Ovarian cancer (OC) is the most deadly gynaecological cancer, contributing significantly to female cancer-related deaths worldwide. Improving the outlook for OC patients depends on the identification of more reliable prognostic biomarkers for early diagnosis and survival prediction. The various roles of long non-coding RNAs (lncRNAs) in OC have attracted increasing attention. This study aimed to identify a lncRNA-based signature for survival prediction in OC patients.RNA expression data and clinical information from a large number of OC patients were downloaded from a public database. These data were regarded as a training set to construct a weighed gene co-expression network analysis (WGCNA) network, mine stable modules, and screen differentially expressed lncRNAs. The prognostic lncRNAs were screened using univariate Cox regression analysis and the optimal prognosis lncRNA combination was screened using a Cox-PH model. The finalised lncRNA combination was used to construct the risk score system, which was validated and assessed for effectiveness using other independent datasets. Further functional pathway enrichment was performed using gene set enrichment analysis (GSEA).A co-expression network was constructed and four stable modules with OC-related biological functions were obtained. A total of 19 lncRNAs significantly related to prognosis of ovarian cancer were obtained using univariate Cox regression analysis, and the 5 prognostic signature lncRNAs GAS5, HCP5, PART1, SNHG11, and SNHG5 were used to establish a risk assessment system. The reliability of the prognostic scoring system was further confirmed using validation sets, which indicated that the risk assessment system could be used as an independent prognostic factor. Pathway enrichment analysis revealed that the network modules related to the above five prognostic genes were significantly associated with cell local adhesion, cancer signaling pathways, JAK-STAT signalling, and endogenous cell receptor interaction.The risk score system established in this study could provide a novel reliable method to identify individuals at high risk of OC. In addition, the five prognostic lncRNAs identified here are promising potential prognostic biomarkers that could help to elucidate the pathogenesis of OC. Ovarian cancer (OC) is the most deadly gynaecological cancer and a primary cause of female cancer-related deaths worldwide, with recurrent OC being incurable in almost all cases , 2. Due Long non-coding RNAs (lncRNAs) are a group of non-coding RNAs longer than 200 nucleotides. Growing bodies of evidence have demonstrated the involvement of lncRNAs in OC, with at least 56 OC-related lncRNAs having been identified so far . LncRNAsRecently, models for disease risk prediction and subsequent prognostic evaluation have attracted increasing interest from researchers, as it has become clear that the pathogenesis of most diseases is mediated by multiple, rather than single genes –16. RiskIn the present study, RNA expression data and clinical information from a large number of OC patients were downloaded from a public database, and a co-expression network was built to excavate network modules with OC-related biological functions. LncRNAs in stable functional modules were identified as important factors associated with OC. Combined with the clinical survival and prognostic information of OC samples; we identified a molecular lncRNA combination, which was significantly associated with OC prognosis. Based on these prognostic-related lncRNAs, a prognostic risk assessment model was constructed to identify significant differences between high-risk and low-risk prognostic samples. The reliability of the model was validated using the clinical survival and prognostic information of OC samples in independent validation datasets.https://portal.gdc.cancer.gov/). A total of 419 ovarian cancer samples were detected by the Illumina HiSeq 2000 RNA sequencing platform. For the validation datasets, we first used the key word “ovarian cancer” to search all publicly uploaded expression data in the NCBI GEO (http://www.ncbi.nlm.nih.gov/geo/) database. The following criteria were then used for inclusion into the single validation datasets: 1) all data contained gene expression profiles; 2) detection objects were solid tumour tissues from patients with OC, exclusive of blood and cell lines; 3) expression profiles were all from human subjects; and 4) the sample number was no less than 40. The two final datasets GSE32062 and GSE17260 satisfied all the requirements, and included 260 and 100 samples, respectively.The mRNA-seq expression profiles of OC in the training dataset were downloaded from the TCGA database (http://bioconductor.org/packages/release/bioc/html/preprocessCore.html). Next, lncRNAs were annotated using Ref_seq and Transcript_ID of the annotation platform and aligned by Blast to human genome