1.1 --- a/grant.txt Mon Apr 20 17:02:27 2009 -0700 1.2 +++ b/grant.txt Mon Apr 20 17:09:48 2009 -0700 1.3 @@ -228,12 +228,42 @@ 1.4 \newpage 1.5 1.6 == Preliminary work == 1.7 +\begin{wrapfigure}{L}{0.4\textwidth}\centering 1.8 +%%\includegraphics[scale=.31]{singlegene_SS_corr_top_1_2365_jet.eps}\includegraphics[scale=.31]{singlegene_SS_corr_top_2_242_jet.eps}\includegraphics[scale=.31]{singlegene_SS_corr_top_3_654_jet.eps} 1.9 +%%\\ 1.10 +%%\includegraphics[scale=.31]{singlegene_SS_lr_top_1_654_jet.eps}\includegraphics[scale=.31]{singlegene_SS_lr_top_2_685_jet.eps}\includegraphics[scale=.31]{singlegene_SS_lr_top_3_724_jet.eps} 1.11 +%%\caption{Top row: Genes Nfic, A930001M12Rik, C130038G02Rik are the most correlated with area SS (somatosensory cortex). Bottom row: Genes C130038G02Rik, Cacna1i, Car10 are those with the best fit using logistic regression. Within each picture, the vertical axis roughly corresponds to anterior at the top and posterior at the bottom, and the horizontal axis roughly corresponds to medial at the left and lateral at the right. The red outline is the boundary of region SS. Pixels are colored according to correlation, with red meaning high correlation and blue meaning low.} 1.12 + 1.13 +\includegraphics[scale=.31]{singlegene_SS_corr_top_1_2365_jet.eps}\includegraphics[scale=.31]{singlegene_SS_corr_top_2_242_jet.eps} 1.14 +\\ 1.15 +\includegraphics[scale=.31]{singlegene_SS_lr_top_1_654_jet.eps}\includegraphics[scale=.31]{singlegene_SS_lr_top_2_685_jet.eps} 1.16 + 1.17 +\caption{Top row: Genes Nfic and A930001M12Rik are the most correlated with area SS (somatosensory cortex). Bottom row: Genes C130038G02Rik and Cacna1i are those with the best fit using logistic regression. Within each picture, the vertical axis roughly corresponds to anterior at the top and posterior at the bottom, and the horizontal axis roughly corresponds to medial at the left and lateral at the right. The red outline is the boundary of region SS. Pixels are colored according to correlation, with red meaning high correlation and blue meaning low.} 1.18 +\label{SScorrLr}\end{wrapfigure} 1.19 + 1.20 + 1.21 + 1.22 1.23 === Format conversion between SEV, MATLAB, NIFTI === 1.24 We have created software to (politely) download all of the SEV files from the Allen Institute website. We have also created software to convert between the SEV, MATLAB, and NIFTI file formats, as well as some of Caret's file formats. 1.25 1.26 1.27 === Flatmap of cortex === 1.28 + 1.29 + 1.30 +We downloaded the ABA data and applied a mask to select only those voxels which belong to cerebral cortex. We divided the cortex into hemispheres. 1.31 + 1.32 +Using Caret\cite{van_essen_integrated_2001}, we created a mesh representation of the surface of the selected voxels. For each gene, for each node of the mesh, we calculated an average of the gene expression of the voxels "underneath" that mesh node. We then flattened the cortex, creating a two-dimensional mesh. 1.33 + 1.34 +We sampled the nodes of the irregular, flat mesh in order to create a regular grid of pixel values. We converted this grid into a MATLAB matrix. 1.35 + 1.36 +We manually traced the boundaries of each of 49 cortical areas from the ABA coronal reference atlas slides. We then converted these manual traces into Caret-format regional boundary data on the mesh surface. We projected the regions onto the 2-d mesh, and then onto the grid, and then we converted the region data into MATLAB format. 1.37 + 1.38 +At this point, the data is in the form of a number of 2-D matrices, all in registration, with the matrix entries representing a grid of points (pixels) over the cortical surface: 1.39 + 1.40 +* A 2-D matrix whose entries represent the regional label associated with each surface pixel 1.41 +* For each gene, a 2-D matrix whose entries represent the average expression level underneath each surface pixel 1.42 + 1.43 \begin{wrapfigure}{L}{0.2\textwidth}\centering 1.44 \includegraphics[scale=.31]{holeExample_2682_SS_jet.eps} 1.45 \caption{Gene Pitx2 is selectively underexpressed in area SS (somatosensory).