Getting started with scRNA-seq analyses with Bioconductor
5/2/2021
Contents
This code is part of a presentation for the Human Cell Atlas - Latin America workshop on scRNA-seq data analysis. The code is based on Peter Hickey’s WEHI scRNA-seq workshop which itself is based on the Orchestrating Single Cell Analysis (OSCA) with Bioconductor book and paper.
You can find the Slides that accompany this code on Google Slides.
1 Install R
If you don’t have the latest R release (currently 4.0), you will need to install it following these instructions http://bioconductor.org/install/#install-R. You might also be interested in this video where I show how to install multiple R versions on macOS and winOS.
Yesterday at @LIBDrstats I explained how to install multiple R & @Bioconductor versions on macOS and winOS using
— 🇲🇽 Leonardo Collado-Torres (@lcolladotor) May 1, 2021
* CRAN
* RSwitch https://t.co/uJ023n6ZKq by @hrbrmstr
* @rstudio
Useful now that R 4.1 is in “alpha” & BioC 3.13 is almost ready#rstats
📹 https://t.co/FzfXpp6nrU
2 Install Bioconductor
Once you have R installed, you can then install Bioconductor.
## Install BiocManager which is the utility for installing
## Bioconductor packages
## For installing Bioconductor packages
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")Once you have BiocManager installed, you should verify that you are indeed using the latest Bioconductor release, which right now is 3.12.
BiocManager::version()## [1] '3.12'Next we can install the R/Bioconductor packages we’ll use later in the session.
## Install required packages
BiocManager::install(
c(
'SingleCellExperiment',
# 'usethis',
# 'here',
'scran',
'scater',
'scRNAseq',
# 'org.Mm.eg.db',
'AnnotationHub',
'ExperimentHub',
# 'BiocFileCache',
# 'DropletUtils',
# 'EnsDb.Hsapiens.v86',
# 'TENxPBMCData',
# 'BiocSingular',
# 'batchelor',
'uwot',
'Rtsne',
# 'pheatmap',
# 'fossil',
# 'ggplot2',
# 'cowplot',
# 'RColorBrewer',
# 'plotly',
# 'iSEE',
'pryr',
'sessioninfo'
)
)3 Data Infraestructure code
Now we can use the code from https://github.com/lcolladotor/osca_LIIGH_UNAM_2020/blob/master/02-data-infrastructure-and-import.R#L57-L208, which I’ve copy-pasted below. The equivalent version with comments in Spanish is available from https://github.com/ComunidadBioInfo/cdsb2020/blob/master/scRNAseq/02-data-infrastructure-and-import.R#L57-L215.
## ----all_code, cache=TRUE--------------------------------------------------------------------------------------------
library('scRNAseq')## Loading required package: SingleCellExperiment## Loading required package: SummarizedExperiment## Loading required package: MatrixGenerics## Loading required package: matrixStats##
## Attaching package: 'MatrixGenerics'## The following objects are masked from 'package:matrixStats':
##
## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars## Loading required package: GenomicRanges## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:base':
##
## expand.grid## Loading required package: IRanges## Loading required package: GenomeInfoDb## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.##
## Attaching package: 'Biobase'## The following object is masked from 'package:MatrixGenerics':
##
## rowMedians## The following objects are masked from 'package:matrixStats':
##
## anyMissing, rowMedianssce.416b <- LunSpikeInData(which = "416b")## using temporary cache /var/folders/cx/n9s558kx6fb7jf5z_pgszgb80000gn/T//RtmpD4KAVX/BiocFileCache## snapshotDate(): 2020-10-27## see ?scRNAseq and browseVignettes('scRNAseq') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## see ?scRNAseq and browseVignettes('scRNAseq') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## see ?scRNAseq and browseVignettes('scRNAseq') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## snapshotDate(): 2020-10-27## see ?scRNAseq and browseVignettes('scRNAseq') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## snapshotDate(): 2020-10-27## loading from cache## require("ensembldb")# Load the SingleCellExperiment package
library('SingleCellExperiment')
# Extract the count matrix from the 416b dataset
counts.416b <- counts(sce.416b)
# Construct a new SCE from the counts matrix
sce <- SingleCellExperiment(assays = list(counts = counts.416b))
# Inspect the object we just created
sce## class: SingleCellExperiment
## dim: 46604 192
## metadata(0):
## assays(1): counts
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
## ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(0):
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(0):
