cg

diff grant.html @ 80:165189ffc588

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author bshanks@bshanks.dyndns.org
date Mon Apr 20 17:17:17 2009 -0700 (16 years ago)
parents 91e9bb96cf68
children d89a99c9ea9a
line diff
1.1 --- a/grant.html Mon Apr 20 17:13:37 2009 -0700 1.2 +++ b/grant.html Mon Apr 20 17:17:17 2009 -0700 1.3 @@ -324,16 +324,15 @@ 1.4 Figure 1: Top row: Genes Nfic and 1.5 A930001M12Rik are the most correlated with 1.6 area SS (somatosensory cortex). Bottom row: 1.7 -Genes C130038G02Rik and Cacna1i are those 1.8 +Genes C130038G02Rik and Cacna1i are those 1.9 with the best fit using logistic regression. Within 1.10 -each picture, the vertical axis roughly corre- 1.11 -sponds to anterior at the top and posterior at 1.12 -the bottom, and the horizontal axis roughly 1.13 -corresponds to medial at the left and lateral 1.14 -at the right. The red outline is the boundary 1.15 -of region SS. Pixels are colored according to 1.16 -correlation, with red meaning high correlation 1.17 -and blue meaning low. Format conversion between SEV, MATLAB, NIFTI 1.18 +each picture, the vertical axis roughly corresponds 1.19 +to anterior at the top and posterior at the bot- 1.20 +tom, and the horizontal axis roughly corresponds 1.21 +to medial at the left and lateral at the right. The 1.22 +red outline is the boundary of region SS. Pixels are 1.23 +colored according to correlation, with red meaning 1.24 +high correlation and blue meaning low. Format conversion between SEV, MATLAB, NIFTI 1.25 We have created software to (politely) download all of the SEV files from 1.26 the Allen Institute website. We have also created software to convert 1.27 between the SEV, MATLAB, and NIFTI file formats, as well as some of 1.28 @@ -362,7 +361,7 @@ 1.29 ∙ For each gene, a 2-D matrix whose entries represent the average expres- 1.30 sion level underneath each surface pixel 1.31 1.32 -Figure 2: Gene Pitx2 1.33 +Figure 2: Gene Pitx2 1.34 is selectively underex- 1.35 pressed in area SS. We created a normalized version of the gene expression data by subtracting each gene’s mean 1.36 expression level (over all surface pixels) and dividing each gene by its standard deviation.