The availability of genomic datasets in association with clinical, phenotypic, and drug sensitivity information represents an invaluable source for potential therapeutic applications, supporting the identification of new drug sensitivity biomarkers and pharmacological targets. Drug discovery and precision oncology can largely benefit from the integration of treatment molecular discriminants obtained from cell line models and clinical tumor samples; however this task demands comprehensive analysis approaches for the discovery of underlying data connections. Here we introduce PATRI , a standalone tool accessible through a user-friendly graphical interface, conceived for the identification of treatment sensitivity biomarkers from user-provided genomics data, associated with information on sample characteristics. PATRI streamlines a translational analysis workflow: first, baseline genomics signatures are statistically identified, differentiating treatment sensitive from resistant preclinical models; then, these signatures are used for the prediction of treatment sensitivity in clinical samples, via random forest categorization of clinical genomics datasets and statistical evaluation of the relative phenotypic features. The same workflow can also be applied across distinct clinical datasets. The ease of use of the PATRI tool is illustrated with validation analysis examples, performed with sensitivity data for drug treatments with known molecular discriminants. The recognition of cancer as a genetic disease has raised in recent years huge “omics” efforts that have generated extensive molecular information on cancer cell lines and tumor samples, along with clinical characterization and drug sensitivity information. These data are often accessible through public resources, such as CCLE , 2, TCGA KRAS gene mutations in the observed resistance to cetuximab and panitumumab treatment in colorectal cancers samples [ EGFR mutated tumors [ BRAF mutated tumors [NTRK1-2-3) in tumors harboring activating rearrangements of these kinases [Biomarkers resulting from the complex task of complementing cancer preclinical findings with clinical knowledge have found application as prognostic or diagnostic indicators, favoring the design of companion diagnostics for targeted drugs and facilitating therapeutic developments . This is samples , 30 or o samples  and of ad tumors , 33. Othd tumors  or to lad tumors , as well kinases , 37. Ind kinases , 39. Esp kinases , 41.Platform for the Analysis of TRanslational Integrated data” (PATRI) tool, requiring data imported by users and integrating this workflow with an intuitive user-friendly graphical interface. First, drug response information is required to set up statistical analyses for the identification of potential drug sensitivity baseline genomic traits in cell lines  or in tumor samples ; lists of resulting relevant markers can then be used to predict genomics responsiveness in annotated datasets of tumor samples, which are stratified into putative “Sensitive” or “Resistant” populations by the algorithm and correlated with the respective relevant clinical characteristics.The aim of our work was to provide a flexible and quick solution to streamline an analysis workflow for the search of potential treatment biomarkers across preclinical and clinical datasets and to make it accessible for application to user-provided genomics and treatment sensitivity data. For this purpose, we designed the “https://www.parametricdesign.bio/.PATRI is conceived for basic end-users and is freely distributed as a virtual machine, portable on Windows, Linux, and MacOS platforms. The PATRI tool is accessible for open download at  PATRI Platform User's Guide (Suppl. File) and in the PATRI Platform Installation and Configuration Guide (Suppl. File) downloadable documents. Export of analysis results was enabled as tab-delimited text files. All PATRI functions were made accessible through a web graphical user interface developed using Joomla and Zend Frameworks. Statistical analysis libraries from CRAN [https://www.parametricdesign.bio/ as an Oracle VM Virtual Box file, populated with an artificial demonstration dataset, which can be removed and replaced with the desired data following instructions in the PATRI Platform User's Guide (Suppl. File).In PATRI, the Database Management System (DBMS) implementation was based on MariaDB. The database schema . All cell lines were authenticated by STR analysis  using the GeneMarker HID v 2.4.0 software  and comparative analysis was performed with CLIFF .Genomics data for 1036 cell lines were imported from CCLE ; mutatioPlatform for the Analysis of TRanslational Integrated data), an open-source tool offering a flexible genomic data integration resource to basic end-users for the identification of predictive biomarkers of differential sensitivity to drugs or any other treatments, such as siRNA or CRISPR-Cas9, starting from user-provided data. Central to the design was the ease of use, through an intuitive graphical user interface, based on a simple workflow of streamlined data analysis, extraction, and visualization procedures, directly correlating biomarkers identified in cell line or tumor sample genomics data to clinical information, aided by the introduction of mouseover and pop-up interactive options. PATRI is a web-based application  on panels of cancer cell lines treated with the well-known targeted drugs lapatinib, vemurafenib, or entrectinib and tested PATRI for the ability to identify significant treatment sensitivity-related molecular markers through the “Translational” workflow , using d ERBB2 amplified breast cancers in combination with capecitabine or letrozole [ ERBB2 kinase gene amplification and overexpression were correctly identified by PATRI within a group of lapatinib sensitive versus resistant breast cancer cell lines , neighboring the ERBB2 coding sequence [∧-4, log2 FC> |1.5|) revealed a considerable number of known or predicted protein interactions, supporting the functional interconnections in the selected list (not shown). Using the “Translational” workflow implemented in PATRI, the above marker list was used to categorize potentially “Sensitive” or “Resistant” cases in a panel of breast cancer clinical samples from TCGA data collection with the respective associated clinical feature annotations via random forest classification. In the resulting breast sample hierarchical analysis heatmap, most of the predicted “Sensitive” breast cancer samples were clustered in a compact group , likely driven by the other overexpressed and amplified genes from the “ERBB2 amplicon” included in the signature.Lapatinib  is a duaetrozole . In our ll lines  tested ill lines . Many ofsequence –68. STRIsequence  of the 1ct group , charactct group  having mct group . Gene li BRAF activating mutation, being inactive on WT BRAF cells [ BRAF was the only feature common to the 3 highly sensitive melanoma cell lines, but also present in the resistant RPMI-7951, harboring a V600E BRAF mutated gene , a gene participating in the mismatch repair (MMR) system. Indeed, BRAF mutations have been observed to frequently occur in colorectal tumors cases with MSI characterized by deficient DNA mismatch repair (dMMR) [ ALPK2 kinase to be preferentially mutated in BRAF wt-vemurafenib resistant melanoma cell lines; mutations in ALPK2 have been proposed to be involved in cutaneous melanoma [ BRAF, MSH3, and ALPK2 mutations for the execution of the “Translational” workflow on a set of clinical genomics data for about 470 melanoma samples from the TCGA database [ BRAF and WT ALPK2; only a small fraction of melanoma samples showed mutated MSH3 without a clear clustering pattern  is a B-Raf inhibitor approved for the treatment of late-stage melanoma. It selectively inhibits melanoma cells harboring the V600EAF cells . Using tted gene . This cer (dMMR) . Besidesmelanoma . Due to database . Hierarc pattern . In the a tumors . We repe NPM-ALK rearrangement [−7, logFC> |5|, Figures −5, logFC> |4.5|) not containing ALK, though with a less defined heatmap “Sensitive” versus “Resistant” cluster pattern (not shown). In GSE14879, predicted “Sensitive” samples included most of the ALK-positive ALCL samples and also included 5 Peripheral T-Cell lymphoma, unspecified (PTCL-NOS) samples, however displaying again ALK-positive diagnosis and lower age among the top ranking significant clinical associated parameters (not shown). Mutational analysis of the entrectinib-treated lymphoma cell line panel did not provide significant results, while copy number analysis with ANOVA revealed only two markers with significant microalterations, namely, TCR gamma alternate reading frame protein (TARP) loss and ADAM metallopeptidase domain 6 pseudogene (ADAM6) gain in entrectinib sensitive, ALK-positive cell lines. The significance of these two markers could not be explored using the PATRI translational workflow, since only gene expression data were available for the same samples in the two lymphoma clinical datasets.We then considered a panel of lymphoma cell lines formerly tested in our labs for sensitivity to entrectinib  and Figu, a new Tangement , and all−10, logFC> |1|) was used for exploration and sensitivity prediction in the lymphoma GSE19069 dataset .We have proposed examples of use of PATRI with in-house sensitivity data from representative targeted drugs with well-established mutated or overexpressed biomarkers; however, PATRI might also be applied to support the identification of new relevant biomarkers and indicators of sensitivity in other types of treatments, such as RNA interference or CRISPR/Cas9 screenings, as well as for the evaluation of their frequency and relevance in the clinics.The PATRI structure can be integrated with further analysis methods, available as R packages, making the tool a suitable platform for future implementation of innovative analysis approaches in biomarker discovery, such as the integration of novel prediction algorithms –86, poss


