Estimation of bacterial community composition from a high-throughput sequenced sample is an important task in metagenomics applications. As 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 time-consuming in a desktop computing environment. Using sparsity enforcing methods from the general sparse signal processing field (such as 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 reasonably fast community composition estimation method, which is shown to be more robust to input data variation than a recently introduced related method.
Saikat Chatterjee is a researcher – holding permanent position - jointly with the Communication Theory Lab and the Signal Processing Lab, KTH-Royal Institute of Technology, Sweden. He was also with the Sound and Image Processing Lab at the same institution as a postdoctoral fellow for one year. Currently, at KTH, he is responsible for teaching several courses and advising several masters and PhD students. Before moving to Sweden, he received Ph.D. degree in 2009 from Indian Institute of Science, India. He was a co-author of the paper that won the best student paper award at ICASSP 2010. His current research interests are source coding, speech and audio processing, estimation and detection, sparse signal processing, compressive sensing, wireless communications and computational biology.