Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang W
PROVIDER: S-EPMC8136772 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BACKGROUND:Analyzing the human transcriptome is crucial in advancing precision medicine, and the plethora of over half a million human microarray samples in the Gene Expression Omnibus (GEO) has enabled us to better characterize biological processes at the molecular level. However, transcriptomic analysis is challenging because the data is inherently noisy and high-dimensional. Gene set analysis is currently widely used to alleviate the issue of high dimensionality, but the user-defined choice of gene sets can introduce biasness in results. In this paper, we advocate the use of a fixed set of transcriptomic modules for such analysis. We apply independent component analysis to the large collection of microarray data in GEO in order to discover reproducible transcriptomic modules that can be used as features for machine learning. We evaluate the usability of these modules across six studies, and demonstrate (1) their usage as features for sample classification, and also their robustness in dealing with small training sets, (2) their regularization of data when clustering samples and (3) the biological relevancy of differentially expressed features. RESULTS:We identified 139 reproducible transcriptomic modules, which we term fundamental components (FCs). In studies with less than 50 samples, FC-space classification model outperformed their gene-space counterparts, with higher sensitivity (p <?0.01). The models also had higher accuracy and negative predictive value (p <?0.01) for small data sets (less than 30 samples). Additionally, we observed a reduction in batch effects when data is clustered in the FC-space. Finally, we found that differentially expressed FCs mapped to GO terms that were also identified via traditional gene-based approaches. CONCLUSIONS:The 139 FCs provide biologically-relevant summarization of transcriptomic data, and their performance in low sample settings suggest that they should be employed in such studies in order to harness the data efficiently.
| S-EPMC6142401 | biostudies-literature
Project description:As public microarray repositories rapidly accumulate gene expression data, these resources contain increasingly valuable information about cellular processes in human biology. This presents a unique opportunity for intelligent data mining methods to extract information about the transcriptional modules underlying these biological processes. Modeling cellular gene expression as a combination of functional modules, we use independent component analysis (ICA) to derive 423 fundamental components of human biology from a 9395-array compendium of heterogeneous expression data. Annotation using the Gene Ontology (GO) suggests that while some of these components represent known biological modules, others may describe biology not well characterized by existing manually-curated ontologies. In order to understand the biological functions represented by these modules, we investigate the mechanism of the preclinical anti-cancer drug parthenolide (PTL) by analyzing the differential expression of our fundamental components. Our method correctly identifies known pathways and predicts that N-glycan biosynthesis and T-cell receptor signaling may contribute to PTL response. The fundamental gene modules we describe have the potential to provide pathway-level insight into new gene expression datasets.
| S-EPMC2991480 | biostudies-literature
Project description:The dynamic adaptation of bacteria to environmental changes is achieved through the coordinated expression of many genes, which constitutes a transcriptional regulatory network (TRN). Bradyrhizobium diazoefficiens USDA110 is an important model strain for the study of symbiotic nitrogen fixation (SNF), and its SNF ability largely depends on the TRN. In this study, independent component analysis was applied to 226 high-quality gene expression profiles of B. diazoefficiens USDA110 microarray datasets, from which 64 iModulons were identified. Using these iModulons and their condition-specific activity levels, we (1) provided new insights into the connection between the FixLJ-FixK2-FixK1 regulatory cascade and quorum sensing, (2) discovered the independence of the FixLJ-FixK2-FixK1 and NifA/RpoN regulatory cascades in response to oxygen, (3) identified the FixLJ-FixK2 cascade as a mediator connecting the FixK2-2 iModulon and the Phenylalanine iModulon, (4) described the differential activation of iModulons in B. diazoefficiens USDA110 under different environmental conditions, and (5) proposed a notion of active-TRN based on the changes in iModulon activity to better illustrate the relationship between gene regulation and environmental condition. In sum, this research offered an iModulon-based TRN for B. diazoefficiens USDA110, which formed a foundation for comprehensively understanding the intricate transcriptional regulation during SNF.
| S-EPMC10454721 | biostudies-literature
Project description:The connectivity information contained in diffusion tensor imaging (DTI) has previously been used to parcellate cortical and subcortical regions based on their connectivity profiles. The aim of the current study is to investigate the utility of a novel approach to connectivity based parcellation of the thalamus using probabilistic tractography and independent component analysis (ICA). We use ICA to identify spatially coherent tractograms as well as their underlying seed regions, in a single step. We compare this to seed-based tractography results and to an established and reliable approach to parcellating the thalamus based on the dominant cortical connection from each thalamic voxel (Behrens et al., 2003a,b). The ICA approach identifies thalamo-cortical pathways that correspond to known anatomical connections, as well as parcellating the underlying thalamus in a spatially similar way to the connectivity based parcellation. We believe that the use of such a multivariate method to interpret the complex datasets created by probabilistic tractography may be better suited than other approaches to parcellating brain regions.
| S-EPMC3032893 | biostudies-literature
Project description:Gene expression time series (GETS) analysis aims to characterize sets of genes according to their longitudinal patterns of expression. Due to the large number of genes evaluated in GETS analysis, an useful strategy to summarize biological functional processes and regulatory mechanisms is through clustering of genes that present similar expression pattern over time. Traditional cluster methods usually ignore the challenges in GETS, such as the lack of data normality and small number of temporal observations. Independent Component Analysis (ICA) is a statistical procedure that uses a transformation to convert raw time series data into sets of values of independent variables, which can be used for cluster analysis to identify sets of genes with similar temporal expression patterns. ICA allows clustering small series of distribution-free data while accounting for the dependence between subsequent time-points. Using temporal simulated and real (four libraries of two pig breeds at 21, 40, 70 and 90 days of gestation) RNA-seq data set we present a methodology (ICAclust) that jointly considers independent components analysis (ICA) and a hierarchical method for clustering GETS. We compare ICAclust results with those obtained for K-means clustering. ICAclust presented, on average, an absolute gain of 5.15% over the best K-means scenario. Considering the worst scenario for K-means, the gain was of 84.85%, when compared with the best ICAclust result. For the real data set, genes were grouped into six distinct clusters with 89, 51, 153, 67, 40, and 58 genes each, respectively. In general, it can be observed that the 6 clusters presented very distinct expression patterns. Overall, the proposed two-step clustering method (ICAclust) performed well compared to K-means, a traditional method used for cluster analysis of temporal gene expression data. In ICAclust, genes with similar expression pattern over time were clustered together.
