Computational protocol: Autoantibody Profiling in Multiple Sclerosis Using Arrays of Human Protein Fragments*

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Protocol publication

[…] Data analysis and visualizations were performed using R () and various R packages, unless otherwise indicated. The analysis of the discovery stage data on planar arrays consisted of two parts. First, the degree of heterogeneity of the plasma autoantibody profiles was investigated. To this aim, arbitrary sample-specific intensity thresholds were applied for each antigen batch data. IgG reactivity in a sample was dichotomized by transforming it to a binary variable that is set equal to 1 or 0 based on exceeding the median signal for that specific sample over the 384 antigens in a batch plus 5× the standard deviation. Second, the antigen profiles across various sample groups were compared via different statistical approaches to identify antigens with a group separation power. The Wilcoxon rank-sum test was applied for a comparison between ONDs and the entire MS group and the Kruskal-Wallis test was applied for a multigroup comparison between MS subtype groups. Similarly, ANOVA was applied and carried out on Qlucore Omics Explorer software (Qlucore AB, Lund, Sweden). As multivariate methods, Between-Group Analysis (BGA, ()) by applying the “MADE4” package () and Partial-Least Squares-Linear Discriminant Analysis (PLS-DA, ()) by applying the “caret” package () were used. Antigens fulfilling the criteria set by the sample-specific intensity threshold and group discrimination were selected for verification on the suspension bead array platform.The data from the antigen suspension bead array was normalized to the signal intensity of the control analyte, the anti-human IgG as follows. The median of the signals for anti-human IgG across all samples was determined and a normalization factor was calculated for each sample by dividing its signal for anti-human IgG to the median across all samples. Signal intensities for all antigens within each sample were then divided by the corresponding normalization factor for that sample. The intensity threshold for an antigen was set to 90% quantile of the data for each sample. It was also checked that this value was 50% greater than from the fusion tag His6-ABP. Based on this data was dichotomized for each sample by transforming it to a binary variable. A Fisher's exact test was performed for the statistical evaluation of differences in proportion of antibody-positive subjects per different sample groups.GO terms were extracted using the tool IDConverter (v.2.0) () (http://idconverter.bioinfo.cnio.es/) for the identified targets based on ENSEMBL Gene IDs. The GO terms for “biological process” were compared using QuickGo () (http://www.ebi.ac.uk/QuickGO/) for ancestry comparison. STRING (v.3.0) () (http://string.embl.de/) and FunCoup (v.2.0) () (http://funcoup.sbc.su.se/) were used to investigate any known or predicted protein–protein interaction networks. […]

Pipeline specifications

Software tools caret, Asterias, QuickGO
Applications Miscellaneous, Protein interaction analysis
Organisms Homo sapiens
Diseases Autoimmune Diseases, Multiple Sclerosis