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The pseudocode, reported in Figure 1(b), succinctly describes the essential steps of the algorithm for defining the signatures and computing the matrix of their reciprocal distances, starting from a set of array data in the form of a matrix (one probe per row, one array per column).
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This paper demonstrates how stringent statistical analysis can be combined with hierarchical clustering to gain much better insight from a set of expression array data.
Create a TABLECrate from a set of arrays, a dictionary, or a structured array.
Four sets of array data were generated: hLF on human arrays, mSMM on mouse arrays, fused cells on human arrays and fused cells on mouse arrays.
The remaining 483 sets of array data were processed using Robust Multi-array Average (RMA) separately for each of the 3 array types in the dataset.
Starting from the perfect-match probe-level data of a set of arrays, this software performs the background correction and the normalization and finally summarizes the results as a set of expression measures for each probe set.
Performance characteristics were determined from data analyzed for a set of 26 arrays run after protocol optimization (Table 1).
First, a set of 2-s data segments were extracted from the intertrial intervals, comprising ±1 s around the "start" cue that preceded each digit array.
Altogether, 195 sets of individual array data were used for further analysis.
All clones on the array are taken from a sub-set of the EST data set analysed in [ 27] (accession numbers: dbEST: 49109381 49125759, Genbank: EW744731– EW744731).
Based on phased array data extracted from compressible flow simulations, sound source strengths are computed on a set of points in the source region using phased array techniques assuming monopole propagation.
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