DOT/FAA/AM-20/09 Office of Aerospace Medicine Washington, D.C. 20591 Comparison Study of Microarray and RNA- seq for Differential Expression Susan K. Munster1, Vicky L. White1, David C. Hutchings2, Dennis M. Burian1, Scott J. Nicholson1 1Federal Aviation Administration Civil Aerospace Medical Institute, Oklahoma City, OK 2Venesco, LLC, Chantilly, VA Civil Aerospace Medical Institute Federal Aviation Administration Oklahoma City, OK 73125 October 2018 Final Report NOTICE This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The United States Government assumes no liability for the contents thereof. ___________ This publication and all Office of Aerospace Medicine technical reports are available in full-text from the Civil Aerospace Medical Institute’s publications Web site: http://www.faa.gov/library/reports/medical/oamtechreports/ 1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. DOT/FAA/AM-20/09 4. Title and Subtitle 5. Report Date Comparison Study of Microarray and RNA-seq for Differential October, 2018 Expression 6. Performing Organization Code 7. Author(s) 8. Performing Organization Report No. Munster SK1, White VL1, Hutchings, DC2, Burian DM1, Nicholson SJ1 9. Performing Organization Name and Address 10.Work Unit No. (TRAIS) 1FAA Civil Aerospace Medical Institute, P. O. Box 25082 Oklahoma City, OK 73126 11. Contract or Grant No. 2Venesco LLC, 14801 Murdock St., Ste. 125, Chantilly, VA 20151 12. Sponsoring Agency Name and Address 13. Type of Report and Period Covered Office of Aerospace Medicine Federal Aviation Administration 800 Independence Ave., S.W. Washington, DC 20591 14. Sponsoring Agency Code AAM-1 15. Supplemental Notes CAMI Aerospace Medical Research Division Project No. 2017-AAM-612-GEN-10020 16. Abstract As costs for performing RNA-seq approach the costs affiliated with utilizing microarray technology, it is worthwhile to determine which methodology is the most efficient, cost effective, and accurate. In this study, we examined the relative capacities of microarrays and total RNA-seq to detect differential gene expression between total RNA samples containing the following RNA mixtures: blood, brain, 2:1 blood:brain, and 1:2 blood:brain. Two microarray hybridization library amplification methods were compared to optimize microarray results, which were then compared to total RNA-seq results for the same samples. Bioinformatic analysis was performed using commonly accepted analysis software for microarray and RNA-seq data to determine which method produced the greatest number of significant differentially expressed genes. It was determined that total RNA-seq outperformed microarrays in efficiency, result quality, and total numbers of detected genes and that cost was similar to utilizing microarrays for quantifying RNA expression levels from mixtures of brain and blood tissue. 17. Key Words 18. Distribution Statement Microarray, total RNA-seq, differential expression Document is available to the public through the Defense Technical Information Center, Ft. Belvior, VA 22060; and the National Technical Information Service, Springfield, VA 22161 19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price Unclassified Unclassified 59 Form DOT F 1700.7 (8-72) Reproduction of completed page is authorized i CONTENTS INTRODUCTION ...........................................................................................................................1 MATERIALS AND METHODS .....................................................................................................4 Sample preparation and processing ..............................................................................................4 Microarray preparation and processing ........................................................................................5 Microarray data QC ......................................................................................................................6 Microarray data filtering ..............................................................................................................6 Microarray differential expression comparisons .........................................................................7 RNA-seq preparation ....................................................................................................................8 RNA-seq data QC and processing ................................................................................................8 RNA-seq differential expression processing ...............................................................................9 Comparisons between microarray and RNA-seq differential expression ...................................9 RESULTS ......................................................................................................................................10 DISCUSSION ................................................................................................................................33 CONCLUSION ..............................................................................................................................37 REFERENCES ..............................................................................................................................38 APPENDIX ....................................................................................................................................42 ii LIST OF FIGURES Figure 1: PCA plots of microarray expression values ............................................................. 13-14 Figure 2: Violin plots of microarray antigenomic-filtered value ranges .......................................15 Figure 3: Violin plots of microarray median-filtered value ranges ...............................................16 Figure 4: Volcano plots of microarray antigenomic-filtered differential expression values ... 18-19 Figure 5: Volcano plots of microarray median-filtered differential expression values ........... 19-20 Figure 6: Volcano plots of microarray antigenomic-filtered biomarker subset differential expression values ..................................................................................................................... 21-22 Figure 7: Volcano plots of microarray median-filtered biomarker subset differential expression values .............................................................................................................................................23 Figure 8: PCA plot of Affymetrix microarray and RNA-seq values common to both datasets ....28 Figure 9: Violin plot of entrez IDs common to both RNA-seq and Affymetrix median-filtered microarray dataset log fold change ranges .....................................................................................28 Figure 10: Violin plot of significant RNA-seq and Affymetrix median-filtered microarray differential expression ranges ........................................................................................................29 Figure 11: Violin plot of RNA-seq and Affymetrix median-filtered microarray biomarker subset differential expression ranges ........................................................................................................30 Figure 12: Volcano plot of RNA-seq and Affymetrix median-filtered microarray biomarker subset differential expression values ....................................................................................... 30-31 iii LIST OF TABLES Table 1: Microarray transcript cluster counts ................................................................................12 Table 2: Differentially expressed transcript cluster counts ............................................................16 Table 3: Microarray tissue comparison of log fold change counts .......................................... 17-18 Table 4: Microarray tissue comparison of potential biomarker log fold change counts ...............21 Table 5: Affymetrix tissue comparison of filtering method overlap counts ..................................24 Table 6; Affymetrix tissue comparison biomarker subset differential expression counts ....... 24-25 Table 7: RNA-seq read trimming, mapping, annotation, and featureCount counts ................ 25-26 Table 8: RNA-seq and Affymetrix median-filtered biomarker subset differential expression counts .............................................................................................................................................32 Table 9: Cost comparison for Affymetrix, NuGEN, and RNA-seq processing methods ..............33 iv ABBREVIATIONS Abbreviation Explanation cDNA complementary DNA DE differential expression FDR false discovery rate LFC log2 fold change all-blood a sample comprised of 100% blood-derived RNA 2/3-blood a sample mixture comprised of 67% blood-derived RNA and 33% brain- derived RNA 1/3-blood a sample mixture comprised of 33% blood-derived RNA and 67% brain- derived RNA all-brain a sample comprised of 100% brain-derived RNA PCA principal components analysis PC1 principal component 1 PC2 principal component 2 [Sequence data has been submitted to NCBI’s Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) and to NCBI’s Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) under BioProject accession number PRJNA492492] v COMPARISON STUDY OF MICROARRAY AND RNA-SEQ FOR DIFFERENTIAL EXPRESSION INTRODUCTION In the past four decades, since Sanger sequencing (Sanger et al.,