SUMMARY: vSPACE is a web-based application presenting a spatial representation of scRNAseq data obtained from human articular cartilage by emulating the concept of spatial transcriptomics technology, but virtually. This virtual 2D plot presentation of human articular cartage cells generates several zonal distribution patterns, for one or multiple genes at a time, revealing patterns that scientists can appreciate as imputed spatial distribution patterns along the zonal axis. AVAILABILITY AND IMPLEMENTATION: vSPACE is implemented in Python Dash as a web-based toolbox designed for data visualization of zonal gene expression patterns in articular cartilage chondrocytes. This tool is freely accessible at: https://vspace.cse.uconn.edu/The source code and extra materials for this service can be downloaded from: https://github.com/zhacheny/vSPACE.

Identifying target genes of a transcription factor is crucial in biomedical research. Thanks to ChIP-seq technology, scientists can estimate potential genome-wide target genes of a transcription factor. However, finding the consistently behaving Up/Down targets of a transcription factor in a given biological context is difficult because it requires analysis of a large number of studies under the same or comparable context. We present a transcription target prediction method, called Cohort-based TF target prediction system (cTAP). This method assumes that the pathway involving the transcription factor of interest is featured with multiple functional groups of marker genes pertaining to the concerned biological process. It uses the notion of gene-presence and gene-absence in addition to log2 ratios of gene expression values for the prediction. Target prediction is made by applying multiple machine-learning models that learn the patterns of genepresence and gene-absence from log2 ratio and four types of Z scores from the normalized cohort's gene expression data. The learned patterns are then associated with the putative targets of the concerned transcription factor to elicit genes exhibiting Up/Down gene regulation patterns “consistently” within the cohort. Totally 11 publicly available GEO data sets related to osteoclastogenesis are used in our experiment. The learned models using gene-presence and gene-absence produce target genes different from using only log2 ratios such as CASP1, BID, and IRF5. Our literature survey reveals that all these predicted targets have known roles in bone remodeling, specifically related to immune and osteoclasts, suggesting confidence in our method and potential merit for a wet-lab experiment for validation.

One known challenge in analyzing gene expression data is to combine analysis outcomes obtained disparately by applying multiple, independent meta-analysis methods. Here we present an integrative computational system that narrows down biological hypotheses by integrating gene expression patterns, transcription factor (TF) binding site analysis outcomes, and Gene Ontology (GO) enrichment analysis outcomes. This system identifies regulated genes from microarray experiments through statistical processes, categorizes similarly behaving groups of genes and then carries out binding site analysis and gene function enrichment analysis based on some significant clusters. The output is an ordered set of "putative" pair-wise relationships between TFs and their potential target genes. The relationships are ranked based on their closeness to the experimental context. We demonstrate the effectiveness of our framework using two independent microarray data sets.

Microarray experiments produce expression patterns for thousands of genes at once. On the other hand, biomedical literature contains large amounts of gene regulation relationship information accumulated over the years. One obvious requirement is an automated way of comparing microarray data with the collection of known gene regulation relationships. Such an automated comparison is imperative because it can help biologists rapidly understand the context of a given microarray experiment. In addition, the consistency measure can be used to either validate or refute the hypothesis being tested using the microarray experiment. In this paper we present a systematic way of examining the consistency between a given set of microarray data and known gene regulation relationships. We first introduce a simple gene regulation network model with two separate algorithms designed to isolate a maximally consistent network. Subsequently, we extend the model to take into account multiple regulating factors for a single gene while highlighting both consistencies and inconsistencies. We illustrate the effectiveness of our approach with two practical examples, one that picks the peroxisome proliferator-activated receptor (PPAR) pathway as highly consistent from multiple pathways of Kyoto encyclopedia of genes and genomes (KEGG), and another that isolates key regulatory relationships involving nfkb1 and others known for macrophage's counter response to inflammation.

Using microarray technology for genetic analysis in biological experiments requires computationally intensive tools to interpret results. The main objective here is to develop a ldquometa-analysisrdquo tool that enables researchers to ldquosprayrdquo microarray data over a network of relevant gene regulation relationships, extracted from a database of published gene regulatory pathway models. The consistency of the data from a microarray experiment is evaluated to determine if it agrees or contradicts with previous findings. The database is limited to ldquoactivaterdquo and ldquoinhibitrdquo gene regulatory relationships at this point and a heuristic graph based approach is developed for consistency checking. Predictions are made for the regulation of genes that were not a part of the microarray experiment, but are related to the experiment through regulatory relationships. This meta-analysis will not only highlight consistent findings but also pinpoint genes that were missed in earlier experiments and should be considered in subsequent analysis.

A Bayesian network is a model to study the structures of gene regulatory networks. It has the ability to integrate information from both prior knowledge and experimental data. Some previous works have explored the advantage of using prior knowledge. Unfortunately, most of the existing works only utilize biological knowledge about local structures of each gene in the network. In this study, we propose an approach to efficiently integrate global ordering information into model learning, where the ordering information specifies the indirect relationships among genes. We study the model behaviors with synthetic data. We demonstrate that, compared with a traditional Bayesian network model that uses only local prior knowledge, utilizing additional global ordering knowledge can significantly improve the modelpsilas performance. The magnitude of this improvement depends on how much global ordering information is integrated and how much noise the data includes.

Microarray experiments produce gene expression data at such a high speed and volume that it is imperative to use highly specialized computational tools for their analyses. One group of such computational tools deals with, namely, "meta-analysis" of microarray data. This step attempts to extract biological interpretations from the identified gene expression pattern. One particular aspect of meta-analysis is sorting out which gene regulation pathways are active and/or inhibited. The focus on this paper is to propose a computational framework with which scientists can compare microarray data with known gene regulation networks that are formed by two known binary gene regulation relationships, activate and inhibit. Using this framework scientists can conduct numerous analysis tasks including (i) identify active or inhibited sub-networks out of massively interconnected gene regulation pathways, (ii) find key genes, namely hubs, that are inferred to be widely involved in multiple aspects of gene regulation, (iii) identify regions of the network that contradict the known regulation, and (iv) estimate the direction of expression of genes that were not included in the microarray experiment. One known utility of this meta-analysis is to help scientists to identify a group of genes that they have missed in earlier experiments and should include in their subsequent experiments. Another utility is to enable them to isolate the group of genes that should be followed more closely using higher accuracy gene expression assays. We introduce three separate but inter-related meta-analysis methodologies, namely, FCFS, Hub majority, and SDP-based. We illustrate our proposed framework using microarray data derived from noggin treated osteoblast cells. The example clearly shows finding sub-networks related to B-catenin which should be expected and thus demonstrates the effectiveness of our proposed framework