GBA1 leading to defective glucocerebrosidase (GCase) function and consequent excess accumulation of glucosylceramide/glucosylsphingosine in visceral organs. Enzyme replacement therapy (ERT) with the biosimilars, imiglucerase (imig) or velaglucerase alfa (vela) improves/reverses the visceral disease. Comparative transcriptomic effects (microarray and mRNA-Seq) of no ERT and ERT (imig or vela) were done with liver, lung, and spleen from mice having Gba1 mutant alleles, termed D409V/null. Disease-related molecular effects, dynamic ranges, and sensitivities were compared between mRNA-Seq and microarrays and their respective analytic tools, i.e. Mixed Model ANOVA (microarray), and DESeq and edgeR (mRNA-Seq). While similar gene expression patterns were observed with both platforms, mRNA-Seq identified more differentially expressed genes (DEGs) (∼3-fold) than the microarrays. Among the three analytic tools, DESeq identified the maximum number of DEGs for all tissues and treatments. DESeq and edgeR comparisons revealed differences in DEGs identified. In 9V/null liver, spleen and lung, post-therapy transcriptomes approximated WT, were partially reverted, and had little change, respectively, and were concordant with the corresponding histological and biochemical findings. DEG overlaps were only 8–20% between mRNA-Seq and microarray, but the biological pathways were similar. Cell growth and proliferation, cell cycle, heme metabolism, and mitochondrial dysfunction were most altered with the Gaucher disease process. Imig and vela differentially affected specific disease pathways. Differential molecular responses were observed in direct transcriptome comparisons from imig- and vela-treated tissues. These results provide cross-validation for the mRNA-Seq and microarray platforms, and show differences between the molecular effects of two highly structurally similar ERT biopharmaceuticals.Gaucher disease type 1, an inherited lysosomal storage disorder, is caused by mutations in  GBA1, which results in dysfunction of the lysosomal enzyme, glucocerebrosidase (GCase) and subsequent excess accumulation of glucosyl-ceramide (GluCer)/-sphingosine (GluSph) in various tissues Gaucher disease type 1, a common glycolipid storage disease, is caused by deleterious mutations in ERT reverses or ameliorates many of the manifestations of Gaucher disease type 1, including anemia, thrombocytopenia, hepatosplenomegaly and organ dysfunction, growth retardation and bone pain, and leads to dramatically improved quality of life for many patients in vitro kinetic properties, interactions with substrates and inhibitors in vitro stabilities or the kinetic properties of either enzyme Here, two structurally/biochemically similar FDA approved GCases, imiglucerase  and velaglucerase alfa  were compared for their molecular therapeutic effects in liver, lung, and spleen. Imig and vela have essentially identical Here, a comprehensive study was conducted to evaluate the transcriptomic similarities and differences in the differentially expressed genes (DEGs) in the 9V/null model using both mRNA-Seq and microarray platforms. Also, the performance of the different analytic tools, i.e., ANOVA for microarrays, edgeR and DESeq for mRNA-Seq, were evaluated. The main objectives of these studies were to determine the effects on the Gaucher disease processes of imig and vela treatments, and to directly compare the molecular differences elicited by these two highly similar ERTs. The comparisons of the results with both platforms and analytic approaches also highlighted their advantages/disadvantages in identifying the DEGs profiles.Comparative transcriptome analyses were performed in strain- and age-matched 9V/null mice that received weekly injections beginning at 20 wks of imig or vela  or of saline (n = 17), and untreated WT mice (n = 12). The lungs, livers, and spleens were harvested one week after the last injection (28-wk old). Organ GluCer levels, as indication of correction of substrate accumulation in this model, were determined previously The disease-related comparative transcriptomic changes as a result of ERT were evaluated by mRNA-Seq and microarray in the different tissues from 9V/null and WT mice. The data obtained from the two platforms were analyzed with the three appropriate analytic methods  used here facilitated evaluation of the sample homogeneity in each tissue group. Principle Component Analyses (PCA) were used to show the overall structure of the data and how replicates grouped. PCA indicated general similarity in overall expression patterns within a tissue group. The PCA of microarray and mRNA-Seq showed distinct tissue separations in both platforms . mRNA-SeTo validate the DEGs obtained from the microarray platform, mRNA-Seq was performed on the identical samples and analyzed by two different statistical methods. The analyses of the mRNA from the treated 9V/null mice are referenced to WT transcriptomes, which provided insight into the ERT effects on the disease-related molecular events. The mRNA-Seq and the microarray outputs are different; the former are discrete intensities of the read counts, while the latter are continuous intensity distributions. To perform correlations between the DEGs patterns from the two platforms, common sets of genes were selected, which were above the detectable threshold and common to both the platforms. Using these criteria, 17,157 genes were identified. The correlations  between Fold-Change (FC) based comparisons were also performed to evaluate the ability of the two platforms to capture the different responses of gene expressions among three analytical methods under different condition. In 9V/null saline vs. WT data sets, the FC values of 105 DEGs were determined by all three analytical methods , and werDEGs from saline-, imig-, and vela-treated 9V/null tissues were identified using three different analytical tools: Mixed Model ANOVA (microarray), and edgeR and DESeq (mRNA-Seq). The cut off criteria for selection of DEGs were based on a FC ±1.5 and an FDR   = 0.05. DEGs are listed in In the spleen, the DESeq and edgeR methods identified a larger number of DEGs as compared to microarray by Mixed Model ANOVA (termed microarray in the figures) . These cIn liver, significantly fewer DEGs were identified . The DEGDifferent analytical techniques were used to evaluate the transcriptome effects of imig or vela on the Gaucher disease processes using the saline-, imig-, or vela-treated mice compared to untreated WT. The functional categorizations were determined using DAVID. Based on the p value and the number of DEGs involved, cellular process genes contained 55–60% of the DEGs identified by the three analytic methods . HoweverWith the combination of the two mRNA-Seq analytic methods, most of the functional groups overlapped in the saline- and ERT-treated spleens with a few exceptions . DespiteTo evaluate the biological process, functions, and pathways in the ERT-treated mice, the DEGs identified by at least two of the three analytic methods were used. These were designated as core DEGs and are represented by the regions of intersections of the three-way proportional Venn diagram . The parBcl2l1, Bnip3l, Camp, Cox5a, Nqo1, Snca and Sod1. In addition, imig-treated spleen has Sphk1 and saline-treated spleen has both Sphk1 and Usp1.The interactions between the significant biological functions under the different conditions are presented as an abstracted view developed with ToppCluster and Cytoscape . The numJak3.New functional groups, e.g., cellular growth and proliferation and immune cell trafficking, were identified in the post-treatment groups, suggesting relationships to disease repair mechanisms. In the saline-, vela- and imig-treated spleens, there were 62 (11.4%), 80 (13%), and 98 (9.5%) hematopoietic DEGs, respectively. Of these, 32 were common to imig-, vela-, and saline-treated spleen and 17 (53%) were in the myelopoietic gene cluster . All theJak-3 is an important component of the JAK/STAT signaling pathway, and additional genes in this pathway showed abnormal expression. For example, the Bcl2-like gene that is involved in a wide variety of cellular activities had decreased expression levels in ERT and saline treated spleens. Stat3 levels were increased by treatment with either enzyme, whereas Socs  showed increased expression levels only in the imig-treated spleen.Apart from its hematopoietic function GATA1 (globin transcription factor 1) and PU.1/Sfpi1  are the two DEGs that are lineage specification genes for the erythropoiesis and myelopoiesis lines, respectively. Gata1 was within the core DEGs in spleen  and Hmox1 [hemeoxygenase (decycling) 1]. Treatment with vela  and p70S6K  signaling, which is important for cell cycle progression, and G (1) and translation regulation The protein ubiquitination pathway was unique to imig-treated spleen . This paAlas2 (δ-aminolevulinic acid synthase 2), Glrx5 [glutaredoxin 5 homolog (S. cerevisiae)], Slc25a38  and solute carrier Slc25a39  and Mycn  were significantly altered in saline-, imig- and vela-treated spleens. The protein encoded by Myc plays a role in cell cycle progression, apoptosis and cellular transformation. It functions to regulate transcription of specific target genes. In addition Rbpjl ; Nf2l2 (nuclear factor erythroid derived 2-like 2) and Nrf1 (nuclear respiratory factor 1) were unique to vela-treated spleens, whereas E2f4 (E2F transcription factor 4) was unique to imig-treated spleens  post-ERT compared to spleen in which the number of DEGs related to these functions increased post-ERT with either drug . InflammSaline-treated spleen and liver shared three functional groups: cell death, hematopoiesis and small molecule biochemistry. In the spleen and liver several disease-related DEGs were shared and derived from 3 pathways, including mitochondrial dysfunction, oxidative phosphorylation and ubiquinone biosynthesis. In the untreated liver, there were 10 DEGs with abnormal expression in the network. Unlike the spleen, 100% correction of those genes to WT levels was observed in the liver post imig- or vela-treatment . The FC The above analyses were conducted to assess the disease-related molecular changes as a result of ERT in the different tissues. To explore the potential molecular effects of two similar, but different, biopharmaceuticals, direct comparison were conducted with the transcriptomic profiles of imig- vs. vela-treatment. For these analyses, only the imig- and vela-data sets were used without reference to the WT data set. This direct comparison of imig- and vela-treatments would enable the detection of potential differences at molecular level between the effects of the two enzymes. mRNA-Seq analyses (DESeq