During the past decade, the development of high throughput nucleic sequencing and mass spectrometry analysis techniques have enabled the characterization of microbial communities through metagenomics, metatranscriptomics, metaproteomics and metabolomics data. To reveal the diversity of microbial communities and interactions between living conditions and microbes, it is necessary to introduce comparative analysis based upon integration of all four types of data mentioned above. Comparative meta-omics, especially comparative metageomics, has been established as a routine process to highlight the significant differences in taxon composition and functional gene abundance among microbiota samples. Meanwhile, biologists are increasingly concerning about the correlations between meta-omics features and environmental factors, which may further decipher the adaptation strategy of a microbial community.We developed a graphical comprehensive analysis software named MetaComp comprising a series of statistical analysis approaches with visualized results for metagenomics and other meta-omics data comparison. This software is capable to read files generated by a variety of upstream programs. After data loading, analyses such as multivariate statistics, hypothesis testing of two-sample, multi-sample as well as two-group sample and a novel function—regression analysis of environmental factors are offered. Here, regression analysis regards meta-omic features as independent variable and environmental factors as dependent variables. Moreover, MetaComp is capable to automatically choose an appropriate two-group sample test based upon the traits of input abundance profiles. We further evaluate the performance of its choice, and exhibit applications for metagenomics, metaproteomics and metabolomics samples.http://cqb.pku.edu.cn/ZhuLab/MetaComp/and https://github.com/pzhaipku/MetaComp/.MetaComp, an integrative software capable for applying to all meta-omics data, originally distills the influence of living environment on microbial community by regression analysis. Moreover, since the automatically chosen two-group sample test is verified to be outperformed, MetaComp is friendly to users without adequate statistical training. These improvements are aiming to overcome the new challenges under big data era for all meta-omics data. MetaComp is available at: The online version of this article (doi:10.1186/s12859-017-1849-8) contains supplementary material, which is available to authorized users. High-throughput meta-omic approaches over the last few years have facilitated researches on understanding of the unculturable majority of microorganisms on earth. Environmental and clinical microbiota samples are characterized in metagenomics, metatranscriptomics, metaproteomics and metabolomics levels. Metagenome reveals taxonomic composition and functional genes abundance. Metatranscriptome accompany with metaproteome further reflect the temporal fluctuation of gene expression. Metabolome identifies metabolites associated with phenotype and physiology as biomarkers. Previously, biologists focused on one or part of all types of meta-omic information, while the integration of metagenomics, metatranscriptomics, metaproteomics and metabolomics data has begun to gain attention for the purpose of systematically characterizing complex microbial communities . Therefop-value from hypothesis testing or correlation analysis but also to unveil intrinsic mechanisms by statistical modeling such as regression analysis.Though the combination of meta-omics approaches may describe a single microbiota in a systems-level, the functional genomic traits associated to host niches and ecological habitats remains obscure. Therefore, it is necessary to introduce comparative meta-omic methods, which refers to statistically comparing meta-omics data from two or more microbiota samples. During the past decades, comparative meta-omics analysis has been established as a routine procedure applied in human pathology and ecology studies. Researchers have already discovered host-specific genes in human gut microbiotas from comparisons between obese and lean volunteers , betweent-test in two-group samples. [For comparative metagenomics, the first tool named as XIPE-TOTEC offered two-sample test and utilized metagenomic shotgun sequences as input . Then, Msamples. . MetaStasamples. . Later osamples. .Several bioinformatic programs had been developed for comparative metagenomics, however few tools were specialized for metatranscriptomics, metaproteomics and metabolomics data comparison  saved as txt or BIOM format  and the http://cqb.pku.edu.cn/ZhuLab/MetaComp/ or at the GitHub site https://github.com/pzhaipku/MetaComp/. The website of MetaComp provided highlight descriptions, pages about software workflow, convenient download pages, online user guides, detailed demonstration of all application examples with input data and contacts of authors. As illustrated in Fig. File (for data input) and Analysis . In the following subsections, we first review the preparation of abundance profiles for four types of meta-omics data. Then, based on outputs of these pipelines, we further introduce the various standard input formats for MetaComp. Finally, integrated statistical analysis options and visualization for these analysis are demonstrated. The structure as well as work flow of MetaComp is displayed in Fig. MetaComp is implemented in C# and R programming languages. The software installer for Windows system, R program and databases of COG, KO and Pfam categories for Linux system and user guide can be found at the website According to Fig. After these experimental processing, the rest procedures for functional gene, taxon and physiological metabolite abundance profiling within a sample are conducted mainly by bioinformatics approaches. There are three major workflows for generalizing functional gene abundance profiles from meta-omics data. The workflow for metagenomics and metatranscriptomics amplicon sequencing data are directly mapped to marker genes through microarray techniques, and after reads per kilobases million (RPKM) normalization or other complicated normalization the gene abundance profiles are obtained.To extract taxon profile of a metagenomic sample, both 16S rRNA reads and binning results are utilized. The reads of amplicon sequenced 16S rRNA are primarily clustered into operational taxonomic units (OTUs), then each OTU is classified using RDP classifier , QIIME , Mothur The profiling of shotgun sequencing metageomics data is consist of three steps: reads assembly, gene prediction and gene annotation. DNA reads are first assembled into contigs or scaffolds through IDBA-UD , CABOG , MAP 3939 or IntThough the processing of shotgun sequenced metatranscriptomics data is consist of three steps as well, the second step of transcriptomic analysis is contig mapping other than gene prediction. After assembled by trinity , RNA conThe LC-MS measured metaproteomics data are profiled in just two steps: peptide identification and protein annotation. As for peptide identification step, MS data are matched with amino acid or nucleotide sequences via search engines such as SEQUEST  and MascThe physiological biomarker reflected by metabolomics data are detected in a unique procedure and consist of tandem MS data filtering or smoothing, nonlinear retention time alignment of peaks and spectral matching of the tandem MS data to METLIN  and Massi (Fi) observed in metagenomic sample j (Sj) is represented by cij  employ BIOM format files as output, meanwhile binning programs always offer simply two column hit results. Besides, the output of physiological biomarker detection is always arranged in a tabular format such as MZmine. After loaded, input files are automatically transferred into APM whose rows correspond to features and columns correspond to individual meta-omic samples. Moreover multiple file selection is supported. Here, the features refer to functional gene categories or phylotype categories. The total number of features We integrated a series of statistical analysis options in MetaComp see Fig. , rangingk-means clustering and hierarchical clustering) to present an overview of the differences among the given sets of meta-omics samples and highlight main features for each sample. Though it is a descriptive statistical function, these results are indispensable visualizations of meta-omics features. For example, enterotypes is illustrated by PCA figure.MetaComp employs principal component analysis (PCA) and clustering approaches /(Ni1+Ni2). Since z-test is not valid if the feature size is insufficient, the prerequisite of z-test is where Mode of multi-sample test: In this mode, pairwise tests between all conceivable pairs of samples are executed by z-test. The p-value of a specific feature i is the minimum of all conceivable p-values. Thus we can identify that the selected feature is significantly different in at least one pair of samples.Mode of two-group sample test: During this test, all samples are classified into two groups. In MetaComp, we provide four statistical hypothesis test methods  to assess whether a specific feature is significantly different between two groups of samples. Users can choose a proper method themselves or let MetaComp determine the most suitable test method according to the criterion shown in Table If MetaComp judges that input data follow a Gaussian distribution, parametric hypothesis testing should be introduced. Otherwise when sample size is small or normality assumption is violated, nonparametric hypothesis testing should be conducted. If two groups of samples are consist of matched pairs for resemble units, or one group of units that has been tested twice, it indicates that two groups of samples are correlated. This automatical selection will be helpful for users lacking of adequate statistical training.Moreover, odds ratio (OR) test was also implemented to evaluate the relative abundance for each feature as Table G1 and G2 is in short for Group 1 and Group 2. cjk denotes as counts for the j-th feature from the k-th group samples.Here, i is Considering the possibility of unevenness between two groups, an empirical continuity correction has been introduced to improve the accuracy of the test. Consequently, OR statistic for feature R=M1/M2. According to the formula above, features are categorized as group 1 enrichment (when log2OR(i)>1) or group 1 scarcity (when log2OR(i)<1).Where Statistical hypothesis tests for comparative meta-omics are provided in MetaComp through three test modes: As the typical meta-omics profile consists of hundreds to thousands of features , direct application of statistical method described above may probably lead to large numbers of false positives. For example, choosing a threshold of 0.05 will introduce 500 false positives in a profile contains 10000 features. Therefore, two correction methods are implemented in the MetaComp software to solve this problem, including false discovery rate (FDR) as the default option and a stricter option Bonferroni correction.ith environmental factor in jth sample  is considered as independent variable, and the jth frequency of kth feature  is considered as dependent variable. Therefore, the regression function is: MetaComp provides a novel function, regression analysis of environmental factors, which means regression analysis of the influence exerted by environmental factors on microbial communities. This original function is implemented by nonlinear regression analysis via the lasso algorithm. MetaComp first normalizes the data of both meta-omics samples and environmental factors. After that, the xmj·xnj means the co-effect of environmental factor xmj and xnj to feature ykj. Then, αki and βkmn represent the regression coefficient of the function. For any specific feature, the influence of environmental factors on samples is appraised by coefficient value and correlation value. Moreover, the reliability of the regression coefficient is estimated by p-value. Only when all p-values meet the prescribed standard, the result of regression would be accepted by MetaComp.where Bar plot: Bar plot is exhibited for the top 10 significantly different features with their frequencies in each sample.Hierarchical clustering dendrogram and multi-dimensional scaling map: Hierarchical clustering dendrogram and multi-dimensional scaling map are presented to illustrate the clustering and distance information of meta-omics samples respectively. Features with significant differences (p<0.05) are involved in this clustering.Two-dimensional heat-map: Two-dimensional heat-map is performed to investigate the relative abundance of each feature and the similarity among independent samples.For the MetaComp software, the visualizations of the hypothesis testing results are displayed in Fig. Moreover, our software enables to save the figures in many formats  that can be used directly for publication.File menu. Outputs of BLAST, HMMER, Kraken, MG-RAST, MZmine and PhymmBL, BIOM format and APM saved as txt files are able to load by MetaComp. Additional environmental factors input data are required if users intend to conduct environmental factors analysis on APMs of samples. These environmental factors are also arranged as APMs.Meta-omics input data are loaded for the further statistical processing through Analysis menu and parameters is set in pop-up dialog boxes.After loading input data, users should choose an analysis from multivariate statistics, statistical hypothesis tests and environmental factor analysis. The option is made through The result of analysis is displayed as Excel spreadsheet with corresponding visualization.The analysis workflow of MetaComp can be described as follows  are found in all three Sargasso Sea samples, while these proteins are hardly detected in other samples. This protein is involved in obtaining light energy. In addition, since the content of potassium is apparently higher in AMD and soil, the quantity of potassium ion channel protein  in AMD and Minnesota farm soil greatly surpasses that in other samples , intermediate (labeled as GS1) and late (labeled as GS2) growth stages. Significantly correlated proteins were identified by significance analysis of microarrays (SAM) or clustered by self-organizing tree algorithm (SOTA) in previous study  in early growth stage than intermediate stage. Meanwhile, previously identified intermediate stage expressed proteins indicate a p-value less than or equal to 4.18×10−30. With this p-value as threshold, 19 proteins still express significantly larger in intermediate stage than early stage, within which 10 proteins are engaged in environmental sensing procedure, others also correspond with specific cell processing and metabolic processing  which may exert toxic effect to human liver and secreted by human gut microbiota [Since metabolome data indirectly reflect the conditional response of a microbial community, which is distinct with metatranscriptome and metaproteome, it is necessary to examine the performance of MetaComp on this data. We applied MetaComp on metabolomics data of fecal microbiota detected by n et al. . The oricrobiota . VOCs wez-test on NAFLD and control group, and results indicate that 15 out of 220 VOCs are significantly different between two groups . Moreover, some of the selected COG features are related to the generation and consumption of Adenosine Triphosphate (ATP), which is evidently related to phosphate and oxygen. For instance, COG0378, as a Ni 2+-binding GTPase involved in regulation of expression and maturation of urease and hydrogenase, will generate organic phosphorus as well as dehydrogenase. Moreover, this reaction may consume oxygen. It is obvious that this COG is linked to both DIP and OC. Details of these protein families are shown in Table We applied the novel function of MetaComp, regression analysis of environmental factors, on metagenomic samples from Hawaii Ocean  (see Addp<0.1). Among the selected genes, fumarate and nitrate reductase (fnr) gene is related to the respiratory chain of bacteria and the reaction of it requires a mass of sulfur. Therefore the abundance of fnr is apparently linked to DO and SOA total of 40 AMD samples distinct in environmental characteristics were previously collected across Southeast China. Sampling procedure and data processing were described previously  , where xij was hit number after resampling and p=1/q to control the alteration between two groups. Therefore, we obtained two groups of samples with hits of 1,800,000 and 1,744,981 in total, respectively. The dataset generated from resampling is demonstrated in Additional file Variations embedded in meta-omics are always difficult to recognize when the hit number of a feature is slightly different from one group of samples to another but evidently fluctuated among samples of the same group. Since that, to evaluate the differentially abundant features detection ability of current comparative meta-omics methods, we simulated two groups of count data from twin gut samples , of whict-test , paired t-test (employed by XCMS and MetaComp in two-group sample test for correlated samples), non-parametric t-test (employed by STAMP in two-group sample test), Wilcoxon Mann-Whitney U test (employed by XCMS and MetaComp in two-group sample test for independent samples) and Wilcoxon signed-rank test (employed by XCMS and MetaComp in two-group sample test for correlated samples) were mainly employed hypothesis testing methods, we took these methods for camparison. The result is listed in Table %) and a decent specificity (99.9%). Here, sensitivity (SN) and specificity (SP) were calculated with true positive (TP), true negative (TN), false positive (FP) and false negative (FN) as: Since We further plotted the Receiver operating characteristic (ROC) curve : Linux and Windows 7, 8 and 10.Programming language: C# & ROther requirements: R 3.1.3 or higher.Any restrictions to use by non-academics: none.Additional file 1Table S1. The input functional gene APM data of eight typical environmental metagenomic samples. (TXT 291 kb)Additional file 2Table S2. The detailed analysis results of eight typical environmental metagenomic samples. (XLS 2109 kb)Additional file 3Table S3. The input functional gene APM data of Acid Mine Drainage metaproteomic samples. (XLS 83kb)Additional file 4Table S4. The detailed analysis results of Acid Mine Drainage metaproteomic samples. (XLS 132 kb)Additional file 5Figure S1 to Figure S4. Visualized analysis results for Acid Mine Drainage metaproteomic samples and human fecal metabolomic samples. (PDF 204 kb)Additional file 6Table S5. The input metabolite APM data of human fecal metabolomic samples from healthy control people and NAFLD patients. (XLS 41kb)Additional file 7Table S6. The detailed analysis results of human fecal metabolomic samples from healthy control people and NAFLD patients. (XLS 51kb)Additional file 8Table S7. The input functional gene APM data of Hawaii Ocean metagenomic samples. (TXT 72kb)Additional file 9Table S8. The input vector of environmental factors related with Hawaii Ocean metagenomic samples. (TXT 1 kb)Additional file 10Table S9. The detailed analysis results of Hawaii Ocean metagenomic samples. (XLS 50 kb)Additional file 11Table S10. The input functional gene APM data of Acid Mine Drainage metagenomic samples. (TXT 62 kb)Additional file 12Table S11. The input vector of environmental factors related with Acid Mine Drainage metagenomic samples. (TXT 1 kb)Additional file 13Table S12. The detailed analysis results of Acid Mine Drainage metagenomic samples. (XLS 43 kb)Additional file 14Table S13. The original, resampled and downsampled datasets of twin gut data for comparison of significance detection. (XLS 622 kb)Additional file 15Table S14. The hypothesis testing results of t-test, paired t-test, non-parametric t-test, Wilcoxon rank-sum test and Wilcoxon signed-rank test. (XLS 168 kb)


Accurate transcriptional sequencing (RNA-seq) from formalin-fixed paraffin-embedded (FFPE) tumor samples presents an important challenge for translational research and diagnostic development. In addition, there are now several different protocols to prepare a sequencing library from total RNA. We evaluated the accuracy of RNA-seq data generated from FFPE samples in terms of expression profiling.We designed a biospecimen study to directly compare gene expression results from different protocols to prepare libraries for RNA-seq from human breast cancer tissues, with randomization to fresh frozen (FF) or FFPE conditions. The protocols were compared using multiple computational methods to assess alignment of reads to a reference genome, the uniformity and continuity of coverage, the variance and correlation of overall gene expression, patterns of measuring coding sequence, phenotypic patterns of gene expression, and measurements from representative multigene signatures.v FFPE) and phenotypic differences between breast cancers. One protocol, with RNase H-based ribosomal RNA depletion, exhibited the least variability of gene expression measurements and strongest correlation between FF and FFPE samples and was generally representative of the transcriptome from standard FF RNA-seq protocols.The principal determinant of variance in gene expression was use of exon capture probes, followed by the conditions of preservation (FF Method of RNA-seq library preparation from FFPE samples had a marked effect on the accuracy of gene expression measurement compared with matched FF samples. Nevertheless, some protocols produced highly concordant expression data from FFPE RNA-seq data, compared with RNA-seq results from matched frozen samples. However, the variably fragmented and chemically modified RNA derived from FFPE samples presents a challenge for accurate measurement of gene expression.In a different context, there is great interest to perform transcriptome sequencing (RNA-seq) for biomarker discovery research using large cohorts of precious archival FFPE samples from completed clinical trials. However, an unfavorable signal-to-noise ratio from FFPE samples could reduce the accuracy of biomarker discovery. Therefore, it is essential to select a protocol for FFPE RNA-seq libraries that yields data that are comparable with a gold standard result from FF samples. But there is more than one standard protocol for RNA-seq of high-quality RNA from FF tumor samples. Different approaches to generating libraries for RNA-seq include selection of mRNA by targeting the poly(A) tail (mRNA protocol), depletion of more abundant ribosomal RNA (rRNA depletion) using bead-based method  or enzymic method , and exon capture probes for known coding region (CR) sequence from an RNA-seq library prepared (CR protocol).3 This protocol is not appropriate for degraded mRNAs from FFPE samples, however.4 On the other hand, total RNA library protocols do not restrict enrichment to poly(A)+ tailed mRNA, allowing less biased quantification of isoform abundance.46Data generated from the popular mRNA protocol using FF tissue samples (FF.mRNA library) are highly concordant with microarray data in tumor gene expression signature studies.7 Exon capture using the CR protocol has potential for stronger correlation