Non-negative Independent Factor Analysis disentangles discrete and continuous sources of variation in scRNA-seq data.

Motivation: Single-cell RNA-seq analysis has emerged as a powerful tool for understanding inter-cellular heterogeneity. Due to the inherent noise of the data, computational techniques often rely on dimensionality reduction (DR) as both a pre-processing step and an analysis tool. Ideally, DR should preserve the biological information while discarding the noise.

Assessing the contribution of tumor mutational phenotypes to cancer progression risk.

Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, which act in different combinations or degrees in different cancers.

Neural Network Deconvolution Method for Resolving Pathway-Level Progression of Tumor Clonal Expression Programs With Application to Breast Cancer Brain Metastases.

Metastasis is the primary mechanism by which cancer results in mortality and there are currently no reliable treatment options once it occurs, making the metastatic process a critical target for new diagnostics and therapeutics. Treating metastasis before it appears is challenging, however, in part because metastases may be quite distinct genomically from the primary tumors from which they presumably emerged. Phylogenetic studies of cancer development have suggested that changes in tumor genomics over stages of progression often result from shifts in the abundance of clonal cellular populations, as late stages of progression may derive from or select for clonal populations rare in the primary tumor.

DataRemix: a universal data transformation for optimal inference from gene expression datasets.

Motivation: RNA-seq technology provides unprecedented power in the assessment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery. However, raw gene expression values have to be normalized for nuisance biological variation and technical covariates, and different normalization strategies can lead to dramatically different results in the downstream study.

Results: We describe a generalization of singular value decomposition-based reconstruction for which the common techniques of whitening, rank-k approximation and removing the top k principal components are special cases.

Robust and accurate deconvolution of tumor populations uncovers evolutionary mechanisms of breast cancer metastasis.

Motivation: Cancer develops and progresses through a clonal evolutionary process. Understanding progression to metastasis is of particular clinical importance, but is not easily analyzed by recent methods because it generally requires studying samples gathered years apart, for which modern single-cell sequencing is rarely an option. Revealing the clonal evolution mechanisms in the metastatic transition thus still depends on unmixing tumor subpopulations from bulk genomic data.

Modeling Effects of RNA on Capsid Assembly Pathways via Coarse-Grained Stochastic Simulation.

The environment of a living cell is vastly different from that of an in vitro reaction system, an issue that presents great challenges to the use of in vitro models, or computer simulations based on them, for understanding biochemistry in vivo. Virus capsids make an excellent model system for such questions because they typically have few distinct components, making them amenable to in vitro and modeling studies, yet their assembly can involve complex networks of possible reactions that cannot be resolved in detail by any current experimental technology. We previously fit kinetic simulation parameters to bulk in vitro assembly data to yield a close match between simulated and real data, and then used the simulations to study features of assembly that cannot be monitored experimentally.

Medoidshift clustering applied to genomic bulk tumor data.

Despite the enormous medical impact of cancers and intensive study of their biology, detailed characterization of tumor growth and development remains elusive. This difficulty occurs in large part because of enormous heterogeneity in the molecular mechanisms of cancer progression, both tumor-to-tumor and cell-to-cell in single tumors. Advances in genomic technologies, especially at the single-cell level, are improving the situation, but these approaches are held back by limitations of the biotechnologies for gathering genomic data from heterogeneous cell populations and the computational methods for making sense of those data.

Automated identification of abnormal respiratory ciliary motion in nasal biopsies.

Motile cilia lining the nasal and bronchial passages beat synchronously to clear mucus and foreign matter from the respiratory tract. This mucociliary defense mechanism is essential for pulmonary health, because respiratory ciliary motion defects, such as those in patients with primary ciliary dyskinesia (PCD) or congenital heart disease, can cause severe sinopulmonary disease necessitating organ transplant. The visual examination of nasal or bronchial biopsies is critical for the diagnosis of ciliary motion defects, but these analyses are highly subjective and error-prone.

