Research


The Bacher Group focuses on developing statistical solutions for analyzing and pre-processing (e.g. normalizing) high-throughput genomic and next-generation sequencing data. We also use statistical models and machine learning techniques to better understand disease in our collaborations with scientists and clinicians in the UF Diabetes Institute and the UF Sepsis and Critical Illness Research Center.

Publications





Automated minute scale RNA-seq of pluripotent stem cell differentiation reveals early divergence of human and mouse gene expression kinetics


In this paper, we measured the immediate-early gene expression responses to neural differentiation signals in mouse and human pluripotent stem cells. We quantitatively summarized the gene expression patterns using a flexible model that allowed us to identify shared trends and dynamic changes across species. We found that responses occurred equally early across species, but expression patterns were prolonged in human cells and the rate of upregulation in gene expression was significantly faster in mouse cells. Our results represent a significant contribution in furthering the knowledge of early developmental transcriptional programs.


Barry, C., Schmitz, M.T., Argus, C., Bolin, J.M., Probasco, M.D., Leng, N., Duffin, B., Steill, J., Swanson, S., McIntosh, B.E., Stewart, R., Kendziorski, C., Thomson, J.A., and Bacher,R.*\(\dagger\). Automated minute scale RNA-seq of pluripotent stem cell differentiation reveals early divergence of human and mouse gene expression kinetics. PLOS Computational Biology. 15.12 (2019).





Trendy: Segmented regression approach to reveal expression dynamics in high throughput profiling data with ordered conditions


High throughput expression profiling experiments with ordered conditions (e.g. time-course or spatial-course) are becoming more common for profiling detailed differentiation processes or spatial patterns. Identifying dynamic changes at both the individual gene and whole transcriptome level can provide important insights about genes, pathways, and critical time-points. We developed Trendy, a flexible R package that utilizes segmented regression models to characterize gene-specific expression patterns and summarizes changes of global dynamics over ordered conditions.


Bacher,R.*\(\dagger\), Leng,N.*, Chu,L.-F.,Ni, Z., Thomson,J.A., Kendziorski,C., and Stewart, R.M.\(\dagger\). Trendy: Segmented regression approach to reveal expression dynamics in high throughput profiling data with ordered conditions. BMC Bioinformatics. 19.1 (2018): 380.











SCnorm: normalization for single-cell RNA-seq data


Single cell RNA-sequencing (scRNA-seq) is a promising tool that facilitates study of the transcriptome at the resolution of a single cell. However, along with the many advantages of scRNA-seq come technical artifacts not observed in bulk RNA-seq studies including an abundance of zeros, varying levels of technical bias across gene groups, and systematic variation in the effects of sequencing depth. The normalization methods traditionally used in bulk RNA-seq were not designed to accommodate these features and, consequently, applying them to the single-cell setting results in poor expression estimates and increased error rates in downstream analyses. To address this, we developed a latent-class regression framework to enable efficient and accurate scRNA-seq normalization. Simulation and case study results suggest that the framework provides for improvements in gene expression estimation as well as downstream inference.


Bacher,R.*, Chu,L.-F.*, Leng,N., Thomson,J.A., Gasch,A., Stewart,R.M., Newton,M., and Kendziorski, C. SCnorm: robust normalization of single-cell RNA-seq data. Nature Methods 14.6 (2017): 584-586.





Design and computational analysis of single-cell RNA-sequencing experiments


Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allows us to address scientific questions that eluded examination just a few years ago. With the advantages of scRNA-seq come computational challenges that are just beginning to be addressed. In this article, we highlight the computational methods available for the design and analysis of scRNA-seq experiments, their advantages and disadvantages in various settings, the open questions for which novel methods are needed, and expected future developments in this exciting area.


Bacher, R. and Kendziorski, C. Design and computational analysis for single-cell RNA-sequencing experiments. Genome Biology. 17, 63 (2016).


Key: * indicates co-first authors; \(\dagger\) indicates corresponding authors; students/trainees are underlined

Barry, C., Schmit , M.T., Jiang, P., Schwarz , M.P., Duffin, B.M., Swanson, S., Bacher, R., Bolin, J.M, Elwell, A.L., McIntosh, B.E., Stewart, R., Thomson, J.A. Species-Specific Developmental Timing is Maintained by Pluripotent Stem Cells Ex Utero. Developmental Biology. 423, 101-110 (2017).


Fischer, B.L., Bacher, R., Bendlin, B.B., Birdsill, A.C., Ly, M., Hoscheidt, S.M., Chappell, R.J., Mahoney, J.E., Gleason, C.E. An Examination of Brain Abnormalities and Mobility in Individuals with Mild Cognitive Impairment and Alzheimer’s Disease. Frontiers in Aging Neuroscience. 9, 86 (2017).


Ye, S., Bacher, R., Keller, M.P., A ie, A.D., and Kendziorski, C. Statistical Methods for Latent Class Quantitative Trait Loci Mapping. Genetics (2017): genetics-117.


Gasch, A.P., Yu, B., Hose, J., Escalante, L., Place, M., Bacher, R., Kanbar, J., Ciobanu, D., Sandor, L., Grigoriyev, I.V., Kendziorski, C., Quake, S., McClean, M. Single-cell RNA-seq reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress. PLOS Biology. 15(12):e2004050. (2017).


Keller, M.P., Simecek, P., Schueler, K.L, Rabaglia, M.E., Stapleton, D.S., Broman, A.T., Gatti, D.M. Vincent, M., Allen, S., Bacher, R., Kendziorski, K., Broman, K.W., Yandell, B.S., Churchill, G.A., Attie, A.D. Genetic drivers of pancreatic islet function. Genetics. genetics-300864, (2018).