but involves selected coverage.8 Finally, because pretreatment heat and methyl saturation are claimed to reduce methylol adducts on FFPE RNA,9 we evaluated preanalytical demethylation of total RNA before library preparation using the CR and sRNA protocols (Corresponding protocols for RNA-seq from FFPE tumor samples include an adaptation of the mRNA protocol that combines random and poly(A) primers (sense RNA [sRNA] protocol) optimized for gene expression microarrays  or are unchanged for the I.TotalRNA, K.TotalRNA, and CR protocols . Total Rrotocols .Consequently, this study was designed to directly compare the results from RNA-seq library protocols between optimally matched sample pairs (FF and FFPE) from representative breast cancers, to address three scenarios in translational research: biomarker discovery from FF samples phase, with intention to translate for FFPE samples in future studies for validation and diagnostic development; biomarker discovery from FFPE samples that is intended to be representative had high-quality FF samples been available; and translation of existing biomarkers, developed using a different method (such as microarrays or RNA-seq using mRNA protocol), for use with RNA-seq data from FFPE samples.10 Phenotypically, the nine breast cancers were defined by the pathologic status of hormone receptors (HRs) and human epidermal growth factor receptor 2 (HER2) as HR-positive/HER2-negative in five, HR-positive/HER2-positive in one, and triple receptor-negative in three. RNA was purified from FF samples using the RNeasy Mini Kit  and FFPE samples from 10-μm sections using High Pure FFPE RNA Isolation Kit . A DNase-I treatment step was included in both.Fresh tumor tissue was collected at intraoperative pathology evaluation, diced into pieces of 1- to 2-mm diameter, stirred, and randomly assigned to RNAlater solution later stored in a −80°C freezer (FF) or 10% neutral buffered formalin and paraffin-embedded as an FFPE tissue block.Full details of all methods for library construction and sequencing of RNA samples are in the Data Supplement. An overview diagram of the different RNA-seq library protocols is shown in + mRNA enrichment, followed by standard procedures of TruSeq RNA Sample Prep Kit v2 . The I.TotalRNA protocol used Ribo-Zero Magnetic Gold Kit to deplete ribosomal RNA (rRNA) from total RNA, followed by library preparation using the Truseq Stranded Total RNA Sample Prep Kit (Illumina). The K.TotalRNA protocol used an RNase H-based method to deplete rRNA from total RNA, followed by library preparation using KAPA Stranded RNA-Seq Kit with RiboErase . The sRNA protocol used SensationPlus Amplification kit (Affymetrix) with oligo-dT and random primers designed for whole-transcriptome amplification. The sRNA was then synthesized by in vitro transcription, followed by rRNA depletion using the Ribo-Zero Magnetic Gold Kit. Then the rRNA-depleted sRNA was used as the template for the mRNA protocol described above.The mRNA protocol (FF only) used oligo-dT beads for poly(A)The CR protocol was performed using Truseq Access RNAseq kit (Illumina), using random primers. Next, sequencing adapters were ligated to the resulting cDNA followed by the first polymerase chain reaction . The CRs in those libraries were enriched using capture probes and amplified by PCR.4Cl, pH7.0).11 Starting with demodified RNA, we tested two additional FFPE library preparation methods: FFPE.deM.CR and FFPE.deM.sRNA.CR.The demodification (deM) protocol used heat in an amine-rich solution (70°C for 30 minutes in 1 × Tris-EDTA buffer containing 20 µM NHLibraries were randomly assigned to a lane (four per lane) and paired-end sequenced with Illumina HiSeq 2000 Sequencing System. We generated 100 base-paired reads for sample C and 50 base-paired reads for the other eight samples for the FF.mRNA and FFPE.sRNA protocols. All remaining libraries had 75 base-paired reads. For the mRNA and sRNA protocols, the libraries were prepared with two technical replicates to test reproducibility. No technical replicates could share the same sequencing lane.Full details of all data analysis methods and an overview diagram of the analysis plan are provided in the Data Supplement. Briefly, the different protocols for FF and FFPE samples were compared with respect to metrics as follows: mapping rates of RNA-seq reads , read coverage uniformity and continuity, principal component analysis and hierarchical clustering analysis on expression levels, and pairwise comparison per gene over the coding sequence, of all genes, and of selected breast cancer gene expression signatures that had previously been developed from reverse transcription–PCR or microarray data from FF samples.RNA extracted from FFPE samples was severely degraded, with RNA integrity number of 1.2 to 2.2, versus 6.7 to 9.3 from FF samples (Data Supplement). All libraries generated > 49 million raw reads .P < .001), more intronic , and comparable for intergenic sequence reads (Data Supplement). The RNAseq data generated from the sRNA protocol had a significantly lower concordant pair alignment rate as compared with those from non-sRNA protocols . Using the CR protocol, mapping was highly concordant between FF and FFPE and the exonic mapping rate was increased compared with non-CR methods (Data Supplement). Overall, the number of genes with read coverage (transcripts per million [TPM] > 0.1) was slightly higher in FFPE samples than in FF samples for both non-CR and CR protocols (Data Supplement).12Postalignment mapping rates from FFPE samples differed from FF samples when using libraries from mRNA and TotalRNA protocols as follows: fewer exonic . FFPE.I.TotalRNA and FFPE.K.TotalRNA libraries demonstrated the most uniform and continuous coverage among protocols for FFPE samples and were equivalent to protocols for FF samples. In contrast, the CR protocol produced nonuniform coverage, with a high percentage of gaps, in both FF and FFPE libraries. The FFPE.sRNA protocol introduced modest nonuniformity.13 We therefore applied the variance-stabilizing transformation method to approximate the independence between variance and mean (Data Supplement). Principal component analysis of expression of a total of 20,381 CR protocol targeted poly(A)+ genes for all libraries showed that the 26.5% of total variation captured by the first principal component was due to use of exon capture probes (CR protocol), and 20.6% from the second and third components due to the combined effects of FFPE and biologic differences (v HR-negative) and the source tumor clustered together with FFPE samples (Data Supplement). Technical replicates  were highly correlated (Spearman ρ ≥ 0.992) for all samples after normalization by total count and transformation to log2 count per million (Data Supplement).In RNA-seq studies, the variance across samples usually grows with the mean of gene expression , and this can be problematic for correctly uncovering the underlying pattern in data using techniques such as distance-based clustering.ferences . HierarcP < .001) or any other FFPE protocol  resulted in a median rank correlation of 0.980 between FF and FFPE, but the FF.CR had much lower correlation with non-CR libraries . Fewer FP genes would suggest fewer artifacts introduced by a protocol. FFPE.K.TotalRNA RNA-seq data had the fewest genes with significantly differential expression at various We used a pattern dissimilarity score to measure the differences in expression patterns of coding DNA sequences between library protocols, allowing direct comparison of non-CR and CR protocols. A smaller value of the score indicates higher similarity between a protocol and an FF reference. The distributions of dissimilarity scores across all genes were similar within each protocol but varied across protocols (Data Supplement). FFPE.K.TotalRNA had the lowest mean dissimilarity score when using FF non-CR libraries as the reference (Data Supplement).P value from DE analysis followed the ideal uniform distribution for non-DE genes, with a spike close to zero for the DE genes (Data Supplement). Receiver operating characteristic curves represented the sensitivity and specificity of the DE analyses using each FF reference as the gold standard. FFPE.K.TotalRNA achieved high and stable area under the curve (0.921 to 0.933) at different cutoffs set for each FF gold standard, even after the strongest DE genes in the gold standards had been filtered out (Data Supplement). The best agreement between FFPE protocols and each FF standard was as follows: FFPE.sRNA with FF.mRNA, FFPE.K.TotalRNA with both FF.I.TotalRNA and FF.K.TotalRNA, and FFPE.CR with FF.CR (Data Supplement).We analyzed differential expression (DE) of genes comparing HR-positive/HER2-negative and triple receptor–negative breast cancers within each protocol. Overall, the normalized data were distributed around zero relative log expression and were clustered by tumor phenotypes in the first two principal components. The 1419 Those were compared between three FFPE protocols  and three FF protocols . Best correlations using FFPE protocols with FF.mRNA  were not as strong as with FF.I.TotalRNA  or FF.K.TotalRNA  protocols (Data Supplement). The FFPE.K.TotalRNA protocol had the highest observed Spearman correlation coefficient in 13 of these 15 comparisons.We compared five published breast cancer gene expression signatures: recurrence score (Oncotype DX), PAM50, sensitivity to endocrine therapy index, mammaprint, and PI3-kinase index (PI3K).Overall, FFPE RNA-seq data reliably captured transcriptional profiles and differences in tumor phenotype-based expression in breast cancer samples, but not quite as well as FF RNA-seq data. Principal component analyses demonstrated the following order of variables influencing gene expression measurements from RNA-sequencing: whether the library preparation protocol used exon capture for CR, whether the sample was from FF tissue or FFPE tissue, and the biologic phenotype of the breast cancer based on HRs and HER2 receptor status . GeneralThe first translational research scenario that we posed, in the Introduction section, considered the best pairing of protocols that would enable discovery using FF samples with intention to later translate for use with FFPE samples. Overall, we favor the K.TotalRNA as consistently best or close to best performance with FFPE protocols when compared with FF.mRNA, FF.I.TotalRNA, or FF.K.TotalRNA as reference FF protocols. This interpretation was supported by the quality of read coverage, pattern of coding sequence expression, and translation of overall or phenotype-related gene expression profiles and prognostic signatures.The CR protocols yielded concordant results, but different from all other (non-CR) protocols. Therefore, a CR protocol used for discovery (FF) would preclude other protocols for later translation to FFPE samples  and 4. AThe most generalizable results for discovery research from FFPE samples were obtained using the Total.RNA protocols without exon capture. Although similar, the FFPE.K.TotalRNA protocol produced slightly stronger results than the FFPE.I.TotalRNA protocol. For our second scenario, therefore, we prefer the K.TotalRNA protocol for best representation of the transcriptome in FFPE samples used for discovery research—aiming to represent the transcriptional information that FF samples would have provided.Our third translational research scenario involves the translation of an existing gene expression signature that was previously developed using a different method  or a particular RNA-seq protocol. Again, the FFPE.K.TotalRNA protocol had the best performance for total transcriptional profile, coding sequence, phenotypic discrimination, and specific gene expression signatures.2 Although we tested a method of chemical demodification of total RNA, our results showed negligible effect and argue against the incorporation of this method for RNA-seq of FFPE samples ; optimally short time to fixation of tissues and possibly, as a result, a modest degree of degradation of FFPE samples (DV200 ranges from 65% to 85%); optimal amount of input RNA used for non-CR protocols (at least 100 ng); and lack of generalizability (single-institution conditions of tissue processing). Also, the effects of long-term storage of FFPE samples could not be tested—but would be expected from a completed clinical trial. Also, several of the cases had prolonged storage of cut FFPE sections (at 4°C) until RNA purification. This could have compromised the FFPE library protocols for this comparison but can also be viewed as stress testing the FFPE-derived RNA. One tumor (sample N) seems to be compromised by unknown technical processing; our study conclusions, whenever involving this sample, are based on robust point estimates (eg, median estimates in


