No examples are presented Open Science: The data analysis methods mentioned are straightforward and readily developed using Matlab. There are no Open Source Contributions in this paper. The difficult balance faced by many and reflected in this work is that making freely-available software is very useful and should be applauded - however, Matlab is not freely available. It seems to be too early for this work to have been published.
It seems to be too early for this work to have been published. It does not seem to be ready for being tested by others. The content of the paper describe a project in the early stages of developement. The novelty of their work is the application of these well stablished image classification algorithms to a leading edge field, by combining it with molecular marking of the cellular tissues. No code was available. There is no code review at this time. Discusses the on-going development of a Matlab-based tool for studying the multivariate space that describes the metabolic environment of a cell. This paper describes the very early stages of a software system for performing Molecular Phenotyping through the use of image analysis. Paper incomplete, details missing, no results or images by Paul Yushkevich on for revision 1 expertise: No evidence is provided. Suggestions for future work: Although the field of application of this paper is extremly interesting, the actual state of the software seems to be in a very early stage. This work is intended to replicate and extend functionality currently available in a toolkit that was developed by the same people and that has been released as a "pre-compiled" IDL application. The paper is extremely sparse on details. I did some investigation on the web based on one sentence in the abstract I look forward to future revisions of this submission to include more details in the paper, demonstration data, and the matlab source code. The paper suggests that the combination of image analysis and computational molecular phenotyping can be used successfully for identifiying cell classes in tissue samples. Source code is probably open, but no links are given. The methodology suggested by the authors is based on the use of K-Means for classifying the images. There are no Open Source Contributions in this paper. Use of Open Source Software: The project is in early stages of design and prototyping. None Not stated in the paper Code Quality: The paper does not satisfy the requirements of open science since there are no actual experiments reported, and there is no sharing of source code of data that could be used by others in order to verify the work of the authors. The data analysis methods mentioned are straightforward and readily developed using Matlab.
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