Single‐cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene marker panels for such populations remains a challenge. In this work, we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single‐cell RNA‐seq data. We show that COMET outperforms other methods for the identification of single‐gene panels and enables, for the first time, prediction of multi‐gene marker panels ranked by relevance. Staining by flow cytometry assay confirmed the accuracy of COMET's predictions in identifying marker panels for cellular subtypes, at both the single‐ and multi‐gene levels, validating COMET's applicability and accuracy in predicting favorable marker panels from transcriptomic input. COMET is a general non‐parametric statistical framework and can be used as‐is on various high‐throughput datasets in addition to single‐cell RNA‐sequencing data. COMET is available for use via a web interface (http://www.cometsc.com/) or a stand‐alone software package (https://github.com/MSingerLab/COMETSC).

Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs.

Single-cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene-marker panels for such populations remains a challenge. In this work we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single-cell RNA-seq data. We show that COMET outperforms other methods for the identification of single-gene panels, and enables, for the first time, prediction of multi-gene marker panels ranked by relevance. Staining by flow-cytometry assay confirmed the accuracy of COMET's predictions in identifying marker-panels for cellular subtypes, at both the single-and multi-gene levels, validating COMET's applicability and accuracy in predicting favorable marker-panels from transcriptomic input. COMET is a general non-parametric statistical framework and can be used as-is on various high-throughput datasets in addition to single-cell RNA-sequencing data. COMET is available for use via a web interface (http://www.cometsc.com/) or a standalone software package (https://github.com/MSingerLab/COMETSC).

Animals exhibit extreme diversity in regenerative ability. This likely reflects different, lineage-specific selective pressures in their evolutionary histories, but how specific molecular features of regenerative programs help solve species-specific challenges has not been examined in detail. Here we discover that, in the highly-regenerative axolotl salamander, a conserved, body-wide stem cell activation response triggered in response to limb removal primes undisturbed limbs for regeneration upon subsequent amputation. This response should be particularly useful to salamanders, which frequently lose limbs in response to cannibalism. We further demonstrate the body-wide response requires both peripheral nervous system input at these distant sites and mTOR signaling. We defined gene expression changes within the nerves and nearby tissues, harboring responsive stem cells, leading to identification of candidate genetic pathways influencing distant stem cell activation following amputation. Functional experimentation confirmed a requirement for adrenergic signaling in amputation-induced activation of distant stem cells. These findings reveal a direct link between systemic cellular activation responses to local tissue damage and overall regenerative ability. Similar systemic activation responses to tissue removal have been observed in animals with widely differing regenerative abilities (e.g., planaria to mice), suggesting that it is the responses downstream of these signals, likely sculpted by differing evolutionary pressures, that ultimately distinguish regenerators from non-regenerators.

The mixed-membership stochastic block model (MMSBM) is a common model for social networks. Given an n-node symmetric network generated from a K-community MMSBM, we would like to test K = 1 versus K > 1. We first study the degree-based χ 2 test and the orthodox Signed Quadrilateral (oSQ) test. These two statistics estimate an order-2 polynomial and an order-4 polynomial of a "signal" matrix, respectively. We derive the asymptotic null distribution and power for both tests. However, for each test, there exists a parameter regime where its power is unsatisfactory. It motivates us to propose a power enhancement (PE) test to combine the strengths of both tests. We show that the PE test has a tractable null distribution and improves the power of both tests. To assess the optimality of PE, we consider a randomized setting, where the n membership vectors are independently drawn from a distribution on the standard simplex. We show that the success of global testing is governed by a quantity βn(K, P, h), which depends on the community structure matrix P and the mean vector h of memberships. For each given (K, P, h), a test is called optimal if it distinguishes two hypotheses when βn(K, P, h) → ∞. A test is called optimally adaptive if it is optimal for all (K, P, h). We show that the PE test is optimally adaptive, while many existing tests are only optimal for some particular (K, P, h), hence, not optimally adaptive.