title: Becoming data-savvy in a big-data world.
abstract: Plant biology is becoming a data-driven science. High-throughput technologies generate data quickly from molecular to ecosystem levels. Statistical and computational approaches enable describing and interpreting data quantitatively. We highlight the purpose, common problems, and general principles in data analysis. We use RNA sequencing (RNAseq) analysis to illustrate the rationale behind some of the choices made in statistical data analysis. Finally, we provide a list of free online resources that emphasize intuition behind quantitative data analysis.
authors.affiliation: Carnegie Institution for Science, Department of Plant Biology, 260 Panama Street, Stanford, CA 94305, USA.
authors.forename: Meng
authors.initials: M
authors.lastname: Xu
authors.affiliation: Carnegie Institution for Science, Department of Plant Biology, 260 Panama Street, Stanford, CA 94305, USA. Electronic address: srhee@carnegiescience.edu.
authors.forename: Seung Yon
authors.initials: SY
authors.lastname: Rhee
mesh_terms: D001901:Botany; D019295:Computational Biology / Q000379:methods*; D017753:Ecosystem; D017423:Sequence Analysis, RNA; D013223:Statistics as Topic
keywords: RNAseq analysis; big data; data analysis; statistics