Heterogeneous Gene Expression Data Research Articles

YADA - Reference Free Deconvolution of RNA Sequencing Data

Introduction: We present YADA, a cellular content deconvolution algorithm for estimating cell type proportions in heterogeneous cell mixtures based on gene expression data. YADA utilizes curated gene signatures of cell type-specific marker genes, either obtained intrinsically from pure cell type expression matrices or provided by the user. Method: YADA implements an accessible and extensible deconvolution framework uniquely capable of handling marker genes alone as inputs. Adoption barriers are lowered significantly by relying solely on literature-supported cell type-specific signatures rather than full transcriptomic profiles from purified isolates. However, flexible inputs do not necessitate sacrificing rigor - predictions match metrics of current methodologies through an integrated optimization scheme balancing multiple inference algorithms. Efficiency optimizations via compiled runtimes enable rapid execution. Packaging as an importable Python toolkit promotes community enhancement while retaining codebase extensibility. Result: Validation studies demonstrate that YADA matches or exceeds the performance of current deconvolution methods on benchmark datasets. To demonstrate the utility and enable immediate usage, we provide an online Jupyter Notebook implementation coupled with tutorials. Conclusion: YADA provides an accurate, efficient, and extensible Python-based toolkit for cellular deconvolution analysis of heterogeneous gene expression data.

Unveiling Prognostic RNA Biomarkers through a Multi-Cohort Study in Colorectal Cancer.

Because cancer is a leading cause of death and is thought to be caused by genetic errors or genomic instability in many circumstances, there have been studies exploring cancer's genetic basis using microarray and RNA-seq methods, linking gene expression data to patient survival. This research introduces a methodological framework, combining heterogeneous gene expression data, random forest selection, and pathway analysis, alongside clinical information and Cox regression analysis, to discover prognostic biomarkers. Heterogeneous gene expression data for colorectal cancer were collected from TCGA-COAD (RNA-seq), and GSE17536 and GSE39582 (microarray), and were integrated with Entrez Gene IDs. Using Cox regression analysis and random forest, genes with consistent hazard ratios and significantly affecting patient survivability were chosen. Predictive accuracy was evaluated using ROC curves. Pathway analysis identified potential RNA biomarkers. The authors identified 28 RNA biomarkers. Pathway analysis revealed enrichment in cancer-related pathways, notably EGFR downstream signaling and IGF1R signaling. Three RNA biomarkers (ZEB1-AS1, PI4K2A, and ITGB8-AS1) and two clinical biomarkers (stage and age) were chosen for a prognostic model, improving predictive performance compared to using clinical biomarkers alone. Despite biomarker identification challenges, this study underscores integration of heterogenous gene expression data for discovery.

SPSNet: subpopulation-sensitive network-based analysis of heterogeneous gene expression data

BackgroundTranscriptomic datasets often contain undeclared heterogeneity arising from biological variation such as diversity of disease subtypes, treatment subgroups, time-series gene expression, nested experimental conditions, as well as technical variation due to batch effects, platform differences in integrated meta-analyses, etc. However, current analysis approaches are primarily designed to handle comparisons between experimental conditions represented by homogeneous samples, thus precluding the discovery of underlying subphenotypes. Unsupervised methods for subtype identification are typically based on individual gene level analysis, which often result in irreproducible gene signatures for potential subtypes. Emerging methods to study heterogeneity have been largely developed in the context of single-cell datasets containing hundreds to thousands of samples, limiting their use to select contexts.ResultsWe present a novel analysis method, SPSNet, which identifies subtype-specific gene expression signatures based on the activity of subnetworks in biological pathways. SPSNet identifies the gene subnetworks capturing the diversity of underlying biological mechanisms, indicating potential sample subphenotypes. In the presence of extrinsic or non-biological heterogeneity (e.g. batch effects), SPSNet identifies subnetworks that are particularly affected by such variation, thus helping eliminate factors irrelevant to the biology of the phenotypes under study.ConclusionUsing multiple publicly available datasets, we illustrate that SPSNet is able to consistently uncover patterns within gene expression data that correspond to meaningful heterogeneity of various origins. We also demonstrate the performance of SPSNet as a sensitive and reliable tool for understanding the structure and nature of such heterogeneity.

