@article {45585,
	title = {NetShift: a methodology for understanding {\textquoteleft}driver microbes{\textquoteright} from healthy and disease microbiome datasets},
	journal = {ISME Journal},
	volume = {13},
	year = {2019},
	month = {JAN},
	pages = {442-454},
	type = {Article},
	abstract = {The combined effect of mutual association within the co-inhabiting microbes in human body is known to play a major role in determining health status of individuals. The differential taxonomic abundance between healthy and disease are often used to identify microbial markers. However, in order to make a microbial community based inference, it is important not only to consider microbial abundances, but also to quantify the changes observed among inter microbial associations. In the present study, we introduce a method called {\textquoteleft}NetShift{\textquoteright} to quantify rewiring and community changes in microbial association networks between healthy and disease. Additionally, we devise a score to identify important microbial taxa which serve as {\textquoteleft}drivers{\textquoteright} from the healthy to disease. We demonstrate the validity of our score on a number of scenarios and apply our methodology on two real world metagenomic datasets. The {\textquoteleft}NetShift{\textquoteright} methodology is also implemented as a web-based application available at },
	doi = {10.1038/s41396-018-0291-x},
	author = {Kuntal, B. K. and Chandrakar, P. and Sadhu, S. and Mande, S. S.}
}
