Deconvolution_Benchmark_DLPFC
Louise Huuki-Myers
Lieber Institute for Brain Development, Johns Hopkins Medical Campuslahuuki@gmail.com
2 May 2024
Source:vignettes/Deconvolution_Benchmark_DLPFC.Rmd
Deconvolution_Benchmark_DLPFC.Rmd#Introduction
What is Deconvolution?
Inferring the composition of different cell types in a bulk RNA-seq data
Deconvolution is a analysis that aims to calculate the proportion of different cell types that make up a sample of bulk RNA-seq, based off of cell type gene expression profiles in a single cell/nuclei RNA-seq dataset.
Use single cell data to infer the composition of
bulk RNA-seq samples
Deconvolution Methods
| Approach | Method | Citation | Availability |
|---|---|---|---|
| weighted least squares | DWLS | Tsoucas et al, Nature Comm, 2019 | R Package Cran |
| Bias correction: Assay | Bisque | Jew et al, Nature Comm, 2020 | R Package github |
| Bias correction: Sourse | MuSiC | Wang et al, Nature Communications, 2019 | R Package github |
| Machine Learning | CIBERSORTx | Newman et al., Nature BioTech, 2019 | Webtool |
| Bayesian | BayesPrism | Chu et al., Nature Cancer, 2022 | Webtool/R Package |
| linear | Hspe | Hunt et al., Ann. Appl. Stat, 2021 | R package github |
Goals of this Vignette
We will be demonstrating how to use DeconvoBuddies tools
when applying deconvolution with the Bisque package.
- Install and load required packages
- Download DLPFC RNA-seq data, and reference snRNA-seq data
- Find marker genes with
DeconvoBuddiestools - Run deconvolution with
BisqueRNA - Explore deconvolution output and create compostion plots with
DeconvoBuddiestools - Check proportion against RNAScope estimated proportions
1. Install and load required packages
R is an open-source statistical environment which can be
easily modified to enhance its functionality via packages. DeconvoBuddies
is a R package available via the Bioconductor repository for packages.
R can be installed on any operating system from CRAN after which you can install
DeconvoBuddies
by using the following commands in your R session:
Install DeconvoBuddies
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("DeconvoBuddies")
## Check that you have a valid Bioconductor installation
BiocManager::valid()Load Other Packages
## install Bisque from cran
# install.packages("BisqueRNA")
library("spatialLIBD")
library("DeconvoBuddies")
library("SummarizedExperiment")
library("SingleCellExperiment")
library("BisqueRNA")
library("dplyr")
library("tidyr")
library("tibble")2. Download DLPFC RNA-seq data, and reference snRNA-seq data.
Bulk RNA-seq data
Access the 110 sample Human DLPFC bulk RNA-seq dataset for LIBD.
These samples
are from 19 tissue blocks, and 10 neurotypical adult donors. Samples
were sequenced with two different library_types (polyA and
RiboZeroGold), and three different RNA_extraction (Cyto,
Total, Nuc), post quality control n=110 samples.
## use fetch deconvon data to load rse_gene
rse_gene <- fetch_deconvo_data("rse_gene")
#> 2024-05-02 22:23:12.350492 loading file /github/home/.cache/R/BiocFileCache/2343769e391_rse_gene.Rdata%3Frlkey%3Dsw2djr71y954yw4o3xrmjv59b%26dl%3D1
rse_gene
#> class: RangedSummarizedExperiment
#> dim: 21745 110
#> metadata(1): SPEAQeasy_settings
#> assays(2): counts logcounts
#> rownames(21745): ENSG00000227232.5 ENSG00000278267.1 ...