statistics) showed 290, 78 and 12 more DEGs in imig compared to vela- treated spleens, livers, and lungs. Similarly, microarray analyses identified 97, 1, and 0 DEGs in the respective tissues . Comparep = 2.03e-06 to 2.67e-02), 2) hematopoietic system , and 3) inflammatory/macrophage .Direct comparison of the imig- and vela-treated spleen identified 47 DEGs, which were common by both microarray (47/97) and mRNA-Seq (47/290) analyses . There wArg2, Cd77, Cd44, Cd00lb, Cdl, Ifi204, Il1f9, Irg1, Ifi204, Mmp19, and Tarm1  were applied in the analyses of mRNA-Seq data and compared with that from cDNA microarrays. These studies were designed to understand the molecular effects of Gaucher disease and of two biosimilar ERTs on the disease processes in different tissues and to compare the different platforms and statistical approaches to their analyses. An unexpected result was the transcriptomic effect differences between the two biosimilars, imig and vela since they differ by only a few mannosyl residues on their N-linked oligosaccharides. By direct comparison of these two biosimilars without any normalization to the WT or saline-treated 9V/null mice, differences were clearly evident in the transcriptomes. The molecular differences imply differential mechanisms and molecular pathways in the therapeutic responses of Gaucher disease to these two biopharmaceuticals.mRNA-Seq allows a comprehensive evaluation and quantification of all subtypes of RNAs in cells or tissues Several comparison studies of mRNA-Seq and microarrays have addressed different biological questions, i.e., the relative merits of the two techniques and their inherent biases The output from NGS mRNA-Seq gives a discrete intensity of read counts and Poisson's distribution is most suitable for mRNA-Seq data DESeq is similarly modeled on the NB distribution. The main difference between edgeR and DESeq is the different approaches to estimate dispersion parameter There was reasonable concordance of DEGs between DESeq and edgeR for the liver, lung, and spleen mRNAs, thereby facilitating identification of the tissue and treatment specific transcriptomic alterations by either method. Pearson's correlation coefficients of the FC were used to assess similarity between microarray/Mixed Model ANOVA, mRNA-Seq/DESeq and mRNA-Seq/edgeR. All three methods showed good agreement when using a subset of DEGs between all three tissues.Based on the previous biochemical and histopathological studies, the liver, spleen, and lungs of the 9V/null mice had complete, partial, or little response, respectively, to either imig or vela, and there were only small differences in these therapeutic effects of these biosimilars δ–Alas2, Abcb7 , Glrx5 [glutaredoxin 5 homolog (S. cerevisiae)] and Slc25a38  are causal to different forms of sideroblastic anemias Slc25a39 is important for maintaining mitochondrial iron homeostasis and regulating heme levels Biologically, both platforms indicate a tissue specific correlation of the DEGs as observed in the PCA plots where the liver, lung, and spleen are clustered into three distinct groups. Analyses of the biological functions, pathways and networks with the core spleen DEGs showed cell growth and proliferation, cell division, cell death, and heme metabolism are common functions across all tissues and treatment conditions in 9V/null spleen. The Gene Ontologies for the common DEGs suggest that the top functions, based on the number of DEGs involved and the p value, include cell growth and proliferation, cell death and survival, and inflammation. The unique genes in the imig and vela group mainly coincided with the same functional groups with some differences. The number of genes associated with the common functions is greater in the imig treated group compared to vela. This suggests that even though the two drugs are very similar structurally and functionally there are differences at the molecular level. Overall functional analyses suggest overlap of some significant canonical pathways, i.e., the oxidative phosphorylation, mitochondrial dysfunction and ubiquinone biosynthesis in the saline-treated 9V/null liver and spleen samples . InteresGBA1 mutations occur with greater frequency in patients afflicted with Parkinson's disease Mitochondrial dysfunction has been reported in lysosomal diseases in part due to the involvement of the autophagy/mitophagy system(s) Jak3, in the JAK/STAT pathway, is the only hematopoietic gene with increased expression in treatment with either enzyme or saline. Both STAT3, and SOCS have been recognized for their anti-inflammatory actions Direct comparison of the transcriptomes from imig- vs. vela-treated spleen without reference to the WT data set identified total 90 genes involved in hematopoiesis. The majority (81/90) of these network genes were also detected in the imig-spleen normalized to WT controls , which iGata1, Gfi1b, Tal1, E2f2, and Birc5 that encode regulatory proteins in erythroid lineage specification and cell division control. The direct comparisons in spleens identified increased cytokine/macrophage and decreased hematopoietic proliferation gene expression in imig-treatment relative to vela-treatment. Thus, two major molecular pathways were differentially responding in the spleen with either imig- or vela- treatment.The large numbers of inflammatory/macrophage response-related genes in the imig- vs. vela- treated spleen indicate different molecular events in the therapeutic pathways of these two highly similar biologics. Interestingly, 5 DEGs (imig vs. vela) that showed decreased expression were present in all 3 networks. They include Gata1 expression levels were decreased across all 3 networks derived from imig- vs. vela-treated spleen  and treatment (imig or vela) conditions. PU.1 is a key transcription factor that, with GATA 1, regulates the balance between the myeloid/erythroid pathways.d spleen . GATA1  is a recombinant human GCase from overexpressing CHO cells; velaglucerase alfa  is a gene-activated GCase from human fibrosarcoma cells. RNA Later and TOTALLY RNA kit . Trizol . Affymetrix Mouse Gene 1.0 ST chip . Illumina TruSeq RNA library preparation kit and Illumina HiSeq2000 . Avadis® NGS software, Version 1.3.0, . JMP Genomics 5  and Ingenuity Pathway Analysis (IPA) .Gba1 point mutation encoding Valine (V) 409 for the WT Aspartate (D) on one missense allele and a null heteroallele [D409V/null (9V/null)] and WT littermates were of mixed, but matched, genetic backgrounds of C57BL/129Sv/FVB Knock-in mice with a 9V/null mice were injected weekly via tail-vein bolus with 60 U/kg of imig or vela for 8 wk For total RNA isolation, the lungs, livers, and spleens were collected from untreated WT, and saline and enzyme treated 9V/null mice. The RNA sample sets were prepared by pooling of mRNA from three individual mice for each tissue type in a treatment cohort. Total RNA was isolated from the organs of control and imig and vela injected 9V/null mice (28 wk of age) and WT mice. Collected tissues were immediately immersed in RNA Later and RNAs were extracted using the TOTALLY RNA kit (lung and liver) or Trizol (spleen). Whole transcriptome analyses of identical aliquots of RNA were performed using the Affymetrix Mouse Gene 1.0 ST Array (microarray) and Illumina TruSeq RNA Sequencing kit (mRNA-Seq). Each treatment cohort was composed of 3 or 4 sets RNA samples: 2 to 3 single tissue RNA samples from individual mice and one pooled RNA of those individual samples.Sample sets were as follows: 32 lung and liver sample sets included 4 each of imig, vela, or saline from 9V null mice and 4 WT controls for each tissue. Twenty-three spleen sets included 4 imig, 4 vela, 7 saline from 9V/null mice and 8 WT mice. Whole transcriptome analyses were performed using aliquots from identical sample sets applied to the Affymetrix Mouse Gene 1.0 ST chip (microarray) at the Gene Expression Microarray Core and the Illumina Hi-Seq2000 (mRNA-Seq) at the Genetic Variation and Gene Discovery Core of CCHMC The microarray and mRNA-seq data set obtained from the 55 samples used in this study are available at the Gene Expression Omnibus (GEO) accessible through GEO series accession number: GSE44675. The subset of 23 samples used for the direct comparion of the two enzymes are available in GEO with the series accession number: GSE44641.Total RNA concentration was determined by Qubit high sensitivity spectrofluorometric assay. The polyA RNAs in the total RNA samples (350 to 900 ng) were selected, sheared, and reverse transcribed using the TruSeq RNA library preparation kit. Each sample was fitted with one of 12 adapters containing a different 6 base molecular barcode to allow pooling of multiple samples during sequencing. After 12 cycles of PCR amplification, completed libraries were sequenced on an Illumina HiSeq2000, generating 10 to 20 million of high quality 50 base-long reads per sample.mRNA-Seq reads were aligned to the mm9 version of the mouse genome reference using the TopHat/Cufflinks pipeline. First, sequences were aligned to the genome with TopHat Methods for microarray analyses were described previously Post Binary Alignment/Map (BAM) aligned files of mRNA-Seq data were uploaded in Avadis NGS software. Data analysis was performed using Avadis® NGS software, Version 1.3.0. Reads were filtered to remove a) duplicate reads, b) non-primary-matched reads and c) reads with alignment scores <95. Quantification was performed on the filtered reads against the RefSeq annotation. The initial number of reads was 632783594, which dropped to 292320998 post filtering amounting to 46.19% of the original read counts. PCA and multivariate correlations were performed to access reproducibility and variability among biological replicates. The outliers identified were subsequently removed.DESeq and edgeR were used to evaluate the DEGs from the mRNA-Seq data. DESeq via R script was performed on the filtered reads by Avadis NGS software using three functions .For DESeq normalization, the sequencing depth is estimated by the read count of the gene with the median read count ratio across all genes. The normalized counts are computed:'ij where r'ij = rij/mi. rij is the read count of gene gi in sample Sj. For each gene gi, the geometric mean of the read counts of all the samples for that gene is calculated. Let it be mi. While computing this median, the genes with mi = 0 are ignored.For each sample Sj, the normalization factor Nj is calculated as the median of the values rij for gene gi in sample Sj are computed as rij/Nj.Finally the normalized counts nThe method is based on the negative binomial distribution, which allows for less restrictive variance parameter assumptions than does the Poisson distribution