Biology subjects

Rogozhnikov, A.

Publications and source records attributed to Rogozhnikov, A..

3 recordsLinked to original sources

bioRxiv2021

Demuxalot: scaled up genetic demultiplexing for single-cell sequencing

Demultiplexing methods have facilitated the widespread use of single-cell RNA sequencing (scRNAseq) experiments by lowering costs and reducing technical variations. Here, we present demuxalot: a method for probabilistic genotype inference from aligned reads, with no assumptions about allele ratios and efficient incorporation of prior genotype information from historical experiments in a multi-batch setting. Our method efficiently incorporates additional information across reads originating from the same transcript, enabling up to 3x more calls per read relative to naive approaches. We also propose a novel and highly performant tradeoff between methods that rely on reference genotypes and methods that learn variants from the data, by selecting a small number of highly informative variants that maximize the marginal information with respect to reference single nucleotide variants (SNVs). Our resulting improved SNV-based demultiplex method is up to 3x faster, 3x more data efficient, and achieves significantly more accurate doublet discrimination than previously published methods. This approach renders scRNAseq feasible for the kind of large multi-batch, multi-donor studies that are required to prosecute diseases with heterogeneous genetic backgrounds.

bioinformatics↗

bioRxiv2021

Hierarchical confounder discovery in the experiment-machine learning cycle

The promise of using machine learning (ML) to extract scientific insights from high dimensional datasets is tempered by the frequent presence of confounding variables, and it behooves scientists to determine whether or not a model has extracted the desired information or instead may have fallen prey to bias. Due both to features of many natural phenomena and to practical constraints of experimental design, complex bioscience datasets tend to be organized in nested hierarchies which can obfuscate the origin of a confounding effect and obviate traditional methods of confounder amelioration. We propose a simple non-parametric statistical method called the Rank-to-Group (RTG) score that can identify hierarchical confounder effects in raw data and ML-derived data embeddings. We show that RTG scores correctly assign the effects of hierarchical confounders in cases where linear methods such as regression fail. In a large public biomedical image dataset, we discover unreported effects of experimental design. We then use RTG scores to discover cross-modal correlated variability in a complex multi-phenotypic biological dataset. This approach should be of general use in experiment-analysis cycles and to ensure confounder robustness in ML models.

bioinformatics↗

bioRxiv2020

Optimization and scaling of patient-derived brain organoids uncovers deep phenotypes of disease

Cerebral organoids provide unparalleled access to human brain development in vitro. However, variability induced by current culture methodologies precludes using organoids as robust disease models. To address this, we developed an automated Organoid Culture and Assay (ORCA) system to support longitudinal unbiased phenotyping of organoids at scale across multiple patient lines. We then characterized organoid variability using novel machine learning methods and found that the contribution of donor, clone, and batch is significant and remarkably consistent over gene expression, morphology, and cell-type composition. Next, we performed multi-factorial protocol optimization, producing a directed forebrain protocol compatible with 96-well culture that exhibits low variability while preserving tissue complexity. Finally, we used ORCA to study tuberous sclerosis, a disease with known genetics but poorly representative animal models. For the first time, we report highly reproducible early morphological and molecular signatures of disease in heterozygous TSC+/- forebrain organoids, demonstrating the benefit of a scaled organoid system for phenotype discovery in human disease models.

neuroscience↗