## reducedDimNames(0):
## altExpNames(0):## How big is it?
pryr::object_size(sce)## Registered S3 method overwritten by 'pryr':
## method from
## print.bytes Rcpp## 40.1 MB# Access the counts matrix from the assays slot
# WARNING: This will flood RStudio with output!
# 1. The general method
assay(sce, "counts")[1:6, 1:3]## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0# 2. The special method for the assay named "counts"
counts(sce)[1:6, 1:3]## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0sce <- scater::logNormCounts(sce)
# Inspect the object we just updated
sce## class: SingleCellExperiment
## dim: 46604 192
## metadata(0):
## assays(2): counts logcounts
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
## ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(0):
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(1): sizeFactor
## reducedDimNames(0):
## altExpNames(0):## How big is it?
pryr::object_size(sce)## 112 MB# 1. The general method
assay(sce, "logcounts")[1:6, 1:3]## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0# 2. The special method for the assay named "logcounts"
logcounts(sce)[1:6, 1:3]## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1
## ENSMUSG00000102693 0
## ENSMUSG00000064842 0
## ENSMUSG00000051951 0
## ENSMUSG00000102851 0
## ENSMUSG00000103377 0
## ENSMUSG00000104017 0# assign a new entry to assays slot
assay(sce, "counts_100") <- assay(sce, "counts") + 100
# List the assays in the object
assays(sce)## List of length 3
## names(3): counts logcounts counts_100assayNames(sce)## [1] "counts" "logcounts" "counts_100"## How big is it?
pryr::object_size(sce)## 183 MB# Extract the sample metadata from the 416b dataset
colData.416b <- colData(sce.416b)
# Add some of the sample metadata to our SCE
colData(sce) <- colData.416b[, c("phenotype", "block")]
# Inspect the object we just updated
sce## class: SingleCellExperiment
## dim: 46604 192
## metadata(0):
## assays(3): counts logcounts counts_100
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
## ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(0):
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(2): phenotype block
## reducedDimNames(0):
## altExpNames(0):# Access the sample metadata from our SCE
colData(sce)## DataFrame with 192 rows and 2 columns
## phenotype block
## <character> <integer>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 wild type phenotype 20160113
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 wild type phenotype 20160113
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 wild type phenotype 20160113
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 induced CBFB-MYH11 o.. 20160113
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 induced CBFB-MYH11 o.. 20160113
## ... ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 o.. 20160325
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 o.. 20160325
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 o.. 20160325
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 o.. 20160325
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 wild type phenotype 20160325# Access a specific column of sample metadata from our SCE
table(sce$block)##
## 20160113 20160325
## 96 96# Example of function that adds extra fields to colData
sce <- scater::addPerCellQC(sce.416b)
# Access the sample metadata from our updated SCE
colData(sce)## DataFrame with 192 rows and 18 columns
## Source Name cell line
## <character> <character>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 SLX-9555.N701_S502.C.. 416B
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 SLX-9555.N701_S503.C.. 416B
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 SLX-9555.N701_S504.C.. 416B
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 SLX-9555.N701_S505.C.. 416B
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 SLX-9555.N701_S506.C.. 416B
## ... ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 SLX-11312.N712_S505... 416B
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 SLX-11312.N712_S506... 416B
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 SLX-11312.N712_S507... 416B
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 SLX-11312.N712_S508... 416B
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 SLX-11312.N712_S517... 416B