sequences (GRCh38 version) using Clustal2 . The β parameters were the square values of R coefficients. A higher RP value less than 0.05 obtained by the log-rank test was chosen as the threshold for identification. Next, the optimal combinations of prognostic-related lncRNAs were screened using the Cox-Proportional Hazards (Cox-PH) model [http://bioconductor.org/packages/penalised/). The optimized parameter “lambda” in the screening model was obtained by 1000 cyclic calculations using a cross-validation likelihood (CVL) algorithm. Based on the regression coefficient of each lncRNA in the optimal lncRNA combination, lncRNA expression weighted by these coefficients was used to establish the risk prediction model, from which the risk score (RS) of each sample was obtained. The RS calculation formula was as follows:The lncRNAs associated with prognosis were further identified by univariate Cox regression analysis of the survival package in R3.4.1 language. A H) model  based onH) model  in R3.4.In order to validate the risk prediction model, GSE32062 and GSE17260 were used as single validation sets. Datasets meeting the following criteria were selected as independent validation datasets: 1) all data contained gene expression profiles; 2) detection objects were solid tumour tissues from patients with OC, exclusive of blood and cell lines; 3) expression profiles were all from human subjects; 4) samples had prognostic clinical information and 5) the sample number was no less than 50. This led to the selection of the three microarray datasets GSE49997, GSE26712, and GSE31245 containing 204, 185, and 58 samples, respectively. We also downloaded the mRNA-seq expression profiles of cervical cancer (CESC) samples with the corresponding clinical information data from TCGA to further validate the efficiency of the risk prediction model. Of 309 samples that were downloaded, 293 had clinical information available and were therefore used for validation. The risk-score model was used to assess significant prognostic differences between high and low risk groups, with the results determining the stability of this established model.http://software.broadinstitute.org/gsea/index.jsp) was then used to identify KEGG pathways enriched in these optimal lncRNA-related gene sets. The GSEA-based pathway enrichment analysis was performed on each lncRNA counter-gene in the lncRNA-mRNA network. Three key statistical values were used in this analysis. The first of these, the enrichment score (ES), is the original result of the GSEA analysis, reflecting the degree of enrichment of one functional gene set locating at the anterior or posterior of this gene sequence after all the hybridization data are ordered. The fundamental calculation principle is to scan the collating sequence; when a gene of this set is prepared, the ES value will increase, otherwise it will decrease. The second key statistical value is the normalised enrichment score (NES) that results from standardized processing of ES values. The final statistical value is the nominal P value, which describes the statistical significance of the enrichment scores of a functional gene subset; a smaller P value indicates a better degree of gene enrichment. When the NES absolute value increases, the P value will spontaneously decrease, suggesting a higher degree of enrichment and a higher significance of the result. In this study, a P value less than 0.05 was chosen as the threshold to screen KEGG pathways that were significantly enriched in the relevant module genes.Gene sets were isolated from the modules that contained the optimal lncRNAs significantly associated with OC prognosis. The GSEA  method   containing RNA expression data for OC were used for analysis. We identified 13,251 mRNAs and 646 lncRNAs overlapping in the three datasets . TCGA expression data was used as the training set and the remaining two datasets were regarded as validation datasets. First, the consistency of the expression values of the overlapped RNA in the three datasets was checked. The results indicated that the correlation distribution of RNA expression levels between each pair of datasets was higher than 0.9 with WGCNA analysis was further conducted to screen stable modules associated with OC. The β (power) value of 3 was chosen as this was its value when R^2 was approximately 0.9 for the first time  of the blue, yellow, brown and green modules were all higher than 5, whereas those for the gray and turquoise modules were not. RNAs included in these four stable functional modules were likely associated with OC pathogenesis. Table The corresponding clinical information of samples in the TCGA dataset was integrated and the correlation between RNA obtained by partitioning in each module and clinical factors was calculated Fig. . The resWe used Cox univariate regression analysis (with the regression threshold P set at 0.05) to