Since the mutation in SARS-COV2 poses new challenges in designing vaccines, it is imperative to develop advanced tools for visualizing the genetic information. Specially, it remains challenging to address the patient-to-patient variability and identify the signature for severe/critical conditions. In this endeavor we analyze the large-scale RNA-sequencing data collected from broncho-alveolar fluid. In this work, we have used PCA and tSNE for the dimension-reduction. The novelty of the current work is to depict a detailed comparison of k-means, HDBSAN and neuro-fuzzy method in visualization of high-dimension data on gene expression. Clinical Relevance- The subpopulation profiling can be used to study the patient-to patient variability when infected by SARS-COV-2 and its variants. The distribution of cell types can be relevant in designing new drugs that are targeted to control the distribution of epithelial cells T cells and macrophages.