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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
As part of a recent effort in my research group to engage in some career development activities, I made, or rather remade, my website. For those of you who may have found my previous site, allow me to apologize for the poor organization, lack of content, awful choice of colors, and all around meh-ness of my former site. To those of you who never saw the old one, it doesn’t exist anymore and you’re welcome for that.
Published in Conference Proceedings for the Physics Education Research Conference, 2017
Projects and Practices in Physics (P3) is a transformed, first-year introductory mechanics course offered at Michigan State University. The focus of the course is concept-based group learning implemented through solving analytic problems and computational modeling problems using the VPython programming environment. Interviews with students from P3 were conducted to explore the variation of students’ perceptions of the utility of solving computational physics problems in the classroom setting. A phenomenographic method is being used to develop categories of student experience with computational physics problems based on themes emerging across the different students’ interviews. This paper will focus on exploring the variation within the theme of Computation Helps to Learn Physics that arose from our preliminary analysis of the data from a larger phenomenographic study. When examined on an individual basis, this theme provides important insights into students’ perception of the use of computation, such as the way that students can engage with computation as a learning tool in a Physics classroom.
Recommended citation: N. Hawkins, M. Obsniuk, P. Irving, and M. Caballero, PERC 2017 Proceedings, 168-171. https://doi.org/10.1119/perc.2017.pr.037
Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters
Published in BMC Bioinformatics, 2019
Single cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq, and unsupervised clustering combined with Principal Component Analysis (PCA) can be used to overcome this limitation. After clustering, however, one has to interpret the average expression of markers on each cluster to identify the corresponding cell types, and this is normally done by hand by an expert curator. We present a computational tool for processing single cell RNA-seq data that uses a voting algorithm to automatically identify cells based on approval votes received by known molecular markers. Using a stochastic procedure that accounts for imbalances in the number of known molecular signatures for different cell types, the method computes the statistical significance of the final approval score and automatically assigns a cell type to clusters without an expert curator. We demonstrate the utility of the tool in the analysis of eight samples of bone marrow from the Human Cell Atlas. The tool provides a systematic identification of cell types in bone marrow based on a list of markers of immune cell types, and incorporates a suite of visualization tools that can be overlaid on a t-SNE representation. The software is freely available as a Python package at https://github.com/sdomanskyi/DigitalCellSorter. This methodology assures that extensive marker to cell type matching information is taken into account in a systematic way when assigning cell clusters to cell types. Moreover, the method allows for a high throughput processing of multiple scRNA-seq datasets, since it does not involve an expert curator, and it can be applied recursively to obtain cell sub-types. The software is designed to allow the user to substitute the marker to cell type matching information and apply the methodology to different cellular environments.
Recommended citation: Domanskyi, S., Szedlak, A., Hawkins, N.T. et al. Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters. BMC Bioinformatics 20, 369 (2019). https://doi.org/10.1186/s12859-019-2951-x https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2951-x
ISMB 2020 went virtual this year due to the COVID-19 pandemic, but that didn’t stop the science from happening. I was fortunate to be selected to give a poster presentation on my current research. In our work, we create word embeddings from sample metadata and use these as features for training logisitic regression classifiers. Our models predict annotations for tissue and cell type labels from the UBERON ontology on the basis of text alone. Our approach outperforms two other classes of text-based annotation methods. While we do not outperform similarly tasked models trained from gene expression features, our approach can be used on novel data types without needing to retrain.
I was fortunate to be selected to give a poster presentation on my current research. In our work, we create word embeddings from sample metadata and use these as features for training logisitic regression classifiers. Our models predict annotations for tissue and cell type labels from the UBERON ontology on the basis of text alone. Our approach outperforms two other classes of text-based annotation methods. While we do not outperform similarly tasked models trained from gene expression features, our approach can be used on novel data types without needing to retrain. And yes, this is the same work I presented as ISMB 2020, but the constraints for posters in this conference were a little looser so I could put more content on there. Also, in the time between ISMB and Genome Informatics, some new analyses were done, and I was excited to be able to include that work as well.