Many high-grade serous carcinomas (HGSCs) of the pelvis are thought to originate in the distal portion of the fallopian tube. Serous tubal intra-epithelial carcinoma (STIC) lesions are the putative precursor to HGSC and identifiable in ~ 50% of advanced stage cases. To better understand the molecular etiology of HGSCs, we report a multi-center integrated genomic analysis of advanced stage tumors with and without STIC lesions and normal tissues. The most significant focal DNA SCNAs were shared between cases with and without STIC lesions. The RNA sequence and the miRNA data did not identify any clear separation between cases with and without STIC lesions. HGSCs had molecular profiles more similar to normal fallopian tube epithelium than ovarian surface epithelium or peritoneum. The data suggest that the molecular features of HGSCs with and without associated STIC lesions are mostly shared, indicating a common biologic origin, likely to be the distal fallopian tube among all cases. High-grade serous carcinomas (HGSCs) are associated with precursor lesions (STICs) in the fallopian epithelium in only half of the cases. Here the authors report the molecular analysis of HGSCs with and without associated STICs and show similar profiles supporting a common origin for all HGSCs. Serous tubal intra-epithelial carcinoma (STIC) lesions are suspected to be the precursor for most HGSCs of the pelvis. STIC lesions have been demonstrated to have a clonal relationship with established HGSCs based on both shared TP53 mutations as well as integrated molecular analyses4. Originally identified in the fimbriated end of serially sectioned fallopian tubes, prophylactically removed from women at high-risk for developing ovarian cancer, these lesions have since transformed our understanding of the origins of this malignancy. It is now clear that with detailed processing of the tubal fimbria, many cases of presumed ovarian cancer contain evidence of tubal origin as manifested by the presence of a STIC. For example, recent reports have documented that 40–60% of advanced pelvic HGSCs contain identifiable STICs7. The absence of STIC lesions in a higher proportion of HGSCs may be due to tubal overgrowth and STIC obliteration by the invasive tumor, lesions not sampled through contemporary pathology protocols, or shedding of normal tubal cells prior to malignant transformation.Ovarian cancer has traditionally been thought to develop from the ovarian surface epithelium (OSE) or cortical inclusion cysts (CICs), but the recent data suggest that a majority of advanced high-grade serous carcinomas (HGSC) likely originate from the epithelium of the distal fallopian tube8. The former has been disproven using studies of telomere length and clonality9. The latter is biologically plausible, but with little concrete data for support. If either of these alternative hypotheses were correct, we would then expect to find molecular differences between HGSCs with and without associated STICs.Alternatives to the tubal hypothesis suggest that the tubal epithelium is colonized in a metastatic manner from the primary ovarian tumor or that some pelvic HGSCs originate from tubal epithelium and others originate from other pelvic structures such as the “secondary Müllerian system”12.Molecular profiling has yielded promising results in the field of predictive classifiers, led to a greater understanding of the pathobiology of cancer, and moved us toward precision medicine. Robust examples can be found across many solid tumors, including breast, colon, ovarian, and endometrial cancer. In ovarian cancer, studies have defined molecular subtypes of advanced ovarian carcinoma, identified deregulated cancer-related biologic pathways, and identified therapeutically relevant predictive classifiers. In a limited number of cases, these models are sufficiently reliable and robust to consider direct application in clinical practiceAlthough highly provocative, these hypotheses require further validation to determine whether there are subsets of HGSC, which have different molecular profiles based on their presumed site of origin or whether there are no differences, and they are essentially indistinguishable based on putative sites of origin. A multi-institutional analysis of pelvic HGSC with STICs uniformly classified will provide a unique assessment of the molecular profiles to determine if a uniform tissue of origin exists. We have analyzed a large set of tubo-ovarian HGSCs diagnosed using traditional criteria with and without STICs in women whose fallopian tubes have been processed using the SEE-FIM technique, currently the most comprehensive method of evaluating fallopian tube epithelium. We tested the hypothesis that advanced pelvic HGSCs with or without associated STICs have similar clinical characteristics, indicating a common underlying origin from the fallopian tube. As described in detail in “Methods” section, we designed the study with sufficient power to detect most molecular differences in the moderate to large range, reasoning that a negative result would provide strong evidence in support of the single origin theory.Biospecimens were qualified until an equal number of cases with and without STIC lesions were ascertained to a total of 96 patient samples. The copy number data are available for all 96 samples, as well as the miRNA expression data for 95 samples, and the RNA expression data for 85 samples. The demographic and clinical data are provided in Table Using the focal somatic copy number alterations (SCNAs) identified from all 96 cases, we compared the frequency distribution between tumors with and without STIC lesions. Of the 82 regions identified as significantly altered in the GISTIC analysis, none were over-represented in either the tumors with and without STIC lesions at a statistically significant level  in order to reconcile the competing views of the origin of ovarian carcinoma. Though most data from model systems and human studies suggest that the distal fallopian tube is the site of origin for most high-grade serous ovarian cancers35. Gynecologic tumors of similar histologic subtypes but different organs or origin have also been shown to have different gene expression subtypes36. Additional examples exist for histologic subtypes of tumors that originate from similar anatomic sites such as breast and lung cancers that can be discriminated through both copy number alterations and gene expression39. If STIC is the precursor lesion to most HGSCs, the data generated by our studies will demonstrate molecular similarity between tumors with and without identifiable precursor lesions. If tumors with and without STIC lesions originate from independent sites of origin, we would expect multi-platform genomic analyses to display clear differences. Our data support the rationale to focus further efforts on the distal fallopian tube when considering surgical or medical approaches for the prevention of HGSC.We hypothesized that STIC is the precursor and not a metastasis of many, if not most, HGSCs. We have carefully characterized the morphologic and molecular genetic features of HGSCs and their associated precursor lesions. Molecular profiling has demonstrated the ability to distinguish tumors of different sites of origin. Gastrointestinal tumors originating from the appendix and the colon show similar morphologic features of mucinous differentiation, but have distinct gene expression profiles40. These findings highlight the consistent inter-patient heterogeneity seen in both this study population and the previously published TCGA work.Analyses of the data from copy number alterations, messenger RNA sequencing and microRNA profiling fail to identify any significant differences between HGSCs with or without precursor STIC lesions. The few microRNAs that were differentially expressed between groups were not highly expressed in the data from TCGA, suggesting a limited role for these genes in ovarian cancer. If tumors with and without STIC lesions had different cellular origins, we would expect molecular distinctions to be identified based on this classification. In order to support our negative hypothesis, we performed various ancillary analyses to demonstrate the robustness of these data. We first confirmed that the focal copy number alterations identified in our data substantially overlapped with those identified by TCGA in their analysis of HGSC of presumed ovarian origin. We then confirmed the presence of four gene expression subtypes that have been reproducibly demonstrated in the published literature41.These data provide evidence from a multi-platform genomic perspective that HGSCs with or without STIC lesions originate from a common site of origin. However, one could argue that though the site of origin is likely to be similar between tumors with and without associated STIC lesions, from this data alone, it is difficult to conclude that the distal fallopian tube is that common site of origin. Therefore, to infer the most probable site of origin, we calculated the similarity between HGSC and normal fallopian tube, normal ovary and normal peritoneum through molecular barcodes, finding the most likely site of origin in the distal fallopian tube. This conclusion is supported by prior work, indicating that lineage markers, such as PAX8, for HGSC are shared with those from the distal fallopian tube and not the OSE or mesothelium43. These clinical trials and others will help to determine if the knowledge of ovarian cancer origins can be translated into broader and acceptable prevention approaches. Biomarker discovery will also be focused on the fallopian tube to identify better approaches for early detection and these data augment the scientific underpinning for reducing the burden of ovarian cancer.The implications of these findings are to direct efforts for prevention and early detection toward the distal fallopian tube, rather than the OSE. Ongoing and planned clinical trials are investigating bilateral salpingectomy with ovarian preservation as a viable option for ovarian cancer risk-reduction. Other promising approaches include direct sampling of normal fallopian tubes and detection of premalignant or early invasive tumors through shed biomarkers detected with pap smears or vaginal secretions45. If sufficient tubal epithelium was not processed to determine whether or not a STIC lesion was present, the case was excluded from further consideration. Tumor specimens with <60% tumor cell nuclei were excluded. Qualifying cases were obtained from Memorial Sloan Kettering Cancer Center , The Johns Hopkins Hospital , and University Health Network . Biospecimens were collected at diagnosis after obtaining informed consent. Local Institutional Review Board approval was obtained from each of the three contributing sites. Normal epithelial cells from ovarian, fallopian tube and peritoneal tissue were collected through cytologic brushings to provide a normal gene expression profile from each tissue type. These brushings were collected in the operating room from patients without cancer during surgical procedures upon entry into the abdomen and prior to vascular ligation or other intraoperative manipulation. Representative photomicrographs confirmed that the target normal tissue type was collected without substantial contamination from adjacent tissues .We reviewed the records of all women diagnosed with HGSC of the pelvis, including diagnoses of ovarian, fallopian tube, or primary peritoneal carcinoma during the time period when our pathologists began routinely using SEE-FIM to evaluate the fallopian tubes. Patients with FIGO stage I to IV disease who had received primary cytoreductive surgery and pathologic processing of their fallopian tubes at one of the study sites were included. Patients with non-serous histologic subtypes  were excluded. Women with both optimal and suboptimal cytoreductive procedures were included. Women who had their primary surgery at an outside facility were excluded. Cases were all re-reviewed by a specialty gynecologic pathologist to confirm the presence or absence of STIC lesions using criteria published by the authorsDNA and RNA were co-isolated from frozen tissue tumor blocks, and RNA was isolated from normal tissues utilizing Ambion’s ToTALLY RNA™ RNA Isolation Kit . This protocol allows sequential isolation and collection of both DNA and RNA. The concentration of DNA and RNA was initially obtained using a NanoDrop spectrophotometer in our laboratory. DNA concentration was confirmed using a PicoGreen protocol. RNA quality was confirmed by utilizing an Agilent Bioanalyzer and assigning an RNA integrity number (RIN) to each sample.Total genomic DNA (500 ng) was collected from all 96 samples included in the study, and digested with Nsp I and Sty I restriction enzymes and ligated to adapters. PCR conditions preferentially amplify fragments in the 200–1100 bp size range. PCR amplification products for each restriction enzyme digest are combined and purified using polystyrene beads. The amplified DNA is then fragmented, labeled, and hybridized to a SNP Array 6.0 containing > 906,600 single-nucleotide polymorphisms (SNPs) and > 946,000 probes for the detection of copy number variation. All 96 samples were assayed at once, in a single batch.Total RNA (100 ng) was obtained from 95 of 96 samples, and hybridized to the nCounter Human miRNA sample probes . Subsequently, the samples were placed into the nCounter Prep Station for automated sample purification and subsequent reporter capture. Each sample was scanned for 600 FOV on the nCounter Digital Analyzer to extract the data. All 95 samples were assayed in a single batch.RNA sequencing libraries were prepared using the Illumina TruSeq Stranded Total RNA Sample Preparation Kit. Briefly, 500 ng of total RNA was purified by Ribo-Zero to remove rRNA and fragmented by divalent cations under elevated temperature. The fragmented RNA underwent first strand synthesis using reverse transcriptase and random primers. Second strand synthesis created the cDNA fragments using DNA polymerase I and RNaseH. The cDNA fragments then went through end repair, adenylation of the 3′ ends, and ligation of adapters. The cDNA library was enriched using 10 cycles of PCR and purified. Quality control consisted of assaying the final library size using the Agilent Bioanalyzer and quantifying the final library by real-time PCR and PicoGreen (fluorescence) methods. A single peak between 250 and 350 bp indicates a properly constructed and amplified library ready for sequencing. Eleven of 96 samples failed RNA quality criteria, or library prep and the remaining 85 samples were analyzed in two batches, with STIC and NOSTIC samples well-represented in both.46. Sequencing libraries were loaded onto the HiSeq 2500 flowcell for clustering on the cBot using the instrument specific clustering protocol. The HiSeq 2500 is capable of generating 200–250 M passed filter 2 × 50 bp sequencing reads per flow cell lane. In order to obtain a minimum of 40 M PF reads per sample, we sequenced 6 samples per lane.Sequencing was performed on the HiSeq 2500 instrument using v4 SBS chemistry according to the Illumina protocol, as described in Bentley et al47. Quantification of genes annotated in Gencode (version 18) was performed using featureCounts (v1.4.3)48. DESeq2 (doi:10.1101/002832) was used to normalize feature counts. Picard and RSeQC49 was used to collect QC metrics (http://broadinstitute.github.io/picard/).RNA sequences were aligned to the NCBI human genome build 37 using STAR aligner (v2.3.1z)52. The nanoStringNorm procedure as implemented in the nanoStringNorm Bioconductor package was used to preprocess the miRNA data, followed by differential expression analysis with Empirical Bayes methods via the limma Bioconductor package53. miR pathway analysis was performed with Diana Tools miRPath v2.054.Post-alignment normalization of the RNAseq data and differential expression analysis was performed using Voom in the limma package from Bioconductor55. A standard HapMap reference included in the APT toolbox was used to represent a diploid genome. Circular binary segmentation as implemented in the DNAcopy  package from Bioconductor, using standard settings56. The GISTIC2.0 algorithm was applied to the segmented data via the genome pattern online tool suite, using default settings, to make gene level copy number calls and identify significantly, copy number altered regions57.The Affy Power Tools (APT) copy number workflow was used to preprocess the SNP arrays and derive log R ratios representing relative copy number abundance58. Unsupervised cluster analyses were performed using the hclust function with Euclidean and Pearson Correlation distances and Ward’s method. Analyses of overall and progression-free survival were performed under Cox proportional hazards assumption, with a P value of 0.05 considered statistically significant. Differentially expressed genes were considered statistically significant at a Benjamini–Hochberg FDR of 10%59. A t-test was used to test for association between STIC status and age, and Fisher’s exact test was used to evaluate potential associations between STIC status and clinical covariates including race, stage, platinum response, and surgical outcome.Statistical analyses were performed and figures prepared in the R statistical software suite using standard functions and custom routines13, together with a set of 100 genes selected to optimally classify HGSC tumors. We defined a prototype expression profile for each subtype by calculating the average expression for each gene, over samples assigned to the subtype. Then, for each tumor sample, we calculated the correlation with each subtype profile, definitively assigning the tumor to the best-correlated class only when there was a single, clear best match. Specifically, a definitive assignment was made only if the maximum correlation was at least 1.5 × the second highest value.The TCGA network identified 4 subtypes of ovarian cancer based on RNA expression patterns53. The 50 most differentially expressed genes from each tissue-specific analysis were combined into a signature of tissue type. Tumors were matched to normal expression profiles obtained from normal ovarian, fallopian tube and peritoneal tissue, by correlation as described above for TCGA expression subtypes. Specifically, correlation coefficients were calculated between each tumor and each normal pool, over the tissue-specific genes. Each tumor was assigned to the tissue type with the highest average correlation.Tissue-specific expression patterns were identified by performing differential expression analysis comparing each tissue type to the other twoOur key assumption was that distinct cancer types would show moderate differences (0.75–1.25 s.d.) in expression in many genes and large differences (>1.5 s.d) in some genes, with similar differences in other data types. Accordingly, we developed a study with sufficient power to detect most differences in this range, reasoning that a negative result would provide strong evidence in support of the single origin theory. We calculated power by simulation, assuming that t-tests are used to detect differential expression, with type-I error controlled at an FDR of 0.10.Assuming that even 50 out of 20,000 genes were differentially expressed, with 40–50 samples per group, we expect to detect many differentially expressed genes at an effect size of 0.75 s.d., most genes at an effect size of 1.0 s.d., and virtually all with an effect size of 1.5 s.d. or above, as shown below. Detection rates are higher when more genes are differentially expressed (Supplementary Table https://gdc.cancer.gov/. The authors declare that all the other data supporting the findings of this study are available within the article and its supplementary information files and from the corresponding author upon reasonable request.All the genomic data including microRNA, RNAseq and copy number data have been deposited in the GEO database under the accession code GSE102094. The TCGA data referenced in this study are available in a public repository from the National Cancer Institute’s Genomic Data Commons at Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4 Supplementary Data 5Supplementary Data 6


The advance of omics technologies has made possible to measure several data modalities on a system of interest. In this work, we illustrate how the Non-Parametric Combination methodology, namely NPC, can be used for simultaneously assessing the association of different molecular quantities with an outcome of interest. We argue that NPC methods have several potential applications in integrating heterogeneous omics technologies, as for example identifying genes whose methylation and transcriptional levels are jointly deregulated, or finding proteins whose abundance shows the same trends of the expression of their encoding genes.We implemented the NPC methodology within “omicsNPC”, an R function specifically tailored for the characteristics of omics data. We compare omicsNPC against a range of alternative methods on simulated as well as on real data. Comparisons on simulated data point out that omicsNPC produces unbiased / calibrated p-values and performs equally or significantly better than the other methods included in the study; furthermore, the analysis of real data show that omicsNPC (a) exhibits higher statistical power than other methods, (b) it is easily applicable in a number of different scenarios, and (c) its results have improved biological interpretability.The omicsNPC function competitively behaves in all comparisons conducted in this study. Taking into account that the method (i) requires minimal assumptions, (ii) it can be used on different studies designs and (iii) it captures the dependences among heterogeneous data modalities, omicsNPC provides a flexible and statistically powerful solution for the integrative analysis of different omics data. Recent developments in various high-throughput technologies have heightened the need for integrative analysis methods. Nowadays, several