## cell type
## <character>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 embryonic stem cell
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 embryonic stem cell
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 embryonic stem cell
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 embryonic stem cell
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 embryonic stem cell
## ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 embryonic stem cell
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 embryonic stem cell
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 embryonic stem cell
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 embryonic stem cell
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 embryonic stem cell
## single cell well quality
## <character>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 OK
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 OK
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 OK
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 OK
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 OK
## ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 OK
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 OK
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 OK
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 OK
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 OK
## genotype
## <character>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 Doxycycline-inducibl..
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 Doxycycline-inducibl..
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 Doxycycline-inducibl..
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 Doxycycline-inducibl..
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 Doxycycline-inducibl..
## ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 Doxycycline-inducibl..
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 Doxycycline-inducibl..
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 Doxycycline-inducibl..
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 Doxycycline-inducibl..
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 Doxycycline-inducibl..
## phenotype strain
## <character> <character>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 wild type phenotype B6D2F1-J
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 wild type phenotype B6D2F1-J
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 wild type phenotype B6D2F1-J
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 induced CBFB-MYH11 o.. B6D2F1-J
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 induced CBFB-MYH11 o.. B6D2F1-J
## ... ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 o.. B6D2F1-J
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 o.. B6D2F1-J
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 o.. B6D2F1-J
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 induced CBFB-MYH11 o.. B6D2F1-J
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 wild type phenotype B6D2F1-J
## spike-in addition block sum
## <character> <integer> <numeric>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 ERCC+SIRV 20160113 865936
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ERCC+SIRV 20160113 1076277
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 ERCC+SIRV 20160113 1180138
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 ERCC+SIRV 20160113 1342593
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 ERCC+SIRV 20160113 1668311
## ... ... ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 Premixed 20160325 776622
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 Premixed 20160325 1299950
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 Premixed 20160325 1800696
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 Premixed 20160325 46731
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 Premixed 20160325 1866692
## detected altexps_ERCC_sum
## <numeric> <numeric>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 7618 65278
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 7521 74748
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 8306 60878
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 8143 60073
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 7154 136810
## ... ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 8174 61575
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 8956 94982
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 9530 113707
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 6649 7580
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 10964 48664
## altexps_ERCC_detected
## <numeric>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 39
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 40
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 42
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 42
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 44
## ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 39
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 41
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 40
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 44
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 39
## altexps_ERCC_percent altexps_SIRV_sum
## <numeric> <numeric>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 6.80658 27828
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 6.28030 39173
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 4.78949 30058
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 4.18567 32542
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 7.28887 71850
## ... ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 7.17620 19848
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 6.65764 31729
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 5.81467 41116
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 13.48898 1883
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 2.51930 16289
## altexps_SIRV_detected
## <numeric>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 7
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 7
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 7
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 7
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 7
## ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 7
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 7
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 7
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 7
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 7
## altexps_SIRV_percent total
## <numeric> <numeric>
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 2.90165 959042
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 3.29130 1190198
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 2.36477 1271074
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 2.26741 1435208
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 3.82798 1876971
## ... ... ...
## SLX-11312.N712_S505.H5H5YBBXX.s_8.r_1 2.313165 858045
## SLX-11312.N712_S506.H5H5YBBXX.s_8.r_1 2.224004 1426661
## SLX-11312.N712_S507.H5H5YBBXX.s_8.r_1 2.102562 1955519
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1 3.350892 56194
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1 0.843271 1931645# Inspect the object we just updated
sce## class: SingleCellExperiment
## dim: 46604 192
## metadata(0):
## assays(1): counts
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
## ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(1): Length
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(18): Source Name cell line ... altexps_SIRV_percent total
## reducedDimNames(0):
## altExpNames(2): ERCC SIRV## How big is it?
pryr::object_size(sce)## 41.4 MB## Add the lognorm counts again
sce <- scater::logNormCounts(sce)
