Author: Mathew Chamberlain
Date: 2021-11-18

# Benchmarking SignacX and SingleR with synovial flow cytometry data

#### Mathew Chamberlain

#### 2021-11-18

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.

## Load data

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]

## SingleR

    require(SingleR)
    data("hpca")
    Q = SingleR(sc_data = E, ref_data = hpca$data, types = hpca$main_types, fine.tune = F, numCores = 4)
    save(file = "fls/SingleR_Results.rda", Q)
    True_labels = M$type[match(colnames(E), M$cell_name)]
    saveRDS(True_labels, file = "fls/celltypes_amp_synovium_true.rds")

## Seurat

Start with the standard pre-processing steps for a Seurat object.

    library(Seurat)

Create a Seurat object, and then perform SCTransform normalization. Note:

- You can use the legacy functions here (i.e., NormalizeData, ScaleData, etc.), use SCTransform or any other normalization method (including no normalization). We did not notice a significant difference in cell type annotations with different normalization methods.
- We think that it is best practice to use SCTransform, but it is not a necessary step. Signac will work fine without it.

    # load data
    synovium <- CreateSeuratObject(counts = E, project = "FACs")

    # run sctransform
    synovium <- SCTransform(synovium, verbose = F)

Perform dimensionality reduction by PCA and UMAP embedding. Note:

- Signac actually needs these functions since it uses the nearest neighbor graph generated by Seurat.

    # These are now standard steps in the Seurat workflow for visualization and clustering
    synovium <- RunPCA(synovium, verbose = FALSE)
    synovium <- RunUMAP(synovium, dims = 1:30, verbose = FALSE)
    synovium <- FindNeighbors(synovium, dims = 1:30, verbose = FALSE)

## SignacX

    library(SignacX)

Generate Signac labels for the Seurat object. Note:

- Optionally, you can do parallel computing by setting num.cores > 1 in the Signac function.
- Run time is ~10 minutes for <100,000 cells.

    # Run Signac
    labels <- Signac(synovium, num.cores = 4)
    celltypes = GenerateLabels(labels, E = synovium)

## Compare SignacX and SingleR with FACs labels

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:

- These data were flow-sorted for CD3, and therefore lack NK cells. Signac detected no NK cells.
- F = Fibroblasts; M = monocytes; T = T cells; B = B cells
- Note that SingleR inaccurately annotated the majority of fibroblast cells as chondrocytes (Chondr.) and non immune cells, and also significantly identified NK cells (even though the data were flow-sorted for CD3, and thus lacked NK cells).

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

    SingleR_Accuracy = round(sum(xx == True_labels)/sum(logik) * 100, 2)
    SingleR_Accuracy

    ## [1] 55.21

Save results

    saveRDS(synovium, file = "synovium_signac.rds")
    saveRDS(celltypes, file = "synovium_signac_celltypes.rds")

Session Info

    ## R version 4.0.3 (2020-10-10)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 18.04.5 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
    ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    ##  [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_4.0.3    magrittr_2.0.1    formatR_1.7       htmltools_0.5.1.1
    ##  [5] tools_4.0.3       yaml_2.2.1        stringi_1.5.3     rmarkdown_2.6
    ##  [9] highr_0.8         knitr_1.30        stringr_1.4.0     digest_0.6.27
    ## [13] xfun_0.20         rlang_0.4.10      evaluate_0.14