single cell Mapper (scMappR)

Transforming summary statistics of differentially expressed genes by cell-type specific information

• scMappR_and_pathway_analysis(): This function generates cell-weighted fold changes (cellWeighted_Foldchanges), visualizes them in a heatmap, and completes pathway enrichment of cellWeighted_Foldchanges and bulk gene list.
• two_method_pathway_enrichment(): This function completes additional pathway enrichment tests from cwFold-changes calculated in scMappR_and_pathway_analysis. Specifically, it re-ranks cwFoldChanges based on their absolute ct specificity scores (per-celltype) as well as their rank increase in cell-type specificity before completing an ordered pathway analysis. In the second method, only genes with a rank increase in cell-type specificity were included.
• cwFoldChange_evaluate(). At the level of the gene, cwFold-changes are scored so that they sum to 1. For each gene, cell-types whose cwFold-change are greater than the cell-type proportion while accounting for an abnormally high proportion of the fold change (+ 3 median absolute deviations from median cell-type specificity) are considered cell-type specific. At the level of the cell-type, bulk DEGs and cwFold-changes for each cell-type are correlated. The difference in the rank of DEG is also measured.

Enriching cell-type markers in a list of genes.

• tissue_scMappR_custom(): This function visualizes signature matrix, clusters subsetted genes, completes enrichment of individual cell-types and co-enrichment.
• tissue_scMappR_internal(): This function loops through every signature matrix in a particular tissue and generates heatmaps, cell-type preferences, and co-enrichment.
• tissue_by_celltype_enrichment(): This function completes a fishers exact test of an input list of genes against one of the two curated tissue by cell-type marker datasets from scMappR.

Processing scRNA-seq count data into a gene list.

• process_dgTMatrix_lists(): This function takes a list of count matrices, processes them, calls cell-types, and generates signature matrices.

Example

data(PBMC_scMappR) # load data example of PBMC bulk- and cell-sorted RNA-seq data

bulk_DE_cors <- PBMC_example$bulk_DE_cors # 59 sex-specific DEGs in bulk PBMC (up-regulated = female-biased)

bulk_normalized <- PBMC_example$bulk_normalized # log CPM normalized bulk RNA-seq data

odds_ratio_in <- PBMC_example$odds_ratio_in # signature matrix developed from cell-sorted RNA-seq

case_grep <- "_female" # flag for 'cases' (up-regulated), index is also acceptable

control_grep <- "_male" # flag for 'control' (down-regulated), index is also acceptable

max_proportion_change <- 10 # maximum cell-type proportion change -- this is good for cell-types that are uncomon in population and small absolute changes may yield large relative changes

theSpecies <- "human" # these RNA-seq data have human gene symbols (and are also from human)

# When running scMappR, it is strongly recommended to use scMappR_and_pathway analysis with the parameters below.
toOut <- scMappR_and_pathway_analysis(bulk_normalized, odds_ratio_in, 
                                      bulk_DE_cors, case_grep = case_grep,
                                      control_grep = control_grep, rda_path = "", 
                                      max_proportion_change = 10, print_plots = TRUE, 
                                      plot_names = "scMappR_vignette_", theSpecies = "human", 
                                      output_directory = "scMappR_vignette_",
                                      sig_matrix_size = 3000, up_and_downregulated = TRUE, 
                                      internet = TRUE, toSave = TRUE, path = tempdir())

Output

Saved outputs

Assuming toSave = T and up_and_downregulated = T, scMappR_and_pathway_analysis() will also generate a folder in your current directory with a considerable amount of data/figures.

Here, we will walk through the output of the above example sorting by name and working downwards.

• BP_barplot: a directory with pdf’s of the top 10 pathways of DEGs that have been re-normlaized by their cwFold-change.
• Downregulated: a directory with pdf’s and Rdata files of pathway and TF enrichment of downregulated DEGs re-normalized by their cwFold-change.
• TF_barplot: a directory with pdf’s of the top 10 TFs of DEGs that have been re-normlaized by their cwFold-change.
• Upregulated: a directory with pdf’s and Rdata files of pathway and TF enrichment of upregulated DEGs re-normalized by their cwFold-change.
• bulk_pathway_enrichment: png and Rdata file of traditional g:ProfilR analysis of GOBP, REAC, and KEGG databases.
• bulk_TF_enrichment: png and Rdata file of traditional g:ProfilR analysis of TF database.
• scMappR_vignette_cell_proportion_changes_summary: t-tests looking at difference in estimated cell-type proporitons between conditions.
• scMappR_vignette_celltype_proportions: Estimated cell-type proportions of all inputted samples.
• scMappR_vignette_cellWeighted_Foldchanges: cwFold-changes of all estimated DEGs.
• scMappR_vignette_cwFC_downregulated_DEGs: heatmap of downregulated cwFold-changes.
• scMappR_vignette_cwFC_signature_matrix_downregulated_DEGs: heatmap of downregulated cwFold-changes overlapping with signature matrix.
• scMappR_vignette_DEG_signature_matrix: Heatmap of signature matrix.
• scMappR_vignette_leaveOneOut_gene_proportions: estimated cell-type proportions of all samples after each DEG is removed.
• scMappR_vignette_reordered_pathways: g:ProfileR enrichment of DEGs after they’re re-ordered by cwFold-changes.
  