## How big is it?
pryr::object_size(sce)## 113 MB# E.g., subset data to just wild type cells
# Remember, cells are columns of the SCE
sce[, sce$phenotype == "wild type phenotype"]## class: SingleCellExperiment
## dim: 46604 96
## metadata(0):
## assays(2): counts logcounts
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
## ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(1): Length
## colnames(96): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
## SLX-11312.N712_S504.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(19): Source Name cell line ... total sizeFactor
## reducedDimNames(0):
## altExpNames(2): ERCC SIRV# Access the feature metadata from our SCE
# It's currently empty!
rowData(sce)## DataFrame with 46604 rows and 1 column
## Length
## <integer>
## ENSMUSG00000102693 1070
## ENSMUSG00000064842 110
## ENSMUSG00000051951 6094
## ENSMUSG00000102851 480
## ENSMUSG00000103377 2819
## ... ...
## ENSMUSG00000094621 121
## ENSMUSG00000098647 99
## ENSMUSG00000096730 3077
## ENSMUSG00000095742 243
## CBFB-MYH11-mcherry 2998# Example of function that adds extra fields to rowData
sce <- scater::addPerFeatureQC(sce)
# Access the feature metadata from our updated SCE
rowData(sce)## DataFrame with 46604 rows and 3 columns
## Length mean detected
## <integer> <numeric> <numeric>
## ENSMUSG00000102693 1070 0.0000000 0.000000
## ENSMUSG00000064842 110 0.0000000 0.000000
## ENSMUSG00000051951 6094 0.0000000 0.000000
## ENSMUSG00000102851 480 0.0000000 0.000000
## ENSMUSG00000103377 2819 0.0104167 0.520833
## ... ... ... ...
## ENSMUSG00000094621 121 0.0 0
## ENSMUSG00000098647 99 0.0 0
## ENSMUSG00000096730 3077 0.0 0
## ENSMUSG00000095742 243 0.0 0
## CBFB-MYH11-mcherry 2998 50375.7 100## How big is it?
pryr::object_size(sce)## 114 MB# Download the relevant Ensembl annotation database
# using AnnotationHub resources
library('AnnotationHub')## Loading required package: BiocFileCache## Loading required package: dbplyr##
## Attaching package: 'AnnotationHub'## The following object is masked from 'package:Biobase':
##
## cacheah <- AnnotationHub()## snapshotDate(): 2020-10-27query(ah, c("Mus musculus", "Ensembl", "v97"))## AnnotationHub with 1 record
## # snapshotDate(): 2020-10-27
## # names(): AH73905
## # $dataprovider: Ensembl
## # $species: Mus musculus
## # $rdataclass: EnsDb
## # $rdatadateadded: 2019-05-02
## # $title: Ensembl 97 EnsDb for Mus musculus
## # $description: Gene and protein annotations for Mus musculus based on Ensem...
## # $taxonomyid: 10090
## # $genome: GRCm38
## # $sourcetype: ensembl
## # $sourceurl: http://www.ensembl.org
## # $sourcesize: NA
## # $tags: c("97", "AHEnsDbs", "Annotation", "EnsDb", "Ensembl", "Gene",
## # "Protein", "Transcript")
## # retrieve record with 'object[["AH73905"]]'# Annotate each gene with its chromosome location
ensdb <- ah[["AH73905"]]## loading from cachechromosome <- mapIds(ensdb,
keys = rownames(sce),
keytype = "GENEID",
column = "SEQNAME")## Warning: Unable to map 563 of 46604 requested IDs.rowData(sce)$chromosome <- chromosome
