Compute the term-frequency inverse-document-frequency

Run term frequency inverse document frequency (TF-IDF) normalization

RunTFIDF(object, ...)

# S3 method for default
RunTFIDF(
  object,
  assay = NULL,
  method = 1,
  scale.factor = 10000,
  verbose = TRUE,
  ...
)

# S3 method for Assay
RunTFIDF(
  object,
  assay = NULL,
  method = 1,
  scale.factor = 10000,
  verbose = TRUE,
  ...
)

# S3 method for Seurat
RunTFIDF(
  object,
  assay = NULL,
  method = 1,
  scale.factor = 10000,
  verbose = TRUE,
  ...
)

Arguments

object	A Seurat object
...	Arguments passed to other methods
assay	Name of assay to use
method	Which TF-IDF implementation to use. Choice of: 1: The LSI implementation used by Stuart & Butler et al. 2019 (https://doi.org/10.1101/460147). 2: The standard LSI implementation used by Cusanovich & Hill et al. 2018 (https://doi.org/10.1016/j.cell.2018.06.052). 3: The log-TF method 4: The 10x Genomics method (no TF normalization)
scale.factor	Which scale factor to use. Default is 10000.
verbose	Print progress

Value

Returns a Seurat object

Examples

mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
RunTFIDF(object = mat)
#> Performing TF-IDF normalization
#> 5 x 5 sparse Matrix of class "dgCMatrix"
#>                                                  
#> [1,] 8.335112 8.586373 .        .        .       
#> [2,] .        7.082948 .        8.335112 7.929766
#> [3,] 8.112028 7.265130 7.824446 .        7.706713
#> [4,] .        7.775676 .        .        8.217359
#> [5,] 7.524481 6.677903 8.335112 7.929766 7.119286
RunTFIDF(atac_small[['peaks']])
#> Performing TF-IDF normalization
#> Assay data with 100 features for 100 cells
#> Top 10 variable features:
#>  chr1:1549446-1552535, chr1:1051006-1053102, chr1:1240091-1245762,
#> chr1:1333514-1336003, chr1:1309645-1311492, chr1:928630-937949,
#> chr1:1166366-1168282, chr1:1446312-1448163, chr1:1562519-1567986,
#> chr1:1259506-1261414 
RunTFIDF(object = atac_small)
#> Performing TF-IDF normalization
#> An object of class Seurat 
#> 300 features across 100 samples within 3 assays 
#> Active assay: peaks (100 features, 90 variable features)
#>  2 other assays present: bins, RNA
#>  2 dimensional reductions calculated: lsi, umap