An official website of the United States government
Finding the network biomarkers of cancers and the analysis of cancer driving genes that are involved in these biomarkers are essential for understanding the dynamics of cancer. Clusters of genes in co-expression networks are commonly known as functional units. This work is based on the hypothesis that the dense clusters or communities in the gene co-expression networks of cancer patients may represent functional units regarding cancer initiation and progression. In this study, RNA-seq gene expression data of three cancers - Breast Invasive Carcinoma (BRCA), Colorectal Adenocarcinoma (COAD) and Glioblastoma Multiforme (GBM) - from The Cancer Genome Atlas (TCGA) are used to construct gene co-expression networks using Pearson Correlation. Six well-known community detection algorithms are applied on these networks to identify communities with five or more genes. A permutation test is performed to further mine the communities that are conserved in other cancers, thus calling them conserved communities. Then survival analysis is performed on clinical data of three cancers using the conserved community genes as prognostic co-variates. The communities that could distinguish the cancer patients between high- and low-risk groups are considered as cancer biomarkers. In the present study, 16 such network biomarkers are discovered.
more »
« less
Bipartite Tight Spectral Clustering (BiTSC) Algorithm for Identifying Conserved Gene Co-clusters in Two Species
Abstract Motivation Gene clustering is a widely-used technique that has enabled computational prediction of unknown gene functions within a species. However, it remains a challenge to refine gene function prediction by leveraging evolutionarily conserved genes in another species. This challenge calls for a new computational algorithm to identify gene co-clusters in two species, so that genes in each co-cluster exhibit similar expression levels in each species and strong conservation between the species. Results Here we develop the bipartite tight spectral clustering (BiTSC) algorithm, which identifies gene co-clusters in two species based on gene orthology information and gene expression data. BiTSC novelly implements a formulation that encodes gene orthology as a bipartite network and gene expression data as node covariates. This formulation allows BiTSC to adopt and combine the advantages of multiple unsupervised learning techniques: kernel enhancement, bipartite spectral clustering, consensus clustering, tight clustering, and hierarchical clustering. As a result, BiTSC is a flexible and robust algorithm capable of identifying informative gene co-clusters without forcing all genes into co-clusters. Another advantage of BiTSC is that it does not rely on any distributional assumptions. Beyond cross-species gene co-clustering, BiTSC also has wide applications as a general algorithm for identifying tight node co-clusters in any bipartite network with node covariates. We demonstrate the accuracy and robustness of BiTSC through comprehensive simulation studies. In a real data example, we use BiTSC to identify conserved gene co-clusters of D. melanogaster and C. elegans, and we perform a series of downstream analysis to both validate BiTSC and verify the biological significance of the identified co-clusters. Availability and implementation The Python package BiTSC is open-access and available at https://github.com/edensunyidan/BiTSC.
more »
« less
- Award ID(s):
- 1846216
- PAR ID:
- 10219953
- Editor(s):
- Birol, Inanc
- Date Published:
- Journal Name:
- Bioinformatics
- ISSN:
- 1367-4803
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Obulisamy Parthiba Karthikeyan (Ed.)Glycoside hydrolases (GHs) are carbohydrate-active enzymes essential for many environmental (e.g., carbon cycling) and biotechnological (e.g., biofuels) processes. The complete processing of carbohydrates by bacteria requires many enzymes acting synergistically. Here, I investigated the clustered or scattered distribution of 406,337 GH-genes and their association with transporter genes identified in 15,640 completely sequenced bacterial genomes. Different bacterial lineages displayed conserved levels of clustered or scattered GH-genes, but overall, the GH-genes clustering was generally higher than in randomized genomes. In lineages with highly clustered GH-genes (e.g.,Bacteroides, Paenibacillus), clustered genes shared the same orientation. These codirectional gene clusters potentially facilitate the genes' co-expression by allowing transcriptional read-through and, at least in some cases, forming operons. In several taxa, the GH-genes clustered with distinct types of transporter genes. The type of transporter genes and the distribution of the so-called GH:TR-genes clusters were conserved in selected lineages. Globally, the phylogenetically conserved clustering of the GH-genes with transporter genes highlights the central function of carbohydrate processing across bacterial lineages. In addition, in bacteria with the most identified GH-genes, the genomic adaptations for carbohydrate processing also mirrored the broad environmental origin of the sequenced strains (e.g., soil and mammal gut) suggesting that a combination of evolutionary history and the environment selects for the specific supragenic organization of the GH-genes supporting the carbohydrate processing in bacterial genomes.more » « less
