1.1 --- a/grant.txt Sun Apr 19 14:44:41 2009 -0700 1.2 +++ b/grant.txt Sun Apr 19 14:50:20 2009 -0700 1.3 @@ -267,10 +267,10 @@ 1.4 1.5 One of the simplest methods in this class is to use correlation as the match score. We calculated the correlation between each gene and each cortical area. The top row of Figure \ref{SScorrLr} shows the three genes most correlated with area SS. 1.6 1.7 -\begin{figure} 1.8 +\begin{figure}\centering 1.9 \includegraphics[scale=.31]{singlegene_SS_corr_top_1_2365_jet.eps} 1.10 \includegraphics[scale=.31]{singlegene_SS_corr_top_2_242_jet.eps} 1.11 -\includegraphics[scale=.31]{singlegene_SS_corr_top_3_654_jet.eps}\\ 1.12 +\includegraphics[scale=.31]{singlegene_SS_corr_top_3_654_jet.eps} 1.13 \\ 1.14 \includegraphics[scale=.31]{singlegene_SS_lr_top_1_654_jet.eps} 1.15 \includegraphics[scale=.31]{singlegene_SS_lr_top_2_685_jet.eps} 1.16 @@ -310,7 +310,7 @@ 1.17 To show that gradient similarity can provide useful information that cannot be detected via pointwise analyses, consider Fig. \ref{AUDgeometry}. The top row of Fig. \ref{AUDgeometry} displays the 3 genes which most match area AUD, according to a pointwise method\footnote{For each gene, a logistic regression in which the response variable was whether or not a surface pixel was within area AUD, and the predictor variable was the value of the expression of the gene underneath that pixel. The resulting scores were used to rank the genes in terms of how well they predict area AUD.}. The bottom row displays the 3 genes which most match AUD according to a method which considers local geometry\footnote{For each gene the gradient similarity between (a) a map of the expression of each gene on the cortical surface and (b) the shape of area AUD, was calculated, and this was used to rank the genes.} The pointwise method in the top row identifies genes which express more strongly in AUD than outside of it; its weakness is that this includes many areas which don't have a salient border matching the areal border. The geometric method identifies genes whose salient expression border seems to partially line up with the border of AUD; its weakness is that this includes genes which don't express over the entire area. Genes which have high rankings using both pointwise and border criteria, such as $Aph1a$ in the example, may be particularly good markers. None of these genes are, individually, a perfect marker for AUD; we deliberately chose a "difficult" area in order to better contrast pointwise with geometric methods. 1.18 1.19 1.20 -\begin{figure} 1.21 +\begin{figure}\centering 1.22 \includegraphics[scale=.31]{singlegene_AUD_lr_top_1_3386_jet.eps} 1.23 \includegraphics[scale=.31]{singlegene_AUD_lr_top_2_1258_jet.eps} 1.24 \includegraphics[scale=.31]{singlegene_AUD_lr_top_3_420_jet.eps} 1.25 @@ -333,7 +333,7 @@ 1.26 1.27 %%Gene mtif2\footnote{"mitochondrial translational initiation factor 2"; EntrezGene ID 76784} is shown in figure the upper-right of Fig. \ref{MOcombo}. Mtif2 captures MO's upper-left boundary, but not its lower-right boundary. Mtif2 does not express very much on the medial surface. By adding together the values at each pixel in these two figures, we get the lower-left of Figure \ref{MOcombo}. This combination captures area MO much better than any single gene. 1.28 1.29 -\begin{figure} 1.30 +\begin{figure}\centering 1.31 \includegraphics[scale=.36]{MO_vs_Wwc1_jet.eps} 1.32 \includegraphics[scale=.36]{MO_vs_Mtif2_jet.eps} 1.33 1.34 @@ -350,7 +350,7 @@ 1.35 Underexpression of a gene can sometimes serve as a marker. See, for example, Figure \ref{hole}. 1.36 1.37 1.38 -\begin{figure} 1.39 +\begin{figure}\centering 1.40 \includegraphics[scale=.31]{holeExample_2682_SS_jet.eps} 1.41 \caption{Gene Pitx2 is selectively underexpressed in area SS (somatosensory).} 1.42 \label{hole}\end{figure} 1.43 @@ -381,7 +381,7 @@ 1.44 In addition, there are a number of areas which are almost identified by single genes: COAa+NLOT (anterior part of cortical amygdalar area, nucleus of the lateral olfactory tract), ENT (entorhinal), ACAv (ventral anterior cingulate), VIS (visual), AUD (auditory). 1.45 1.46 1.47 -\begin{figure} 1.48 +\begin{figure}\centering 1.49 \includegraphics[scale=.31]{singlegene_example_2682_Pitx2_SS_jet.eps} 1.50 \includegraphics[scale=.31]{singlegene_example_371_Aldh1a2_SSs_jet.eps} 1.51 \includegraphics[scale=.31]{singlegene_example_2759_Ppfibp1_PIR_jet.eps} 1.52 @@ -391,7 +391,7 @@ 1.53 \includegraphics[scale=.31]{singlegene_example_925_Col12a1_ACA+PL+ILA+DP+ORB+MO_jet.eps} 1.54 \includegraphics[scale=.31]{singlegene_example_1334_Ets1_post_lat_vis_jet.eps} 1.55 1.56 -\caption{From left to right and top to bottom, single genes which roughly identify areas SS (somatosensory primary + supplemental), SSs (supplemental somatosensory), PIR (piriform), FRP (frontal pole), RSP (retrosplenial), COApm (Cortical amygdalar, posterior part, medial zone). Grouping some areas together, we have also found genes to identify the groups ACA+PL+ILA+DP+ORB+MO (anterior cingulate, prelimbic, infralimbic, dorsal peduncular, orbital, motor), posterior and lateral visual (VISpm, VISpl, VISI, VISp; posteromedial, posterolateral, lateral, and primary visual). The genes are $Pitx2$, $Aldh1a2$, $Ppfibp1$, $Slco1a5$, $Tshz2$, $Trhr$, $Col12a1$, $Ets1$.} 1.57 +\caption{From left to right and top to bottom, single genes which roughly identify areas SS (somatosensory primary + supplemental), SSs (supplemental somatosensory), PIR (piriform), FRP (frontal pole), RSP (retrosplenial), COApm (Cortical amygdalar, posterior part, medial zone). Grouping some areas together, we have also found genes to identify the groups ACA+PL+ILA+DP+ORB+MO (anterior cingulate, prelimbic, infralimbic, dorsal peduncular, orbital, motor), posterior and lateral visual (VISpm, VISpl, VISI, VISp; posteromedial, posterolateral, lateral, and primary visual; the posterior and lateral visual area is distinguished from its neighbors, but not from the entire rest of the cortex). The genes are $Pitx2$, $Aldh1a2$, $Ppfibp1$, $Slco1a5$, $Tshz2$, $Trhr$, $Col12a1$, $Ets1$.} 1.58 \label{singleSoFar}\end{figure} 1.59 1.60