Language-Agnostic Reproducible Data Analysis Using Literate Programming

A modern biomedical research project can easily contain hundreds of analysis steps and lack of reproducibility of the analyses has been recognized as a severe issue. While thorough documentation enables reproducibility, the number of analysis programs used can be so large that in reality reproducibility cannot be easily achieved. Literate programming is an approach to present computer programs to human readers. The code is rearranged to follow the logic of the program, and to explain that logic in a natural language. The code executed by the computer is extracted from the literate source code. As such, literate programming is an ideal formalism for systematizing analysis steps in biomedical research. We have developed the reproducible computing tool Lir (literate, reproducible computing) that allows a tool-agnostic approach to biomedical data analysis. We demonstrate the utility of Lir by applying it to a case study. Our aim was to investigate the role of endosomal trafficking regulators to the progression of breast cancer. In this analysis, a variety of tools were combined to interpret the available data: a relational database, standard command-line tools, and a statistical computing environment. The analysis revealed that the lipid transport related genes LAPTM4B and NDRG1 are coamplified in breast cancer patients, and identified genes potentially cooperating with LAPTM4B in breast cancer progression. Our case study demonstrates that with Lir, an array of tools can be combined in the same data analysis to improve efficiency, reproducibility, and ease of understanding. Lir is an open-source software available at github.com/borisvassilev/lir.现代生物医学研究项目往往包含数百个分析步骤，分析结果的可复现性不足已被公认为一项严峻问题。尽管详尽的文档记录可助力实现可复现性，但所用分析程序的体量往往过于庞大，实际中难以轻易达成可复现的分析结果。文学化编程（Literate Programming）是一种面向人类读者展示计算机程序的方法：程序代码会被重构以契合程序的运行逻辑，并以自然语言对该逻辑进行阐释，计算机实际运行的代码可从该文学化源代码中提取得到。因此，文学化编程是系统化整理生物医学研究中分析步骤的理想形式化方法。我们开发了一款可复现计算工具Lir（全称为literate, reproducible computing，即文学化可复现计算），其支持以工具无关的方式开展生物医学数据分析。我们通过一项案例研究展示了Lir的应用价值：本研究旨在探究内体运输调控因子在乳腺癌进展中的作用。本次分析中，我们结合了多种工具以解读现有数据，包括关系型数据库、标准命令行工具以及统计计算环境。分析结果显示，脂运输相关基因LAPTM4B与NDRG1在乳腺癌患者中存在共扩增现象，并筛选出了可能在乳腺癌进展中与LAPTM4B协同发挥作用的潜在基因。本案例研究证实，借助Lir，可在同一数据分析流程中整合多种工具，从而提升分析效率、可复现性与可读性。Lir是一款开源软件，源代码托管于github.com/borisvassilev/lir。