The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not yet been established. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~3000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size. There has been a rapid rise in single cell RNA-seq methods and associated pipelines. Here the authors use simulated data to systematically evaluate the performance of 3000 possible pipelines to derive recommendations for data processing and analysis of different types of scRNA-seq experiments. All these goals have the first analysis steps in common in that they require expression counts or normalised counts. Here, we focus on these important first choices made in any scRNA-seq study, using DE-inference as performance read-out. Benchmarking studies exist only separately for each analysis step, which are library preparation protocols3, alignment5, annotations6, count matrix preprocessing8 and normalisation9. However, the impact of the combined choices of the separate analysis steps on overall pipeline performance has not been quantified. In order to achieve a fair and unbiased comparison of computational pipelines, simulations of realistic data sets are necessary. This is because the ground truth of real data is unknown and alternatives, such as concordance analyses are bound to favour similar and not necessarily better methods.Many experimental protocols and computational analysis approaches exist for single cell RNA sequencing (scRNA-seq). Furthermore, scRNA-seq analyses can have different goals including differential expression (DE) analysis, clustering of cells, classification of cells and trajectory reconstruction10. As the basis for simulations, powsimR uses raw count matrices to describe the mean-variance relationship of gene expression measures. This includes the variance introduced during the experiment itself as well as extra variance due to the first to computational steps of expression quantification. Adding DE then provides us with detailed performance measures based on how faithfully DE-genes can be recovered.To this end, we integrate popular methods for each analysis step into our simulation framework powsimR11. This assumption is no longer true when diverse cell types are considered. For example, Zeisel et al.12 find up to 60% DE genes and differing amounts of total mRNA levels between cell types. This issue of asymmetry is conceptually one of the characteristics that distinguishes single cell from bulk RNA-seq and has not been addressed so far. Therefore, we simulate varying numbers of DE-genes in conjunction with small to large differences in mRNA content including the entire spectrum of possible DE-settings.One main assumption in traditional DE-analysis is that differences in expression are symmetric. This implies that either a small fraction of genes is DE while the expression of the majority of genes remains constant or similar numbers of genes are up- and down-regulated so that the mean total mRNA content does differ between groupsRealistic simulations in conjunction with a wide array of scRNA-seq methods, allow us not only to quantify the performance of individual pipeline steps, but also to quantify interdependencies among the steps. Moreover, the relative importance of the various steps to the overall pipeline can be estimated. Hence, our analysis provides sound recommendations regarding the construction of an optimal computational scRNA-seq pipeline for the data at hand.13) and four UMI methods16. The UMI strategies encompass two plate-based  and the most common non-commercial and commercial droplet-based protocols . CEL-seq2 differs from SCRB-seq in that it relies on linear amplification by in vitro transcription, while SCRB-seq relies on PCR amplification using the same strategy as 10X Chromium . We then combine the library preparation protocols with three mapping approaches20 and three annotation schemes23 resulting in 45 distinct raw count matrices (see “Methods”). We simulated 27 distinct DE-setups per matrix, each with 20 replicates, resulting in a total of 19,980 simulated data sets , (2) alignment to the transcriptome (BWA19) and (3) pseudo-alignment of reads guided by a transcriptome .We then combined these with three annotation schemes including two curated schemes (RefSeq21 and Vega23) and the more inclusive GENCODE22 ,  alignmenFirst, we assessed the performance by the number of reads or UMIs that were aligned and assigned to genes Fig. . AlignmeThis said, it remains to be seen what impact the differences in read or UMI counts obtained through the different alignment strategies and annotations have on the power to detect DE-genes.2, UMI protocols have a noticeably higher power than Smart-seq2  on normalisation performance. Note, that we use the imputation or filtering only to obtain size factor estimates, that are then used together with the raw count matrix for DE-testing.If the main reason why normalisation methods perform worse for scRNA-seq than for bulk data is the sparsity of the count matrix, reducing this sparsity by either more stringent filtering or imputation of missing values should remedy the problemWe find that simple frequency filtering has no effect on normalisation results Fig. . PerformThese observations suggest that data sets with a high gene dropout rate might benefit more from imputation than data sets with a relatively low gene dropout rate . The same is true for annotation (ΔR2 ≤ 2%) and aligner choices (ΔR2 ≤ 5%). For aligner and annotation, it is important to note that these are upper bounds, because our simulations do not include differences in gene detection rates  explain ~50 and ~60% of the variance in performance, for 20 and 60% DE-genes, respectively Fig. . Choicestes Fig. .Fig. 5EvR2 = 12–38%), where the importance increases with increasing levels of DE-genes and increasing asymmetry. Spike-ins are only necessary if many asymmetric changes are expected and have little or no impact if only 5% of the genes are DE or the changes are symmetric  pipeline to identify DE-genes between the cell types. Cross-referencing the identified DE-genes with known differences in marker gene expression39, we find that the good pipeline always identifies a higher fraction of the marker genes as DE than the naive pipeline try Fig. . In part10. Hence, it is not surprising that in concordance with Soneson et al.31, we find that relatively straight forward DE-testing methods adapted from bulk RNA-seq perform well with scRNA-seq data.The choice in DE-testing method, our final pipeline step had relatively little impact on overall pipeline performance. A good normalisation prior to DE-testing alleviates the need for more complex and thus vulnerable methods, such as for example MASTs hurdle model which implicitly assumes that the CPM values were generated from zero inflated negative binomial count distribution. Indeed, we previously showed that also scRNA-seq data fit a negative binomial distribution rather well and that the previously reported zero-inflation in scRNA-seq data is mainly due to amplification noise which is removed in UMI-dataFinally, we want to remark that paying attention to the details in a computational pipeline and in particular to normalisation pays off. The effect of using a good pipeline as compared to a naively compiled one has a similar or even greater effect on the potential to detect a biological signal in scRNA-seq data as an increase in cell numbers from 96 to 384 cells per group Fig. .2 43 were spiked in for all methods except Drop-seq. All raw cDNA sequencing reads were cut to a length of 45 bases and downsampled to one million cDNA reads per cell .Finally, we added a 10X Chromium data set sequencing mouse NIH3T3 cellsThese choices of library preparation protocols cover the diversity in current protocols without imposing partiality due to biological differences and technical handling.44 (v.0.0.3) pipeline with STAR18 (v.2.5.3a) and the mouse genome (Mus_musculus.GRm38) together with annotation files (gtf) for GENCODE (vM15), Vega (VEGA68) and RefSeq (Release 85)  to align the scRNA-seq reads to these transcriptomes. We only removed reads that aligned equally well to transcripts of different genes as truly multi-mapped. The remaining reads were tallied up per gene. For UMI data, the reads were collapsed per gene by identity, similar to the strategy recommended in zUMIs.For transcriptome alignment, we downloaded transcriptome fasta files corresponding to the annotations listed above. We used BWA24 (v0.43.1), a transcriptome-guided pseudo-alignment method, we followed the recommended quantification procedure for scRNA-seq data to yield abundance estimates per equivalence class. To be comparable with other alignment methods, the counts per equivalence class were collapsed per gene. The counts of equivalence classes representing multiple genes were filtered out. For SCRB-seq, CEL-seq2, Drop-seq and 10× Genomics libraries, we chose the UMI-aware quantification option. The ERCC spike-in sequences were appended to the genome or transcriptome sequences for quantification.For kallisto10. PowsimR has been independently validated for benchmarking DE-approaches31 and consistently reproduces the mean-variance relationship and dropout rates of genes of scRNA-seq data ) ) defining the log2 fold change, which is then added to the sampled mean expression. The combination of these parameters results in a total of 27 DE-setups that were then applied to the parameter estimates from 37 different count matrices to simulate 20 replicates for each setting, producing a total of 19,980 simulated data sets.For a detailed evaluation of the pipelines, we simulated two groups of cells for pairwise comparisons with the following three sample size setups: 96 vs. 96, 384 vs. 384 or 50 vs. 200 cells Fig. . For sim1)) Fig. . This di31 and used in conjunction with the raw count matrices for DE-testing using four representative approaches . The resulting p-values were corrected for multiple testing with Benjamini-Hochberg FDR and we applied a threshold level of 10% to define positive test results. All these steps were seamlessly implemented into powsimR (github: https://github.com/bvieth/powsimR). In total we analysed 2,979 different RNA-seq pipelines.These data sets were then analysed by a nearly exhaustive number of combinations of four imputation strategies , gene dropout filtering  together with seven normalisation approaches  with or without spike-ins, depending on library preparation protocol and method Fig. . Normali49 (v.7.3–51.1)  of a robust linear model using the difference between estimated and simulated library size factors as response variable in rlm implemented in R-package MASS50. We then summarise these curves by their partial area under curve (pAUC) of TPR versus observed FDR that still ensures FDR control at the nominal level of 10% Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


A commercially available microtiter plate reader was applied as a high-throughput counterpart of ultraviolet-visible (UV–Vis) spectrophotometer to identify the producing location of extra virgin olive oils (EVOOs). Multiplicative scatter correction and the first derivative was used to denoise the UV–Vis spectra and eliminate the effects of background drift. The spectra were analyzed using chemometrics methods including the principal component analysis (PCA) and the partial least squares-discriminant analysis (PLS-DA). The PLS-DA model on full spectra using 5 latent variables showed a classification accuracy of 97.92% by cross-validation. The overall results demonstrated that the use of a UV–Vis spectrophotometer based on the microtiter plate reader combined with chemometrics can be applied to the quality assessment of EVOOs. It is demonstrated that the microtiter plate reader can be a high-throughput tool in the quality assessment of food ingredients. Olive oil comprises about 4% of the total vegetable oil production . It is uBecause of the increasing popularity of the Mediterranean diet and remarkable nutritional properties, olive oil consumption and production had been expanding to nontraditional producing countries during the past two decades, which may result in inferior quality . This glThe classical chemical analysis procedures, including the analyses of fatty acids, polyphenol and phenolic acid, have been reported as sources of analytical data to establish olive oil region discrimination using chemometrics patterns ,13. HoweThe fingerprinting methods are usually combined with chemometrics to resolve the complex and convoluted nature of spectroscopic fingerprint data. Chemometrics involves a wide range of techniques. Specifically, preprocessing, classification, and clustering have been widely applied in spectroscopic data modelling, and are able to determine which grade or what producing location the commodity belongs to for the quality assessment of food. For instance, Forina et al. combined artificial nose, NIR and UV–Vis spectroscopy, using stepwise-linear discriminant analysis (STEP-LDA) as a feature selection technique, and built a model with linear discriminant analysis (LDA) and quadratic discriminant analysis-UNEQual dispersed