  The objective of many high-dimensional microarray and RNA-seq studies is to
develop a classifier of cancer patients based on characteristics of their
disease. The germinal center B-cell (GCB) classifier study in lymphoma and the
National Cancer Institute's Director's Challenge lung (DC-lung) study are two
examples. In recent years, such classifiers are often developed using
regularized regression, such as the lasso. A critical question is whether a
better classifier can be developed from a larger training set size and, if so,
how large the training set should be. This paper examines these two questions
using an existing sample size method and a novel sample size method developed
here specifically for lasso logistic regression. Both methods are based on
pilot data. We reexamine the lymphoma and lung cancer data sets to evaluate the
sample sizes, and use resampling to assess the estimation methods. We also
study application to an RNA-seq data set. We find that it is feasible to
estimate sample size for regularized logistic regression if an adequate pilot
data set exists. The GCB and the DC-lung data sets appear adequate, under
specific assumptions. Existing human RNA-seq data sets are by and large
inadequate, and cannot be used as pilot data. Pilot RNA-seq data can be
simulated, and the methods in this paper can be used for sample size
estimation. A MATLAB program is made available.
