csDeconv: Improve reference-free deconvolution using cross-cell type...
In TOAST: Tools for the analysis of heterogeneous tissues

Description Usage Arguments Value Author(s) References Examples

This function improve the feature selection in reference-free deconvolution through cross-cell type differential analysis

1 2	csDeconv(Y_raw, K, FUN, nMarker = 1000, InitMarker = NULL, TotalIter = 30, bound_negative = FALSE)

`Y_raw`	A G*N matrix, G is the number of features, N is the number of subjects; or a SummarizedExperiment object.
`K`	The number of cell types. Need to be specified a priori.
`FUN`	The reference-free deconvolution function, this function should take Y_raw and K, and the return values should be a N by K proportion matrix. N is the number of samples and K is the number of cell types. Default function is a wrapper of the RefFreeCellMix() function from CRAN package RefFreeEWAS.
`nMarker`	The number of markers used in the deconvolution. Default is 1000.
`InitMarker`	A vector of length L to represent the selection of inital markers. L should be equal or smaller than G. If G is large, it is recommended that L is much smaller than G. If not specified, the most variable nMarker features will be used.
`TotalIter`	The total number of iterations of applying cross-cell type differential analysis. Default is 30.
`bound_negative`	Whether to bound all negative parameter estimators to zero.

`allProp`	A list of estimated proportions from all iterations.
`allRMSE`	A vector of root mean squared errors (RMSE) from all iteratoins.
`estProp`	A N*K matrix representing the mixture proportions of K cell types in N subjects, chosen from allProp with the smallest RMSE.
`updatedInx`	Selected variable index from the algorithm.

Ziyi Li <ziyi.li@emory.edu>

Ziyi Li and Hao Wu. "Improving reference-free cell composition estimation by cross-cell type differential analysis".

Y_raw <- abs(matrix(runif(10000*20, 0,1),10000,20))
K <- 3

## wrap your reference-free
## deconvolution method into a function
## this function should take Y and K as input
## and output a N by K proprotion matrix
## here we use RefFreeCellMix() as an example
outT <- csDeconv(Y_raw, K)

RefFreeCellMix_wrapper <- function(Y, K){
     outY = RefFreeEWAS::RefFreeCellMix(Y,
     mu0=RefFreeEWAS::RefFreeCellMixInitialize(Y,
     K = K))
     Prop0 = outY$Omega
     return(Prop0)
}

outT <- csDeconv(Y_raw, K,
            FUN = RefFreeCellMix_wrapper)