classes (QDA-UNEQ) to verify the protected designation of origin (PDO) of Italian olive oil . Alves eThe nondestructive, fast and real-time UV–Vis spectroscopic method may constitute one of the most promising and effective detection methods for olive oil quality control. However, the shortcomings of its complicated operation are prominent when the number of samples expands greatly. In particular, it requires manual operation in loading the sample into cuvette. Secondly, the cuvette must be cleaned between each measurement. The residue may increase the deviations of measurements. As a result, traditional commercially available benchtop UV–Vis spectrophotometer models may not be able to meet the need without modification. Increasing sample throughput and incorporating automation are needed.Unlike the traditional UV–Vis spectrophotometer, the multi-row porous characteristic of multi-well plate furnishes the microtiter plate reader with the ability to detect samples in a high-throughput, fully automatic manner. The microtiter plate reader is also a popular and versatile instrument for a conventional biochemistry laboratory, because it offers flexibility and ease of use over a broad range of applications, such as enzyme-linked immunosorbent assays, protein detections and drug screening assays. Measurements performed by the microtiter plate reader save time and effort of sample-loading, as well as decreasing possible deviations among parallel groups.In this work, a high-throughput determination of the geographic origin of extra virgin olive oil (EVOO) by the microtiter plate reader instead of a benchtop UV–Vis instrument were tested. The dataset was processed by chemometrics modelling to demonstrate a rapid method for quality assessment of EVOO. The classical UV–Vis measurements using a benchtop model were also performed and compared. This work will help to extend the capabilities of the microtiter plate reader combined with chemometrics modelling in the quality and safety assessment of food ingredients.Twelve commercially available EVOO samples produced from three countries including Spain, Italy and Turkey were purchased from local Chinese grocery stores. For each region, the sample set consisted of EVOO with four different brands. Specifically, Samples S1–S4, I1–I4, and T1–T4 were from Spain, Italy and Turkey, respectively.Eight commercially available vegetable oils  were purchased from local Chinese grocery stores. All samples were tested directly without further treatments.UV–Vis spectra data were collected at room temperature (25 °C) within 230–1000 nm, at 1 nm resolution by an Evolution 300 UV–Vis spectrophotometer . The radiation source was a xenon flash lamp. All samples were analyzed in triplicate in a rectangular quartz cuvette with a path length of 10 mm. The sample volume required for each measurement is around 3.5 mL. The UV–Vis data of the samples were compared to the microtiter plate reader assay after spectral baseline adjustment.Samples were weighted at 200 mg in quintuplicate and placed into Nunc MicroWell transparent 96-well plates (Thermo Fisher Scientific). All samples were analyzed by an Infinite M1000 PRO microtiter plate reader  at room temperature. Using a Quad4 monochromator, the microtiter plate reader gives flexibility of wavelength selection from UV to NIR. The radiation source was a xenon arc discharge lamp. The wavelength was set at 230–1000 nm with a 1 nm resolution, and the number of flashes was 25. The MTP200 dataset is then obtained from the spectra.To determine whether the sampling amount is adequate, the microtiter plate reader assay procedure was also repeated at the weight of 100, 150 and 300 mg, and obtained datasets were labelled as MTP100, MTP150 and MTP300, respectively. The density of each sample was measured by using a DDM 2910 densimeter . Because the bands between 230 and 307 nm were saturated in the MTP assay, they were removed from all the spectra and only the data ranging in 308–1000 nm were analyzed. The datasets consist of a 60 × 693 matrix, where the rows represent the analyzed samples and the columns represent the variables.The particles and turbidity of EVOO and variations in the optical path length can affect the UV–Vis spectra . TherefoIn addition to MSC, derivative methods are often used for processing one-dimensional signals to eliminate the effects of background drift. The first-level derivative eliminates the background constant and the second-level derivative eliminates the linear background shift. If the sampling wavelength intervals are equal, the first-level derivatives can be expressed asHere Classification is involved in both ‘supervised’ and ‘unsupervised’ methods. Supervised methods extract general principles from observed examples guided by a specific prediction objective . A set oThis study focuses on two multivariate analysis methods: the unsupervised principal component analysis (PCA) and the supervised partial least squares-discriminant analysis (PLS-DA). The PCA is a widely used qualitative method in spectral data analysis . The PCAThe PLS-DA combines the properties of partial least squares regression (PLSR) with the discrimination power of a classification technique . The PLSa, pheophorbide a and pyropheophytin a [α-, β-, and γ-carotene at 447, 451, 462 nm, respectively [Olive oil possesses a unique assignment of the major visible absorption bands. Its visible spectra characteristics of MTP200 are shown in phytin a . The secectively . The finectively . HoweverSince the microtiter plate reader was applied in the quality assessment of food ingredients, it is important to investigate whether the spectra obtained by the microtiter plate reader is equivalent or similar to the counterparts obtained by the benchtop UV–Vis spectrophotometer. The UV–Vis spectra of benchtop spectrophotometer are shown in Verification of the spectral similarity of two assays was carried out. Comparisons of the mean optical density/absorbance of all the EVOO samples along the entire wavelength range with different instruments and different volumes were performed. The correlation matrix is shown in Since the two instruments possess different characteristics, further assessments of the spectral similarity are necessary. A typical absorbance peak at 415 nm was selected for all the subsequent calculations. First, the linearity of the microtiter plate reader response was performed by examining different sampling amounts. The average optical density of all samples and their weight showed the linear relationship2) of 0.9996, suggesting that the EVOO measured by the microtiter plate reader is also in accordance with the Beer-Lambert’s Law.OD stands for optical density and w for weight (w/mg), with a squared correlation coefficient (R3 and the diameter of each microwell was 0.86 cm. Therefore, the estimated pathlengths for 100, 150, 200 and 300 mg EVOOs were 1.9, 2.8, 3.8 and 5.7 mm, respectively, provided that the meniscus was flat. The pathlength of the cuvette was 10 mm, which was respectively 5.2, 3.6, 2.6 and 1.8 times greater than the microtiter plate reader measurements by 100, 150, 200 and 300 mg EVOOs. In comparison, the average absorbances in 415 nm by the UV–Vis spectrophotometer were respectively 3.1, 2.0, 1.5, and 1.0 times greater than the microtiter plate reader using 100, 150, 200 and 300 mg EVOOs. According to the Beer-Lambert’s Law, the pathlength is proportional to absorbance when the absorptivity coefficient and concentration of the sample are unchanged. The actual performance differences can be due to other factors like instrument differences, such as differences in the optical parts. On the other hand, a weight of 321.58 mg would theoretically result in a similar spectrum in the microtiter plate reader as in UV–Vis spectrophotometer, provided that the optical performances were ideally equal between the two instruments. Summing up, the result indicated that the two methods can achieve similar theoretical performance. Consequently, the microtiter plate reader can be a proper alternative when measuring the absorbance of EVOOs.The relationship of actual linearity of pathlength and absorbance was also examined. The average density of the sample was 0.9113 g/cmPre-processing is frequently used to improve signal-to-noise before chemometrics modelling in spectroscopy . The ligA PCA was performed on the MSC-Der1 processed MTP200 dataset. First, the optimal number of PCs was determined before analysis. The first four PCs were accounted for 96.28%, 2.47%, 0.94%, and 0.23% of the total variance, suggesting a model with two PCs is proper to describe the overall model variance. However, it is unprecise to solely regard a small variance captured as background noise, given that such variance may bring some information regarding the small quantities of a chemical compound . TherefoThe classification model was built by PLS-DA on the MTP200 dataset. The numbers of latent variables for three class PLS-DA and the corresponding prediction accuracy were calculated. The prediction accuracy reached 97.92% in PLS-DA model built using five latent variables, indicating the feasibility to classify olive oils from different regions using the microtiter plate reader spectra. The prediction accuracy did not increase greatly as the number of latent variables increases further. The best classification accuracy was 99.58% using models built by 10 latent variables, however the high number of latent variables was not preferred because it may lead to overfitting. In summary, both PCA and PLS-DA models had adequate discrimination power in differentiating the producing regions of EVOO.An experiment was further carried out by expanding the current sample set to examine the capability of the microtiter plate reader in the quality assessment of EVOOs. The spectral data of 8 vegetable oil samples were added into the original spectral data set. Some spectra of vegetable oils contain overflown optical density readings. The overflown intensities were replaced by 4, which is the maximum indication of the microtiter plate reader. All other data-processing methods were kept unchanged from those of the original data set. From A high-throughput and rapid UV–Vis spectroscopic method using the microtiter plate reader was examined to differentiate the producing region of EVOOs. The microtiter plate reader offers high-throughput measurement with a relatively small sample volume requirement, while maintaining adequate precision and accuracy compared to its benchtop counterparts. From the spectra comparison and statistic evaluation it can be observed that the microtiter plate reader might be a valid substitute of benchtop UV–Vis spectrophotometer. In addition, the PCA and PLS-DA are proved to be successful modelling approaches applied in EVOOs region discrimination based on the microtiter plate reader spectra. Besides, the application of MSC and first derivative treatment in spectral data improved the clustering in PCAs scores scatter plots. The result suggests microtiter plate readers may potentially be applied in many other areas of food quality assessments.This study was an initial attempt to apply the microtiter plate reader as a high-throughput spectroscopic instrument. It should be mentioned that all samples in this study were bought from the market and may lack authentication, therefore the models built in this study may not be used directly for practice. More authentic samples, such as those directly obtained from related local olive oil producers, may be needed to support food authentication reliably.