-
Pisani, Davide (Ed.)Abstract Molecular studies of animal regeneration typically focus on conserved genes and signaling pathways that underlie morphogenesis. To date, a holistic analysis of gene expression across animals has not been attempted, as it presents a suite of problems related to differences in experimental design and gene homology. By combining orthology analyses with a novel statistical method for testing gene enrichment across large data sets, we are able to test whether tissue regeneration across animals shares transcriptional regulation. We applied this method to a meta-analysis of six publicly available RNA-Seq data sets from diverse examples of animal regeneration. We recovered 160 conserved orthologous gene clusters, which are enriched in structural genes as opposed to those regulating morphogenesis. A breakdown of gene presence/absence provides limited support for the conservation of pathways typically implicated in regeneration, such as Wnt signaling and cell pluripotency pathways. Such pathways are only conserved if we permit large amounts of paralog switching through evolution. Overall, our analysis does not support the hypothesis that a shared set of ancestral genes underlie regeneration mechanisms in animals. After applying the same method to heat shock studies and getting similar results, we raise broader questions about the ability of comparative RNA-Seq to reveal conserved gene pathways across deep evolutionary relationships.more » « less
-
Martelli, Pier Luigi (Ed.)Abstract Motivation Clustering spatial-resolved gene expression is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression co-localizations in tissue for detecting spatial expression patterns or functional relationships among the genes for biological interpretation in the spatial context. In this article, we present a convolutional neural network (CNN) regularized by the graph of protein–protein interaction (PPI) network to cluster spatially resolved gene expression. This method improves the coherence of spatial patterns and provides biological interpretation of the gene clusters in the spatial context by exploiting the spatial localization by convolution and gene functional relationships by graph-Laplacian regularization. Results In this study, we tested clustering the spatially variable genes or all expressed genes in the transcriptome in 22 Visium spatial transcriptomics datasets of different tissue sections publicly available from 10× Genomics and spatialLIBD. The results demonstrate that the PPI-regularized CNN constantly detects gene clusters with coherent spatial patterns and significantly enriched by gene functions with the state-of-the-art performance. Additional case studies on mouse kidney tissue and human breast cancer tissue suggest that the PPI-regularized CNN also detects spatially co-expressed genes to define the corresponding morphological context in the tissue with valuable insights. Availability and implementation Source code is available at https://github.com/kuanglab/CNN-PReg. Supplementary information Supplementary data are available at Bioinformatics online.more » « less
-
Two graph theoretic concepts—clique and bipartite graphs—are explored to identify the network biomarkers for cancer at the gene network level. The rationale is that a group of genes work together by forming a cluster or a clique-like structures to initiate a cancer. After initiation, the disease signal goes to the next group of genes related to the second stage of a cancer, which can be represented as a bipartite graph. In other words, bipartite graphs represent the cross-talk among the genes between two disease stages. To prove this hypothesis, gene expression values for three cancers— breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COAD) and glioblastoma multiforme (GBM)—are used for analysis. First, a co-expression gene network is generated with highly correlated gene pairs with a Pearson correlation coefficient ≥ 0.9. Second, clique structures of all sizes are isolated from the co-expression network. Then combining these cliques, three different biomarker modules are developed—maximal clique-like modules, 2-clique-1-bipartite modules, and 3-clique-2-bipartite modules. The list of biomarker genes discovered from these network modules are validated as the essential genes for causing a cancer in terms of network properties and survival analysis. This list of biomarker genes will help biologists to design wet lab experiments for further elucidating the complex mechanism of cancer.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/bioinformatics/btaa741
