Package 'midasHLA'

Description

aaVariationToCounts convert amino acid variation data frame into counts table.

Usage

aaVariationToCounts(aa_variation)

Arguments

Value

Amino acid counts data frame. First column holds samples ID's, further columns, corresponding to specific amino acid positions, give information on the number of their occurrences in each sample.

Description

Given a set of p-values, returns p-values adjusted using one of several methods.

Usage

adjustPValues(p, method, n = length(p))

Arguments

Details

This function modifies stats::p.adjust method such that for Bonferroni correction it is possible to specify n lower than length(p). This feature is useful in cases when knowledge about the biology or redundance of alleles reduces the need for correction.

See p.adjust for more details.

Value

A numeric vector of corrected p-values (of the same length as p, with names copied from p).

Description

Accessed on 28.07.20

Usage

allele_frequencies

Format

A data frame with 2096 rows and 3 variables:

var

allele number, character

population

reference population name, character

frequency

allele frequency in reference population, float

Details

A dataset containing allele frequencies across 5697 alleles For details visit the search results page in the allelefrequencies.net database website.

Source

www.allelefrequencies.net

Description

analyzeAssociations perform association analysis on a single variable level using a statistical model of choice.

Usage

analyzeAssociations(
  object,
  variables,
  placeholder = "term",
  correction = "bonferroni",
  n_correction = NULL,
  exponentiate = FALSE
)

Arguments

Details

correction specifies p-value adjustment method to use, common choice is Benjamini & Hochberg (1995) ("BH"). Internally this is passed to p.adjust.

Value

Tibble containing combined results for all variables. The first column "term" hold the names of variables. Further columns depends on the used model and are determined by associated tidy function. Generally they will include "estimate", "std.error", "statistic", "p.value", "conf.low", "conf.high", "p.adjusted".

Examples

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

# analyzeAssociations expects model data to be a data.frame
midas_data <- as.data.frame(midas)

# define base model
object <- lm(disease ~ term, data = midas_data)

# test for alleles associations
analyzeAssociations(object = object,
                    variables = c("B*14:02", "DRB1*11:01"))

Description

analyzeConditionalAssociations perform stepwise conditional testing adding the previous top-associated variable as covariate, until there are no more significant variables based on a self-defined threshold.

Usage

analyzeConditionalAssociations(
  object,
  variables,
  placeholder = "term",
  correction = "bonferroni",
  n_correction = NULL,
  th,
  th_adj = TRUE,
  keep = FALSE,
  rss_th = 1e-07,
  exponentiate = FALSE
)

Arguments

Value

Tibble with stepwise conditional testing results or a list of tibbles, see keep argument. The first column "term" hold the names of variables. Further columns depends on the used model and are determined by associated tidy function. Generally they will include "estimate", "std.error", "statistic", "p.value", "conf.low", "conf.high", "p.adjusted".

Examples

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

# analyzeConditionalAssociations expects model data to be a data.frame
midas_data <- as.data.frame(midas)

# define base model
object <- lm(disease ~ term, data = midas_data)
analyzeConditionalAssociations(object,
                            variables = c("B*14:02", "DRB1*11:01"),
                            th = 0.05)

Description

Helper function transforming experiment counts to selected inheritance_model.

Usage

applyInheritanceModel(
  experiment,
  inheritance_model = c("dominant", "recessive", "additive", "overdominant")
)

## S3 method for class 'matrix'
applyInheritanceModel(
  experiment,
  inheritance_model = c("dominant", "recessive", "additive", "overdominant")
)

## S3 method for class 'SummarizedExperiment'
applyInheritanceModel(
  experiment,
  inheritance_model = c("dominant", "recessive", "additive", "overdominant")
)

Arguments

Details

Under "dominant" model homozygotes and heterozygotes are coded as 1. In "recessive" model homozygotes are coded as 1 and other as 0. In "additive" model homozygotes are coded as 2 and heterozygotes as 1. In "overdominant" homozygotes (both 0 and 2) are coded as 0 and heterozygotes as 1.

Value

experiment converted to specified inheritance model.

Description

Coerce MiDAS to Data Frame

Usage

## S3 method for class 'MiDAS'
as.data.frame(x, ...)

Arguments

Value

Data frame representation of MiDAS object. Consecutive columns hold values of variables from MiDAS's experiments and colData. The metadata associated with experiments is not preserved.

Description

backquote places backticks around elements of character vector

Usage

backquote(x)

Arguments

Details

backquote is useful when using HLA allele numbers in formulas, where '*' and ':' characters have special meanings.

Value

Character vector with its elements backticked.

Description

characterMatches checks if all elements of a character vector matches values in choices.

Usage

characterMatches(x, choice)

Arguments

Value

Logical indicating if x's elements matches any of the values in choice.

Description

checkAlleleFormat test if the input character follows HLA nomenclature specifications.

Usage

checkAlleleFormat(allele)

Arguments

Details

Correct HLA number should consist of HLA gene name followed by "*" and sets of digits separated with ":". Maximum number of sets of digits is 4 which is termed 8-digit resolution. Optionally HLA numbers can be supplemented with additional suffix indicating its expression status. See http://hla.alleles.org/nomenclature/naming.html for more details.

HLA alleles with identical sequences across exons encoding the peptide binding domains might be designated with G group allele numbers. Those numbers have additional G or GG suffix. See http://hla.alleles.org/alleles/g_groups.html for more details. They are interpreted as valid HLA alleles designations.

Value

Logical vector specifying if allele elements follows HLA alleles naming conventions.

Examples

allele <- c("A*01:01", "A*01:02")
checkAlleleFormat(allele)

Description

checkColDataFormat asserts if the colData data frame has proper format.

Usage

checkColDataFormat(data_frame)

Arguments

Value

Logical indicating if data_frame is properly formatted. Otherwise raise an error.

Description

checkHlaCallsFormat asserts if hla calls data frame have proper format.

Usage

checkHlaCallsFormat(hla_calls)

Arguments

Value

Logical indicating if hla_calls follows hla calls data frame format. Otherwise raise an error.

Description

checkKirCallsFormat asserts if KIR counts data frame have proper format.

Usage

checkKirCallsFormat(kir_calls)

Arguments

Value

Logical indicating if kir_calls follow KIR counts data frame format. Otherwise raise an error.

Description

checkKirGenesFormat test if the input character follows KIR gene names naming conventions.

