Expression Profile ClusteringExpression Profile = the pattern of signal values for one gene over several chips. Expression Profile Clustering = the clustering of similar profiles

Why? Similar expression profiles suggest regulation (by shared factor or cluster member)related functionALSO: opposite expression profiles suggestregulation (e.g. inhibition)

Software:EpClustJ-ExpressEtc.

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Guided Tour of EpClust

Data file formatCan enter in a variety of formats.See EpClusts data upload page

One simple way:Download tab-delimited data from NASCOpen with (or paste into) excelDelete all but one name column and Signal value columns. Do not include any hyphens! (I try to avoid all punctuation)Save as tab delimited text

Annotation can be added as either:A second file with the same first column of namesA single second column in the signal file (if specified)

To Upload Data

To select uploaded data

Select Specific Experiments within Input File

To Remove Unreliable Data

To Choose Algorithm TypeHierarchicalMeasures distance between each profile (i.e. gene) with each other profileSo time increases exponentially with each geneThen clusters closest genes together, followed by increasingly distant ones Into a tree of clusters within clusterscan be visualised and finally split at chosen distance with knowledge of treeK-meansInitially choose a set number (K) of clusters1) Chooses the K most different profiles2) Clusters each remaining profile with one of the KSo, much less time than Hierarchical (if many genes)

Cluster size can vary so can return too many or few geneswith no way to select.Then must repeat with different K values.

So Which is Better?K-Means is better able to manage large data-sets.

Hierarchical seams a more objective aproachIn that you dont need to decide cluster number at start

But which is more biologically informative?

Opinion divided.Both artificial.Dont prove, just suggest.K-means gives more consistent results.

.and then theres SOTA too!

Alternative Distance Measurements

Pearson-Based Distance MeasurementThe most commonly usedHow similar the SHAPES of the two profiles areBased on average of values and the standard deviationRates from identical (1) to completely uncorrelated (0) to perfect opposites (-1)

Centred Test?If profiles have identical shape, but offset from each other by a fixed value (or magnitude)Centred: Identical (1)Uncentred: Not (

Euclidean-Based Distance Measurementbetween gene expression levels directly based on magnitude of changes More about signal VALUES. Less about profile shapes. data must be suitably normalizede.g. use log-ratios of signals

Euclidean: shortest path between points Manhattan: the sum of distances along each dimension

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To Choose Algorithm Type

RESULTS

RESULTS_MyGene

_MyGene

Extra Options

Extra OptionsPhylip FormatSearch for Promoter MotifsFormat Results PictureExport Lists as TextStatisticsExport clusters as:text (lists of gene names) tree cut at chosen height.Or formatted for Phylip (tree drawing software)

Visualise promoter region next to each profile

Highlight motifs You must tell it the sequences of motifs though.

Must prepare file of upstream sequences Currently must arrange with EpClust staff to upload Can get from querying a database such as PlantCare using the upstream sequence of our particular gene of most interestCan get from TAIR using a list of gene names

PlantCARECis-Acting Regulatory Elementshttp://intra.psb.ugent.be:8080/PlantCARE/

Database of CAREs

ToolsSearch for CAREEnter upstream sequence of a geneLists and then highlights known motifs from database

Motif SamplerEnter upstream sequences of your clusterHighlights 8mers conserved more in cluster members than in genome

Other toolsClusteringQuery for info on motifs

Gene OntologyA hierarchical structure to describe gene function.

As PlantCARE compares:Expression Profile Clusters to Promoter Motif Conservation

There are also tools to compare:Expression Profile Clusters to Gene Ontology