evaluate the expression levels of the 33 lncRNAs within the stable modules of the TCGA dataset with regard to the OC sample clinical prognosis information. This resulted in the identification of 19 prognostic related-lncRNAs Table .Table 2LNext, we optimized and screened the 19 OC prognostic-related lncRNAs using a Cox-PH model based on an L1-penalised regularization regression algorithm in the penalised package, in which the value of the “lambda” parameter was obtained by 1000 cyclic calculations using a CVL algorithm. This resulted in an optimal prognosis combination containing 5 lncRNAs: GAS5, HCP5, PART1, SNHG11 and SNHG5 Table .Table 3CBased on the corresponding Cox-PH regression coefficients of the optimal combination of 5 lncRNAs obtained in the previous section, the prediction model of sample risk scoring was established as follows:p value < 0.01)  was calculated according to the constructed model. These samples were divided into high- and low-risk groups based on the scoring medians. Next, Kaplan-Meier survival curve analysis was used to examine the significant differences in survival time between the two sample groups. The result indicated that this model had an excellent distinguishing effect on the sample groups (01) Fig. ; therefoP value < 0.05). The stability of this model was further validated by the independent validation datasets GSE49997, GSE26712, and GSE31245, as well as the CESC data from TCGA exclusive of any processing . The model displayed significant differentiation effects for all these validation sets, indicating a high robustness of this prediction model. Taken together, the above results show that the 5 lncRNAs comprising the key components of this risk prediction model , were significantly associated with OC prognosis, and form a stable combination for distinguishing high- and low-risk prognostic samples, as well as other unprocessed samples.As shown in Fig. The previous analysis identified the 5 important OC-related lncRNAs; GAS5, HCP5, PART1, SNHG11, and SNHG5. These five lncRNAs were distributed in the blue, green, and yellow stable modules discovered by the WGCNA method. The mRNAs closely related to the expression of these five lncRNAs can be found in these modules, and may be the target genes of these key lncRNAs Fig. .Fig. 8GrGSEA was used to perform pathway enrichment analysis of these mRNAs. This showed that the network modules related to the 5 prognostic genes were significantly associated with cell local adhesion, cancer signalling pathways, JAK-STAT signalling, and endogenous cell receptor interaction Table .Table 4KOC is the most deadly gynaecological cancer and a primary cause of female cancer-related deaths worldwide, yet the regulatory machinery underlying OC development remains unclear. Increasing evidence has identified lncRNAs in multiple biological functions at various stages of OC development, and deregulated expression of lncRNA is closely associated with OC early diagnosis, prognosis, and response to chemotherapy –9. To idAs a bioinformatics algorithm for construction of co-expression networks, WGCNA is commonly used to identify modules associated with diseases and consequently screen important pathogenic mechanisms or potential therapeutic targets . So far,Prognostic genes are informative for cancer prognosis and treatment because of their potential as biomarkers, and can help to predict patients’ survival, as well as providing insights into the molecular mechanisms of tumour progression –35. In tThe five prognostic genes have all been reported to be associated with human cancer, and three  have reported associations with OC. GAS5 (growth arrest-specific transcript 5) was originally isolated from NIH 3 T3 cells using subtraction hybridization . The latIt has been demonstrated that lncRNAs play important roles in a variety of biological processes by regulating target genes at transcriptional, posttranscriptional and epigenetic levels , 52. TheAlthough the independent validation performed in this study and the results of previous reports both indicate that the present model should be effective, there are limitations of the present study. Primarily, as this was an extensive bioinformatics study based on previously published data, our results should be further validated using in vitro and in vivo models. However, our results form a strong basis for other researchers to carry out the relevant future research.In conclusion, our study constructed a co-expression network and excavated four modules with specific biological functions related to OC. A risk assessment tool for predicting prognosis of OC was further identified and validated based on the expression of 5 prognostic genes. The present risk assessment tool could provide a novel reliable method to identify individuals at high risk of OC, and the 5 prognostic genes could be promising prognostic biomarkers for OC.


