Installation

if (!requireNamespace("BiocManager",quietly=TRUE))    install.packages("BiocManager")BiocManager::install("clustifyr")

BiocManager::install("rnabioco/clustifyr")

Example usage

library(clustifyr)# calculate correlationres<- clustify(input=pbmc_matrix_small,metadata=pbmc_meta$classified,ref_mat=cbmc_ref,query_genes=pbmc_vargenes)# print assignmentscor_to_call(res)#> # A tibble: 9 × 3#> # Groups:   cluster [9]#>   cluster      type           r#>   <chr>        <chr>      <dbl>#> 1 B            B          0.909#> 2 CD14+ Mono   CD14+ Mono 0.915#> 3 FCGR3A+ Mono CD16+ Mono 0.929#> 4 Memory CD4 T CD4 T      0.861#> 5 Naive CD4 T  CD4 T      0.889#> 6 DC           DC         0.849#> 7 Platelet     Mk         0.732#> 8 CD8 T        NK         0.826#> 9 NK           NK         0.894# plot assignments on a projectionplot_best_call(cor_mat=res,metadata=pbmc_meta,cluster_col="classified")

# for SingleCellExperimentsce_small<- sce_pbmc()clustify(input=sce_small,# an SCE objectref_mat=cbmc_ref,# matrix of RNA-seq expression data for each cell typecluster_col="cell_type",# name of column in meta.data containing cell clustersobj_out=TRUE# output SCE object with cell type inserted as "type" column)#> class: SingleCellExperiment#> dim: 2000 2638#> metadata(0):#> assays(2): counts logcounts#> rownames(2000): PPBP LYZ ... CLIC2 HEMGN#> rowData names(0):#> colnames(2638): AAACATACAACCAC AAACATTGAGCTAC ... TTTGCATGAGAGGC#>   TTTGCATGCCTCAC#> colData names(8): cell_source sum ... type r#> reducedDimNames(1): UMAP#> mainExpName: NULL#> altExpNames(0):# for Seuratlibrary(Seurat)s_small<- so_pbmc()clustify(input=s_small,cluster_col="RNA_snn_res.0.5",ref_mat=cbmc_ref,seurat_out=TRUE)#> An object of class Seurat#> 2000 features across 2638 samples within 1 assay#> Active assay: RNA (2000 features, 2000 variable features)#>  2 layers present: counts, data#>  1 dimensional reduction calculated: umap# New output option, directly as a vector (in the order of the metadata), which can then be inserted into metadata dataframes and other workflowsclustify(input=s_small,cluster_col="RNA_snn_res.0.5",ref_mat=cbmc_ref,vec_out=TRUE)[1:10]#>  [1] "CD4 T"      "B"          "CD4 T"      "CD14+ Mono" "NK"#>  [6] "CD4 T"      "NK"         "NK"         "CD4 T"      "CD16+ Mono"

# make reference from SingleCellExperiment objectssce_small<- sce_pbmc()sce_ref<- object_ref(input=sce_small,# SCE objectcluster_col="cell_type"# name of column in colData containing cell identities)# make reference from seurat objectss_small<- so_pbmc()s_ref<- seurat_ref(seurat_object=s_small,cluster_col="RNA_snn_res.0.5")head(s_ref)#>                 0          1          2          3          4         5#> PPBP   0.04883837 0.06494743 0.28763857 0.09375021 0.35662599 0.2442300#> LYZ    1.40165143 1.39466552 5.21550849 1.42699419 1.35146753 3.4034309#> S100A9 0.55679700 0.58080250 4.91453355 0.62123058 0.58823794 2.6277996#> IGLL5  0.03116080 0.04826212 0.02434753 2.44576997 0.03284986 0.2581198#> GNLY   0.46041901 0.41001072 0.53592906 0.37877736 2.53161887 0.2903092#> FTL    3.35611600 3.31062958 5.86217774 3.66698837 3.37056910 5.9518479#>                 6          7         8#> PPBP   0.00000000 0.06527347 6.0941782#> LYZ    1.32701580 4.84714962 2.5303912#> S100A9 0.52098541 2.53310734 1.6775692#> IGLL5  0.05247669 0.10986617 0.2501642#> GNLY   4.70481754 0.46959958 0.3845813#> FTL    3.38471536 4.21848878 4.5508242

clustify_lists(input=pbmc_matrix_small,metadata=pbmc_meta,cluster_col="classified",marker=pbmc_markers,marker_inmatrix=FALSE)#>                      0        1        2         3         4        5        6#> Naive CD4 T  1.5639055 20.19469 31.77095  8.664074 23.844992 19.06931 19.06931#> Memory CD4 T 1.5639055 20.19469 31.77095 10.568007 23.844992 17.97875 19.06931#> CD14+ Mono   0.9575077 14.70716 76.21353 17.899569 11.687739 49.86699 16.83210#> B            0.6564777 12.70976 31.77095 26.422929 13.536295 20.19469 13.53630#> CD8 T        1.0785353 17.97875 31.82210 12.584823 31.822099 22.71234 40.45383#> FCGR3A+ Mono 0.6564777 13.63321 72.43684 17.899569  9.726346 56.48245 14.61025#> NK           0.6564777 14.61025 31.82210  7.757206 31.822099 22.71234 45.05072#> DC           0.6564777 15.80598 63.34978 19.069308 13.758144 40.56298 17.97875#> Platelet     0.5428889 13.34769 59.94938 14.215244 15.158755 46.92861 19.49246#>                      7          8#> Naive CD4 T   6.165348  0.6055118#> Memory CD4 T  6.165348  0.9575077#> CD14+ Mono   25.181595  1.0785353#> B            17.899569  0.1401901#> CD8 T         7.882145  0.3309153#> FCGR3A+ Mono 21.409177  0.3309153#> NK            5.358651  0.3309153#> DC           45.101877  0.1401901#> Platelet     19.492465 59.9493793clustify_lists(input=s_small,marker=pbmc_markers,marker_inmatrix=FALSE,cluster_col="RNA_snn_res.0.5",seurat_out=TRUE)#> An object of class Seurat#> 2000 features across 2638 samples within 1 assay#> Active assay: RNA (2000 features, 2000 variable features)#>  2 layers present: counts, data#>  1 dimensional reduction calculated: umap

Additional resources