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The Influence of the Global Gene Expression Shift on Downstream Analyses.

Xu Q, Zhang X - PLoS ONE (2016)

Bottom Line: Most existing gene expression data were generated without considering this possibility, and are therefore at the risk of having produced unreliable results if such global shift effect exists in the data.To evaluate this risk, we conducted a systematic study on the possible influence of the global gene expression shift effect on differential expression analysis and on molecular classification analysis.Classification accuracy is not sensitive to the shift and actually can benefit from it, but genes selected for the classification can be greatly affected.

View Article: PubMed Central - PubMed

Affiliation: MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing, China.

ABSTRACT
The assumption that total abundance of RNAs in a cell is roughly the same in different cells is underlying most studies based on gene expression analyses. But experiments have shown that changes in the expression of some master regulators such as c-MYC can cause global shift in the expression of almost all genes in some cell types like cancers. Such shift will violate this assumption and can cause wrong or biased conclusions for standard data analysis practices, such as detection of differentially expressed (DE) genes and molecular classification of tumors based on gene expression. Most existing gene expression data were generated without considering this possibility, and are therefore at the risk of having produced unreliable results if such global shift effect exists in the data. To evaluate this risk, we conducted a systematic study on the possible influence of the global gene expression shift effect on differential expression analysis and on molecular classification analysis. We collected data with known global shift effect and also generated data to simulate different situations of the effect based on a wide collection of real gene expression data, and conducted comparative studies on representative existing methods. We observed that some DE analysis methods are more tolerant to the global shift while others are very sensitive to it. Classification accuracy is not sensitive to the shift and actually can benefit from it, but genes selected for the classification can be greatly affected.

No MeSH data available.


Related in: MedlinePlus

Overlap proportions of differentially expressed genes detected by SAM from the data with corrected and uncorrected global shift effects on Loven et al’s data.(A) Up-regulated DE genes. (B) Down-regulated DE genes. The settings are the same with Fig 2.
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pone.0153903.g003: Overlap proportions of differentially expressed genes detected by SAM from the data with corrected and uncorrected global shift effects on Loven et al’s data.(A) Up-regulated DE genes. (B) Down-regulated DE genes. The settings are the same with Fig 2.

Mentions: Fig 3 shows the results of SAM on the same data. We can see that the overlap is much poorer and more complicated. There is almost no overlap among the top genes. This tells that the global shift causes more changes in the variances of gene expression as the mean shifted, especially when sample size is very small.


The Influence of the Global Gene Expression Shift on Downstream Analyses.

Xu Q, Zhang X - PLoS ONE (2016)

Overlap proportions of differentially expressed genes detected by SAM from the data with corrected and uncorrected global shift effects on Loven et al’s data.(A) Up-regulated DE genes. (B) Down-regulated DE genes. The settings are the same with Fig 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836657&req=5

pone.0153903.g003: Overlap proportions of differentially expressed genes detected by SAM from the data with corrected and uncorrected global shift effects on Loven et al’s data.(A) Up-regulated DE genes. (B) Down-regulated DE genes. The settings are the same with Fig 2.
Mentions: Fig 3 shows the results of SAM on the same data. We can see that the overlap is much poorer and more complicated. There is almost no overlap among the top genes. This tells that the global shift causes more changes in the variances of gene expression as the mean shifted, especially when sample size is very small.

Bottom Line: Most existing gene expression data were generated without considering this possibility, and are therefore at the risk of having produced unreliable results if such global shift effect exists in the data.To evaluate this risk, we conducted a systematic study on the possible influence of the global gene expression shift effect on differential expression analysis and on molecular classification analysis.Classification accuracy is not sensitive to the shift and actually can benefit from it, but genes selected for the classification can be greatly affected.

View Article: PubMed Central - PubMed

Affiliation: MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing, China.

ABSTRACT
The assumption that total abundance of RNAs in a cell is roughly the same in different cells is underlying most studies based on gene expression analyses. But experiments have shown that changes in the expression of some master regulators such as c-MYC can cause global shift in the expression of almost all genes in some cell types like cancers. Such shift will violate this assumption and can cause wrong or biased conclusions for standard data analysis practices, such as detection of differentially expressed (DE) genes and molecular classification of tumors based on gene expression. Most existing gene expression data were generated without considering this possibility, and are therefore at the risk of having produced unreliable results if such global shift effect exists in the data. To evaluate this risk, we conducted a systematic study on the possible influence of the global gene expression shift effect on differential expression analysis and on molecular classification analysis. We collected data with known global shift effect and also generated data to simulate different situations of the effect based on a wide collection of real gene expression data, and conducted comparative studies on representative existing methods. We observed that some DE analysis methods are more tolerant to the global shift while others are very sensitive to it. Classification accuracy is not sensitive to the shift and actually can benefit from it, but genes selected for the classification can be greatly affected.

No MeSH data available.


Related in: MedlinePlus