Microbiome data are inherently sparse, multivariate, and compositional. These characteristics create many challenges for statistical analysis. I will present our recent developments to tackle some of these challenges, including using dimension reduction techniques to remove batch effects and identify microbial signatures discriminating sample groups.
Statistical Genomics, Melbourne Integrative Genomics, School of Mathematics and Statistics, The university of Melbourne
A/Prof Kim-Anh Lê Cao develops computational methods, software and tools to interpret big biological data and answer research questions efficiently. Kim-Anh has a mathematical engineering background and graduated with a PhD in statistics from the Université de Toulouse, France. She then moved to Australia to forge her own non-linear career path, first working as a biostatistician consultant at QFAB Bioinformatics, then as a research group leader at the biomedical University of Queensland Diamantina Institute.
She currently continues her strong research focus at the University of Melbourne. Kim-Anh has secured two consecutive NHMRC fellowships from 2014. In 2019 she received the Australian Academy of Science’s Moran Medal for her contributions to Applied Statistics. She was selected to the international HomewardBound leadership program for women in STEMM, culminating to a trip to Antarctica in 2019, and the superstars of STEM program from Science Technology Australia.