Review for jacob_frelinger - *Fcm - A python library for flow cytometry*

[davclark]

Reviewer: Dav Clark
Department/Center/Division: D-Lab
Institution/University/Company: UC Berkeley
Field of interest / expertise: Computational Social Science / Neuroscience
Country: USA

Article reviewed: Fcm - A python library for flow cytometry

GENERAL EVALUATION

Please rate the paper using the following criteria (please use the abbreviation
to the right of the description)::

below doesn't meet standards for academic publication
meets meets or exceeds the standards for academic publication
n/a not applicable

• Quality of the approach: meets
• Quality of the writing: meets
• Quality of the figures/tables: meets

SPECIFIC EVALUATION

For the following questions, please respond with 'yes' or 'no'. If you
answer 'no', please provide a brief, one- to two-sentence explanation.

• Is the code made publicly available and does the article sufficiently
  describe how to access it?

  Yes. But a little more on navigating the code would be nice (along with code
  for figures in the paper, etc.)

• Does the article present the problem in an appropriate context?
  Specifically, does it:

  • explain why the problem is important,

    Yes!

  • describe in which situations it arises,

    Yes!

  • outline relevant previous work,

    Yes!

  • provide background information for non-experts

    Yes!

• Is the content of the paper accessible to a computational scientist
  with no specific knowledge in the given field?

  I think so. The editors expressed some concern about this, though. Even if you
  don't fully understand the biology, the methods are quite straightforward
  (single parameter or quadrant-based "gates" or otherwise commonly used
  mixture and k-means models).

• Does the paper describe a well-formulated scientific or technical
  achievement?

  Yes

• Are the technical and scientific decisions well-motivated and
  clearly explained?

  Yes! Commendable in the clear explanation of the scientific problem. Extra
  points for explaining the importance of improving methodology / automation.

• Are the code examples (if any) sound, clear, and well-written?

  Yes, though they are pretty thin.

  "Sensible defaults for hyperparameters have been chosen that in our experience
  perform satisfactorily on all FCS data samples we have analyzed." Might
  aggrivate some readers, but you can only put so much in such a paper... Can
  you refer readers to where they can find these hyperparameters in your code?

• Is the paper factually correct?

  As far as I can tell.

• Is the language and grammar of sufficient quality?

  Yes.

• Are the conclusions justified?

  Yes.

• Is prior work properly and fully cited?

  Reference, but no citation for other packages mentioned (proprietary and R
  bioconductor). Note, however, that mentioning other packages is already
  above-average for scipy 2012 (based on my limited sample ;)

• Should any part of the article be shortened or expanded? Please explain.

  Yes - I'd like more code (or pointers to code) if it's not to much trouble.

• In your view, is the paper fit for publication in the conference proceedings?
  Please suggest specific improvements and indicate whether you think the
  article needs a significant rewrite (rather than a minor revision).

  Yes

[jfrelinger]

• Can you refer readers to where they can find these hyperparameters in your code?

Hyperparameters are instance variables of the various model generating objects. This information is now included in the paper.

• Reference, but no citation for other packages mentioned

a citation for Bioconductor has been added.

• Yes - I'd like more code (or pointers to code) if it's not to much trouble.

More examples can be found in the package documentation at http://packages.python.org/fcm/. The document has been updated to include this information.