annotate summary.txt @ 119:e61f822e0375
.
| author |
bshanks@bshanks.dyndns.org |
| date |
Tue Jul 07 14:57:48 2009 -0700 (16 years ago) |
| parents |
8f12af1c821d |
| children |
|
| rev |
line source |
| bshanks@117 | 1 === Intellectual merit ===
|
| bshanks@117 | 2
|
| bshanks@119 | 3 Modern experimental techniques allow the expression levels of many genes at many locations to be compared. This goal of this project is to develop automated methods to relate spatial variation in gene expression to anatomy. Marker genes for specific anatomical regions will be found, and new anatomical maps will be drawn based on gene expression patterns. These methods will be validated by applying them to 46 anatomical areas within the cerebral cortex, using the Allen Mouse Brain Atlas coronal dataset (ABA).
|
| bshanks@117 | 4
|
| bshanks@117 | 5 This project has three primary goals:\\
|
| bshanks@117 | 6
|
| bshanks@117 | 7 (1) develop an algorithm to screen spatial gene expression data for combinations of marker genes which selectively target anatomical regions.\\
|
| bshanks@117 | 8
|
| bshanks@117 | 9 (2) develop an algorithm to suggest new ways of carving up a structure into anatomically distinct regions, based on spatial patterns in gene expression.\\
|
| bshanks@117 | 10
|
| bshanks@119 | 11 (3) adapt those tools for the analysis of multi/hyperspectral imaging data from the Geographic Information Systems (GIS) community.\\
|
| bshanks@117 | 12
|
| bshanks@119 | 13 The project involves the creation and publication of a 2-D "flat map" that contains a flattened version of the ABA data, as well as the boundaries of cortical areas. This dataset will be used to validate the algorithms that are developed.
|
| bshanks@117 | 14
|
| bshanks@119 | 15 Although this particular application involves the 3D spatial distribution of gene expression, the methods developed will generalize to any high-dimensional data over points located in a low-dimensional space. In particular, the methods could be applied to the analysis of multi/hyperspectral imaging data.
|
| bshanks@117 | 16
|
| bshanks@119 | 17 All algorithms will be implemented in a GPL open-source software toolkit.
|
| bshanks@117 | 18
|
| bshanks@117 | 19
|
| bshanks@117 | 20 === Broader impacts ===
|
| bshanks@119 | 21 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 by finding 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.
|
| bshanks@117 | 22
|
| bshanks@119 | 23 The 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.
|