Vermillion, K.L., Bacher, R., Tannenbaum A.P., Swanson S., Callahan K.A., Jiang P., Stewart R.M., Thomson J.A., Vereide D.T. Spatial patterns of gene expression are unveiled in the chick primitive streak by ordering single-cell transcriptomes. Developmental Biology. DOI: 10.1016/j.ydbio.2018.04.007, (2018).


Chu, L.-F., Mamott, D., Ni Z., Bacher, R. , Liu, C., Swanson, S., Kendziorski, C., Stewart, R.M., and Thomson, J.A. An In Vitro Human Segmentation Clock Model Derived from Embryonic Stem Cells. Cell Reports. 28(9), 2247-2255, (2019).


Shapiro, M.R., Wasserfall, C.H., McGrail, S.M., Posgai, A.L., Bacher, R., Muir, A., Haller, M.J., Schatz, D.A., Wesley, J.D., von Herrath, M. and Hagopian, W.A., Speake, C., Atkinson, M.A., Brusko, T.M. Insulin-Like Growth Factor Dysregulation Both Preceding and Following Type 1 Diabetes Diagnosis. Diabetes. (2020).


Motwani, K., Peters, L.D., Vliegen, W.H., Gomaa El-sayed, A., Seay, H.R., Cecilia Lopez, M., Baker, H.V., Posgai, A.L., Brusko, M.A., Perry, D.J., Bacher, R., Larkin, J., Haller, M.J., Brusko, T.M. Human Regulatory T Cells From Umbilical Cord Blood Display Increased Repertoire Diversity and Lineage Stability Relative to Adult Peripheral Blood. Frontiers in Immunology. (2020).


Darden, D.B., Stortz, J.A., Hollen, M.K., Cox, M.C., Apple, C.G., Hawkins R.B., Rincon, J.C., Lopez, M., Wang, Z., Navarro, E., Hagen, J.E., Parvataneni, H.K., Brusko, M.A., Kladde, M., Bacher, R., Brumback, B.A., Brakenridge, S.C., Baker, H.V. Cogle, C.R., Mohr, A.M., Efron, P.A. Identification of Unique mRNA and miRNA Expression Patterns in Bone Marrow Hematopoietic Stem and Progenitor Cells After Trauma in Older Adults. Frontiers in Immunology. (2020).


Seirup, M., Chu, L. F., Sengupta, S., Leng, N., Browder, H., Kapadia, K., … & Bacher, R. (2020). Reproducibility across single-cell RNA-seq protocols for spatial ordering analysis. PloS one, 15(9), e0239711.


Japp, A. S., Meng, W., Rosenfeld, A. M., Perry, D. J., Thirawatananond, P., Bacher, R. L., … & HPAP Consortium. (2021). TCR+/BCR+ dual-expressing cells and their associated public BCR clonotype are not enriched in type 1 diabetes. Cell, 184(3), 827-839.


Williams, M. D., Bacher, R., Perry, D. J., Grace, C. R., McGrail, K. M., Posgai, A. L., … & Wasserfall, C. H. (2021). Genetic Composition and Autoantibody Titers Model the Probability of Detecting C-Peptide Following Type 1 Diabetes Diagnosis. Diabetes, 70(4), 932-943.


Ross, J. J., Wasserfall, C. H., Bacher, R., Perry, D. J., McGrail, K., Posgai, A. L., … & Atkinson, M. A. (2021). Exocrine Pancreatic Enzymes Are a Serological Biomarker for Type 1 Diabetes Staging and Pancreas Size. Diabetes, 70(4), 944-954.


Darden, D. B., Bacher, R., Brusko, M. A., Knight, P., Hawkins, R. B., Cox, M. C., … & Efron, P. A. (2021). Single cell RNA-SEQ of human myeloid derived suppressor cells in late sepsis reveals multiple subsets with unique transcriptional responses: a pilot study. Shock, 55(5), 587.




Preprints


methylscaper: an R/Shiny app for joint visualization of DNA methylation and nucleosome occupancy in single-molecule and single-cell data Parker Knight, Marie-Pierre L. Gauthier, Carolina E. Pardo, Russell P. Darst, Alberto Riva, Michael P. Kladde, Rhonda Bacher bioRxiv 2020.11.13.382465; doi: https://doi.org/10.1101/2020.11.13.382465


Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization Rhonda Bacher , Li-Fang Chu, Cara Argus, Jennifer M. Bolin, Parker Knight, James A. Thomson, Ron Stewart, Christina Kendziorski bioRxiv 2020.10.05.326553; doi: https://doi.org/10.1101/2020.10.05.326553


Normalization by distributional resampling of high throughput single-cell RNA-sequencing data Jared Brown, Zijian Ni, Chitrasen Mohanty, Rhonda Bacher, Christina Kendziorski bioRxiv 2020.10.28.359901; doi: https://doi.org/10.1101/2020.10.28.359901


Ogut, F., Newman, J.R.B., Bacher, R., Concannon P.J., Verhoeven K.J.F., and McIntyre, L.M. Experimental design for large scale omic studies. bioRxiv, DOI: 532580, (2019).




Book Chapters


Bacher, R. Normalization for single-cell RNA-seq data analysis. In Computational methods for single-cell data analysis, Guo-Cheng Yuan (Ed.). Methods in Molecular Biology. DOI 10.1007/978-1-4939-9057-3, (2019).