Phylogenetic analysis of multiple FISH markers in oral tongue squamous cell carcinoma suggests that a diverse distribution of copy number changes is associated with poor prognosis.

Oral tongue squamous cell carcinoma (OTSCC) is associated with poor prognosis. To improve prognostication, we analyzed four gene probes (TERC, CCND1, EGFR and TP53) and the centromere probe CEP4 as a marker of chromosomal instability, using fluorescence in situ hybridization (FISH) in single cells from the tumors of sixty-five OTSCC patients (Stage I, n = 15; Stage II, n = 30; Stage III, n = 7; Stage IV, n = 13). Unsupervised hierarchical clustering of the FISH data distinguished three clusters related to smoking status.

A Novel Subset of Human Tumors That Simultaneously Overexpress Multiple E2F-responsive Genes Found in Breast, Ovarian, and Prostate Cancers.

Reasoning that overexpression of multiple E2F-responsive genes might be a useful marker for RB1 dysfunction, we compiled a list of E2F-responsive genes from the literature and evaluated their expression in publicly available gene expression microarray data of patients with breast cancer, serous ovarian cancer, and prostate cancer. In breast cancer, a group of tumors was identified, each of which simultaneously overexpressed multiple E2F-responsive genes. Seventy percent of these genes were concerned with cell cycle progression, DNA repair, or mitosis.

Single-cell genetic analysis reveals insights into clonal development of prostate cancers and indicates loss of PTEN as a marker of poor prognosis.

Gauging the risk of developing progressive disease is a major challenge in prostate cancer patient management. We used genetic markers to understand genomic alteration dynamics during disease progression. By using a novel, advanced, multicolor fluorescence in situ hybridization approach, we enumerated copy numbers of six genes previously identified by array comparative genomic hybridization to be involved in aggressive prostate cancer [TBL1XR1, CTTNBP2, MYC (alias c-myc), PTEN, MEN1, and PDGFB] in six nonrecurrent and seven recurrent radical prostatectomy cases.

Algorithms to model single gene, single chromosome, and whole genome copy number changes jointly in tumor phylogenetics.

We present methods to construct phylogenetic models of tumor progression at the cellular level that include copy number changes at the scale of single genes, entire chromosomes, and the whole genome. The methods are designed for data collected by fluorescence in situ hybridization (FISH), an experimental technique especially well suited to characterizing intratumor heterogeneity using counts of probes to genetic regions frequently gained or lost in tumor development. Here, we develop new provably optimal methods for computing an edit distance between the copy number states of two cells given evolution by copy number changes of single probes, all probes on a chromosome, or all probes in the genome.

Applying molecular crowding models to simulations of virus capsid assembly in vitro.

Virus capsid assembly has been widely studied as a biophysical system, both for its biological and medical significance and as an important model for complex self-assembly processes. No current technology can monitor assembly in detail and what information we have on assembly kinetics comes exclusively from in vitro studies. There are many differences between the intracellular environment and that of an in vitro assembly assay, however, that might be expected to alter assembly pathways.

Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM.

Unlabelled: High-dimensional '-omics' profiling provides a detailed molecular view of individual cancers; however, understanding the mechanisms by which tumors evade cellular defenses requires deep knowledge of the underlying cellular pathways within each cancer sample. We extended the PARADIGM algorithm (Vaske et al., 2010, Bioinformatics, 26, i237-i245), a pathway analysis method for combining multiple '-omics' data types, to learn the strength and direction of 9139 gene and protein interactions curated from the literature.

Phylogenetic analysis of multiprobe fluorescence in situ hybridization data from tumor cell populations.

Motivation: Development and progression of solid tumors can be attributed to a process of mutations, which typically includes changes in the number of copies of genes or genomic regions. Although comparisons of cells within single tumors show extensive heterogeneity, recurring features of their evolutionary process may be discerned by comparing multiple regions or cells of a tumor. A useful source of data for studying likely progression of individual tumors is fluorescence in situ hybridization (FISH), which allows one to count copy numbers of several genes in hundreds of single cells.