http://www.bioconductor.org/packages/2.11/bioc/html/edgeR.html. edgeR is based on a NB underlying distribution to account for variability of replicates on gene-wise dispersions. The Trimmed Mean of M value normalization (TMM) method is incorporated in the package, where different library sizes across samples are adjusted with scaling factors prior to DE analyses to avoid biased detection. DE analysis in edgeR includes two factors in the generalized linear model (glmFit), the treatment specific effect (main factor) and a nuisance factor, to address the variation in the biological replicates which may affect the measured changes of gene expression, and therefore needs to be controlled in the model. Under the null hypothesis, there is no significant difference in change of expression for each tissue between two different biopharmaceuticals for a given gene; glmLRT function is next performed to carry out the likelihood ratio test in edgeR.The threshold for detection of the DEGs was set at ±1.5 FC and a FDR of 0.05. Imig, vela, and saline-treated samples from 9V/null spleen, liver and lung were compared with their age and strain matched untreated WT tissue. edgeR  is available from Bioconductor at the URL: Correlations between the signals obtained from microarray and the mRNA-Seq data were evaluated with two-way Venn diagrams on the RefSeq genes common to both platforms and above the detection threshold. The genes in the overlapping region were selected for performing the scatterplot.Scatter plots were developed from log-transformed intensity and read count values of the selected genes common to the microarray and mRNA-Seq in liver, lung, and spleen samples. Correlation plots on FC values were generated on the DEGs common to all three DE methods in liver, lung and spleen tissues. Correlation between the two platforms and the correlation of the FC values between the three DE methods was evaluated by the Pearson's correlation coefficient and statistical significance with the JMP5 Genomics software.Various statistical algorithms have been developed for identifying DEGs between different groups. Each of these methods use different strategies and thus identify different gene set, which overlap in part. The choice of proper method can have a profound influence on the DEGs identified. We expect that the genes identified by more than one method/algorithm are more likely to be true DEGs. As such our final lists of DEGs were identified in at least two of the algorithms. This approach should result in a more consistent set of DEGs than relying on a single algorithm. The core DEGs are the DEGs which overlap between any two methods.To identify the core DEGs, a three-way proportional Venn diagram was developed with each circle representing the DEGs determined by one of the DE methods. The overlapping region between any two circles represented the core DEGs. The total number of core DEGs were arrived at by combining the DEGs in all the overlapping regions. These core DEGs were used for functional analyses.http://david.abcc.ncifcrf.gov/home.jsp, ToppCluster, and public information and literature references. For DAVID, the hypergeometric distribution was performed to detect the referenced significant functional categories (p<0.05). For ToppCluster a p≤0.05 was chosen as the cutoff value.The functional classifications were performed using the Gene Ontology (GO) classification obtained through the DAVID Bioinformatics Resources 6.7 The functional analyses were done in two stages as described in For direct comparison a total of 23 RNA sample sets were analyzed: 8 lung sets including 4 (imig) and 4 (vela); 8 liver sets: 4 (imig) and 4 (vela); 7 spleen sets: 4 (imig) and 3 (vela), and were interrogated using the microarray and mRNA-Sequencing as described above. To identify the differential expression between the drugs, imig and vela, imig-treated samples were compared with their vela-treated counterparts in the liver, lung and spleen. Mixed Model ANOVA (microarray) and DESeq (mRNA-Seq) were used to identify DEGs of imig/vela, a ±1.5 FC with an FDR of 0.05 was used as selection criteria. GO and network analysis was performed in the DEGs as described above.Figure S1Flowchart of microarray and mRNA-Seq data analysis methodology. The analysis performed simultaneously on two platforms, microarray and mRNA-Seq, to identify DEGs and associated biological functions.(TIF)Click here for additional data file.Figure S2Principal Component Analysis (PCA). a) Eight outliers were identified from total 55 sample sets from the microarray data. b) Four outliers out of 55 sample sets were found in mRNA-Seq data. Green circles indicate the outliers. PCA was applied to assess the variables in the data set. The proportion of variables in each component  shown under the graph. The addition of three components yielded 82.1% for microarray data and 67.6% for mRNA-Seq data of variation in measure correlations. The first principal component accounts for as much of the variability in the data as possible, the linear combination of X-variables that has maximum variance , and each succeeding component accounts for as much of the remaining variability as possible.(TIF)Click here for additional data file.Figure S3Comparisons of the liver DEGs between microarray and mRNA-Seq. DEGs were identified by Mixed Model ANOVA (microarray) and DESeq and edgeR (mRNA-Seq). The colors correspond to the analytic methods. (a) saline-treated, (b) imig-treated, (c) vela-treated 9V/null livers. d) The number of DEGs in 9V/null liver identified by DE methods in imig-, vela- and saline-treatment. The genes with increased expression level are shown in dark grey and the genes with decreased expression level are in light grey.(TIF)Click here for additional data file.Figure S4Comparison of the lung DEGs between microarray and mRNA-Seq by Venn diagrams. DEGs were identified by Mixed Model ANOVA (microarray) and DESeq and edgeR (mRNA-Seq). The colors correspond to each method. (a) saline-treated, (b) imig-treated and (c) vela-treated 9V/null lung. d) The number of DEGs in 9V/null lung identified by DE methods in imig-, vela- and saline-treatment. The genes with increased expression level are shown in dark grey and the genes with decreased expression level in light grey.(TIF)Click here for additional data file.Table S1(XLSX)Click here for additional data file.Table S2(XLSX)Click here for additional data file.Table S3(XLSX)Click here for additional data file.Table S4(XLSX)Click here for additional data file.Table S5(XLSX)Click here for additional data file.Table S6(XLSX)Click here for additional data file.Table S7(XLSX)Click here for additional data file.Table S8(XLSX)Click here for additional data file.Table S9(XLSX)Click here for additional data file.Table S10(XLSX)Click here for additional data file.Table S11(XLSX)Click here for additional data file.Table S12(XLSX)Click here for additional data file.Table S13(XLSX)Click here for additional data file.Table S14(XLSX)Click here for additional data file.


We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year), and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with  Prostate cancer is the most frequently diagnosed male cancer and the second leading cause of cancer death in men in the United States Enormous efforts have been invested in the development of biomarkers for prognosis of prostate cancer with an emphasis on features of the tumor epithelial component in retrospective samples. However, few accepted and clinically employed biomarkers have been developed. One barrier to biomarker discovery may be the cell-type heterogeneity and the polyclonal/multifocal nature of the accumulated genetic alterations at the time of diagnosis Our data sets GSE8218 and GSE17951, which are publically available in the Gene Expression Omnibus (GEO) database, are based on post-prostatectomy frozen tissue samples obtained by informed consent using IRB-approved and HIPPA-compliant protocols. All tissues were collected at surgery and escorted to pathology for expedited review, dissection, and snap freezing in liquid nitrogen. Clinical follow-up data was assimilated by the UCI SPECS program and maintained in a relational database. RNA for expression analysis was made directly from frozen tissue following dissection of OCT (optimum cutting temperature compound) blocks prepared from the snap frozen samples with the aid of a cryostat. Stroma from tumor-bearing samples was prepared from the OCT-embedded tissue that was mounted in a cryostat by etching a line between tumor and stroma with a scalpel and then preparing frozen sections which appear as two pieces one of which is tumor adjacent stroma as described in For expression analysis 50 micrograms (10 micrograms for biopsy tissue) of total RNA samples were processed for hybridization to Affymetrix GeneChips . For these two data sets, the distributions for the four principal cell types  were estimated by up to four pathologists, whose estimates were averaged as described in silico method to determine the tumor percentage of samples based on the expression values for the multi-gene signatures that are invariant with tumor surgical pathology parameters of Gleason and stage (available at http://www.webarraydb.org/webarray/index.html) in silico method Data set GSE25136 (U133A platform), which consists of 79 tumor-bearing cases (>10% tumor cells), was independently developed and used as a test set. The cell-type distribution of this data set was estimated using CellPred, an http://www.pathology.uci.edu/faculty/mercola/UCISpecsHome.html) Normalization was carried out across multiple data sets using the ∼22,000 probe sets in common to all Data sets. First, data set GSE8218 was quantile-normalized using the function ‘normalizeQuantiles’ of the LIMMA routine p values <0.05 and fold change >1.6 (either up-regulated or down-regulated) were identified and used to develop a PAM classifier. The resulting classifier was subsequently tested against the patient samples that had not been used for training (32 rapid relapse patient samples and 2 indolent patient samples). This process was repeated 1,000 times and 3,625 probe sets were selected at least once out of 1,000 times based on criterions of p values <0.05 and fold change >1.6. The average sensitivity and specificity of the cross-validation process were 69% and 82%, respectively. A total of 131 probe sets were selected by PAM no less than 500 times out of the 1,000 iterations.Two methods were employed to define genes differentially expressed in stroma of high and low risk cases. Short disease-free survival (DFS) time is a commonly used indicator of aggressiveness vs. indolent comparison which consist of 44 relapsed cases  and 5 non-relapsed cases. We