Loading in 2 Seconds...
Loading in 2 Seconds...
Clustered alignments of gene-expression time series data. Adam A. Smith, Aaron Vollrath , Cristopher A. Bradfield and Mark Craven Department of Biosatatistics & Medical Informatics, Department of Computer Sciences and Department of Oncology, University of Wisconsin, Madison, USA
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Adam A. Smith, Aaron Vollrath, Cristopher A. Bradfield and Mark Craven
Department of Biosatatistics & Medical Informatics, Department of Computer Sciences and Department of Oncology, University of Wisconsin, Madison, USA
BIOINFROMATICS Vol. 25 pages i119-i127, 2009
Shorting : The alignment path that represents shorting ends in the top row or the right column of the alignment space diagram, but not in the top-right cell.
COW search for good segment boundaries in only a limited area of alignment space.
The segment are assumed to be of constant length andusually evenly spaced in q
The vector K contains the coordinates of the knots (segment endpoints) in q
Variable in d
q(a,b) : Subseries of q from a to b
d is defined likewise.
The predecessor function list valid starting locations in d for segments ending at x
Second step : SCOW alternates horizontal and vertical movement of each knot until it converges.
The first step : seach independently in both dimensions.
The stretching si is defined as the ratio of lengths between qi and di, and ai is the amplitude ratio between the two as determined by a weighted least squares fitting procedure.
The first step is to assign the initial alignment centroids, to select a representative set of gene alignments as the centroids.
Subroutine Align returns the best alignment between two sereis based on a give set of genes.
ScoreGene returns the score of two series when aligned using a given alignment and a specified gene.
Record the best score so far that gene using one of the current centroidls.
It alternates between assigning genes to cluster and recomputing the alignment for each cluster using the genes assigned to it.
The top line : treatment accuracy with different orders of splines
The middle line : alignment accuracy by adding the criterion that the average time error in the mapping is less than or equal to 24 h
The bottom line : alignment accuracy where this tolerance is decreased to 12 h.