search

IanevskiAleksandr / sc-type

GNU General Public License v3.0
238 stars 45 forks source link

Not able to assess mouse database #4

Open vravik opened 3 years ago

vravik commented 3 years ago

Dear Aleksandr,

Thank you for this developing this very useful tool. I am trying to annotate a mouse intestinal single cell dataset, but I'm not able to access the ScType database to generate the marker genesets. Could you please tell me how to proceed?

The example database file you have provided has only few genesets for Human tissues.

jenchien commented 2 years ago

Hi, thanks a lot for the super nice resources! I'm also interested in the mouse DB. It would be helpful if you could kindly point us to it.

RENXI-NUS commented 2 years ago

Just chiming in to say that I am interested in annotating my mouse data using scType, too.

VitorAguiar commented 2 years ago

Hello, I'm also interested in the mouse database.

Or maybe you use the same database for humans and mice, and convert the gene names?

Thank you!

hongjianjin commented 2 years ago

I would download databases from CellMaker and make mouse version by myself http://bio-bigdata.hrbmu.edu.cn/CellMarker/CellMarker_download.html

yangfeizZZ commented 1 year ago

I would download databases from CellMaker and make mouse version by myself http://bio-bigdata.hrbmu.edu.cn/CellMarker/CellMarker_download.html

How to make mouse version database

jsangalang commented 1 year ago

For those interested, here is a mouse immune cell Excel that I compiled from the CellMarker 2.0 mouse genes.

I categorized the cells to the progenitor lineage, into just 11 cell types: Macrophages, monocytes, eosinophils, neutrophils, basophils, DC, pDC, NK, T-cell, B-cell, and platelets. This is not tissue-type specific, so I compiled all the marker genes based on the cell-type.

I also attach the Excel file of the filtered CellMarker 2.0 of the cell-type of interest.

sc_type_mouse_alltumours.xlsx immune_cell_no_progenitors.xlsx

I also edited the functions a bit for the mouse genes:

# Modified gene_sets_prepare
gene_sets_prepare <- function(path_to_db_file, cell_type){
  cell_markers = openxlsx::read.xlsx(path_to_db_file)
  cell_markers = cell_markers[cell_markers$tissueType == cell_type,] 
  cell_markers$geneSymbolmore1 = gsub(" ",
                                      "",
                                      cell_markers$geneSymbolmore1)
  cell_markers$geneSymbolmore2 = gsub(" ",
                                      "",
                                      cell_markers$geneSymbolmore2)

  # correct gene symbols from the given DB (up-genes)
  cell_markers$geneSymbolmore1 = sapply(1:nrow(cell_markers), function(i){
    markers_all = gsub(" ", 
                       "", 
                       unlist(strsplit(cell_markers$geneSymbolmore1[i],",")))
    # markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) # Only for human
    markers_all = sort(markers_all)

    if(length(markers_all) > 0){
      # suppressMessages({markers_all = unique(na.omit(checkGeneSymbols(markers_all, species = "mouse")$Suggested.Symbol))}) # This is used if the symbols are not in the format of HGNC
      paste0(markers_all, collapse = ",")
    } else {
      ""
    }
  })

  # correct gene symbols from the given DB (down-genes)
  cell_markers$geneSymbolmore2 = sapply(1:nrow(cell_markers), function(i){
    markers_all = gsub(" ", "", unlist(strsplit(cell_markers$geneSymbolmore2[i],",")))
    # markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) # Only for human
    markers_all = sort(markers_all)

    if (length(markers_all) > 0){
      # suppressMessages({markers_all = unique(na.omit(checkGeneSymbols(markers_all)$Suggested.Symbol))}) # This is used if the symbols are not in the format of HGNC
      paste0(markers_all, collapse = ",")
    } else {
      ""
    }
  })

  cell_markers$geneSymbolmore1 = gsub("///",",",cell_markers$geneSymbolmore1)
  cell_markers$geneSymbolmore1 = gsub(" ","",cell_markers$geneSymbolmore1)
  cell_markers$geneSymbolmore2 = gsub("///",",",cell_markers$geneSymbolmore2)
  cell_markers$geneSymbolmore2 = gsub(" ","",cell_markers$geneSymbolmore2)

  gs = lapply(1:nrow(cell_markers), function(j) {
    gsub(" ",
         "",
         unlist(strsplit(toString(cell_markers$geneSymbolmore1[j]),","))
         )
    })
  names(gs) = cell_markers$cellName

  gs2 = lapply(1:nrow(cell_markers), function(j) {
    gsub(" ",
         "",
         unlist(strsplit(toString(cell_markers$geneSymbolmore2[j]),","))
         )
    })
  names(gs2) = cell_markers$cellName

  list(gs_positive = gs, gs_negative = gs2)
}
# Modified sctype_score
sctype_score <- function(scRNAseqData, scaled = !0, gs, gs2 = NULL, gene_names_to_uppercase = !0, ...){

