Database details

The clustermole meta-database collects gene signatures from public marker databases and cell atlases. The same cell type can have several signatures that can differ by organ, species, and study.

The database includes data from the following sources:

• CellMarker (Hu et al. Nucleic Acids Research 2023)
• CellMatch (Shao et al. iScience 2020)
• Cell Taxonomy (Jiang et al. Nucleic Acids Research 2023)
• DISCO (Li et al. Nucleic Acids Research 2022)
• HPA (Karlsson et al. Science Advances 2021)
• MSigDB (Liberzon et al. Bioinformatics 2011)
• PanglaoDB (Franzen et al. Database 2019)
• SaVanT (Lopez et al. BMC Genomics 2017)
• ScType (Ianevski et al. Nature Communications 2022)
• TISSUES (Palasca et al. Database 2018)
• xCell (Aran et al. Genome Biology 2017)

Many of these are static, but some are updated periodically.

The raw source data is processed to make the structure and content consistent across databases. Database preparation steps include:

• Converting recognized human and mouse gene aliases to canonical symbols.
• Checking that the species labels are supported by the provided genes.
• Removing signatures with fewer than five or more than 1,000 genes.
• Mapping genes to orthologs in the other species.

Database data frame

The summaries below describe the database based on the actual package contents.

library(clustermole)
library(dplyr)

Retrieve a data frame of all cell type markers in the database. The species argument selects the appropriate gene symbols and does not restrict signatures to the selected species.

markers <- clustermole_markers(species = "hs")
markers
#> # A tibble: 521,262 × 8
#>    celltype_full         db    species organ celltype gene_origi…¹ gene  n_genes
#>    <chr>                 <chr> <chr>   <chr> <chr>    <chr>        <chr>   <int>
#>  1 (Pro-) Subiculum | H… ScTy… ""      Hipp… (Pro-) … ADAMTS2      ADAM…      10
#>  2 (Pro-) Subiculum | H… ScTy… ""      Hipp… (Pro-) … FN1          FN1        10
#>  3 (Pro-) Subiculum | H… ScTy… ""      Hipp… (Pro-) … KLHL1        KLHL1      10
#>  4 (Pro-) Subiculum | H… ScTy… ""      Hipp… (Pro-) … LIPM         LIPM       10
#>  5 (Pro-) Subiculum | H… ScTy… ""      Hipp… (Pro-) … NPSR1        NPSR1      10
#>  6 (Pro-) Subiculum | H… ScTy… ""      Hipp… (Pro-) … NTS          NTS        10
#>  7 (Pro-) Subiculum | H… ScTy… ""      Hipp… (Pro-) … RAB38        RAB38      10
#>  8 (Pro-) Subiculum | H… ScTy… ""      Hipp… (Pro-) … RXFP1        RXFP1      10
#>  9 (Pro-) Subiculum | H… ScTy… ""      Hipp… (Pro-) … STAC         STAC       10
#> 10 (Pro-) Subiculum | H… ScTy… ""      Hipp… (Pro-) … TLE4         TLE4       10
#> # ℹ 521,252 more rows
#> # ℹ abbreviated name: ¹​gene_original

The output data frame is in an R-friendly tidy/long format with one gene-to-signature mapping per row.

Total cell types

Check the total number of available cell type signatures. Each celltype_full value uniquely identifies a signature (cell type, organ/tissue, species, and source).

length(unique(markers$celltype_full))
#> [1] 13130

Cell types per source

Check the number of cell type signatures from each source database.

distinct(markers, db, celltype_full) |> count(db)
#> # A tibble: 11 × 2
#>    db               n
#>    <chr>        <int>
#>  1 CellMarker    7922
#>  2 CellMatch      818
#>  3 CellTaxonomy   647
#>  4 DISCO          388
#>  5 HPA             70
#>  6 MSigDB        1082
#>  7 PanglaoDB      336
#>  8 SaVanT         619
#>  9 ScType         247
#> 10 TISSUES        512
#> 11 xCell          489

Cell types per species

Check the number of cell type signatures per original source species. Not all sources provide information about the species, so it can be unknown or blank.

distinct(markers, species, celltype_full) |> count(species)
#> # A tibble: 3 × 2
#>   species     n
#>   <chr>   <int>
#> 1 ""       1216
#> 2 "HS"     8946
#> 3 "MM"     2968

Cell types per organ

Check the number of available cell type signatures per organ or tissue. This label is not standardized and is not always available.

distinct(markers, celltype_full, organ) |> count(organ, sort = TRUE)
#> # A tibble: 383 × 2
#>    organ                  n
#>    <chr>              <int>
#>  1 ""                  3686
#>  2 "Brain"              798
#>  3 "Lung"               659
#>  4 "Liver"              534
#>  5 "Peripheral blood"   521
#>  6 "Skin"               497
#>  7 "Kidney"             428
#>  8 "Bone marrow"        427
#>  9 "Pancreas"           315
#> 10 "Breast"             292
#> # ℹ 373 more rows

Package version

Check the package version since the database contents can change.

packageVersion("clustermole")
#> [1] '1.2.0'