Serin Harmanci Lab

Serin Lab Projects


Enhanced Computational Tools for Identifying and Integrating Genomic Variants from Single Cell and Spatial Tanscriptomics Data 

Single-cell or bulk RNA-Seq and spatial transcriptomics datasets contain a substantial amount of information about the genomic variants in the samples and are severely underutilized. Despite substantial advances, significant challenges remain in the identification of variants from scRNA-Seq to better understand the correlation betwee¬¬n the genomic and the transcriptomic properties of different cell types, states, or clones. Identification of variants can enable a more complete characterization and present higher utility for RNA-seq and spatial transcriptomics data. Therefore, we developed one of the first methods, CaSpER, that identifies visualizes, and integrates CNV events using scRNA-Seq data.

Challenges of Quantifying Dynamic Pathways Within Heterogonous Cell Subpopulations

The complex cellular changes and interactions between different cell types or states define the characteristics of brain pathology. Using novel computational approaches, we can estimate the future state of an individual cell and identify the regulatory changes in transient cell states from scRNA-Seq data. Therefore, we developed a novel computational algorithm named scReguloCity for inferring pathway level transcriptional dynamics from single-cell RNA-Seq data. 

Deconvolving Different Cell Types or States in Bulk RNA-Seq and Spatial Transcriptomics Data

Understanding the clonal architecture of the tumor and the interplay between the malignant and non-malignant cells within the tumor ecosystem provides significant insights into the tumor recurrence, treatment, initiation, progression, and metastasis. Since, clinical data is mostly extracted from tissues containing a mixture of different cell types, inferring the ratio of malignant and non-malignant cell types and heterogeneous tumor populations (subclones) in bulk tumor has a great significance in the clinical care of cancer patients. Using our deconvolution algorithm, we can estimate cell type ratios in bulk-tumor using single cell RNA Sequencing as a reference.