1.1 --- a/grant.txt Mon Apr 13 14:53:12 2009 -0700 1.2 +++ b/grant.txt Mon Apr 13 19:38:30 2009 -0700 1.3 @@ -147,7 +147,7 @@ 1.4 \cite{thompson_genomic_2008} describes an analysis of the anatomy of 1.5 the hippocampus using the ABA dataset. In addition to manual analysis, 1.6 two clustering methods were employed, a modified Non-negative Matrix 1.7 -Factorization (NNMF), and a hierarchial recursive bifurcation clustering scheme based on correlation as the similarity score. The paper yielded impressive results, proving the usefulness of such research. We have run NNMF on the cortical dataset and while the results are promising (see Preliminary Data), we think that it will be possible to find a better method\footnote{We ran "vanilla" NNMF, whereas the paper under discussion used a modified method. Their main modification consisted of adding a soft spatial contiguity constraint. However, on our dataset, NNMF naturally produced spatially contiguous clusters, so no additional constraint was needed. The paper under discussion mentions that they also tried a hierarchial variant of NNMF, but since they didn't report its results, we assume that those result were not any more impressive than the results of the non-hierarchial variant.} (we also think that more automation of the parts that this paper's authors did manually will be possible). 1.8 +Factorization (NNMF), and a hierarchial recursive bifurcation clustering scheme based on correlation as the similarity score. The paper yielded impressive results, proving the usefulness of such research. We have run NNMF on the cortical dataset\footnote{We ran "vanilla" NNMF, whereas the paper under discussion used a modified method. Their main modification consisted of adding a soft spatial contiguity constraint. However, on our dataset, NNMF naturally produced spatially contiguous clusters, so no additional constraint was needed. The paper under discussion mentions that they also tried a hierarchial variant of NNMF, but since they didn't report its results, we assume that those result were not any more impressive than the results of the non-hierarchial variant.} and while the results are promising (see Preliminary Data), we think that it will be possible to find a better method (we also think that more automation of the parts that this paper's authors did manually will be possible). 1.9 1.10 1.11 \cite{ng_anatomic_2009} describes AGEA, "Anatomic Gene Expression 1.12 @@ -164,11 +164,9 @@ 1.13 1.14 * Clusters: AGEA includes a precomputed hierarchial clustering of voxels based on a recursive bifurcation algorithm with correlation as the similarity metric. 1.15 1.16 -At first glance AGEA seems similar to this proposal, but in fact it is different. 1.17 - 1.18 -Gene Finder is different from our Aim 1 in at least four ways. First, although the user chooses a seed voxel, Gene Finder, not the user, chooses the cluster for which genes will be found, and in our experience it never chooses cortical areas, instead preferring cortical layers. Therefore, Gene Finder cannot be used to find marker genes for cortical areas. Second, Gene Finder finds only single genes, whereas we will also look for combinations of genes. Third, gene finder can only use overexpression as a marker, whereas we will also look for underexpression. Fourth, Gene Finder uses a simple pointwise metric ("expression energy ratio", which captures overexpression), whereas we will also use geometric metrics such as gradient similarity. 1.19 - 1.20 -The hierarchial clustering is different from our Aim 2 in at least two ways. todo 1.21 +Gene Finder is different from our Aim 1 in at least four ways. First, although the user chooses a seed voxel, Gene Finder, not the user, chooses the cluster for which genes will be found, and in our experience it never chooses cortical areas, instead preferring cortical layers\footnote{\label{layersNotAreas}Because of the way in which Gene Finder chooses a cluster, layers will always be preferred to areas if pairwise correlations between the gene expression of voxels in different areas but the same layer are stronger than pairwise correlatios between the gene expression of voxels in different layers but the same area. This appears to be the case.}. Therefore, Gene Finder cannot be used to find marker genes for cortical areas. Second, Gene Finder finds only single genes, whereas we will also look for combinations of genes\footnote{See Preliminary Data for an example of an area which cannot be marked by any single gene in the dataset, but which can be marked by a combination.}. Third, gene finder can only use overexpression as a marker, whereas in the Preliminary Data we show that underexpression can also be used. Fourth, Gene Finder uses a simple pointwise score\footnote{"Expression energy ratio", which captures overexpression.}, whereas we will also use geometric metrics such as gradient similarity. 1.22 + 1.23 +The hierarchial clustering is different from our Aim 2 in at least three ways. First, the clustering finds clusters corresponding to layers, but no clusters corresponding to areas\footnote{This is for the same reason as in footnote \ref{layersNotAreas}.} \footnote{There are clusters which presumably correspond to the intersection of a layer and an area, but since one area will have many layer-area intersection clusters, further work is needed to make sense of these.} Our Aim 2 will not be accomplished until a clustering is produced which yields areas. Second, AGEA uses perhaps the simplest possible similarity score (correlation), and does no dimensionality reduction before calculating similarity. While it is possible that a more complex system will not do any better than this, we believe further exploration of alternative methods of scoring and dimensionality reduction is warranted. Third, AGEA did not look at clusters of genes; in Preliminary Data we have shown that clusters of genes may identify intersting spatial subregions such as cortical areas. 1.24 1.25 1.26