Hossein Khiabanian

The paper identifies a distinct breast tumor group with monoallelic gene expression linked to poor prognosis and develops a method to detect allelic imbalance.

This paper introduces the average overlap metric to improve clustering and annotation in single-cell RNA sequencing by comparing gene rankings across clusters.

The paper introduces a revised workflow for scRNA-seq analysis that performs feature selection before normalization, leading to improved clustering results.

This paper evaluates computational methods to infer germline vs. somatic variants from tumor-only sequencing data using 1,608 patients and compares results to germline testing.

All-FIT estimates tumor purity from high-depth sequencing data by analyzing allele frequencies, outperforming existing methods in simulated and clinical data.

TuBA identifies clinically relevant tumor transcriptional profiles in breast cancer by detecting biclusters with significant gene set agreement and clinical implications across multiple datasets.

MERIT quantifies sequencing errors in ultra-deep sequencing data and identifies optimal sequencing depth to minimize false positives and negatives.

This paper develops a statistical model for sequencing noise in high-depth data to identify mutations associated with tumor progression and recurrence.