#> ENSG00000210195.2 ENSG00000210196.2
#> rowData names(11): Length gencodeID ... gencodeTx passExprsCut
#> colnames(110): 2107UNHS-0291_Br2720_Mid_Bulk
#> 2107UNHS-0291_Br2720_Mid_Cyto ... AN00000906_Br8667_Mid_Cyto
#> AN00000906_Br8667_Mid_Nuc
#> colData names(78): SAMPLE_ID Sample ... diagnosis qc_class
# lobstr::obj_size(rse_gene)
# 41.16 MB
## bulk RNA seq samples were sequenced with different library types, and RNA extractions
table(rse_gene$library_type, rse_gene$library_prep)
#>
#> Bulk Cyto Nuc
#> polyA 19 18 18
#> RiboZeroGold 19 19 17Refernce snRNA-seq data
This data is paired with a single nucleus RNA-seq data set from
spatialLIBD. This dataset can be accessed with
spatialLIBD::fetch_data().
## Use spatialLIBD to fetch the snRNA-seq dataset
sce_path_zip <- fetch_deconvo_data("sce")
#> 2024-05-02 22:23:14.468243 loading file /github/home/.cache/R/BiocFileCache/23443ff239b_sce_DLPFC_annotated.zip%3Fdl%3D1
sce_path <- unzip(sce_path_zip, exdir = tempdir())
sce <- HDF5Array::loadHDF5SummarizedExperiment(
file.path(tempdir(), "sce_DLPFC_annotated"))
# lobstr::obj_size(sce)
# 172.28 MB
## Check the broad cell type distribution
table(sce$cellType_broad_hc)
#>
#> Astro EndoMural Micro Oligo OPC Excit Inhib Ambiguous
#> 3979 2157 1601 10894 1940 24809 11067 21157
## We're going to subset to the first 1k genes to save memory
sce <- sce[seq_len(1000),]3. Find marker genes with
DeconvoBuddies::get_mean_ratio
Use Mean Ratio to find less noisy marker genes
htan 1vALL
marker_stats <- get_mean_ratio(sce,
cellType_col = "cellType_broad_hc",
gene_ensembl = "gene_id",
gene_name = "gene_name")
marker_stats |> group_by(cellType.target) |> slice(1)
#> # A tibble: 8 × 10
#> # Groups: cellType.target [8]
#> gene cellType.target mean.target cellType.2nd mean.2nd MeanRatio
#> <chr> <fct> <dbl> <fct> <dbl> <dbl>
#> 1 PRDM16 Astro 1.97 EndoMural 0.142 13.9
#> 2 SLC2A1 EndoMural 1.49 Ambiguous 0.183 8.14
#> 3 LINC01141 Micro 1.57 Excit 0.0640 24.5
#> 4 RNF220 Oligo 4.48 EndoMural 1.32 3.40
#> 5 CSMD2 OPC 2.82 Excit 1.33 2.11
#> 6 RAP1GAP Excit 0.983 Ambiguous 0.506 1.94
#> 7 SCMH1 Inhib 2.04 OPC 1.43 1.43
#> 8 CAMK2N1 Ambiguous 3.16 Excit 2.25 1.40
#> # ℹ 4 more variables: MeanRatio.rank <int>, MeanRatio.anno <chr>,
#> # gene_ensembl <chr>, gene_name <chr>Reproducibility
The DeconvoBuddies package (Huuki-Myers, Maynard, Hicks, Zandi, Kleinman, Hyde, Goes, and Collado-Torres, 2024) was made possible thanks to:
- R (R Core Team, 2024)
- BiocStyle (Oleś, 2023)
- knitr (Xie, 2024)
- RefManageR (McLean, 2017)
- rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2024)
- sessioninfo (Wickham, Chang, Flight, Müller, and Hester, 2021)
- testthat (Wickham, 2011)
This package was developed using biocthis.
Code for creating the vignette
## Create the vignette
library("rmarkdown")
system.time(render("Deconvolution_Benchmark_DLPFC.Rmd", "BiocStyle::html_document"))
## Extract the R code
library("knitr")
knit("Deconvolution_Benchmark_DLPFC.Rmd", tangle = TRUE)Date the vignette was generated.
#> [1] "2024-05-02 22:23:34 UTC"Wallclock time spent generating the vignette.
#> Time difference of 34.535 secsR session information.
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#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────Bibliography
This vignette was generated using BiocStyle (Oleś, 2023) with knitr (Xie, 2024) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
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