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brain-map

http://www.brain-map.org. ALLEN BRAIN ATLAS: ADULT HUMAN. “Whole brain” microarrays: Agilent 8x60k array , starting from 4x44k Agilent Whole Human Genome probe set 2+ probes for 93% of genes [~21k unique Entrez Ids]. Gene Finder.

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brain-map

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  1. http://www.brain-map.org

  2. ALLEN BRAIN ATLAS: ADULT HUMAN • “Whole brain” microarrays: Agilent 8x60k array, starting from 4x44k Agilent Whole Human Genome probe set • 2+ probes for 93% of genes [~21k unique Entrez Ids]

  3. Gene Finder • User navigates tovoxel-of-interest in reference atlas volume andafixed threshold AGEA correlation map appears • Get a gene list fromABA is returned.

  4. AGEA Gene Finder Tool enables users to search a local anatomic region of interest for genes that exhibit localized enrichment Finding genes with highly localized expressionis of neuroscientificinterest - structural relationships, evidence for refinement of structural boundaries.

  5. The Finder Algorithm For seed s, correlation value t, find set of voxelsN(t,s) Let B(s) = N(T,s) Let A(s) be local neighborhood of highest correlated voxels

  6. Ranked List of Genes • Computation is independent for 16 brain regions R with unique intra‐correlation patterns • Regions include - cortex, hippocampus, striatum, thalamus, olfactory bulb, cerebellar cortex, hypothalamus, midbrain and hindbrain. • Special Regions - Ventricular areas, medial habenula, caudoputamen, deep cortical layers, olfactory nerve layer of the olfactory bulb, zonaincerta and inferior

  7. Cortical Map • Genes in superficial layers have sharp drop in correlation depth-wise • Transition not smooth – L5 & L6: column a • Vice-versa; Expression in deep layers reduces correlation in superficial layers • Laminar effects - seeds in somatosensory L6 have lower L4 correlation (column d) than seeds in L2/3

  8. Visualizing Correlations • Allows interpretation of relative correlations across layers and regions. • Mean correlation is highest in the domain containing the seed • Use representation to determine dominant area (columns) or layer (rows) to show that adjacent layers have positive expression correlation • Strongest concordance between L5 and L6 • Non-adjacent layers- negative correlation with anatomic proximity: physically distant layers less likely to exhibit gene coexpression.

  9. Multi-dimensional Scaling • Domain-to-domain correlations as measure of similarity • Data is visualized by multidimensional scaling (MDS) • Clustering method • Distance between points (domains) is proportional to their correlation • MDS recapitulatesthe basic laminar and areal relationships of the neocortex • Proximal and functional relationship of SSp and SSs • Lower concordance of VISp with other regions.

  10. Multi Dimensional Scaling

  11. Multidimensional Scaling From a matrix ofdistances… Kruskal & Wish, 1978

  12. MDS …it calculates a map…

  13. MDS • What does the MDS algorithm do? …but it cannot tell the orientationand the meaning ofthe axes. Tuesday, May 5, 2009

  14. MDS Shepard, 1963: • Morse-codes presented in pairs to naïve observers (each possiblecombination) •Task - Same/different • Confusion matrix (% same responses): can be interpreted as adissimilarity matrix

  15. MDS Algorithm • Given a set of similarities (or distances) between every pairof N items • Find a representation of the items in few dimensions • Inter-item proximities “nearly” match the original similarities(or distances) 65 Tuesday, May 5, 2009

  16. Objective

  17. Kruskal’s Stress

  18. Next

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