R: Infer a subject-specific genotype using a Bayesian approach

inferGenotypeBayesian {tigger} | R Documentation

## Infer a subject-specific genotype using a Bayesian approach

### Description

`inferGenotypeBayesian` infers an subject's genotype by applying a Bayesian framework with a Dirichlet prior for the multinomial distribution. Up to four distinct alleles are allowed in an individual’s genotype. Four likelihood distributions were generated by empirically fitting three high coverage genotypes from three individuals (Laserson and Vigneault et al, 2014). A posterior probability is calculated for the four most common alleles. The certainty of the highest probability model was calculated using a Bayes factor (the most likely model divided by second-most likely model). The larger the Bayes factor (K), the greater the certainty in the model.

### Usage

    inferGenotypeBayesian(
      data,
      germline_db = NA,
      novel = NA,
      v_call = "v_call",
      seq = "sequence_alignment",
      find_unmutated = TRUE,
      priors = c(0.6, 0.4, 0.4, 0.35, 0.25, 0.25, 0.25, 0.25, 0.25)
    )

### Arguments

`data` | a `data.frame` containing V allele calls from a single subject. If `find_unmutated` is `TRUE`, then the sample IMGT-gapped V(D)J sequence should be provided in column `sequence_alignment`
`germline_db` | named vector of sequences containing the germline sequences named in `allele_calls`. Only required if `find_unmutated` is `TRUE`.
`novel` | an optional `data.frame` of the type novel returned by findNovelAlleles containing germline sequences that will be utilized if `find_unmutated` is `TRUE`. See Details.
`v_call` | column in `data` with V allele calls. Default is `"v_call"`.
`seq` | name of the column in `data` with the aligned, IMGT-numbered, V(D)J nucleotide sequence. Default is `"sequence_alignment"`.
`find_unmutated` | if `TRUE`, use `germline_db` to find which samples are unmutated. Not needed if `allele_calls` only represent unmutated samples.
`priors` | a numeric vector of priors for the multinomial distribution. The `priors` vector must be nine values that defined the priors for the heterozygous (two allele), trizygous (three allele), and quadrozygous (four allele) distributions. The first two values of `priors` define the prior for the heterozygous case, the next three values are for the trizygous case, and the final four values are for the quadrozygous case. Each set of priors should sum to one. Note, each distribution prior is actually defined internally by set of four numbers, with the unspecified final values assigned to `0`; e.g., the heterozygous case is `c(priors[1], priors[2], 0, 0)`. The prior for the homozygous distribution is fixed at `c(1, 0, 0, 0)`.

### Details

Allele calls representing cases where multiple alleles have been assigned to a single sample sequence are rare among unmutated sequences but may result if nucleotides for certain positions are not available. Calls containing multiple alleles are treated as belonging to all groups. If `novel` is provided, all sequences that are assigned to the same starting allele as any novel germline allele will have the novel germline allele appended to their assignent prior to searching for unmutated sequences.

### Value

A `data.frame` of alleles denoting the genotype of the subject with the log10 of the likelihood of each model and the log10 of the Bayes factor. The output contains the following columns:

- `gene`: The gene name without allele.

- `alleles`: Comma separated list of alleles for the given `gene`.

- `counts`: Comma separated list of observed sequences for each corresponding allele in the `alleles` list.

- `total`: The total count of observed sequences for the given `gene`.

- `note`: Any comments on the inferrence.

- `kh`: log10 likelihood that the `gene` is homozygous.

- `kd`: log10 likelihood that the `gene` is heterozygous.

- `kt`: log10 likelihood that the `gene` is trizygous

- `kq`: log10 likelihood that the `gene` is quadrozygous.

- `k_diff`: log10 ratio of the highest to second-highest zygosity likelihoods.

### Note

This method works best with data derived from blood, where a large portion of sequences are expected to be unmutated. Ideally, there should be hundreds of allele calls per gene in the input.

### References

1. Laserson U and Vigneault F, et al. High-resolution antibody dynamics of vaccine-induced immune responses. PNAS. 2014 111(13):4928-33.

### See Also

plotGenotype for a colorful visualization and genotypeFasta to convert the genotype to nucleotide sequences. See inferGenotype to infer a subject-specific genotype using a frequency method

### Examples

    # Infer IGHV genotype, using only unmutated sequences, including novel alleles
    inferGenotypeBayesian(AIRRDb, germline_db=SampleGermlineIGHV, novel=SampleNovel,
                          find_unmutated=TRUE, v_call="v_call", seq="sequence_alignment")

[Package tigger version 1.0.0 Index]