They proposed a graph-based Tunable Biclustering Algorithm (TuBA) based on a novel pairwise proximity measure, examining the relationship of samples at the extremes of genes' expression profiles to identify similarly altered signatures. Even though biclustering is a well studied area, there is a need for new tools. However insufficient comparison is made to other methods. Major comments: 1.The consistency of the biclusters at different parameters remain unclear. 2.The authors did not provide performance evaluation of TuBA with previously published approaches such as ISA, BIMAX, SAMBA and DeBi. 3.The samples are ranked by their expression value for each gene and a percentile cutoff is applied in the first step of the algorithm. However some genes might not be expressed in any of the samples, therefore ranking genes for each sample and applying percentile cutoff for each sample might be better. Can you discuss the rationale for ranking samples for each gene instead of ranking genes for each sample? 4.Run time of the algorithm with different size datasets is not shown.

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Authors' responses to reviews: (https://drive.google.com/open?id=1Opuc4BLagbgGxigy4sSyI-c7AeX35BTQ)

The revised version of the manuscript addresses all the concerns. I believe the revised version is suitable for publication.

Declaration of competing interests Please complete a declaration of competing interests, considering the following questions: Have you in the past five years received reimbursements, fees, funding, or salary from an organisation that may in any way gain or lose financially from the publication of this manuscript, either now or in the future? Do you hold any stocks or shares in an organisation that may in any way gain or lose financially from the publication of this manuscript, either now or in the future? Do you hold or are you currently applying for any patents relating to the content of the manuscript? Have you received reimbursements, fees, funding, or salary from an organization that holds or has applied for patents relating to the content of the manuscript? Do you have any other financial competing interests? Do you have any non-financial competing interests in relation to this paper? If you can answer no to all of the above, write 'I declare that I have no competing interests' below. If your reply is yes to any, please give details below. I declare that I have no competing interests.

I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.
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Authors' responses to reviews:

Reviewer 3: In this paper the authors present a tunable biclustering algorithm (TuBA), based in the use of a proximity measure. TuBA has been applied to Breast Cancer Analysis. The paper is well organized and the contents include an extensive analysis on different datasets. Nevertheless, I have the following concerns regarding its appropriateness for being accepted.

Response: We thank the reviewer for their positive comments and hope that in the revised manuscript, we have clarified and addressed these concerns.

Reviewer 3: In section METHODS, subsection "Proximity Measure" does not provide enough details in order to reproduce its computation. It is not easy to understand how it does evaluate biclusters.

Response: TuBA’s proximity measure is based on computing the significance of the overlap between the samples that exhibit high (or low) expression of any given gene pair. As noted in lines 110-114, “The number of samples shared between any pair of percentile sets follows the hypergeometric distribution; therefore, we can compute the significance (p-value) of overlaps between pairs of percentile sets based on the numbers of shared samples by using the one-sided Fisher's exact test.” The lower the p-value of overlap, the stronger is the association between the gene-pair. We illustrate the basic idea and the setup of the test underlying our proximity measure in Figure 1.

Reviewer 3: In section METHODS, subsection "Datasets" it is not clear how many datasets have been finally analysed. Although in line 248 it is said that "we applied TuBA to three BRCA datasets", TCGA, METABRIC and GEO, from line 258 some other datasets are introduced: Bicmix or GTEx.

Response: The primary and clinically relevant results of the paper are based on the application of TuBA to three large breast cancer (BRCA) datasets – TCGA, METABRIC, and GEO. We additionally used the Genotype-Tissue Expression (GTEx) and ESTIMATE datasets to reaffirm that some of our biclusters from the three BRCA datasets were associated with normal/stromal tissue gene expression signatures. Moreover, for comparing TuBA with the Bicmix algorithm, we applied TuBA to the breastCancerNKI dataset. We have now separated this subsection in Methods to clearly indicate primary and validation datasets (lines 248-289).

Reviewer 3: In section RESULTS, subsection "Benchmarking TuBA's Proximity Measure", proximity measure has been checked against correlation coefficients such as Pearson or Spearman, but not against other evaluation measures specific for biclusters.

Response: TuBA’s proximity measure, like Pearson’s and Spearman’s correlation coefficients, is a pairwise measure of proximity. It enables us to identify gene-pairs that show strong association by virtue of a significant overlap in their top (or bottom) samples. Such gene-pairs are represented graphically as a pair of nodes linked together; the link represents the set of samples that are shared in their respective top (or bottom) percentile sets. Thus, our proximity measure provides the basis for the generation of graphs that are subsequently analyzed by our iterative graph-based approach to identify biclusters. By itself, the proximity measure does not evaluate biclusters.

We should note that TuBA does not involve modeling of gene expression data in order to identify biclusters. Thus, bicluster evaluation measures based on models of patterns of gene expression data are not applicable to its results. However, in the Enrichment of bicluster samples in top (bottom) sample sets of bicluster genes subsection of Results, we are now proposing a simple metric to assess the overall quality of the biclusters discovered by TuBA (Lines 404-412): “… the FDR values for each gene (sample) in any given bicluster can be viewed as their scores – the closer the value of the FDR is to zero for a gene (sample), the stronger is the association of the gene (sample) to the bicluster. We used the FDR values for the genes and samples within bicluster i to evaluate its overall quality, Q(Bi), defined as the minimum of the fraction of the genes in the bicluster with FDR < 0.05 or the fraction of samples in the bicluster with FDR < 0.05. Q(Bi) takes values between 0 and 1, where values close to zero indicate weak associations between the constituent genes and samples within the bicluster, while values close to one would indicate strong associations.” Supplementary Table 5 is now updated to include these quality scores for the biclusters.

Reviewer 3: In section RESULTS, subsection "Choice of parameters for TuBA" it not specified a clear strategy for choosing the parameter values when analysing a new dataset.

Response: We have discussed the considerations that underlie the choices for TuBA’s parameters in the Tuning TuBA subsection of Methods, also illustrated in Figure 3. For the choice of the second parameter (Overlap significance cutoff), we proposed the following heuristic in the same section: “the cutoff for the significance level of overlap should be such that a decrease in the significance level by an order of magnitude leads to an 40-60% increase in the number of edges that get added to the graph.” (Lines 233-237)

Reviewer 3: Furthermore, being the results section so large, I also suggest to include a summary table or diagram in order to provide the reader with a quick snapshot of the results.

Response: We have summarized the key results and discussions in Supplementary Figure 10.