Inadvertent sample swaps are a real threat to data quality in any medium to large scale omics studies. While matches between samples from the same individual can in principle be identified from a few well characterized single nucleotide polymorphisms (SNPs), omics data types often only provide low to moderate coverage, thus requiring integration of evidence from a large number of SNPs to determine if two samples derive from the same individual or not.We select about six thousand SNPs in the human genome and develop a Bayesian framework that is able to robustly identify sample matches between next generation sequencing data sets.We validate our approach on a variety of data sets. Most importantly, we show that our approach can establish identity between different omics data types such as Exome, RNA-Seq, and MethylCap-Seq. We demonstrate how identity detection degrades with sample quality and read coverage, but show that twenty million reads of a fairly low quality RNA-Seq sample are still sufficient for reliable sample identification.Our tool, SMASH, is able to identify sample mismatches in next generation sequencing data sets between different sequencing modalities and for low quality sequencing data. Because no laboratory tracking method is perfect, there is always a risk of error in sample identification in next generation sequencing (NGS), which increases as the size and scope of a study increases . SequencWhile there are methods for ensuring that a NGS sample on the flow cell is the same sample that arrived at the sequencing center, there is no method for validating whether a sample that has already been sequenced is the correct sample. Also, inadvertent sample swaps can already occur before delivering samples to the sequencing facility. In tumor-normal, knock down/knock out analysis in primary cultures, or drug trial studies, an incorrectly identified sample can have egregious effects on the resulting data.Existing sample identification methods, such as STR profiling  or SNP pSample Matching using SNPs in Humans). Unlike existing methods, such as STR profiling, SNP panels, or post hoc computational analysis of microarray data, SMaSH uses human-aligned NGS data to determine whether or not two or more samples were derived from the same patient. SMaSH uses a similar statistical approach as presented by Korneliussen et al.  for each SNP in a sample and the genotype hs of SNP s in a sample. The latter can have the values WW (homozygous wildtype), AA , and WA (heterozygous). The probability to observe read counts ms and ns from one sample and fs, hs, x, respectively, is then given byWe also introduce the apparent alternate allele frequency We model sequencing itself as a binomial process, i.e., we usef we expect values close to zero (but not quite zero due to sequencing errors) for h=WW, values close to one for h=AA, and values around 1/2 for h=WA. To model these expectations, we choose a beta distributionFor the apparent alternate allele frequencies αWW=βAA=1, βWW=αAA=30, and αWA=βWA=2, where B is the beta function. Since we will apply a Bayesian approach, the relevant combination of these two probability distributions iswith We then need to calculateQ for each SNP s into a 3x3 matrix This can be most conveniently done by arranging the integrals x=I  and another 3x3 matrix x=D . These are expressed in terms of the population allele frequency qs of the current SNP being tested asSimilarly, the term ) in Eq.  can be wFinally we obtain the probability that the two samples are from the same individual aswhere Tr is the trace over the 3x3 matrices.Qs,i defined in Eq.  with i<j. We also used the experimental design of the respective data sets to determine the reference set The tradeoff between sensitivity and specificity of our algorithm was quantified using Receiver Operating Characteristic (ROC) analysis. To this end, we precalculated the d in Eq.  for eversing Eq.  for eachpi,j and calculated for each cutoff value p in this list the true positive rateof true positives. We then sorted the and the false positive rateThe ROC curve is then obtained by plotting the true positive rate as a function of the false positive rate.In order to thoroughly test our algorithm, we applied it to a variety of data sets and compared it against a similar tool, VerifyBamID . Some ofData set 1 and 4 libraries were generated using the stranded total transcriptome Illumina kit . Total RNA was quantified using Invitrogen Qubit 2.0 Fluorometer  and 200 ng was used as input. Samples were required to have a RIN (RNA Integrity Number) >4 as assessed with Agilent 2100 Bioanalyzer RNA 6000 Nanochip . Libraries with compatible barcodes were pooled and sequenced on Illumina HiSeq 2500 Sequencer using High Output Mode to achieve 35 to 40 million passed filtered paired-end 50bp reads per sample.Data set 2 consisted of RNA-Seq and MethylCap-Seq