analyzed the 49 stroma samples from relapse case by a correlation analysis and identified 115 DFS-associated probe sets using Pearson’s correlation analysis with correlation coefficients >0.46 and associated p values <0.001). The Pearson’s correlation coefficients for these 115 probe sets range from −0.46 to −0.61 or from 0.46 to 0.69. Different disease-progression relevant genes, beyond those found in early and late relapse cases (131 genes above) were assumed to be uncovered in this gene identification step because median-risk cases (relapse time between 1 year and 4 years) were included.Second, in order to identify probe sets associated with a broader class of risk values, the data set GSE8218 again was used to identify probe sets that correlate with disease-free survival time, including patients that relapsed between one and four years after surgery. Data set GSE8218 included 49 pure stroma samples from 49 patients who underwent prostate cancer relapse after surgery. Note that the 49 stroma samples used for correlation analysis are not identical to the 49 stroma cases used for rapid relapse vs. the DFS time are shown in Figure S1.There were 19 common probe sets between the 131 probe sets identified by permutated PAM analysis and the 115 probe sets identified from correlation analysis. A simulation study showed that the chance of observing 19 overlap between randomly selected 131 probe sets and 115 probe sets from a basis of 22,000 probe sets is <0.0001. Thus these 19 overlapping probe sets (a figure greater than random) represent significant agreement between two non-identical sets of case using different methods of analysis. The 19 common probe sets, which represent 15 unique genes, are listed in Classifier development and testing with independent data sets.The 19 overlapping probe sets were used to develop a classifier. From 40 rapid relapse cancer samples from the first step containing stroma near tumor, we selected 9 samples with the shortest DFS times, which were combined with all 9 samples containing stroma near indolent tumor to form a training set. We used the 19 probe sets identified in the previous step as PAM Figure S2) illustrates that the 194 genes  had distinct profiles between rapid relapse cases and indolent cases in the 18 training stroma samples. A volcano plot  illustrates that some of the probe sets have large fold changes and low p values. The 115 probe sets have 19 probe sets (15 unique genes) in common with the 131 probe sets identified from the permutated PAM analysis. The volcano plot in Figure S3 illustrates that these 19 probe sets are among the most promising probe sets which have the largest fold changes and lowest p values.A heat map (Test). The overall positive predictive value (PPV) and the negative predictive value (NPV) of the test based on the 47 independent samples were 97.9% and 44.4%, respectively.In order to provide an objective test of the prognostic classifier, 47 independent test samples including 36 samples from data set GSE8218 and 11 samples from data set GSE17951 (not used in training) were employed for testing. A sensitivity of 88.1% and a specificity of 80% were observed yielding the average accuracy was 87% , Test. Tp = 0.02). These observations indicated that the combination of a training method based on selected survivorship in combination with a correlation method that utilized the full available range of disease-free survival times yielded a classifier with accurate results when applied to an independent test cohort. A Kaplan-Meier representation of the test results for the 47 test samples in combination with the test results for these 19 median-risk stroma samples is summarized in p = 0.0018.In order to test further whether the 15-gene prognostic classifier generally applies to entire range of outcomes and is not limited to the specific selected survivorship selected for training in the first step, we tested 19 samples (not included in training) from patients who either suffered relapse between year 1 and year 4 after surgery or did not relapse but had less than 4 years’ follow-up data. These 19 samples included 9 stroma samples from near tumor and 10 tumor-bearing samples (tumor <10%). The Kaplan-Meier analysis indicated that the 15-gene prognostic classifier dichotomized these ambiguous samples into two groups with significantly distinct risks  and TNM (p = 0.0383) were significantly associated with survival. The result indicated that gene signature has better predictive value and adds predictive value to known clinical and pathological variables.We also checked if including clinical information, such as Gleason Scores, tumor stage and pre-operative PSA added prognostic value to the classifier. We used these three variables in combination with the 19 probe sets as PAM input and let PAM select the best predictive features. None of these three variables were picked by PAM. In addition, we analyzed 65 cases . Thus, when stroma is tested from remote positions with a low likelihood of being affected by tumor paracrine factors, a stroma response represented by the expression changes of these 15 genes was not detected in contrast to such changes detectable in stroma near tumor. To check whether the 15-gene classifier was insensitive to large amounts of contaminating tumor, we tested it on 117 tumor-bearing samples (>10% tumor cells with average of 48.5% of tumor component) in three data sets  with an overall accuracy of 41%. However, when the classifier was tested on 9 samples that contain <10% tumor cells, the accuracy was 89% (The 19 probe sets (15 genes) form a prognostic signature specific to stroma near tumor. To examine whether the 15-gene classifier extended to stroma that is far from primary tumor, we tested the classifier on 9 indolent stroma samples 8 of which are  was 89% Table 2.Table S1.We analyzed the 19 probe sets (15 genes) using the DAVID bioinformatics tool We previously showed that there are hundreds of significant gene expression changes between tumor-adjacent stroma and normal stroma that were used to develop a stroma-specific high accuracy Diagnostic Classifier for detecting the presence-of-tumor based on the RNA expression of stroma alone same training samples and the 1,000 sets of 19 probe sets selected at random and tested these random classifiers with the same test samples as used for testing the 19-probe set Prognostic Stroma Classifier. The average number of probe sets selected by PAM in the 1,000 random sets is 3.7 which are assumed to be a noise. That is for any randomly picked set of 19 probe sets, a small number of probe sets would be correlating with the high/low risk status by coincidence, which explains why the average training accuracy of random classifiers was ∼70%. However, these random classifiers would not work for independent test sets. On the contrary, the 19 probe sets were identified through both rigorous approaches; therefore, they are potentially general prognostic markers that apply to other test sets. The comparison favored our 15-gene (19 probe set) classifier over those classifiers generated through random processes  with the 339 probe sets used to develop our diagnostic classifier p value of 0.08. Thus, gene expression of a few genes we identified as of potential prognostic value might be altered due to genotypic changes, and are of particular future interest, but most genes we identified do not yet show such an association.In a recent study, a genome-wide LOH/allelic imbalance (AI) scan of DNA was conducted to identify LOH/AI hot/cold spots in prostate epithelium, or in prostate stroma, or in both which identified 156 gene associated with clinicopathologic phenotypes including relapse p value = 0.1312, by a simulation-based test). Another recent study identified 36 prognostic markers also specifically drawn from reactive stroma p value of 0.0001, indicating substantial agreement. Moreover, all four gene products are well documented diagnostic or prognostic biomarkers for prostate cancer A subset of the more aggressive samples in our study will have reactive stroma, which has been shown to correlate with poor outcome We analyzed the biological functions for the prognostic 19 probe sets (15 genes) Table 1 uTable S1) of which 59 are in common among the two lists. Furthermore, most of these 194 genes are also associated with other cancers such as colorectal neoplasms, breast neoplasms and lung neoplasms, indicating these genes may be commonly involved in cancer related pathways. The pathway analysis also showed that a significant fraction of these 194 genes interact with transcriptional factors, such as P53, SP1, FOXO3A, AR, BCL6, STAT5A, STAT5B, C-Jun, NRF2, MYOD and STAT1, which play crucial roles in cancer development and progression. For example, transcriptional factor SP1 is functionally associated with 94 genes from the 194 gene list . SP1 is a transcriptional factor that is over expressed in a variety of cancers and regulates gene expression by interacting with GC rich SP1 binding sites We further analyzed the 194 genes  using a pathway software MetaCore. The result of pathway analysis by using ‘smooth muscle + disease biomarker’ as a filtering parameter indicated that this set of 194 genes are significantly enriched in genes associated with ‘prostatic neoplasms transcription’. The seven genes associated with this description were NCOA3 (TRAM-1), c/EBP (CEBP), NR77 (NR4A1), NK31 (NKX3-1), P53 (TP53), KL5 (KL5), CEBPD. Moreover, 3 genes STAT1, ERBB3, P21 (CDKN1A) were found to be associated with ‘prostatic neoplasms regulation of progression through cell cycle’ and 1 gene STAT1 is associated with ‘prostatic neoplasms inflammatory response’. Pathway analysis using ‘smooth muscle + disease’ as a filtering parameter indicated that 67 of the 194 genes are known to be significantly associated with prostatic diseases and 66 of these 194 genes are known to be significantly associated with prostatic neoplasms Click here for additional data file.Figure S2Heat map of the 227 probe sets  in the 18 training cases. The cases labeled with red are high-risk stroma samples and the cases labeled with green are low-risk stroma samples.(PDF)Click here for additional data file.Figure S3Volcano plot of probe set ratios and probabilities based on 18 training samples.(PDF)Click here for additional data file.Figure S4Among the 194 stroma genes correlated with tumor prognosis there are 94 genes that are functionally associated with transcriptional factor SP1 .(PDF)Click here for additional data file.Table S1MetaCore pathway analysis of the 194 genes  using ‘smooth muscle + disease biomarker’ and ‘prostatic neoplasms transcription’ as filtering parameters.(XLS)Click here for additional data file.


