Mekan Myradov

I'm currently an MSc student at Sabanci University, affiliated with the VPA Lab under the guidance of Öznur Taştan and Hilal Kazan. My research focuses on machine learning applications in biology, specifically single-cell RNA sequencing data integration.

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We introduce a novel method for batch effect correction named SCITUNA, a Single-Cell data Integration Tool Using Network Alignment. The method aligns batches iteratively, progressively integrating each batch into a unified representation to correct for batch effects. We perform evaluations on 39 individual batches from four real datasets and a simulated dataset, which include both scRNA-seq and scATAC-seq datasets, spanning multiple organisms and tissues. A thorough comparison of existing batch correction methods using 13 metrics reveals that SCITUNA outperforms current approaches and is successful at preserving biological signals present in the original data.

I implemented a comprehensive Python toolbox for statistical analysis implementing one-way ANOVA, two-way ANOVA, and multiple linear regression with various hypothesis testing and confidence interval methods.

I implemented a synthetic data generation algorithm in Python using Gaussian Mixture Models.

Website template credits to Jon Barron.