} 1.46 @@ -241,34 +271,6 @@ 1.47 1.48 1.49 1.50 -We downloaded the ABA data and applied a mask to select only those voxels which belong to cerebral cortex. We divided the cortex into hemispheres. 1.51 - 1.52 -Using Caret\cite{van_essen_integrated_2001}, we created a mesh representation of the surface of the selected voxels. For each gene, for each node of the mesh, we calculated an average of the gene expression of the voxels "underneath" that mesh node. We then flattened the cortex, creating a two-dimensional mesh. 1.53 - 1.54 -We sampled the nodes of the irregular, flat mesh in order to create a regular grid of pixel values. We converted this grid into a MATLAB matrix. 1.55 - 1.56 -We manually traced the boundaries of each of 49 cortical areas from the ABA coronal reference atlas slides. We then converted these manual traces into Caret-format regional boundary data on the mesh surface. We projected the regions onto the 2-d mesh, and then onto the grid, and then we converted the region data into MATLAB format. 1.57 - 1.58 -At this point, the data is in the form of a number of 2-D matrices, all in registration, with the matrix entries representing a grid of points (pixels) over the cortical surface: 1.59 - 1.60 -* A 2-D matrix whose entries represent the regional label associated with each surface pixel 1.61 -* For each gene, a 2-D matrix whose entries represent the average expression level underneath each surface pixel 1.62 - 1.63 -\begin{wrapfigure}{L}{0.4\textwidth}\centering 1.64 -%%\includegraphics[scale=.31]{singlegene_SS_corr_top_1_2365_jet.eps}\includegraphics[scale=.31]{singlegene_SS_corr_top_2_242_jet.eps}\includegraphics[scale=.31]{singlegene_SS_corr_top_3_654_jet.eps} 1.65 -%%\\ 1.66 -%%\includegraphics[scale=.31]{singlegene_SS_lr_top_1_654_jet.eps}\includegraphics[scale=.31]{singlegene_SS_lr_top_2_685_jet.eps}\includegraphics[scale=.31]{singlegene_SS_lr_top_3_724_jet.eps} 1.67 -%%\caption{Top row: Genes Nfic, A930001M12Rik, C130038G02Rik are the most correlated with area SS (somatosensory cortex). Bottom row: Genes C130038G02Rik, Cacna1i, Car10 are those with the best fit using logistic regression. Within each picture, the vertical axis roughly corresponds to anterior at the top and posterior at the bottom, and the horizontal axis roughly corresponds to medial at the left and lateral at the right. The red outline is the boundary of region MO. Pixels are colored according to correlation, with red meaning high correlation and blue meaning low.} 1.68 - 1.69 -\includegraphics[scale=.31]{singlegene_SS_corr_top_1_2365_jet.eps}\includegraphics[scale=.31]{singlegene_SS_corr_top_2_242_jet.eps} 1.70 -\\ 1.71 -\includegraphics[scale=.31]{singlegene_SS_lr_top_1_654_jet.eps}\includegraphics[scale=.31]{singlegene_SS_lr_top_2_685_jet.eps} 1.72 - 1.73 -\caption{Top row: Genes Nfic and A930001M12Rik are the most correlated with area SS (somatosensory cortex). Bottom row: Genes C130038G02Rik and Cacna1i are those with the best fit using logistic regression. Within each picture, the vertical axis roughly corresponds to anterior at the top and posterior at the bottom, and the horizontal axis roughly corresponds to medial at the left and lateral at the right. The red outline is the boundary of region MO. Pixels are colored according to correlation, with red meaning high correlation and blue meaning low.} 1.74 -\label{SScorrLr}\end{wrapfigure} 1.75 - 1.76 - 1.77 - 1.78 We created a normalized version of the gene expression data by subtracting each gene's mean expression level (over all surface pixels) and dividing each gene by its standard deviation. 1.79 1.80 The features and the target area are both functions on the surface pixels. They can be referred to as scalar fields over the space of surface pixels; alternately, they can be thought of as images which can be displayed on the flatmapped surface.