1.1 --- a/grant.txt Sun Apr 12 15:34:12 2009 -0700 1.2 +++ b/grant.txt Mon Apr 13 03:07:26 2009 -0700 1.3 @@ -142,7 +142,12 @@ 1.4 1.5 As noted above, there has been much work on both supervised learning and clustering, and there are many available algorithms for each. Many of these algorithms are flexible enough to accomodate new scoring measures; and the performance of most of the algorithms is greatly affected by preprocessing and by the choice of which representation to use for feature values. We think it likely that for this application, the development of domain-specific scoring measures (such as gradient similarity, which is discussed in Preliminary Work) will be necessary in order to achieve the best results. In essence, the machine learning community has provided algorithms, but the scientist must provide a framework for representing the problem domain, and the way that this framework is set up has a large impact on performance. Creating a good framework can require creatively reconceptualizing the problem domain, and is not merely a mechanical "fine-tuning" of numerical parameters. Therefore, the completion of Aims 1 and 2 involves more than just reimplementing an existing algorithm, and more than just choosing between a set of existing algorithms, and will constitute a substantial contribution to biology. 1.6 1.7 -We are aware of one other effort to computationally analyze spatial gene expression data. 1.8 +We are aware of two existing efforts to relate spatial gene expression data to anatomy through computational methods. 1.9 + 1.10 +\cite{thompson_genomic_2008} describes an analysis of the anatomy of the hippocampus using the ABA dataset. In addition to manual analysis, two clustering methods were employed, a modified Non-negative Matrix Factorization (NNMF), and a hierarchial 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 the result were not any more impressive than the non-hierarchial variant.} (we also think that more automation of the parts that this paper's authors did manually will be possible). 1.11 + 1.12 + 1.13 + and \cite{ng_anatomic_2009} describes AGEA. todo 1.14 1.15 1.16 In the Preliminary Work, we show that 1.17 @@ -237,6 +242,9 @@ 1.18 1.19 **Raw dimensionality reduction results** 1.20 1.21 +todo 1.22 + 1.23 +(might want to incld nnMF since mentioned above) 1.24 1.25 **Dimensionality reduction plus K-means or spectral clustering** 1.26 1.27 @@ -309,4 +317,8 @@ 1.28 1.29 1.30 1.31 -todo: replace aim # bullet pts with #s 1.32 +if we need citations for aim 3 significance, http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WSS-4V70FHY-9&_user=4429&_coverDate=12%2F26%2F2008&_rdoc=1&_fmt=full&_orig=na&_cdi=7054&_docanchor=&_acct=C000059602&_version=1&_urlVersion=0&_userid=4429&md5=551eccc743a2bfe6e992eee0c3194203#app2 has examples of genetic targeting to specific anatomical regions 1.33 + 1.34 +--- 1.35 + 1.36 +note: