Run tf-idf on single-cell data

Source: R/run_tfidf.R

Run tf-idf on single-cell data

run_tfidf(
  object = NULL,
  reduction = "UMAP",
  label_var = "label",
  cluster_var = "seurat_clusters",
  replace_regex = "[.]|[_]|[-]",
  terms_per_cluster = 3,
  force_new = F,
  return_all_results = F,
  verbose = T
)

Arguments

object

Single-cell data object. Can be in SingleCellExperiment or Seurat format.
reduction

Name of the reduction to use (case insensitive).
label_var

Which cell metadata column to input to tf-idf enrichment analysis.
cluster_var

Which cell metadata column to use to identify which cluster each cell is assigned to.
replace_regex

Characters by which to split label_var into terms (i.e. tokens) for tf-idf enrichment analysis.
terms_per_cluster

The number of top significantly enriched terms to include per cluster.
force_new

If tf-idf results are already detected the metadata, set force_new=T to replace them with new results.
return_all_results

Whether to return just the object with updated metadata (return_all_results=F), or all intermediate results (return_all_results=F).
verbose

Whether to print messages.

Examples

 
library(scNLP)
data("pseudo_seurat")
pseudo_seurat_tfidf <- run_tfidf(object = pseudo_seurat,
                                 reduction = "UMAP",
                                 cluster_var = "cluster",
                                 label_var = "celltype") 

#> [1] "+ Extracting data from Seurat object."
#> [1] "+ Using reduction: umap"
#> Joining, by = "word"
#> Joining, by = "cluster"
#> Joining, by = "cluster"
head(pseudo_seurat_tfidf@meta.data)  

#>                         cluster       batch species     dataset celltype label
#> human.DRONC_human.ASC1        5 DRONC_human   human DRONC_human     ASC1  ASC1
#> human.DRONC_human.ASC2        5 DRONC_human   human DRONC_human     ASC2  ASC2
#> human.DRONC_human.END         9 DRONC_mouse   mouse DRONC_mouse      END   END
#> human.DRONC_human.exCA1       0 DRONC_human   human DRONC_human    exCA1 exCA1
#> human.DRONC_human.exCA3       0 DRONC_human   human DRONC_human    exCA3 exCA3
#> human.DRONC_human.exDG        0 DRONC_human   human DRONC_human     exDG  exDG
#>                         nCount_RNA nFeature_RNA RNA_snn_res.0.8 seurat_clusters
#> human.DRONC_human.ASC1    237.9283          671               5               5
#> human.DRONC_human.ASC2    232.7748          638               5               5
#> human.DRONC_human.END     288.9631          616               9               9
#> human.DRONC_human.exCA1   245.1858          669               0               0
#> human.DRONC_human.exCA3   225.1575          675               0               0
#> human.DRONC_human.exDG    237.8823          678               0               0
#>                             UMAP_1      UMAP_2             enriched_words
#> human.DRONC_human.ASC1  -0.4796632  0.17629431      glia; schwann; radial
#> human.DRONC_human.ASC2  -0.6386602 -0.05231967      glia; schwann; radial
#> human.DRONC_human.END   -7.7066403 -1.84134831 vascular; peric; pericytes
#> human.DRONC_human.exCA1  6.2326443  1.51104526          lpn; adpn; neuron
#> human.DRONC_human.exCA3  6.0303471  1.47096417          lpn; adpn; neuron
#> human.DRONC_human.exDG   5.9316036  1.49563257          lpn; adpn; neuron
#>                                                                             tf_idf
#> human.DRONC_human.ASC1     0.198360552120631; 0.181900967132288; 0.111766521696813
#> human.DRONC_human.ASC2     0.198360552120631; 0.181900967132288; 0.111766521696813
#> human.DRONC_human.END                         0.528096815017439; 0.042313284392222
#> human.DRONC_human.exCA1 0.0527542246967963; 0.0523351433907082; 0.0428030761818744
#> human.DRONC_human.exCA3 0.0527542246967963; 0.0523351433907082; 0.0428030761818744
#> human.DRONC_human.exDG  0.0527542246967963; 0.0523351433907082; 0.0428030761818744