Data sources used for gene function prediction and network construction.

<p>Primary networks assessed individually and in aggregate are shown with sparsities calculated over the full genes set.</p

A small number of edges dominate precision-recall in the mouse gene network.

<p>A) Average precision as exceptional edges are added, B) Network performance is predicted by overlap with a network of the 100 edges predicted to be most exceptional. The 10 constituent networks of the combined kernel are assessed individually for their precisions and overlap with the 100 edge network.</p

Large-scale functional connectivity discriminates between unattended, conscious processing of fearful and neutral faces.

<p>(<b>A</b>) Decoding accuracy when classifying F vs. N as a function of the number of features (1 to 40) included ranked in descending order by their absolute t-score. Maximum accuracy for F vs. N classification (100%, p<0.002, corrected) was achieved when learning was based on the top 25 features in each training set. Mean accuracy scores for shuffled data are plotted along the bottom, with error bars representing standard deviation about the mean. Posterior (<b>B</b>), ventral (<b>C</b>) and right lateralized (<b>D</b>) anatomical representation of the top 25 features when classifying supraliminal fearful vs. supraliminal neutral face conditions (F vs. N). The thalamus (large red sphere in the center of each view) is the largest contributor of connections the differentiate the F from N. Red indicates correlations that are greater in F, and blue represents correlations that are greater in N. For display purposes, the size of each sphere is scaled according to the sum of the SVM weights of each node's connections, while the color of each sphere is set according to the sign of this value; positive sign, red, F>N and negative sign, blue, N>F. In addition, the thickness of each connection was made proportional to its SVM weight.</p

Experimental paradigm for the interaction of attention and affect (adapted from Etkin, et. al. 2004).

<p>Stimuli were either fearful (F) or neutral (N) expression faces, pseudocolored in red, yellow,or blue. Each event was comprised of a face which was either masked (33 ms for a fearful or neutral face, followed by 167 ms of a neutral face mask of the same gender and color, but different individual; MF or MN, respectively), or unmasked (200 ms for each face; F or N) or masked. Ten events of the same type, spaced 2 seconds apart, were presented within each 20 second block, followed by 15 seconds of crosshair with black background. There were four blocks per condition, giving 40 time points in the correlation estimates per condition per subject. In view of our specific hypotheses, only the unmasked conditions are discussed in the main text, while results for unmasked conditions are presented elsewhere (manuscript in preparation).</p

Node definitions and anatomical locations.

<p>Cortical and subcortical regions (ROIs) were parcellated according to bilateralized versions of the Harvard-Oxford Cortical and subcortical-atlases, and the cerebellum was parcellated according to AAL (left panel). ROIs were trimmed to ensure there was no overlap between them and that they contained voxels present in each subject. The top two eigenvariates from each ROI was extracted, resulting in 270 total nodes throughout the brain (right panel). For display purposes, node locations (black spheres) correspond to the peak loading value from each time-course's associated eigenmap averaged over all subjects.</p

F vs. N, Top 25 features (consensus features are in bold).

<p>F vs. N, Top 25 features (consensus features are in bold).</p

Data analysis scheme.

<p>Time series from each condition (unmasked fearful and unmasked neutral, F and N) and for N regions (R1 though RN) were segmented from each subject's whole run and concatenated (concatenation of two blocks for each condition shown in figure). There were four 20 second (10 TR) blocks of each condition; hence each example was comprised of 40 time points per condition per subject. For each of example, correlation matrices were estimated, in which each off-diagonal element contains Pearson's correlation coefficient between region <i>i</i> and region <i>j</i>. The lower <b><i>triangular</i></b> region of each of these matrices were used as input features in subsequent classifiers that learned to predict the example (i.e. F or N) based on their observed patterns of the correlations. Here, we used a filter feature selection based on t-scores in the training sets during each iteration of leave-two-out cross validation. The difference map consists of the set of most informative features (those that are included in the most rounds of cross-validation and have the highest SVM weights.)</p

Hierarchical clustering results of all 36 024 comparable pairs of probes

<p><b>Copyright information:</b></p><p>Taken from "Experimental comparison and cross-validation of the Affymetrix and Illumina gene expression analysis platforms"</p><p>Nucleic Acids Research 2005;33(18):5914-5923.</p><p>Published online 19 Oct 2005</p><p>PMCID:PMC1258170.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p> The dilution step is shown as a graph at the top of the figure (Blood/Placenta). Black bars at the side indicate large clusters of genes that appear to show clear dilution effects in both platforms. Gray bars indicate examples of clusters that appear to show dilution effects in one platform but not consistently in the other. Lighter colors indicate higher relative levels of expression on an arbitrary scale. Note that in this figure, if a gene occurs multiple times on one platform, it is shown in all possible valid comparisons with matching probes on the other platform