# Access the feature metadata from our updated SCE
rowData(sce)## DataFrame with 46604 rows and 4 columns
## Length mean detected chromosome
## <integer> <numeric> <numeric> <character>
## ENSMUSG00000102693 1070 0.0000000 0.000000 1
## ENSMUSG00000064842 110 0.0000000 0.000000 1
## ENSMUSG00000051951 6094 0.0000000 0.000000 1
## ENSMUSG00000102851 480 0.0000000 0.000000 1
## ENSMUSG00000103377 2819 0.0104167 0.520833 1
## ... ... ... ... ...
## ENSMUSG00000094621 121 0.0 0 GL456372.1
## ENSMUSG00000098647 99 0.0 0 GL456381.1
## ENSMUSG00000096730 3077 0.0 0 JH584292.1
## ENSMUSG00000095742 243 0.0 0 JH584295.1
## CBFB-MYH11-mcherry 2998 50375.7 100 NA## How big is it?
pryr::object_size(sce)## 114 MB# E.g., subset data to just genes on chromosome 3
# NOTE: which() needed to cope with NA chromosome names
sce[which(rowData(sce)$chromosome == "3"), ]## class: SingleCellExperiment
## dim: 2876 192
## metadata(0):
## assays(2): counts logcounts
## rownames(2876): ENSMUSG00000098982 ENSMUSG00000098307 ...
## ENSMUSG00000105990 ENSMUSG00000075903
## rowData names(4): Length mean detected chromosome
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(19): Source Name cell line ... total sizeFactor
## reducedDimNames(0):
## altExpNames(2): ERCC SIRV# Access the metadata from our SCE
# It's currently empty!
metadata(sce)## list()# The metadata slot is Vegas - anything goes
metadata(sce) <- list(favourite_genes = c("Shh", "Nck1", "Diablo"),
analyst = c("Pete"))
# Access the metadata from our updated SCE
metadata(sce)## $favourite_genes
## [1] "Shh" "Nck1" "Diablo"
##
## $analyst
## [1] "Pete"# E.g., add the PCA of logcounts
# NOTE: We'll learn more about PCA later
sce <- scater::runPCA(sce)
# Inspect the object we just updated
sce## class: SingleCellExperiment
## dim: 46604 192
## metadata(2): favourite_genes analyst
## assays(2): counts logcounts
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
## ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(4): Length mean detected chromosome
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(19): Source Name cell line ... total sizeFactor
## reducedDimNames(1): PCA
## altExpNames(2): ERCC SIRV# Access the PCA matrix from the reducedDims slot
reducedDim(sce, "PCA")[1:6, 1:3]## PC1 PC2 PC3
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 18.717668 27.598132 -5.939654
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 2.480705 27.564583 -4.916567
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 42.034018 7.552435 -12.126964
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 -8.494303 -31.833727 -15.760853
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 -49.737390 -4.226795 -6.123169
## SLX-9555.N701_S507.C89V9ANXX.s_1.r_1 -44.528081 3.215503 -10.384939# E.g., add a t-SNE representation of logcounts
# NOTE: We'll learn more about t-SNE later
sce <- scater::runTSNE(sce)
# Inspect the object we just updated
sce## class: SingleCellExperiment
## dim: 46604 192
## metadata(2): favourite_genes analyst
## assays(2): counts logcounts
## rownames(46604): ENSMUSG00000102693 ENSMUSG00000064842 ...
## ENSMUSG00000095742 CBFB-MYH11-mcherry
## rowData names(4): Length mean detected chromosome
## colnames(192): SLX-9555.N701_S502.C89V9ANXX.s_1.r_1
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 ...
