Feature-Selection

This repository contains R code for various feature selection methods. The code is organized as outlined below.

• Model Fits & GOF: Includes functions for fitting the Cox, PO & YP models, as well as testing for goodness-of-fit. For the Cox and PO cases, there are options for adjusting for age and stage, if desired.

  • Required: R packages (survival, timereg, YPmodel)
  • Inputs:
    • predictor, x (gene expression)
    • data frame with n rows (number of subjects) and columns with survival information (time = survival time, censor = censoring information) and p genes
  • Output: model coefficient (beta), coefficient standard error, significance p-value, GOF p-value

• Pseudo-R² Measures: Includes functions for calculating each pseudo-R² measures (PO, CO, CH, ModCH and PH). Note, each measure has a separate function. In the future, we plan to combine the code into one R² function with option type = c("PO", "CO", "CH", "ModCH", "PH").

  • Required: R package (survival)
  • Inputs:
    • predictor, x (gene expression)
    • survival time
    • censoring indicator
  • Output: R² measure

• R²_LR & R²_I Measures: This code contains functions for R²_LR, R²_IPO & R²_IPH. There are options for adjusting for age and stage, if desired.

  • Required: R packages (survival, timereg)
  • Inputs:
    • predictor, x (gene expression)
    • survival time
    • censoring indicator
  • Output: R² measures

• I Measures: This code contains functions for I_PO and I_YP. There are options for adjusting for age and stage for I_PO, if desired.

  • Required: R packages (survival, timereg, YPmodel)
  • This code assumes that your data is in the the following form:
    • Column 1 = survival time (time)
    • Column 2 = censoring indicator (censor)
    • Columns 3+ = genes
  • Output:
    • I_PO, outPO (I, I test statistic, I p-value)
    • I_YP, outYP (I, I test statistic, I p-value)

• Youden & AUC: Computes Youden & AUC values based on gene ranking by a specified feature selection method

  • Required: R packages (MESS)
  • Inputs:
    • data frame with n rows (number of genes) and 1 column (feature selection measure)
    • effectGenes: number of significant genes
  • Output option: Specificity, Sensitivity, Youden & AUC

• Venn Diagrams: This code creates Venn Diagrams showing various interestions between different feature selection measures.

  • Required: R packages (gpplots, VennDiagram, latex2exp)
  • Inputs:
    • Obtain a data frame named "out" with n rows (number of genes) and 1 column for each of the measures listed above (except I_YP). All measures here are based on continuous gene expression.
    • Obtain a data frame named "out2" with n rows (number of genes) and 1 column for each of the measures based on dichotomized expression (I_YP, I_PO, concreg).
  • Venn Diagrams created:
    • I_PO, I_YP, & concreg (dichotomized case is coded separately)
    • R²_PO, R²_IPO & R²_LR
    • R²_PO, R²_ModCH & R²_CO

• Other Existing Measures: This code contains functions for computing some existing measures in the literature.

  • Required: R packages (concreg, survAUC, survival, pec, timereg)
  • Measures computed:
    • Concreg (Dunkler et al. 2010)
    • Uno's C (Uno et al. 2011)
    • R²_G - PH & PO cases (Graf et al. 1999; Gerds & Schumacher 2006)
    • R²_SH (Schemper & Henderson 2000)
  • Inputs:
    • predictor, x (gene expression)
    • survival time
    • censoring indicator
  • Output: Concreg (absolute effect size), Uno's C, R²_G (PH), R²_G (PO) and R²_SH.

• Simulations: Contains R code for simulating data

  • Scheme 1: Univariate aproach; genes linked to survival one at a time
  • Scheme 2: Multivariate approach; incorporates correlation between features
  • For both schemes, there are options to simulate from the following models: LN, LL1, LL2, W1, W2

• Complete Example: This example does the following

  1. Creates a simulated data set (Scheme 1 - W, 33% censoring)
  2. Obtains PH, PO & YP model fits and GOF
  3. Computes all proposed measures for feature selection:
     • I measures - I_PO, I_YP
     • R²_measures - R²_LR, R²_IPO, R²_IPH, R²_PO, R²_CO, R²_ModCH
     • R²_measures (by Rouam et al. 2010, 2011) - R²_CH, R²_PH
  4. Computes other existing measures (concreg, Uno's C, R²_G & R²_SH)
  5. Computes Sensitivity, Specificity, Youden & AUC for each measure.
  6. Creates venn diagrams showing overlaps between measures.
  • Note: Before running this example, some functions need to be run from the other R code in this repository. All required functions are noted thoughout the example.
  • Required: R packages (survival, timereg, YPmodel, concreg, survAUC, pec, MESS, gplots, VennDiagram, latex2exp)

Copyright & Citations

Copyright © Lauren Spirko-Burns and Karthik Devarajan

Spirko, L.N., Devarajan, K. Unified methods for variable selection in large-scale genomic studies with censored survival outcomes. Under review. COBRA pre-print series, Article 120 (June 2019). http://biostats.bepress.com/cobra/art120.

Spirko, L. (2017). Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes. Ph.D. Dissertation. Department of Statistical Science, Temple University, Philadelphia.

License

This work by Lauren Spirko-Burns and Karthik Devarajan is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.