using fecal microbiota composition as a non-invasive diagnostic tool to classify colorectal carcinoma and adenoma.

We profiled the microbiota of 1002 fecal samples of colorectal carcinoma and adenoma patients and normal controls from southeastern China using 16S rRNA Illumina PE sequencing. We built random forest tree based bi-class classifier of CRC and normal and multi-class classifier of cancer, adenomas, and normal, and then test them independently. Batch effects were studied and spike-in methods were proposed as an effective strategy to model such effects and improve the results of multi-class classification. Effects of confounding factors were also studied.