Identification of differentially expressed genes in breast cancer

Salceda, Susana Alicia; Drumright, Carrie; DiEgidio, Anthony; Liang, Brandon; Hu, Ping; Sun, Yongming; Hoang, Vu Viet; Munteanu, Andrei; Récipon, Hervé; Cafferkey, Robert; Macina, Roberto A.

doi:10.1038/87276

Cited by 4 publications

(2 citation statements)

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“…In examining the results for both the breast and ovarian cancer datasets, a number of genes were overlooked using traditional methods, when missing values were introduced and processed, which independent experiments [40] have confirmed alter expressions in tumor lines and so can be very important in oncogenesis. This set of genes have not only been selected by the BSS/WSS algorithm, but have been revalidated using the modified t -test with greedy pairs method [41] which minimizes the bias of the gene selection strategy towards either a particular imputation technique or a set of genes.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

How to Improve Postgenomic Knowledge Discovery Using Imputation

Sehgal¹,

Gondal²,

Dooley³

et al. 2009

EURASIP Journal on Bioinformatics and Systems Biology

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While microarrays make it feasible to rapidly investigate many complex biological problems, their multistep fabrication has the proclivity for error at every stage. The standard tactic has been to either ignore or regard erroneous gene readings as missing values, though this assumption can exert a major influence upon postgenomic knowledge discovery methods like gene selection and gene regulatory network (GRN) reconstruction. This has been the catalyst for a raft of new flexible imputation algorithms including local least square impute and the recent heuristic collateral missing value imputation, which exploit the biological transactional behaviour of functionally correlated genes to afford accurate missing value estimation. This paper examines the influence of missing value imputation techniques upon postgenomic knowledge inference methods with results for various algorithms consistently corroborating that instead of ignoring missing values, recycling microarray data by flexible and robust imputation can provide substantial performance benefits for subsequent downstream procedures.

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Section: Discussion Of Resultsmentioning

confidence: 99%

How to Improve Postgenomic Knowledge Discovery Using Imputation

Sehgal¹,

Gondal²,

Dooley³

et al. 2009

EURASIP Journal on Bioinformatics and Systems Biology

View full text Add to dashboard Cite

show abstract

“…While the final validation of HCMVI as an imputation strategy will only be truly achieved when the role of newly predicted genes are validated in biological experiments, it is instructive to examine the list of candidate genes to determine whether any are independently validated. In examining both the breast and ovarian cancer datasets, HCMVI identified a number of genes overlooked by all the other algorithms and which, independent experiments [34] confirm, alter expressions in tumor lines and so could be important in oncogenesis. This set of genes has not only been selected by BSS/WSS algorithm but has been revalidated using the modified t-test with greedy pairs method [35] which minimizes the bias of the gene selection strategy towards either a particular imputation technique or a set of genes.…”

Section: Biological Significancementioning

confidence: 99%

Heuristic Non Parametric Collateral Missing Value Imputation: A Step Towards Robust Post-genomic Knowledge Discovery

Sehgal

Gondal

Dooley

et al. 2008

Pattern Recognition in Bioinformatics

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Ameliorative missing value imputation for robust biological knowledge inference

Sehgal

Gondal

Dooley

et al. 2008

Journal of Biomedical Informatics

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Gene expression data is widely used in various post genomic analyses. The data is often probed using microarrays due to their ability to simultaneously measure the expressions of thousands of genes. The expression data, however, contains significant numbers of missing values, which can impact on subsequent biological analysis. To minimize the impact of these missing values, several imputation algorithms including Collateral Missing Value Estimation (CMVE), Bayesian Principal Component Analysis (BPCA), Least Square Impute (LSImpute), Local Least Square Impute (LLSImpute), and K-Nearest Neighbour (KNN) have been proposed. These algorithms, however, exploit either only the global or local correlation structure of the data, which normally can lead to higher estimation errors. This paper presents an Ameliorative Missing Value Imputation (AMVI) technique which has ability to exploit global/local and positive/negative correlations in a given dataset by automatic selection of the optimal number of predictor genes k using a wrapper non-parametric method based on Monte Carlo simulations. The AMVI technique has CMVE strategy at its core because CMVE has demonstrated improved performance compared to both low variance methods like BPCA, LLSImpute, and high variance methods such as KNN and ZeroImpute, as CMVE exploits positive/negative correlations. The performance of AMVI is compared with CMVE, BPCA, LLSImpute, and KNN by randomly removing between 1% and 15% missing values in eight different ovarian, breast cancer and yeast datasets. Together with the standard NRMS error metric, the True Positive (TP) rate of the significant genes selection, biological significance of the selected genes and the statistical significance test results are presented to investigate the impact of missing values on subsequent biological analysis. The enhanced performance of AMVI was demonstrated by its lower NRMS error, improved TP rate, bio significance of the selected genes and statistical significance test results, when compared with the aforementioned imputation methods across all the datasets. The results show that AMVI adapted to the latent correlation structure of the data and proved to be an effective and robust approach compared with the trial and error methodology for selecting k. The results confirmed that AMVI can be successfully applied to accurately impute missing values prior to any microarray data analysis.

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Identification of differentially expressed genes in breast cancer

Cited by 4 publications

References 0 publications

How to Improve Postgenomic Knowledge Discovery Using Imputation

How to Improve Postgenomic Knowledge Discovery Using Imputation

Heuristic Non Parametric Collateral Missing Value Imputation: A Step Towards Robust Post-genomic Knowledge Discovery

Ameliorative missing value imputation for robust biological knowledge inference

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