Krishnan troyanskaya

A key challenge for the diagnosis and treatment of complex human diseases is identifying their molecular basis. Here, we developed a unified computational framework, URSAHD (Unveiling RNA Sample Annotation for Human Diseases), that leverages machine learning and the hierarchy of anatomical relationships present among diseases to integrate thousands of clinical gene expression profiles and identify molecular characteristics specific to each of the hundreds of complex diseases. URSAHD can distinguish between closely related diseases more accurately than literature-validated genes or traditional differential-expression-based computational approaches and is applicable to any disease, including rare and understudied ones...

GIANT2 (Genome-wide Integrated Analysis of gene Networks in Tissues) is an interactive web server that enables biomedical researchers to analyze their proteins and pathways of interest and generate hypotheses in the context of genome-scale functional maps of human tissues. The precise actions of genes are frequently dependent on their tissue context, yet direct assay of tissue-specific protein function and interactions remains infeasible in many normal human tissues and cell-types. With GIANT2, researchers can explore predicted tissue-specific functional roles of genes and reveal changes in those roles across tissues, all through interactive multi-network visualizations and analyses...

A common goal in data-analysis is to sift through a large data-matrix and detect any significant submatrices (i.e., biclusters) that have a low numerical rank. We present a simple algorithm for tackling this biclustering problem. Our algorithm accumulates information about 2-by-2 submatrices (i.e., 'loops') within the data-matrix, and focuses on rows and columns of the data-matrix that participate in an abundance of low-rank loops. We demonstrate, through analysis and numerical-experiments, that this loop-counting method performs well in a variety of scenarios, outperforming simple spectral methods in many situations of interest...

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes-about 65 genes out of an estimated several hundred-are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence...

IMP (Integrative Multi-species Prediction), originally released in 2012, is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides biologists with a framework to analyze their candidate gene sets in the context of functional networks, expanding or refining their sets using functional relationships predicted from integrated high-throughput data...

Functional Networks of Tissues in Mouse (FNTM) provides biomedical researchers with tissue-specific predictions of functional relationships between proteins in the most widely used model organism for human disease, the laboratory mouse. Users can explore FNTM-predicted functional relationships for their tissues and genes of interest or examine gene function and interaction predictions across multiple tissues, all through an interactive, multi-tissue network browser. FNTM makes predictions based on integration of a variety of functional genomic data, including over 13 000 gene expression experiments, and prior knowledge of gene function...

Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, identify the changing functional roles of genes across tissues and illuminate relationships among diseases...

The diagnosis of Parkinson's disease (PD) is usually not established until advanced neurodegeneration leads to clinically detectable symptoms. Previous blood PD transcriptome studies show low concordance, possibly resulting from the use of microarray technology, which has high measurement variation. The Leucine-rich repeat kinase 2 (LRRK2) G2019S mutation predisposes to PD. Using preclinical and clinical studies, we sought to develop a novel statistically motivated transcriptomic-based approach to identify a molecular signature in the blood of Ashkenazi Jewish PD patients, including LRRK2 mutation carriers...

We present SEEK (search-based exploration of expression compendia; https://seek.princeton.edu/), a query-based search engine for very large transcriptomic data collections, including thousands of human data sets from many different microarray and high-throughput sequencing platforms. SEEK uses a query-level cross-validation-based algorithm to automatically prioritize data sets relevant to the query and a robust search approach to identify genes, pathways and processes co-regulated with the query. SEEK provides multigene query searching with iterative metadata-based search refinement and extensive visualization-based analysis options...

MOTIVATION: Leveraging the large compendium of genomic data to predict biomedical pathways and specific mechanisms of protein interactions genome-wide in metazoan organisms has been challenging. In contrast to unicellular organisms, biological and technical variation originating from diverse tissues and cell-lineages is often the largest source of variation in metazoan data compendia. Therefore, a new computational strategy accounting for the tissue heterogeneity in the functional genomic data is needed to accurately translate the vast amount of human genomic data into specific interaction-level hypotheses...

MOTIVATION: Leveraging gene expression data through large-scale integrative analyses for multicellular organisms is challenging because most samples are not fully annotated to their tissue/cell-type of origin. A computational method to classify samples using their entire gene expression profiles is needed. Such a method must be applicable across thousands of independent studies, hundreds of gene expression technologies and hundreds of diverse human tissues and cell-types. RESULTS: We present Unveiling RNA Sample Annotation (URSA) that leverages the complex tissue/cell-type relationships and simultaneously estimates the probabilities associated with hundreds of tissues/cell-types for any given gene expression profile...