International consortia such as the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA) or the International Human Epigenetics Consortium (IHEC) have produced a wealth of genomic datasets with the goal of advancing our understanding of cell differentiation and disease mechanisms. However, utilizing all of these data effectively through integrative analysis is hampered by batch effects, large cell type heterogeneity and low replicate numbers. To study if batch effects across datasets can be observed and adjusted for, we analyze RNA-seq data of 215 samples from ENCODE, Roadmap, BLUEPRINT and DEEP as well as 1336 samples from GTEx and TCGA. While batch effects are a considerable issue, it is non-trivial to determine if batch adjustment leads to an improvement in data quality, especially in cases of low replicate numbers.Cell Ontology to establish if batch adjustment leads to a better agreement between observed pairwise similarity and similarity of cell types inferred from the ontology. A comparison of state-of-the art batch effect adjustment methods suggests that batch effects in heterogeneous datasets with low replicate numbers cannot be adequately adjusted. Better methods need to be developed, which can be assessed objectively in the framework presented here.We present a novel method for assessing the performance of batch effect adjustment methods on heterogeneous data. Our method borrows information from the https://github.com/SchulzLab/OntologyEval.Our method is available online at Bioinformatics online. A growing number of international consortia such as The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) project and the International Human Epigenome Consortium (IHEC) have generated a wealth of epigenomic profiling data of cell lines, sorted primary cells and tissue samples. These data will be of tremendous help in unraveling mechanisms of cell differentiation and in identifying patterns of epigenetic dysregulation in various diseases. A number of studies have shown that joint analysis of data from multiple projects enable novel applications of biological relevance (Various approaches have been developed for batch effect adjustment (BEA)  and are In this study, we are interested in learning how methods for BEA can be compared best and to understand where methods fail in such difficult application scenarios. A common approach for assessing BEA performance is visual inspection in reduced dimensions  before and after BEA. However, we and others find this visual inspection to be highly subjective and non-interpretable, especially if the batch is not associated to the highest variance present in the data  gene expression (RNA-seq) data. Additionally, we consider a less diverse set comprised of uniformly processed data from TCGA  with Salmon (v. 0.8.2) using reference transcript sequences from Gencode Release v26 (GRCh38.p10). ENCODE accession numbers, DEEP and Blueprint sample IDs, tissue/cell-type assignments as well as sample number per consortia are listed in We downloaded FASTQ files for 36 ENCODE and 112 Roadmap RNA-Seq experiments from the ENCODE web portal. Furthermore, we obtained FASTQ files for 12 samples from the DEEP data portal and for 56 samples from the BLUEPRINT data portal. Gene expression is quantified in FASTQ files for 6575 RNA-Seq samples were downloaded from the GTEx project (RNA-Seq data is mapped against the human genome version hg19 with TopHat2 (v.2.0.12)  and Biocmode = Union, inter.feature = FALSE. Only primary read alignments are retained. Sample normalization values of the counts are calculated using DESeq2  between terms it and jt using both Jaccard coefficients (jacsim) and cosine similarity (cossim) (A(it) returns the set of ancestors for a given term in the cell ontology. Note that A(it) contains it as well and that only subclass relationships are considered. For the cosine similarity we obtain a vector representation t. The vector has |CL | entries, where |CL | is the number of terms in the Cell Ontology and each entry corresponds to one term. We set every entry in A(t) to one. The Jaccard similarity and cosine similarity are then defined asBetween all terms included in the Cell Ontology terms. Using the entire set of similarities for all terms and the manually generated sample-term mappings, we can generate matrices holding pairwise sample similarities sim for any combination of samples by considering the respective similarity measures sim and sample types jacO and cos.O We point out that these similarity matrices can be easily converted to distance matrices by An example is provided in % of variance) we compute a sample similarity matrix D based on Spearman correlation.In order to generate a gene expression-based distance matrix for all samples, we run a PCA on a full RNA-seq dataset of interest, thereby reducing its dimensionality. The first Principal Components (PCs) capture most of the variability between the samples, thus they can be used to distinguish the samples from each other. Using the first four PCs   . SVA estRUV) is an alternative approach that uses a set of control genes to identify factors associated with batch effects . RUV is applied through the RUVNormalize R-package (v 1.12.0). In this study, we provide Combat with information on the present batches, i.e. the data source, as well as the tissue assignment of the samples. In SVA, we define the probability for a gene g to be a control gene gpc asσ(g) being the standard deviation of the expression of gene g across all samples and rank returns the corresponding rank of σ(g) compared to all genes. The motivation for this is to preferably use genes as controls that have stable expression across samples. SVA is also provided with the tissue to sample assignment. For RUV, we use a set of housekeeping genes suggested by Cell Ontology for computing ontology scores. Second, we add artificial noise to GTEx data to assess the robustness of the score. Third, we add an artificial batch effect to GTEx data to investigate if the ontology score drops as expected and to study to what extent the score can be recovered through BEA. Finally, we apply the ontology score to TCGA, GTEx and IHEC data to demonstrate what can be learned from this score when applied to batch-adjusted heterogeneous data.We developed the ontology score to assess if BEA is beneficial on heterogeneous datasets. To assess if the score fulfills this expectation, we present several analyses. First, we use randomization experiments to assess the benefit of using the To learn about the robustness of the ontology score, we conduct randomization experiments using GTEx data. To this end, we generate 100 sets in which each sample is assigned a random tissue-term from the available ontology terms. For each set, we compute corresponding expected distance matrices and subsequently recompute the ontology scores.As shown in In N to all genes across all tissues for different fractions of GTEx samples reaching from 0 to 50%. As shown in μ ≥ 10. Similar observations can be made with alternative scores  we combined data from TCGA and GTEx as well as (ii) data from the IHEC members Blueprint, DEEP, ENCODE and Roadmap.RUV method does not perform well according to the ontology score, while SVA seems to be able to successfully adjust GTEx data, while exhibiting poor performance on TCGA data. Combat shows only marginal improvement in the ontology scores. SVA introduces negative scores for prostate samples  originating from four consortia, namely DEEP, Blueprint, Roadmap and ENCODE. Here, only few samples are available per tissue/cell-type and the overlap of tissues/cell-types being present across multiple consortia is small.RUV method seems to perform favorably for DEEP and Roadmap data, whereas it is the worst performing method on Blueprint data. For the latter, Combat shows the highest increase in the ontology score. Interestingly, according to the ontology score, no method leads to an improvement on ENCODE data. Similar observation can be made for the alternative score variations (As shown in riations . In a diRUV performs well on hepatocyte data (see also Combat or SVA while others are either unaffected or show a decrease in ontology score.Our ontology score allows a cell-type/tissue specific analysis of the data. An example is shown in see also , it doesCell Ontology. The ontology score can thus answer the question if BEA leads to a better separation of samples according to the relative similarity changes to other sample types. Second, it allows for different BEA methods to be compared against each other and to identify strengths and limitations of each approach.Heterogeneous datasets such as the one produced by IHEC do not yet offer sufficient sample numbers to leverage existing BEA methods without careful considerations. A major concern is that the procedure may further reduce statistical power or, in the worst case, introduce an additional confounding variable that obscures group differences such as those between sample types. To our knowledge there is no objective approach for assessing the quality improvement of BEA on heterogeneous datasets with few sample numbers. This motivated us to develop the ontology score, which offers two major advantages in comparison to existing methodology. First, it overcomes the problem that few samples of the same type are available by comparing each sample to all other samples. This is achieved by utilizing information captured in the Cell Ontology leads to significantly higher scores than a randomized assignment of labels or an ontology in which we only recognize sample type identity but use a default for all other sample types. While this is encouraging and demonstrates that the ontology score works as expected, it should be noted that the ontology score is also limited by the quality of the ontology. Indeed it is not likely that biological reality can be fully captured in an ontology as previous studies have already shown. For instance, cell differentiation during hematopoiesis is more complex than previously believed (To characterize the ontology score we used GTEx gene expression data from five different tissues. Our results show that using the Moreover, we point out that it is not clear when a good ontology score is reached. In fact, the ontology score can only be used for relative comparisons, e.g. between original and batch-adjusted data or between datasets adjusted through different methods.Finally, the quality of the results depends on the assignments between sample type labels and the ontology terms. We did this in a manual, error-prone fashion and learned in the process that it is not always straightforward to find the correct assignment. We thus encourage consortia to incorporate expert-curated mappings to ontology terms in their meta data.We further study how the ontology score is affected by adding varying degrees of noise to the data. Here, the ontology score is reduced as expected, highlighting that it may also be used for assessing the performance of normalization methods (as illustrated in Another possible concern is the influence of sample numbers. In the GTEx data this did not seem to play a major role. For instance, thyroid and colon are represented through large sample numbers, yet thyroid samples achieved far higher scores than colon samples.SVA, Combat and RUV to a mixed dataset of GTEx and TCGA data, the ontology score suggests that BEA was generally not beneficial. The reason for this could be that TCGA control samples, which are collected in the vicinity of a tumor, are markedly different from non-cancerous tissue samples (SVA batch adjustment. However, the ontology score reveals that prostate samples do now show a negative correlation with the expected similarities, indicating that SVA may have isolated the different sample types perfectly, while removing important information that characterize group differences. This is not surprising, since SVA attempts to maximize the differences between the sample types. This means that the ontology score, by leveraging the additional information provided by the ontology, can be used to test whether a BEA method removed important group characteristics. By considering how relative similarities to other sample types are affected, the ontology score gains an advantage over alternative methods such as the mixture score, which only test if individual sample types cluster well. Considering only the latter would not indicate an issue in this case.When applying The IHEC data is the most heterogeneous example we consider in this study with very few samples overlapping between different projects. Blueprint, DEEP and ENCODE samples, which consist mainly of primary cells, benefit little from BEA. Roadmap samples, which are mostly obtained from tissues, show a more pronounced improvement depending on the method used. Here, we speculate that BEA may be more meaningful for tissue samples in which a heterogeneous mix of different cell types is distorting the results. Removal of a latent variable representing, for instance, the contribution of immune cells to a tissue sample, could explain the increase in the ontology score. Primary cells, on the other hand, are already purified and do thus not benefit from this procedure.SVA and Combat often outperformed RUV, likely because they received the sample type labels as input. The poor performance of the RUV method for GTEx and TCGA data, may be due to our choice of housekeeping genes. On the other hand, we find examples where RUV leads to considerably higher ontology scores than all other methods, e.g. in DEEP and Roadmap samples, where it improved the clustering of hepatocyte and kidney samples considerably (iderably .For the future, we see a demand for the development of novel BEA methods that can accommodate the heterogeneity observed in large international consortia like IHEC. We note that this task could be tremendously simplified by including a set of reference samples such as a panel of cell lines into each batch of a project and recommend considering this for future studies. Already today, empirical Bayes methods exist that can utilize such reference samples even when batches are otherwise unbalanced with respect to their sample types (Another major challenge in batch effect analysis is the lack of meta data annotation, which forces data analysts to rely on surrogate variables such as the processing date. This has been realized and efforts for harmonizing such data in IHEC are currently under way.Here, we use RNA-seq data as the most widespread data source used in genomics studies. However, batch effects are a concern in all types of genomic and epigenomic data (Finally, we envision that the availability of multi-omics data of the same samples, as is the case for IHEC, offers the opportunity to develop BEA methods that adjust for batch effects in a joint model that borrows information from other data types (In recent years, more and more emphasis has been put on collaborative efforts for generating molecular profiling data. While many genomics projects handle impressive sample numbers, this is not yet the case for relatively new technologies allowing to study the epigenome. Moreover, many projects focus on a particular condition, such as cancer, whereas the data available through the IHEC consortium is very heterogeneous and includes cell lines, primary cells and tissues for samples from healthy donors as well as samples from patients carrying various diseases.While it is always preferable to include batch variables directly in statistical analysis, many downstream analysis tools do not offer this possibility. This leads to the requirement of BEA as an essential part of integrative analysis. However, it is currently unclear how useful established methods are on epigenetic data, in particular considering that these methods have been developed with larger sample numbers and balanced datasets in mind in which all sample types are included in all batches.The ontology score we propose here is, to our knowledge, the first to robustly assess the performance of BEA, in particular when biologically relevant group variables such as the sample type are part of the adjustment procedure. Moreover, this approach is not limited to batch adjustment but can also be used to show if data normalization leads to improved results.Supplementary DataClick here for additional data file.


