PRESTO, a new tool for integrating large-scale -omics data and discovering disease-specific signatures

Abstract

Background: Cohesive visualization and interpretation of hyperdimensional, large-scale -omics data is an ongoing challenge, particularly for biologists and clinicians involved in current highly complex sequencing studies. Multivariate studies are often better suited towards non-linear network analysis than differential expression testing. Here, we present PRESTO, a 'PREdictive Stochastic neighbor embedding Tool for Omics', which allows unsupervised dimensionality reduction of multivariate data matrices with thousands of subjects or conditions. PRESTO is intuitively integrated into an interactive user interface that helps to visualize the multidimensional patterns in genome-wide transcriptomic data from basic science and clinical studies.Results: PRESTO was tested with multiple input omics' platforms, including microarray and proteomics from both mouse and human clinical datasets. PRESTO can analyze up to tens of thousands of genes and shows no increase in processing time with a large number of samples or patients. In complex datasets, such as those with multiple time points, several patient groups, or diverse mouse strains, PRESTO outperformed conventional methods. Core co-expressed gene networks were intuitively grouped in clusters, or gates, after dimensionality reduction and remained consistent across users. Networks were identified and assigned to physiological and pathological functions that cannot be gleaned from conventional bioinformatics analyses. PRESTO detected gene networks from the natural variations among mouse macrophages and human blood leukocytes. We applied PRESTO to clinical transcriptomic and proteomic data from large patient cohorts and detected disease-defining signatures in antibody-mediated kidney transplant rejection, renal cell carcinoma, and relapsing acute myeloid leukemia (AML). In AML, PRESTO confirmed a previously described gene signature and found a new signature of 10 genes that is highly predictive of patient outcome. Conclusions: PRESTO offers an important integration of powerful bioinformatics tools with an interactive userinterface that increases data analysis accessibility beyond bioinformaticians and 'coders'. Here, we show that PRESTO out performs conventional methods, such as DE analysis, in multi-dimensional datasets and can identify biologically relevant co-expression gene networks. In paired samples or time points, co-expression networks could be compared for insight into longitudinal regulatory mechanisms. Additionally, PRESTO identified disease-specific signatures in clinical datasets with highly significant diagnostic and prognostic potential.

The Signaling Pathways Project: an integrated ‘omics knowledgebase for mammalian cellular signaling pathways

“…Many excellent tools and resources have been developed in the field of cell signaling 'omics [30][31][32][33][34][35][36][37][38][39][40][41] . Here, we set out to complement these resources to allow researches to routinely answer questions such as: What cell cycle-related factors are regulated by FGF receptors in the liver?…”

Data-Driven Kidney Transplant Phenotyping as a Histology-Independent Framework for Biomarker Discovery

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“…The robustness of the method has been previously tested. 27(preprint) K-Means is subsequently used to define clusters of genes, which can be interrogated in downstream analyses. The number of clusters was determined as a function of a standard statistical significance level (Supplemental Figure 2).…”

“…The number of clusters was determined as a function of a standard statistical significance level (Supplemental Figure 2). 27(preprint) In Figure 2, we used GSE36059 ( n =411) for PRESTO network analysis with 2296 genes (covariation threshold of 1.9) and t-SNE perplexity of 50. For the analysis of cell type–specific gene network activation, the PRESTO gene network mean activity was determined for each network (average gene expression of all genes per network) and correlated (Pearson) with the KID9plus3 results.…”

Tools and resources for metabolomics research community: A 2017–2018 update

“…PREdictive Stochastic neighbor embedding Tool for Omics (PRESTO) , implemented in MATLAB, allows unsupervised dimensionality reduction of multivariate data matrices with thousands of subjects or conditions . Further, PRESTO outperforms conventional methods, such as differential expression analysis, in multidimensional datasets and can identify biologically relevant coexpression gene networks.…”