Usage

checkKirGenesFormat(genes)

Arguments

Details

KIR genes: "KIR3DL3", "KIR2DS2", "KIR2DL2", "KIR2DL3", "KIR2DP1", "KIR2DL1", "KIR3DP1", "KIR2DL1", "KIR3DP1", "KIR2DL4", "KIR3DL1", "KIR3DS1", "KIR2DL5", "KIR2DS3", "KIR2DS5", "KIR2DS4", "KIR2DS1", "KIR3DL2".

Value

Logical vector specifying if genes elements follow KIR genes naming conventions.

Examples

checkKirGenesFormat(c("KIR3DL3", "KIR2DS2", "KIR2DL2"))

Description

checkStatisticalModel asserts if object is an existing fit from a model functions such as lm, glm and many others. Containing MiDAS object as its data atribute.

Usage

checkStatisticalModel(object)

Arguments

Value

Logical indicating if object is an existing fit from a model functions such as lm, glm and many others. Containing MiDAS object as its data attribute. Otherwise raise an error.

Description

colnamesMatches check if data frame's columns are named as specified

Usage

colnamesMatches(x, cols)

Arguments

Value

Logical indicating if x's colnames equals choice.

Description

convertAlleleToVariable converts input HLA allele numbers to additional variables based on the supplied dictionary.

Usage

convertAlleleToVariable(allele, dictionary)

Arguments

Details

dictionary file should be a tsv format with header and two columns. First column should hold allele numbers, second additional variables (eg. expression level).

Type of the returned vector depends on the type of the additional variable.

Value

Vector containing HLA allele numbers converted to additional variables according to dictionary.

Examples

dictionary <- system.file("extdata", "Match_allele_HLA_supertype.txt", package = "midasHLA")
convertAlleleToVariable(c("A*01:01", "A*02:01"), dictionary = dictionary)

Description

countsToVariables converts counts table to additional variables.

Usage

countsToVariables(counts, dictionary, na.value = NA, nacols.rm = TRUE)

Arguments

Details

dictionary file should be a tsv format with header and two columns. First column should be named "Name" and hold variable name, second should be named "Expression" and hold expression used to identify variable (eg. "KIR2DL3 & ! KIR2DL2" will match all samples with KIR2DL3 and without KIR2DL2). Optionally a data frame formatted in the same manner can be passed instead.

Dictionaries shipped with the package:

kir_haplotypes

KIR genes to KIR haplotypes dictionary.

Value

Data frame with variable number of columns. First column named "ID" corresponds to "ID" column in counts, further columns hold indicators for converted variables. 1 and 0 code presence and absence of a variable respectively.

Examples

countsToVariables(MiDAS_tut_KIR, "kir_haplotypes")

Description

Function deletes 'ID' column from a df, then transpose it and sets the column names to values from deleted 'ID' column.

Usage

dfToExperimentMat(df)

Arguments

Value

Matrix representation of df.

Description

Integer vector giving Grantham distance values between pairs of amino acid residues.

Usage

dict_dist_grantham

Format

Named integer vector of length 400.

Description

distGrantham calculates normalized Grantham distance between two amino acid sequences. For details on calculations see Grantham R. 1974..

Usage

distGrantham(aa1, aa2)

Arguments

Details

Distance between amino acid sequences is normalized by length of compared sequences.

Lengths of aa1 and aa2 must be equal.

Value

Numeric vector of normalized Grantham distance between aa1 and aa2.

Description

Function transpose mat and inserts column names of input mat as a 'ID' column.

Usage

experimentMatToDf(mat)

Arguments

Value

Data frame representation of mat.

Description

Filter MiDAS object by frequency

Usage

filterByFrequency(
  object,
  experiment,
  lower_frequency_cutoff = NULL,
  upper_frequency_cutoff = NULL,
  carrier_frequency = FALSE
)

Arguments

Value

Filtered MiDAS object.

Examples

filterByFrequency(object = MiDAS_tut_object, 
                  experiment = "hla_alleles",
                  lower_frequency_cutoff = 0.05,
                  upper_frequency_cutoff = 0.95,
                  carrier_frequency = TRUE)

Description

Filter MiDAS object by omnibus groups

Usage

filterByOmnibusGroups(object, experiment, groups)

Arguments

Value

Filtered MiDAS object.

Examples

filterByOmnibusGroups(object = MiDAS_tut_object,
                      experiment = "hla_aa",
                      groups = c("A_3", "A_6", "C_1"))

Description

Filter MiDAS object by features

Usage

filterByVariables(object, experiment, variables)

Arguments

Value

Filtered MiDAS object.

Examples

filterByVariables(object = MiDAS_tut_object,
                  experiment = "hla_alleles",
                  variables = c("A*25:01", "A*26:01", "B*07:02"))

Description

Helper function for experiments filtering

Usage

filterExperimentByFrequency(
  experiment,
  carrier_frequency = FALSE,
  lower_frequency_cutoff = NULL,
  upper_frequency_cutoff = NULL
)

## S3 method for class 'matrix'
filterExperimentByFrequency(
  experiment,
  carrier_frequency = FALSE,
  lower_frequency_cutoff = NULL,
  upper_frequency_cutoff = NULL
)

## S3 method for class 'SummarizedExperiment'
filterExperimentByFrequency(
  experiment,
  carrier_frequency = FALSE,
  lower_frequency_cutoff = NULL,
  upper_frequency_cutoff = NULL
)

Arguments

Value

Filtered experiment matrix.

Description

Helper function for experiments filtering

Usage

filterExperimentByVariables(experiment, variables)

## S3 method for class 'matrix'
filterExperimentByVariables(experiment, variables)

## S3 method for class 'SummarizedExperiment'
filterExperimentByVariables(experiment, variables)

Arguments

Value

Filtered experiment object.

Description

Filter two level list by its secondary elements and remove empty items

Usage

filterListByElements(list, elements)

Arguments

Value

List filtered according to elements argument.

Description

formatResults format statistical analysis results table to html or latex format.

Usage

formatResults(
  results,
  filter_by = "p.value <= 0.05",
  arrange_by = "p.value",
  select_cols = c("term", "estimate", "std.error", "p.value", "p.adjusted"),
  format = c("html", "latex"),
  header = NULL,
  scroll_box_height = "400px"
)

Arguments

Value

Character vector of formatted table source code.

