Pseudo-bulk profiles and benchmarking

library(multideconv)
metacell_obj = multideconv::metacells_data
metacell_metadata = multideconv::metacells_metadata

Pseudo-bulk profiles

To create pseudo-bulk profiles from the original single-cell objects, simulating a bulk RNA-seq dataset, you can use the following function:

NOTE: You can input either your original single-cell object or the metacell object. Just be sure to select the same object when examining the real cell proportions (if needed).

metacells_seurat = Seurat::CreateSeuratObject(metacell_obj, meta.data = metacell_metadata)
#> Warning: Data is of class matrix. Coercing to dgCMatrix.
pseudobulk = create_sc_pseudobulk(metacells_seurat, cells_labels = "annotated_ct", sample_labels = "sample", normalized = TRUE, file_name = "Tutorial")
#> Warning: Layer 'data' is empty
#> Warning: Layer 'scale.data' is empty
#> Aggregating assay 'counts' using 'rowMeans2'.
#> Converting input to matrix.

Creating cell type signatures

To create cell type signatures, multideconv uses four methods: CIBERSORTx, DWLS, MOMF, and BSeq-SC. You must provide single-cell data as input. Signatures are saved in the Results/custom_signatures directory, and returned as a list. From now and after compute.deconvolution() will use these signatures additionally to the default ones! So if you would like to have the deconvolution results based on your new files, make sure to run compute.deconvolution()

To run BSeq-SC, supply the cell_markers argument, which should contain the differential markers for each cell type (these can be obtained using FindMarkers() or FindAllMarkers() from Seurat).

bulk_pseudo = multideconv::pseudobulk
signatures = create_sc_signatures(metacell_obj, 
                                  metacell_metadata, 
                                  cells_labels = "annotated_ct", 
                                  sample_labels = "sample", 
                                  bulk_rna = bulk_pseudo, 
                                  cell_markers = NULL, 
                                  name_signature = "Test",
                                  methods_sig = c("DWLS", "CIBERSORTx", "MOMF", "BSeqsc"))

Cell types signatures benchmark

To validate the generated signatures, we provide a benchmarking function to compare deconvolution outputs against known cell proportions (e.g., from single-cell or imaging data). The cells_extra argument should include any non-standard cell types present in your ground truth. Make sure cell names match those in the deconvolution matrix (e.g., use B.cells instead of B cells if that is the naming convention used - see README for more information).

deconv_pseudo = multideconv::deconvolution
cells_groundtruth = multideconv::cells_groundtruth
benchmark = compute.benchmark(deconv_pseudo, 
                              cells_groundtruth, 
                              cells_extra = c("Mural.cells", "Myeloid.cells"), 
                              corr_type = "spearman",
                              scatter = FALSE, 
                              plot = TRUE, 
                              pval = 0.05, 
                              file_name = "Tutorial", 
                              width = 10, 
                              height = 15)
#> No id variables; using all as measure variables
#> No id variables; using all as measure variables

Figure 1. Example of performance of different methods and signature combinations on the pseudo bulk.