Empirical comparison of cross-platform normalization methods for gene expression data

Abstract

BackgroundSimultaneous measurement of gene expression on a genomic scale can be accomplished using microarray technology or by sequencing based methods. Researchers who perform high throughput gene expression assays often deposit their data in public databases, but heterogeneity of measurement platforms leads to challenges for the combination and comparison of data sets. Researchers wishing to perform cross platform normalization face two major obstacles. First, a choice must be made about which method or methods to employ. Nine are currently available, and no rigorous comparison exists. Second, software for the selected method must be obtained and incorporated into a data analysis workflow.ResultsUsing two publicly available cross-platform testing data sets, cross-platform normalization methods are compared based on inter-platform concordance and on the consistency of gene lists obtained with transformed data. Scatter and ROC-like plots are produced and new statistics based on those plots are introduced to measure the effectiveness of each method. Bootstrapping is employed to obtain distributions for those statistics. The consistency of platform effects across studies is explored theoretically and with respect to the testing data sets.ConclusionsOur comparisons indicate that four methods, DWD, EB, GQ, and XPN, are generally effective, while the remaining methods do not adequately correct for platform effects. Of the four successful methods, XPN generally shows the highest inter-platform concordance when treatment groups are equally sized, while DWD is most robust to differently sized treatment groups and consistently shows the smallest loss in gene detection. We provide an R package, CONOR, capable of performing the nine cross-platform normalization methods considered. The package can be downloaded at http://alborz.sdsu.edu/conor and is available from CRAN.

Genome-driven integrated classification of breast cancer validated in over 7,500 samples

“…This was achieved using the normalizeFeatures function. The function also implements other normalization methods from the CONOR R package [39]. In the third step a classifier was trained using the probes that were matched using the pamr R package [22], based on shrunken centroids.…”

Interhemispheric gene expression differences in the cerebral cortex of humans and macaque monkeys

“…Human and macaque datasets were always analyzed independently to avoid potential problems with cross-platform normalization (Rudy and Valafar, 2011). In the human dataset 22,011 probes were collapsed to 17,634 annotated genes.…”

XMS: Cross-Platform Normalization Method for Multimodal Mass Spectrometric Tissue Profiling

“…More sophisticated strategies that can be used to estimate cross-platform normalization factors are described elsewhere [14][15][16][17][18][19][20][21], but the simple strategy described above was sufficient to account for platformspecific variation in our study. Figure 3 shows the symmetrical distribution of analyte-specific normalization factors between pairs of platforms in logarithmic scale, with the majority of analytes having consistent intensities between platforms.…”

“…In turn, a large scale MS driven tissue identification system requires a histologically assigned database using fused, platform-independent spectral data. Several cross-platform normalization approaches have recently been developed in the field of transcriptomics [14][15][16][17][18][19][20][21] with the aim of combining multiple datasets to increase sample size for improved statistical analysis. These approaches utilize a single expression per gene to derive cross-platform normalization gene-or gene cluster-specific factors to bring intensities from one platform in line with the other.…”