cg
changeset 56:1a2a8d08b7c3
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author | bshanks@bshanks.dyndns.org |
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date | Sat Apr 18 23:46:49 2009 -0700 (16 years ago) |
parents | 51c00dc05ff4 |
children | 0caab6fd7e51 |
files | grant.pdf grant.txt |
line diff
1.1 Binary file grant.pdf has changed
2.1 --- a/grant.txt Sat Apr 18 23:33:04 2009 -0700
2.2 +++ b/grant.txt Sat Apr 18 23:46:49 2009 -0700
2.3 @@ -263,9 +263,17 @@
2.4
2.5 One class of feature selection scoring method are those which calculate some sort of "match" between each gene image and the target image. Those genes which match the best are good candidates for features.
2.6
2.7 -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.
2.8 -
2.9 -todo: fig
2.10 +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. Figure \ref{SScorr} shows the three genes most correlated with area SS.
2.11 +
2.12 +\begin{figure}\label{SScorr}
2.13 +\includegraphics[scale=.31]{singlegene_SS_corr_top_1_2365_jet.eps}
2.14 +\includegraphics[scale=.31]{singlegene_SS_corr_top_2_242_jet.eps}
2.15 +\includegraphics[scale=.31]{singlegene_SS_corr_top_3_654_jet.eps}
2.16 +
2.17 +\caption{Genes (and predicted genes) Nfic, A930001M12Rik, C130038G02Rik are the most correlated with area SS (somatosensory cortex). 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.}
2.18 +\end{figure}
2.19 +
2.20 +
2.21
2.22 \vspace{0.3cm}**Conditional entropy**
2.23 An information-theoretic scoring method is to find features such that, if the features (gene expression levels) are known, uncertainty about the target (the regional identity) is reduced. Entropy measures uncertainty, so what we want is to find features such that the conditional distribution of the target has minimal entropy. The distribution to which we are referring is the probability distribution over the population of surface pixels.
2.24 @@ -319,7 +327,7 @@
2.25 \includegraphics[scale=.36]{MO_vs_Mtif2_jet.eps}
2.26
2.27 \includegraphics[scale=.36]{MO_vs_Wwc1_plus_Mtif2_jet.eps}
2.28 -\caption{Upper left: $wwc1$. Upper right: $mtif2$. Lower left: wwc1 + mtif2 (each pixel's value on the lower left is the sum of the corresponding pixels in the upper row). 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 approximately according to the density of expressing cells underneath each pixel, with red meaning a lot of expression and blue meaning little.}
2.29 +\caption{Upper left: $wwc1$. Upper right: $mtif2$. Lower left: wwc1 + mtif2 (each pixel's value on the lower left is the sum of the corresponding pixels in the upper row). }
2.30 \end{figure}
2.31
2.32
2.33 @@ -421,6 +429,8 @@
2.34
2.35 \vspace{0.3cm}**Develop algorithms to suggest a division of a structure into anatomical parts**
2.36
2.37 +# mixture models, etc
2.38 +
2.39 # Explore dimensionality reduction algorithms applied to pixels: including TODO
2.40 # Explore dimensionality reduction algorithms applied to genes: including TODO
2.41 # Explore clustering algorithms applied to pixels: including TODO
2.42 @@ -462,7 +472,6 @@
2.43
2.44 note:
2.45
2.46 -do we need to cite: no known markers, impressive results?
2.47
2.48
2.49