From: Seeking gene relationships in gene expression data using support vector machine regression
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Theme | Genes that contained highly correlated genes | From the same biological family | Across biological families | Random Walk (all genes) |
Sample size | 1000 | 55 RPa 49 ZFPb | 49 | 3554 |
Sample selection criteria | A total of 1000 genes that contained 100 highly correlated genes | all in RP family, all in ZFP family | RP, ZFP, and DEADc | The full data set of all 3554 genes |
Training size | 2 genes per training, 3 trainings | 2 to 10 genes | 3 genes per training | 3 to 20 genes |
Training selection criteria | Corr > 0.85, p < 0.001 | Randomly from 55 RP genes or from 49 ZFP genes | Only from RP family | Randomly from entire sample |
Best training size | 2 genes | 4–5 genes | 3 genes | 3–7 genes |
Example of training genes | 1. 200088_x_at and 200809_x_at (both are different problems for RPL12) (Pearson corr > 0.92 and Spearman corr > 0.90, p < 0.0001) 2. RPL32 and RPS18 (Pearson corr > 0.94, p < 0.0001) 3. DDX3Y and EIF1AY (Pearson corr > 0.9875, p < 0.0001)d | RPS11 RPS10 RPS3A (201257_x_at) RPS16 | RPS4X RPS4Y1 RPS5 | C1D ALOX5 ENO2 RERE |
Example of captured genes | 1. 200088_x_at and 200809_x_at 2. RPL32, RPS15, RPS18, RPS3A, and RPS28 3. DDX3Y and EIF1AY | 1. RPL27, RPS3A(2000099_s_at), RPS3A(201257_x_at), RPS29, RPS28 2. RPS15A, RPS18, RPS12, RPS19 3. Similar results were seen among genes with ZFP family | DDX39 DDX3Y DDX58 DDX26 | SCAP1 SGPP1 TGFBR3 CD9 VAMP8 |