Genomic profiles among different breast cancer survivors who received similar treatment may provide clues about the key biological processes involved in the cells and finding the right treatment. More specifically, such profiling may help personalize the treatment based on the patients’ gene expression. In this paper, we present a hierarchical machine learning system that predicts the 5-year survivability of the patients who underwent though specific therapy; The classes are built on the combination of two parts that are the survivability information and the given therapy. For the survivability information part, it defines whether the patient survives the 5-years interval or deceased. While the therapy part denotes the therapy has been taken during that interval, which includes hormone therapy, radiotherapy, or surgery, which totally forms six classes. The Model classifies one class vs. the rest at each node, which makes the tree-based model creates five nodes. The model is trained using a set of standard classifiers based on a comprehensive study dataset that includes genomic profiles and clinical information of 347 patients. A combination of feature selection methods and a prediction method are applied on each node to identify the genes that can predict the class at that node, the identified genes for each class may serve as potential biomarkers to the class’s treatment for better survivability. The results show that the model identifies the classes with high-performance measurements. An exhaustive analysis based on relevant literature shows that some of the potential biomarkers are strongly related to breast cancer survivability and cancer in general. Despite the fast increase in the breast cancer incidence rate, the survival rates have also increased due to improvements in the treatments because of new technologies . Breast Traditional laboratory techniques like CAT scans and magnetic resonance imaging (MRI) have been proven to be useful. However, they provide very little information about the mechanism of the cancer progression. On the contrary, advances in DNA microarray technology have provided high throughput samples of gene expression. Machine learning approaches have been utilized to detect breast cancer treatment or survivals . many reUsing samples from patients with high-risk clinical features in the early stages of breast cancer, In this present paper, we are extending an earlier supervised learning model that shows preliminary results to predict which BC patients will survive beyond 5 years after undergoing a given treatment therapy . This exWe used a publicly accessible dataset that contains samples for 2,433 breast cancer patients . The genTo avoid overfitting, we performed the filter feature selection first for each class, before running the wrapper feature selection or even the classification model on all the samples from all classes. The number of genes after the filter feature selection for each class are reported in Based on the available data, only three treatment therapies are covered in this study: surgery, hormone therapy, and radiotherapy. Our model uses hierarchical classifiers to classify one-versus-the-rest classes. The classes are imbalanced. Hence, standard classification methods will yield poor performance results. The pipeline starts with feature selection methods like Chi-square  and infoThe patient class distribution for the studied model is shown in This model uses a one-versus-rest scheme to tackle the multi-class problem, which leads to an imbalanced class dataset at each node of the classification model. Therefore, we applied the following techniques to handle this issue:Oversampling the minority class by using synthetic data generators. Several algorithms are used to achieve this. We used one of the most popular ones, SMOTE .Using penalizing models that apply additional weight to the minority class to achieve class balancing. This, in turn, biases the model to pay more attention to the minority class than others. The algorithm used in this work is called Cost-Sensitive Classifier in Weka machine learning tool using a penalty matrix to overcome the imbalance .Replicating the dataset can be using one of two methods: (1) adding copies of the data instances to the minority class, which is called over-sampling (2) deleting some instances of the majority class, which is called under-sampling. We used the over-sampling technique .The gene expression dataset contains 24,368 genes for each of the 347 samples. The curse of dimensionality makes it difficult to classify the dataset in its current form. Thus, engaging in feature selection is essential to narrow down the number of genes to a handful at each node. Chi-square and Info-Gain are applied to select the best information gain of the selected genes, this step  will drop down the number of genes to a couple of hundreds based on the correlation between each class and the gene expressions based on the default correlation threshold in WEKA. After that, mRMR is applied to identify the best subset of significant genes. mRMR  is an algorithm that is commonly used in greedy searches to identify the characteristics of features and correctly narrow down their relevance.In the trial to find the best feature selection wrapper method to select handful gene biomarker for each class, we applied both mRMD 2.0 and mRMR on the filtered genes on each class. mRMD 2.0 outperformed mRMR fourth and the fifth node as seen in We applied a multi-class approach, the one-versus-rest technique. This approach involves classifying one class against the remaining classes and then removing that class from the dataset. Afterward, we selected another class to classify it against the rest, and so on. Using a greedy method to find the starting node, the method involves classifying all possible combinations, such as DH, against the rest, then DR against the rest, and so on for all six classes. Afterward, the best starting node is selected as the root node for the classification tree based on the best performance.Several classifiers were tested to achieve these results, including random forest, SVM, and naive Bayes, random forest outperformed the others and has shown a better classification power for the hierarchical model. Therefore, we used it in all nodes. The classification model was built using 10-fold cross-validation. The data is divided into 10 equal folds of samples, then the learning method will loop 10 times, at each time, it will learn from 90 folds and test on the remaining (left out) fold. At each time in the loop, it will take out a unique fold that has not been shown up in the previous loop steps as a left out. The 10-fold modules will increase the learning samples to 90% of the samples, while it will test on 100% of the samples. The sample will be classified around 9 times; the class is voted more will be considered as the predicted class. The accuracy and other performance measurements are calculated based on the testing folds; therefore, the accuracy here is a testing accuracy.The developed multi-class model also shows the final results for each node and the performance measures that were considered, such as accuracy, sensitivity, F1-measure, and specificity. Moreover, it also shows the number of the correctly and incorrectly classified instances in each node.In Our method was used to identify the 47 gene biomarkers that are listed in At each node, we tried different standard classifiers to select the classifier with the best accuracy at that node as seen in A combination of gene regulation analysis and biological analysis have been done to validate some of the biomarker genes. Biological validation was carried out using relevant literature . Figure x-axis represent the gene expression values in the column side, where the y-axis represents the gene expression values at the row side.In the first node, FGF16 gene is a member of the fibroblast growth factors (FGFs) family, which is involved in a variety of cellular processes, such as stemness, proliferation, anti-apoptosis, drug resistance, and angiogenesis . Figure In the second node, ASAP1 is shown to be a breast cancer biomarker; it is precisely correlated to its invasive phenotypes that have not been accurately identified . Sabe etin situ DCIS  was found to be a direct target of miR124 in breast cancer . Nam et Regarding the fifth node, The use of a machine learning model for identifying gene biomarkers for breast cancer survival is a significant step in determining the proper treatment for each patient and will potentially increase survival rates. This study analyzes the gene activities of the survival vs. deceased for each therapy, and the potential biomarkers will help to identify the best therapy for the patients based on their gene expression test. This model has very high accuracy levels, and it uses a hierarchical model as a tree that includes one-versus-rest classifications.The computational model pulls sets of biomarkers for patients who received different treatments. These biomarkers can be used to distinguish whether the patient survived or died in a 5-year time window for a specific treatment therapy. Related literature was used to verify the relationships between these biomarkers and breast cancer survivability.Future work includes testing these gene biomarkers in biomedical labs. This novel model can be improved to be used to identify the proper biomarker genes (signature) for different cancer types or even in cases in which patients need or have received more than one type of therapy. Considering additional patient data will enable researchers to cover all missing treatments. With this considerable data size, big data tools, such as Hadoop and Spark, can be utilized to devise an enhanced model.http://www.cbioportal.org/study?id=brca_metabric.Publicly available datasets were analyzed in this study. This data can be found here: AT and AA applied the method. AT retained the results. All authors have equally contributed in brainstorming and writing the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Differential expression analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is important how to select features which best discriminate between different groups of patients. Despite the emergence of multivariate analysis approaches, prevailing feature selection methods primarily focus on multiple hypothesis testing on individual variables, and then combine them for an explanatory result. Besides, these methods, which are commonly based on hypothesis testing, view classification as a posterior validation of the selected variables.Based on previously provided A5 feature selection strategy, we develop a joint covariate detection tool for differential expression analysis on tumor expression profiles. This software combines hypothesis testing with testing according to classification results. A model selection approach based on Gaussian mixture model is introduced in for automatic selection of features. Besides, a projection heatmap is proposed for the first time.http://bio-nefu.com/resource/jcd-dea.Joint covariate detection strengthens the viewpoint for selecting variables which are not only individually but also jointly significant. Experiments on simulation and realistic data show the effectiveness of the developed software, which enhances the reliability of joint covariate detection for differential expression analysis on tumor expression profiles. The software is available at The online version of this article (10.1186/s12859-019-2893-3) contains supplementary material, which is available to authorized users. The ys (SAM) , provide testing , 