Matrix Clustering


Mandatory inputs


Optional inputs


Advanced options


Run analysis

RSAT server matrix-clustering

Identify groups (clusters) of similarities between motifs from one (or several) collections, align them and generate dyanmic visualisation.
Jaime A Castro-Mondragon, Morgane Thomas-Chollier, Jacques van Helden.
Sample output
User Manual
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Cite the publication:
Castro-Mondragon JA, Jaeger S, Thieffry D, Thomas-Chollier M#, van Helden J#. "RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections.", Nucleic Acid Research, 45:13 e119 (2017) [Pubmed][Full text]
Analysis Title
Motif Collection
Info
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Motif Collection 2
Info
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Motif Collection 3
Info
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Clustering options
Motif comparison options

Main Thresholds to define the clusters
Metrics Lower
Threshold		 Upper
Threshold		 description
w Width = number of aligned columns
cor Pearson correlation (computed on residue occurrences in aligned columns)
Ncor Relative width-normalized Pearson correlation

Supplementary thresholds to define the clusters (slower version of the program)
Metrics Lower
Threshold		 Upper
Threshold		 description
logoDP dot product of sequence logos
logocor correlation computed on sequence logos
Nlogocor Relative width-normalized logocor
Icor Pearson correlation computed on Information content
NIcor Relative width-normalized Icor
dEucl Euclidian distance between residue occurrences in aligned columns
NdEucl Relative width-normalized dEucl
NsEucl similarity derived from Relative width-normalized Euclidian distance
SSD Sum of square deviations

Output options

Output