studies measure heterogeneous data modalities, as for example methylation levels, protein abundance, transcriptomics, etc., on the same or partially overlapping biological samples/subjects. The key idea is to measure several aspects of the same system in order to gain a deeper understanding of the underlying biological mechanisms. In such settings, a common tasks is identifying molecular quantities that are (a) measured by different omics technologies, (b) related to each other , and (c) that are conjointly affected by the factor(s) under study or associated to a relevant outcome, in a statistically significant way. A typical example is the identification of differentially expressed genes that are also characterized by one or more differentially methylated epigenetic markers –3. OtherMore in general, the presence of multiple omics data allows the identification of “differentially behaving genes”, i.e., genes that are affected by the factors under study in one or more of the transcription, translation or epigenetic levels.In this work we introduce and evaluate a novel application of a known statistical methodology, the Non-Parametric Combination (NPC) of dependent permutation tests , for theNPC has several advantages that make it especially suitable for being applied on the analysis of multiple, heterogeneous omics data. Particularly, NPC:▪allows the integration of data modalities characterized by different encodings, ranges and data distributions▪employs minimal assumptions; particularly, it does not assume independence across data modalities, taking in due account correlations among datasets▪provides an interpretable metric as final output, a p-value, which reflects the overall evidence of rejecting or not the global null hypothesis▪identifies changes that are supported by at least one modality, assigning a lower p-value to findings which are supported by more modalities▪provides the user with the flexibility of weighting differently the information from each dataset based on biological knowledge and experience▪exhibits higher statistical power than analyzing each data modality in isolation and thus increase the number of true findings.NPC is a general methodology, and must be tailored on the idiosyncrasies of the specific data at hand. In order to allow the application of NPC on omics data, we realized the R function omicsNPC, freely available in the STATegRa R-Bioconductor package . The omiWe characterize the performances of omicsNPC in comparison with other methods for the integrative analysis of omics information. To this end simulated as well as real data are used. In the simulated data, each data modality is first analyzed independently using an appropriate statistical approach, and then all data modalities are conjointly analyzed using in turn omicsNPC and other, alternative integrative methods. The results show that the p-values provided by omicsNPC are calibrated, meaning that they follow a uniform distribution when no association is present between any data modality and the outcome of interest. Leek and Storey  showed tWe further explored NPC/omicsNPC capabilities on three separate, real data applications: (a) the identification of gene/protein pairs that are deregulated in Glioblastoma, (b) pathway enrichment analysis on expression / methylation profiles in Schizophrenia, and (c) the integration of RNA-seq and microarray expression profiles measured on the same Breast Cancer subjects. The results of these different case-studies pointed out that applying omicsNPC provides more biological insights than analyzing each modality in isolation.The simplest approach adopted in the literature for assessing the conjoint deregulation of multiple, linked molecular quantities consists in analysing each data modality separately, using one of the numerous available methods ,16, and Meta-analysis methods are a more theoretically grounded alternative . Meta-anCombining p-values approaches has become popular in the integrative analysis of datasets with common properties , where tCombining ranks approaches require the genes to be ranked in each study / data modality according to some measure of differential expression. Then a combined rank is computed by taking the product, the mean, or the sum of the partial ranks. The statistical significance of the combined rank is usually assessed through a permutation-based procedure . The iniFinally, meta-analysis approaches typically do not consider correlations that may be present among the different data modalities ,26. In a1, M2, …, Mi) over the same biological samples. We also make the simplistic assumption that each modality provides exactly one measurement for each gene; dropping this assumption is discussed later in the text. Our question of interest is which genes (Gs) are associated with a given outcome of interest / experimental factor, e.g., which genes behave differently between two conditions. NPC is applied by following the steps reported below and depicted in The omicsNPC function relies heavily on the Non-Parametric Combination (NPC) methodology. Here we use an example to overview the method, state some important points and show the modifications we employ in the case of omicsNPC. Let us assume that we have measured different omics modalities  permute the samples in a manner consistent with the global null hypothesis, and b) calculate the test statistic in the permuted data, urements . This isi calculate its partial p-value on the observed data, by comparing its values on the observed data, Step 4: Calculate the permutation p-values of the partial tests, namely partial p-values, Step 5: Use an appropriate convex function to combine p-values across data modalities into a single “global” test statistic. A final vector of length B + 1 is produced by combining the p-values computed at step 4. The first element Step 6: Calculate a p-value of the global test. Similarly to the partial p-values, the global p-value is computed by equation (1), Step 7: Assess the evidence of the global null hypothesis by using the global p-value. Each partial hypothesis is evaluated based on its partial p-value. If necessary, both global and partial p-values should be adjusted for multiple testing.An important point of this methodology is the selection of a convex function to combine p-values in step 5. Pesarin and Salmaso  discuss The Tippett function, i such that ∑wi = 1, the Fisher function becomes In contrast, the normal combining function, Liptak, In the simplified explanation presented in the “Non-Parametric Combination methodology” section, each omics dataset was assumed to provide exactly one measurement for each gene. This is often not the case in real studies, where each data modality is usually defined over a distinct set of measurements, and the relation among these sets is often complex and strictly dependent on their biological significance. For example, multiple methylation markers can be associated with the transcriptional activity of the same gene; furthermore, this association is not always certain, especially in case of markers close to the genomic region of the gene but not physically laying on it. The same applies for genomic variants, for the interplay between genes and proteins, or proteins and metabolites.1 can be mapped to multiple features in data modality M2. A first possible approach is pairing each M1 measurement in turn with all their corresponding M2 measurements. Another solution is summarizing the information from each group of M2 measurements corresponding to a single feature in M1. If methylation and expression data are available, the first approach leads to pairing the expression value of each gene with the methylation level of each epigenetic markers attributable to that gene, in turn. In contrast, the second solution requires to compute a single methylation value for each gene.The NPC methodology requires features measured by different data modalities to be mapped to each other. While the exact mapping strategy can be defined only in the context of each specific study, general guidelines can be provided for the general case where each measurement from data modality MIntegrating more than two modalities requires more complicate mapping strategies. If suitable, a convenient solution is defining a reference set of elements, typically genes, and summarizing all data modalities in such a way that there is one measurement for each element and data type.The NPC was originally conceived for co-analyzing datasets measured over the exact same samples. This is often too restrictive in biology, where different datasets might share only part of the samples. For example, proteomics profiles might be available for all the patients involved in a study, while expression profiles are available only for a subset of them. The simplistic solution of co-analyzing only the samples in common clearly discards potentially relevant information. We argue that a better solution is co-analyzing all samples, by preserving the same permutation schema for the overlapping ones. In such a way the correlation among datasets are preserved, while all available information is employed in the analysis.global indicates that not all data modalities comply with the global null-hypothesis; however, it does not indicate which omics datasets departs from it. To this scope, the partial p-values A significant global p-value λWe implemented the “omicsNPC” function within the STATegRa  BioconduWe evaluated the performance of the omicsNPC function in an exhaustive comparative study over simulated data. The scope of these analysis is to quantify the capability of each method in retrieving the genes that show a deregulated behavior in one or more heterogeneous datasets.These simulations investigate how omicsNPC and the other methods behave when analysing data characterized by encodings and distributions that commonly occur in biological studies. Particularly, we simulated count data following a negative binomial distribution (representative of RNA-seq data), continuous data distributed (approximately) normally (microarray data), three-level ordinal data , as well as data generated by the uniform distribution (for reference purposes).For sake of simplicity, across all experimentations we assume a case-control study design, where the factor under study is a dichotomous variables and no additional covariates are taken into account. The issue of mapping features across different data modalities is not addressed in these analysis, and each data modality is assumed to provide one measurement for each gene.For each simulation we fixed the total number of measurements, Num_genes, and Num_samples, the total number of samples per group. The RNAseq dataset, namely RNAseq_sim, was simulated using the R package compcodeR , while tWe perform two main experiments using simulated data, named Different Modalities and Correlated Modalities. The first investigates the impact of combining statistics / p-values computed with different statistical tests, while the latter focuses on the impact of correlation among data modalities. In both cases the objective is discriminating deregulated elements from the others.▪Different Modalities: The purpose of this experiments was to identify how the performances of the different integrative methods are influenced by a) the number of samples, b) the number of modalities and c) the number of permutations. Regarding a), we set the simulation parameters as follows: Num_genes = 2000, RNAseq_DE = 1600, Microarray_DE = 1200, SNP_DE = 800, Unif_DE = 400 and Common_DE = 400. The number of samples per group was varied within {4, 6, 8, 10}. Regarding b), we set the simulation parameters as in a), except for the number of samples that was set to 10 and the number of datasets that was varied between 2 and 4. Regarding c), we generated data for all four data modalities as in a), with the Num_samples set to 10, and the number of permutations varied within {100, 500, 1000, 2000, 5000}. We stress that no correlation structures were added across data modalities in these experiments.