Examples

## Not run: 
midas <- prepareMiDAS(hla_calls = MiDAS_tut_HLA,
                      colData = MiDAS_tut_pheno,
                      experiment = "hla_alleles")
object <- lm(disease ~ term, data = midas)
res <- runMiDAS(object, 
                experiment = "hla_alleles", 
                inheritance_model = "dominant")
formatResults(res,
              filter_by = c("p.value <= 0.05", "estimate > 0"),
              arrange_by = c("p.value * estimate"),
              select_cols = c("allele", "p-value" = "p.value"),
              format = "html",
              header = "HLA allelic associations")

## End(Not run)

Description

getAAFrequencies calculates amino acid frequencies in amino acid data frame.

Usage

getAAFrequencies(aa_variation)

Arguments

Details

Both gene copies are taken into consideration for frequencies calculation, frequency = n / (2 * j) where n is the number of amino acid occurrences and j is the number of samples in aa_variation.

Value

Data frame with each row holding specific amino acid position, it's count and frequency.

Examples

aa_variation <- hlaToAAVariation(MiDAS_tut_HLA)
getAAFrequencies(aa_variation)

Description

getAlleleResolution returns the resolution of input HLA allele numbers.

Usage

getAlleleResolution(allele)

Arguments

Details

HLA allele resolution can take the following values: 2, 4, 6, 8. See http://hla.alleles.org/nomenclature/naming.html for more details.

NA values are accepted and returned as NA.

Value

Integer vector specifying allele resolutions.

Examples

allele <- c("A*01:01", "A*01:02")
getAlleleResolution(allele)

Description

List HLA alleles and amino acid residues at a given position.

Usage

getAllelesForAA(object, aa_pos)

Arguments

Value

Data frame containing HLA alleles, their corresponding amino acid residues and frequencies at requested position.

Examples

getAllelesForAA(object = MiDAS_tut_object, aa_pos = "A_9")

Description

getExperimentFrequencies calculate features frequencies.

Usage

getExperimentFrequencies(
  experiment,
  pop_mul = NULL,
  carrier_frequency = FALSE,
  ref = NULL
)

## S3 method for class 'matrix'
getExperimentFrequencies(
  experiment,
  pop_mul = NULL,
  carrier_frequency = FALSE,
  ref = NULL
)

## S3 method for class 'SummarizedExperiment'
getExperimentFrequencies(
  experiment,
  pop_mul = NULL,
  carrier_frequency = FALSE,
  ref = NULL
)

Arguments

Value

Data frame with each row holding specific variable, it's count and frequency.

Description

getExperimentPopulationMultiplicator extracts population multiplicator from experiment's metadata.

Usage

getExperimentPopulationMultiplicator(experiment)

## S3 method for class 'matrix'
getExperimentPopulationMultiplicator(experiment)

## S3 method for class 'SummarizedExperiment'
getExperimentPopulationMultiplicator(experiment)

Arguments

Value

Experiment's population multiplicator number.

Description

Get available experiments in MiDAS object.

Usage

getExperiments(object)

Arguments

Value

Character vector giving names of experiments in object.

Examples

getExperiments(object = MiDAS_tut_object)

Description

Calculate features frequencies for a given experiment in MiDAS object.

Usage

getFrequencies(
  object,
  experiment,
  carrier_frequency = FALSE,
  compare = FALSE,
  ref_pop = list(hla_alleles = c("USA NMDP African American pop 2", "USA NMDP Chinese",
    "USA NMDP European Caucasian", "USA NMDP Hispanic South or Central American",
    "USA NMDP Japanese", "USA NMDP North American Amerindian",
    "USA NMDP South Asian Indian"), kir_genes = c("USA California African American KIR",
    "USA California Asian American KIR", "USA California Caucasians KIR",
    "USA California Hispanic KIR")),
  ref = list(hla_alleles = allele_frequencies, kir_genes = kir_frequencies)
)

Arguments

Value

Data frame with features from selected experiment and their corresponding frequencies. Column "term" hold features names, "Counts" hold number of feature occurrences, "Freq" hold feature frequencies. If argument compare is set to TRUE, further columns will hold frequencies in reference populations.

Examples

# using default reference populations
getFrequencies(object = MiDAS_tut_object, 
               experiment = "hla_alleles", 
               compare = TRUE)

# using customized set of reference populations
getFrequencies(
  object = MiDAS_tut_object, 
  experiment = "hla_alleles", 
  compare = TRUE,
  ref_pop = list(
    hla_alleles = c("USA NMDP Chinese", "USA NMDP European Caucasian")
  ),
  ref = list(hla_alleles = allele_frequencies)
)

Description

Helper function for filtering frequency data frame

Usage

getFrequencyMask(
  df,
  lower_frequency_cutoff = NULL,
  upper_frequency_cutoff = NULL
)

Arguments

Value

Character vector containing names of variables after filtration.

Description

Get HLA calls from MiDAS object.

Usage

getHlaCalls(object)

Arguments

Value

HLA calls data frame.

Examples

getHlaCalls(object = MiDAS_tut_object)

Description

getHlaCallsGenes get's genes found in HLA calls.

Usage

getHlaCallsGenes(hla_calls)

Arguments

Value

Character vector of genes in hla_calls.

Description

getHlaFrequencies calculates allele frequencies in HLA calls data frame.

Usage

getHlaFrequencies(
  hla_calls,
  carrier_frequency = FALSE,
  compare = FALSE,
  ref_pop = c("USA NMDP African American pop 2", "USA NMDP Chinese",
    "USA NMDP European Caucasian", "USA NMDP Hispanic South or Central American",
    "USA NMDP Japanese", "USA NMDP North American Amerindian",
    "USA NMDP South Asian Indian"),
  ref = allele_frequencies
)

Arguments

Details

Both gene copies are taken into consideration for frequencies calculation, frequency = n / (2 * j) where n is the number of allele occurrences and j is the number of samples in hla_calls.

Value

Data frame with each row holding HLA allele, it's count and frequency.

Examples

getHlaFrequencies(MiDAS_tut_HLA)

Description

getHlaKirInteractions calculate presence-absence matrix of HLA - KIR interactions.

Usage

getHlaKirInteractions(
  hla_calls,
  kir_calls,
  interactions_dict = system.file("extdata", "Match_counts_hla_kir_interactions.txt",
    package = "midasHLA")
)

Arguments

Details

hla_calls are first reduced to all possible resolutions and converted to additional variables, such as G groups, using dictionaries shipped with the package.

interactions_dict file should be a tsv format with header and two columns. First column should be named "Name" and hold interactions names, second should be named "Expression" and hold expression used to identify interaction (eg. "C2 & KIR2DL1" will match all samples with C2 and KIR2DL1). The package is shipped with an interactions file based on Pende et al., 2019.

