vocal {ComICS}

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)