• scMappR_vignette_reordered_transcription_factors: g:ProfileR enrichment of DEGs after they’re re-ordered by cwFold-changes.
  
• scMappR_vignette_signature_matrix: heatmap of signature matrix.
• scMappR_vignette_signature_matrix_downregulated_DEGs: heatmap of signature matrix overlapping downregulated DEGs.
• scMappR_vignette_signature_matrix_upregulated_DEGs: heatmap of signature matrix overlapping upregulated DEGs.
• scMappR_vignetteabs_cwFC: heatmap of absolute valued cwFold-changes (up- and down-regulated DEGs).
• scMappR_vignettecwFC_signature_matrix_upregulated_DEGs: heatmap of absolute valued cwFold-changes (up- and down-regulated DEGs) overlapping with the signature matrix.

Additional pathway enrichments of cwFold-changes

In addition to completing pathway enrichment of DEGs re-ranked by their cwFold-change, it may be informative to interrogate which DEGs were the most influenced by their cell-type specificity in the context of each cell-type.

twoOutFiles <- two_method_pathway_enrichment(bulk_DE_cors, "human",
scMappR_vals = toOut$cellWeighted_Foldchange, background_genes = rownames(bulk_normalized), 
output_directory = "newfun_test",plot_names = "nonreranked_", toSave = FALSE)

Example

In this example, we are extending the output from the scMappR_and_pathway_analysis function above.

evaluated <- cwFoldChange_evaluate(toOut$cellWeighted_Foldchange, toOut$cellType_Proportions, bulk_DE_cors)

Output

• cwFoldChange_normalized: A gene by cell-type data.frame of cwFold-changes. Here, the cwFold-change of each gene is divided by it’s sum such that the cwFold-change can be represented as how much that gene is accounting for the fold-change of the DEG.
• gene_level_investigation: A data.frame where each row is a gene. The first column is that gene’s normalized cwFold-change’s (see previous point) euclidean distance from 1. The following columns test if that gene’s normalized cwFold-change fits a normal distribution using a Shapiro test.
• cwFoldchange_gene_assigned: A list of cell-types. Each cell-type contains genes where scMappR would predict that the cell-type is driving the gene’s expression in the researcher’s bulk sample. For each gene, outliers are measured using mean + 3sd or median + 3mad depending on if the cwFold-change of the gene fit the normal distribution. As a second threshold, the cwFold-change of the DEG in the outlier cell-type needed needed to be greater than the cell-type proportion of that cell-type. The normalized cwFold-change for that gene in the assigned cell-type is also stored.
  • cwFoldchange_gene_flagged_FP: In our cell-type proportion scaling step, we multiply the fold-change of each DEG by the reciprical of the cell-type proporitons between case and controls. In the condition when there are many DEGs that do not overlap with the signature matrix and there are one or more cell-types with a very large difference in cell-type proportion. The scaling step can overpredit and assign non-celltype specific DEGs to a cell-type based on the change in cell-type proportion. These DEGs have a very predictable pattern and are stored in the cwFoldchange_gene_flagged_FP list. There is a possibility that these genes are not false positives, but should be inspected further by the researcher if they are to be included in downstream analysis. By default we suggest that these genes are not considered cell-type specific.
• celltype_level_investigation: data frame where the columns are cell-type and the rows are the Pearson and Spearman correlation coefficients (and p-values) between bulk DEGs and cwFold-changes.
• cwFoldchange_vs_bulk_rank_change: A gene by cell-type matrix populated with what rank the gene is differentially expressed given that cell-type.