Value

Data frame with variable number of columns. First column named "ID" corresponds to "ID" column in counts, further columns hold indicators for HLA - KIR interactions. 1 and 0 code presence and absence of a variable respectively.

Examples

getHlaKirInteractions(
  hla_calls = MiDAS_tut_HLA, 
  kir_calls = MiDAS_tut_KIR, 
  interactions_dict = system.file(
    "extdata", "Match_counts_hla_kir_interactions.txt", 
    package = "midasHLA")
)

Description

Get KIR calls from MiDAS object.

Usage

getKirCalls(object)

Arguments

Value

KIR calls data frame.

Examples

getKirCalls(object = MiDAS_tut_object)

Description

getKIRFrequencies calculates KIR genes frequencies in KIR calls data frame.

Usage

getKIRFrequencies(kir_calls)

Arguments

Value

Data frame with each row holding KIR gene, it's count and frequency.

Examples

getKIRFrequencies(MiDAS_tut_KIR)

Description

getObjectDetails extracts some of the statistical model object attributes that are needed for runMiDAS internal calculations.

Usage

getObjectDetails(object)

Arguments

Value

List with following elements:

call

Object's call

formula_vars

Character containing names of variables in object formula

data

MiDAS object associated with model

Description

Get omnibus groups from MiDAS object.

Usage

getOmnibusGroups(object, experiment)

Arguments

Details

For some experiments features can be naturally divided into groups (here called omnibus groups). For example, in "hla_aa" experiment features can be grouped by amino acid position ("B_46_E", "B_46_A") can be grouped into B_46 group). Such groups can be then used to perform omnibus test, see runMiDAS for more details.

Value

List of omnibus groups for a given experiment.

Examples

getOmnibusGroups(object = MiDAS_tut_object,
                 experiment = "hla_aa")

Description

Get placeholder name from MiDAS object.

Usage

getPlaceholder(object)

Arguments

Value

String giving name of placeholder.

Examples

getPlaceholder(object = MiDAS_tut_object)

Description

Helper transforming reference frequencies

Usage

getReferenceFrequencies(ref, pop, carrier_frequency = FALSE)

Arguments

Value

Wide format data frame with population frequencies as columns.

Description

getVariableAAPos finds variable amino acid positions in protein sequence alignment.

Usage

getVariableAAPos(alignment, varchar = "[A-Z]")

Arguments

Details

The variable amino acid positions in the alignment are those at which different amino acids can be found. As the alignments can also contain indels and unknown characters, the user choice might be to consider those positions as variable or not. This can be achieved by passing appropriate regular expression in varchar. Eg. when varchar = "[A-Z]" occurence of deletion/insertion (".") will not be treated as variability. In order to detect this kind of variability varchar = "[A-Z\\.]" should be used.

Value

Integer vector specifying which alignment columns are variable.

Examples

alignment <- readHlaAlignments(gene = "TAP1")
getVariableAAPos(alignment)

Description

hasTidyMethod check if there is a tidy method available for a given class.

Usage

hasTidyMethod(class)

Arguments

Value

Logical indicating if there is a tidy method for a given class.

Description

Helper function returning alignment for Grantham distance calculations

Usage

hlaAlignmentGrantham(gene, aa_sel = 2:182)

Arguments

Value

HLA alignment processed for grantham distance calculation. Processing includes extracting specific amino acids, masking indels, gaps and stop codons.

Description

hlaCallsGranthamDistance calculate Grantham distance between two HLA alleles of a given, using original formula by Grantham R. 1974..

Usage

hlaCallsGranthamDistance(
  hla_calls,
  genes = c("A", "B", "C"),
  aa_selection = "binding_groove"
)

Arguments

Details

Grantham distance is calculated only for class I HLA alleles. First exons forming the variable region in the peptide binding groove are selected. Here we provide option to choose either "binding_groove" - exon 2 and 3 (positions 1-182 in IMGT/HLA alignments, however here we take 2-182 as many 1st positions are missing), "B_pocket" - residues 7, 9, 24, 25, 34, 45, 63, 66, 67, 70, 99 and "F_pocket" - residues 77, 80, 81, 84, 95, 116, 123, 143, 146, 147. Then all the alleles containing gaps, stop codons or indels are discarded. Finally distance is calculated for each pair.

See Robinson J. 2017. for more details on the choice of exons 2 and 3.

Value

Data frame of normalized Grantham distances between pairs of alleles for each specified HLA gene. First column (ID) is the same as in hla_calls, further columns are named as given by genes.

Examples

hlaCallsGranthamDistance(MiDAS_tut_HLA, genes = "A")

Description

hlaCallsToCounts converts HLA calls data frame into a counts table.

Usage

hlaCallsToCounts(hla_calls, check_hla_format = TRUE)

Arguments

Value

HLA allele counts data frame. First column holds samples ID's, further columns, corresponding to specific alleles, give information on the number of their occurrences in each sample.

Description

hlaToAAVariation convert HLA calls data frame to a matrix of variable amino acid positions.

Usage

hlaToAAVariation(hla_calls, indels = TRUE, unkchar = FALSE, as_df = TRUE)

Arguments

Details

Variable amino acid positions are found by comparing elements of the alignment column wise. Some of the values in alignment can be treated specially using indels and unkchar arguments. Function processes alignments for all HLA genes found in hla_calls.

Variable amino acid position uses protein alignments from EBI database.

Value

Matrix or data frame containing variable amino acid positions. Rownames corresponds to ID column in hla_calls, and colnames to alignment positions. If no variation is found one column matrix filled with NA's is returned.

Examples

hlaToAAVariation(MiDAS_tut_HLA)

Description

hlaToVariable converts HLA calls data frame to additional variables.

Usage

hlaToVariable(
  hla_calls,
  dictionary,
  reduce = TRUE,
  na.value = 0,
  nacols.rm = TRUE
)

Arguments

Details

dictionary file should be a tsv format with header and two columns. First column should hold allele numbers and second corresponding additional variables. Optionally a data frame formatted in the same manner can be passed instead.

dictionary can be also used to access dictionaries shipped with the package. They can be referred to by using one of the following strings:

"allele_HLA_Bw"

Translates HLA-B alleles together with A*23, A*24 and A*32 into Bw4 and Bw6 allele groups. In some cases HLA alleles containing Bw4 epitope, on nucleotide level actually carries a premature stop codon. Meaning that although on nucleotide level the allele would encode a Bw4 epitope it's not really there and it is assigned to Bw6 group. However in 4-digit resolution these alleles can not be distinguished from other Bw4 groups. Since alleles with premature stop codons are rare, Bw4 group is assigned.