7. The rrection . Besidesrrection .2-test [Apart from multiple hypothesis testing on individual variables, multivariate hypothesis testing which indicates whether two distributions of samples are differential or not  holds a2-test ), genes In fact, it has been pointed out that hypothesis testing is regarded to be explanatory, while classification-based methods are viewed to be predictive . As to mIn this paper, we propose a joint covariate detection software for differential expression analysis on tumor expression profiles . In addition, we make three more improvements. Firstly, a model selection method based on Gaussian mixture model (GMM)  is introOur JCD-DEA is concisely expressed, as illustrated in Fig. At step A2, values of expressions or projections with two kinds of classification labels are resampled at 90% in each dimension. Welch’s t-test is used on the one dimensional values of two categories for hypothesis testing. Permutations of data are alternatively utilized for overcoming the limitation of sample size. In addition, a classifier is trained using resampled 70% specimens and tested using the left 30% samples. An average classification error rate is calculated after certain rounds of resampling. More details about step A1 and step A2 can be seen in .p-values derived from hypothesis testing and average classification error rates are confirmed respectively. An intersection of features derived from the two minimum-mean-value Gaussian components respectively for hypothesis testing and classification-based testing is obtained and voted with one score for bonus point, as labeled with symbol At step A3, hypothesis testing results are combined with those of classification-based testing. Unlike the voting strategy applied in , a GMM-bmentclass2pt{minimmentclass2pt{minimBased on proposed bottom-up enumeration strategy on features with different dimensions, the above procedure is repeated beneath the upper bound of computing capacity. Tuples with different dimensions are voted and accumulated. GMM-based model selection  is againmentclass2pt{minimAt step A5, we present a projection heatmap other than traditional expression heatmap for further decision. Projection values are derived from the expression values of selected candidates using the same projection method at previous steps. In fact, the thought of using a projection heatmap derives from the procedure of accumulations on classification results. Following the treatment of using projections at step A1 and step A2, it is obvious to use projection values for clustering other than to use simple expression values. The performance of candidates with different dimensions is evaluated by their projection heatmaps. According to Occam’s razor criteria , a candiJCD-DEA is written mainly in MATLAB, distributed under GNU GPLv3. Variables which are either individually differential or jointly significant for distinguishing between groups of samples are identified. Due to the lack of adjacent normal tissues in some cancer diseases , Fisher’s linear discriminative analysis (LDA) other than corresponding bilinear projection  is also Client.zip for analyzing expression profiles on personal computers or workstations, and a server part in Server.zip which is designed to run on cluster servers that using Portable Batch System(PBS) as scheduling program.Due to the existence of repeating steps in JCD-DEA, we make a two-step implementation: a client part in S1_feature_selection.m for selection of feature(s) associated with differential expression analysis, as shown in Fig. Step A1, step A2 and step A3 correspond to a MATLAB m-file S2_plot_heatmap.m for selection of feature(s) with high accumulation score(s), as shown in Fig. Step A4 and step A5 correspond to a MATLAB m-file http://bio-nefu.com/resource/jcd-dea.Detailed software documentation and tutorial are presented on In order to exhibit the effectiveness of JCD-DEA, we made a simulated data containing 500 samples equally divided into two categories in a 40 dimensional space. 34 variables of them are independently and identically distributed, each of which keeps a random mean value ranging from 10 to 30 and a same standard deviation 0.01. The left three variable pairs have jointly but not individually significant distributions respectively, subjecting to the following guidelines.miRNA-alternative 1 and miRNA-alternative 2 has a good sample distribution form and also a clear category distinction. The mean vectors corresponding to the two categories of samples are T and T. The two categories of samples keep a same covariance matrix, which is expressed as As illustrated in Fig. miRNA-alternative 3 and miRNA-alternative 4, it ought to keep a good sample distribution form but an inferior category distinction. In order to achieve the above objectives, one fifth of samples are randomly and evenly selected and exchanged between the two categories, of which the mean vectors and the covariance matrix keep the same as the former pair before sample exchange, as plotted in Fig. As to variable pair miRNA-alternative 5 and miRNA-alternative 6 appears an inferior sample distribution form but a superior category distinction. Logically speaking, this might be caused by a very small amount of singular points that significantly different from others with the same label. We’ve found this situation in the expression values of miRNA hsa-mir-450 from data set GSE22058 and make the following surmises for the existence of such points. It is just a special case among the expression values of a particular feature, and the corresponding sample should be removed in statistical view.This is caused by an unbalanced sampling, which means that there might be undiscovered samples between the singular points and others see Fig. d.Scattered as Fig. T and T and the corresponding covariance matrix In order to achieve the above objectives, five samples of each category are resampled as singular points with their mean vectors miRNA-alternative 1 and miRNA-alternative 5, which illustrates a noncorrelation across different variable pairs.Figure Significant feature may not be composed of individual variables expressed differentially between two patient groups.Significant feature ought to keep not only a good sample distribution form but also a clear category distinction.Projection heatmap corresponding to the classifier selected before may present a better clustering result other than traditional expression heatmap.In fact, we made such a simulated data in order to verify the following three facts. Fisher’s LDA was utilized for combined projection and dimension reduction projection at step A1 and the classifier at step A2. Besides, 100 rounds of resampling were performed at step A2 and step A3, with GMM priori probability for eliminating redundant Gaussian components set to 0.001. Correspondingly, GMM priori probability used at step A4 was set to 0.001.p-values of Welch’s t-test and the average classification error rate derived from 100 rounds of Fisher’s LDA trained on 70% randomly selected samples and tested on 30% rest samples were calculated. The corresponding pairwise and individual results on simulation data are listed in Tables A5 scores  together with the p-values  exhibit false positives. Besides, variable importance of each miRNA is calculated using random forest [In Table miRNA-alternative 1 and miRNA-alternative 2 which keeps a statistically good distribution and also a clear category distinction, has the highest A5 score, the minimal p-value and the smallest average of classification error rate. As to the variable pair miRNA-alternative 3 and miRNA-alternative 4 which keeps a statistically good distribution but an inferior category distinction, a smaller p-value and a bigger average of classification error rate are listed. As to the variable pair miRNA-alternative 5 and miRNA-alternative 6 which has a statistically inferior distribution but a superior category distinction, it keeps a bigger p-value and a smaller average of classification error rate. As the result indicates, only the variable pair miRNA-alternative 1 and miRNA-alternative 2 has been selected by JCD-DEA, which shows the effectiveness of our method.In Table In addition, we made projection heatmaps  as plotted in Figs. We also performed experiments on GSE6857 which is a public dataset containing 29 samples associated with metastasis cases and 102 samples corresponded to liver cancer without metastasis using linear and bilinear projection. Limited by computing capacity, we have only enumerated features in 2-dimensional space.hsa-mir-29b-1No1 and hsa-mir-338No1 has been selected with GMM priori probability set to 1e-5.Results with GMM priori probability set to 5e-5 are listed in Table However, the result is not very ideal. As shown in Fig. On account of this, we made new hierarchical clusterings using linear projection on tumor and normal tissues instead of bilinear projection based on the pair selected above respectively. We found that the result on tumor is better than normal tissues, as shown in Fig. hsa-mir-194-2No1 and hsa-mir-346No1 is selected with GMM priori probability set to 1e-5. Compared to Figs. Thus, we performed new experiments using only linear projection on tumor data with GMM priori probability set to 5e-5. Results are presented in Table As illustrated in Fig. Though improvements have been made in Fig. JCD-DEA is a bottom-up enumeration tool for seeking not only explanatory but also predictive variables associated with the categories of patients on tumor expression profiles. Other than prevailing differential expression analysis, we concern various dimensional features expressed differentially on tumor expression profiles. In order to strengthen the reliability of selected candidates, both distribution-based and classification-based testing are considered. In addition, we introduce GMM-based model selection for automatic feature selection, which helps to choose features objectively. Finally, a projection heatmap is proposed for hierarchical clustering. On account of the potential possibilities on complicated distributions of samples, we plan to develop new top-down feature selection methods in the near future.Project name: JCD-DEAProject home page: http://bio-nefu.com/resource/jcd-deaOperating system(s): Linux, WindowsProgramming language: Matlab (≥R2012b), Python (≥ 3.0)License: GPLv3Any restrictions to use by non-academics: noneAdditional file 1Pairwise results on simulation data with a descending order of A5 scores. (PDF 153 kb)


