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diff summary.txt @ 117:abdedf8a8cf2
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| author | bshanks@bshanks.dyndns.org |
|---|---|
| date | Mon Jul 06 15:43:14 2009 -0700 (16 years ago) |
| parents | |
| children | 8f12af1c821d |
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1.1 --- /dev/null Thu Jan 01 00:00:00 1970 +0000
1.2 +++ b/summary.txt Mon Jul 06 15:43:14 2009 -0700
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1.4 +=== Intellectual merit ===
1.5 +
1.6 +Modern experimental techniques allow the expression levels of many genes at many locations to be compared. Our goal is to develop automated methods to relate spatial variation in gene expression to anatomy. We want to find marker genes for specific anatomical regions, and also to draw new anatomical maps based on gene expression patterns. We will validate these methods by applying them to 46 anatomical areas within the cerebral cortex, using the Allen Mouse Brain Atlas coronal dataset (ABA). %%This gene expression dataset was generated using ISH, and contains over 4,000 genes. For each gene, a digitized 3-D raster of the expression pattern is available: for each gene, the level of expression at each of 51,533 voxels is recorded.
1.7 +
1.8 +This project has three primary goals:\\
1.9 +
1.10 +(1) develop an algorithm to screen spatial gene expression data for combinations of marker genes which selectively target anatomical regions.\\
1.11 +
1.12 +(2) develop an algorithm to suggest new ways of carving up a structure into anatomically distinct regions, based on spatial patterns in gene expression.\\
1.13 +
1.14 +(3) adapt our tools for the analysis of multi/hyperspectral imaging data from the Geographic Information Systems (GIS) community.\\
1.15 +
1.16 +We also will create and make publically available a 2-D "flat map" that contains a flattened version of the ABA data, as well as the boundaries of cortical areas. We will use this dataset to validate the algorithms that we develop.
1.17 +
1.18 +Although our particular application involves the 3D spatial distribution of gene expression, the methods we will develop will generalize to any high-dimensional data over points located in a low-dimensional space. In particular, our methods could be applied to the analysis of multi/hyperspectral imaging data.
1.19 +
1.20 +All algorithms that we develop will be implemented in a GPL open-source software toolkit.
1.21 +
1.22 +
1.23 +=== Broader impacts ===
1.24 +The algorithm developed in Goal 1 will be applied to each cortical area to find a set of marker genes that uniquely picks out the target area. This will will be useful for experimentation and also drug discovery because marker genes can be used to design interventions which selectively target individual cortical areas. This algorithm will support the development of new neuroanatomical methods; we will find a small panel of genes that can find many of the areal boundaries at once. The algorithm developed in Goal 2 will contribute to the creation of a better cortical map.
1.25 +
1.26 +Our project will draw attention to an area of overlap between the fields of neuroanatomy and geographic information systems (GIS), and may lead to future collaborations between these two fields. The flat map will be useful for other neuroanatomy projects and also as a sample dataset for the machine learning community.