RunTFIDF {Signac}

Compute the term-frequency inverse-document-frequency

Description

Run term frequency inverse document frequency (TF-IDF) normalization on a matrix.

Usage

RunTFIDF(object, ...)

## Default S3 method:
RunTFIDF(
  object,
  assay = NULL,
  method = 1,
  scale.factor = 10000,
  idf = NULL,
  verbose = TRUE,
  ...
)

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

## S3 method for class 'StdAssay'
RunTFIDF(
  object,
  assay = NULL,
  method = 1,
  scale.factor = 10000,
  idf = NULL,
  verbose = TRUE,
  ...
)

## S3 method for class 'Seurat'
RunTFIDF(
  object,
  assay = NULL,
  method = 1,
  scale.factor = 10000,
  idf = NULL,
  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 TF-IDF implementation used by Stuart & Butler et al. 2019 (doi:10.1101/460147). This computes \log(TF \times IDF).

• 2: The TF-IDF implementation used by Cusanovich & Hill et al. 2018 (doi:10.1016/j.cell.2018.06.052). This computes TF \times (\log(IDF)).

• 3: The log-TF method used by Andrew Hill. This computes \log(TF) \times \log(IDF).

• 4: The 10x Genomics method (no TF normalization). This computes IDF.

scale.factor

Which scale factor to use. Default is 10000.

idf

A precomputed IDF vector to use. If NULL, compute based on the input data matrix.

verbose

Print progress

Details

Four different TF-IDF methods are implemented. We recommend using method 1 (the default).

Value

Returns a Seurat object

References

https://en.wikipedia.org/wiki/Latent_semantic_analysis#Latent_semantic_indexing

Examples

mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
RunTFIDF(object = mat)
RunTFIDF(atac_small[['peaks']])
RunTFIDF(atac_small[['peaks']])
RunTFIDF(object = atac_small)