Module analysis captures pancancer genetically and epigenetically deregulated cancer driver genes for smoking and antiviral response

Champion, Magali; Brennan, Kevin; Croonenborghs, Tom; Gentles, Andrew J.; Pochet, Nathalie; Gevaert, Olivier

doi:10.1101/216754

Cited by 8 publications

(18 citation statements)

References 96 publications

(111 reference statements)

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“…The solution to such least squares problem usually results in a dense incidence matrix W . Yet, in practice, it is believed that only a subset of driver genes regulate a given target, and hence the connection in the graph should be sparse [16,7]. In order to have a more robust model and sparse incidence matrices, the most common optimization problem for GRN inference is expressed as:…”

Section: Simic: Lasso With Similarity Constraintsmentioning

confidence: 99%

“…With the advent of next-generation sequencing, GRN inference has become one of the most important steps in determining cellular functions and modeling different systemic behaviors [4,3]. Although they do not reflect post-transcriptional modifications, GRNs have been successfully used in many applications to elucidate new biological mechanisms and gene-level relationships in cells [7,6]. evaluation [27,11].…”

Section: Introductionmentioning

confidence: 99%

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A single-cell gene regulatory network inference method for identifying complex regulatory dynamics across cell phenotypes

Peng

Chembazhi

Bangru

et al. 2020

Preprint

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Motivation: With the use of single-cell RNA sequencing (scRNA-Seq) technologies, it is now possible to acquire gene expression data for each individual cell in samples containing up to millions of cells. These cells can be further grouped into different states along an inferred cell differentiation path, which are potentially characterized by similar, but distinct enough, gene regulatory networks (GRNs). Hence, it would be desirable for scRNA-Seq GRN inference methods to capture the GRN dynamics across cell states. However, current GRN inference methods produce a unique GRN per input dataset (or independent GRNs per cell state), failing to capture these regulatory dynamics. Results: We propose a novel single-cell GRN inference method, named SimiC, that jointly infers the GRNs corresponding to each state. SimiC models the GRN inference problem as a LASSO optimization problem with an added similarity constraint, on the GRNs associated to contiguous cell states, that captures the intercell-state homogeneity. We show on a mouse hepatocyte single-cell data generated after partial hepatectomy that, contrary to previous GRN methods for scRNA-Seq data, SimiC is able to capture the transcription factor (TF) dynamics across liver regeneration, as well as the cell-level behavior for the regulatory program of each TF across cell states. In addition, on a honey bee scRNA-Seq experiment, SimiC is able to capture the increased heterogeneity of cells on whole-brain tissue with respect to a regional analysis tissue, and the TFs associated specifically to each sequenced tissue. Availability: SimiC is written in Python and includes an R API. It can be downloaded from https:// These networks are usually represented as graphs, where the nodes are genes, and the edges represent a regulatory (or co-expression) relationship between the genes that they connect. These graphs can be classified, among others, as: directed, if the regulatory direction is known; weighted, where the weight of each edge represents the regulatory strength of the connection; or bipartite, where genes are split into disjoint sets and edges only connect genes of distinct sets. In addition, some GRN inference methods follow a module-based approach, where genes are first clustered in modules and then a GRN is inferred per module, in contrast to other methods that build a unique single GRN for the data [16,30].Until recently, most available gene expression data were derived from bulk RNA sequencing (RNA-Seq). These sequencing techniques are inherently agnostic to differences among diversity of cell type within a given sample, and can therefore only give an average measure of the gene expressions across all cells. Hence, GRNs inferred from bulk RNA-Seq data represent the transcriptional regulatory landscape of the sequenced tissue rather than of the individual cells.With the advancement of single-cell RNA Sequencing technologies (scRNA-Seq), it is now possible to acquire gene expression data for individual cells in samples containing up to millions of cells. scRNA-Seq ...