Finally, we use pan-cancer TCGA protein expression data to show the high reproducibility of CaDrA’s search procedure. Collectively, this work demonstrates the utility of our framework for supporting the fast querying of large, publicly available multi-omics datasets, including but not limited to TCGA and CCLE, for potential drivers of a given target profile of interest.The identification of genetic alteration combinations as drivers of a given phenotypic outcome, such as drug sensitivity, gene or protein expression, and pathway activity, is a challenging task that is essential to gaining new biological insights and to discovering therapeutic targets. Existing methods designed to predict complementary drivers of such outcomes lack analytical flexibility, including the support for joint analyses of multiple genomic alteration types, such as somatic mutations and copy number alterations, multiple scoring functions, and rigorous significance and reproducibility testing procedures. To address these limitations, we developed Candidate Driver Analysis or CaDrA, an integrative framework that implements a step-wise heuristic search approach to identify functionally relevant subsets of genomic features that, together, are maximally associated with a specific outcome of interest. We show CaDrA’s overall high sensitivity and specificity for typically sized multi-omic datasets using simulated data, and demonstrate CaDrA’s ability to identify known mutations linked with sensitivity of cancer cells to drug treatment using data from the Cancer Cell Line Encyclopedia (CCLE). We further apply CaDrA to identify novel regulators of oncogenic activity mediated by Hippo signaling pathway effectors YAP and TAZ in primary breast cancer tumors using data from The Cancer Genome Atlas (TCGA), which we functionally validate  Advances in high-throughput sequencing technology has led to a rapid rise in the availability of large multi-omic datasets through compendia such as the CCLE, TCGA, the Genotype-Tissue Expression (GTEx), and others . These dRecently, computational methods and models have been developed for performing driver gene analyses applied to high-dimensional ‘omics’ data from cancer cell lines and patients. These are typically motivated either by frequency or exclusivity of alterations across samples , or theifeatures , associated with a user-provided ranking of samples within a dataset. Our method specifically employs a stepwise heuristic search to identify a subset of features whose union is maximally associated with the observed sample ranking, and carries out rigorous statistical significance testing based on sample permutation, thereby allowing for the identification of candidate genetic drivers associated with aberrant pathway activity or drug sensitivity, while still exploiting aspects of feature complementarity and sample heterogeneity. To highlight the method’s overall performance, along with its relevance and ability to select sets of genomic features that indeed drive certain oncogenic phenotypes in cancer, we perform extensive evaluation of CaDrA based on simulated data, as well as real genomic data from cancer cell lines and primary human tumors. The results from simulations show that CaDrA has high sensitivity for mid- to large-sized datasets, and high specificity for all sample sizes considered. Using genomic data drawn from CCLE and TCGA, we demonstrate CaDrA’s capacity to correctly identify well-characterized driver mutations in cancer cell lines and primary tumors spanning multiple cancer types, along with its ability to discover novel features associated with invasive phenotypes in human breast cancer samples, which we functionally validate in vitro. Our framework, which is publicly available as an R package, will allow for rapidly mining numerous multi-omics datasets for candidate drivers of user-specified molecular readouts, such as pathway activity, drug sensitivity, protein expression, or other quantitative measurements of interest, further enabling targeted queries and novel hypothesis generation.Here, we present CaDrA, a methodology that searches for the set of genomic alterations, here denoted as An overview of CaDrA’s workflow is summarized in true-positive datasets consist of both left-skewed  as well as uniformly distributed  features; and (ii) the null datasets consist of null features only (see section “Methods” and n = 500 true positive and null datasets for each sample size), we first evaluated the resulting meta-features based on the number of true positive features and the total number of features contained within each returned meta-feature  significantly associated meta-features, we simulated two types of datasets for a range of sample sizes: (i) the α = 0.05 . The TPRf N = 50 . Furtherf N = 50 , the majf N = 50 .These results suggest that CaDrA requires mid- to large-sized datasets for sufficient sensitivity, while maintaining high specificity at all sample sizes assessed.BRAF and NRAS, two upstream oncogenes and potent regulators of Ras/Raf/Mek/ERK signaling (The mitogen-activated protein kinase (MAPK) kinase (MEKK)/extra-cellular signal-regulated kinase (ERK) pathway is a well-conserved kinase cascade known to play a regulatory role in cell proliferation, differentiation, and survival in response to extracellular signaling . Increasignaling . Small mignaling . To highignaling , along wignaling , to rankV600E and NRAS somatic mutations in the first two iterations, respectively. Subsequent iterations identified mutations in APAF1, TGFBR2, and AMHR2, before terminating the search process (P ≤ 0.001). APAF1 is a pro-apoptotic factor and known regulator of cell survival and tumor development  as well as its downstream effector (MEK).The resulting feature set  is shown in elopment , the depelopment . TGFBR2 elopment . Notablyelopment , suggestCollectively, these results confirm CaDrA’s capability to accurately identify upstream drivers of cellular response to treatment that are both components of independently linked pathways, as well as part of the same signaling branch, which in turn suggests their role in driving the disease state of interest.EGFR mutation status can be used to predict response to existing anti-EGFR treatments in patients with lung cancers (Tyr1068) protein expression levels to stratify TCGA lung adenocarcinomas (LUAD), and seeded the search process with EGFR mutations. Subsequent search iterations selected well-known regulators of EGFR activity in lung cancers, including mutations in epithelial-to-mesenchymal transition mediators SMAD4 and LAMC2, as well as ERBB2  expression, for instance, together with  cancers . To demoas ERBB2 , with thn = 57). In particular, CaDrA was applied to search for sets of genomic features associated with elevated protein expression for each protein under consideration. The features selected by CaDrA were then pooled across all protein queries, and the resulting feature set was tested for enrichment against the reference COSMIC list of frequently mutated oncogenes and tumor suppressor genes . We observed a significant enrichment of the reference cancer driver mutations among the CaDrA-identified features in all cancer types tested  were first ranked in decreasing order of their overall YAP/TAZ activity estimates. The ranked binary matrix of mutation and SCNA features were then used as input to CaDrA. In the first iteration, CaDrA identified the top scoring genomic feature to be a deletion on chromosomal locus chr5q21.3 , validating the association of their loss of function with increased YAP/TAZ transcriptional activity . To do so, we implemented a sub-sampling procedure and applied it to the search for YAP/TAZ activity drivers in TCGA BRCAs. Specifically, the original meta-feature returned by the search on the full dataset, and the meta-feature returned when performing the same search on a random subset (80%) of samples were compared by the Jaccard (J) index (see section “Methods”). We performed this sub-sampling search procedure both with respect to the original sample ranking (n = 100 iterations each). Comparison of the resulting J index distributions yielded a significantly higher reproducibility of results when sub-sampling from the original sample ranking, than from the randomly permuted one  were more reproducible than those corresponding to a randomly selected subset of five non-significant protein queries (CaDrA P > 0.05). To this end, the J index distribution obtained upon sub-sampling from the significant queries (n = 100 iterations each) was compared to the equivalent distribution from the non-significant queries, and a significantly higher reproducibility of the former was observed in all nine cancer types tested genetic drivers of molecular readouts is of fundamental importance to determining alternative mechanisms influencing the phenotype in question. Existing methods attempting to extract functionally relevant sets of genomic alterations associated with a given context either do not support the analysis of data beyond somatic mutations, do not incorporate multiple feature scoring functions and search modes, or do not implement rigorous statistical significance testing of the obtained results. Importantly, a computational framework package bundling all of these features does not exist, and can significantly help identify novel drivers of signature activity.NRAS and BRAF. Importantly, BRAFV600E mutations account for >90% of BRAF mutations and is generally found to be mutually exclusive to NRAS mutations , and high specificity for all sample sizes considered. Importantly, multi-omics datasets produced by networks such as CCLE and TCGA, also presented in this study, are well above this sample size limit. CaDrA’s specificity was further evident when querying genetic drivers of increased sensitivity to treatment with PLX4720, a potent and selective inhibitor designed to preferentially inhibit active B-Raf protein bearing the V600E allele  YAP/TAZ activity ranking, compared to a randomly permuted sample ranking. This sub-sampling procedure was independently assessed using a systematic pan-cancer comparison of reproducibility results from significant and non-significant protein queries, which revealed a significantly higher concordance of the former compared to the latter in all cases tested. Together, these results confirm the agreement between the estimated permutation rank-based prediction in this context, which we believe is well-suited to: (i) model the noisy relationship between (epi)genetic alterations and a functional readout, and (ii) privilege the accurate prediction of highly ranked samples over lowly ranked samples, a desirable feature when modeling oncogenic activity. Furthermore, the framework as defined is flexible enough such that non-rank-based scoring functions can be easily incorporated. We emphasize that using rank-based scoring functions, while advantageous for the reasons mentioned, rely on accurate stratification of samples based on the dependent variable to yield concordant associations for a given biological question. Thus, the soundness of predictions is dependent on the quality of signatures used to query the target profile of interest.Previously developed methods have indeed been shown to aid in the selection of functionally relevant genomic features in cancer . HoweverThe method that most-resembles CaDrA in its approach is REVEALER , an iterCurrent implementations of CaDrA and other similar methods are limited to the use of summarized input genomic features that are treated as binary events, denoting the presence or absence of a given mutation or SCNA in a sample. As we have demonstrated, this summarization approach is indeed sufficient to identifying genomic feature sets that may drive the target profile of interest. However, since different types of point mutations  may impose differing functional impacts in oncogenes versus tumor suppressor genes, we surmise that these methods could be further improved by qualitatively differentiating between the different types of alterations being considered. One possibility would be to separate mutations by predicted gain or loss-of-function, as well as to distinguish between low (1) and high (≥2) DNA copy number gains or losses, although this may lead to excessive sparsity in the input matrix for low-frequency point mutations and SCNAs.While our evaluations focused on somatic mutations and SCNAs, CaDrA’s search functionality can be applied to additional sequencing readouts capturing regulatory features, including and not limited to, DNA methylation and microRNA expression, albeit with proper discretization of these continuous features. A joint analysis of these additional data types might provide insight into epigenetic mechanisms that