Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Feature Selection to Reduce Dimensionality
2.3. Handling Data Imbalance
2.4. Deep Learning Application for Cancer Prediction
- Dataset is separated into positive and negative tumor outcomes.
- The limiting outcome is randomly separated into two sets containing 70% (for training) and 30% (for testing) of the data.
- A subset of the non-limiting outcome, equal to 70% of the limiting outcome, is randomly chosen.
- The two subsets of the two outcomes, equivalent in number, is combined to form the training data.
- All remaining samples are combined to form the testing data set.
- Both data sets are randomly shuffled internally.
2.5. Gene Set Enrichment Analysis (GSEA)
2.6. Survival Analysis
3. Results
3.1. Feature Selection to Reduce Dimensionality
3.2. Deep Learning Application for Cancer Prediction
3.3. Gene Set Enrichment Analysis (GSEA)
3.4. Survival Analysis Using Seven Overlapping Genes
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
GSEA | Gene Set Enrichment Analysis |
FDR | False Discovery Rate |
SMOTE | Synthetic Minority Over Sampling Technique |
ANOVA | Analysis of Variance |
KNN | K-Nearest Neighbor |
TCGA | The Cancer Genome Atlas |
Appendix A
Dataset | Trials | Accuracy | Precision | Recall | F1 Score | Cohen Kappa | RUC |
---|---|---|---|---|---|---|---|
All markers | 1 | 0.02694 | 0 | 0 | 0 | 0 | 0.5 |
2 | 0.97306 | 0.97306 | 1 | 0.98635 | 0 | 0.5 | |
3 | 0.02694 | 0 | 0 | 0 | 0 | 0.5 | |
4 | 0.97306 | 0.97306 | 1 | 0.98635 | 0 | 0.5 | |
5 | 0.97306 | 0.97306 | 1 | 0.98635 | 0 | 0.5 | |
Avg. | 0.59461 | 0.58384 | 0.6 | 0.59181 | 0 | 0.5 | |
St.dev. | 0.51822 | 0.53297 | 0.54772 | 0.54025 | 0 | 0 | |
Anova_RF | 1 | 0.50168 | 1 | 0.48789 | 0.65581 | 0.04882 | 0.74395 |
2 | 0.92593 | 0.99628 | 0.92734 | 0.96057 | 0.36216 | 0.90117 | |
3 | 0.52525 | 1 | 0.51211 | 0.67735 | 0.05352 | 0.75606 | |
4 | 0.49158 | 1 | 0.47751 | 0.64637 | 0.04692 | 0.73875 | |
5 | 0.63636 | 1 | 0.62630 | 0.77021 | 0.08281 | 0.81315 | |
Avg. | 0.61616 | 0.99926 | 0.60623 | 0.74206 | 0.11885 | 0.79061 | |
St.dev. | 0.18252 | 0.00166 | 0.18904 | 0.13165 | 0.13679 | 0.06854 | |
Smote | 1 | 0.98370 | 0.96842 | 1 | 0.98396 | 0.96739 | 0.98370 |
2 | 0.99457 | 1 | 0.98913 | 0.99454 | 0.98913 | 0.99457 | |
3 | 0.97826 | 1 | 0.95652 | 0.97778 | 0.95652 | 0.97826 | |
4 | 0.97283 | 0.97802 | 0.96739 | 0.97268 | 0.94565 | 0.97283 | |
5 | 0.97283 | 0.98876 | 0.95652 | 0.97238 | 0.94565 | 0.97283 | |
Avg. | 0.98043 | 0.98704 | 0.97391 | 0.98027 | 0.96087 | 0.98043 | |
St.dev. | 0.00909 | 0.01385 | 0.01975 | 0.00926 | 0.01819 | 0.00909 |
Dataset | Trials | Accuracy | Precision | Recall | F1 Score | Cohen Kappa | ROC AUC |
---|---|---|---|---|---|---|---|