## SLX-11312.N712_S508.H5H5YBBXX.s_8.r_1
## SLX-11312.N712_S517.H5H5YBBXX.s_8.r_1
## colData names(19): Source Name cell line ... total sizeFactor
## reducedDimNames(2): PCA TSNE
## altExpNames(2): ERCC SIRV# Access the t-SNE matrix from the reducedDims slot
head(reducedDim(sce, "TSNE"))## [,1] [,2]
## SLX-9555.N701_S502.C89V9ANXX.s_1.r_1 6.117248 2.3252320
## SLX-9555.N701_S503.C89V9ANXX.s_1.r_1 3.223016 -0.7115595
## SLX-9555.N701_S504.C89V9ANXX.s_1.r_1 6.331325 4.9120344
## SLX-9555.N701_S505.C89V9ANXX.s_1.r_1 -1.653247 4.0296996
## SLX-9555.N701_S506.C89V9ANXX.s_1.r_1 -4.892326 -7.0317003
## SLX-9555.N701_S507.C89V9ANXX.s_1.r_1 -3.711729 -7.6197516# E.g., add a 'manual' UMAP representation of logcounts
# NOTE: We'll learn more about UMAP later and a
# simpler way to compute it.
u <- uwot::umap(t(logcounts(sce)), n_components = 2)
# Add the UMAP matrix to the reducedDims slot
# Access the UMAP matrix from the reducedDims slot
reducedDim(sce, "UMAP") <- u
# List the dimensionality reduction results stored in # the object
reducedDims(sce)## List of length 3
## names(3): PCA TSNE UMAP4 Quality Control
Below I copy-pasted the code from https://github.com/lcolladotor/osca_LIIGH_UNAM_2020/blob/master/03-quality-control.R#L57-L226. The equivalent version with comments in Spanish is available from https://github.com/ComunidadBioInfo/cdsb2020/blob/master/scRNAseq/03-quality-control.R#L57-L233.
## ----all_code, cache=TRUE--------------------------------------------------------------------------------------------
## Data
library('scRNAseq')
sce.416b <- LunSpikeInData(which = "416b")## snapshotDate(): 2020-10-27## see ?scRNAseq and browseVignettes('scRNAseq') for documentation## loading from cache## see ?scRNAseq and browseVignettes('scRNAseq') for documentation## loading from cache## see ?scRNAseq and browseVignettes('scRNAseq') for documentation## loading from cache## snapshotDate(): 2020-10-27## see ?scRNAseq and browseVignettes('scRNAseq') for documentation## loading from cache## snapshotDate(): 2020-10-27## loading from cachesce.416b$block <- factor(sce.416b$block)
# Download the relevant Ensembl annotation database
# using AnnotationHub resources
library('AnnotationHub')
ah <- AnnotationHub()## snapshotDate(): 2020-10-27query(ah, c("Mus musculus", "Ensembl", "v97"))## AnnotationHub with 1 record
