Ellen Tsaprailis, January 12, 2022
Photo credit: Lindsay Ralph

Canada Research Chair in Data Science and Analytics Awarded to Sanjeena Dang who is Pioneering Statistical Analysis of Biological Systems

Carleton’s Sanjeena Dang will apply cutting-edge statistical algorithms to achieve a comprehensive understanding of biological systems through her newly-designated Tier 2 Canada Research Chair (CRC) in Data Science and Analytics.

In her first year at Carleton, Dang is also an Assistant Professor in the School of Mathematics and Statistics. She plans to focus her research on developing efficient and scalable statistical models to analyze large-scale genomic data.

“It’s wonderful to be recognized as a Canada Research Chair at Carleton. I am thankful for the support from my students and mentors throughout my career, and from colleagues at Carleton University,” says Dang.

“As a CRC, I will primarily focus on developing statistical approaches that combine information from various different biological platforms to get a comprehensive global understanding of complex biological systems.”

Sanjeena Dang is the Canada Research Chair in Data Science and Analytics

In trying to get insight into a particular biological process—for example, disease progression—Dang models and analyzes data from various biological levels to get to the root of key events affecting the process. For modelling biological datasets such as RNA-sequencing data as well as microarray and microbiome data, she is developing novel cluster-analysis models.

“Different biological platforms provide different but partial views of an underlying process,” says Dang. “My work will focus on integrating the information together in a meaningful way to provide a unified understanding. Integrating these datasets is challenging because of the heterogeneity in the data types.”

Genomic datasets have many attributes that must be factored in.

“An RNA-sequence study with measurements on thousands of genes or a genome-wide association study with the genotypes of millions of SNPs (single nucleotide polymporphisms) is an example where analyzing these complex datasets efficiently is an ongoing challenge,” says Dang. “We will move the field forward by developing models and algorithms capable of scaling efficiently for these large-scale datasets.”

Dang notes that her research program is data-centric, and the research directions are data-driven.

“I am fortunate to have the opportunity to collaborate with and learn from many talented researchers from diverse academic backgrounds. These collaborations have shaped many of my research directions,” says Dang.

The five boxplots shows the distribution of the relative abundant of microbes from different phyla using only the tumor samples and healthy controls while the sixth plot shows the proportions of normal, adenoma and carcinoma samples in each group. Image published by Royal Statistical Society from a recent paper by Dang.

She is currently developing a model for clustering human microbiome (microorganisms that co-exist in the human body) compositions. Imbalance in the microbiota has been associated with several diseases and emerging therapeutic approaches are aiming to restore the microbial diversity and functionality that is lost in patients with disease. Through her collaborations, the statistical models that Dang and her team are pioneering are being used to better understand the human gut microbiome composition of patients with ulcerative colitis and identify novel diagnostic microbiome biomarkers that are indicative of disease status and severity.

“Understanding changes in biological systems at various levels and how these interactions influence human health is crucial,” says Dang.

“Developing models that can efficiently analyze and integrate information from these massive and heterogeneous datasets in a meaningful way is key to gaining a better understanding of biological systems and accurately tailoring personalized medical care. This is especially important for complex diseases that have highly-variable phenotypes due to complex interactions between genetic/genomic, environmental, and microbiota factors.”

Tier 2 Canada Research Chairs
Tier 2 Chairs are intended for emerging scholars who have demonstrated particular research creativity and innovation, with the potential to achieve international recognition in their fields, as well as showing a strong commitment to attracting and developing excellent trainees, students and future researchers.

The Government of Canada established its Canada Research Chairs program in 2000.

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