|
The mean square errors (MSE) of estimated gene expression levels (up:down = 9:1) for simulated cDNA microarray data with the R-I plots similar to Figure 3. |
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| Percentage of DEG |
Descriptive Statistics |
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|
|
|||||||
| Method |
Mean |
Minimum |
25% Quantile |
Median |
75% Quantile |
Maximum |
|
|
|
|||||||
| 1% |
OLS |
0.0370 |
0.0116 |
0.0258 |
0.0323 |
0.0410 |
2.2092 |
| Huber |
0.0248 |
0.0112 |
0.0195 |
0.0223 |
0.0255 |
1.7156 |
|
| Tukey |
0.0238 |
0.0109 |
0.0191 |
0.0217 |
0.0247 |
1.5648 |
|
| LOWESS |
0.0375 |
0.0119 |
0.0251 |
0.0321 |
0.0419 |
2.2162 |
|
| 5% |
OLS |
0.0489 |
0.0126 |
0.0265 |
0.0333 |
0.0422 |
1.5836 |
| Huber |
0.0324 |
0.0110 |
0.0198 |
0.0228 |
0.0263 |
1.1765 |
|
| Tukey |
0.0299 |
0.0108 |
0.0194 |
0.0222 |
0.0254 |
1.0278 |
|
| LOWESS |
0.0493 |
0.0125 |
0.0255 |
0.0325 |
0.0429 |
1.6134 |
|
| 10% |
OLS |
0.0692 |
0.0124 |
0.0285 |
0.0359 |
0.0464 |
1.6907 |
| Huber |
0.0455 |
0.0098 |
0.0210 |
0.0245 |
0.0288 |
1.1667 |
|
| Tukey |
0.0404 |
0.0102 |
0.0204 |
0.0236 |
0.0276 |
1.0175 |
|
| LOWESS |
0.0692 |
0.0119 |
0.0270 |
0.0349 |
0.0461 |
1.6846 |
|
| 20% |
OLS |
0.0961 |
0.0137 |
0.0324 |
0.0428 |
0.0570 |
1.8614 |
| Huber |
0.0632 |
0.0124 |
0.0235 |
0.0282 |
0.0354 |
1.2969 |
|
| Tukey |
0.0547 |
0.0127 |
0.0225 |
0.0266 |
0.0329 |
1.0525 |
|
| LOWESS |
0.0950 |
0.0154 |
0.0325 |
0.0431 |
0.0580 |
1.8834 |
|
| 40% |
OLS |
0.1439 |
0.0147 |
0.0493 |
0.0665 |
0.1007 |
2.5021 |
| Huber |
0.0960 |
0.0134 |
0.0330 |
0.0446 |
0.0673 |
1.9988 |
|
| Tukey |
0.0821 |
0.0136 |
0.0305 |
0.0401 |
0.0602 |
1.6771 |
|
| LOWESS |
0.1562 |
0.0187 |
0.0832 |
0.1121 |
0.1418 |
3.0480 |
|
| *70% |
OLS |
0.1530 |
0.0138 |
0.0554 |
0.1146 |
0.1882 |
1.2717 |
| Huber |
0.1040 |
0.0121 |
0.0366 |
0.0791 |
0.1267 |
0.9153 |
|
| Tukey |
0.0901 |
0.0115 |
0.0337 |
0.0700 |
0.1098 |
0.7778 |
|
| LOWESS |
0.4082 |
0.0350 |
0.1651 |
0.3563 |
0.6331 |
1.0816 |
|
|
DEG: differentially expressed genes. *: all DEG are up-regulated. OLS: the TW-SLM using the ordinary least squares. Huber: the robust TW-SLM using Huber's weight function. Tukey: the robust TW-SLM using Tukey's weight function. | |||||||
Wang et al. BMC Bioinformatics 2005 6:14 doi:10.1186/1471-2105-6-14 |
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