Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-Up Proteomics

Analysis of differential abundance in proteomics data sets requires careful application of missing value imputation. Missing abundance values widely vary when performing comparisons across different sample treatments. For example, one would expect a consistent rate of “missing at random” (MAR) across batches of samples and varying rates of “missing not at random” (MNAR) depending on the inherent difference in sample treatments within the study. The missing value imputation strategy must thus be selected that best accounts for both MAR and MNAR simultaneously. Several important issues must be considered when deciding the appropriate missing value imputation strategy: (1) when it is appropriate to impute data; (2) how to choose a method that reflects the combinatorial manner of MAR and MNAR that occurs in an experiment. This paper provides an evaluation of missing value imputation strategies used in proteomics and presents a case for the use of hybrid left-censored missing value imputation approaches that can handle the MNAR problem common to proteomics data.

Model-based imputation of sound level data at thoroughfare using computational intelligence

The aim of the paper was to present the methodology of imputation of the missing sound level data, for a period of several months, in many noise monitoring stations located at thoroughfares by applying one model which describes variability of sound level within the tested period. To build the model, at first the proper set of input attributes was elaborated, and training dataset was prepared using recorded equivalent sound levels at one of thoroughfares. Sound level values in the training data were calculated separately for the following 24-hour sub-intervals: day (6–18), evening (18–22) and night (22–6). Next, a computational intelligence approach, called Random Forest was applied to build the model with the aid of Weka software. Later, the scaling functions were elaborated, and the obtained Random Forest model was used to impute data at two other locations in the same city, using these scaling functions. The statistical analysis of the sound levels at the abovementioned locations during the whole year, before and after imputation, was carried out.

Multiple Imputation Approaches Applied to the Missing Value Problem in Bottom-up Proteomics

ABSTRACTAnalysis of differential abundance in proteomics data sets requires careful application of missing value imputation. Missing abundance values vary widely when performing comparisons across different sample treatments. For example, one would expect a consistent rate of “missing at random” (MAR) across batches of samples and varying rates of “missing not at random” (MNAR) depending on inherent difference in sample treatments within the study. The missing value imputation strategy must thus be selected that best accounts for both MAR and MNAR simultaneously. Several important issues must be considered when deciding the appropriate missing value imputation strategy: (1) when it is appropriate to impute data, (2) how to choose a method that reflects the combinatorial manner of MAR and MNAR that occurs in an experiment. This paper provides an evaluation of missing value imputation strategies used in proteomics and presents a case for the use of hybrid left-censored missing value imputation approaches that can handle the MNAR problem common to proteomics data.

Estimating the First 90 of the UNAIDS 90-90-90 Goal: A Review

Estimating the population with undiagnosed HIV (PUHIV) is the most methodologically challenging aspect of evaluating 90-90-90 goals. The objective of this review is to discuss assumptions, strengths, and shortcomings of currently available methods of this estimation. Articles from 2000 to 2018 on methods to estimate PUHIV were reviewed. Back-calculation methods including CD4 depletion and test–retest use diagnosis CD4 count, or previous testing history to determine likely infection time thus, providing an estimate of PUHIV for previous years. Biomarker methods use immunoassays to differentiate recent from older infections. Statistical techniques treat HIV status as missing data and impute data for models of infection. Lastly, population surveys using HIV rapid testing most accurately calculates the current HIV prevalence. Although multiple methods exist to estimate the number of PUHIV, the appropriate method for future applications depends on multiple factors, namely data availability and population of interest.

Flexible, Free Software for Multilevel Multiple Imputation: A Review of Blimp and jomo

Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one’s imputation model must be congenial to (appropriate for) one’s intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo, to multiply impute data in a manner congenial with three prototypical multilevel modeling analyses: (1) a random intercept model, (2) a random slope model, and (3) a cross-level interaction model. Following these analysis examples, I review and discuss both software packages.

SNPs and GVHD prediction: where to next?

The article by Chien at al in this issue of Blood uses a novel approach to assess the role of single nucleotide polymorphisms (SNPs) in acute graft-versus-host disease (GVHD). Using a genome-wide association study (GWAS) employing an Affymetrix GeneChip Genome-Wide Human 500 000 SNP array, they screened 1298 allogeneic hematopoietic stem cell transplant donors and recipients and tested whether the results from 40 previously reported candidate SNPs could be replicated. They also used a novel approach to impute data using IMPUTE software (http://nathgen.stats-ox.ac.uk/impute/impute.html) where the genotyping data were not available.1