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WormBase Tree Display for Expression_cluster: WBPaper00025032:cluster_89

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Name Class

WBPaper00025032:cluster_89DescriptionC-lineage related expression profile.
SpeciesCaenorhabditis elegans
AlgorithmQT clustering
ReferenceWBPaper00025032
Microarray_results171743_x_at
172973_at
173606_s_at
173665_at
173992_at
174010_at
175066_at
175419_at
175690_s_at
176730_at
182959_s_at
183254_at
183509_at
186925_at
188696_at
193775_at
173564_at
182696_s_at
GeneWBGene00019357
WBGene00004050
WBGene00001005
WBGene00000065
WBGene00008948
WBGene00000460
WBGene00000480
WBGene00013728
WBGene00018923
WBGene00020823
WBGene00009739
WBGene00013383
WBGene00011038
WBGene00004808
WBGene00012546
Attribute_ofMicroarray_experimentWBPaper00025032:N2_0_min
WBPaper00025032:N2_23_min
WBPaper00025032:N2_41_min
WBPaper00025032:N2_53_min
WBPaper00025032:N2_66_min
WBPaper00025032:N2_83_min
WBPaper00025032:N2_101_min
WBPaper00025032:N2_122_min
WBPaper00025032:N2_143_min
WBPaper00025032:N2_186_min
WBPaper00025032:mex-3_skn-1_0_min
WBPaper00025032:mex-3_skn-1_23_min
WBPaper00025032:mex-3_skn-1_41_min
WBPaper00025032:mex-3_skn-1_53_min
WBPaper00025032:mex-3_skn-1_66_min
WBPaper00025032:mex-3_skn-1_83_min
WBPaper00025032:mex-3_skn-1_101_min
WBPaper00025032:mex-3_skn-1_122_min
WBPaper00025032:mex-3_skn-1_143_min
WBPaper00025032:mex-3_skn-1_186_min
WBPaper00025032:pie-1_0_min
WBPaper00025032:pie-1_23_min
WBPaper00025032:pie-1_41_min
WBPaper00025032:pie-1_53_min
WBPaper00025032:pie-1_66_min
WBPaper00025032:pie-1_83_min
WBPaper00025032:pie-1_101_min
WBPaper00025032:pie-1_122_min
WBPaper00025032:pie-1_143_min
WBPaper00025032:pie-1_186_min
RemarkThis clustering algorithm assembles a series of clusters ordered by size with a defined limit on the largest pair-wise distance allowed between any two profiles in a cluster. Distance between profiles is measured as 1-R, where R is the Pearson correlation coefficient. Although we limited this distance to 0.3, some genes are included in clusters simply by chance. To reduce the spurious inclusion of these genes in the final clusters, we systematically re-sampled our data (100 times) with two forms of synthetic noise added at each reiteration to generate an Ravg. Noise was added to log2 scale RMA expression data, and was generated by a two-component model consisting of an additive Gaussian background with standard deviation 0.2, and a multiplicative Gaussian sampling error with a standard deviation of 0.05. Simulated data were floored at 1 RMA unit.
Type: Co-expression Cluster