CausalBiome: Developing a unified statistical framework for analyzing microbiome data
The CausalBiome project will develop a new unified framework for statistical analysis and causal inference on human microbiome data.
Microorganisms, such as bacteria, fungi and viruses interact in diverse ways with their surroundings. The human body is estimated to be a habitat for more than 10,000 different microbial species and they have been associated with various health outcomes such as cardiovascular disease, metabolic diseases, obesity, mental illness, and autoimmune disorders. Thanks to recent advances in gene sequencing technology, scientists are now able to directly measure these microbes. However, to understand how they interact with their human host, sophisticated statistical tools are needed to analyze the highly complex data. Unfortunately, current techniques do not offer a unified approach that incorporates all available knowledge into the analysis.
The CausalBiome project will fill this gap by developing novel statistical and data science analysis methods, which will lead to a better understanding of how the microbiome interacts with its host. All results will be made publicly available to help other scientists gain new insights into how microbes affect our health.