The Computational article format: Software as a research output.

Robust software, tools, methods, and pipelines are essential to analyzing modern cytometry experiments. Where should we, researchers working in this field, turn to learn about new tools, software, and methods? In the broader field of bioinformatics, computational cytometry remains a niche area (although its star is rising with the successes of immunotherapies for cancer treatment and increased application of high dimensional cytometry across a broad spectrum of research areas). Researchers who produce new software do not necessarily have access to novel data sets leading to high impact biological insights, and high impact journals, for better or worse, often relegate software and computational methods to supplementary notes. Broad scope bioinformatics journals, on the other hand, do not always have the interest to publish cytometry‐related articles (and when they do, those articles do not necessarily reach their intended audience).

Cytometry A is inarguably, a specialist journal. But it is also unique; a multidisciplinary journal by the very nature of the scientific society, ISAC, which it supports. ISAC is a multidisciplinary scientific society of engineers, biologists, immunologists, computational researchers, computer scientists, technologists, and others. It reflects the multidisciplinary technical origins of cytometry. It has always been welcoming to research articles highlighting technical innovations in the field. Until now there has been no domain‐specific venue where computational researchers could highlight new software tools as a specific research output.

This month, Cytometry A is debuting its' new “Computational Article” format. Cytometry A is a natural home for such work to reach its intended audience and have the greatest impact. The goals of the new format are straightforward: “to highlight novel or improved software and tools, algorithm implementations, databases, or utilities for visualization, analysis, processing, modeling, managing or otherwise working with cytometry data.”

Computational articles provide a venue for authors to present work motivated by clearly defined problems in the field, with results weighed against existing solutions. Examples are work that makes new methodological developments, contributes to an existing ecosystem of tools, implements ISAC standards, or otherwise makes contributions to the field by solving an existing problem or filling a clearly defined need. Trivial software implementations should be avoided and software should follow the Cytometry A software policy.

Defining trivial versus non‐trivial software implementations can be subjective and vary from one individual or reviewer to another, therefore an operational definition is warranted. Can the software have an impact on the field? Then it may be non‐trivial. Is the implementation simple, for example, a script or wrapper around an existing tool, with little demonstrated benefit? Then it may be trivial. Not all complex software is necessarily non‐trivial and not all simple software is necessarily trivial. For example, an implementation of an FCS file reader may be trivial, whereas a library of tools where an FCS file reader is one component may be non‐trivial. The former is not impactful, while the latter is more likely to be so. Review would weigh these factors and authors should aim to demonstrate benefit and impact.

Importantly, the principles of reproducible research are encouraged 1, 2, 3. Software presented in a computational article should be free, open source, well documented and otherwise usable by the community. The software is the research output, not just an afterthought, so work presented must be fully reproducible by reviewers and readership, and all code and data necessary to reproduce the work should be publicly, and permanently available online. Specifically, cytometry data should be deposited in FlowRepository and follow the MiFlowCyt standards 4, 5. Articles should describe the motivation for the work, discuss implementation, demonstrate usage, and place the work in the context of the existing state of the art in the field. Although the computational article format has no page limit, brevity is encouraged.

In this issue, the article “CytoML for Cross‐Platform Cytometry Data Sharing” provides a first example of the new format. There we describe a new tool that implements the different flavors and variants of Gating ML 6 used by several popular cytometry software vendors (Cytobank, DiVa, and FlowJo) as well as the open source R/Bioconductor platform in order to represent gated and analyzed cytometry data. We show how this tool can be used to share cytometry data amongst these platforms, how it can be used to reproduce and validate data analysis, and how it can be used to combine computational and manual analysis. There is no novel biology, and this new format does not require it, but the software fills a clear gap in capabilities in the field, is novel and has potential to be impactful. Publicly available data sets are used to demonstrate concepts, and all code is made publicly available and its reproducibility has been verified through the peer review process.

The Computational Article format recognizes software as a research output, thus the quality of the research software is itself becomes a focus of peer review and this new format provides our field the opportunity to set a higher standard for reproducibility and software quality in computational and computer‐aided cytometry.

Conflict of Interest

The authors declare that they have no conflicts of interest.