Learning regulatory programs by threshold SVD regression

Ma, Xin; Xiao, Luo; Wong, Wing Hung

doi:10.1073/pnas.1417808111

Cited by 26 publications

(24 citation statements)

References 35 publications

(34 reference statements)

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Section: Rna-binding Proteinmentioning

confidence: 99%

“…Meanwhile, the TCGA project generated datasets for over 18 cancer types, which include gene expression, copy number alteration (CNA), DNA methylation, and somatic mutation profiles (10). All of these resources provided a rich base for cancer integrative analysis (11,12).Despite the rapid growth of genomic data, the knowledge on how gene expression programs in tumors are controlled by TFs is still limited. As one challenge, the experimental condition of public ChIP-seq data, such as stem cell line, may not match the physiological condition of a specific cancer type.…”

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confidence: 99%

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Inference of transcriptional regulation in cancers

Jiang

Freedman

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Despite the rapid accumulation of tumor-profiling data and transcription factor (TF) ChIP-seq profiles, efforts integrating TF binding with the tumor-profiling data to understand how TFs regulate tumor gene expression are still limited. To systematically search for cancerassociated TFs, we comprehensively integrated 686 ENCODE ChIPseq profiles representing 150 TFs with 7484 TCGA tumor data in 18 cancer types. For efficient and accurate inference on gene regulatory rules across a large number and variety of datasets, we developed an algorithm, RABIT (regression analysis with background integration). In each tumor sample, RABIT tests whether the TF target genes from ChIP-seq show strong differential regulation after controlling for background effect from copy number alteration and DNA methylation. When multiple ChIP-seq profiles are available for a TF, RABIT prioritizes the most relevant ChIP-seq profile in each tumor. In each cancer type, RABIT further tests whether the TF expression and somatic mutation variations are correlated with differential expression patterns of its target genes across tumors. Our predicted TF impact on tumor gene expression is highly consistent with the knowledge from cancer-related gene databases and reveals many previously unidentified aspects of transcriptional regulation in tumor progression. We also applied RABIT on RNAbinding protein motifs and found that some alternative splicing factors could affect tumor-specific gene expression by binding to target gene 3′UTR regions. Thus, RABIT (rabit.dfci.harvard.edu) is a general platform for predicting the oncogenic role of gene expression regulators.regulatory inference | tumor profiling | transcription factor | RNA-binding proteinT umorigenesis is a multistep process requiring alterations in gene expression programs (1, 2). Transcription factors (TFs) are instrumental in driving these gene expression programs, and misregulation of these TFs can result in the acquisition of tumorrelated properties (3). For example, E2F1 is overexpressed in many cancer types and promotes tumor proliferation by regulating expression of genes involved in cell differentiation, metabolism, and development (4). As another example, FOXM1 plays an important role in promoting cell proliferation and cell cycle progression through transcriptional activation of many G2/M-specific genes. Increased FOXM1 gene expression was detected in numerous cancer types, and FOXM1 is a promising therapeutic target for cancer treatment (5). TFs also play critical roles in inducing the tumor microenvironment for metastasis. For example, SNAI1/2, TWIST, and ZEB1/2 orchestrate the expression of genes involved in cell polarity, cell-cell contact, cytoskeleton structure, and extracellular matrix degradation. The joint effect of these TFs promotes cancer cell motility and invasion in the metastatic process (2, 6).With the rapid development of high-throughput technologies, large amounts of datasets have been generated for regulatory proteins. For example, the ENCODE project generated 689 C...

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Section: Rna-binding Proteinmentioning

confidence: 99%

mentioning

confidence: 99%

Inference of transcriptional regulation in cancers

Jiang

Freedman

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…Recently, we casted the matched data integration into a multivariate regression framework and proposed a new method, T-SVD [43]. The application example is to analyze miRNA and lncRNA data from The Cancer Genome Atlas (TCGA) consortium.…”

Section: Models and Algorithms For Matched Samplementioning

confidence: 99%

Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data

Wang

Jiang

Wong

2016

National Science Review

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Cell packs a lot of genetic and regulatory information through a structure known as chromatin, i.e. DNA is wrapped around histone proteins and is tightly packed in a remarkable way. To express a gene in a specific coding region, the chromatin would open up and DNA loop may be formed by interacting enhancers and promoters. Furthermore, the mediator and cohesion complexes, sequence-specific transcription factors, and RNA polymerase II are recruited and work together to elaborately regulate the expression level. It is in pressing need to understand how the information, about when, where, and to what degree genes should be expressed, is embedded into chromatin structure and gene regulatory elements. Thanks to large consortia such as Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomic projects, extensive data on chromatin accessibility and transcript abundance are available across many tissues and cell types. This rich data offer an exciting opportunity to model the causal regulatory relationship. Here, we will review the current experimental approaches, foundational data, computational problems, interpretive frameworks, and integrative models that will enable the accurate interpretation of regulatory landscape. Particularly, we will discuss the efforts to organize, analyze, model, and integrate the DNA accessibility data, transcriptional data, and functional genomic regions together. We believe that these efforts will eventually help us understand the information flow within the cell and will influence research directions across many fields.

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“…The reduced-rank representation has been further extended to enable variable selection [11, 6, 14], and it can be distilled from many other multivariate tools such as PCA, canonical correlation analysis, and matrix completion [9, 27]. It is evident that the reduced-rank methods have greatly facilitated scientific investigations in various disciplines, e.g., finance [39], ecology [12], neuroscience [55], genetics [31], etc.…”

Section: Introductionmentioning

confidence: 99%

Model diagnostics in reduced-rank estimation

Chen

2016

Statistics and Its Interface

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Reduced-rank methods are very popular in high-dimensional multivariate analysis for conducting simultaneous dimension reduction and model estimation. However, the commonly-used reduced-rank methods are not robust, as the underlying reduced-rank structure can be easily distorted by only a few data outliers. Anomalies are bound to exist in big data problems, and in some applications they themselves could be of the primary interest. While naive residual analysis is often inadequate for outlier detection due to potential masking and swamping, robust reduced-rank estimation approaches could be computationally demanding. Under Stein's unbiased risk estimation framework, we propose a set of tools, including leverage score and generalized information score, to perform model diagnostics and outlier detection in large-scale reduced-rank estimation. The leverage scores give an exact decomposition of the so-called model degrees of freedom to the observation level, which lead to exact decomposition of many commonly-used information criteria; the resulting quantities are thus named information scores of the observations. The proposed information score approach provides a principled way of combining the residuals and leverage scores for anomaly detection. Simulation studies confirm that the proposed diagnostic tools work well. A pattern recognition example with hand-writing digital images and a time series analysis example with monthly U.S. macroeconomic data further demonstrate the efficacy of the proposed approaches.

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Learning regulatory programs by threshold SVD regression

Cited by 26 publications

References 35 publications

Inference of transcriptional regulation in cancers

Inference of transcriptional regulation in cancers

Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data

Model diagnostics in reduced-rank estimation

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