Multi-gene interactions likely play an important role in the development of complex phenotypes, and relationships between interacting genes pose a challenging statistical problem in microarray analysis, since the genes involved in these interactions may not exhibit marginal differential expression. As a result, it is necessary to develop tools that can identify sets of interacting genes that discriminate phenotypes without requiring that the classification boundary between phenotypes be convex.We describe an extension and application of a new unsupervised statistical learning technique, known as the Partition Decoupling Method (PDM), to gene expression microarray data. This method may be used to classify samples based on multi-gene expression patterns and to identify pathways associated with phenotype, without relying upon the differential expression of individual genes. The PDM uses iterated spectral clustering and scrubbing steps, revealing at each iteration progressively finer structure in the geometry of the data. Because spectral clustering has the ability to discern clusters that are not linearly separable, it is able to articulate relationships between samples that would be missed by distance- and tree-based classifiers. After projecting the data onto the cluster centroids and computing the residuals ("scrubbing"), one can repeat the spectral clustering, revealing clusters that were not discernible in the first layer. These iterations, each of which provide a partition of the data that is decoupled from the others, are carried forward until the structure in the residuals is indistinguishable from noise, preventing over-fitting. We describe the PDM in detail and apply it to three publicly available cancer gene expression data sets. By applying the PDM on a pathway-by-pathway basis and identifying those pathways that permit unsupervised clustering of samples that match known sample characteristics, we show how the PDM may be used to find sets of mechanistically-related genes that may play a role in disease. An R package to carry out the PDM is available for download.We show that the PDM is a useful tool for the analysis of gene expression data from complex diseases, where phenotypes are not linearly separable and multi-gene effects are likely to play a role. Our results demonstrate that the PDM is able to distinguish cell types and treatments with higher accuracy than is obtained through other approaches, and that the Pathway-PDM application is a valuable technique for identifying disease-associated pathways. However, the high-dimensional data produced in these experiments--often comprising many more variables than samples and subject to noise--also presents analytical challenges.Since their first use nearly fifteen years ago , microark-means clustering [k-means appears to perform the best [The analysis of gene expression data can be broadly grouped into two categories: the identification of differentially expressed genes (or gene-sets) between two or more known conditions, and the unsupervised identification (clustering) of samples or genes that exhibit similar profiles across the data set. In the former case, each gene is tested individually for association with the phenotype of interest, adjusting at the end for the vast number of genes probed. Pre-identified gene sets, such as those fulfilling a common biological function, may then be tested for an overabundance of differentially expressed genes ; this austering ,5 and Seustering ; a briefustering . Of thesthe best -9. Relatthe best  searchesthe best ) search While these approaches have been fruitful, they also have the potential to miss causative mechanisms that can be deleteriously affected in several ways  as well as those which are only deleteriously affected through a combination of particular alterations . It is well known that complex diseases, such as cancers, exhibit considerable molecular heterogeneity for these reasons . As a reco-expressed with its regulatory target MYL2 in Wagyu cattle, whereas the nonfunctional variant in the Piedmontese cattle did not exhibit co-expression with MYL2. The correct identification of this system, in the absence of differential expression at the gene level in MSTN or MYL2, is crucial to understanding the molecular determinants of the double-muscled phenotype. This example serves to underscore the pressing need for analysis methods that can reveal systems-level differences in cases and controls even when the constituent genes do not exhibit differential expression.As an example of how causative genes can be missed in gene-centric analyses, consider a recent study in which gene expression profiles in the Wagyu cattle are compared to those of the double-muscled Piedmontese cattle . The PieAs an alternative approach, we propose here an analysis technique that is designed to reveal relationships between samples based on multi-gene expression profiles without requiring that the genes be differentially expressed , and that has the power to reveal relationships between samples at various scales, permitting the identification of phenotypic subtypes. Our approach adapts a new unsupervised machine-learning technique, the Partition Decoupling Method (PDM) ,15, to gThe PDM has a number of satisfying features. The use of spectral clustering allows identification of clusters that are not necessarily separable by linear surfaces, permitting the identification of complex relationships between samples. This means that clusters of samples can be identified even in situations where the genes do not exhibit differential expression, a trait that makes it particularly well-suited to examining gene expression profiles of complex diseases. The PDM employs a low-dimensional embedding of the feature space, reducing the effect of noise in microarray studies. Because the data itself is used to determine both the optimal number of clusters and the optimal dimensionality in which the feature space is represented, the PDM provides an entirely unsupervised method for classification without relying upon heuristics. Importantly, the use of a resampled null model to determine the optimal dimensionality and number of clusters prevents clustering when the geometric structure of the data is indistinguishable from chance. By scrubbing the data and repeating the clustering on the residuals, the PDM permits the resolution of relationships between samples at various scales; this is a particularly useful feature in the context of gene-expression analysis, as it permits the discovery of distinct sample subtypes. By applying the PDM to gene subsets defined by common pathways, we can use the PDM to identify gene subsets in which biologically meaningful topological structures exist, and infer that those pathways are related to the clinical characteristics of the samples . This pathway-based approach has the benefit of incorporating existing knowledge and being interpretable from a biological standpoint in a way that searching for sets of highly significant but mechanistically unrelated genes does not.A number of other operationally similar, yet functionally distinct, methods have been considered in the literature. First, simple spectral clustering has been applied to gene expression data in , with mik-means clustering. However, the method as described in [Another novel clustering method is proposed in , where aribed in  is compuAlternatively, SPACC  uses theA fourth approach, QUBIC  is a graThe PDM is thus unique in a number of ways: not only is it able to partition clusters with nonlinear and nonconvex boundaries, it does so in an unsupervised manner (permitting the identification of unknown subtypes) and in the context of comparison to a null distribution that both prevents clustering by chance and reduces the influence of noisy features. Moreover, the PDM's iterated clustering and scrubbing steps permit the identification of independent  partitions in the data.In this manuscript, we describe the PDM algorithm and demonstrate its application to several publicly-available gene-expression data sets. To illustrate the PDM's ability to articulate independent partitions of samples, we apply it to genome-wide expression data from a four phenotype, three exposure radiation response study . The PDMThe partition decoupling method (PDM) was first described in . We summij sbetween each pair, we wish to partition data in such a way that the samples within one cluster are significantly more similar to each other than they are to the remainder of the samples. A summary of the spectral clustering algorithm is given in Table The first step, spectral clustering, serves to identify clusters of samples in high-dimensional gene-expression space. The motivation is simple: given a set of samples and a measure of pairwise similarity k-means clustering as used in [Spectral clustering offers several advantages over traditional clustering algorithms such as those reviewed in . Most im used in ,5, and i used in . Spectraij scorrespond to some measure of similarity between a pair of nodes i and j. Spectral graph theory  and the diagonal degree matrix D , normalized according to the formulaIn spectral clustering, the data are represented as a complete graph in which nodes correspond to samples and edge weights e, e.g., ) is broue, e.g., -22 that ij sis computed from the pairwise distances ij rbetween samples i and j using a Gaussian kernel [In spectral clustering, the similarity measure n kernel -22 to moσ controls the width of the Gaussian neighborhood, i.e., the scale at which distances are deemed to be similar.  Folquestion , we use ijs) and is motivated by the desire to distinguish between samples that positively activate a pathway from those that down-regulate it.The use of the signed correlation coefficient implies that samples with strongly anticorrelated gene expression profiles will be dissimilar , the eigenvector for the second smallest  eigenvalue  encodes a coarse geometry of the data, in which the coordinates of the normalized Fiedler vector provide a one-dimensional embedding of the network. This is a "best" embedding in the sense that it solves a relaxation of an optimization problem that seeks to determine an optimal partitioning of the data . Thi. ThiL gik-means [k-means clustering is by nature stochastic [k-means runs are performed and the clustering yielding the smallest within-cluster sum of squares is chosen. In order to use k-means on the embedded data, two parameters need to be chosen: the number of eigenvectors l to use  and the number of clusters k into which the data will be clustered.By embedding the data into a smaller-dimensional space defined by the low-frequency eigenvectors and clustering the embedded data using k-means , the geoochastic , multiplThe optimal dimensionality of the embedded data is obtained by comparing the eigenvalues of the Laplacian to the distribution of Fiedler values expected from null data. The motivation of this approach follows from the observation that the size of eigenvalues corresponds to the degree of structure (see ), with sij s(preserving ij s= ji sand ii s= 1). This process may be thought of as "rewiring" the network while retaining the same distribution of edge weights. This has the effect of destroying structure by dispersing clusters (subgraphs containing high edge weights) and creating new clusters by random chance. Because the raw data itself is not resampled, the resulting resampled network is one which has the same marginal gene expression distributions and gene-gene correlations as the original data, and is thus a biologically comparable network to that in the true data. Note that the resampling-based (and hence nonparametric) construction of the reference distribution here differs from the previous description of the PDM [The null distribution of Fiedler values is obtained through resampling  the PDM  that empEigenvectors whose eigenvalues are significantly small with respect to the resampled null model are retained as the coordinates that describe the geometry of the system that distinguishable from noise, yielding a low-dimensional embedding of the significant geometry. If none of the eigenvalues are significant with respect to the resampled null reference distribution, we conclude that no coordinate encodes more significant cluster structure than would be obtained by chance, and halt the process.k suitable for partitioning a data set is an area of active research  To obtain this value, we fit a Gaussian mixture model [The development of methods for obtaining the number of clusters e, e.g., ,23 and re, e.g., ,22 that re model  with 2-3re model ,26).k and l have been assigned, the data embedded via the coordinates given by the eigenvectors of the smallest l nonzero eigenvalues is clustered using k-means [k-means: first, the Fiedler vector provides a natural means to estimate the number of clusters; and second, because spectral clustering operates on similarity of the samples, rather than planar cuts of the high-dimensional feature space, complex correlation structures can be identified. A complete discussion of the advantages of spectral clustering is given in [Once  k-means . The spegiven in -22.k-means alone can only identify clusters with convex hulls, k-means clustering using k = 2 produces an arbitrary, linear division of the data as shown in Figure k-means took k = 2 as an input from the user, the spectral clustering example determined k = 2 from the data, as shown in Figure To illustrate the power of this method, consider a toy data set called "two_circles" in which 200 data points are placed in two-dimensional space in two concentric circles, as depicted in Figure While the two_circles data is simulated, we note that patterns of this type will