"allele_HLA-B_only_Bw"

Translates HLA-B alleles (without A*23, A*24 and A*32) into Bw4 and Bw6 allele groups.

"allele_HLA-C_C1-2"

Translates HLA-C alleles into C1 and C2 allele groups.

"allele_HLA_supertype"

Translates HLA-A and HLA-B alleles into supertypes, a classification that group HLA alleles based on peptide binding specificities.

"allele_HLA_Ggroup"

Translates HLA alleles into G groups, which defines amino acid identity only in the exons relevant for peptide binding. Note that alleles DRB1*01:01:01 and DRB1*01:16 match more than one G group, here this ambiguity was removed by deleting matching with DRB5*01:01:01G group.

reduce control if conversion should happen in a greedy way, such that if some HLA number cannot be converted, it's resolution is reduced by 2 and another attempt is taken. This process stops when alleles cannot be further reduced or all have been successfully converted.

Value

Data frame with variable number of columns. First column named "ID" corresponds to "ID" column in hla_calls, further columns holds converted HLA variables.

Examples

hlaToVariable(MiDAS_tut_HLA, dictionary = "allele_HLA_supertype")

Description

Test experiment features for Hardy Weinberg equilibrium.

Usage

HWETest(
  object,
  experiment = c("hla_alleles", "hla_aa", "hla_g_groups", "hla_supertypes",
    "hla_NK_ligands"),
  HWE_group = NULL,
  HWE_cutoff = NULL,
  as.MiDAS = FALSE
)

Arguments

Details

Setting as.MiDAS to TRUE will filter MiDAS object based on p-value cut-off given by HWE_cutoff.

Value

Data frame with Hardy Weinberg Equilibrium test results or a filtered MiDAS object.

Examples

# create MiDAS object
midas <- prepareMiDAS(hla_calls = MiDAS_tut_HLA,
                      colData = MiDAS_tut_pheno,
                      experiment = "hla_alleles"
)

# get HWE p-values as data frame
HWETest(midas, experiment = "hla_alleles")

# get HWE in groups defined by disease status
# grouping by `disease == 1` will divide samples into two groups:
# `disease == 1` and `not disease == 1`
HWETest(midas, experiment = "hla_alleles", HWE_group = disease == 1)

# filter MiDAS object by HWE test p-value
HWETest(midas, experiment = "hla_alleles", HWE_cutoff = 0.05, as.MiDAS = TRUE)

Description

isCharacterOrNULL checks if the object is a character vector or NULL.

Usage

isCharacterOrNULL(x)

Arguments

Value

Logical indicating if object is character vector or NULL

Description

isClassOrNULL checks if object is an instance of a specified class or is null.

Usage

isClass(x, class)

Arguments

Value

Logical indicating if x is an instance of class.

Description

isClassOrNULL checks if object is an instance of a specified class or is null.

Usage

isClassOrNULL(x, class)

Arguments

Value

Logical indicating if x is an instance of class.

Description

isCountOrNULL check if object is a count (a single positive integer) or NULL.

Usage

isCountOrNULL(x)

Arguments

Value

Logical indicating if object is count or NULL

Description

isCountsOrZeros checks if vector contains only positive integers or zeros.

Usage

isCountsOrZeros(x, na.rm = TRUE)

Arguments

Value

Logical indicating if provided vector contains only positive integers or zeros.

Description

isExperimentCountsOrZeros checks if experiment contains only positive integers or zeros.

Usage

isExperimentCountsOrZeros(x, na.rm = TRUE)

Arguments

Value

Logical indicating if x contains only positive integers or zeros.

Description

isExperimentInheritanceModelApplicable check experiment's metadata for presence of "inheritance_model_applicable" flag, indicating if inheritance model can be applied.

Usage

isExperimentInheritanceModelApplicable(experiment)

## S3 method for class 'matrix'
isExperimentInheritanceModelApplicable(experiment)

## S3 method for class 'SummarizedExperiment'
isExperimentInheritanceModelApplicable(experiment)

Arguments

Value

Logical flag.

Description

isFlagOrNULL checks if object is flag (a length one logical vector) or NULL.

Usage

isFlagOrNULL(x)

Arguments

Value

Logical indicating if object is flag or NULL

Description

isNumberOrNULL checks if object is number (a length one numeric vector) or NULL.

Usage

isNumberOrNULL(x)

Arguments

Value

Logical indicating if object is number or NULL

Description

isStringOrNULL checks if object is string (a length one character vector) or NULL.

Usage

isStringOrNULL(x)

Arguments

Value

Logical indicating if object is string or NULL

Description

isTRUEorFALSE check if object is a flag (a length one logical vector) except NA.

Usage

isTRUEorFALSE(x)

Arguments

Value

Logical indicating if object is TRUE or FALSE flag

Description

iterativeLRT performs likelihood ratio test in an iterative manner over groups of variables given in omnibus_groups.

Usage

iterativeLRT(object, placeholder, omnibus_groups)

Arguments

Value

Data frame containing summarised likelihood ratio test results.

Description

Information about variable statistic from each model is extracted using tidy function.

Usage

iterativeModel(object, placeholder, variables, exponentiate = FALSE)

Arguments

Value

Tibble containing per variable summarised model statistics. The exact output format is model dependent and controlled by model's dedicated tidy function.

Description

kableResults convert results table (runMiDAS output) to HTML or LaTeX format.

Usage

kableResults(
  results,
  colnames = NULL,
  header = "MiDAS analysis results",
  pvalue_cutoff = NULL,
  format = getOption("knitr.table.format"),
  scroll_box_height = "400px"
)

Arguments

Value

Association analysis results table in HTML or LaTeX.

Examples

midas <- prepareMiDAS(hla_calls = MiDAS_tut_HLA,
                      colData = MiDAS_tut_pheno,
                      experiment = "hla_alleles")
object <- lm(disease ~ term, data = midas)
res <- runMiDAS(object, experiment = "hla_alleles", inheritance_model = "additive")
kableResults(results = res,
             colnames = c("HLA allele" = "allele"))

Description

Accessed on 28.08.20

Usage

kir_frequencies

Format

A data frame with 3744 rows and 3 variables:

var

allele number, character

population

reference population name, character

frequency

KIR genes carrier frequency in reference population, float

Details

A dataset containing KIR genes frequencies across 16 genes. For details visit the search results page in the allelefrequencies.net database website.