▪Correlated Modalities: In this scenario we simulated two correlated microarray datasets. We set Num_genes = 2000, Num_samples = 10, Microarray_DE = 1000, Common_DE = 1000. We introduced different levels of correlation among the non-differential expressed genes of the two datasets, specifically Cor_level = {0.6, 0.7, 0.8, 0.9, 1}. We forgo introducing a fixed amount of correlation among differentially expressed quantities, since to the best of our knowledge there is no procedure able to ensure both a given amount of correlation and the desired level of differential expression. We also avoid adding within-dataset correlations, since in the NPC framework each gene is analysed across datasets independently by the others, and thus within-dataset correlations would not affect the behaviour of the omicsNPC function.We analysed RNAseq_sim, Micr_sim, SNP_sim and Unif_sim either (a) independently using respectively voom/limma, limma, the R package scrime  and the We compared omicsNPC against several integrative analyses methods often used in the literature:NPCno-correction: we use the standard NPC methodology without correction for zero p-values, in order to evaluate the relevance of this modification.RankSum: this is a standard ranks combining method, where the sum of the ranks is used for providing a statistic of global differential expression.omicsNPCRankSum: we use the NPC permutation schema for providing p-values for the combined rank computed with the RankSum approach. More specifically, the sum of the ranks is used as global test statistic and its null distribution is generated through permutations, as depicted in panels c) and d) of Figure A in RankProd : this approach follows the same steps as RankSum but the product of the ranks is used as the global statistic instead of the sum.omicsNPCRankProd: as in omicsNPCRankSum, the omicsNPC permutation schema is employed to generate p-values for assessing the significance of RankProd statistics.Combining P-values (CP): we use the standard meta-analysis process of combining p-values. Specifically, here we employ the classical Fisher combination method [Benjamini : Benjamini and Heller proposed a method for testing whether at least u out of n partial null-hypotheses are false. Their method is valid also when some specific dependency structures hold among the partial p-values, and the global p-value is computed as:j have been ordered so that λj ≤ λj+1. When u = 1, the null hypotheses of the Benjamini and NPC method coincide. Interestingly, for u = 1 the Benjamini equation reduces to the Benjamini-Hochberg formula for controlling the false discovery rate [n method . Benjamiery rate .We evaluated the capability of each method in discriminating differentially expressed quantities by employing the partial AUC, a metric commonly used in Information Retrieval applications. The Receiver Operator Characteristic (ROC) Area Under the Curve (AUC) , is a meIn all experiments conducted in this study, the whole procedure from data simulation to pAUC calculation was performed 20 times and the median pAUC was calculated. Finally, we applied the two sided Wilcoxon test to evaluate the significance of the differences in performances between the best method and all the other ones.We performed a separated analysis for assessing the correctness  of the p-values computed by each integrative method through the Joint Null Criterion, JNC . AccordiAlternatively, the JNC criterion can also be checked by computing at each iteration the posterior probability that the p-values come from a uniform distribution . Both dkWe further applied omicsNPC in three different case-studies, for demonstrating (a) omicsNPC applicability on the integration of heterogeneous datasets, and (b) the increase in relevant biological findings obtained by integrating several omics datasets.We co-analyzed a gene expression  and a meProteomics and transcriptomics profiles of Glioblastoma patients were downloaded from the The Cancer Genome Atlas (TCGA) repository. We used the data as preprocessed by the TCGA consortium (“Level 3” preprocessing); expression data were measured with the Affymetrix HT Human Genome U133 microarray, while proteomics information was produced with the reverse phase protein array technique . Both daThe expression profiles of 16 distinct BReast invasive CArcinoma (BRCA) patients were downloaded from TCGA repository. For each patient two types of tissues are available, i.e., tumor and healthy ones, and each tissue is profiled with three different technologies, “RNAseq”, “RNAseqV2” and “Exp-Gene”, leading to a total of six expression profiles for each patient. RNAseq and RNAseqV2 are both produced by mRNA sequencing but with slightly different pipelines, while the Exp-Gene dataset is generated with microarray technology. The selected 16 patients belong to the same experimental batch, namely batch 93. We used the data as pre-processed by the TCGA consortium at “Level 3”, meaning that count matrices are provided for the sequencing datasets and normalized expression values for the microarray samples. In total, 16146 genes were in common among all datasets and were subsequently analyzed.In all case-studies the datasets were first analyzed in isolation with limma (voom/limma for RNAseq data). Furthermore, datasets were co-analyzed with omicsNPC by setting the number of permutations to 10000. All Schizophrenia and Glioblastoma analyses were corrected for age and gender.We applied the joint null criterion to evaluate whether the p-values produced by each method follow the desired uniform distribution when the null-hypotheses are all true. To this end, we simulated 1000 times either two uncorrelated or two perfectly correlated microarray datasets, and we applied the double KS test process.no-correction generated slightly non-calibrated p-values, in both uncorrelated and correlated scenarios. In the uncorrelated scenario . In the correlated scenario NPCno-correction generated non-calibrated p-values, regardless of the combining function used  and analyzed them either independently or by employing integrative analysis methods. We evaluated performances by employing the pAUC metric. First, we restricted our analysis on genes that have the same behavior across all modalities, i.e., they are either deregulated in all datasets or in none of them, and examined the effect of the sample size on the performances. Overall, integrative approaches performed better in identifying differential expressed genes, in comparison with methods that analyze each data modality independently .RankSum and RankSum, whereas for bigger sample sizes CP was as well performing as omicsNPCFisher.This stands also for low sample sizes (samples per group = 4). In all cases CP performed equally or slightly better than omicsNPC, however the observed differences were often not statistically significant: when the sample size was equal to 4, CP performed equally well with omicsNPCFisher performed equally well with CP in all other cases . Also in this case, omicsNPC retrieves additional findings that seem to have biological relevance: proteins Myc, MSH6 and STAT3 were significant at 0.1 FDR level for omicsNPC, even if they were not significant when proteomics and transcriptomics data were analyzed in isolation. Genes encoding Myc, MSH6 and STAT3 are well known for their role as oncogenes –64. MoreIn this study we have shown that the Non-Parametric Combination, NPC, is a valuable methodology for the integration of heterogeneous omics datasets. NPC is particularly useful for identifying molecular quantities that, considered together, are deregulated / associated to an outcome of interested in a statistically significant way.We realized the omicsNPC function for facilitating the analysis of omics data within the NPC framework. The function is freely available in the R Bioconductor package STATegRa, and is able to address a variety of different study designs as well as analyzing datasets having only part of their samples in common.Fisher performed better than other methods in most scenarios conducted in this study. When no correlation structures were present in the data, the CP method was slightly more performant than omicsNPCFisher, with the observed differences often being not statistically significant. In contrast, when between-datasets correlation structures were introduced, omicsNPC outperformed all other methods, while approaches that did not take those correlations into account performed poorly, achieving lower performances than single-dataset analyses for moderate to high correlation levels  versatile enough to be used for the integration of different omics data; (b) able to include co-variates into the analysis and process sets of datasets that share only part of the samples; (c) finally, omicsNPC have shown, at least in the context of our analysis, to produce additional, relevant biological insights with respect to analyzing data modalities in isolation. Particularly, we concentrated on results deemed statistically significant by omicsNPC but discarded by single-dataset analysis, retrieving interesting findings both at gene , pathway  and disease levels (gynecologic cancer diseases in BRCA).In conclusion, several important features of the NPC methodology make it appropriate for the integrative analysis of omics data. First, the underlying assumptions are minimal: NPC permutation schema requires observations to be exchangeable under the null hypothesis, a requirement common in permutation-based approaches. Furthermore, the partial p-values are assumed to be adequate for assessing the partial null-hypothesis, marginally unbiased and consistent , while tFinally, we would like to underline the limitations of the present study. First, we restricted our simulations to the special case where exactly one measurement per gene is provided by each data modality, and this is rarely the case in real applications. All analyses were performed solely in the context of case-control studies, without taking into consideration other types of study designs. The performances of the different integrative methods, as well as the joint-null criterion, were evaluated only on simulated data, since the ground-truth necessary for the evaluation is not available in the case-studies on real data.To the best of our knowledge, this is the first study investigating the applicability of the Non-Parametric Combination methodology in the analysis of heterogeneous omics data. Our results indicate omicsNPC and the NPC framework as versatile and valuable tools for performing integrative analyses in biological studies.S1 FileThis file contains Figure A, which explain OmicsNPC employing ranking methods, Figures B–K which illustrate the diagnostic plots of the joint null hypothesis criterion. Further it includes Figure L which demonstrates how omicsNPC performances are influenced by the number of permutations. Finally it includes Tables A–F, which report the median pAUCs values for the experimentations on simulated data and their respective significance levels. Table G reports the number of deregulated genes in the BRCA case-study.(PDF)Click here for additional data file.S2 File(XLSX)Click here for additional data file.