All markers base model | 1 | 0.04231 | 0 | 0 | 0 | 0 | 0.5 |
2 | 0.95769 | 0.95769 | 1 | 0.97839 | 0 | 0.5 | |
3 | 0.95769 | 0.95769 | 1 | 0.97839 | 0 | 0.5 | |
4 | 0.04231 | 0 | 0 | 0 | 0 | 0.5 | |
5 | 0.95769 | 0.95769 | 1 | 0.97839 | 0 | 0.5 | |
Avg. | 0.59154 | 0.57461 | 0.6 | 0.58703 | 0 | 0.5 | |
St.dev. | 0.50137 | 0.52455 | 0.54772 | 0.53588 | 0 | 0 | |
AnovaRF base model | 1 | 0.79831 | 0.99814 | 0.79087 | 0.8825 | 0.23336 | 0.87877 |
2 | 0.72355 | 0.99589 | 0.71429 | 0.8319 | 0.15957 | 0.82381 | |
3 | 0.72496 | 1 | 0.71281 | 0.83233 | 0.17359 | 0.85641 | |
4 | 0.83216 | 0.99822 | 0.82622 | 0.90411 | 0.27686 | 0.89644 | |
5 | 0.79408 | 1 | 0.78498 | 0.87954 | 0.23603 | 0.89249 | |
Avg. | 0.77461 | 0.99845 | 0.76583 | 0.86608 | 0.21588 | 0.86958 | |
St.dev. | 0.04828 | 0.00169 | 0.05027 | 0.03242 | 0.04845 | 0.03 | |
AnovaRF- SMOTE base model | 1 | 0.98889 | 1 | 0.97778 | 0.98876 | 0.97778 | 0.98889 |
2 | 0.97778 | 1 | 0.95556 | 0.97727 | 0.95556 | 0.97778 | |
3 | 0.96889 | 1 | 0.93778 | 0.96789 | 0.93778 | 0.96889 | |
4 | 0.97778 | 1 | 0.95556 | 0.97727 | 0.95556 | 0.97778 | |
5 | 0.96222 | 0.99524 | 0.92889 | 0.96092 | 0.92444 | 0.96222 | |
Avg. | 0.97511 | 0.99905 | 0.95111 | 0.97442 | 0.95022 | 0.97511 | |
St.dev. | 0.01011 | 0.00213 | 0.01886 | 0.01057 | 0.02022 | 0.01011 |
Dataset | Trials | Accuracy | Precision | Recall | F1 Score | Cohen Kappa | ROC AUC |
---|---|---|---|---|---|---|---|
All markers large model | 1 | 0.95769 | 0.95769 | 1 | 0.97839 | 0 | 0.5 |
2 | 0.04231 | 0 | 0 | 0 | 0 | 0.5 | |
3 | 0.04231 | 0 | 0 | 0 | 0 | 0.5 | |
4 | 0.95769 | 0.95769 | 1 | 0.97839 | 0 | 0.5 | |
5 | 0.04231 | 0 | 0 | 0 | 0 | 0.5 | |
Avg. | 0.40846 | 0.38307 | 0.4 | 0.39135 | 0 | 0.5 | |
St.dev. | 0.50137 | 0.52455 | 0.54772 | 0.53588 | 0 | 0 | |
AnovaRF large model | 1 | 0.93089 | 0.99685 | 0.93078 | 0.96268 | 0.50331 | 0.93206 |
2 | 0.89563 | 0.99672 | 0.89396 | 0.94255 | 0.39113 | 0.91365 | |
3 | 0.86601 | 1 | 0.86009 | 0.92478 | 0.3422 | 0.93004 | |
4 | 0.95628 | 1 | 0.95435 | 0.97664 | 0.63885 | 0.97717 | |
5 | 0.91537 | 0.99839 | 0.91311 | 0.95385 | 0.45727 | 0.93989 | |
Avg. | 0.91283 | 0.99839 | 0.91046 | 0.9521 | 0.46656 | 0.93856 | |
St.dev. | 0.03431 | 0.00161 | 0.0359 | 0.01971 | 0.11432 | 0.0236 | |
AnovaRF-SMOTE large model | 1 | 0.98222 | 0.99543 | 0.96889 | 0.98198 | 0.96444 | 0.98222 |
2 | 0.99556 | 1 | 0.99111 | 0.99554 | 0.99111 | 0.99556 | |
3 | 0.98667 | 1 | 0.97333 | 0.98649 | 0.97333 | 0.98667 | |
4 | 0.98 | 1 | 0.96 | 0.97959 | 0.96 | 0.98 | |
5 | 0.99333 | 1 | 0.98667 | 0.99329 | 0.98667 | 0.99333 | |
Avg. | 0.98756 | 0.99909 | 0.976 | 0.98738 | 0.97511 | 0.98756 | |
St.dev. | 0.00678 | 0.00204 | 0.0128 | 0.00693 | 0.01355 | 0.00678 |