Source

www.allelefrequencies.net

Description

Used to run function iteratively over list, while using tryCatch to catch warnings and errors to finally present a summary of issues rather than error on each and every one. Used in iterativeLRT and iterativeModel.

Usage

lapply_tryCatch(X, FUN, err_res, ...)

Arguments

Value

List of elements as returned by FUN.

Description

listMiDASDictionaries lists dictionaries shipped with the MiDAS package. See hlaToVariable for more details on dictionaries.

Usage

listMiDASDictionaries(pattern = "allele", file.names = FALSE)

Arguments

Value

Character vector giving names of available HLA alleles dictionaries.

Description

LRTest carry out an asymptotic likelihood ratio test for two models.

Usage

LRTest(mod0, mod1)

Arguments

Details

mod0 have to be a reduced version of mod1. See examples.

Value

Data frame with the results of likelihood ratio test of the supplied models.

Column term holds new variables appearing in mod1, df difference in degrees of freedom between models, logLik difference in log likelihoods, statistic Chisq statistic and p.value corresponding p-value.

Description

Example HLA calls data used in MiDAS tutorial

Usage

MiDAS_tut_HLA

Format

Data frame with 1000 rows and 19 columns. First column holds samples ID's, following columns holds HLA alleles calls for different genes.

ID

Character sample ID

A_1

Character

A_2

Character

B_1

Character

B_2

Character

C_1

Character

C_2

Character

DPA1_1

Character

DPA1_2

Character

DPB1_1

Character

DPB1_2

Character

DQA1_1

Character

DQA1_2

Character

DQB1_1

Character

DQB1_2

Character

DRA_1

Character

DRA_2

Character

DRB1_1

Character

DRB1_2

Character

Description

Example KIRR presence/absence data used in MiDAS tutorial

Usage

MiDAS_tut_KIR

Format

Data frame with 1000 rows and 17 columns. First column holds samples ID's, following columns holds presence/absence indicators for different KIR genes.

ID

Character sample ID

KIR3DL3

Integer

KIR2DS2

Integer

KIR2DL2

Integer

KIR2DL3

Integer

KIR2DP1

Integer

KIR2DL1

Integer

KIR3DP1

Integer

KIR2DL4

Integer

KIR3DL1

Integer

KIR3DS1

Integer

KIR2DL5

Integer

KIR2DS3

Integer

KIR2DS5

Integer

KIR2DS4

Integer

KIR2DS1

Integer

KIR3DL2

Integer

Description

Example MiDAS object created with data used in MiDAS tutorial: MiDAS_tut_HLA, MiDAS_tut_KIR, MiDAS_tut_pheno. Used in code examlpes and unit tests.

Usage

MiDAS_tut_object

Format

MiDAS object with following experiments defined:

hla_alleles

SummarizedExperiment with 447 rows and 1000 columns

hla_aa

SummarizedExperiment with 1223 rows and 1000 columns

hla_g_groups

SummarizedExperiment with 46 rows and 1000 columns

hla_supertypes

SummarizedExperiment with 12 rows and 1000 columns

hla_NK_ligands

SummarizedExperiment with 5 rows and 1000 columns

kir_genes

SummarizedExperiment with 16 rows and 1000 columns

kir_haplotypes

SummarizedExperiment with 6 rows and 1000 columns

hla_kir_interactions

SummarizedExperiment with 29 rows and 1000 columns

hla_divergence

matrix with 4 rows and 1000 columns

hla_het

SummarizedExperiment with 9 rows and 1000 columns

Description

Example phenotype data used in MiDAS tutorial

Usage

MiDAS_tut_pheno

Format

Data frame with 1000 rows and 4 columns.

ID

Character sample ID

disease

Integer

lab_value

Numeric

outcome

Integer

Description

The MiDAS class is a MultiAssayExperiment object containing data and metadata required for MiDAS analysis.

Valid MiDAS object must have unique features names across all experiments and colData. It's metadata list needs to have a placeholder element, which is a string specifying name of column in colData used when defining statistical model for downstream analyses (see runMiDAS for more details). Optionally the object's metadata can also store 'hla_calls' and 'kir_calls' data frames (see prepareMiDAS for more details).

Usage

## S4 method for signature 'MiDAS'
getExperiments(object)

## S4 method for signature 'MiDAS'
getHlaCalls(object)

## S4 method for signature 'MiDAS'
getKirCalls(object)

## S4 method for signature 'MiDAS'
getPlaceholder(object)

## S4 method for signature 'MiDAS'
getOmnibusGroups(object, experiment)

## S4 method for signature 'MiDAS'
getFrequencies(
  object,
  experiment,
  carrier_frequency = FALSE,
  compare = FALSE,
  ref_pop = list(hla_alleles = c("USA NMDP African American pop 2", "USA NMDP Chinese",
    "USA NMDP European Caucasian", "USA NMDP Hispanic South or Central American",
    "USA NMDP Japanese", "USA NMDP North American Amerindian",
    "USA NMDP South Asian Indian"), kir_genes = c("USA California African American KIR",
    "USA California Asian American KIR", "USA California Caucasians KIR",
    "USA California Hispanic KIR")),
  ref = list(hla_alleles = allele_frequencies, kir_genes = kir_frequencies)
)

## S4 method for signature 'MiDAS'
filterByFrequency(
  object,
  experiment,
  lower_frequency_cutoff = NULL,
  upper_frequency_cutoff = NULL,
  carrier_frequency = FALSE
)

## S4 method for signature 'MiDAS'
filterByOmnibusGroups(object, experiment, groups)

## S4 method for signature 'MiDAS'
filterByVariables(object, experiment, variables)

## S4 method for signature 'MiDAS'
getAllelesForAA(object, aa_pos)

Arguments

Value

Instance of class MiDAS

Description

Transform MiDAS to wide format data.frame

Usage

midasToWide(object, experiment)

Arguments

Value

Data frame representation of MiDAS object. Consecutive columns holds values of variables from MiDAS's experiments and colData. The metadata associated with experiments is not preserved.

Description

isTRUEorFALSE check if object is a flag (a length one logical vector) except NA.

Usage

objectHasPlaceholder(object, placeholder)

Arguments

Value

Logical indicating if placeholder is present in object formula.

Description

OmnibusTest calculates overall p-value for linear combination of variables using likelihood ratio test.

