1.1 --- a/grant.html Sun Apr 19 14:44:41 2009 -0700 1.2 +++ b/grant.html Sun Apr 19 14:50:20 2009 -0700 1.3 @@ -366,8 +366,8 @@ 1.4 This finds pairs of genes which are most informative (at least at these discretization thresholds) relative to the question, 1.5 “Is this surface pixel a member of the target area?”. 1.6 1.7 - 1.8 - 1.9 + 1.10 + 1.11 Figure 1: Top row: Genes Nfic, A930001M12Rik, C130038G02Rik are the most correlated with area SS (somatosensory 1.12 cortex). Bottom row: Genes C130038G02Rik, Cacna1i, Car10 are those with the best fit using logistic regression. Within 1.13 each picture, the vertical axis roughly corresponds to anterior at the top and posterior at the bottom, and the horizontal 1.14 @@ -409,14 +409,17 @@ 1.15 ____________________________ 1.16 17For each gene, a logistic regression in which the response variable was whether or not a surface pixel was within area AUD, and the predictor 1.17 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 1.18 +they predict area AUD. 1.19 + 18For 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, 1.20 +was calculated, and this was used to rank the genes. 1.21 1.22 - 1.23 - 1.24 + 1.25 + 1.26 Figure 2: The top row shows the three genes which (individually) best predict area AUD, according to logistic regression. 1.27 The bottom row shows the three genes which (individually) best match area AUD, according to gradient similarity. From 1.28 left to right and top to bottom, the genes are Ssr1, Efcbp1, Aph1a, Ptk7, Aph1a again, and Lepr 1.29 - 1.30 - 1.31 + 1.32 + 1.33 Figure 3: Upper left: wwc1. Upper right: mtif2. Lower left: wwc1 + mtif2 (each pixel’s value on the lower left is the 1.34 sum of the corresponding pixels in the upper row). Acccording to logistic regression, gene wwc1 is the best fit single gene 1.35 for predicting whether or not a pixel on the cortical surface belongs to the motor area (area MO). The upper-left picture in 1.36 @@ -427,8 +430,17 @@ 1.37 does not express very much on the medial surface. By adding together the values at each pixel in these two figures, we get 1.38 the lower-left image. This combination captures area MO much better than any single gene. 1.39 1.40 - 1.41 + 1.42 Figure 4: Gene Pitx2 is selectively underexpressed in area SS (somatosensory). 1.43 + 1.44 + 1.45 +Figure 5: From left to right and top to bottom, single genes which roughly identify areas SS (somatosensory primary + 1.46 +supplemental), SSs (supplemental somatosensory), PIR (piriform), FRP (frontal pole), RSP (retrosplenial), COApm (Corti- 1.47 +cal amygdalar, posterior part, medial zone). Grouping some areas together, we have also found genes to identify the groups 1.48 +ACA+PL+ILA+DP+ORB+MO (anterior cingulate, prelimbic, infralimbic, dorsal peduncular, orbital, motor), posterior 1.49 +and lateral visual (VISpm, VISpl, VISI, VISp; posteromedial, posterolateral, lateral, and primary visual; the posterior and 1.50 +lateral visual area is distinguished from its neighbors, but not from the entire rest of the cortex). The genes are Pitx2, 1.51 +Aldh1a2, Ppfibp1, Slco1a5, Tshz2, Trhr, Col12a1, Ets1. 1.52 Underexpression of a gene can serve as a marker Underexpression of a gene can sometimes serve as a marker. 1.53 See, for example, Figure 4. 1.54 Specific to Aim 1 (and Aim 3) 1.55 @@ -449,6 +461,8 @@ 1.56 In addition, there are a number of areas which are almost identified by single genes: COAa+NLOT (anterior part of 1.57 cortical amygdalar area, nucleus of the lateral olfactory tract), ENT (entorhinal), ACAv (ventral anterior cingulate), VIS 1.58 (visual), AUD (auditory). 1.59 +____________________ 1.60 + 195-fold cross-validation. 1.61 Specific to Aim 2 (and Aim 3) 1.62 Raw dimensionality reduction results 1.63 todo 1.64 @@ -457,20 +471,6 @@ 1.65 Many areas are captured by clusters of genes 1.66 todo 1.67 todo 1.68 -_________________________________________ 1.69 -they predict area AUD. 1.70 - 18For 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, 1.71 -was calculated, and this was used to rank the genes. 1.72 - 195-fold cross-validation. 1.73 - 1.74 - 1.75 - 1.76 -Figure 5: From left to right and top to bottom, single genes which roughly identify areas SS (somatosensory primary + 1.77 -supplemental), SSs (supplemental somatosensory), PIR (piriform), FRP (frontal pole), RSP (retrosplenial), COApm (Corti- 1.78 -cal amygdalar, posterior part, medial zone). Grouping some areas together, we have also found genes to identify the groups 1.79 -ACA+PL+ILA+DP+ORB+MO (anterior cingulate, prelimbic, infralimbic, dorsal peduncular, orbital, motor), posterior 1.80 -and lateral visual (VISpm, VISpl, VISI, VISp; posteromedial, posterolateral, lateral, and primary visual). The genes are 1.81 -Pitx2, Aldh1a2, Ppfibp1, Slco1a5, Tshz2, Trhr, Col12a1, Ets1. 1.82 Research plan 1.83 Further work on flatmapping 1.84 In anatomy, the manifold of interest is usually either defined by a combination of two relevant anatomical axes (todo),