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Project description:We have developed a computational approach that uses self-organizing maps for integrative genomic analysis. We utilize this approach to identify the single-cell chromatin and transcriptomic profiles during mouse pre-B cell differentiation.
2019-02-14 | GSE89362 | GEO
Project description:We have developed a computational approach that uses self-organizing maps for integrative genomic analysis. We utilize this approach to identify the single-cell chromatin and transcriptomic profiles during mouse pre-B cell differentiation.
2019-02-14 | GSE89280 | GEO
Project description:Rapid advances in single-cell assays have outpaced methods for analysis of those data types. Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq regions with scRNA-seq genes that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar single-cell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of heterogeneous data.
| S-EPMC6855564 | biostudies-literature
Project description:Integrative analysis of single-cell ATAC-seq and RNA-seq using Self-Organizing Maps
| PRJNA351241 | ENA
Project description:PurposeCorneal epithelial homeostasis is maintained by coordinated gene expression across distinct cell populations, but the gene regulatory programs underlying this cellular diversity remain to be characterized. Here we applied single-cell multi-omics analysis to delineate the gene regulatory profile of mouse corneal epithelial cells under normal homeostasis.MethodsSingle cells isolated from the cornea epithelium (with marginal conjunctiva) of adult mice were subjected to scRNA-seq and scATAC-seq using the 10×Genomics platform. Cell types were clustered by the graph-based visualization method uniform manifold approximation and projection and unbiased computational informatics analysis. The scRNA-seq and scATAC-seq datasets were integrated following the integration pipeline described in ArchR and Seurat.ResultsWe characterized diverse corneal epithelial cell types based on gene expression signatures and chromatin accessibility. We found that cell type-specific accessibility regions were mainly located at distal regions, suggesting essential roles of distal regulatory elements in determining corneal epithelial cell diversity. Trajectory analyses revealed a continuum of cell state transition and higher coordination between transcription factor (TF) motif accessibility and gene expression during corneal epithelial cell differentiation. By integrating transcriptomic and chromatin accessibility analysis, we identified cell type-specific and shared gene regulation programs. We also uncovered critical TFs driving corneal epithelial cell differentiation, such as nuclear factor I (NFI) family members, Rarg, Elf3. We found that nuclear factor-κB (NF-κB) family members were positive TFs in limbal cells and some superficial cells, but they were involved in regulating distinct biological processes.ConclusionsOur study presents a comprehensive gene regulatory landscape of mouse cornea epithelial cells, and provides valuable foundations for future investigation of corneal epithelial homeostasis in the context of cornea pathologies and regenerative medicine.
| S-EPMC10043503 | biostudies-literature
Project description:BackgroundParallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.ResultsThe method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.ConclusionsThe SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'.
| S-EPMC3161046 | biostudies-literature
Project description:MotivationMapping of chromatin accessibility landscapes in single-cells and the integration with gene expression enables a better understanding of gene regulatory mechanisms defining cell identities and cell-fate determination in development and disease. Generally, raw data generated from single-cell Assay for Transposase-Accessible Chromatin sequencing (scATAC-seq) are deposited in repositories that are generally inaccessible due to lack of in-depth knowledge of computational programming.ResultsWe have developed ShinyArchR.UiO, an R-based shiny app, that facilitates scATAC-seq data accessibility and visualization in a user-friendly, interactive and open-source web interface. ShinyArchR.UiO is an application that can streamline collaborative efforts for interpretation of massive chromatin accessibility datasets and allow for open access data sharing for wider audiences.Availability and implementationhttps://Github.com/EskelandLab/ShinyArchRUiO and a demo server with a hematopoietic tutorial dataset https://cancell.medisin.uio.no/ShinyArchR.UiO.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8756194 | biostudies-literature
Project description:The k nearest neighbor is one of the most important and simple procedures for data classification task. The kNN, as it is called, requires only two parameters: the number of k and a similarity measure. However, the algorithm has some weaknesses that make it impossible to be used in real problems. Since the algorithm has no model, an exhaustive comparison of the object in classification analysis and all training dataset is necessary. Another weakness is the optimal choice of k parameter when the object analyzed is in an overlap region. To mitigate theses negative aspects, in this work, a hybrid algorithm is proposed which uses the Self-Organizing Maps (SOM) artificial neural network and a classifier that uses similarity measure based on information. Since SOM has the properties of vector quantization, it is used as a Prototype Generation approach to select a reduced training dataset for the classification approach based on the nearest neighbor rule with informativeness measure, named iNN. The SOMiNN combination was exhaustively experimented and the results show that the proposed approach presents important accuracy in databases where the border region does not have the object classes well defined.
| S-EPMC5547710 | biostudies-other
Project description:ObjectiveGenetic studies on ankylosing spondylitis (AS) have identified more than 100 pathogenic genes. Building a bridge between these genes and biologically targeted therapies is the current research hotspot.MethodsWe integrated single-cell assaying transposase-accessible chromatin sequencing (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) to explore the key genes and related mechanisms associated with AS pathogenesis.ResultsWe identified 18 cell types in peripheral mononuclear cells from patients with AS and normal controls and summarized the cell-type-specific abnormal genes by scRNA-seq. Interestingly, we found that the pathogenic gene NFKB involved in AS progression originated from CD8+ T cells. Moreover, we observed an abnormal tumor TNF pathway mediated by abnormal expression of TNF, NFKB, FOS, JUN, and JUNB, and scATAC-seq results confirmed the abnormal accessible binding sites of transcriptional factors FOS, JUN, and JUNB. The final magnetic bead sorting and quantitative real-time PCR(RT-qPCR) confirmed that NFKB, FOS, JUN, and JUNB in CD8+ T cells differed in the AS group.ConclusionsOur results revealed a possible mechanism by which NFKB abnormally regulates FOS, JUN, and JUNB and drives AS progression, providing a novel perspective from a single cell point of view in AS.
| S-EPMC8647172 | biostudies-literature
Project description:Design of multitarget drugs and polypharmacological compounds has become popular during the past decade. However, the main approach to design such compounds is to link two selective ligands via a flexible linker. Although such chimeric ligands often have reasonable potency in vitro, the in vivo efficacy is low due to high molecular weight, low ligand efficiency, and poor pharmacokinetic profile. We developed an unprecedented in silico approach for fragment-based design of multitarget ligands. It relies on superposition of the chemical spaces related to the affinity on single targets represented by self-organizing maps. We used this approach for screening of molecular fragments, which bind to the enzymes 5-lipoxygenase (5-LO) and soluble epoxide hydrolase (sEH). Using STD-NMR and activity-based assays, we were able to identify fragments binding to both targets. Furthermore, we were able to expand one of the fragments to a potent dual inhibitor bearing a reasonable molecular weight (MW = 446) and high affinity to both targets (IC50 of 0.03 ?M toward 5-LO and 0.17 ?M toward sEH).
| S-EPMC4027374 | biostudies-literature