| S-EPMC5513449 | biostudies-other
Project description:Cataloging the association of transcripts to genetic variants in recent years holds the promise for functional dissection of regulatory structure of human transcription. Here, we present a novel approach, which aims at elucidating the joint relationships between transcripts and single-nucleotide polymorphisms (SNPs). This entails detection and analysis of modules of transcripts, each weakly associated to a single genetic variant, together exposing a high-confidence association signal between the module and this 'main' SNP. To explore how transcripts in a module are related to causative loci for that module, we represent such dependencies by a graphical model. We applied our method to the existing data on genetics of gene expression in the liver. The modules are significantly more, larger and denser than found in permuted data. Quantification of the confidence in a module as a likelihood score, allows us to detect transcripts that do not reach genome-wide significance level. Topological analysis of each module identifies novel insights regarding the flow of causality between the main SNP and transcripts. We observe similar annotations of modules from two sources of information: the enrichment of a module in gene subsets and locus annotation of the genetic variants. This and further phenotypic analysis provide a validation for our methodology.
| S-EPMC3401454 | biostudies-literature
Project description:BackgroundGenetic pathways involved with flower color and shape are thought to play an important role in the development of flowers associated with different pollination syndromes, such as those associated with bee, butterfly, or hummingbird pollination. Because pollination syndromes are complex traits that are orchestrated by multiple genes and pathways, the gene regulatory networks have not been explored. Gene co-expression networks provide a systems level approach to identify important contributors to floral diversification.MethodsRNA-sequencing was used to assay gene expression across two stages of flower development (an early bud and an intermediate stage) in 10 species of Achimenes (Gesneriaceae). Two stage-specific co-expression networks were created from 9,503 orthologs and analyzed to identify module hubs and the network periphery. Module association with bee, butterfly, and hummingbird pollination syndromes was tested using phylogenetic mixed models. The relationship between network connectivity and evolutionary rates (d N/d S) was tested using linear models.ResultsNetworks contained 65 and 62 modules that were largely preserved between developmental stages and contained few stage-specific modules. Over a third of the modules in both networks were associated with flower color, shape, and pollination syndrome. Within these modules, several hub nodes were identified that related to the production of anthocyanin and carotenoid pigments and the development of flower shape. Evolutionary rates were decreased in highly connected genes and elevated in peripheral genes.DiscussionThis study aids in the understanding of the genetic architecture and network properties underlying the development of floral form and provides valuable candidate modules and genes for future studies.
| S-EPMC7071821 | biostudies-literature
Project description:BackgroundGlucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset.MethodsWe used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher's exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher's exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters.ResultsThe density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.
| S-EPMC8349163 | biostudies-literature
Project description:Data integration to align cells across batches has become a cornerstone of single cell data analysis, critically affecting downstream results. Yet, how much biological signal is erased during integration? Currently, there are no guidelines for when the biological differences between samples are separable from batch effects, and thus, data integration usually involve a lot of guesswork: Cells across batches should be aligned to be "appropriately" mixed, while preserving "main cell type clusters". We show evidence that current paradigms for single cell data integration are unnecessarily aggressive, removing biologically meaningful variation. To remedy this, we present a novel statistical model and computationally scalable algorithm, CellANOVA, to recover biological signal that is lost during single cell data integration. CellANOVA utilizes a "pool-of-controls" design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest. When applied with existing integration methods, CellANOVA allows the recovery of subtle biological signals and corrects, to a large extent, the data distortion introduced by integration. Further, CellANOVA explicitly estimates cell- and gene-specific batch effect terms which can be used to identify the cell types and pathways exhibiting the largest batch variations, providing clarity as to which biological signals can be recovered. These concepts are illustrated on studies of diverse designs, where the biological signals that are recovered by CellANOVA are shown to be validated by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nuclei data integration, where the recovered biological signals are replicated in an independent study.
| S-EPMC10197537 | biostudies-literature
Project description:Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networks. While ICA produces reliable group-level estimates, single-subject ICA often produces noisy results. Template ICA is a hierarchical ICA model using empirical population priors to produce more reliable subject-level estimates. However, this and other hierarchical ICA models assume unrealistically that subject effects are spatially independent. Here, we propose spatial template ICA (stICA), which incorporates spatial priors into the template ICA framework for greater estimation efficiency. Additionally, the joint posterior distribution can be used to identify brain regions engaged in each network using an excursions set approach. By leveraging spatial dependencies and avoiding massive multiple comparisons, stICA has high power to detect true effects. We derive an efficient expectation-maximization algorithm to obtain maximum likelihood estimates of the model parameters and posterior moments of the latent fields. Based on analysis of simulated data and fMRI data from the Human Connectome Project, we find that stICA produces estimates that are more accurate and reliable than benchmark approaches, and identifies larger and more reliable areas of engagement. The algorithm is computationally tractable, achieving convergence within 12 hours for whole-cortex fMRI analysis.
| S-EPMC10292763 | biostudies-literature