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Section: Simic: Lasso With Similarity Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A single-cell gene regulatory network inference method for identifying complex regulatory dynamics across cell phenotypes

Peng

Chembazhi

Bangru

et al. 2020

Preprint

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“…The complex nature of disease development and interplay between interacting biological aberrations -genetic, epigenetic, somatic or germline -often makes it difficult to elucidate causal mechanisms of cancer development'. Furthermore, there is still much work in multi-omics to elucidate causal flows of information influencing cellular physiology and pathology and to discriminate how separate phenomena are linked to create cancer (3,5,56,58). However, integrated multi-omic approaches like ProteoMix can provide additional insights into pathways and processes involved in oncogenesis and how they manifest as clinical phenotypes.…”

Section: Moreover Early Proteomic Techniques Such As Those Utilized mentioning

confidence: 99%

“…Therefore, comprehensive studies are needed to understand the molecular basis of disease. Toward this end a multi-institutional consortium, The Cancer Genome Atlas (TCGA), has extensively characterized numerous cancer sites producing genome wide data for mutations, copy number alterations (CNA), RNA expression, microRNA expression, and DNA methylation (1)(2)(3)(4)(5).…”

Section: Introductionmentioning

confidence: 99%

Proteogenomic view of cancer epigenetics: the impact of DNA methylation on the cancer proteome

Prunello

Brennan

et al. 2018

Preprint

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Aberrant DNA methylation disrupts normal gene expression in cancer and broadly contributes to oncogenesis. We previously developed MethylMix, a model-based algorithmic approach to identify epigenetically regulated driver genes. MethylMix identifies genes where methylation likely executes a functional role by using transcriptomic data to select only methylation events that can be linked to changes in gene expression. However, given that proteins more closely link genotype to phenotype recent high-throughput proteomic data provides an opportunity to more accurately identify functionally relevant abnormal methylation events. Here we present ProteoMix, which refines nominations for epigenetic driver genes by leveraging quantitative highthroughput proteomic data to select only genes where DNA methylation is predictive of protein abundance. Applying our algorithm across three cancer cohorts we find that ProteoMix narrows candidate nominations, where the effect of DNA methylation is often buffered at the protein level. Next, we find that ProteoMix genes are enriched for biological processes involved in cancer including functions involved in epithelial and mesenchymal transition. ProteoMix results are also enriched for tumor markers which are predictive of clinical features like tumor stage and we find clustering on ProteoMix genes captures cancer subtypes.

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“…RNA sequencing data and DNA methylation data have been widely investigated in pan-cancer analyses to identify cancer driver genes or biomarkers (Byron, et al, 2016;Gevaert, et al, 2015;Kulis and Esteller, 2010;Litovkin, et al, 2015;Tomczak, et al, 2015). Many such studies on pan-cancer genomics require complete datasets (Champion, et al, 2018). However, missing values are frequently present in these data due to various reasons including low resolution, missing probes, and artifacts (Baghfalaki, et al, 2016;Libbrecht and Noble, 2015).…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework for imputing missing values in genomic data

Qiu

Zheng

Gevaert

2018

Preprint

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Motivation:The presence of missing values is a frequent problem encountered in genomic data analysis. Lost data can be an obstacle to downstream analyses that require complete data matrices. State-of-the-art imputation techniques including Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN) based methods usually achieve good performances, but are computationally expensive especially for large datasets such as those involved in pan-cancer analysis. Results:This study describes a new method: a denoising autoencoder with partial loss (DAPL) as a deep learning based alternative for data imputation. Results on pan-cancer gene expression data and DNA methylation data from over 11,000 samples demonstrate significant improvement over standard denoising autoencoder for both data missing-at-random cases with a range of missing percentages, and missing-not-at-random cases based on expression level and GCcontent. We discuss the advantages of DAPL over traditional imputation methods and show that it achieves comparable or better performance with less computational burden.

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Module analysis captures pancancer genetically and epigenetically deregulated cancer driver genes for smoking and antiviral response

Cited by 8 publications

References 96 publications

A single-cell gene regulatory network inference method for identifying complex regulatory dynamics across cell phenotypes

A single-cell gene regulatory network inference method for identifying complex regulatory dynamics across cell phenotypes

Proteogenomic view of cancer epigenetics: the impact of DNA methylation on the cancer proteome

A deep learning framework for imputing missing values in genomic data

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