About

I’m a computational biologist whose main research interest lies in developing and applying computational and statistical tools to help improve our understanding of gene regulatory processes. My current research focuses on the development and application of machine learning approaches for analyzing single-cell RNA-Seq (scRNA-Seq) data. As part of this research, I have recently developed ENHANCE (Wagner et al., 2019), an algorithm that accurately removes technical noise from scRNA-Seq datasets, Galapagos (Wagner, 2019), a straightforward clustering approach for scRNA-Seq data, and Monet (Wagner, 2020), an open-source Python package for analyzing scRNA-Seq data, which contains implementations of these methods.

In 2017, I received my PhD in Computational Biology and Bioinformatics from Duke University. As part of my dissertation work, I developed GO-PCA (Wagner, 2015), a new method for systematic exploratory analysis of RNA-Seq data using prior knowledge. In the past, I have also been involved in a project studying the development of the nematode C. elegans using microarray expression analysis of precisely staged embryos (Levin et al., 2012), and in the development of CEL-Seq (Hashimshony et al., 2012), an experimental method for linear amplification and high-throughput sequencing of mRNA from single cells.

What I enjoy most about doing research is learning new things all the time, thinking critically, being creative, solving technical challenges, and working collaboratively.

Contact

Email: florian.wagner@uchicago.edu
Twitter: @flo_compbio

Software

  • Monet: An open-source Python package for analyzing and integrating scRNA-Seq data using PCA-based latent spaces (contains implementation of ENHANCE)
  • ENHANCE: Accurate denoising of single-cell RNA-Seq data (stand-alone Python/R implementations)
  • GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge

Publications

Preprints

  1. Wagner, F. (2020). Monet: An open-source Python package for analyzing and integrating scRNA-Seq data using PCA-based latent spaces. BioRxiv. https://doi.org/10.1101/2020.06.08.140673

  2. Wagner, F. (2020). Straightforward clustering of single-cell RNA-Seq data with t-SNE and DBSCAN. BioRxiv, 770388. https://doi.org/10.1101/770388

  3. Wagner, F., Barkley, D., & Yanai, I. (2019). Accurate denoising of single-cell RNA-Seq data using unbiased principal component analysis. BioRxiv, 655365. https://doi.org/10.1101/655365

  4. Wagner, F., & Yanai, I. (2018). Moana: A robust and scalable cell type classification framework for single-cell RNA-Seq data. BioRxiv, 456129. https://doi.org/10.1101/456129

  5. Wagner, F., Yan, Y., & Yanai, I. (2018). K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data. BioRxiv, 217737. https://doi.org/10.1101/217737

  6. Wagner, F. (2017). The XL-mHG test for gene set enrichment (No. e1962v3). PeerJ Preprints. Retrieved from https://peerj.com/preprints/1962

  7. Wagner, F. (2016). pyAffy: An efficient Python/Cython implementation of the RMA method for processing raw data from Affymetrix expression microarrays. PeerJ Preprints, 4, e1790v1. https://doi.org/10.7287/peerj.preprints.1790v1

  8. Wagner, F. (2015). GO-PCA: An Unsupervised Method to Explore Biological Heterogeneity Based on Gene Expression and Prior Knowledge. BioRxiv, 018705. https://doi.org/10.1101/018705

  9. Wagner, F. (2015). The XL-mHG Test For Enrichment: A Technical Report. ArXiv:1507.07905 [Stat]. Retrieved from http://arxiv.org/abs/1507.07905

Peer-reviewed research

  1. Moncada, R., Barkley, D., Wagner, F., Chiodin, M., Devlin, J. C., Baron, M., … Yanai, I. (2020). Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nature Biotechnology, 38(3), 333–342. https://doi.org/10.1038/s41587-019-0392-8

  2. Xia, B., Yan, Y., Baron, M., Wagner, F., Barkley, D., Chiodin, M., … Yanai, I. (2020). Widespread Transcriptional Scanning in the Testis Modulates Gene Evolution Rates. Cell, 180(2), 248–262.e21. https://doi.org/10.1016/j.cell.2019.12.015

  3. Wagner, F. (2015). GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge. PloS One, 10(11), e0143196. https://doi.org/10.1371/journal.pone.0143196

  4. Hashimshony, T., Wagner, F., Sher, N., & Yanai, I. (2012). CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Reports, 2(3), 666–673. https://doi.org/10.1016/j.celrep.2012.08.003

  5. Levin, M., Hashimshony, T., Wagner, F., & Yanai, I. (2012). Developmental milestones punctuate gene expression in the Caenorhabditis embryo. Developmental Cell, 22(5), 1101–1108. https://doi.org/10.1016/j.devcel.2012.04.004