detecting relevant genes with destiny 3
Single Cell RNA-Sequencing data and gene relevance
Libraries
We need of course destiny, scran for preprocessing, and some tidyverse niceties.
Data
Let’s use data from the scRNAseq[1] package. If
necessary, install it via
BiocManager::install('scRNAseq').
[1] Risso D, Cole M (2019). scRNAseq: A Collection of Public Single-Cell RNA-Seq Datasets.
# The parts of the help we’re interested in
help('scRNAseq-package', package = 'scRNAseq') %>%
repr::repr_html() %>%
stringr::str_extract_all(stringr::regex('<p>The dataset.*?</p>', dotall = TRUE)) %>%
unlist() %>%
paste(collapse = '') %>%
knitr::raw_html()The dataset fluidigm contains 65 cells from Pollen et al. (2014), each sequenced at high and low coverage.
The dataset th2 contains 96 T helper cells from Mahata et al. (2014).
The dataset allen contains 379 cells from the mouse visual cortex. This is a subset of the data published in Tasic et al. (2016).
379 cells seems sufficient to see something!
Preprocessing
We’ll mostly stick to the scran vignette here. Let’s add basic information to the data and choose what to work with.
As scran expects the raw counts in the
counts assay, we rename the more accurate RSEM counts to
counts. Our data has ERCC spike-ins in an
altExp slot:
rowData(allen)$Symbol <- rownames(allen)
rowData(allen)$EntrezID <- AnnotationDbi::mapIds(org.Mm.eg.db::org.Mm.eg.db, rownames(allen), 'ENTREZID', 'ALIAS')## ## 'select()' returned 1:many mapping between keys and columnsrowData(allen)$Uniprot <- AnnotationDbi::mapIds(org.Mm.eg.db::org.Mm.eg.db, rownames(allen), 'UNIPROT', 'ALIAS', multiVals = 'list')## 'select()' returned 1:many mapping between keys and columnsassayNames(allen)[assayNames(allen) == 'rsem_counts'] <- 'counts'
assayNames(altExp(allen, 'ERCC'))[assayNames(altExp(allen, 'ERCC')) == 'rsem_counts'] <- 'counts'
allen## class: SingleCellExperiment
## dim: 20816 379
## metadata(2): SuppInfo which_qc
## assays(4): tophat_counts cufflinks_fpkm counts rsem_tpm
## rownames(20816): 0610007P14Rik 0610009B22Rik ... Zzef1 Zzz3
## rowData names(3): Symbol EntrezID Uniprot
## colnames(379): SRR2140028 SRR2140022 ... SRR2139341 SRR2139336
## colData names(22): NREADS NALIGNED ... Animal.ID passes_qc_checks_s
## reducedDimNames(0):
## mainExpName: endogenous
## altExpNames(1): ERCCNow we can use it to renormalize the data. We normalize the
counts using the spike-in size factors and logarithmize
them into logcounts.
## class: SingleCellExperiment
## dim: 20816 379
## metadata(2): SuppInfo which_qc
## assays(5): tophat_counts cufflinks_fpkm counts rsem_tpm logcounts
## rownames(20816): 0610007P14Rik 0610009B22Rik ... Zzef1 Zzz3
## rowData names(3): Symbol EntrezID Uniprot
## colnames(379): SRR2140028 SRR2140022 ... SRR2139341 SRR2139336
## colData names(23): NREADS NALIGNED ... passes_qc_checks_s sizeFactor
## reducedDimNames(0):
## mainExpName: endogenous
## altExpNames(1): ERCCWe also use the spike-ins to detect highly variable genes more accurately:
decomp <- modelGeneVarWithSpikes(allen, 'ERCC')
rowData(allen)$hvg_order <- order(decomp$bio, decreasing = TRUE)We create a subset of the data containing only rasonably highly variable genes:
Let’s create a Diffusion map. For rapid results, people often create
a PCA first, which can be stored in your
SingleCellExperiment before creating the Diffusion map or
simply created implicitly using
DiffusionMap(..., n_pcs = <number>).
However, even with many more principal components than necessary to get a nicely resolved Diffusion Map, the close spatial correspondence between diffusion components and genes are lost.
#To go from PCA: reducedDim(allen_hvg, 'pca') <- irlba::prcomp_irlba(t(assay(allen, 'logcounts')), 50)$xThe chosen distance metric has big implications on your results, you should try at least cosine and rankcor.
set.seed(1)
dms <- c('euclidean', 'cosine', 'rankcor') %>% #, 'l2'
set_names() %>%
map(~ DiffusionMap(allen_hvg, distance = ., knn_params = list(method = 'covertree')))## Warning in DiffusionMap(allen_hvg, distance = ., knn_params = list(method =
## "covertree")): You have 5000 genes. Consider passing e.g. n_pcs = 50 to speed
## up computation.
## Warning in DiffusionMap(allen_hvg, distance = ., knn_params = list(method =
## "covertree")): You have 5000 genes. Consider passing e.g. n_pcs = 50 to speed
## up computation.
## Warning in DiffusionMap(allen_hvg, distance = ., knn_params = list(method =
## "covertree")): You have 5000 genes. Consider passing e.g. n_pcs = 50 to speed
## up computation.dms %>%
imap(function(dm, dist) plot(dm, 1:2, col_by = 'driver_1_s') + ggtitle(dist)) %>%
cowplot::plot_grid(plotlist = ., nrow = 1)
gms <- imap(grs, function(gr, dist) plot(gr, iter_smooth = 0) + ggtitle(dist))
cowplot::plot_grid(plotlist = gms, nrow = 1)
As you can see, despite the quite different embedding, the rankcor and Cosine diffusion Maps display a number of the same driving genes.
options(readr.show_col_types = FALSE)
tryCatch({
httr::GET('https://rest.uniprot.org/uniprotkb/search', query = list(
fields = 'accession,gene_names,cc_tissue_specificity',
format = 'tsv',
query = rowData(allen)$Uniprot[gms$cosine$ids[1:6]] %>% unlist() %>% paste(collapse = ' OR ')
)) %>%
httr::content(type = 'text/tab-separated-values', encoding = 'utf-8')
}, error = function(e) e)## # A tibble: 25 × 3
## Entry `Gene Names` `Tissue specificity`
## <chr> <chr> <chr>
## 1 Q8K2G4 Bbs7 Bbs2l1 <NA>
## 2 P58044 Idi1 <NA>
## 3 Q922E6 Fastkd2 TISSUE SPECIFICITY: Ubiquitously expressed (Pub…
## 4 P63054 Pcp4 Pep19 <NA>
## 5 Q8VEH8 Erlec1 <NA>
## 6 G3XA48 Idi1 <NA>
## 7 Q8QZR0 Tram1l1 <NA>
## 8 Q3V165 Bbs7 <NA>
## 9 Q9CWA6 Erlec1 <NA>
## 10 B1ATV5 Bbs7 CT7-314H19.3-001 <NA>
## # ℹ 15 more rows