Events

Speaker : Ms. Madhulika Verma (Research Scholar)

Single-cell RNA sequencing technologies have given a major boost to the area of genomics and transcriptomics since it has helped in studying the gene expression profiles of individual cells. The major advantages of SS RNA-seq are in the areas such as developmental biology, neurology, immunology, oncology, cardiovascular research, and infectious diseases. Typically, for analysis of single-cell data, the gene expression patterns are identified by means of clustering, which in turn helps in classifying different cell types present in our data. A plethora of tools is available for this purpose along with the differential expression analysis protocol. Here, we present a pipeline that makes use of "Semi-soft clustering of single-cell data" (SOUP), which is a soft clustering method that helps in identifying the presence of pure and transitional/developmental cells in the data. The pipeline also contains the method to identify the cell types present in the data by finding out the significantly expressed canonical markers for a particular cell type, in a cluster. The gene expression patterns are identified by means of clustering, which in turn helps in classifying different cell types present in our data.