Usage

omnibusTest(
  object,
  omnibus_groups,
  placeholder = "term",
  correction = "bonferroni",
  n_correction = NULL
)

Arguments

Details

Likelihood ratio test is conducted by comparing a model given in an object with an extended model, that is created by including the effect of variables given in variables as their linear combination.

Value

Data frame with columns:

• "group" Omnibus group name

• "term" Elements of omnibus group added to base model

• "df" Difference in degrees of freedom between base and extended model

• "logLik" Difference in log likelihoods between base and extended model

• "statistic" Chisq statistic

• "p.value" P-value

• "p.adjusted" Adjusted p-value

Examples

midas <- prepareMiDAS(hla_calls = MiDAS_tut_HLA,
                      colData = MiDAS_tut_pheno,
                      experiment = "hla_aa")

# define base model
object <- lm(disease ~ term, data = midas)
omnibusTest(object,
            omnibus_groups = list(
              A_29 = c("A_29_D", "A_29_A"),
              A_43 = c("A_43_Q", "A_43_R")
            ))

Description

prepareMiDAS transform HLA alleles calls and KIR calls according to selected experiments 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

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")

Description

Prepare MiDAS data on HLA amino acid level

Usage

prepareMiDAS_hla_aa(hla_calls, indels = TRUE, unkchar = FALSE, ...)

Arguments

Value

SummarizedExperiment

Description

Prepare MiDAS data on HLA allele level

Usage

prepareMiDAS_hla_alleles(hla_calls, ...)

Arguments

Value

Matrix

Description

Prepare MiDAS data on custom HLA level

Usage

prepareMiDAS_hla_custom(hla_calls, hla_dictionary, ...)

Arguments

Value

Matrix

Description

Prepare MiDAS data on HLA divergence level

Usage

prepareMiDAS_hla_divergence(
  hla_calls,
  hla_divergence_aa_selection = "binding_groove",
  ...
)

Arguments

Value

Matrix

Description

Prepare MiDAS data on HLA allele's G groups level

Usage

prepareMiDAS_hla_g_groups(hla_calls, ...)

Arguments

Value

Matrix

Description

Prepare MiDAS data on HLA heterozygosity level

Usage

prepareMiDAS_hla_het(hla_calls, hla_het_resolution = 8, ...)

Arguments

Value

Matrix

Description

Prepare MiDAS data on HLA - KIR interactions level

Usage

prepareMiDAS_hla_kir_interactions(hla_calls, kir_calls, ...)

Arguments

Value

Matrix

Description

Prepare MiDAS data on HLA allele's groups level

Usage

prepareMiDAS_hla_NK_ligands(hla_calls, ...)

Arguments

Value

Matrix

Description

Prepare MiDAS data on HLA allele's supertypes level

Usage

prepareMiDAS_hla_supertypes(hla_calls, ...)

Arguments

Value

Matrix

Description

Prepare MiDAS data on custom KIR level

Usage

prepareMiDAS_kir_custom(kir_calls, kir_dictionary, ...)

Arguments

Value

Matrix

Description

Prepare MiDAS data on KIR genes level

Usage

prepareMiDAS_kir_genes(kir_calls, ...)

Arguments

Value

Matrix

Description

Prepare MiDAS data on KIR haplotypes level

Usage

prepareMiDAS_kir_haplotypes(kir_calls, ...)

Arguments

Value

Matrix

Description

readHlaAlignments read HLA allele alignments from file.

Usage

readHlaAlignments(file, gene = NULL, trim = FALSE, unkchar = "")

Arguments

Details

HLA allele alignment file should follow EBI database format, for details see ftp://ftp.ebi.ac.uk/pub/databases/ipd/imgt/hla/alignments/README.md.

All protein alignment files from the EBI database are shipped with the package. They can be easily accessed using gene parameter. If gene is set to NULL, file parameter is used instead and alignment is read from the provided file. In EBI database alignments for DRB1, DRB3, DRB4 and DRB5 genes are provided as a single file, here they are separated.

Additionally, for the alleles without sequence defined in the original alignment files we have infered thier sequence based on known higher resolution alleles.

Value

Matrix containing HLA allele alignments.

Rownames correspond to allele numbers and columns to positions in the alignment. Sequences following the termination codon are marked as empty character (""). Unknown sequences are marked with a character of choice, by default "". Stop codons are represented by a hash (X). Insertion and deletions are marked with period (.).

Examples

hla_alignments <- readHlaAlignments(gene = "A")

Description

readHlaCalls read HLA allele calls from file

Usage

readHlaCalls(file, resolution = 4, na.strings = c("Not typed", "-", "NA"))

Arguments

Details

Input file has to be a tsv formatted table with a header. First column should contain sample IDs, further columns hold HLA allele numbers. See system.file("extdata", "MiDAS_tut_HLA.txt", package = "midasHLA") file for an example.

resolution parameter can be used to reduce HLA allele numbers. If reduction is not needed resolution can be set to 8. resolution parameter can take the following values: 2, 4, 6, 8. For more details about HLA allele numbers resolution see http://hla.alleles.org/nomenclature/naming.html.

Value

HLA calls data frame. First column hold sample IDs, further columns hold HLA allele numbers.

Examples

file <- system.file("extdata", "MiDAS_tut_HLA.txt", package = "midasHLA")
hla_calls <- readHlaCalls(file)

Description

readKirCalls read KIR calls from file.

Usage

readKirCalls(file, na.strings = c("", "NA", "uninterpretable"))

Arguments

Details

Input file has to be a tsv formatted table. First column should be named "ID" and contain samples IDs, further columns should hold KIR genes presence / absence indicators. See system.file("extdata", "MiDAS_tut_KIR", package = "midasHLA") for an example.

Value

Data frame containing KIR gene's counts. First column hold samples IDs, further columns hold KIR genes presence / absence indicators.

Examples

file <- system.file("extdata", "MiDAS_tut_KIR.txt", package = "midasHLA")
readKirCalls(file)

Description

reduceAlleleResolution reduce HLA allele numbers resolution.

Usage

reduceAlleleResolution(allele, resolution = 4)

Arguments

Details

In cases when allele number contain additional suffix their resolution can not be unambiguously reduced. These cases are returned unchanged. Function behaves in the same manner if resolution is higher than resolution of input HLA allele numbers.

NA values are accepted and returned as NA.

TODO here we give such warning when alleles have G or GG suffix (see http://hla.alleles.org/alleles/g_groups.html) "Reducing G groups alleles, major allele gene name will be used." I dond't really remember why we are doing this xd These allele numbers are processed as normal alleles (without suffix). Let me know if this warning is relevant or we could go without it. If we want to leave it lets also add text in documentation.

