In this work, we provide a computational method of regularized logistic regression for discovering biomarkers of spontaneous preterm birth (SPTB) from gene expression data. The successful identification of SPTB biomarkers will greatly benefit the interference of infant gestational age for reducing the risks of pregnant women and preemies. Obviously, the proposed method of discovering biomarkers for SPTB can be easily extended for other complex diseases.
- LogReg: A method of regularized logistic regression for biomarker discovery from gene expression data.
- In this work, we also compared LogReg with the other five recursive feature elimination (RFE) feature selection methods, namely, AB-RFE, NN-RFE, RF-RFE, KNN-RFE and SVM-RFE.
Li, Lingyu, and Zhi-Ping Liu. "Biomarker discovery for predicting spontaneous preterm birth from gene expression data by regularized logistic regression." Computational and Structural Biotechnology Journal 18 (2020): 3434-3446. LogReg paper website
- In the Data and Data37 files, we only give some necessary files by each R program.
- Some of these input files only give the first few lines, but this does not affect the results of the work (LogReg).
The serial number (1) (2) ... (10) represents the order in which the program runs in our work.
- (1) GSE59491_expr.R ---- Processing original data of GSE59491.
- (2) GSE73685_expr.R ---- Processing original data of GSE73685.
- (3) Ttest.R ---- It contains some functions used to select differentially expressed genes (DEGs), and is included in “DEgene.R”.
- (4) DEgene.R ---- Identify the differentially expressed genes (DEGs) on a dataset and find candidates with adjusted P-value < 0.05.
- (5) GSE59491_cel37_rep.R ---- Solve the regularized logistic regression with seven effective penalties, i.e., ridge, lasso, elastic net, L0, L1/2, SCAD and MCP.
- (6) Feature_select.R ---- Extract data from independent dataset and origina discovery dataset based on identified biomarker.
- (7) Class_ROC.R" ---- Verify the identified biomarkers on an independent dataset by the AUC value. (Figure 5 a in our work)
- (8) Box_PRS.R ---- Obtain the boxplot of of preterm risk score (PRS) on the independent dataset. (Figure 5 b in our work)
- (9) Hyper_test.R ---- Calculate the number of overlapping genes selected by hypergeometric test. (Table 4 in our work)
- (10) Stability.R ---- Calculate the stability of selected features/genes by seven different methods.
R code for five RFE feature selection methods.
- (1)"svmrfe.py" ---- SVM: SVM-RFE.
- (2) "abrfe.py" ---- SVM: AB-RFE.
- (3)"rfe_nnet.R" ---- SVM: NN-RFE.
- (4)"rfsvm.py" ---- SVM: RF-RFE.
- (5)"ref_knn.R" ---- SVM: KNN-RFE.
- (6)"feature_overlap_clear.R" ---- SVM: Intersection of feature subsets obtained by 5 RFE methods to identify biomarkers.
- (7)"feature_select_clear.R" ---- SVM: Extract features from the test set and verification set separately.
- (8)"RFE_ROC_clear.R" ---- SVM classifier.
- (9)"class_feature_clear.R" ---- SVM: Perform independent data set verification and draw ROC curve.
- (10) "cluster_clear.R" ---- SVM: Enrichment analysis of identified biomarkers.
This LogReg R code is the same as the one in our Lab's Website