datE.Admixture provides the expression values from a mixture of cell types (admixed
population) and you want to estimate the proportion of each pure cell type in the mixed samples (rows of
datE.Admixture). The function allows you to do this as long as you provide a data frame
MarkerMeansPure that reports the mean expression values of markers in each of the pure cell types.proportionsInAdmixture(MarkerMeansPure, datE.Admixture, calculateConditionNumber = FALSE, coefToProportion = TRUE)datE.Admixture could
correspond to thousands of genes. The rows of datE.Admixture correspond to the admixed samples for
which the function estimates the proportions of pukappa
function to calculates the condition number of the matrix MarkerMeansPure[,-1]. This allows one to
determine whether the linear model for estimating the proportPredictedProportions correspond to the admixed samples, i.e. the rows of datE.Admixture. The columns of PredictedProportions correspond to the pure populations, i.e. the columns of MarkerMeansPure[,-1].PredictedProportions. In general, datCoef will only be different from PredictedProportions if coefToProportion=TRUE. See the description of coefToProportionkappa function. See the description of calculateConditionNumber.MarkerMeansPure) that match column names of datE.Admixture and that contain non-missing pure mean values.datE.MarkersAdmixtureTranspose and MarkerMeansPure are comparable. When dealing with Affymetrix data: we have successfully used it on untransformed MAS5 data.
For statisticians: To estimate the proportions, we use the coefficients
of a linear model. Specifically:
datCoef= t(lm(datE.MarkersAdmixtureTranspose ~MarkerMeansPure[,-1])$coefficients[-1,])
where datCoef is a matrix whose rows correspond to the mixed samples (rows of datE.Admixture) and the columns correspond to pure populations (e.g. cell types), i.e. the columns of MarkerMeansPure[,-1].
More details can be found in Abbas et al (2009).lm, kappa