Monoclonal antibody production processes control critical quality attributes (CQAs), which are the measures that provide proof of a product's identity and quality. Critical decisions rely on the accuracy and precision of these measures, as well as their appropriate statistical treatment. Many measures require special attention. For example, the charge heterogeneity CQA measured by ionic exchange chromatography reports proportions or percentages of the total integrated peak area of known species. Since proportions sum to a constant (1 or 100%), they fall into a special class of data called compositional data that have a unit sum constraint and therefore an inherent correlation. However, these measures are often analyzed assuming independence which is incorrect. Estimating statistics with incorrect assumptions can lead to inferential failures (e.g., shelf life failures), or can lead to missing important structural patterns in the data. Presented here is a new class of analysis methods for CQAs compositional data called Biologic Compositional Data Analysis (Bio-CoDa). The method is based on the elegant solution to analysis issues by Aitchison (1986). An introduction to the Bio-CoDa methods with rational is presented as well as examples demonstrating its strengths. (c) 2017 Wiley Periodicals, Inc.