prepareMiDAS {midasHLA} | R Documentation

## Construct a MiDAS object

### Description

`prepareMiDAS` transform HLA alleles calls and KIR calls according to selected `experiment`s creating a `MiDAS` object.

### Usage

    prepareMiDAS(
      hla_calls = NULL,
      kir_calls = NULL,
      colData,
      experiment = c("hla_alleles", "hla_aa", "hla_g_groups", "hla_supertypes",
        "hla_NK_ligands", "kir_genes", "kir_haplotypes", "hla_kir_interactions",
        "hla_divergence", "hla_het", "hla_custom", "kir_custom"),
      placeholder = "term",
      lower_frequency_cutoff = NULL,
      upper_frequency_cutoff = NULL,
      indels = TRUE,
      unkchar = FALSE,
      hla_divergence_aa_selection = "binding_groove",
      hla_het_resolution = 8,
      hla_dictionary = NULL,
      kir_dictionary = NULL
    )

### Arguments

`hla_calls` | HLA calls data frame, as returned by `readHlaCalls` function.
`kir_calls` | KIR calls data frame, as returned by `readKirCalls` function.
`colData` | Data frame holding additional variables like phenotypic observations or covariates. It have to contain `'ID'` column holding samples identifiers corresponding to identifiers in `hla_calls` and `kir_calls`. Importantly rows of `hla_calls` and `kir_calls` without corresponding phenotype are discarded.
`experiment` | Character vector indicating analysis type for which data should be prepared. Valid choices are `"hla_alleles"`, `"hla_aa"`, `"hla_g_groups"`, `"hla_supertypes"`, `"hla_NK_ligands"`, `"kir_genes"`, `"hla_kir_interactions"`, `"hla_divergence"`, `"hla_het"`. See details for further explanations.
`placeholder` | String giving name for dummy variable inserted to `colData`. This variable can be than used to define base statistical model used by `runMiDAS`.
`lower_frequency_cutoff` | Number giving lower frequency threshold. Numbers greater than 1 are interpreted as the number of feature occurrences, numbers between 0 and 1 as fractions.
`upper_frequency_cutoff` | Number giving upper frequency threshold. Numbers greater than 1 are interpreted as the number of feature occurrences, numbers between 0 and 1 as fractions.
`indels` | Logical indicating whether indels should be considered when checking amino acid variability in `'hla_aa'` experiment.
`unkchar` | Logical indicating whether unknown characters in the alignment should be considered when checking amino acid variability in `'hla_aa'` experiment.
`hla_divergence_aa_selection` | String specifying variable region in peptide binding groove which should be considered for Grantham distance calculation. Valid choices includes: `"binding_groove"`, `"B_pocket"`, `"F_pocket"`. See details for more information.
`hla_het_resolution` | Number specifying HLA alleles resolution used to calculate heterogeneity in `"hla_het"` experiment.
`hla_dictionary` | Data frame giving HLA allele dictionary used in `'hla_custom'` experiment. See `hlaToVariable` for more details.
`kir_dictionary` | Data frame giving KIR genes dictionary used in `'kir_custom'` experiment. See `countsToVariables` for more details.

### Details

`experiment` specifies analysis types for which `hla_calls` and `kir_call` should be prepared.

`'hla_alleles'`

  `hla_calls` are transformed to counts matrix describing number of allele occurrences for each sample. This experiment is used to test associations on HLA alleles level.

`'hla_aa'`

  `hla_calls` are transformed to a matrix of variable amino acid positions. See `hlaToAAVariation` for more details. This experiment is used to test associations on amino acid level.

`"hla_g_groups"`

  `hla_calls` are translated into HLA G groups and transformed to matrix describing number of G group occurrences for each sample. See `hlaToVariable` for more details. This experiment is used to test associations on HLA G groups level.

`"hla_supertypes"`

  `hla_calls` are translated into HLA supertypes and transformed to matrix describing number of G group occurrences for each sample. See `hlaToVariable` for more details. This experiment is used to test associations on HLA supertypes level.

`"hla_NK_ligands"`

  `hla_calls` are translated into NK ligands, which includes HLA Bw4/Bw6 and HLA C1/C2 groups and transformed to matrix describing number of their occurrences for each sample. See `hlaToVariable` for more details.This experiment is used to test associations on HLA NK ligands level.

`"kir_genes"`

  `kir_calls` are transformed to counts matrix describing number of KIR gene occurrences for each sample. This experiment is used to test associations on KIR genes level.

`"hla_kir_interactions"`

  `hla_calls` and `kir_calls` are translated to HLA - KIR interactions as defined in Pende et al., 2019.. See `getHlaKirInteractions` for more details. This experiment is used to test associations on HLA - KIR interactions level.

`"hla_divergence"`

  Grantham distance for class I HLA alleles is calculated based on `hla_calls` using original formula by Grantham R. 1974.. See `hlaCallsGranthamDistance` for more details. This experiment is used to test associations on HLA divergence level measured by Grantham distance.

`"hla_het"`

  `hla_calls` are transformed to heterozygosity status, where `1` designates a heterozygote and `0` homozygote. Heterozygosity status is calculated only for classical HLA genes (A, B, C, DQA1, DQB1, DRA, DRB1, DPA1, DPB1). This experiment is used to test associations on HLA divergence level measured by heterozygosity.

### Value

Object of class `MiDAS`

### Examples

    midas <- prepareMiDAS(hla_calls = MiDAS_tut_HLA,
                          kir_calls = MiDAS_tut_KIR,
                          colData = MiDAS_tut_pheno,
                          experiment = "hla_alleles")

[Package midasHLA version 1.2.0 Index]