## # snapshotDate(): 2020-10-27
## # names(): AH73905
## # $dataprovider: Ensembl
## # $species: Mus musculus
## # $rdataclass: EnsDb
## # $rdatadateadded: 2019-05-02
## # $title: Ensembl 97 EnsDb for Mus musculus
## # $description: Gene and protein annotations for Mus musculus based on Ensem...
## # $taxonomyid: 10090
## # $genome: GRCm38
## # $sourcetype: ensembl
## # $sourceurl: http://www.ensembl.org
## # $sourcesize: NA
## # $tags: c("97", "AHEnsDbs", "Annotation", "EnsDb", "Ensembl", "Gene",
## # "Protein", "Transcript")
## # retrieve record with 'object[["AH73905"]]'# Annotate each gene with its chromosome location
ens.mm.v97 <- ah[["AH73905"]]## loading from cachelocation <- mapIds(
ens.mm.v97,
keys = rownames(sce.416b),
keytype = "GENEID",
column = "SEQNAME"
)## Warning: Unable to map 563 of 46604 requested IDs.# Identify the mitochondrial genes
is.mito <- which(location == "MT")
library('scater')## Loading required package: ggplot2sce.416b <- addPerCellQC(sce.416b,
subsets = list(Mito = is.mito))
## ----qc_metrics, cache=TRUE, dependson='all_code'--------------------------------------------------------------------
plotColData(sce.416b, x = "block", y = "detected")
plotColData(sce.416b, x = "block", y = "detected") +
scale_y_log10()
plotColData(sce.416b,
x = "block",
y = "detected",
other_fields = "phenotype") +
scale_y_log10() +
facet_wrap( ~ phenotype)
## ----all_code_part2, cache = TRUE, dependson='all_code'--------------------------------------------------------------
# Example thresholds
qc.lib <- sce.416b$sum < 100000
qc.nexprs <- sce.416b$detected < 5000
qc.spike <- sce.416b$altexps_ERCC_percent > 10
qc.mito <- sce.416b$subsets_Mito_percent > 10
discard <- qc.lib | qc.nexprs | qc.spike | qc.mito
# Summarize the number of cells removed for each reason
DataFrame(
LibSize = sum(qc.lib),
NExprs = sum(qc.nexprs),
SpikeProp = sum(qc.spike),
MitoProp = sum(qc.mito),
Total = sum(discard)
)## DataFrame with 1 row and 5 columns
## LibSize NExprs SpikeProp MitoProp Total
## <integer> <integer> <integer> <integer> <integer>
## 1 3 0 19 14 33qc.lib2 <- isOutlier(sce.416b$sum, log = TRUE, type = "lower")
qc.nexprs2 <- isOutlier(sce.416b$detected, log = TRUE,
type = "lower")
qc.spike2 <- isOutlier(sce.416b$altexps_ERCC_percent,
type = "higher")
qc.mito2 <- isOutlier(sce.416b$subsets_Mito_percent,
type = "higher")
discard2 <- qc.lib2 | qc.nexprs2 | qc.spike2 | qc.mito2
# Extract the thresholds
attr(qc.lib2, "thresholds")## lower higher
## 434082.9 Infattr(qc.nexprs2, "thresholds")## lower higher
## 5231.468 Inf# Summarize the number of cells removed for each reason.
DataFrame(
LibSize = sum(qc.lib2),
NExprs = sum(qc.nexprs2),
SpikeProp = sum(qc.spike2),
MitoProp = sum(qc.mito2),
Total = sum(discard2)
)## DataFrame with 1 row and 5 columns
## LibSize NExprs SpikeProp MitoProp Total
## <integer> <integer> <integer> <integer> <integer>
## 1 4 0 1 2 6## More checks
plotColData(sce.416b,
x = "block",
y = "detected",
other_fields = "phenotype") +
scale_y_log10() +
facet_wrap( ~ phenotype)
batch <- paste0(sce.416b$phenotype, "-", sce.416b$block)
qc.lib3 <- isOutlier(sce.416b$sum,
log = TRUE,
type = "lower",
batch = batch)
qc.nexprs3 <- isOutlier(sce.416b$detected,
log = TRUE,
type = "lower",
batch = batch)
qc.spike3 <- isOutlier(sce.416b$altexps_ERCC_percent,
type = "higher",
batch = batch)
qc.mito3 <- isOutlier(sce.416b$subsets_Mito_percent,
type = "higher",
batch = batch)
discard3 <- qc.lib3 | qc.nexprs3 | qc.spike3 | qc.mito3
# Extract the thresholds
attr(qc.lib3, "thresholds")## induced CBFB-MYH11 oncogene expression-20160113
## lower 461073.1
## higher Inf
## induced CBFB-MYH11 oncogene expression-20160325
## lower 399133.7
## higher Inf
## wild type phenotype-20160113 wild type phenotype-20160325
## lower 599794.9 370316.5
## higher Inf Infattr(qc.nexprs3, "thresholds")## induced CBFB-MYH11 oncogene expression-20160113
## lower 5399.24
## higher Inf
## induced CBFB-MYH11 oncogene expression-20160325
## lower 6519.74
## higher Inf
## wild type phenotype-20160113 wild type phenotype-20160325
## lower 7215.887 7586.402
## higher Inf Inf# Summarize the number of cells removed for each reason
DataFrame(
LibSize = sum(qc.lib3),
NExprs = sum(qc.nexprs3),
SpikeProp = sum(qc.spike3),
MitoProp = sum(qc.mito3),
Total = sum(discard3)
)## DataFrame with 1 row and 5 columns
## LibSize NExprs SpikeProp MitoProp Total
## <integer> <integer> <integer> <integer> <integer>
## 1 5 4 6 2 9## ----use_case, cache=TRUE, dependson= c('all_code', 'all_code_part2')------------------------------------------------
sce.grun <- GrunPancreasData()## snapshotDate(): 2020-10-27## see ?scRNAseq and browseVignettes('scRNAseq') for documentation## downloading 1 resources## retrieving 1 resource## loading from cache## snapshotDate(): 2020-10-27## see ?scRNAseq and browseVignettes('scRNAseq') for documentation## loading from cachesce.grun <- addPerCellQC(sce.grun)
plotColData(sce.grun, x = "donor", y = "altexps_ERCC_percent")## Warning: Removed 10 rows containing non-finite values (stat_ydensity).## Warning: Removed 10 rows containing missing values (position_quasirandom).
