Digitizing omics profiles by divergence from a baseline

Dinalankara, Wikum; Ke, Qian; Xu, Yiran; Ji, Lanlan; Pagane, Nicole; Lien, Anching; Matam, Tejasvi; Fertig, Elana J.; Price, Nathan D.; Younes, Laurent; Marchionni, Luigi; Geman, Donald

doi:10.1073/pnas.1721628115

Cited by 25 publications

(51 citation statements)

References 30 publications

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“…In the above results we have showcased a variety of analyses that can be performed with divergence at the univariate and multivariate level. While we have used RNA-seq data here, the software is applicable to many other modalities of high dimensional omics data, such as microarray data, CpG level methylation data, protein data, and microRNA expression data, some of which we have showed in [1]. Once the data has been processed as necessary and the baseline cohort identified, the R package can be used to compute the divergence coding quite easily.…”

Section: Resultsmentioning

confidence: 99%

An R package for divergence analysis of omics data

Dinalankara¹,

Geman

et al. 2019

Preprint

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Given the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. The divergence package, available on the R platform through the bioconductor repository collection, provides easy-to-use functions for carrying out this transformation.

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Section: Resultsmentioning

confidence: 99%

An R package for divergence analysis of omics data

Dinalankara¹,

Geman

et al. 2019

Preprint

Self Cite

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“…For the sake of simplicity, suppose that we know whether each biomelecule is depleted, enriched, or within its normal range. This simplified framing is similar to [8], who binarize omics profiles based on divergence from baseline. In our simple setting, we may write x i ∈ {−1, 0, 1} p .…”

Section: Background and Notationsmentioning

confidence: 99%

Refining interaction search through signed iterative Random Forests

Kumbier¹,

Basu

Brown

et al. 2018

Preprint

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Advances in supervised learning have enabled accurate prediction in biological systems governed by complex interactions among biomolecules. However, state-of-the-art predictive algorithms are typically "black-boxes," learning statistical interactions that are difficult to translate into testable hypotheses. The iterative Random Forest (iRF) algorithm took a step towards bridging this gap by providing a computationally tractable procedure to identify the stable, high-order feature interactions that drive the predictive accuracy of Random Forests (RF). Here we refine the interactions identified by iRF to explicitly map responses as a function of interacting features. Our method, signed iRF (s-iRF), describes "subsets" of rules that frequently occur on RF decision paths. We refer to these "rule subsets" as signed interactions. Signed interactions share not only the same set of interacting features but also exhibit similar thresholding behavior, and thus describe a consistent functional relationship between interacting features and responses. We describe stable and predictive importance metrics (SPIMs) to rank signed interactions in terms of their stability, predictive accuracy, and strength of interaction. For each SPIM, we define null importance metrics that characterize its expected behavior under known structure. We evaluate our proposed approach in biologically inspired simulations and two case studies: predicting enhancer activity and spatial gene expression patterns. In the case of enhancer activity, s-iRF recovers one of the few experimentally validated high-order interactions and suggests novel enhancer elements where this interaction may be active. In the case of spatial gene expression patterns, s-iRF recovers all 11 reported links in the gap gene network. By refining the process of interaction recovery, our approach has the potential to guide mechanistic inquiry into systems whose scale and complexity is beyond human comprehension.

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“…This variation drives tumor progression through dysregulation of key cancer pathways and contributes to the evolutionary fitness of tumors 3,4 . Differential variability analysis of bulk transcriptional data from microarrays and RNA-sequencing have also demonstrated that tumors with worse prognosis have a corresponding increase in transcriptional variation [5][6][7][8] . Single-cell RNA-sequencing (scRNA-seq) technologies provide an unprecedented ability to measure gene expression from individual cells, enabling in-depth exploration of tumor heterogeneity 9,10 .…”

Section: Introductionmentioning

confidence: 99%

Expression variation analysis for tumor heterogeneity in single-cell RNA-sequencing data

Davis-Marcisak

Orugunta

Stein-O’Brien

et al. 2018

Preprint

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Tumor heterogeneity provides a complex challenge to cancer treatment and is a critical component of therapeutic response, disease recurrence, and patient survival. Singlecell RNA-sequencing (scRNA-seq) technologies reveal the prevalence of intra-and inter-tumor heterogeneity. Computational techniques are essential to quantify the differences in variation of these profiles between distinct cell types, tumor subtypes, and patients to fully characterize intra-and inter-tumor molecular heterogeneity. We devised a new algorithm, Expression Variation Analysis in Single Cells (EVAsc), to perform multivariate statistical analyses of differential variation of expression in gene sets for scRNA-seq. EVAsc has high sensitivity and specificity to detect pathways with true differential heterogeneity in simulated data. We then apply EVAsc to several public domain scRNA-seq tumor datasets to quantify the landscape of tumor heterogeneity in several key applications in cancer genomics, i.e. immunogenicity, cancer subtypes, and metastasis. Immune pathway heterogeneity in hematopoietic cell populations in breast tumors corresponded to the amount diversity present in the T-cell repertoire of each individual. In head and neck squamous cell carcinoma (HNSCC) patients, we found dramatic differences in pathway dysregulation across basal primary tumors. Within the basal primary tumors we also identified increased immune dysregulation in individuals with a high proportion of fibroblasts present in the tumor microenvironment. Moreover, cells in HNSCC primary tumors had significantly more heterogeneity across pathways than cells in metastases, consistent with a model of clonal outgrowth. These results demonstrate the broad utility of EVAsc to quantify inter-and intra-tumor heterogeneity from scRNA-seq data without reliance on low dimensional visualization.

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Digitizing omics profiles by divergence from a baseline

Cited by 25 publications

References 30 publications

An R package for divergence analysis of omics data

An R package for divergence analysis of omics data

Refining interaction search through signed iterative Random Forests

Expression variation analysis for tumor heterogeneity in single-cell RNA-sequencing data

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