complement the assessed genetic features in driving phenotypic variation. Furthermore, we envision the adoption of CaDrA for the study of germ-line variation as well, thus contributing to move beyond the “one feature at a time” paradigm typical of GWAS studies, although issues of computational efficiency in that problem space will likely become more challenging.CaDrA enables the efficient identification of subsets of genomic features, including somatic mutations and SCNAs, as candidate drivers of a pre-defined phenotypic variable. Given the rapid rise in the availability of multi-omics datasets, as well as an increased need to interrogate targeted molecular readouts within these contexts, we believe that our methodology will accelerate feature prioritization for further follow-up and consideration, in turn aiding in the discovery of potential drivers of the phenotype of interest. Thus, we propose CaDrA as a tool for both targeted hypotheses testing, and novel hypothesis generation.left-skewness of a binary vector with respect to the sample ranking. The more the occurrences of an alteration are skewed toward higher rankings , the higher the score. The scores currently implemented are the KS test (default), and the Wilcoxon rank-sum test, but additional scoring functions can easily be added.An overview of CaDrA’s workflow is summarized in p-value per search result. Since the CaDrA algorithm specifically returns feature-sets maximally left-skewed given the provided sample ranking variable, it can be applied to identify features that are either positively correlated or anti-correlated with the continuous variable of interest by ranking samples in decreasing or increasing order of that variable, respectively.Given the sample ranking, the matrix of binary features, and the score of choice (KS or Wilcoxon), CaDrA implements a step-wise greedy search: it begins by first selecting the single feature that maximizes the score Step 1; . It thenCaDrA supports multiple search modalities: it allows for the selection of a user-specified feature from which to start the search , which can be visualized as a ‘meta-plot’. This includes (panels from top to bottom): an area plot of the sample-specific measurements used to obtain the sample ranks; a color-coded matrix of all features in the meta-feature (in the step-wise order that they were added), one feature per row, with the corresponding union of the meta-feature (red) last; and a corresponding enrichment score (ES) plot below. Additionally, top-N search results can be visualized for overlapping features to evaluate robustness across different search starting points.≥500 iterations . In order to speed up this potentially time-consuming task, CaDrA supports exploiting parallel computing with the help of the parallel R package functionality, should multiple compute cores be available to users.The generation of the empirical null distribution for significance testing is typically done for p-values almost instantaneously, avoiding the need for repetitive computation, thus significantly reducing overall query run time.Since the generation of the null distribution used for significance testing is a time-consuming step, and since the null distribution of scores depends solely on the feature dataset and the search parameters specified , and not on the input sample ranking, we can implement cacheing of the null distribution corresponding to each dataset and search parameters. When submitting multiple subsequent queries (each with its own sample ranking) that utilize the same dataset and search criteria, CaDrA can then fetch the corresponding cached null distribution to generate permutation https://github.com/montilab/CaDrA, and will further be deposited and maintained for future use under Bioconductor, including complete code and example use-cases.CaDrA is freely available for download and use as a documented R package under the git repository https://portals.broadinstitute.org/ccle and processed as previously described (DNA copy number (GISTIC2), mutation and RPPA data for TCGA analyses were obtained using Firehose v0.4.3 corresponding to the Jan 28th, 2016 (SCNA and somatic mutations) and Jul 15th, 2016 (RPPA) Firehose release. Somatic mutation data was processed at the gene level by assigning either 1 or 0 based on the presence or absence of any given mutation in that gene, respectively (excluding synonymous mutations). Annotated Level 3 RPPA data was used for all protein-related TCGA data queries. For pan-cancer analyses, these three data sets were obtained for nine cancer types, including bladder urothelial carcinoma (BLCA), breast invasive carcinomas (BRCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), and prostate adenocarcinoma (PRAD). RNASeq version 2 data processed as Level 3 RSEM-normalized gene expression values corresponding to the Feb 4th, 2015 Firehose release was used for the TCGA BRCA analysis. CCLE genomic data were downloaded from escribed . Somaticescribed  with allescribed .In all cases presented, SCNA and somatic mutation data were jointly analyzed as a single input dataset to CaDrA, thereby including samples for which both data were available. All input data to CaDrA were further pre-filtered so as to exclude alteration frequencies below 3% and above 60% to reduce feature sparsity and redundancy, respectively, across samples (CaDrA’s default feature pre-filtering settings).ith position denoting the occurrence (non-occurrence) of the genetic event  in the ith sample. This simulation of binary features relies on the following parameters:To evaluate both the sensitivity and specificity of CaDrA, we generated simulated data to represent cases where there was a mix of left-skewed (“true positive”) and randomly distributed (“null”) features, as well as cases where there were only null features. The left-skewness of a feature is a measure of its association with the sample ranking, since samples are sorted from left (high rank) to right (low rank). The design and parameter specification of the simulated data matrix is shown in N: Dataset sample size (number of columns in the matrix).n: Total number of features in the dataset (number of rows in the matrix).p: Number of true positive features generated per dataset .f: Left-skew proportion. The proportion of samples that are cumulatively left-skewed in the sample ranking.λ: The mean (and variance) of the Poisson distribution from which the number of events in the null features is sampled. This is equal to the number of 1’s per skewed positive feature. A Poisson distribution is used so that we can partially control (through the mean) the number of 1’s in a null feature, which are then uniformly distributed across samples (see description of Null feature generation below).The resulting simulated binary data matrix will consist of two main types of features:True Positive (TP) Features: A total of p TP features are generated. Events  are assigned to the TP features in a mutually exclusive fashion, with each of these features having (f × N)/p entries set to 1, with their cumulative OR yielding an N-sized vector with the left-most f × N entries set to 1’s. For example, if we generate data for 100 samples and 5 positive features, with the left-skew proportion set to 0.5, each non-overlapping feature will have 10 among the 50 left-most entries (columns) set to 1, such that the union  of the 5 features will have 1’s in the first 50 entries.Null Features: Null features are generated for a total of (n–p) features. To generate these features, we sample the number of 1’s per null feature based on a Poisson distribution with mean parameter λ = (f × N)/p. In this fashion, the number of 1’s in the null features will have a distribution centered on the corresponding number for the TP features. For instance, if we generate data for 100 samples and 5 TP features with left-skew proportion f = 0.5, then each of the TP features will have ten 1’s, and each of the remaining 995 null features will have a number of 1’s sampled from Poisson (λ = 10), uniformly distributed over the N samples.A schematic representation of this data, along with the parameters that define its composition is shown in Evaluation of CaDrA performance was performed considering two main scenarios: (a) True positive datasets: Data containing both true positive and null features (where the sensitivity of CaDrA is tested); and (b) Null datasets: Data containing only null features (where the specificity of CaDrA is tested), with the following parameter specifications for data generation:N = {50, 60, 70, 80, 90, 100, 250, and 500}n = 1000p = 5f = 0.5p-value based on the empirical null search distribution 1 (n = 554), compared to the pooled list of non-selected features.For enrichment analyses, RPPA protein data for the nine cancer types (see section “Data Availability and Processing”) was first restricted to those proteins representing hallmark oncogene or tumor suppressor genes included in the COSMIC v84 database (n = 57)1 . For eacJ) index of the returned meta-feature obtained with and without sub-sampling (repeated for n = 100 independent sub-sampling iterations). To assess reproducibility of drivers associated with YAP/TAZ activity, the search was repeated by either preserving the observed ranking (decreasing YAP/TAZ activity), or by taking a permuted ranking. J indices were then compared between the original and permuted ranking cases using a Wilcox rank sum test. For the pan-cancer protein query analysis, all available proteins profiled as part of the RPPA data were used, with J indices similarly computed for the top 5 protein queries that yielded significant meta-features (P ≤ 0.05), and 5 queries randomly selected from the non-significant list (P > 0.05) in each cancer type. J indices were then pooled for the five significant, and non-significant results, respectively, and compared using a Wilcox rank sum test. FDR correction was used for all pan-cancer analyses tests of significance.For all sub-sampling analyses presented, CaDrA was run after sub-sampling 80% of the original data, with consistency of CaDrA results computed as the Jaccard (n = 717) in MDA-MB-231 cells was obtained based on a previous study  was projected onto the signature genes and per-sample estimates of YAP/TAZ activity were derived using ASSIGN  with control siRNA  or an equal molar mixture of siRNA targeting RELN (Sigma), RBCK1 (Sigma), or TAZ and YAP . 48 h poN = 7, choosing the best resulting meta-feature . For evaluation of simulated data, only the top-scoring feature was considered as a starting feature per search run . The “ks” method was chosen for evaluating skewness of features at each step in all cases presented. All other default input search parameters were used for all cases presented.For evaluation using genomic data, CaDrA was run in the top-N mode using the default of https://tcga-data.nci.nih.gov/docs/publications/tcga), and CCLE repository , and are available from the corresponding author on reasonable request.The datasets generated and/or analyzed during the current study are available in the TCGA repository (VK developed the R package and conducted the analyses. VK and SM wrote the manuscript, with input from PS and XV. JK performed the siRNA and qRT-PCR experiments. LZ assisted in obtaining the gene expression signature for TCGA data projection. PS assisted in the evaluation of CaDrA on simulated data. SM and VK designed the CaDrA framework and features, and interpreted the results. XV designed the experimental validation of novel candidate drivers, and interpreted the results thereof. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.