R: Variation in Cell Abundance Loci

vocal {ComICS} | R Documentation

## Variation in Cell Abundance Loci

### Description

Probing immune system genetics via gene expression. VoCAL is a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune-cell types, and then uses these quantitative traits to uncover the underlying DNA loci (iQTLs) assuming homozygosity (such as in the case of recombinent inbred strains).

### Usage

    vocal(...,reference_data,expression_data,genotyping_data,normalize_data,
     T.i=5,T.e=10,eqtl_association_scores=NULL)

### Arguments

`...` | one or more data frames of one column, each one represents a preselected marker set that likely discriminate well between the immune-cell types given in the reference data. The number of data frames defines the number of association scores that would be combined to generate the final iQTL association score.
`reference_data` | a data frame representing immune cell expression profiles. Each row represents an expression of a gene, and each column represents a different immune cell type. `colnames` contains the name of each immune cell type and the `rownames` includes the genes' symbol. The names of each immune cell type and the symbol of each gene should be unique. Any gene with missing expression values must be excluded.
`expression_data` | a data frame representing RNA-seq or microarray gene-expression profiles of a given complex tissue across a population of genetically distinct (genotyped) individuals. Each row represents an expression of a gene, and each column represents a genetically distinct individual. `colnames` contain the name of each individual, as written in the `genotyping_data`, and `rownames` includes the genes' symbol. The name of each individual sample and the symbol of each gene should be unique. Any gene with missing expression values should be excluded.
`genotyping_data` | a data frame where each row represents a different locus, and each column represents a genetically distinct individual. The genotype should be taken from homozygous individuals only. Where the genotype is unknown `NA` should be used. The first six columns contain the following information: (1) The sequential identifier of the locus; (2) The name of each locus Chr; (3) Chromosome position; (4) Start genome position; (5) End genome position; (6) position in cM.
`normalize_data` | normalization type. The data will be normalized by either: (1) "All" - subtraction of the mean expression of all strains; (2) "None" - data is already normalized, do nothing; (3) name of individual included in `colnames` of `expression_data`;
`T.i` | numerical. significant iQTL association score `(-log10(Pvalue))` cutoff for the refinement step of the VoCAL algorithm.
`T.e` | numerical. significant eQTL association score `(-log10(Pvalue))` cutoff for the refinement step of the VoCAL algorithm.
`eqtl_association_scores` | (optional) a data frame where each entry represents an association score for a gene given the genotype of all the individuals that appear in the expression_data data frame, in a specific locus. This eQTL analysis should be peformed over the normalized expression_data. `colnames` contain the UID (as written in the genotyping_data) and `rownames` includes the genes' symbol (as written in the expression_data). The symbol of each gene should be unique. These scores should be in -log10(P value). Default is NULL, meaning that eQTL analysis will be performed.

### Value

a list of two martices

`final_association_score` | a matrix that contains the output iQTL association score after applying the iterative filteration procedure. Each row represents the genome wide-association result for a specific immune trait over a range of DNA loci. `rownames` provides the identifier of the locus and `colnames` contains the immune-cell type names. Each entry provides the `-log10(P value)` of an iQTL association score.
`marker_info` | the names of all the markers removed from the different marker sets provided

### References

Steuerman Y and Gat-Viks I. Exploiting Gene-Expression Deconvolution to Probe the Genetics of the Immune System (2015), Submitted.

### Examples

    data(commons)
    data(vocalEx)
    ## Not run:
     results <- vocal(DCQ_mar, reference_data=immgen_dat, expression_data=lung_dat,
     genotyping_data=gBXD, normalize_data="B6", eqtl_association_scores=eQTL_res)

    ## End(Not run)

[Package ComICS version 1.0.4 Index]