We developed a method called
ISLET
(Individual-Specific ceLl typE referencing Tool), to deconvolute mixture samples and obtain the individual-specific and cell-type-specific reference panels. ISLET
can leverage on multiple observations or temporal measurements of the same subject. ISLET
adopted a more reasonable assumption that repeated samples from the same subject would share the same reference panel. This unknown panel, treated as missing values, are estimated by an iterative Expectation--Maximization (EM) algorithm in mixed-effect regression framework, when combining all samples from all subjects together. This is the first statistical framework to estimate the subject-level cell-type-specific reference panel, for repeated measures. Our modeling can effectively borrow information across samples within the same subject.
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