arise when out-of-phase oscillatory genes, such as those involved in circadian rhythms or cell cycle processes, are sampled; the radii of the co-expression circles will be dictated by the amplitude of the gene oscillations. An illustration of such patterns in nature is provided in Figure x-axis represents the expression level of one gene, and the y-axis represents another; let us further assume that the inner ring is known to correspond to samples of one phenotype, and the outer ring to another. A situation of this type may arise from differential misregulation of the x and y axis genes. However, while the variance in the x-axis gene differs between the "inner" and "outer" phenotype, the means are the same (0 in this example); likewise for the y-axis gene. In the typical single-gene t-test analysis of this example data, we would conclude that neither the x-axis nor the y-axis gene was differentially expressed; if our gene set consisted of the x-axis and y-axis gene together, it would not appear as significant in GSEA [x-axis and y-axis genes plays a role in the phenotypes of interest. We exploit this property in applying the PDM by pathway to discover gene sets that permit the accurate classification of samples.The benefit of spectral clustering for pathway-based analysis in comparison to over-representation analyses such as GSEA  is also  in GSEA , which mAfter the clustering step has been performed and each data point assigned to a cluster, we wish to "scrub out" the portion of the data explained by those clusters and consider the remaining variation. This is done by computing first the cluster centroids , and then subtracting the data's projection onto each of the centroids from the data itself, yielding the residuals. The clustering step may then be repeated on the residual data, revealing structure that may exist at multiple levels, until either a) no eigenvalues of the Laplacian in the scrubbed data are significant with resepct to those obtained from the resampled graphs as described above; or b) the cluster centroids are linearly dependent. We apply the PDM to several cancer gene expression data sets to demonstrate how it may be used to reveal multiple layers of structure. In the first data set , the PDMl using 60 permutations ; 30 k-means runs were performed and the clustering yielding the smallest within-cluster sum of squares was chosen. Classification results are given in Table We begin by applying the PDM to the radiation response data  to illusk-means, we ran k-means on the original data using k = 3 and k = 4, corresponding to the number of treatment groups and number of cell type groups respectively. As with the spectral clustering, 30 random k means starts were used, and the smallest within-cluster sum of squares was chosen. The results, given in Tables k used here was not derived from the characteristics of the data, but rather is assigned in a supervised way based on additional knowledge of the probable number of categories . While the pure k-means results are noisy, the k = 4 classification yields a cluster that is dominated by the highly radiation-sensitive cells ; we thus conclude that there are only two layers of structure present in the data: the first corresponding to exposure, and the second to radiation sensitivity. That is, there exist patterns in the gene expression space that distinguish UV- and ionizing radiation exposed cells from mock-treated cells (and from each other), and that there exist further patterns that distinguish high-sensitivity cells from the rest. Together, these independent (decoupled) sets of clusters describe six categories, as shown in Figure In the original analysis of this data , the autHaving observed the PDM's ability to decouple independent partitions in the four-phenotype, three-exposure radiation response data, we next consider the PDM's ability to articulate disease subtypes. Because cancers can be molecularly heterogeneous, it is often important to articulate differences between subtypes--a distinction that may be more subtle than than the differences caused by radiation exposure. Here, we apply the PDM to the suite of 21 Affymetrix data sets previously considered in . The usek-means, finite mixture of Gaussians (FMG), and shared nearest-neighbor clustering--to the data using various linkage and distance metrics as available for each. In [k was set manually, ranging over c kis the known number of sample classes and n is the number of samples; in the spectral clustering implementation, l was set equal to the value chosen for k. Note that the PDM differs in several crucial ways from basic spectral clustering as applied in [k and l in the PDM are data-driven . Second, the successive partitioning carried out in the PDM layers can disambiguate mixed clusters. Notably, the PDM partitions are obtained without relying on prior knowledge of the number of clusters. This is an important feature when the data may contain un-identified disease subtypes.In , the auteach. In , the numplied in . First, To illustrate this, we focus on a handful of the benchmark data sets.  of the PDM over standard spectral clustering.Results for the complete set of Affymetrix benchmark data are given in Additional Files  FMG, in . Howeverand l in ). This iand l in  when usiThe above applications of the PDM illustrate its ability to detect clusters of samples with common exposures and phenotypes based on genome-wide expression patterns, without advance knowledge of the number of sample categories. However, it is often of greater interest to identify a set of genes that govern the distinction between samples. Pathway-based application of the PDM permits this by systematically subsetting the genes in known pathways . Because some pathways contain a fairly large number of probes, it is reasonable to ask whether the pathways that permitted clusterings corresponding to tumor status were simply sampling the overall gene expression space. In order to assess this, we also constructed artificial pathways of the same size as each real pathway by randomly selecting the appropriate number of probes, and recomputing the clustering and χ2 p-value as described above. 1000 such random pathways were created for each unique pathway length, and the fraction frand of pathways that yielded a χ2 p-value smaller than that observed in the "true" pathway is used as an additional measure of the pathway significance. Six pathways distinguished the radiation-sensitive samples with frand <0.05 as shown in Figure We applied Pathway-PDM as described above to the radiation response data from , testingχ2 test).To further illustrate Pathway-PDM, we apply it to the Singh prostate gene expression data  the hea. This isfrand in any layer of clustering is given in Table Of the 203 pathways considered, those that yielded significant A number of known prostate cancer-related pathways appear at the top of this list. The urea acid cycle pathway, prion disease pathway, and bile acid synthesis pathways have previously been noted in relationship to prostate cancer . The coaBecause Pathway-PDM does not rely upon single-gene associations and employs a "scrubbing" step to reveal progressively finer relationships, we expect that we will be able to identify pathways missed by other methods. It is of interest to compare the results obtained by Pathway-PDM to those obtained by other pathway analysis techniques. In , the autWe have presented here a new application of the Partition Decoupling Method ,15 to gek-means clustering but could be correctly clustered using spectral clustering. We note that, like the genes in Figure The PDM has a number of features that make it preferable to existing microarray analysis techniques. First, the use of spectral clustering allows identification of clusters that are not necessarily separable by linear surfaces, enabling the identification of complex relationships between samples. As this relates to microarray data, this corresponds to the ability to identify clusters of samples even in situations where the genes do not exhibit differential expression. This is particularly useful when examining gene expression profiles of complex diseases, where single-gene etiologies are rare. We observe the benefit of this feature in the example of Figure Second, the PDM employs not only a low-dimensional embedding of the feature space, thus reducing noise , but also the optimal dimensionality and number of clusters is data-driven rather than heuristically set. This makes the PDM an entirely unsupervised method. Because those parameters are obtained with reference to a resampled null model, the PDM prevents samples from being clustered when the relationships amongst them are indistinguishable from noise. We observed the benefit of this feature in the radiation response data  shown inThird, the independent "layers" of clusters (decoupled partitions) obtained in the PDM provide a natural means of teasing out variation due to experimental conditions, phenotypes, molecular subtypes, and non-clinically relevant heterogeneity. We observed this in the radiation response data , where tThe accuracy of the PDM can be used, in the context of gene subsets defined by pathways, to identify mechanisms that permit the partitioning of phenotypes. In Pathway-PDM, we subset the genes by pathway, apply the PDM, and then test whether the PDM cluster assignments reflect the known sample classes. Pathways that permit accurate partitioning by sample class contain genes with expression patterns that distinguish the classes, and may be inferred to play a role in the biological characteristics that distinguish the classes. This is a novel approach to pathway analysis that improves upon enrichment approaches in that does not require that the pathway's constituent genes be differentially expressed. That is, we expect that Pathway-PDM will identify both the pathways that would be identified in enrichment analyses  as well as those whose constituent genes would not yield high measures of differential expression (such as in the two_circles example or the yeast cell-cycle genes). This makes Pathway-PDM a promising tool for identifying mechanisms that show systems-level differences in their regulation that could be missed by methods that rely on single-gene association statistics.To illustrate Pathway-PDM, we applied the Pathway-PDM to both the radiation response data  and a prWhile our application of Pathway-PDM was such that the clusters found by the PDM for each pathway were compared against known sample class labels, we can just as easily compare them to labels from the cluster assignment from full-genome PDM. Hence, for example, in a situation such as the Golub-1999-v1 data shown in Figure σ = 1, the dependence upon this scaling parameter in Eq. 1 is a known issue in kernel-based methods, including spectral clustering and KPCA [σ are actively being developed, and several adaptive procedures have been suggested . Pathway-PDM serves to address this issue by leveraging expert knowledge to identify mechanisms associated with the phenotypes. Additionally, the nature of the embedding, which relies upon the geometric structure of all the samples, makes the classification of a new sample challenging. These issues might be addressed in several ways: experimentally, by investigation of the Pathway-PDM identified pathways (possibly after further subsetting the genes to subsets of the pathway) to yield a better biological understanding of the dynamics of the system that were "snapshot" in the gene expression data; statistically, by modeling the pathway genes using an approach such as [Despite these benefits, the PDM as applied here has two potential drawbacks. First, while we obtained accurate results from the PDM when setting and KPCA ,22. Methted  that ma such as  that exp such as  that acc such as ,43 that In sum, our findings illustrate the utility of the PDM in gene expression analysis and establish a new technique for pathway-based analysis of gene expression data that is able to articulate phenotype distinctions that arise from systems-level (rather than single-gene) differences. We expect this approach to be of use in future analysis of microarray data as a complement to existing techniques.ij ρbetween samples. In the l-optimization step, 60 resamplings of the correlation coefficients were used to determine the dimension of the embedding l. In the clustering step, 30 k-means runs were performed, choosing the clustering yielding the smallest within-cluster sum of squares. An free, open-source R package to carry out the PDM is available for download from http://braun.tx0.org/PDM.The PDM as described above was implemented in R  and applThese data come from a gene-expression profiling study of radiation toxicity designed to identify the determinants of adverse reaction to radiation therapy . In thisThese data comprise filtered gene expression levels from 21 cancer studies using Affymetrix microarrays along with associated class labels. The data were analyzed previously in , where sThese data come from a gene-expression profiling study of prostate cancer comprising 52 tumor samples (T) and 50 tumor-adjacent normal samples (N) from 52 men who had undergone radical prostatectomy . RNA wasThe BioConductor  annotatiRB, GL, SP, and DR conceived of the study and participated in its design. RB wrote the R PDM implementation, performed the statistical analysis, and drafted the manuscript. DR helped to draft the manuscript. All authors read and approved the final manuscript.Figure S-1. PDM classifications of deSouto benchmark set samples using a correlation-based distance metric (as described in methods).Click here for fileFigure S-2. PDM classifications of deSouto benchmark set samples using a Euclidean distance metric.Click here for fileFigure S-3. Pathway-PDM classifications of radiation response data for pathways that discriminate cells by radiation exposure but not by phenotype, suggesting that these mechanisms are intact across sample types. Exposure is indicated by shape , with phenotypes  indicated by color. The discriminatory pathways relate to DNA metabolism and cell death, as would be expected from radiation exposure.Click here for fileFigure S-4. PDM results in first and second layers of the Singh prostate tumor data using all genes. The top two panels show the Fiedler vector values and clustering results, along with the Fiedler vector density, in the first and second layer; the bottom panel shows the combined classification results. The second layer, but not the first, discriminates the tumor samples.Click here for file


