport
s... |
7e0844b394342a6c866be21cc86611f533fc9b8b9c5ee96c248526bace4e0f1a | R | 15,122 | 267 | ---
title: "explore-items"
author: "Peter Szolovits"
date: "March 29, 2016"
output: html_document
---
Exploring data in MIMIC-III.
We use a slightly incorrect heuristic in comparing CareVue and Metavision data, namely that patients registered in those systems may be recognized by whether the `SUBJECT_ID < 40000`. Thi... |
73bb85d0e4fb8f93966dcd232d2eaf411275ced023603dd21c14af9bb3bb986c | R | 15,129 | 360 | # libraries ---------------------------------------------------------------
library(Seurat)
library(tidyverse)
library(scales)
library(ggrepel)
library(cowplot)
# read the data -----------------------------------------------------------
# read in the dataset
data.combined <- readRDS("../../out/object/sobj_processed_do... |
1ddf73714e2c290b883085e5447d30c0c659ad51804024e703620862cd79629d | R | 15,173 | 494 | # ====感染状況を日本標準マップで表示する画像を作成====
# Returns:
# data.table: データセット
cumSumConfirmedByDateAndRegion <- reactive({
dt <- mapData[date >= input$mapDateRange[1] & date <= input$mapDateRange[2]]
dt
})
output$comfirmedMapWrapper <- renderUI({
if (input$switchMapVersion == T) {
echarts4rOutput("echartsSimpleMap", he... |
7a4ac9d4c2ed823a96ec8a6bb8e0e7c70ef374c0915570b122897120b328ff97 | R | 15,239 | 433 | ---
title: "Custom bar plots for EWCE results from common variants"
author: "Isabel Castanho"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float:
collapsed: false
toc_depth: 4
code_folding: hide
---
---
CIRCUITS Multiregion single-nucleus RNA-seq data
single cell res... |
ba8fb9c3ff6e4bfbeaac4cb09aa00941d6f976b2dd5f679bb5e379054d1b6a86 | R | 15,241 | 467 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == "fukuoka" && is.null(GLOBAL_VALUE$Fukuoka$patients)) {
# GLOBAL_VALUE <- list(Fukuoka = list(
# summary = NULL,
# patients = NULL,
# updateTime = NULL,
# nodes = NULL,
# edges = NULL,
# call = NULL,
# test = NULL
... |
3240ab4e59db295a82ae4fd9966299542e942e219bc3b7ddd45c4528de7b6331 | R | 15,272 | 381 |
library(DESeq2)
library(GenomicRanges)
library(apeglm)
library(dplyr)
library(ggplot2)
library(gplots)
library(gridExtra)
#library(Rtsne)
library(reshape)
library(scales)
#library(VennDiagram)
#library(Seurat)
library(graphics)
#library(MultiPhen)
library(stringr)
#if (length(args)<6) {
# stop("Nee... |
4861dd8097b212eced63b1386b87cc58d17659b6c758707bb10556f1dc73d67b | R | 15,309 | 530 | # Siwei 20 Mar 2023
# Use tximport and EnsDB for gene name translation
# init
library(readr)
library(readxl)
library(stringr)
library(tximport)
library(EnsDb.Hsapiens.v86)
library(AnnotationDbi)
library(edgeR)
library(variancePartition)
library(factoextra)
library(sva)
library(ggplot2)
library(ggrepel)
# library... |
695f7c5f103e1e0a48ff6736f6f860d35ef97eb38bab49f6c16d9cac0caec38e | R | 15,412 | 251 | # this script runs weighted GAMMS on the unimputed dataset
# 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
path = "/data/pt_life/ResearchProjects/LLammer/gamms/Result... |
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