Cell type identification in single cell RNA sequencing (scRNA-seq) remains an essential yet challenging task. Existing algorithms assign cell type nomenclature typically by (i) subgrouping cells by computing proximity of cellular gene expression patterns at the single cell level, (ii) identifying marker genes in each cluster, and (iii) performing a lookup of these genes in the literature and databases to assign cell subtype labels. Unfortunately, this approach falls short when classifying cells based on functions, because biological function(s) of a group of cells cannot be determined based on a limited set of marker genes. Here we propose a new modeling formalism, namely FuncNet, which is a form of neural network-based method capable of discerning the functional profiles of individual cells. The key idea of FuncNet is to incorporate "a secondary learning arm" into the otherwise classical multi-layer neural network in order to bring in "prior knowledge" into the overall learning process. The prior knowledge that is brought into the FuncNet learning is Gene Ontology Biological Processes that should have been captured in the cells being studied. We applied FuncNet to three publicly available single cell transcriptomics datasets from human and mouse brains. The results demonstrate that FuncNet can successfully reproduce the functional nomenclatures associated with brain cells in the literature. FuncNet suggests a new way of subtyping cells, specifically for the functional subtyping of single cells—an area which has not been well established in the current single cell data analysis practices.

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.

The brain’s resident immune cells, microglia, play important roles in the pathological process of Alzheimer's disease (AD). Upon encountering amyloid-β (Aβ) plaque accumulation, microglia change its state to produce proinflammatory cytokines to uptake and clear Aβ but during the chronic state of neuroinflammation they become responsible for neurodegeneration. In this paper we propose a new method of analyzing microglia undergoing multi-stage state changes from homeostasis (HOM) to disease associated microglia (DAM). Our single cell gene expression data analysis method differs from the conventional marker based subtyping methods in the sense that it uses prior knowledge, namely, Gene Ontology’s Biological Processes known for immune functions and other related ones. In this regard, our method can be thought of supervised clustering rather than UMAP/tSNE style unbiased subgrouping of cells. The strengths of this "prior knowledge" using method are multi-faceted: (i) one can assign meaningful functional nomenclatures to identified subtypes of cells, (ii) one can refine GO Biological Process genes to be context-specific (e.g., phagocytosis responsible by "microglia" as opposed to "general immune cells"), and (iii) additional context-specific biological processes can be identified in addition to the ones used as "prior knowledge". We illustrate the advantages of our semi-supervised cell clustering method using a set of publicly available human AD and mouse AD model gene expression datasets.

Microglia are macrophages residing in the brain and spinal cord and responsible for neurological immune defense. A growing number of studies point out the importance of microglia’s role in Alzheimer’s disease (AD) pathology. Like macrophages in other tissues, microglia are polarized to M1/M2 states, Ml inhibiting cell inflammatory and causing tissue damage and M2 resolving inflammation and repairing damaged tissue. Thanks to scRNA-seq technology, single cell gene expression data sets for microglia have become available. However, examining the polarization of microglia at single cell level to study their association with AD is difficult for reasons such as complexity (too many genes, too many cells), expression dropouts, etc. We propose a scRNA-seq data visualization framework, namely, Pola Viz that can associate the polarization states of microglia with gene regulatory pathways that are potentially responsible for defining M1/M2 states or transition between them. For its two-dimensional placement of single cells, the x-axis depicts each cell’s M1/M2 or its transition score and the y-axis denotes what we call, the pathway “route” scores designed to assign the degree of contribution of the concerned pathway routes to each cell’s M1/M2 state. We conducted two case studies involving publicly available AD single cell mRNA datasets, one from human brain and one from mouse brain. Our method identified pathway routes that are highly correlated with known functional roles of microglia. It also discovers similarities between mice and human AD microglia activation such as T cell receptor signaling pathway, cell cycle, NF-kappa B signaling pathways, all highly disturbed at late stage of AD. Our method provides a new way of examining the M1/M2 polarization of microglia at single cell level