Accurate and Efficient Estimation of Small P-values with the Cross-Entropy Method: Applications in Genomic Data Analysis

Small $p$-values are often required to be accurately estimated in large scale genomic studies for the adjustment of multiple hypothesis tests and the ranking of genomic features based on their statistical significance. For those complicated test statistics whose cumulative distribution functions are analytically intractable, existing methods usually do not work well with small $p$-values due to lack of accuracy or computational restrictions. We propose a general approach for accurately and efficiently calculating small $p$-values for a broad range of complicated test statistics based on the principle of the cross-entropy method and Markov chain Monte Carlo sampling techniques. We evaluate the performance of the proposed algorithm through simulations and demonstrate its application to three real examples in genomic studies. The results show that our approach can accurately evaluate small to extremely small $p$-values (e.g. $10^{-6}$ to $10^{-100}$). The proposed algorithm is helpful to the improvement of existing test procedures and the development of new test procedures in genomic studies.Comment: 34 pages, 1 figure, 4 table

Default Patterning Produces Pan-cortical Glutamatergic and CGE/LGE-like GABAergic Neurons from Human Pluripotent Stem Cells

Default differentiation of human pluripotent stem cells has been promoted as a model of cortical development. In this study, a developmental transcriptome analysis of default-differentiated hPSNs revealed a gene expression program resembling in vivo CGE/LGE subpallial domains and GABAergic signaling. A combination of bioinformatic, functional, and immunocytochemical analysis further revealed that hPSNs consist of both cortical glutamatergic and CGE-like GABAergic neurons. This study provides a comprehensive characterization of the heterogeneous group of neurons produced by default differentiation and insight into future directed differentiation strategies

Identification of a small molecule yeast TORC1 inhibitor with a flow cytometry-based multiplex screen

TOR (target of rapamycin) is a serine/threonine kinase, evolutionarily conserved from yeast to human, which functions as a fundamental controller of cell growth. The moderate clinical benefit of rapamycin in mTOR-based therapy of many cancers favors the development of new TOR inhibitors. Here we report a high throughput flow cytometry multiplexed screen using five GFPtagged yeast clones that represent the readouts of four branches of the TORC1 signaling pathway in budding yeast. Each GFP-tagged clone was differentially color-coded and the GFP signal of each clone was measured simultaneously by flow cytometry, which allows rapid prioritization of compounds that likely act through direct modulation of TORC1 or proximal signaling components. A total of 255 compounds were confirmed in dose-response analysis to alter GFP expression in one or more clones. To validate the concept of the high throughput screen, we have characterized CID 3528206, a small molecule most likely to act on TORC1 as it alters GFP expression in all five GFP clones in an analogous manner to rapamycin. We have shown that CID 3528206 inhibited yeast cell growth, and that CID 3528206 inhibited TORC1 activity both in vitro and in vivo with EC50s of 150 nM and 3.9 μM, respectively. The results of microarray analysis and yeast GFP collection screen further support the notion that CID 3528206 and rapamycin modulate similar cellular pathways. Together, these results indicate that the HTS has identified a potentially useful small molecule for further development of TOR inhibitors

Application of multidisciplinary analysis to gene expression.

Molecular analysis of cancer, at the genomic level, could lead to individualized patient diagnostics and treatments. The developments to follow will signal a significant paradigm shift in the clinical management of human cancer. Despite our initial hopes, however, it seems that simple analysis of microarray data cannot elucidate clinically significant gene functions and mechanisms. Extracting biological information from microarray data requires a complicated path involving multidisciplinary teams of biomedical researchers, computer scientists, mathematicians, statisticians, and computational linguists. The integration of the diverse outputs of each team is the limiting factor in the progress to discover candidate genes and pathways associated with the molecular biology of cancer. Specifically, one must deal with sets of significant genes identified by each method and extract whatever useful information may be found by comparing these different gene lists. Here we present our experience with such comparisons, and share methods developed in the analysis of an infant leukemia cohort studied on Affymetrix HG-U95A arrays. In particular, spatial gene clustering, hyper-dimensional projections, and computational linguistics were used to compare different gene lists. In spatial gene clustering, different gene lists are grouped together and visualized on a three-dimensional expression map, where genes with similar expressions are co-located. In another approach, projections from gene expression space onto a sphere clarify how groups of genes can jointly have more predictive power than groups of individually selected genes. Finally, online literature is automatically rearranged to present information about genes common to multiple groups, or to contrast the differences between the lists. The combination of these methods has improved our understanding of infant leukemia. While the complicated reality of the biology dashed our initial, optimistic hopes for simple answers from microarrays, we have made progress by combining very different analytic approaches

A Two-Part Mixed Model for Differential Expression Analysis in Single-Cell High-Throughput Gene Expression Data

The high-throughput gene expression data generated from recent single-cell RNA sequencing (scRNA-seq) and parallel single-cell reverse transcription quantitative real-time PCR (scRT-qPCR) technologies enable biologists to study the function of transcriptome at the level of individual cells. Compared with bulk RNA-seq and RT-qPCR gene expression data, single-cell data show notable distinct features, including excessive zero expression values, high variability, and clustered design. We propose to model single-cell high-throughput gene expression data using a two-part mixed model, which not only adequately accounts for the aforementioned features of single-cell expression data but also provides the flexibility of adjusting for covariates. An efficient computational algorithm, automatic differentiation, is used for estimating the model parameters. Compared with existing methods, our approach shows improved power for detecting differential expressed genes in single-cell high-throughput gene expression data

