Comparison of normalization methods with MicroRNA microarray
MicroRNAs (miRNAs) are a group of RNAs that play important roles in regulating gene expression and protein translation. In a previous study, we established an oligonucleotide microarray platform to detect miRNA expression. Because it contained only hundreds of probes, data normalization was difficult. In this study, the microarray data for eight miRNAs extracted from inflamed rat dorsal root ganglion (DRG) tissue were normalized using 15 methods and compared with the results of real-time polymerase chain reaction. It was found that the miRNA microarray data normalized by the print-tip loess method were the most consistent with results from real-time polymerase chain reaction. Moreover, the same pattern was also observed in 14 different types of rat tissue. This study compares a variety of normalization methods and will be helpful in the preprocessing of miRNA microarray data.
[Show abstract] [Hide abstract] ABSTRACT: The culmination of over a century's work to understand the role of the immune system in tumor control has led to the recent advances in cancer immunotherapies that have resulted in durable clinical responses in patients with a variety of malignancies. Cancer immunotherapies are rapidly changing traditional treatment paradigms and expanding the therapeutic landscape for cancer patients. However, despite the current success of these therapies, not all patients respond to immunotherapy and even those that do often experience toxicities. Thus, there is a growing need to identify predictive and prognostic biomarkers that enhance our understanding of the mechanisms underlying the complex interactions between the immune system and cancer. Therefore, the Society for Immunotherapy of Cancer (SITC) reconvened an Immune Biomarkers Task Force to review state of the art technologies, identify current hurdlers, and make recommendations for the field. As a product of this task force, Working Group 2 (WG2), consisting of international experts from academia and industry, assembled to identify and discuss promising technologies for biomarker discovery and validation. Thus, this WG2 consensus paper will focus on the current status of emerging biomarkers for immune checkpoint blockade therapy and discuss novel technologies as well as high dimensional data analysis platforms that will be pivotal for future biomarker research. In addition, this paper will include a brief overview of the current challenges with recommendations for future biomarker discovery.
- "Different normalization approaches have been extensively studied for data generated from various high-throughput platforms, e.g., gene expression arrays, miRNA arrays, protein microarrays, and RNA-sequencing experiments. For each highthroughput platform, comparisons have been made between the different normalization methods, such as global normalization, Lowess normalization, quantile normalization or conditional quantile normalization, variance stabilizing normalization, Z-Score normalization and robust linear model normalization231232233234235236237238239240241. For epigenomic data such as the epigenetic regulation of immune cells, data preprocessing and normalization includes inverse normal transformation or Z-score normalization. "
- "Almost all works that focused on comparing microarray platforms normalized their data (for instance, [15,16,24]), but this is a non-trivial issue that has to be carefully evaluated. As a matter of fact, to date, normalization for miRNA microarray has been largely debated, with results that have been somehow discordant2526272829, so that no " gold-standard " methods exists. Additionally, normalizing data in the context of assessing platform agreement poses other relevant problems. "
[Show abstract] [Hide abstract] ABSTRACT: MicroRNAs (miRNAs) are small (∼22-nt), stable RNAs that critically modulate post-transcriptional gene regulation. MicroRNAs can be found in the blood as components of serum, plasma and peripheral blood mononuclear cells (PBMCs). Many microRNAs have been reported to be specific biomarkers in a variety of non-neoplastic diseases. To date, no one has globally evaluated these proposed clinical biomarkers for general quality or disease specificity. We hypothesized that the cellular source of circulating microRNAs should correlate with cells involved in specific non-neoplastic disease processes. Appropriate cell expression data would inform on the quality and usefulness of each microRNA as a biomarker for specific diseases. We further hypothesized a useful clinical microRNA biomarker would have specificity to a single disease. We identified 416 microRNA biomarkers, of which 192 were unique, in 104 publications covering 57 diseases. One hundred and thirty-nine microRNAs (33%) represented biologically plausible biomarkers, corresponding to non-ubiquitous microRNAs expressed in disease-appropriate cell types. However, at a global level, many of these microRNAs were reported as "specific" biomarkers for two or more unrelated diseases with 6 microRNAs (miR-21, miR-16, miR-146a, miR-155, miR-126 and miR-223) being reported as biomarkers for 9 or more distinct diseases. Other biomarkers corresponded to common patterns of cellular injury, such as the liver-specific microRNA, miR-122, which was elevated in a disparate set of diseases that injure the liver primarily or secondarily including hepatitis B, hepatitis C, sepsis, and myocardial infarction. Only a subset of reported blood-based microRNA biomarkers have specificity for a particular disease. The remainder of the reported non-neoplastic biomarkers are either biologically implausible, non-specific, or uninterpretable due to limitations of our current understanding of microRNA expression.
- "Even that strategy can be fraught with error if the spiking is done with poor or inconsistent methodology for handling samples [42,49]. Analytical methods on qPCR arrays were also variable with no less than 3 different global normalization methods used to evaluate the data . Despite variable normalization quality, we were unable to associate better normalization procedures with an increased likelihood of obtaining likely microRNA biomarkers. "