Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

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1 Identification of rheumatoid arthritis and osterthritis patients by transcriptome-based rule set generation Bering Limited Report generated on September 19, 2014 Contents 1 Dataset summary Project description Array processing and normalization 3 3 Quality Outlier detection Principal Component Analysis Signal density and box plots Array similarity heatmap and hierarchical clustering Batch correction Quality summary Differential expression analysis 8 5 Gene Ontology enrichment Biological Process Cellular Component Molecular Function Reactome pathway enrichment 11 1

2 1 Dataset summary Number of samples: 30 Number of chip identifiers: Comparison: ra vs. 1.1 Project description ArrayExpression accession number: E-GEO Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased apprch using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy s ( group - CG), as well as 26 osterthritis (OA) and 33 RA patients. Reference: Woetzel D., et al. Identification of rheumatoid arthritis and osterthritis patients by transcriptome-based rule set generation. Arthritis Research & Therapy 2014, 16:R84 file.name GSM ND 1 S... GSM ND 2 S... GSM ND 3 S... GSM ND 4 S... GSM ND 5 S... GSM ND 6 S... GSM ND 7 S... GSM ND 8 S... GSM ND 9 S... GSM ND GSM OA 1 S... GSM OA 2 S... GSM OA 3 S... GSM OA 4 S... GSM OA 5 S... GSM OA 6 S... GSM OA 7 S... GSM OA 8 S... GSM OA 9 S... GSM OA phenotype

3 GSM RA 1 S... ra GSM RA 2 S... ra GSM RA 3 S... ra GSM RA 4 S... ra GSM RA 5 S... ra GSM RA 6 S... ra GSM RA 7 S... ra GSM RA 8 S... ra GSM RA 9 S... ra GSM RA ra Table 1: Sample-data relationships 2 Array processing and normalization After array normalization and detection of present probes, probes were retained. 3 Quality GeneProfiler pipeline aims to identify outliers, batch effects, and overly noisy experiments. Automated quality is carried out using the arrayqualitymetrics Bioconductor package. 3.1 Outlier detection Outlier detection is carried out using three distinct apprches: Box plot: Each box corresponds to one array. Typically, one expects the boxes to have similar positions and widths. If the distribution of an array is very different from the others, this may indicate an experimental problem. Outlier detection is performed by computing the Kolmogorov-Smirnov statistic Ka between each array s distribution and the distribution of the pooled data. Signal Density plot: Typically, the distributions of the arrays should have similar shapes and ranges. Arrays whose distributions are very different from the others should be considered for possible problems. Inter-sample correlation heatmap: Patterns in this plot can indicate clustering of the arrays either because of intended biological or unintended experimental factors (batch effects). The distance between two arrays is computed as the mean absolute difference between the data of the arrays (using the data from all probes without filtering). Outlier detection is performed by

4 looking for arrays for which the sum of the distances to all other arrays is exceptionally large. Results of outlier detection are shown in Table 2. Table columns contain results of a specific outlier detection test. FALSE value indicates that an array is not an outlier, while TRUE value indiciates that an array is an outlier. An array will be considered an outlier, and labeled so in a column Vote, if it is called an outlier by at least two methods. Boxplot Density Heatmap Vote GSM ND 1 S... FALSE FALSE FALSE FALSE GSM ND 2 S... TRUE FALSE FALSE FALSE GSM ND 3 S... FALSE FALSE FALSE FALSE GSM ND 4 S... FALSE FALSE FALSE FALSE GSM ND 5 S... FALSE FALSE FALSE FALSE GSM ND 6 S... FALSE FALSE FALSE FALSE GSM ND 7 S... TRUE FALSE FALSE FALSE GSM ND 8 S... TRUE FALSE FALSE FALSE GSM ND 9 S... FALSE FALSE FALSE FALSE GSM ND FALSE FALSE FALSE FALSE GSM OA 1 S... FALSE FALSE FALSE FALSE GSM OA 2 S... FALSE FALSE FALSE FALSE GSM OA 3 S... FALSE FALSE FALSE FALSE GSM OA 4 S... FALSE FALSE FALSE FALSE GSM OA 5 S... FALSE FALSE FALSE FALSE GSM OA 6 S... FALSE FALSE FALSE FALSE GSM OA 7 S... FALSE FALSE FALSE FALSE GSM OA 8 S... FALSE FALSE FALSE FALSE GSM OA 9 S... FALSE FALSE FALSE FALSE GSM OA TRUE FALSE FALSE FALSE GSM RA 1 S... FALSE FALSE FALSE FALSE GSM RA 2 S... FALSE FALSE FALSE FALSE GSM RA 3 S... FALSE FALSE FALSE FALSE GSM RA 4 S... FALSE FALSE FALSE FALSE GSM RA 5 S... FALSE FALSE FALSE FALSE GSM RA 6 S... FALSE FALSE FALSE FALSE GSM RA 7 S... FALSE FALSE FALSE FALSE GSM RA 8 S... FALSE FALSE FALSE FALSE GSM RA 9 S... FALSE FALSE FALSE FALSE GSM RA FALSE FALSE FALSE FALSE Table 2: Outlying arrays.

