Specific cell type identification in Single Cell Sequencing

I do not know of such software.

However, I believe this effort is a bit misdirected. The purpose of single-cell sequencing is to get a better understanding of cells; their heterogeneity and functional diversity or developmental / biological processes such as differentiation, using a higher "resolution" method. In other words, if we had the methods you are asking for, there would be no need for the single-cell experiment.

Running your data through some pipeline utilizing previous knowledge has the danger of forcing ideas on the data, rather than seeing what the data tells you: it would be better to try and understand and explain the biologically relevant heterogeneity and diversity in your cells, together with a critical comparison of cell type characteristics (expression of genes, pathways) with knowledge described in literature. This question is also very similar to the one you previously asked and was answered.

Also good to recognize that your approach has a lot of implicit assumptions, such as: cells can be clearly categorized as +/- expression for each gene, or it ignores the systems level (networks and pathways), thus again forcing concepts on the data.

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This said, one method would be to construct a table listing cell type markers as described in the linked answer above, then write a script that determines a cutoff value for expression of genes in your data (see this), then ranks cell types for each cell. For example, you can measure the number or proportion of uniquely genes expressed (although that's very simplistic).

Another option, if you are familiar with machine learning, is to train a classifier on an annotated dataset, and then use that on new data.

Also see a convenience function below, which requires a table of marker genes for each cell cluster (i.e. (the Seurat::FindAllMarkers() output), and a reference df listing genes and corresponding cell types in its HGNC_symbol and Cell_type columns, and returns the table with listing clusters, their marker genes and corresponding cell types.

getCelltypes <- function(markers, reference) {
  marker.celltype <- markers
  marker.celltype$Cell_type <- marker.celltype$gene
  marker.celltype$Cell_type <- with(reference, celltype[match(marker.celltype$Cell_type, gene)])
  return(marker.celltype)
}

Many variants of this reference table and function can be created, feel free to modify it.

In the future, I think we can expect such classifier software that uses reference data from the Human Protein Atlas, HCA and similar projects.