Missing value imputation improves clustering and interpretation of gene expression microarray data.
BACKGROUND
Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value imputation approaches are available to the microarray users and new ones are constantly being developed, there is no general consensus on how to choose between the different methods since their performance seems to vary drastically depending on the dataset being used.


RESULTS
We show that this discrepancy can mostly be attributed to the way in which imputation methods have traditionally been developed and evaluated. By comparing a number of advanced imputation methods on recent microarray datasets, we show that even when there are marked differences in the measurement-level imputation accuracies across the datasets, these differences become negligible when the methods are evaluated in terms of how well they can reproduce the original gene clusters or their biological interpretations. Regardless of the evaluation approach, however, imputation always gave better results than ignoring missing data points or replacing them with zeros or average values, emphasizing the continued importance of using more advanced imputation methods.


CONCLUSIONS
The results demonstrate that, while missing values are still severely complicating microarray data analysis, their impact on the discovery of biologically meaningful gene groups can - up to a certain degree - be reduced by using readily available and relatively fast imputation methods, such as the Bayesian Principal Components Algorithm (BPCA).
Tuikkala
Johannes
J
Department of Information Technology and TUCS, University of Turku, FI-20014 Turku, Finland. jotatu@utu.fi
Elo
Laura L
LL
Nevalainen
Olli S
OS
Aittokallio
Tero
T
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D016001:Confidence Intervals; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data*; D016247:Information Storage and Retrieval / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated / Q000379:methods / Q000706:statistics & numerical data; D012016:Reference Values; D018401:Sample Size; D012680:Sensitivity and Specificity