libraries. RNA-Seq libraries were generated using the non-stranded Illumina kit . Total RNA was quantified using Invitrogen Qubit 2.0 Fluorometer  and 200 ng was used as input. Samples were required to have a RIN >7 as assessed with Agilent 2100 Bioanalyzer RNA 6000 Nanochip . Libraries with compatible barcodes were pooled and sequenced on Illumina HiSeq 2500 Sequencer using High Output Mode to achieve 35 to 40 million passed filtered single-end 50bp reads per sample. MethylCap-Seq libraries were prepared by enriching methylated DNA fragments (150-200 bp) using the methyl binding domain (MBD) of human MeCP2  on the Diagenode SX-8G IP-Star Automated System as described by the manufacturer’s protocol. The methylated DNA fragments were eluted with 1M NaCl. Illumina sequencing libraries were generated from the enriched methylated material as previously described [RNA-Seq reads in all data sets were adapter trimmed using AdapterRemoval  and highAdditional file 1 This excel spreadsheet includes p-values for every pairwise comparison of two samples used in the evaluation of SMaSH.Additional file 2 The Labchip tracings in this supplementary figure document the sample quality in the lower quality RNAseq data set used in the evaluation of SMaSH.Additional file 3 This excel spreadsheet contains sample by sample quality control parameters of the lower quality RNASeq data set used in the evaluation of SMaSH.Additional file 4 This excel spreadsheet lists the 6059 SNPs used by SMaSH for identity assessment.Additional file 5 This excel spreadsheet lists metadata for the samples from the lung squamous cell carcinoma data set used in the evaluation of SMaSH.


Objective. To further investigate this biomarker panel, the objective of the current study is to (1) validate the assay reproducibility for the 325 RNA biomarkers and (2) compare gene expression profiles side by side using two technology platforms.  With the development of new drug combinations and targeted treatments for multiple types of cancer, the ability to stratify categories of patient populations and to develop companion diagnostics has become increasingly important. A panel of 325 RNA biomarkers was selected based on cancer-related biological processes of healthy cells and gene expression changes over time during nonmalignant epithelial cell organization. This “cancer in reverse” approach resulted in a panel of biomarkers relevant for at least 7 cancer types, providing gene expression profiles representing key cellular signaling pathways beyond mutations in “driver genes.” r = 0.995 for Affymetrix and r = 0.999 for NanoString. Correlation in multiple days and multiple users was for Affymetrix r = (0.962 − 0.999) and for NanoString r = (0.982 − 0.991).  We have mapped the 325 RNA transcripts and in a custom NanoString nCounter expression panel to be compared to all potential probe sets in the Affymetrix Human Genome U133 Plus 2.0. The experiments were conducted with 10 unique biological formalin-fixed paraffin-embedded (FFPE) breast tumor samples. Each site extracted RNA from four sections of 10-micron thick FFPE tissue over three different days by two different operators using an optimized standard operating procedure and quality control criteria. Samples were analyzed using mas5 in BioConductor and NanoStringNorm in R. Pearson correlation showed reproducibility between sites for all 60 samples with  The 325 RNA biomarkers showed reproducibility in two technology platforms with moderate to high concordance. Future directions include performing clinical validation studies and generating rationale for patient selection in clinical trials using the technically validated assay. We developed a new panel of 325 RNA biomarkers selected based on cancer-related biological processes of healthy cells and gene expression changes over time during nonmalignant epithelial cell organization –5. This In this context, preanalytic variables may interfere with the gene expression profiling results, for example, RNA integrity coming from formalin-fixed paraffin-embedded (FFPE) samples. In the present study, we validate the analytical performance to profile these 325 RNA biomarkers using a custom-designed NanoString assay in comparison to Affymetrix microarray gene chip.FFPE breast tumor biopsies are routinely used in clinical practice, since the collection of fresh or frozen samples is not feasible for the set of histopathological tests to be performed. The quality of the total RNA extracted from FFPE samples is crucial for the evaluation of the gene expression profiles and depends on a series of factors such as the quality of the materials used for fixing, the quality of the material to be fixed, cross-liking, and degradation of paraffin-embedded genetic material over the years, among others . In addiBased on this context, we aimed to compare the expression profiles