No custom signature matrix:

data(POA_example) # region to preoptic area

Signature <- POA_example$POA_Rank_signature # signature matrix 

rowname <- get_gene_symbol(Signature) # get signature

rownames(Signature) <- rowname$rowname

genes <- rownames(Signature)[1:60]

rda_path1 = "" # data directory (if it exists)

# Identify tissues available for tissue_scMappR_internal
data(scMappR_tissues)

"Hypothalamus" %in% toupper(scMappR_tissues)

internal <- tissue_scMappR_internal(genes, "mouse", output_directory = "scMappR_Test_Internal", 
tissue = "hypothalamus", rda_path = rda_path1, toSave = TRUE, path = tempdir())

Custom signature matrix

tissue_scMappR_custom() assumes a custom signature matrix. We suggest a gene x cell-type matrix populated with the -log10(Padj)*sign(FC) (i.e. Rank) of the cell-type marker. We identify the cell-type marker with the FindMarker function from the Seurat package. The statistical test within that function is based on the biological question that the user interested in. Pre-computed signature matrices use the Wilcoxon test. If the value filling the matrix is not rank, users will need to change the gene_cutoff parameter to what they deem significant. If is_pvalue == TRUE, tissue_scMappR_custom() will transform the p-values into Ranks.

# Acquiring the gene list
data(POA_example)

Signature <- POA_example$POA_Rank_signature

rowname <- get_gene_symbol(Signature)

rownames(Signature) <- rowname$rowname

genes <- rownames(Signature)[1:200]
 
#running tisue_scMappR_custom
internal <- tissue_scMappR_custom(genes,Signature,output_directory = "scMappR_Test_custom", toSave = F)

Saved Outputs

• _celltype_preferences.tsv: Enrichment of inputted genes on each cell-type.
• _cell_co_preferences.tsv: Identification and enrichment of cell-types being enriched by the same genes (cell-type markers in common).
• _custom_background_heatmap.pdf: Heatmap of all cell-type markers in each cell-type in the entire signature matrix.
• _custom_background_preferences.pdf: All cell-type markers sorted into each cell-type.
• _custom_genelist_heatmap.pdf: Heatmap of cell-type markers intersected with inputted gene list.
• _custom_genelist_preferences.pdf: Cell-type markers intersected with inputted gene list sorted into each cell-type.

Example

Here, we will investigate the tissue x cell-type enrichment of the top 100 genes in the Preoptic area signature matrix.

data(POA_example)
POA_generes <- POA_example$POA_generes
POA_OR_signature <- POA_example$POA_OR_signature
POA_Rank_signature <- POA_example$POA_Rank_signature
Signature <- POA_Rank_signature
rowname <- get_gene_symbol(Signature)
rownames(Signature) <- rowname$rowname
genes <- rownames(Signature)[1:100]

enriched <- tissue_by_celltype_enrichment(gene_list = genes, 
species = "mouse",p_thresh = 0.05, isect_size = 3)

Three types of outputs.

• tissue x CT enrichment: Always returned when using the tissue_by_celltype_enrichment() function. This function returns gene set enrichment compatible with plotBP() for all cell-type markers significantly enriched via the function.
• gmt_file: Since the bank of signature matrices is updated monthly, it is recommended to periodically download a new human_tissue_celltype_scMappR.rda and mouse_tissue_celltype_scMappR.rda from https://github.com/DustinSokolowski/scMappR_Data. When internet is available, setting “rda_path” to “” will download the most updated pathway files directly using the downloader package. Here, if return_gmt == TRUE, then this downloaded gmt will be returned with the enrichment.
• if toSave == TRUE, then the -log10(P_adj) of the top 10 tissue/cell-types that are enriched are plotted.

Interpreting the signature matrix.

• Each row is a cell-type marker.
• Each column is a different cell-type. Assuming that process_dgTMatrix_lists was used and cell-types were predicted within scMappR, Each cell-type is labelled as followed.
  • cluster X. “cell-type predicted by Cellmarker” and_or “cell-type predicted by panglao”.

Processing scRNA-seq data with multiple scRNA-seq runs.

When there are multiple scRNA-seq runs, we use the integration anchors feature to combine runs. Here, each run is a different element of the dgTMatrix_list parameter.