Epigenetic modifications of DNA, such as cytosine methylation are differentially abundant in diseases such as cancer. A goal for clinical research is finding sites that are differentially methylated between groups of samples to act as potential biomarkers for disease outcome. However, clinical samples are often limited in availability, represent a heterogeneous collection of cells or are of uncertain clinical class. Array-based methods for identification of methylation provide a cost-effective method to survey a proportion of the methylome at single base resolution. The Illumina Infinium array has become a popular and reliable high throughput method in this field and are proving useful in the identification of biomarkers for disease. Here, we compare a commonly used statistical test with a new intuitive and flexible computational approach to quickly detect differentially methylated sites. The method rapidly identifies and ranks candidate lists with greatest inter-group variability whilst controlling for intra-group variability. Intuitive and biologically relevant filters can be imposed to quickly identify sites and genes of interest. Studying DNA methylation profiles and related implications in developmental processes and diseases is currently the focus of much epigenetic research e.g., , an approach to identify potential candidates, which uses a simple heuristic to identify and visualize differential methylation patterns between groups of samples. Importantly, NIMBL can also be used to compare and visualize the results obtained by multiple Infinium array investigation tools.p-value serving as a measure of probe performance. Additionally, annotation is provided for all array sites, setting them in genomic context by specifying chromosomal location, association with a gene or with a CpG island, etc. Methylation levels are usually estimated by calculating the proportion of the methylated signal to the sum of both signals and a constant offset, which avoids an overestimation if measured signals are low  is reflected by a significant deviation of beta value distribution. A Kolmogorov–Smirnov test is performed to assist the identification of low quality samples which may influence downstream analysis and these can be then excluded from further analysis.NIMBL consists of four main modules. The first module (NIMBL-qc) allows for a basic quality assessment of samples. Several output plots are generated to visualize the sample quality including a plot of the beta value distribution of each sample. Deviation from the expected distribution is largely related to the detection d) between non-overlapping groups , which can be masked from each group .NIMBL has been developed in collaboration with researchers working with clinical data particularly those derived from tumor samples. These data have peculiarities which have guided some of the features of NIMBL. One of which is a method to cope with higher heterogeneous methylation common in primary tissue samples, particularly tumor samples when compared to cell lines [as has been noted previously  allows the detailed examination of user defined genes of interest. Genomic information is used to create methylation overview plots of each gene, where methylation measurements are plotted according to their genomic location. Moreover, alignment files in the FASTA format, which contain the DNA sequence of each gene and the aligned DNA probe sequences, provide information of the exact sequence context of array measurements.Finally, the fourth module (NIMBL-compare) can be used to compare two or three lists of sites identified by any method or by different settings of one method. The comparison is performed both on a site level and gene level. The gene information tables, similar to the one obtained from the core module, can be used to identify candidate genes that are common or unique between the input lists or their subsets.Whilst no universally accepted method for the analysis of Infinium arrays exists; widely used approaches are incorporated within the Illumina Methylation Analyzer (IMA) . The scatterplot output of NIMBL-compare shows that whilst the value of m influences overlap , which are both available within IMA. Many sites were detected by IMA using the limma and/or Wilcoxon tests. However, many of these were due to small differences  between groups Table . Whilst When a minimum median beta value difference of 0.2 post-filter is used, many of these additional sites are removed, although a significant number (3023) remain undetected by NIMBL but detected by IMA with the Wilcoxon option. Wilcoxon is the default option of IMA for comparison of categorical data which we have in this test example and is therefore expected to be more appropriate than the limma method. However, there is significant overlap between the Wilcoxon and limma approaches of IMA with the limma option producing the largest number of candidate sites. Limma is an R package for linear modeling of experimental data and was developed for use in the analysis of microarray data Smyth, . As the m = 1 a total of 5740 sites are identified as differentially methylated by NIMBL. 5669 (98.8%) of these are also identified by IMA using either the limma or Wilcoxon method. As the number of masked sites increases the number of NIMBL-specific sites also increases. For example, when m = 2 a total of 15,047 sites are identified as differentially methylated by NIMBL and the majority of these  are also identified by IMA using either the limma or Wilcoxon method whilst a small proportion (1730) of CpGs are detected by NIMBL alone. This may be seen as a controversial approach as the removal of highly variable sites would invalidate the principles of statistical testing. However, the number of individual samples masked is reported in the NIMBL output and should be used as one of the criteria in ranking candidates for verification. We are confident that this option offers flexibility for the user and this approach has identified a number of candidates of interest by ourselves, for example, the identification of SLC38A2 identified here as a potentially epigenetically regulated gene which is differentially expressed in breast cancer cell lines.To overcome the problems when comparing clinical samples such as tumor samples with high heterogeneity, the potential of mis-diagnosis or analysis of a mixed population of cells, NIMBL was developed to allow the masking of a proportion of values from each compared group. The effect of the masking variable can be seen in Table The quality control implemented within NIMBL allows a quick assessment of overall sample performance. An aberrant beta value distribution of samples detected by NIMBL is used as an indicator of a low quality sample on the array. However, such a deviation might be due to biological reasons  rather than technical failure. The user is advised to integrate all quality control information, especially the number of low quality measurements of each sample. A correlation between these data and an aberrant beta value distribution is a strong indicator of an experimental problem. Other tools, for example, the R packages lumi  that are used for the latest array technology . Genomic information including genomic DNA sequence and coordinates of gene coding regions were obtained from the UCSC Genome Browser database  database (GEO accession GSE29290). This dataset consists of eight breast tumor samples and eight normal breast tissue samples   is a Matlab package which is freely available from: p-value of less than 0.05 and sites with missing measurements  were excluded from differential methylation analysis in both software packages. The final dataset contained 480,917 measurements as the input of both packages.Array sites with one or more of the 16 samples having a detection d) between non-overlapping groups. Optionally, a maximum number of samples can be masked within each group. Two different masking limits for each group can be specified within NIMBL. We used the same value (m) for each group within this study. Masking of samples is performed for each array site independently. Group medians are calculated and the distance of each sample to the corresponding group median is determined. The sample most distant to its group median is masked and remaining samples are re-tested for d. A single sample is masked per iteration irrespective of group. This process repeats until either d is reached or m samples are masked for each group. Here, the differential methylation settings used for NIMBL are based on a constant level of beta value inter-group distance (d = 0.1) and three different values for the maximum number of masked samples within each group . The NIMBL input script for the complete methylation analysis of the test data can be obtained from the example directory within the NIMBL package.The detection of differentially methylated sites by NIMBL is controlled by setting a minimum beta value distance  procedure was used for multiple testing correction of both IMA methods and the cut-off for adjusted The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Analyzing data obtained from genome-wide gene expression experiments is challenging due to the quantity of variables, the need for multivariate analyses, and the demands of managing large amounts of data. Here we present the R package pcaGoPromoter, which facilitates the interpretation of genome-wide expression data and overcomes the aforementioned problems. In the first step, principal component analysis (PCA) is applied to survey any differences between experiments and possible groupings. The next step is the interpretation of the principal components with respect to both biological function and regulation by predicted transcription factor binding sites. The robustness of the results is evaluated using cross-validation, and illustrative plots of PCA scores and gene ontology terms are available. pcaGoPromoter works with any platform that uses gene symbols or Entrez IDs as probe identifiers. In addition, support for several popular Affymetrix GeneChip platforms is provided. To illustrate the features of the pcaGoPromoter package a serum stimulation experiment was performed and the genome-wide gene expression in the resulting samples was profiled using the Affymetrix Human Genome U133 Plus 2.0 chip. Array data were analyzed using pcaGoPromoter package tools, resulting in a clear separation of the experiments into three groups: controls, serum only and serum with inhibitor. Functional annotation of the axes in the PCA score plot showed the expected serum-promoted biological processes, e.g., cell cycle progression and the predicted involvement of expected transcription factors, including E2F. In addition, unexpected results, e.g., cholesterol synthesis in serum-depleted cells and NF-κB activation in inhibitor treated cells, were noted. In summary, the pcaGoPromoter R package provides a collection of tools for analyzing gene expression data. These tools give an overview of the input data via PCA, functional interpretation by gene ontology terms , and an indication of the involvement of possible transcription factors. The differences in mRNA levels between the different samples can be ascribed to three different effects: consequences of cellular signal transduction, cellular differentiation or the migration of cells into or out of the tissue. Under these circumstances, key transcription factors are responsible for establishing differences in the mRNA levels. Moreover, the transcription factors involved can often be linked to specific biological processes. For instance, the transcription factor NF-κβ is linked to inflammation Working with genome-wide gene expression data is challenging for the typical molecular biologist with training mainly focusing on laboratory techniques and only to lesser extend in the fields of mathematics or biostatistics. The large number of gene expression measurements available requires a meaningful reduction of the data set to make its results comprehensible. Data typically originate from DNA microarray hybridization experiments or, more recently, from next-generation sequencing experiments. An example of an experiment requiring genome-wide gene expression analysis is the extraction of RNA from a tissue sample taken Several data analysis strategies for genome-wide gene expression data combine an unsupervised approach for reducing the dimension of the dataset with a supervised approach for drawing conclusions  in terms of biological processes (function GOtree) and the transcription factors involved (function primo).The pcaGoPromoter package provides functions that have been designed for use with any gene expression analysis platform. In this report, however, we use data derived from the Affymetrix GeneChip platform for exemplification. The overall idea was to achieve an interpretation of the score plot axes of a PCA , mouse (Mouse