A sequential Monte Carlo approach to gene expression deconvolution.

High-throughput gene expression data are often obtained from pure or complex (heterogeneous) biological samples. In the latter case, data obtained are a mixture of different cell types and the heterogeneity imposes some difficulties in the analysis of such data. In order to make conclusions on gene expresssion data obtained from heterogeneous samples, methods such as microdissection and flow cytometry have been employed to physically separate the constituting cell types. However, these manual approaches are time consuming when measuring the responses of multiple cell types simultaneously. In addition, exposed samples, on many occasions, end up being contaminated with external perturbations and this may result in an altered yield of molecular content. In this paper, we model the heterogeneous gene expression data using a Bayesian framework, treating the cell type proportions and the cell-type specific expressions as the parameters of the model. Specifically, we present a novel sequential Monte Carlo (SMC) sampler for estimating the model parameters by approximating their posterior distributions with a set of weighted samples. The SMC framework is a robust and efficient approach where we construct a sequence of artificial target (posterior) distributions on spaces of increasing dimensions which admit the distributions of interest as marginals. The proposed algorithm is evaluated on simulated datasets and publicly available real datasets, including Affymetrix oligonucleotide arrays and national center for biotechnology information (NCBI) gene expression omnibus (GEO), with varying number of cell types. The results obtained on all datasets show a superior performance with an improved accuracy in the estimation of cell type proportions and the cell-type specific expressions, and in addition, more accurate identification of differentially expressed genes when compared to other widely known methods for blind decomposition of heterogeneous gene expression data such as Dsection and the nonnegative matrix factorization (NMF) algorithms. MATLAB implementation of the proposed SMC algorithm is available to download at https://github.com/moyanre/smcgenedeconv.git.

Sensitivity analysis of cell-type specific differential expression detection in heterogeneous gene expression data

Sensitivity analysis of cell-type specific differential expression detection in heterogeneous gene expression data

Harnessing gene expression networks to prioritize candidate epileptic encephalopathy genes.

We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.

Integrating heterogeneous gene expression data for gene regulatory network modelling

Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors' knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.

CleanEx: a database of heterogeneous gene expression data based on a consistent gene nomenclature.

The main goal of CleanEx is to provide access to public gene expression data via unique gene names. A second objective is to represent heterogeneous expression data produced by different technologies in a way that facilitates joint analysis and cross-data set comparisons. A consistent and up-to-date gene nomenclature is achieved by associating each single experiment with a permanent target identifier consisting of a physical description of the targeted RNA population or the hybridization reagent used. These targets are then mapped at regular intervals to the growing and evolving catalogues of human genes and genes from model organisms. The completely automatic mapping procedure relies partly on external genome information resources such as UniGene and RefSeq. The central part of CleanEx is a weekly built gene index containing cross-references to all public expression data already incorporated into the system. In addition, the expression target database of CleanEx provides gene mapping and quality control information for various types of experimental resource, such as cDNA clones or Affymetrix probe sets. The web-based query interfaces offer access to individual entries via text string searches or quantitative expression criteria. CleanEx is accessible at: http://www.cleanex.isb-sib.ch/.

Classification of heterogeneous gene expression data

Recent advanced technologies in DNA microarray analysis are intensively applied in disease classification, especially for cancer classification. Most recent proposed gene expression classifiers can successfully classify testing samples obtained from the same microarray experiment as training samples with the assumption that the symmetric errors are constant among training and testing samples. However, the classification performance is degraded with heterogeneous testing samples obtained from different microarray experiments. In this paper, we propose the "impact factors" (IFs) to measure the variations between individual classes in training samples and heterogeneous testing samples, and integrate the IFs to classifiers for classification of heterogeneous samples. Two publicly available lung adenocarcinomas gene expression data sets are used in our experiments to demonstrate the effectiveness of the IFs. It shows that, with the integration of the IFs to the Golub and Slonim (GS) and k-nearest neighbors (kNN) classifiers, the classifiers can be further improved on the classification accuracy of heterogeneous samples. Even more, the classification accuracy of the integrated GS classifier is around 90%.