Gene selection in single cell data

Gene selection in unannotated large single cell RNA sequencing (scRNA-seq) data is important and crucial step in the preliminary step of downstream analysis. The existing approaches are primarily based on high variation (highly variable genes) or significant high expression (highly expressed genes) failed to provide stable and predictive feature set due to technical noise present in the data. We propose RgCop, , and sc-REnF, for gene selection from large single cell RNA-seq data. RgCop utilizes copula correlation (Ccor), a robust equitable dependence measure that captures multivariate dependency among a set of genes in single cell expression data. We introduce sc-REnF [robust entropy based feature (gene) selection method], aiming to leverage the advantages of Renyi and Tsallis entropies in gene selection for single cell clustering. Research Image

Single Cell Clustering

A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data. Here we introduce sc-CGconv, (copula based graph convolution network for single clustering), a stepwise robust unsupervised feature extraction and clustering approach that formulates and aggregates cell–cell relationships using copula correlation (Ccor), followed by a graph convolution network based clustering approach. Research Image

In-silico Generation of Single Cell

Here, we present an improved version of generative adversarial network (GAN) called LSH-GAN LSH-GAN,to address this issue by producing new realistic cell samples. Research Image

Cell Type Detection

Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA. Cell type prediction is one of the most challenging goals in single-cell RNA sequencing (scRNA-seq) data. Existing methods use unsupervised learning to identify signature genes in each cluster, followed by a literature survey to look up those genes for assigning cell types. However, finding potential marker genes in each cluster is cumbersome, which impedes the systematic analysis of single-cell RNA sequencing data. To address this challenge, we proposed a framework based on regularized multi-task learning (RMTL) that enables us to simultaneously learn the subpopulation associated with a particular cell type. We also proposed MarkerCapsule, which leverages the landmark advantages of capsule networks achieved in their original applications in single cell typing. Research Image