A concentration-dependent analysis method for high density protein microarrays.
Protein microarray technology is rapidly growing and has the potential to accelerate the discovery of targets of serum antibody responses in cancer, autoimmunity and infectious disease. Analytical tools for interpreting this high-throughput array data, however, are not well-established. We developed a concentration-dependent analysis (CDA) method which normalizes protein microarray data based on the concentration of spotted probes. We show that this analysis samples a data space that is complementary to other commonly employed analyses, and demonstrate experimental validation of 92% of hits identified by the intersection of CDA with other tools. These data support the use of CDA either as a preprocessing step for a more complete proteomic microarray data analysis or as a stand-alone analysis method.
Marina
Ovidiu
O
Cancer Vaccine Center and Division of Hematologic Neoplasia, Dana-Farber Cancer Institute, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard Institutes of Medicine, Boston, MA 02115, USA.
Biernacki
Melinda A
MA
Brusic
Vladimir
V
Wu
Catherine J
CJ
D000465:Algorithms; D000941:Antigens / Q000032:analysis; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D015451:Leukemia, Lymphocytic, Chronic, B-Cell / Q000097:blood; D015464:Leukemia, Myelogenous, Chronic, BCR-ABL Positive / Q000097:blood; D040081:Protein Array Analysis / Q000379:methods*; D015203:Reproducibility of Results; D013223:Statistics as Topic / Q000379:methods*
D000941:Antigens