PM-FSOR

Construction of a prognostic model based on feature selection with orthogonal regression (FSOR).

/Data/ contains:

• FSOR_result.Rdata: Weight of genes and iteration process of FSOR training.
• geodata.Rdata: Expression matrix and group list of four GEO datastes.
• hrgene_exp_1060.Rdata: Expression matrix of 1060 up-regulated genes in TCGA.
• unicox_results.Rdata: Univariate COX regression results of DEGs.

/Results/ contains:

• BP_GO.csv: Results of biological process in GO function enrichment analysis.
• CC_GO.csv: Results of cellular component in GO function enrichment analysis.
• MF_GO.csv: Results of molecular function in GO function enrichment analysis.
• kegg.csv: Results of KEGG pathway enrichment analysis.
• selectgene_effectsize.csv: Combined effect size of up-regulated genes selected from four GEO datastes.
• selectgene_fsor_50_weight.csv: Weights of top 50 genes of FSOR.
• tcga_Up_DEM.csv: Detail information of up-regulated DEGs in TCGA.
• tcga_univariate COX analysis_log2(x+1).csv: Univariate COX regression results of DEGs.

/Scripts/ contains:

• pca.R: Implementation of PCA analysis and visualization procedures for four LUAD GEO datasets.
• FSOR_datapre.R: Preprocessing the gene expression data from TCGA and initializing parameters for FSOR.
• FSOR_train.R: It contains the main training process of FSOR. It may be time-consuming. Please split the experimental data small if necessary.
• prognostic_model.R: This involves further screening candidate genes based on Cox regression results. C-index were considered in this procedure.
• lasso_cox.R: The results were compared with those based on the FSOR approach.

Contact:

Yuqi Wang (yukihhu@foxmail.com); Binhua Tang, PhD (bh.tang@outlook.com).

Citation:

Tang B., Wang Y., Chen Y., Li M., Tao Y. A Novel Early-Stage Lung Adenocarcinoma Prognostic Model Based on Feature Selection With Orthogonal Regression. Frontiers in Cell and Developmental Biology, 2021(8), ArticleID:620746.