# fitConsensus: Fit row-linear models to all loci In consensus: Cross-platform consensus analysis of genomic measurements via interlaboratory testing method

## Description

The main function of this package. Fits a number of row-linear models from a MultiMeasure object, one for each matching row of the data matrices contained within it. Outputs a ConsensusFit object containing per-platform, per locus consensus values for average, sensitivity and precision.

## Usage

 1  fitConsensus(multimeas) 

## Arguments

 multimeas An object of class MultiMeasure.

## Details

For each locus, a row-linear model (Mandel 1994) is fit of the form

Z_{ij} = a_{i} + b_{i}(x_{j} - \bar{x}) + d_{ij}

where Z_{ij} is a matrix of measurements at the same genomic locus k, the row index i=1,…,p labels the platform or condition (microarray, library prep method for sequencing assay etc.) used and the column index j=1,…,n labels the biological samples that are interrogated at that locus on each of the p platforms. Hence a_{i} is the intercept (row averages of Z_{ij}), b_{i} the slope of the regression line (sensitivity) and d_i = (n-2)^{-1}Σ_{j}d_{ij}^{2} the residual mean square (precision) about the ith fitted line, noting that higher d_i corresponds to lower precision. Values of a_i, b_i and d_i can be found in the slots of the ConsensusFit object.

For MultiMeasure objects with 10,000 loci or more, a progress message is printed for every 10,000 loci fitted.

## Value

A ConsensusFit object with slots containing various parameter values from the row-linear fits. More information can be found in the linked class description. Output from this function can then be passed to various plotting functions for data exploration.

## Author(s)

Tim Peters <t.peters@garvan.org.au>

## References

Mandel, J. (1994). Analyzing Interlaboratory Data According to ASTM Standard E691. In Quality and Statistics: Total Quality Management (pp. 59-59-12). 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International.

Mandel, J. (2012). The statistical analysis of experimental data. Courier Corporation. Chapter 13: “The Systematic Evaluation of Measuring Processes”.

Ku, H.H. (1969). Precision Measurement and Calibration. Volume 1. Statistical Concepts and Procedures (No. NBS-SP-300-VOL-1). Issued February 1969. US Department of Commerce. Chapter 3.7: “The Interlaboratory Evaluation of Testing Methods”. Mandel, J. and Lashof, T.W. p. 170.

## Examples

 1 2 3 4 data("TCGA") tcga_mm <- MultiMeasure(names=c("U133A", "Huex", "Agilent", "RNA-Seq"), data=list(U133A, Huex, Agilent, RNASeq)) fit <- fitConsensus(tcga_mm) 

consensus documentation built on Nov. 8, 2020, 8:32 p.m.