5 3.1.1 Principal Component Analysis Figure 1: Scatterplot visualising Principal Component Analysis for 30 arrays. Outliers (if any) are shown in red. Principal Components Analysis (PCA) plots were used to visualize the overall quality of a micrrray dataset. Each point in the PCA plots corresponds to an array. Dissimilar arrays are further apart.

6 3.1.2 Signal density and box plots Figure 2: Boxplots and signal intensity densities for 30 arrays. Outliers (if any) are shown in red Array similarity heatmap and hierarchical clustering Hiearachical clustering was used to determine if sample clusters correspond to the experimental sample groups, rather than to technical sources of variation.

7 Figure 3: Array similarity heatmap for 30 arrays. The color scale is chosen to cover the range of distances encountered in the dataset. There were 0 outlying arrays. 3.2 Batch correction If batches are specified, they are corrected. 3.3 Quality summary Of probes, passed quality protocols. 30 samples passed outlier detection criteria.

8 4 Differential expression analysis Differential expression analysis was carried out comparing ra vs.. There were 692 up-regulated and 801 down-regulated genes (p value 0.05, FDR-correction: No). Top 10 differentially expressed genes are shown in Table 3. Symbol Name logfc P.Value CXCL13 Chemokine (C-X-C motif) ligand 1.1E E SLAMF8 SLAM family member 8 7.4E E-12 TPD52L1 Tumor protein D52-like 1-4.8E E-11 ADAMDEC1 ADAM-like, decysin 1 4.7E E-10 SERPINA1 Serpin peptidase inhibitor, 4.6E E-09 clade A (alpha-1 antiproteinase, antitrypsin), member 1 NOVA1 Neuro-oncological ventral -3.0E E-10 antigen 1 CCL13 Chemokine (C-C motif) ligand 4.5E E ISG20 Interferon stimulated 2.3E E-10 exonuclease gene 20kDa CD27 CD27 molecule 2.9E E-09 CRLF1 Cytokine receptor-like factor 1-4.7E E-07 Table 3: Top 10 differentially expressed genes.

9 Figure 4: Volcano plot of all differentially expressed genes in ra vs.. Top 5 differentially expressed genes are labeled. 5 Gene Ontology enrichment 1493 differentially expressed genes were enriched for Gene Ontology (GO) Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) terms. All micrrray genes (n=8934) were used as background. Headers in Tables 4, 5, and 6, Significant and P.Value reffer to number of significant genes annotated by a term and corresponding significance p-values respectively.

10 5.1 Biological Process Term Significant P.Value Immune Response E-30 Immune System Process E-25 Defense Response E-20 Regulation Of Immune System Process E-19 Regulation Of Immune Response E-19 Positive Regulation Of Immune System Process E-18 Positive Regulation Of Response To Stimulus E-17 Regulation Of Response To Stimulus E-17 Signal Transduction E-17 Signaling E-16 Table 4: Top enriched Gene Ontology Biological Process terms. 5.2 Cellular Component Term Significant P.Value Cell Periphery E-18 Plasma Membrane E-18 Membrane E-17 Extracellular Region E-13 Membrane Part E-11 Intrinsic Component Of Membrane E-11 Integral Component Of Membrane E-10 Extracellular Region Part E-10 Extracellular Space E-10 Side Of Membrane E-10 Table 5: Top enriched Gene Ontology Cellular Component terms. 5.3 Molecular Function Term Significant P.Value Receptor Activity E-12 Signal Transducer Activity E-09 Molecular Transducer Activity E-09 Receptor Binding E-09 Transmembrane Signaling Receptor Activity E-08 Signaling Receptor Activity E-08 Antigen Binding E-08 Sulfur Compound Binding E-07 Heparin Binding E-06 Chemokine Activity E-06 Table 6: Top enriched Gene Ontology Molecular Function terms.

11 6 Reactome pathway enrichment 1493 differentially expressed genes were enriched for Reactome pathways. Top 10 enriched pathways are shown in Table 7. Description P.Value Count Activity.Score Immune System 1.50E Adaptive Immune System 5.40E Phosphorylation of CD3 and TCR zeta 1.90E chains Lipid and lipoprotein metabolism 1.90E Hemostasis 2.00E TCR signaling 2.00E Cytokine Signaling in Immune system 2.60E Platelet activation, signaling and 2.70E aggregation Antigen Activates B Cell Receptor Leading 3.00E to Generation of Second Messengers Alternative complement activation 3.10E Table 7: Top enriched Reactome Pathways. Column P.Value refers to raw enrichment significance p-values. Column Count highlights the total number of differentially expressed genes assigned to a specific pathway. Column Activity.Score corresponds to the average pathway fold change in ra vs. comparison.

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