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Cellular heterogeneity is present in almost all gene expression profiles. However, transcriptome analysis of tissue specimens often ignores the cellular heterogeneity present in these samples. Standard deconvolution algorithms require prior knowledge of the cell type frequencies within a tissue or their in vitro expression profiles.

Furthermore, these algorithms tend to report biased estimations. Here, we describe a Digital Sorting Algorithm (DSA) for extracting cell-type specific gene expression profiles from mixed tissue samples that is unbiased and does not require prior knowledge of cell type frequencies. .

Selecting genes and pathways indicative of disease is a central problem in computational biology. This problem is especially challenging when parsing multi-dimensional genomic data. A number of tools, such as L1-norm based regularization and its extensions elastic net and fused lasso, have been introduced to deal with this challenge. However, these approaches tend to ignore the vast amount of a priori biological network information curated in the literature.

We propose the use of graph Laplacian regularized logistic regression to integrate biological networks into disease classification and pathway association problems. Simulation studies demonstrate that the performance of the proposed algorithm is superior to elastic net and lasso analyses. Utility of this algorithm is also validated by its ability to reliably differentiate breast cancer subtypes using a large breast cancer dataset recently generated by the Cancer Genome Atlas (TCGA) consortium. Many of the protein-protein interaction modules identified by our approach are further supported by evidence published in the literature. .

Combinatorial therapies play increasingly important roles in combating complex diseases. Due to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose and thus there is a great need of new algorithms for drug combination prediction.

Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, web developed a web-based tool [http://www.drug.liuzlab.org/] to allow users to iteratively search for optimal drug combinations given a user-defined gene set.

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