Permutation test for incomplete paired data with application to cDNA microarray data

Asymptotic-based bootstrap approach for matched pairs with missingness in a single arm

The issue of missing values is an arising difficulty when dealing with paired data. Several test procedures are developed in the literature to tackle this problem. Some of them are even robust under deviations and control type-I error quite accurately. However, most of these methods are not applicable when missing values are present only in a single arm. For this case, we provide asymptotic correct resampling tests that are robust under heteroskedasticity and skewed distributions. The tests are based on a meaningful restructuring of all observed information in quadratic form-type test statistics. An extensive simulation study is conducted exemplifying the tests for finite sample sizes under different missingness mechanisms. In addition, illustrative data examples based on real life studies are analyzed.

A cautionary tale on using imputation methods for inference in matched-pairs design

Motivation

Imputation procedures in biomedical fields have turned into statistical practice, since further analyses can be conducted ignoring the former presence of missing values. In particular, non-parametric imputation schemes like the random forest have shown favorable imputation performance compared to the more traditionally used MICE procedure. However, their effect on valid statistical inference has not been analyzed so far. This article closes this gap by investigating their validity for inferring mean differences in incompletely observed pairs while opposing them to a recent approach that only works with the given observations at hand.

Results

Our findings indicate that machine-learning schemes for (multiply) imputing missing values may inflate type I error or result in comparably low power in small-to-moderate matched pairs, even after modifying the test statistics using Rubin’s multiple imputation rule. In addition to an extensive simulation study, an illustrative data example from a breast cancer gene study has been considered.

Availability and implementation

The corresponding R-code can be accessed through the authors and the gene expression data can be downloaded at www.gdac.broadinstitute.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Testing equality of means in partially paired data with incompleteness in single response

In testing differentially expressed genes between tumor and healthy tissues, data are usually collected in paired form. However, incomplete paired data often occur. While extensive statistical researches exist for paired data with incompleteness in both arms, hardly any recent work can be found on paired data with incompleteness in single arm. This paper aims to fill this gap by proposing some new methods, namely, P-value pooling methods and a nonparametric combination test. Simulation studies are conducted to investigate the performance of the proposed methods in terms of type I error and power at small to moderate sample sizes. A real data set from The Cancer Genome Atlas (TCGA) breast cancer study is analyzed using the proposed methods.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.

Propensity score method for partially matched omics studies

This paper focuses on the problem of partially matched samples in the presence of confounders. We propose using propensity score matching to adjust for confounding factors for the subset of data with incomplete pairs, followed by integrating the P-values computed from the complete and incomplete paired samples, respectively. Several simulations and a case study on DNA methylation are considered to evaluate the operating characteristics of the proposed method.