  # For testing
  # gs = gs_list$gs_positive

  # check input matrix
  if(!is.matrix(scRNAseqData)){
    warning("scRNAseqData doesn't seem to be a matrix")
  } else {
    if(sum(dim(scRNAseqData))==0){
      warning("The dimension of input scRNAseqData matrix equals to 0, is it an empty matrix?")
    }
  }

  # marker sensitivity
  marker_stat = sort(table(unlist(gs)), decreasing = T)
  marker_sensitivity = data.frame(
    score_marker_sensitivity = scales::rescale(as.numeric(marker_stat), 
                                               to = c(0,1), 
                                               from = c(length(gs),1)),
    gene_ = names(marker_stat), 
    stringsAsFactors = !1)

  # convert gene names to Uppercase
  if (gene_names_to_uppercase){
    rownames(scRNAseqData) = toupper(rownames(scRNAseqData));
  }

  # subselect genes only found in data
  names_gs_cp = names(gs)
  names_gs_2_cp = names(gs2)
  gs = lapply(1:length(gs), function(d_){ 
    GeneIndToKeep = rownames(scRNAseqData) %in% as.character(gs[[d_]])
    rownames(scRNAseqData)[GeneIndToKeep]
    })
  gs2 = lapply(1:length(gs2), function(d_){ 
    GeneIndToKeep = rownames(scRNAseqData) %in% as.character(gs2[[d_]])
    rownames(scRNAseqData)[GeneIndToKeep]
    })
  names(gs) = names_gs_cp
  names(gs2) = names_gs_2_cp
  cell_markers_genes_score = marker_sensitivity[marker_sensitivity$gene_ %in% unique(unlist(gs)),]

  # z-scale if not
  if (!scaled) {
    Z <- t(scale(t(scRNAseqData)))
  } else {
      Z <- scRNAseqData
  }

  # multiple by marker sensitivity
  for (jj in 1:nrow(cell_markers_genes_score)){
    Z[cell_markers_genes_score[jj,"gene_"], ] = Z[cell_markers_genes_score[jj,"gene_"], ] * cell_markers_genes_score[jj, "score_marker_sensitivity"]
  }

  # subselect only with marker genes
  Z = Z[unique(c(unlist(gs),unlist(gs2))), ]

  # combine scores
  es = do.call("rbind", lapply(names(gs), function(gss_){ 
    sapply(1:ncol(Z), function(j) {
      gs_z = Z[gs[[gss_]], j]
      gz_2 = Z[gs2[[gss_]], j] * -1
      sum_t1 = (sum(gs_z) / sqrt(length(gs_z)))
      sum_t2 = sum(gz_2) / sqrt(length(gz_2))

      if (is.na(sum_t2)){
        sum_t2 = 0;
      }
      sum_t1 + sum_t2
    })
  })) 

  dimnames(es) = list(names(gs), colnames(Z))
  es.max <- es[!apply(is.na(es) | es == "", 1, all),] # remove NA rows

  es.max
}
shaniAmare commented 1 year ago

For those interested, here is a mouse immune cell Excel that I compiled from the CellMarker 2.0 mouse genes.

I categorized the cells to the progenitor lineage, into just 11 cell types: Macrophages, monocytes, eosinophils, neutrophils, basophils, DC, pDC, NK, T-cell, B-cell, and platelets. This is not tissue-type specific, so I compiled all the marker genes based on the cell-type.

I also attach the Excel file of the filtered CellMarker 2.0 of the cell-type of interest.

sc_type_mouse_alltumours.xlsx immune_cell_no_progenitors.xlsx

I also edited the functions a bit for the mouse genes:

# Modified gene_sets_prepare
gene_sets_prepare <- function(path_to_db_file, cell_type){
  cell_markers = openxlsx::read.xlsx(path_to_db_file)
  cell_markers = cell_markers[cell_markers$tissueType == cell_type,] 
  cell_markers$geneSymbolmore1 = gsub(" ",
                                      "",
                                      cell_markers$geneSymbolmore1)
  cell_markers$geneSymbolmore2 = gsub(" ",
                                      "",
                                      cell_markers$geneSymbolmore2)

  # correct gene symbols from the given DB (up-genes)
  cell_markers$geneSymbolmore1 = sapply(1:nrow(cell_markers), function(i){
    markers_all = gsub(" ", 
                       "", 
                       unlist(strsplit(cell_markers$geneSymbolmore1[i],",")))
    # markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) # Only for human
    markers_all = sort(markers_all)

    if(length(markers_all) > 0){
      # suppressMessages({markers_all = unique(na.omit(checkGeneSymbols(markers_all, species = "mouse")$Suggested.Symbol))}) # This is used if the symbols are not in the format of HGNC
      paste0(markers_all, collapse = ",")
    } else {
      ""
    }
  })

