To install the package, start R (version 4.1.0 or higher) and enter:
if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("ISLET")
You may post your question on ISLET’s GitHub Issue section: https://github.com/haoharryfeng/ISLET/issues.
In clinical samples, the observed bulk sequencing/microarray data are often a mixture of different cell types. Because each unique cell type has its own gene expression profile, the real sequencing/microarray data are the weighted average of signals from multiple pure cell types. In high-throughput data analysis, the mixing proportions will confound with the primary phenotype-of-interest, if not properly accounted for. Over the past several years, researchers have gained substantial interests in using computational methods to deconvolute cell compositions. Under the assumption of a commonly shared feature-by-cell-type reference panel across all samples, deconvolution methods were developed. However, this assumption may not hold. For example, when repeated samples are measured for each subject, assuming a shared reference panel across different time points for each subject is a preferred choice over assuming a shared one across all the samples.
Here, we developed a method called
ISLET (Individual-Specific ceLl typE referencing Tool), to solve for the individual-specific and cell-type-specific reference panels, once the cell type proportions are given.
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 subject-specific panel, treated as missing values, are modeled by Gaussian distribution in the mixed-effect regression framework and estimated by an iterative Expectation–Maximization (EM) algorithm, 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.
ISLET can deconvolve reference panels based on the raw counts without batch effect in library size or the normalized counts such as Transcript Per Million (TPM). In the current version,
ISLET performs cell-type-specific differential expression analysis for two groups of subjects. Other covariates and additional groups will be added in future versions.