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We make use of this generalized ILR transform to down weight the influence of taxa with many zero and near-zero counts since these are less reliable and therefore more variable ( Good, 1956 ). Our choice of taxa weights is a heuristic that combines two terms multiplicatively: a measure of the central tendency of counts, such as the mean or median of the raw counts for a taxon across the N samples in a dataset; and, the norm of the vector of relative abundances of a taxon across the N samples in a dataset. We add this vector norm term to weight taxa by their site-specificity. Preliminary studies showed that the geometric mean of the counts (with a pseudocount added to avoid skew from zero values) outperformed both the arithmetic mean and median as a measure of central tendency for the counts (data not shown). Additionally, while both the Euclidean norm and the Aitchison norm improved preliminary benchmark performance compared to using the geometric mean alone, in one case (classification using support vector machine on the global patterns dataset), the Euclidean norm greatly outperformed the Aitchison norm ( XJRHXJR Gold Sequined Spring/Autumn Women Casual Shoes Cheap Price Wholesale Great Deals Cheap Online Perfect Outlet Wholesale Price Wiki Cheap Online iqOcp
). Therefore, our chosen taxa weighting scheme uses the geometric mean times the Euclidean norm:

Note that we add the subscript j to the right-hand side of the above equation to emphasize that this is calculated with respect to a single taxon across the N samples in a dataset. As intended, this scheme tended to assign smaller weights to taxa in our benchmarks with more zero and near-zero counts ( Recommend 2018 Cool SMONSDLE 2018 New Summer Tide Ankle Flat Sandals Casual Great Deals Online wl8wPPHL
). Despite their heuristic nature, we found that our chosen weights provide performance improvements over alternative weights (or the lack thereof) as measured by our benchmark tasks ( Supplementary file 1 ).

Our taxa weighting scheme supplements the use of pseudo-counts and represents a soft-threshold on low abundance taxa. More generally, these taxa weights represent a form of prior information regarding the importance of each taxon. We note that if prior biological information suggests allowing specific taxa to influence the PhILR transform more (or less) strongly, such a weighting could be achieved for taxon j by increasing (or decreasing) p j .

Beyond utilizing the connectivity of the phylogenetic tree to dictate the partitioning scheme for ILR balances, branch length information can be embedded into the transformed space by linearly scaling ILR balances ( y i * ) by the distance between neighboring clades. We call this scaling by phylogenetic distance ‘branch length weighting’. Specifically, for each coordinate y i * , corresponding to node i we use the transform

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to increment the field value on multiple objects - which could be very much faster than pulling them all into Python from the database, looping over them, incrementing the field value of each one, and saving each one back to the database:

F() therefore can offer performance advantages by:

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Another useful benefit of is that having the database - rather than Python - update a field’s value avoids a .

If two Python threads execute the code in the first example above, one thread could retrieve, increment, and save a field’s value after the other has retrieved it from the database. The value that the second thread saves will be based on the original value; the work of the first thread will simply be lost.

If the database is responsible for updating the field, the process is more robust: it will only ever update the field based on the value of the field in the database when the or is executed, rather than based on its value when the instance was retrieved.

F() objects assigned to model fields persist after saving the model instance and will be applied on each save() . For example:

stories_filed will be updated twice in this case. If it’s initially 1 , the final value will be 3 .

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is also very useful in filters, where they make it possible to filter a set of objects against criteria based on their field values, rather than on Python values.

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F() can be used to create dynamic fields on your models by combining different fields with arithmetic:

If the fields that you’re combining are of different types you’ll need to tell Django what kind of field will be returned. Since F() does not directly support output_field you will need to wrap the expression with ExpressionWrapper :

When referencing relational fields such as ForeignKey , F() returns the primary key value rather than a model instance:

Use F() and the nulls_first or nulls_last keyword argument to Expression.asc() or Big Discount Visa Payment Cheap Price Wholesale Custom Short Sleeve Black Design Dress am9wMN3hD
to control the ordering of a field’s null values. By default, the ordering depends on your database.

2) We have substantially increased the number of figure supplements in this revision to include many of the figures previously found only in the response to reviewer’s letter.

3) We have added a new paragraph to the “Correlation of behavior to gene expression” section to the methods wherein we detail corrections for multiple comparisons and the rationale behind including non-significant p-values after FDR correlations in downstream analyses.

4) We have updated the manuscript to remove any references to a MAPK11 promoter and synthesized reviewer 1’s helpful comments into a new Materials and methods “MAPK11 Annotation Note” subsection.

We thank reviewer 1 for his/her positive comments about the revision.

In response to this concern and as suggested by reviewer 3, we have denoted in the figure legend for Figure 3B that we present uncorrected p-values, then direct the reader to subsection “Correlation of Gene Expression to Behavior” wherein we provide rationale for our statistical approach, summarizing what was said in response to the first comment made by reviewer 2 (now reviewer 1) in the previous review.

[…] Rather than having identified the TSS and thus the promoter region for zebra finch MAPK11, it is much more likely that the authors' data provide evidence for FOXP2 binding at an internal, presumably intronic site. This is quite interesting in itself, and consistent with data from other genes and organisms that relevant transcription factor binding sites are not restricted to promoter regions. The author's interpretation that they have identified the promoter region for MAPK11 is less tenable without further data, as it would imply that this gene in zebra finches has a considerable truncation at its 5'-region, resulting in the loss of a N-terminus peptide that is conserved across vertebrates, including other closely related songbirds and mammals. These issues should be acknowledged and the claim that the TSS/promoter has been identified needs to be toned down.

We thank reviewer 1 for this thoughtful analysis and presentation of the data and agree that we have not provided sufficient evidence to suggest that we are looking at the zebra finch MAPK11 promoter region. In this revision, we have removed such claims, acknowledged that the RefSeq model for zebra finch MAPK11 is likely incomplete, and suggested that we are perhaps viewing a regulatory region within MAPK11 that is bound by FoxP2 instead of the promoter. We now refer to this region as the “promoter”, quotes included. Finally, we have added a new subsection ‘MAPK11 Annotation Note’ summarizing these helpful comments in the Materials and methods section and referenced it in the text.

It would also be helpful if the authors could deposit the sequence(s) identified in the ChIP assay for FOXP2 in GenBank, to allow for precise mapping onto the zebra finch genome.

We sequenced a genomic fragment that was pulled down by FoxP2 ChIP and found it aligned well with the NCBI sequence for MAPK11. We provide that sequence here as Figure 6—figure supplement 1 . The sequence is less than 200bp and thus GenBank will not accept it.

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