  # correct gene symbols from the given DB (down-genes)
  cell_markers$geneSymbolmore2 = sapply(1:nrow(cell_markers), function(i){
    markers_all = gsub(" ", "", unlist(strsplit(cell_markers$geneSymbolmore2[i],",")))
    # markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) # Only for human
    markers_all = sort(markers_all)

    if (length(markers_all) > 0){
      # suppressMessages({markers_all = unique(na.omit(checkGeneSymbols(markers_all)$Suggested.Symbol))}) # This is used if the symbols are not in the format of HGNC
      paste0(markers_all, collapse = ",")
    } else {
      ""
    }
  })

  cell_markers$geneSymbolmore1 = gsub("///",",",cell_markers$geneSymbolmore1)
  cell_markers$geneSymbolmore1 = gsub(" ","",cell_markers$geneSymbolmore1)
  cell_markers$geneSymbolmore2 = gsub("///",",",cell_markers$geneSymbolmore2)
  cell_markers$geneSymbolmore2 = gsub(" ","",cell_markers$geneSymbolmore2)

  gs = lapply(1:nrow(cell_markers), function(j) {
    gsub(" ",
         "",
         unlist(strsplit(toString(cell_markers$geneSymbolmore1[j]),","))
         )
    })
  names(gs) = cell_markers$cellName

  gs2 = lapply(1:nrow(cell_markers), function(j) {
    gsub(" ",
         "",
         unlist(strsplit(toString(cell_markers$geneSymbolmore2[j]),","))
         )
    })
  names(gs2) = cell_markers$cellName

  list(gs_positive = gs, gs_negative = gs2)
}
# Modified sctype_score
sctype_score <- function(scRNAseqData, scaled = !0, gs, gs2 = NULL, gene_names_to_uppercase = !0, ...){

  # For testing
  # gs = gs_list$gs_positive

  # check input matrix
  if(!is.matrix(scRNAseqData)){
    warning("scRNAseqData doesn't seem to be a matrix")
  } else {
    if(sum(dim(scRNAseqData))==0){
      warning("The dimension of input scRNAseqData matrix equals to 0, is it an empty matrix?")
    }
  }

  # marker sensitivity
  marker_stat = sort(table(unlist(gs)), decreasing = T)
  marker_sensitivity = data.frame(
    score_marker_sensitivity = scales::rescale(as.numeric(marker_stat), 
                                               to = c(0,1), 
                                               from = c(length(gs),1)),
    gene_ = names(marker_stat), 
    stringsAsFactors = !1)

  # convert gene names to Uppercase
  if (gene_names_to_uppercase){
    rownames(scRNAseqData) = toupper(rownames(scRNAseqData));
  }

  # subselect genes only found in data
  names_gs_cp = names(gs)
  names_gs_2_cp = names(gs2)
  gs = lapply(1:length(gs), function(d_){ 
    GeneIndToKeep = rownames(scRNAseqData) %in% as.character(gs[[d_]])
    rownames(scRNAseqData)[GeneIndToKeep]
    })
  gs2 = lapply(1:length(gs2), function(d_){ 
    GeneIndToKeep = rownames(scRNAseqData) %in% as.character(gs2[[d_]])
    rownames(scRNAseqData)[GeneIndToKeep]
    })
  names(gs) = names_gs_cp
  names(gs2) = names_gs_2_cp
  cell_markers_genes_score = marker_sensitivity[marker_sensitivity$gene_ %in% unique(unlist(gs)),]

  # z-scale if not
  if (!scaled) {
    Z <- t(scale(t(scRNAseqData)))
  } else {
      Z <- scRNAseqData
  }

  # multiple by marker sensitivity
  for (jj in 1:nrow(cell_markers_genes_score)){
    Z[cell_markers_genes_score[jj,"gene_"], ] = Z[cell_markers_genes_score[jj,"gene_"], ] * cell_markers_genes_score[jj, "score_marker_sensitivity"]
  }

  # subselect only with marker genes
  Z = Z[unique(c(unlist(gs),unlist(gs2))), ]

  # combine scores
  es = do.call("rbind", lapply(names(gs), function(gss_){ 
    sapply(1:ncol(Z), function(j) {
      gs_z = Z[gs[[gss_]], j]
      gz_2 = Z[gs2[[gss_]], j] * -1
      sum_t1 = (sum(gs_z) / sqrt(length(gs_z)))
      sum_t2 = sum(gz_2) / sqrt(length(gz_2))

      if (is.na(sum_t2)){
        sum_t2 = 0;
      }
      sum_t1 + sum_t2
    })
  })) 

  dimnames(es) = list(names(gs), colnames(Z))
  es.max <- es[!apply(is.na(es) | es == "", 1, all),] # remove NA rows

  es.max
}

I think this would work.Can you please submit this as a pull request so the authors will accept it (hopefully) for the general public to access this code?

Thanks, Shani.