This vignette shows how to use Signac to annotate flow-sorted synovial cells by integrating SignacX with Seurat. We also compared Signac to another popular cell type annotation tool, SingleR. We start with raw counts.
Read the CEL-seq2 data.
ReadCelseq <- function(counts.file, meta.file) {
E = suppressWarnings(readr::read_tsv(counts.file))
gns <- E$gene
E = E[, -1]
E = Matrix::Matrix(as.matrix(E), sparse = TRUE)
rownames(E) <- gns
E
}
counts.file = "./fls/celseq_matrix_ru10_molecules.tsv.gz"
meta.file = "./fls/celseq_meta.immport.723957.tsv"
E = ReadCelseq(counts.file = counts.file, meta.file = meta.file)
M = suppressWarnings(readr::read_tsv(meta.file))
# filter data based on depth and number of genes detected
kmu = Matrix::colSums(E != 0)
kmu2 = Matrix::colSums(E)
E = E[, kmu > 200 & kmu2 > 500]
# filter by mitochondrial percentage
logik = grepl("^MT-", rownames(E))
MitoFrac = Matrix::colSums(E[logik, ])/Matrix::colSums(E) * 100
E = E[, MitoFrac < 20]
Start with the standard pre-processing steps for a Seurat object.
Create a Seurat object, and then perform SCTransform normalization. Note:
# load data
synovium <- CreateSeuratObject(counts = E, project = "FACs")
# run sctransform
synovium <- SCTransform(synovium, verbose = F)
Perform dimensionality reduction by PCA and UMAP embedding. Note:
Generate Signac labels for the Seurat object. Note:
SignacX (rows are FACs labels, columns are SignacX)
B | F | M | NonImmune | T | Unclassified | |
---|---|---|---|---|---|---|
B | 945 | 0 | 2 | 0 | 0 | 19 |
F | 0 | 2218 | 10 | 223 | 0 | 58 |
M | 1 | 28 | 891 | 18 | 0 | 96 |
T | 4 | 0 | 0 | 0 | 1768 | 21 |
SingleR (rows are FACs labels, columns are SingleR)
B | Chondr. | F | M | NK | NonImmune | T | |
---|---|---|---|---|---|---|---|
B | 958 | 1 | 0 | 6 | 1 | 0 | 0 |
F | 2 | 1468 | 36 | 19 | 23 | 960 | 1 |
M | 4 | 39 | 0 | 964 | 6 | 21 | 0 |
T | 9 | 0 | 0 | 2 | 368 | 0 | 1414 |
Note:
Signac accuracy
logik = xy != "Unclassified"
Signac_Accuracy = round(sum(xy[logik] == True_labels[logik])/sum(logik) * 100, 2)
Signac_Accuracy
## [1] 95.32
SingleR accuracy
## [1] 55.21
Save results
saveRDS(synovium, file = "synovium_signac.rds")
saveRDS(celltypes, file = "synovium_signac_celltypes.rds")
Session Info
## R version 3.5.0 (2018-04-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /site/ne/home/i0369218/.local/share/r-miniconda/envs/r-reticulate/lib/libopenblasp-r0.3.10.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_3.5.0 magrittr_1.5 formatR_1.7 htmltools_0.4.0
## [5] tools_3.5.0 yaml_2.2.1 Rcpp_1.0.4.6 stringi_1.4.6
## [9] rmarkdown_2.1 highr_0.8 knitr_1.28 stringr_1.4.0
## [13] digest_0.6.18 xfun_0.12 rlang_0.4.8 evaluate_0.14