L1/2 constraint (L1/2 gLPCA) on error function for feature (gene) extraction. The error function based on L1/2-norm helps to reduce the influence of outliers and noise. Augmented Lagrange Multipliers (ALM) method is applied to solve the subproblem. This method gets better results in feature extraction than other state-of-the-art PCA-based methods. Extensive experimental results on simulation data and gene expression data sets demonstrate that our method can get higher identification accuracies than others.Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adopting  With the rapid development of gene-chip and deep-sequencing technologies, a lot of gene expression data have been generated. It is possible for biologists to monitor the expression of thousands of genes with the maturation of the sequencing technology –3. It isOne striking feature of gene expression data is the case that the number of genes is far greater than the number of samples, commonly called the high-dimension-small-sample-size problem . TypicalThe high dimensionality of data poses a great challenge to the research, which is called data disaster.Facing with millions of data points, it is reasonable to consider the internal geometric structure of the data.Gene expression data usually contain a lot of outliers and noise, but the above methods cannot effectively deal with these problems.Traditional dimensionality reduction methods have been widely used. For example, Principal Component Analysis (PCA) recombines the original data which have a certain relevance into a new set of independent indicators –11. HoweWith the development of graph theory  and maniLp  (0 < p < 1)-norm was getting more and more popular to replace L1, which was first proposed by Nie et al. , there is no obvious difference. However, when p ∈ 9], L0 PC, L1 PCA , gLPCA, , L1 PCA  on simulA ∈ Rk×j, where k = 2000 and j = 10 are the number of genes and samples, respectively, the simulation data are generated as A ~ . Let k = 1,…, 50, and k = 51,…, 2000;  k = 51,…, 100, and k ≠ 51,…, 100;  k = 101,…, 150, and k ≠ 101,…, 150;  k = 151,…, 200, and k ≠ 151,…, 200. Given a matrix E ~ N as a noise matrix with 2000-dimension and different Signal-to-Noise Ratio (SNR), which is added into 4 can be expressed as k = 1,2, 3,4. Let the four eigenvectors dominate; the eigenvalues of A can be denoted as c1 = 400, c2 = 300, c3 = 200, c4 = 100, and ck = 1 for k = 5,…, 2000.Here, we describe a method to produce simulation data. Supposing we generate the data matrix x-axis is the number of samples. x-axis is the value of parameter μ. The accuracy is defined as follows:t is the iterative times and Acci is the identification accuracy of the ith time. We define Acc as follows:r denotes the number of genes, δ is a function that equals to 0 if m ≠ n and equals to 1 if m = n. We use the function map(I) to map the identification of labels. In L1/2 gLPCA works better than other six methods with higher identification accuracies. This means that our algorithm has lower sensitivity to noise and outliers. This table clearly displays the detail of the identification accuracies in different sparse parameters; our method indicates the superiority when the parameter is larger than 0.4 and the curve is more stable. The accuracy of L0 PCA and L1 PCA starts a precipitous decline when the parameter is larger than 0.7 and 0.8. Compared with L0 PCA and L1 PCA, the methods of L1/2 gLPCA, RgLPCA, gLPCA, PCA, and LE are not sensitive to the parameter, so there is no substantial change. The stability and average accuracy of various methods can be seen from In order to give more accurate experiment results, the average values of the results of 30 times are adopted. For fairness and uniformity, 200 genes are selected by the five methods with their unique parameters. Here, we show the accuracy (%) of these methods. In L1/2 gLPCA, gLPCA, RgLPCA, PCA, and LE. For L0 PCA and L1 PCA, we do not change its parameters, since it can obtain the best result from the author's description. The details of average identification accuracies which use seven methods with different sample numbers can be seen from L1/2 gLPCA is generally better than other methods and increases with the increase of the number of samples. Besides, Furthermore, the number of samples in real gene expression data has a significant influence on the identification accuracy when we select feature gene. Based on this theory, we test different numbers of samples with their best parameters and the average values of the results of 30 times. From the results of https://toppgene.cchmc.org/enrichment.jsp. We set P value cutoff to 0.01 through all the experiment. For fair comparison, about L1/2 gLPCA, RgLPCA, and gLPCA, we both set β = 0.5 to control the degree of Laplacian embedding through all experiments in this paper. When β = 0, β = 1, it results in standard PCA and LE, respectively. Since our algorithm is not sensitive to parameter μ mu in practice, we set μ = 0.3 through our experiment.In this subsection, the features (genes) are selected by these methods and sent to ToppFun to detect the gene-set enrichment analysis, which is a type of GOTermFinder . The prihttps://sites.google.com/site/feipingnie/file. It is made up of 11 types of acute myelogenous leukemia (AML) and 27 types of acute lymphoblastic leukemia (ALL) [P value and hit count of top nine terms about molecular function, biological process, and cellular component of ALLAML data by different methods are listed in P value is significance for these genes enrichment analysis in these GO terms; the smaller the P value is, the more significant these GO terms are. In this Table, the number of hits is the number of genes from input, and the P value was influenced by the number of genes from input and so on. Thus, the difference in number of hits is smaller than the difference in P value. It shows clearly that our method performs better than compared methods in 8 terms. The lower P value shows that the algorithm is less affected by noise and outliers and thus has high efficiency. If the algorithm is affected by noise and outliers significantly, the degree of gene enrichment will be reduced. Nevertheless, LE has the lowest P value in term GO: 0098552. From this table, we can see that there are 93 genes in the item of “immune response” which are selected by our method. This item can be considered as the most probable enrichment item, since it has the lowest P value. And many researches were focused on the immune status of leukemia [The data of ALLAML as a matrix includes 38 samples and 5000 features (genes), which are publicly available at ia (ALL) . This daleukemia –32. Besileukemia . And 30 leukemia . The higL1/2 gLPCA are proved based on gene-set enrichment analysis (GSEA) that is publicly available at http://software.broadinstitute.org/gsea/msigdb/annotate.jsp. We make analysis by GSEA to compute overlaps for selected genes. For the sake of the correlations between the selected genes and ALLAML data, the genes selected by https://tcgadata.nci.nih.gov/tcga/) has been producing multimodal genomics, epigenomics, and proteomics data for thousands of tumor samples across over 30 types of cancer. At the same time, as a multidimensional combination of data, five levels of data are involved, such as gene expression (GE), Protein Expression (PE), DNA Methylation (ME), DNA Copy Number (CN), and microRNA Expression (miRExp). Two disease data sets are downloaded from TCGA to be analyzed in the following two experiments. Pancreatic cancer is a type of disease that threatens human health. In this experiment, pancreatic cancer gene expression data (PAAD-GE) is analyzed by these methods. The data of PAAD-GE data as a matrix includes 180 samples and 20502 features (genes). In this subsection, we extract PAAD-GE data to complete this set of comparative experiments and 500 genes are selected and sent to ToppFun. We select top nine terms from molecular function, biological process, and cellular component by L1/2 gLPCA and compare with other methods. The P value and hit count of these terms are listed in P value in 7 terms. But in terms GO:0045047 and GO:0072599, PCA performs better than other methods. Nevertheless, L1/2 gLPCA has the same P value with gLPCA in terms GO:0045047 and GO:0072599. 196 genes in the item of “extracellular space” are selected by our method.As the largest public database of cancer gene information, The Cancer Genome Atlas . As can be clearly seen from the table, most of these genetic lesions would likely incur pancreas-related diseases. The etiology of pancreatic cancer is not very clear; it is noted that there is a certain relationship between the incidence of chronic pancreatitis and pancreatic cancer, and we find a significant increase in the proportion of chronic pancreatitis patients with pancreatic cancer. This view is consistent with our experimental result. The clinical observation shows that abdominal pain is the most obvious symptom in the early stage of pancreatic cancer. Some literature on these genes also made a further research as follows. The gene PRSS1 variant likely affects disease susceptibility by altering expression of the primary trypsinogen gene [The function of top 7 genes selected by gen gene . The pangen gene .http://www.genecards.org/.Because ALLAML and PPAD are human disease data sets, we can find them directly from GeneCards and they are publicly available at L0 PCA and L1 PCA, demonstrating the usefulness of PCA and LE. (2) gLPCA has a good performance in some conditions and is unstable. Thus, it is necessary to reduce the effects of outliers and noise. (3) L1/2 gLPCA and RgLPCA consistently perform better than other methods, but L1/2 gLPCA has the highest relevance score and highest accuracy.In order to summarize the experiments on gene expression data, we compute the accuracy and highest relevance score of these methods from GeneCards and list the details in L1/2 gLPCA) by applying L1/2-norm constraint on the former method. L1/2-norm constraint is applied on error function to improve the robustness of the PCA-based method. Augmented Lagrange Multipliers (ALM) method is applied to solve the optimization problem. Extensive experiments on both simulation and real gene expression data have been performed. Results on these two kinds of data show that our proposed method performs better than compared methods. Based on our proposed method, many genes have been extracted to analyze. The identified genes are demonstrated that they are closely related to the corresponding cancer data set.This paper investigates a new method of graph-Laplacian PCA (In future, we will modify the model to improve sparse and robustness of the structure at the same time.In the supplementary data, "resultALL_AML_data" are the top 500 genes found by our method on the ALLAML dataset, "find_official_name" is the way to find the official name of these genes and "0.5gLPCA" is the experimental code of our method.