Making multiple run scRNA-seq data. As seen in many cases, the first dataset is identified with “.1” and the second with “.2”

# generating scRNA-seq data with multiple runs.
data(sm)

sm1 <- sm2 <- sm
colnames(sm1) <- paste0(colnames(sm1), ".1")
colnames(sm2) <- paste0(colnames(sm2),".2")
combined_counts <- cbind(sm1,sm2)

Combining datasets with the integration anchors batch correction

toProcess <- list()
for(i in 1:2) {
  toProcess[[paste0("example",i)]] <- combined_counts[,grep(paste0(".",i), colnames(combined_counts))]
}
tst1 <- process_dgTMatrix_lists(toProcess, name = "testProcess", species_name = "mouse",
naming_preference = "eye", rda_path = "", 
toSave = TRUE, saveSCObject = TRUE, path = tempdir())

If you do not want to use the integration anchors feature and process the scRNA-seq data as if it were one run, then keep all of these data in the same dgCMatrix matrix.

tst1 <- process_dgTMatrix_lists(combined_counts, name = "testProcess", species_name = "mouse",
naming_preference = "eye", rda_path = "", 
toSave = TRUE, saveSCObject = TRUE, path = tempdir())

Processing scRNA-seq count data from a different species.

Please continue to use the process_from_count function. The only species specific information required in this function is the mitochondrial gene symbol to regress out mitochondria. In each of your count matrices, make sure that your michondial genes have a “MT-” preface. Then select “human” as the species. This can also be done with an “mt-” preface and selecting mouse.

Processing scRNA-seq count data when cell-types are already named.

It may be common to generate a signature matrix when clusters and cell-types have already been given for every cell. These examples follow how to make this signature matrix from:

1. A Seurat object with named cell-types
2. A Count matrix with named cell-types.

Signature matrix from Seurat object with named cell-type

Generating the Seurat Object for example and making up cell-types. This example will be used from 1-2.

data(sm)

toProcess <- list(sm = sm)

seurat_example <- process_from_count(toProcess, "test_vignette",theSpecies  = "mouse")

levels(seurat_example@active.ident) <- c("Myoblast", "Neutrophil", "cardiomyoblast", "Mesothelial")

1. A Seurat object with named cell-types. Markers for each cell-type are stored in the generes object and each signature matrix is in gene_out.

    generes <- seurat_to_generes(pbmc = seurat_example, test = "wilcox")

    gene_out <- generes_to_heatmap(generes, make_names = FALSE)

2. A count matrix with named cell-types.

#Create the cell-type ids and matrix
Cell_type_id <- seurat_example@active.ident

count_file <- sm

rownames_example <- get_gene_symbol(count_file)

rownames(count_file) <- rownames_example$rowname

# make seurat object
seurat_example <- process_from_count(count_file, "test_vignette",theSpecies  = "mouse")

# Intersect column names (cell-types) with labelled CTs

inters <- intersect(colnames(seurat_example), names(Cell_type_id))

seurat_example_inter <- seurat_example[,inters]

Cell_type_id_inter <- Cell_type_id[inters]

seurat_example_inter@active.ident <- Cell_type_id_inter

# Making signature matrices

    generes <- seurat_to_generes(pbmc = seurat_example_inter, test = "wilcox")

    gene_out <- generes_to_heatmap(generes, make_names = FALSE)

Barplots

# making an example matrix
term_name <- c("one", "two", "three")
log10 <- c(1.5, 4, 2.1)

ordered_back_all <- as.data.frame(cbind(term_name,log10))

#plotting
 g <- ggplot2::ggplot(ordered_back_all, ggplot2::aes(x = stats::reorder(term_name, 
        log10), y = log10)) + ggplot2::geom_bar(stat = "identity", 
        fill = "turquoise") + ggplot2::coord_flip() + ggplot2::labs(y = "-log10(Padj)", 
        x = "Gene Ontology")
    y <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(face = NULL, 
        color = "black", size = 12, angle = 35), axis.text.y = ggplot2::element_text(face = NULL, 
        color = "black", size = 12, angle = 35), axis.title = ggplot2::element_text(size = 16, 
        color = "black"))
    
print(y)

Heatmaps

Here, the heatmaps are for plotting cwFold-changes and cell-type proportions. The same heatmap is used so just an example of one is given.

# Generating a heatmap

# Acquiring the gene list
data(POA_example)

Signature <- POA_example$POA_Rank_signature

rowname <- get_gene_symbol(Signature)

rownames(Signature) <- rowname$rowname

genes <- rownames(Signature)[1:200]
 
#running tisue_scMappR_custom
internal <- tissue_scMappR_custom(genes,Signature,output_directory = "scMappR_Test_custom", toSave = F)

toPlot <- internal$gene_list_heatmap$geneHeat


#Plotting the heatmap

cex = 0.2 # size of genes

myheatcol <- grDevices::colorRampPalette(c("lightblue", "white", "orange"))(256)
    pheatmap::pheatmap(as.matrix(toPlot), color = myheatcol, scale = "row", fontsize_row = cex, fontsize_col = 10)