The Expression Pattern of Adhesion G Protein-Coupled Receptor F5 Is Related to Cell Adhesion and Metastatic Pathways in Colorectal Cancer—Comprehensive Study Based on In Silico Analysis

Adhesion G protein-coupled receptor F5 (ADGRF5) is involved inthe neoplastic transformation of some cancer types. However, the significance of ADGRF5 expression signature and the impact of signaling pathways mediated by ADGRF5 during neoplastic transformation of the colon and colorectal cancer (CRC) progression has been poorly examined. Using Gene Expression Omnibus and The Cancer Genome Atlas datasets, we showed that ADGRF5 is overexpressed in the colons of patients with CRC. In line, combined analysis of ADGRF5 expression with clinical characterization revealed an increased expression of ADGRF5 in patients with more advanced stages of CRC compared to patients with early stages of CRC. The Spearman correlation analysis documented numerous genes positively and negatively correlated with the expression pattern of ADGRF5 in the colon of patients with CRC. In the colon of CRC patients, the expression signature of ADGRF5 was associated with genes participating in phosphatidylinositol 3-kinase/Akt, focal adhesion, cell adhesion molecules, and ribosome signaling pathways. Of note, ADGRF5 expression correlated with the levels of tumor-infiltrating immune cells in the colon of CRC patients. Moreover, we found that CRC patients with high expression of ADGRF5 had a significantly lower probability of overall survival and disease-free survival. In conclusion, our results support the prognostic value of ADGRF5 and its potent therapeutic implication in CRC

Two-step mixed model approach to analyzing differential alternative RNA splicing.

Changes in gene expression can correlate with poor disease outcomes in two ways: through changes in relative transcript levels or through alternative RNA splicing leading to changes in relative abundance of individual transcript isoforms. The objective of this research is to develop new statistical methods in detecting and analyzing both differentially expressed and spliced isoforms, which appropriately account for the dependence between isoforms and multiple testing corrections for the multi-dimensional structure of at both the gene- and isoform- level. We developed a linear mixed effects model-based approach for analyzing the complex alternative RNA splicing regulation patterns detected by whole-transcriptome RNA-sequencing technologies. This approach thoroughly characterizes and differentiates three types of genes related to alternative RNA splicing events with distinct differential expression/splicing patterns. We applied the concept of appropriately controlling for the gene-level overall false discovery rate (OFDR) in this multi-dimensional alternative RNA splicing analysis utilizing a two-step hierarchical hypothesis testing framework. In the initial screening test we identify genes that have differentially expressed or spliced isoforms; in the subsequent confirmatory testing stage we examine only the isoforms for genes that have passed the screening tests. Comparisons with other methods through application to a whole transcriptome RNA-Seq study of adenoid cystic carcinoma and extensive simulation studies have demonstrated the advantages and improved performances of our method. Our proposed method appropriately controls the gene-level OFDR, maintains statistical power, and is flexible to incorporate advanced experimental designs

Default Patterning Produces Pan-cortical Glutamatergic and CGE/LGE-like GABAergic Neurons from Human Pluripotent Stem Cells

Summary: Default differentiation of human pluripotent stem cells has been promoted as a model of cortical development. In this study, a developmental transcriptome analysis of default-differentiated hPSNs revealed a gene expression program resembling in vivo CGE/LGE subpallial domains and GABAergic signaling. A combination of bioinformatic, functional, and immunocytochemical analysis further revealed that hPSNs consist of both cortical glutamatergic and CGE-like GABAergic neurons. This study provides a comprehensive characterization of the heterogeneous group of neurons produced by default differentiation and insight into future directed differentiation strategies. : Default differentiation of human pluripotent stem cell-derived neurons (hPSNs) is thought to be a model of cortical differentiation. The authors performed transcriptome, time-course analysis of developing and mature hPSNs. In addition to cortical glutamatergic neurons, default differentiation led to significant CGE/LGE-specific GABAergic patterning. This work comprehensively characterizes the heterogeneity of neurons produced by default differentiation. Keywords: human pluripotent stem cells, cortical neurons, subpallium, CGE, development, microarray, COUPTFII, CALB

Ovarian Tumor Microenvironment Signaling: Convergence on the Rac1 GTPase

The tumor microenvironment for epithelial ovarian cancer is complex and rich in bioactive molecules that modulate cell-cell interactions and stimulate numerous signal transduction cascades. These signals ultimately modulate all aspects of tumor behavior including progression, metastasis and therapeutic response. Many of the signaling pathways converge on the small GTPase Ras-related C3 botulinum toxin substrate (Rac)1. In addition to regulating actin cytoskeleton remodeling necessary for tumor cell adhesion, migration and invasion, Rac1 through its downstream effectors, regulates cancer cell survival, tumor angiogenesis, phenotypic plasticity, quiescence, and resistance to therapeutics. In this review we discuss evidence for Rac1 activation within the ovarian tumor microenvironment, mechanisms of Rac1 dysregulation as they apply to ovarian cancer, and the potential benefits of targeting aberrant Rac1 activity in this disease. The potential for Rac1 contribution to extraperitoneal dissemination of ovarian cancer is addressed