kl_divergence {clustifyr} | R Documentation

## KL divergence

### Description

Use package entropy to compute Kullback-Leibler divergence. The function first converts each vector's reads to pseudo-number of transcripts by normalizing the total reads to total_reads. The normalized read for each gene is then rounded to serve as the pseudo-number of transcripts. Function entropy::KL.shrink is called to compute the KL-divergence between the two vectors, and the maximal allowed divergence is set to max_KL. Finally, a linear transform is performed to convert the KL divergence, which is between 0 and max_KL, to a similarity score between -1 and 1.

### Usage

    kl_divergence(vec1, vec2, if_log = FALSE, total_reads = 1000, max_KL = 1)

### Arguments

`vec1` | Test vector
`vec2` | Reference vector
`if_log` | Whether the vectors are log-transformed. If so, the raw count should be computed before computing KL-divergence.
`total_reads` | Pseudo-library size
`max_KL` | Maximal allowed value of KL-divergence.

### Value

numeric value, with additional attributes, of kl divergence between the vectors

[Package clustifyr version 1.6.0 Index]