Genome 430 2.0 Array) and rat (Rat Genome 230 2.0 Array) GeneChip arrays. In addition the primoData function allows custom data files for primo to be produced by the user.The pcaGoPromoter package supports Bioconductor's ExpressionSet class iX referring to p probe set IDs on the chip. The data from n hybridization experiments were used to compute PC(1) explains most of the variance of the data, the second principal component PC(2) second most, and so forth.PCA is a well-established method for multivariate analyses pk quantifies the importance of the p′th probe set ID on the chip for the k′th dimension of the reduced predictor space. The sign and magnitude of the loadings were used to find important probe set IDs for functional interpretation.The loading bpca calculates the principal components of a data matrix with hybridization experiments in columns and probe set IDs in rows, by internally calling the function prcomp of the R base package ‘stats’. It should be noted that pca uses the transformed input matrix for calculations, as the convention for PCA is that experiment are in rows and variables in columns. The function getRankedProbeIds works on pca objects and is used to select the most important positive and negative probe set IDs based on their loadings. Going forward, we use a selection of the 2.5% probes set IDs with highest or lowest loadings, respectively, as an example. In a separate section under the In pcaGoPromoter, the function GOtree, which operates either on the Affymetrix probe set IDs, gene symbols, or Entrez IDs. In case the input is not of class ExpressionSet, the input type is controlled with the argument inputType for the function GOtree. Objects obtained with the function GOtree are then visualized in a tree structure of overrepresented GO terms along with their corresponding p-values. The calculation for overrepresentation can be performed using either Fisher's exact test for proportions, or with an exact test for the number of successes in a Bernoulli sequence . The calculation details for overrepresentation of annotation terms in gene lists have earlier been published We were interested in joining a functional interpretation to the directions of the axes for each principal component in the PCA score plot. The Gene Ontology (GO) Consortium Transcription factors can be organized according to their DNA-binding motifs. The TRANSFAC database primoData function is used to generate a table with information about potential transcription factor binding sites discovered by searching promoters for matches to position weight matrices. The function is based on a previously published algorithm primoData function is thus a tool for inclusion of custom promoter sets in the analysis. It takes as inputs two arguments . The promoters argument is a list with two elements. The first element is a list of Refseq identifiers and the second list element is a list of promoter sequences. The R commandThe Promoters <- pcaGoPromoters:::primoData.getPromoter( filename )primoData is a data file to be used with the function primo, which is the function that actually joins the promoter analysis to the PCA analysis.loads promoter sequences from a file in FASTA format into the promoters variable . The matrices argument is an R list of list elements. Each list element contain 3 data elements . baseId is a character vector with a base id, name is a character vector with the common name for the matrix, pwm is a position weight matrix with the base A,C,G,T in rows and the weights in columns. The primoData function is, however, only intended to be used by experts in bioinformatics because it requires a certain level of bioinformatics skills to obtain and format the input files from the public databases. In addition the function requires much processor time. The output of The R command:myPrimoData <- primoData myPrimoData object on custom promters and pwm matrices and the R command:calculates the TFs <- primomyLoadings) using the custom data.Calculates cis-element overrepresentation analysis on the set of probe set IDs . For these genomes, binding sites have been identified for promoter regions upstream of reference sequence mRNA transcripts (Refseq) primo is an object of class ExpressionSet. Alternatively a vector with either Affymetrix probe set IDs, gene symbols or Entrez IDs can be used in which case information about the organism is required and entered as the “org” argument. An option allows for the selection of multiple test correction using the argument p.adjust.method with the default being the false discovery rate. The result is a list of possible transcription factor binding sites that are either over- or underrepresented.The input for http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=nvydbmmukoikora&acc=GSE27071).To illustrate the use of pcaGoPromoter, including the rationale behind the analysis strategy, an experiment was designed and conducted. A serum-starved human monocyte cell line (ATCC Number: CRL-9853) was stimulated by serum in the presence (10 nM) or absence of the specific Erk-1/2 inhibitor, U0126 rma.The Affymetrix CEL files were read into R using the affy package followed by calculation of a normalized gene expression measure for each probe set ID using library(affy)chipdata <- ReadAffychipdataRMA <- rma(chipdata)Load the pcaGoPromoter package.library(pcaGoPromoter)Do everything in one command :pcaInfoPlotst principal component axis (x-axis), which explained 21% of the variance. This axis illustrates the portion of the serum effect influenced by the Erk-1/2 inhibitor. The control group was separated from the others along the 2nd principal component axis (y-axis), which explained 16% of the variance. This axis illustrates the portion of the serum effect that is independent of the Erk-1/2 inhibitor. The axes were annotated with the top overrepresented GO terms and predicted transcription factor binding sites. The cell cycle progression was reflected in the negative direction of the 1st principal component axis. Involvement of E2F and Ets transcription factors was predicted, because binding sites in gene promoters influence this direction of the axis.The resulting score plot  displayspcaInfoPlot function is designed to perform the key calculations required for functional interpretations of the PCA. The underlying calculations can be conducted individually with possibilities for choosing additional options as explained in the following.The Run PCA:pcaObj <- pca(chipdataRMA)st and 2nd principal component. The function getRankedProbeIds generates a ranked list of the probe set IDs that mostly contribute for placing experiments along the chosen principal component, here set by the argument “pc”:The probe set IDs on the GeneChip can now be ranked according to their effect on the projection of the experiments into the new subspace defined by the 1probesPC1neg <- getRankedProbeIds[1∶1365]The number of probe set IDs chosen (1365) constitutes 1365/54613 = 2.5% of the total number of probe set IDs on the HG-U133 Plus 2.0 array.The probes associated with the negative direction of the first principal component axis can now be interpreted in terms of biological processes:GOtreeObj <- GOtree(probesPC1neg)GOtree returns an object that lists all the GO terms with one or more genes, the total number of genes found for the term and a p-value calculated using an exact binominal test ).x: number of successes (number of probes in the extreme loading list having the specified GO term)n: number of experiments p: the hypothesized probability of success Fisher's exact test for proportions can be used (option: significance Method = “fisher”)plot(GOtreeObj) plots a tree view . The function GOtreeHits found eight genes in the PC2 positive loadings annotated with this term. The probes interrogate genes involved in cholesterol synthesis st principal component. This was as expected because the probe set IDs with the most negative loadings of the 1st principal component represent cell cycle progression with activated E2F responsive promoters. Position weight matrix hits for the immediate downstream targets of MAP kinase activation, Ets and Elk transcription factors, are overrepresented in both directions of the 1st principal component. The interpretation is that different Ets and Elk transcription factor targets are activated by serum in the absence and presence of the Erk-1/2 inhibitor.The result “primoRes” contained two lists, overRepresented and underRepresented. Each list holds the respective transcription factor position weight matrix with p-values for over- and underrepresentation calculation using Fisher's exact test for proportions. primoHits as follows:The probe set IDs joined to promoter hits for position weight matrices can be retrieved using the function probeIdsE2F <- primoHitsmget function from the AnnotationDBI package and the hgu133plus2.db package:This function generates the list of probe set IDs associated with promoter hits for the E2F position weight matrix with the Jasper accession number MA0024 and ID 9252. A list of gene names can be retrieved using the geneNamesMA0024 <- mgetAmong the resulting hits is proliferating cell nuclear antigen (PCNA), which is a well-known component of the DNA replication fork  and NF-kB (pwm MA0061)) were also overrepresented in the gene promoters (probe set IDs) with extreme positive loadings for the 1omponent . The intThe first two principal components of the PCA  explain st and 2nd principal component. The probe set IDs with the highest positive loadings had expression patterns with the highest expression levels in the serum + inhibitor group for the 1st principal component. The control group had an intermediate expression level, and the serum only group had the lowest expression level. For the probe set IDs with most influence on the negative direction of the 1st principal component, the reverse was true. Likewise, probe set IDs with high or low expression in the control group relative to the two serum groups (with or without inhibitor) defined the positive or negative direction of the 2nd principal component.Thus, almost 40% of the variance in the original data was due to the four distinct gene expression patterns seen in The test strategy used to calculate GO term overrepresentation is a competitive test strategy The GOtree function can be used in a self-contained test.t.test function in R. The resulting p-values are corrected for multiple tests by the false discovery rate method using p.adjust selfcontained.As an example, the GO terms overrepresented in genes with increased expression in the serum-only samples compared with the control samples can be calculated in a self-contained test. First, genes with higher expression in serum-only samples are calculated using the Then GOtree is used with the binomAlpha argument set :GOselfcontained <- GOtreest principal component  which was found overrepresented in probe set IDs with the 2.5% highest loadings in PC2. The term GO:0007420 (“Brain development”) is annotated to 80 probe set IDs on the chip and was not found overrepresented in the analysis. The calculated p-value for this term remained high irrespective of the fraction of probe set IDs included . In the GOtreeWithLeaveOut and primoWithLeaveOneOut. Thus, a fraction of experiments are left out, and the PCA model builds using a data set with a reduced number of experiments. This process is repeated until all samples have been left out. The default is leave-one-out, but a fraction of the samples to be left out can be given as an argument . For both functions, only GOterms (for GOtreeWithleaveoneOut) or PWMs (for primoWithLeaveOneOut) that are present in all runs are retrieved for subsequent ranking after p-value. Thus, GO terms or PWMs that are only found in some of the cross-validation runs are not present in the final list.Cross-validation is a direct way to judge the robustness of the PCA and the joined functional interpretations of the PC axes. This can be achieved using the functions The command line:GOtreePC2poscv <- GOtreeWithLeaveOutnd principal component using leave-one-out cross-validation. The results , and an indication of the involvement of specific transcription factors. In the present setup, a serum stimulation experiment with a monocyte cell line was used for illustrative purposes. In addition to the expected results, the pcaGoPromoter analysis also revealed unexpected and interesting results when applied to the serum stimulation data, e.g., an indication of cholesterol synthesis in serum-starved cells and NF-κB activation in cells treated with both serum and Erk1/2 map kinase inhibitor. This directly demonstrates how the pcaGoPromoter package can be used to direct attention towards relevant biological issues in various genome-wide gene expression analyses in the future.http://gastro.sund.ku.dk/brew/pcaGoPromoter.html .The serum stimulation experiment used for calculations in this presentation is available as an example. In addition the user can upload data for analysis. The uploaded data should be either a zipped CEL file (with the Affymetrix platform) or a csv table for other formats. The extension R package can also be downloaded and installed locally.A pcaGopromoter online version providing access to the most important plot functions is available at Project name: pcaGoPromoterProject home page:http://gastro.sund.ku.dk/brew/pcaGoPromoter.htmlPublic repositories:https://code.google.com/p/pcagopromoter/downloads/listhttp://www.bioconductor.org/packages/2.10/bioc/html/pcaGoPromoter.htmlOperating system(s): Linux, Windows, Mac OS XProgramming language: The R statistical environmentOther requirements: R version 2.10 or higher, Bioconductor 2.xLicense: GNU GLP3Any restrictions to use by non-academics: None