discard.ercc <- isOutlier(sce.grun$altexps_ERCC_percent,
type = "higher",
batch = sce.grun$donor)## Warning in .get_med_and_mad(metric, batch = batch, subset = subset,
## share.medians = share.medians, : missing values ignored during outlier detectiondiscard.ercc2 <- isOutlier(
sce.grun$altexps_ERCC_percent,
type = "higher",
batch = sce.grun$donor,
subset = sce.grun$donor %in% c("D17", "D2", "D7")
)## Warning in .get_med_and_mad(metric, batch = batch, subset = subset,
## share.medians = share.medians, : missing values ignored during outlier detectionplotColData(
sce.grun,
x = "donor",
y = "altexps_ERCC_percent",
colour_by = data.frame(discard = discard.ercc)
)## Warning: Removed 10 rows containing non-finite values (stat_ydensity).
## Warning: Removed 10 rows containing missing values (position_quasirandom).
plotColData(
sce.grun,
x = "donor",
y = "altexps_ERCC_percent",
colour_by = data.frame(discard = discard.ercc2)
)## Warning: Removed 10 rows containing non-finite values (stat_ydensity).
## Warning: Removed 10 rows containing missing values (position_quasirandom).
# Add info about which cells are outliers
sce.416b$discard <- discard2
# Look at this plot for each QC metric
plotColData(
sce.416b,
x = "block",
y = "sum",
colour_by = "discard",
other_fields = "phenotype"
) +
facet_wrap( ~ phenotype) +
scale_y_log10()
# Another useful diagnostic plot
plotColData(
sce.416b,
x = "sum",
y = "subsets_Mito_percent",
colour_by = "discard",
other_fields = c("block", "phenotype")
) +
facet_grid(block ~ phenotype)
5 Reproducibility information
Below is the R session information used for making this document.
## Reproducibility information
print('Reproducibility information:')## [1] "Reproducibility information:"Sys.time()## [1] "2021-05-02 15:02:20 EDT"proc.time()## user system elapsed
## 112.925 9.019 176.469options(width = 120)
sessioninfo::session_info()## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.5 Patched (2021-03-31 r80247)
## os macOS Big Sur 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2021-05-02
##
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
## package * version date lib source
## AnnotationDbi * 1.52.0 2020-10-27 [1] Bioconductor
## AnnotationFilter * 1.14.0 2020-10-27 [1] Bioconductor
## AnnotationHub * 2.22.1 2021-04-16 [1] Bioconductor
## askpass 1.1 2019-01-13 [1] CRAN (R 4.0.2)
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.2)
## beachmat 2.6.4 2020-12-20 [1] Bioconductor
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##
## [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/libraryCode for making the R file.
knitr::knit("2021-05-03_code_HCA_LA.Rmd", tangle = TRUE)