The blood is probably the most well-studied tissue in the single-cell field, mostly because everything is already dissociated “for free”. Of particular interest has been the use of single-cell genomics to study cell fate decisions in haematopoeisis. Indeed, it was not long ago that dueling interpretations of haematopoeitic stem cell (HSC) datasets were a mainstay of single-cell conferences. Sadly, these times have mostly passed so we will instead entertain ourselves by combining a small number of these datasets into a single analysis.
sce.nestclass: SingleCellExperiment
dim: 46078 1656
metadata(0):
assays(2): counts logcounts
rownames(46078): ENSMUSG00000000001 ENSMUSG00000000003 ... ENSMUSG00000107391
ENSMUSG00000107392
rowData names(3): GENEID SYMBOL SEQNAME
colnames(1656): HSPC_025 HSPC_031 ... Prog_852 Prog_810
colData names(3): cell.type FACS sizeFactor
reducedDimNames(1): diffusion
altExpNames(1): ERCCThe Grun dataset requires a little bit of subsetting and re-analysis to only consider the sorted HSCs.
library(scuttle)
sce.grun.hsc <- sce.grun.hsc[,sce.grun.hsc$protocol=="sorted hematopoietic stem cells"]
sce.grun.hsc <- logNormCounts(sce.grun.hsc)
set.seed(11001)
library(scran)
dec.grun.hsc <- modelGeneVarByPoisson(sce.grun.hsc) Finally, we will grab the Paul dataset, which we will also subset to only consider the unsorted myeloid population. This removes the various knockout conditions that just complicates matters.
sce.paul <- sce.paul[,sce.paul$Batch_desc=="Unsorted myeloid"]
sce.paul <- logNormCounts(sce.paul)
set.seed(00010010)
dec.paul <- modelGeneVarByPoisson(sce.paul) common <- Reduce(intersect, list(rownames(sce.nest),
rownames(sce.grun.hsc), rownames(sce.paul)))
length(common)[1] 17147Combining variances to obtain a single set of HVGs.
combined.dec <- combineVar(
dec.nest[common,],
dec.grun.hsc[common,],
dec.paul[common,]
)
hvgs <- getTopHVGs(combined.dec, n=5000)Adjusting for gross differences in sequencing depth.
library(batchelor)
normed.sce <- multiBatchNorm(
Nestorowa=sce.nest[common,],
Grun=sce.grun.hsc[common,],
Paul=sce.paul[common,]
)We turn on auto.merge=TRUE to instruct fastMNN() to merge the batch that offers the largest number of MNNs.
This aims to perform the “easiest” merges first, i.e., between the most replicate-like batches,
before tackling merges between batches that have greater differences in their population composition.
set.seed(1000010)
merged <- fastMNN(normed.sce, subset.row=hvgs, auto.merge=TRUE)Not too much variance lost inside each batch, hopefully. We also observe that the algorithm chose to merge the more diverse Nestorowa and Paul datasets before dealing with the HSC-only Grun dataset.
metadata(merged)$merge.info[,c("left", "right", "lost.var")]DataFrame with 2 rows and 3 columns
left right lost.var
<List> <List> <matrix>
1 Paul Nestorowa 0.01069374:0.0000000:0.00739465
2 Paul,Nestorowa Grun 0.00562344:0.0178334:0.00702615saveRDS(merged,"output/merged_sce.RDS")Computation Started: 2023-07-21 16:24:08
Finished in 25.439 secs
Git Log
No git history available for this page
Packages
| package | version | date |
|---|---|---|
| Rcpp | 1.0.6 | 2021-01-16 |
| git2r | 0.28.0 | 2021-01-11 |
| batchelor | 1.6.2 | 2020-11-27 |
| compiler | 4.0.1 | 2020-06-07 |
| bluster | 1.0.0 | 2020-10-28 |
| GenomeInfoDb | 1.26.2 | 2020-12-09 |
| XVector | 0.30.0 | 2020-10-29 |
| MatrixGenerics | 1.2.0 | 2020-10-28 |
| methods | 4.0.1 | 2020-06-07 |
| bitops | 1.0-6 | 2020-07-15 |
| BiocNeighbors | 1.8.2 | 2020-12-08 |
| utils | 4.0.1 | 2020-06-07 |
| tools | 4.0.1 | 2020-06-07 |
| DelayedMatrixStats | 1.12.2 | 2021-01-13 |