Value

Character vector containing reduced HLA allele numbers.

Examples

reduceAlleleResolution(c("A*01", "A*01:24", "C*05:24:55:54"), 2)

Description

reduceHlaCalls reduces HLA calls data frame to specified resolution.

Usage

reduceHlaCalls(hla_calls, resolution = 4)

Arguments

Details

Alleles with resolution greater than resolution or optional suffixes are returned unchanged.

Value

HLA calls data frame reduced to specified resolution.

Examples

reduceHlaCalls(MiDAS_tut_HLA, resolution = 2)

Description

runMiDAS perform association analysis on MiDAS data using statistical model of choice. Function is intended for use with prepareMiDAS. See examples section.

Usage

runMiDAS(
  object,
  experiment,
  inheritance_model = NULL,
  conditional = FALSE,
  omnibus = FALSE,
  omnibus_groups_filter = NULL,
  lower_frequency_cutoff = NULL,
  upper_frequency_cutoff = NULL,
  correction = "bonferroni",
  n_correction = NULL,
  exponentiate = FALSE,
  th = 0.05,
  th_adj = TRUE,
  keep = FALSE,
  rss_th = 1e-07
)

Arguments

Details

By default statistical analysis is performed iteratively on each variable in selected experiment. This is done by substituting placeholder in the object's formula with each variable in the experiment.

Setting conditional argument to TRUE will cause the statistical analysis to be performed in a stepwise conditional testing manner, adding the previous top-associated variable as a covariate to object's formula. The analysis stops when there is no more significant variables, based on self-defined threshold (th argument). Either adjusted or unadjusted p-values can be used as the selection criteria, which is controlled using th_adj argument.

Setting omnibus argument to TRUE will cause the statistical analysis to be performed iteratively on groups of variables (like residues at particular amino acid position) using likelihood ratio test.

Argument inheritance_model specifies the inheritance model that should be applyed to experiment's data. Following choices are available:

• "dominant" carrier status is sufficient for expression of the phenotype (non-carrier: 0, heterozygous & homozygous carrier: 1).

• "recessive" two copies are required for expression of the phenotype (non-carrier & heterozygous carrier: 0, homozygous carrier: 1).

• "additive" allele dosage matters, homozygous carriers show stronger phenotype expression or higher risk than heterozygous carriers (non-carrier = 0, heterozygous carrier = 1, homozygous carrier = 2).

• "overdominant" heterozygous carriers are at higher risk compared to non-carriers or homozygous carriers (non-carrier & homozygous carrier = 0, heterozygous carrier = 1).

correction specifies p-value adjustment method to use, common choice is Benjamini & Hochberg (1995) ("BH"). Internally this is passed to p.adjust.

Value

Analysis results, depending on the parameters:

conditional=FALSE, omnibus=FALSE

Tibble with first column "term" holding names of tested variables (eg. alleles). Further columns depends on the used model and are determined by associated tidy function. Generally they will include "estimate", "std.error", "statistic", "p.value", "conf.low", "conf.high", "p.adjusted".

conditional=TRUE, omnibus=FALSE

Tibble or a list of tibbles, see keep argument. The first column "term" hold names of tested variables. Further columns depends on the used model and are determined by associated tidy function. Generally they will include "estimate", "std.error", "statistic", "p.value", "conf.low", "conf.high", "p.adjusted".

conditional=FALSE, omnibus=TRUE

Tibble with first column holding names of tested omnibus groups (eg. amino acid positions) and second names of variables in the group (eg. residues). Further columns are: "df" giving difference in degrees of freedom between base and extended model, "statistic" giving Chisq statistic, "p.value" and "p.adjusted".

conditional=TRUE, omnibus=TRUE

Tibble or a list of tibbles, see keep argument. The first column hold names of tested omnibus groups (eg. amino acid positions), second column hold names of variables in the group (eg. residues). Further columns are: "df" giving difference in degrees of freedom between base and extended model, "statistic" giving Chisq statistic, "p.value" and "p.adjusted".

Examples

# create MiDAS object
midas <- prepareMiDAS(hla_calls = MiDAS_tut_HLA,
                      colData = MiDAS_tut_pheno,
                      experiment = c("hla_alleles", "hla_aa")
)

# construct statistical model
object <- lm(disease ~ term, data = midas)

# run analysis
runMiDAS(object, experiment = "hla_alleles", inheritance_model = "dominant")

# omnibus test
# omnibus_groups_filter argument can be used to restrict omnibus test only
# to selected variables groups, here we restrict the analysis to HLA-A
# positions 29 and 43.
runMiDAS(
  object,
  experiment = "hla_aa",
  inheritance_model = "dominant",
  omnibus = TRUE,
  omnibus_groups_filter = c("A_29", "A_43")
)

Description

Helper getting variables frequencies from MiDAS object. Additionally for binary test covariate frequencies per phenotype are added. Used in scope of runMiDAS.

Usage

runMiDASGetVarsFreq(midas, experiment, test_covar)

Arguments

Value

Data frame with variable number of columns. First column, "term" holds experiment's variables, further columns hold number of variable occurrence and their frequencies.

Description

stringMatches checks if string is equal to one of the choices.

Usage

stringMatches(x, choice)

Arguments

Value

Logical indicating if x matches one of the strings in choice.

Description

List HLA alleles and amino acid residues at a given position.

Usage

summariseAAPosition(hla_calls, aa_pos, aln = NULL, na.rm = FALSE)

Arguments

Value

Data frame containing HLA alleles, their corresponding amino acid residues and frequencies at requested position.

Examples

summariseAAPosition(MiDAS_tut_HLA, "A_9")

Description

updateModel adds new variables to model and re-fit it.

Usage

updateModel(object, x, placeholder = NULL, backquote = TRUE, collapse = " + ")

Arguments

Value

Updated fitted object.

Description

validateFrequencyCutoffs checks if lower_frequency_cutoff and upper_frequency_cutoff are valid.

Usage

validateFrequencyCutoffs(lower_frequency_cutoff, upper_frequency_cutoff)

Arguments

Details

lower_frequency_cutoff and upper_frequency_cutoff should be a positive numbers, giving either frequency or counts. lower_frequency_cutoff has to be lower than upper_frequency_cutoff.

Value

Logical indicating if lower_frequency_cutoff and upper_frequency_cutoff are valid.