WormBase Tree Display for Expression_cluster: WBPaper00025032:cluster_24
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| WBPaper00025032:cluster_24 | Description | C-lineage related expression profile. | |
|---|---|---|---|
| Species | Caenorhabditis elegans | ||
| Algorithm | QT clustering | ||
| Reference | WBPaper00025032 | ||
| Microarray_results | 171956_s_at | ||
| 172434_at | |||
| 173271_s_at | |||
| 173938_s_at | |||
| 174276_at | |||
| 174361_at | |||
| 174623_at | |||
| 174860_at | |||
| 175147_at | |||
| 175771_at | |||
| 176055_at | |||
| 177482_at | |||
| 179130_at | |||
| 180530_at | |||
| 182166_at | |||
| 183557_at | |||
| 184676_s_at | |||
| 184902_at | |||
| 187073_at | |||
| 187153_s_at | |||
| 188059_at | |||
| 188060_s_at | |||
| 188564_at | |||
| 188609_s_at | |||
| 188706_at | |||
| 188754_at | |||
| 189768_at | |||
| 190076_at | |||
| 190279_s_at | |||
| 190491_at | |||
| 190570_at | |||
| 191468_s_at | |||
| 191469_at | |||
| 191470_s_at | |||
| 192242_at | |||
| 192699_s_at | |||
| 193009_at | |||
| 193058_at | |||
| 193319_at | |||
| 193344_at | |||
| 193656_s_at | |||
| 193751_at | |||
| 193759_at | |||
| 194114_s_at | |||
| 194203_x_at | |||
| 172197_x_at | |||
| 172801_x_at | |||
| 172820_x_at | |||
| 173640_at | |||
| 174812_at | |||
| 174910_at | |||
| 176564_at | |||
| 184551_at | |||
| 191052_at | |||
| 192633_at | |||
| Gene | WBGene00004237 | ||
| WBGene00018743 | |||
| WBGene00002282 | |||
| WBGene00001768 | |||
| WBGene00012768 | |||
| WBGene00009657 | |||
| WBGene00003933 | |||
| WBGene00010868 | |||
| WBGene00000969 | |||
| WBGene00003779 | |||
| WBGene00001105 | |||
| WBGene00000179 | |||
| WBGene00014153 | |||
| WBGene00019843 | |||
| WBGene00016641 | |||
| WBGene00015472 | |||
| WBGene00006956 | |||
| WBGene00019927 | |||
| WBGene00013263 | |||
| WBGene00000545 | |||
| WBGene00006893 | |||
| WBGene00004257 | |||
| WBGene00006844 | |||
| WBGene00009342 | |||
| WBGene00006321 | |||
| WBGene00019317 | |||
| WBGene00018682 | |||
| WBGene00006404 | |||
| WBGene00009343 | |||
| WBGene00000170 | |||
| WBGene00001993 | |||
| WBGene00002827 | |||
| WBGene00006366 | |||
| WBGene00001430 | |||
| WBGene00020215 | |||
| WBGene00001948 | |||
| WBGene00002181 | |||
| WBGene00012261 | |||
| WBGene00000667 | |||
| WBGene00000668 | |||
| WBGene00000669 | |||
| WBGene00000620 | |||
| WBGene00005018 | |||
| WBGene00023209 | |||
| WBGene00008205 | |||
| WBGene00022146 | |||
| WBGene00001075 | |||
| WBGene00004057 | |||
| WBGene00000109 | |||
| Attribute_of | Microarray_experiment | WBPaper00025032: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 | |||
| Remark | This 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 |