27K | Original | AnovaRF | SMOTE | ||||
Normal | Cancer | Normal | Cancer | Normal | Cancer | ||
Normal | 3.2 | 4.8 | 7.8 | 0.2 | 90.8 | 1.2 | |
Cancer | 115.6 | 173.4 | 113.8 | 175.2 | 2.4 | 89.6 | |
Prediction Sample Size | 297 | 297 | 184 | ||||
450K base model | Original | AnovaRF | SMOTE | ||||
Normal | Cancer | Normal | Cancer | Normal | Cancer | ||
Normal | 12 | 18 | 28.2 | 1.8 | 224.8 | 0.2 | |
Cancer | 271.6 | 407.4 | 150 | 529 | 11 | 214 | |
Prediction Sample Size | 709 | 709 | 450 | ||||
450K larger model | Original | AnovaRF | SMOTE | ||||
Normal | Cancer | Normal | Cancer | Normal | Cancer | ||
Normal | 18 | 12 | 28.4 | 1.6 | 224.8 | 0.2 | |
Cancer | 407.4 | 271.6 | 86 | 593 | 5.4 | 219.6 | |
Prediction Sample Size | 709 | 709 | 450 |
Appendix B
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Dataset | Total Samples | Tumor Samples | Normal Samples | # CpG Markers |
---|---|---|---|---|
27K | 337 | 309 | 28 | 27,578 |
450K | 851 | 750 | 101 | 485,577 |
Metric | Zero Impute | KNN Impute | Mean Impute | Iterative Impute | |
---|---|---|---|---|---|
27K | MSE | 0.016648 | 0.016755 | 0.016749 | 0.016777 |
STD | 0.007299 | 0.007253 | 0.007245 | 0.007340 | |
450K | MSE | 0.017244 | 0.017253 | 0.017251 | ——– |
STD | 0.005273 | 0.005286 | 0.005307 | ——– |
Dataset | # Features | Sample Size | Tumor Samples | Normal Samples | Runtime | |
---|---|---|---|---|---|---|
27K | All markers | 24,981 | 337 | 309 | 28 | 21 s |
Anova_RF | 336 | 337 | 309 | 28 | 12 s | |
Anova_RF (with Smote) | 475 | 618 | 309 | 309 | 13 s | |
450K | 450K All (base + large) | 395,722 | 851 | 750 | 101 | 1:44:10 s |
Anova_RF (base + large) | 1044 | 851 | 750 | 101 | 38:41 s | |
Anova_RF with SMOTE (base + large) | 1445 | 1500 | 525 | 525 | 13 s |
Dataset | CpG Markers | Total Genes | COSMIC + TSGene Overlap (3326 Genes) | Sample Genes Overlap (100 Genes) |
---|---|---|---|---|
27K all | 24,981 | 18,166 | 1214 | 98 |
27K ANOVA-RF | 336 | 470 | 36 | 2 |
27K ANOVA-RF SMOTE | 475 | 685 | 55 | 6 |
450K all | 395,722 | 35,555 | 1455 | 100 |
450K ANOVA-RF | 1044 | 1208 | 88 | 7 |
450K ANOVA-RF SMOTE | 1445 | 1572 | 136 | 9 |
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Gomes, R.; Paul, N.; He, N.; Huber, A.F.; Jansen, R.J. Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers. Genes 2022, 13, 1557. https://doi.org/10.3390/genes13091557
Gomes R, Paul N, He N, Huber AF, Jansen RJ. Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers. Genes. 2022; 13(9):1557. https://doi.org/10.3390/genes13091557
Chicago/Turabian StyleGomes, Rahul, Nijhum Paul, Nichol He, Aaron Francis Huber, and Rick J. Jansen. 2022. "Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers" Genes 13, no. 9: 1557. https://doi.org/10.3390/genes13091557