%0 Journal Article %1 10.1371/journal.pone.0071462 %A Guo, Yan %A Sheng, Quanhu %A Li, Jiang %A Ye, Fei %A Samuels, David C. %A Shyr, Yu %D 2013 %I Public Library of Science %J PLOS ONE %K MUSTREAD cancer-research comparison fulltext microarray rna-seq rpkm rsem tcga %N 8 %P 1-10 %R 10.1371/journal.pone.0071462 %T Large Scale Comparison of Gene Expression Levels by Microarrays and RNAseq Using TCGA Data %U https://doi.org/10.1371/journal.pone.0071462 %V 8 %X RNAseq and microarray methods are frequently used to measure gene expression level. While similar in purpose, there are fundamental differences between the two technologies. Here, we present the largest comparative study between microarray and RNAseq methods to date using The Cancer Genome Atlas (TCGA) data. We found high correlations between expression data obtained from the Affymetrix one-channel microarray and RNAseq (Spearman correlations coefficients of ∼0.8). We also observed that the low abundance genes had poorer correlations between microarray and RNAseq data than high abundance genes. As expected, due to measurement and normalization differences, Agilent two-channel microarray and RNAseq data were poorly correlated (Spearman correlations coefficients of only ∼0.2). By examining the differentially expressed genes between tumor and normal samples we observed reasonable concordance in directionality between Agilent two-channel microarray and RNAseq data, although a small group of genes were found to have expression changes reported in opposite directions using these two technologies. Overall, RNAseq produces comparable results to microarray technologies in term of expression profiling. The RNAseq normalization methods RPKM and RSEM produce similar results on the gene level and reasonably concordant results on the exon level. Longer exons tended to have better concordance between the two normalization methods than shorter exons.

@article{10.1371/journal.pone.0071462, abstract = {RNAseq and microarray methods are frequently used to measure gene expression level. While similar in purpose, there are fundamental differences between the two technologies. Here, we present the largest comparative study between microarray and RNAseq methods to date using The Cancer Genome Atlas (TCGA) data. We found high correlations between expression data obtained from the Affymetrix one-channel microarray and RNAseq (Spearman correlations coefficients of ∼0.8). We also observed that the low abundance genes had poorer correlations between microarray and RNAseq data than high abundance genes. As expected, due to measurement and normalization differences, Agilent two-channel microarray and RNAseq data were poorly correlated (Spearman correlations coefficients of only ∼0.2). By examining the differentially expressed genes between tumor and normal samples we observed reasonable concordance in directionality between Agilent two-channel microarray and RNAseq data, although a small group of genes were found to have expression changes reported in opposite directions using these two technologies. Overall, RNAseq produces comparable results to microarray technologies in term of expression profiling. The RNAseq normalization methods RPKM and RSEM produce similar results on the gene level and reasonably concordant results on the exon level. Longer exons tended to have better concordance between the two normalization methods than shorter exons.}, added-at = {2017-07-23T21:50:17.000+0200}, author = {Guo, Yan and Sheng, Quanhu and Li, Jiang and Ye, Fei and Samuels, David C. and Shyr, Yu}, biburl = {https://www.bibsonomy.org/bibtex/20b0f0b975a585121cd23c842f2acb6c9/marcsaric}, description = {Large Scale Comparison of Gene Expression Levels by Microarrays and RNAseq Using TCGA Data}, doi = {10.1371/journal.pone.0071462}, interhash = {7c61e34d70eaf3d2096a27479b93d35e}, intrahash = {0b0f0b975a585121cd23c842f2acb6c9}, journal = {PLOS ONE}, keywords = {MUSTREAD cancer-research comparison fulltext microarray rna-seq rpkm rsem tcga}, month = {08}, number = 8, pages = {1-10}, publisher = {Public Library of Science}, timestamp = {2017-07-23T23:30:51.000+0200}, title = {Large Scale Comparison of Gene Expression Levels by Microarrays and RNAseq Using TCGA Data}, url = {https://doi.org/10.1371/journal.pone.0071462}, volume = 8, year = 2013 }