SEK - Sparsity exploiting estimation of bacterial community composition
by
Saikat Chatterjee(KTH Communication Theory)
→
Europe/Stockholm
132:028
132:028
Description
Estimation of bacterial community composition from a
high-throughput sequenced sample is an important task in metagenomics applications. Since the the sample sequence data typically harbors reads of variable lengths and different levels of biological and technical noise, accurate statistical analysis of such data is challenging. Currently popular estimation methods are typically very time consuming in a desktop computing environment.
Using sparsity enforcing methods from general compressed sensing, we derive a solution to the community composition estimation problem by a simultaneous assignment of all sample reads to a pre-processed reference database. A general statistical model based on kernel density estimation techniques is introduced for the assignment task and the model solution is obtained using convex optimization tools. Further, we design a greedy algorithm solution for a fast solution. Our approach offers a very fast community composition estimation method which is shown to be more robust to input data variation than a recently introduced related method.