| grDevices | 4.0.1 | 2020-06-07 |
| zlibbioc | 1.36.0 | 2020-10-29 |
| statmod | 1.4.35 | 2020-10-20 |
| SingleCellExperiment | 1.12.0 | 2020-10-28 |
| evaluate | 0.14 | 2020-06-15 |
| lattice | 0.20-41 | 2020-06-07 |
| pkgconfig | 2.0.3 | 2020-07-15 |
| Matrix | 1.2-18 | 2020-06-07 |
| igraph | 1.2.6 | 2020-10-07 |
| DelayedArray | 0.16.0 | 2020-10-28 |
| parallel | 4.0.1 | 2020-06-07 |
| xfun | 0.39 | 2023-07-17 |
| GenomeInfoDbData | 1.2.4 | 2020-11-03 |
| stringr | 1.4.0 | 2020-07-15 |
| knitr | 1.30 | 2020-09-23 |
| S4Vectors | 0.28.1 | 2020-12-10 |
| graphics | 4.0.1 | 2020-06-07 |
| datasets | 4.0.1 | 2020-06-07 |
| stats | 4.0.1 | 2020-06-07 |
| IRanges | 2.24.1 | 2020-12-13 |
| stats4 | 4.0.1 | 2020-06-07 |
| locfit | 1.5-9.4 | 2020-07-15 |
| grid | 4.0.1 | 2020-06-07 |
| scuttle | 1.0.4 | 2020-12-18 |
| base | 4.0.1 | 2020-06-07 |
| Biobase | 2.50.0 | 2020-10-28 |
| BiocParallel | 1.24.1 | 2020-11-07 |
| limma | 3.46.0 | 2020-10-28 |
| irlba | 2.3.3 | 2020-07-15 |
| magrittr | 2.0.1 | 2020-11-18 |
| BiocSingular | 1.6.0 | 2020-10-28 |
| edgeR | 3.32.1 | 2021-01-15 |
| matrixStats | 0.57.0 | 2020-09-26 |
| sparseMatrixStats | 1.2.0 | 2020-10-28 |
| BiocGenerics | 0.36.0 | 2020-10-28 |
| GenomicRanges | 1.42.0 | 2020-10-28 |
| beachmat | 2.6.4 | 2020-12-21 |
| SummarizedExperiment | 1.20.0 | 2020-10-28 |
| rsvd | 1.0.3 | 2020-07-15 |
| dqrng | 0.2.1 | 2020-07-15 |
| ResidualMatrix | 1.0.0 | 2020-10-28 |
| stringi | 1.5.3 | 2020-09-10 |
| RCurl | 1.98-1.2 | 2020-07-15 |
| scran | 1.18.3 | 2020-12-22 |
System Information
| systemInfo | |
|---|---|
| version | R version 4.0.1 (2020-06-06) |
| platform | x86_64-apple-darwin17.0 (64-bit) |
| locale | en_CA.UTF-8 |
| OS | macOS 10.16 |
| UI | X11 |
Scikick Configuration
cat scikick.yml### Scikick Project Workflow Configuration File
# Directory where Scikick will store all standard notebook outputs
reportdir: report
# --- Content below here is best modified by using the Scikick CLI ---
# Notebook Execution Configuration (format summarized below)
# analysis:
# first_notebook.Rmd:
# second_notebook.Rmd:
# - first_notebook.Rmd # must execute before second_notebook.Rmd
# - functions.R # file is used by second_notebook.Rmd
#
# Each analysis item is executed to generate md and html files, E.g.:
# 1. <reportdir>/out_md/first_notebook.md
# 2. <reportdir>/out_html/first_notebook.html
analysis: !!omap
- index.Rmd:
- notebooks/nestorowa/import.Rmd:
- notebooks/nestorowa/quality_control.Rmd:
- notebooks/nestorowa/import.Rmd
- notebooks/nestorowa/normalization.Rmd:
- notebooks/nestorowa/quality_control.Rmd
- notebooks/nestorowa/further_exploration.Rmd:
- notebooks/nestorowa/normalization.Rmd
- notebooks/grun/import.Rmd:
- notebooks/grun/quality_control.Rmd:
- notebooks/grun/import.Rmd
- notebooks/grun/normalization.Rmd:
- notebooks/grun/quality_control.Rmd
- notebooks/grun/further_exploration.Rmd:
- notebooks/grun/normalization.Rmd
- notebooks/paul/import.Rmd:
- notebooks/paul/quality_control.Rmd:
- notebooks/paul/import.Rmd
- notebooks/paul/normalization.Rmd:
- notebooks/paul/quality_control.Rmd
- notebooks/paul/further_exploration.Rmd:
- notebooks/paul/normalization.Rmd
- notebooks/merged/merge.Rmd:
- notebooks/grun/quality_control.Rmd
- notebooks/paul/quality_control.Rmd
- notebooks/nestorowa/normalization.Rmd
- notebooks/merged/combined_analysis.Rmd:
- notebooks/merged/merge.Rmd
version_info:
snakemake: 6.0.2
ruamel.yaml: 0.16.12
scikick: 0.2.1
# Optional site theme customization
output:
BiocStyle::html_document:
code_folding: hide
theme: readable
toc_float: true
toc: true
number_sections: false
toc_depth: 5
self_contained: trueFunctions