run_edec_stage_1 | R Documentation |
run_edec_stage_1
takes as input the methylation profiles of complex tissue
samples, the set of loci with high variability in methylation across cell
types, and the number of constituent cell types. It then estimates the
average methylation profiles of constituent cell types, and the proportions
of constituent cell types in each input sample.
run_edec_stage_1( meth_bulk_samples, informative_loci, num_cell_types, max_its = 2000, rss_diff_stop = 1e-10 )
meth_bulk_samples |
Matrix with methylation profiles of bulk tissue samples. Rows correspond to loci/probes and columns correspond to different samples. |
informative_loci |
A vector containing names (strings) of rows corresponding to loci/probes that are informative for distinguishing cell types. |
num_cell_types |
Number of cell types to use in deconvolution. |
max_its |
Maximum number of iterations after which the algorithm will stop. |
rss_diff_stop |
Maximum difference between the residual sum of squares of the model in two consecutive iterations for the algorithm to converge. |
The first stage of EDec performs constrained matrix factorization to find
cell type specific methylation profiles and constituent cell type proportions
that minimize the Euclidian distance between their linear combination and the
original matrix of tissue methylation profiles. The minimization algorithm
involves an iterative procedure that, in each round, alternates between
estimating constituent cell type proportions (using estimate_props_qp
function) and methylation profiles (using estimate_meth_qp
function)
by solving constrained least squares problems through quadratic programming.
The minimization problem is made tractable by the constraints that
methylation measurements (beta values) and cell type proportions are numbers
in the 0,1 interval, and that cell type proportions within a sample add up
to one. These constraints restrict the space of possible solutions, thus
making it possible for the local iterative search to reproducibly find a
global minimum and an accurate solution. One key requirement for EDec is that
cell type proportions vary across samples. A second requirement is that there
must be significant differences across constituent cell type methylation
profiles. The latter requirement can be met by providing EDec with loci
expected to vary in methylation levels across constituent cell types.
A list with the following components:
methylation
A matrix with average methylation profiles of constituent cell types. Rows represent different loci/probes and columns represent different cell types.
proportions
A matrix with proportions of constituent cell types in each input sample. Rows represent different samples. Columns represent different cell types.
iterations
Number of iterations the method went through before reaching convergence or maximum number of iterations.
explained.variance
Proportion of variance in input methylation profiles over informative loci explained by the final model.
res.sum.squares
Residual sum of squares for the final model over the set of informative loci.
aic
Akaike Information Criterion for the final model over the set of informative loci.
rss.per.iteration
Vector of residual sum of squares for the models generated in each iteration of the algorithm.
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