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• User navigates to voxel-of-interest in reference

atlas volume and a fixed threshold AGEA correlation map appears

• Get a gene list from ABA is returned.

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 expression is of neuroscientific interest - structural relationships, evidence for refinement of structural boundaries.

For seed s, correlation value t, find set of voxels N(t,s)

Let B(s) = N(T,s)

Let A(s) be local neighborhood of highest correlated voxels

The Finder Algorithm

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, zona incerta and inferior

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

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.

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 recapitulates the basic laminar and areal

relationships of the neocortex Proximal and functional relationship of SSp and SSs Lower concordance of VISp with other regions.

Multi Dimensional Scaling

Multidimensional Scaling

From amatrix of

distances…

Kruskal & Wish, 1978

MDS

…itcalculatesa map…

MDSWhat does the MDS algorithm do?

…but itcannot tell

theorientationand themeaning ofthe axes.

Tuesday, May 5, 2009

MDSShepard, 1963:

• Morse-codes presented in pairs to naïve observers (each possiblecombination)

• Task - Same/different• Confusion matrix (% same responses): can be interpreted as adissimilarity matrix

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)

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Tuesday, May 5, 2009

Objective

Kruskal’s Stress

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