cg
changeset 24:3dc394d192eb
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author | bshanks@bshanks.dyndns.org |
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date | Mon Apr 13 03:20:00 2009 -0700 (16 years ago) |
parents | 161319ea4991 |
children | 8ff9b7b5c242 |
files | grant.html grant.odt grant.pdf grant.txt |
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1.4 Their main modification consisted of adding a soft spatial contiguity constraint. However,
1.5 on our dataset, NNMF naturally produced spatially contiguous clusters, so no additional
1.6 constraint was needed. The paper under discussion mentions that they also tried a hierarchial
1.7 -variant of NNMF, but since they didn’t report its results, we assume that the result were not
1.8 -any more impressive than the non-hierarchial variant.
1.9 +variant of NNMF, but since they didn’t report its results, we assume that those result were
1.10 +not any more impressive than the results of the non-hierarchial variant.
1.11 7
1.12
1.13 Preliminary work
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4.4
4.5 We are aware of two existing efforts to relate spatial gene expression data to anatomy through computational methods.
4.6
4.7 -\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).
4.8 +\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 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).
4.9
4.10
4.11 and \cite{ng_anatomic_2009} describes AGEA. todo