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changeset 100:fa7c0a924e7a

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author bshanks@bshanks.dyndns.org
date Wed Apr 22 06:45:17 2009 -0700 (16 years ago)
parents a48955c639d4
children 89815d210b5c
files grant.doc grant.html grant.odt grant.pdf grant.txt
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2.1 --- a/grant.html Wed Apr 22 06:43:51 2009 -0700 2.2 +++ b/grant.html Wed Apr 22 06:45:17 2009 -0700 2.3 @@ -433,19 +433,15 @@ 2.4 of a better map. The development of present-day cortical maps was driven by the application of histological 2.5 stains. If a different set of stains had been available which identified a different set of features, then today’s 2.6 cortical maps may have come out differently. It is likely that there are many repeated, salient spatial patterns 2.7 +in the gene expression which have not yet been captured by any stain. Therefore, cortical anatomy needs to 2.8 +incorporate what we can learn from looking at the patterns of gene expression. 2.9 _________________________________________ 2.10 7The sagittal data do not cover the entire cortex, and also have greater registration error[15]. Genes were selected by the Allen 2.11 Institute for coronal sectioning based on, “classes of known neuroscientific interest... or through post hoc identification of a marked 2.12 non-ubiquitous expression pattern”[15]. 2.13 8In both cases, the cause is that pairwise correlations between the gene expression of voxels in different areas but the same layer 2.14 are often stronger than pairwise correlations between the gene expression of voxels in different layers but the same area. Therefore, a 2.15 -pairwise voxel correlation clustering algorithm will tend to create clusters representing cortical layers, not areas (there may be clusters 2.16 -which presumably correspond to the intersection of a layer and an area, but since one area will have many layer-area intersection 2.17 -clusters, further work is needed to make sense of these). The reason that Gene Finder cannot the find marker genes for cortical areas 2.18 -is that, although the user chooses a seed voxel, Gene Finder chooses the ROI for which genes will be found, and it creates that ROI by 2.19 -(pairwise voxel correlation) clustering around the seed. 2.20 -in the gene expression which have not yet been captured by any stain. Therefore, cortical anatomy needs to 2.21 -incorporate what we can learn from looking at the patterns of gene expression. 2.22 +pairwise voxel correlation clustering algorithm will tend to create clusters representing cortical layers, not areas. 2.23 While we do not here propose to analyze human gene expression data, it is conceivable that the methods 2.24 we propose to develop could be used to suggest modifications to the human cortical map as well. In fact, the 2.25 methods we will develop will be applicable to other datasets beyond the brain. 2.26 @@ -486,10 +482,10 @@ 2.27 as a boolean mask over the surface pixels. 2.28 We calculated the correlation between each gene and each cortical area. The top row of Figure 2 shows the 2.29 three genes most correlated with area SS. 2.30 -__ 2.31 +Conditional entropy 2.32 +__________________ 2.33 9SEV is a sparse format for spatial data. It is the format in which the ABA data is made available. 2.34 -Conditional entropy 2.35 -Foreach region, we created and ran a forward stepwise procedure which attempted to find pairs of gene 2.36 +For each region, we created and ran a forward stepwise procedure which attempted to find pairs of gene 2.37 expression boolean masks such that the conditional entropy of the target area’s boolean mask, conditioned 2.38 upon the pair of gene expression boolean masks, is minimized. 2.39 This finds pairs of genes which are most informative (at least at these discretization thresholds) relative to the
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5.1 --- a/grant.txt Wed Apr 22 06:43:51 2009 -0700 5.2 +++ b/grant.txt Wed Apr 22 06:45:17 2009 -0700 5.3 @@ -309,8 +309,9 @@ 5.4 5.5 === Related work === 5.6 5.7 -\cite{ng_anatomic_2009} describes the application of AGEA to the cortex. The paper describes interesting results on the structure of correlations between voxel gene expression profiles within a handful of cortical areas. However, this sort of analysis is not related to either of our aims, as it neither finds marker genes, nor does it suggest a cortical map based on gene expression data. Neither of the other components of AGEA can be applied to cortical areas; AGEA's Gene Finder cannot be used to find marker genes for the cortical areas; and AGEA's hierarchical clustering does not produce clusters corresponding to the cortical areas\footnote{In both cases, the cause is that pairwise correlations between the gene expression of voxels in different areas but the same layer are often stronger than pairwise correlations between the gene expression of voxels in different layers but the same area. Therefore, a pairwise voxel correlation clustering algorithm will tend to create clusters representing cortical layers, not areas (there may be 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). The reason that Gene Finder cannot the find marker genes for cortical areas is that, although the user chooses a seed voxel, Gene Finder chooses the ROI for which genes will be found, and it creates that ROI by (pairwise voxel correlation) clustering around the seed.}. 5.8 - 5.9 +\cite{ng_anatomic_2009} describes the application of AGEA to the cortex. The paper describes interesting results on the structure of correlations between voxel gene expression profiles within a handful of cortical areas. However, this sort of analysis is not related to either of our aims, as it neither finds marker genes, nor does it suggest a cortical map based on gene expression data. Neither of the other components of AGEA can be applied to cortical areas; AGEA's Gene Finder cannot be used to find marker genes for the cortical areas; and AGEA's hierarchical clustering does not produce clusters corresponding to the cortical areas\footnote{In both cases, the cause is that pairwise correlations between the gene expression of voxels in different areas but the same layer are often stronger than pairwise correlations between the gene expression of voxels in different layers but the same area. Therefore, a pairwise voxel correlation clustering algorithm will tend to create clusters representing cortical layers, not areas.}. 5.10 + 5.11 +%% (there may be 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). The reason that Gene Finder cannot the find marker genes for cortical areas is that, although the user chooses a seed voxel, Gene Finder chooses the ROI for which genes will be found, and it creates that ROI by (pairwise voxel correlation) clustering around the seed. 5.12 5.13 %% Most of the projects which have been discussed have been done by the same groups that develop the public datasets. Although these projects make their algorithms available for use on their own website, none of them have released an open-source software toolkit; instead, users are restricted to using the provided algorithms only on their own dataset. 5.14