of the 325 RNA biomarkers on the NanoString custom-designed assay against Affymetrix GeneChip. RNA extracted from 10 unique FFPE breast tumor biopsies by two different laboratories on three different days was analyzed with focus on the reproducibility of the assay in a total of 60 samples. The study showed that the custom-designed NanoString assay for the 325 RNA biomarkers is reproducible and showed moderate to high concordance to Affymetrix gene expression profiles.Ten unique FFPE breast tumor biopsies were analyzed, including five triple-negative (TNB) and five estrogen receptor-positive (ER+) breast cancer samples . The numThe Universal Human Reference RNAs (UHR)  were used as an amplification and hybridization positive control for the Affymetrix and NanoString assays.μL of each sample was mixed with 8 μL of the hybridization cocktail (4 μL of the reporter codeset and 4 μL of hybridization buffer). 2 μL of the capture codeset was added; the solution was mixed and spin down. It was placed in a 65°C thermocycler  for 20 hours.We designed a custom nCounter® assay  for quantitative assessment of expression of 325 gene elements. Due to the change of analysis platforms, we designed the probes to hit the maximum number of validated transcripts and with the closest coverage of the probes analyzed in the Affymetrix platform. In addition to the 325 genes, there were 7 housekeeping genes, 6 positive control genes, and 8 negative control genes included in the same panel totaling in 346 genes analyzed. The full list with the 346 genes can be found in The samples were transferred to the preparation station with prepared reagent plates and a cartridge. The samples ran with the standard sensitivity for maximum binding to the cartridge. The preparation station ran approximately 3 hours.The cartridges were transferred to the Digital Analyzer  for analysis. A field of view (FOV) of 280 was used for the cartridges of the project, due to expected lower expression levels of the genes of interest. The Digital Analyzer ran approximately 2.5 hours for each cartridge.All of the cartridges used for the experiment were evaluated with the standard NanoString nCounter™ performance QC metrics . Any samμL of deionized water. The concentration and integrity of the samples were evaluated and then 5 μg of mass was run through the standard NuGEN Biotin fragmentation and labeling protocol .The biological samples were prepared such that 100 ng of mass was added to a 96-well plate and the standard NuGEN-FFPE WTA v3  amplification protocol was used. The samples were cleaned, following single primer isothermal amplification (SPIA), with 35 μL of each sample was added to the Affymetrix cartridges  and placed in the hybridization oven at 65°C with 60 rotations per minute. The Affymetrix cartridges were hybridized for 18 hours, washed and stained in the Affymetrix Wash/Stain equipment, and then scanned.Hybridization solution was added to the samples and 200 The samples were compared with the biological controls for each batch and the following metrics were used to determine the QC metrics: scale factor, percent present, actin 3′ to 5′ ratio, GAPDH 3′ to 5′ ratio, and the RNA degradation slope. Affymetrix CEL files were processed using the BioConductor rma package , as wellR2correlation ≥ 0.98.To assess reproducibility, for each sample, RNA was extracted by a single user on three different days in Laboratory 1, and the same procedure was repeated by an independent user at Laboratory 2, resulting in 6 technical replicates for one biological sample. An intra-assay variability of less than 10% was expected when comparing the normalized results of the technical replicates in a single run. A review of the correlation between the counts of the technical replicates was prepared, with expected R2 expected correlation ≥ 0.98.Reproducibility is the assessment of the same samples run by different users, on different days, and with different reagent lots to verify that there are minimal differences with respect to sample results. An interassay precision of less than 15% was expected when comparing the normalized results of the technical replicates between users, days, and reagents. A review of the correlation between the counts of the technical replicates was also prepared, with The samples were compared between platforms to assess the correlation of gene expression for common targets. We compared the Affymetrix gene expression data with NanoString expression data by normalizing the expression levels within each platform.Total RNA was extracted from 10 unique breast cancer FFPE samples , respectively, in three different days by two different staff members, in two independent laboratories (Laboratory 1 and Laboratory 2), resulting in a total of 60 replicate samples. To achieve a more comprehensive view of the reproducibility in gene expression measurements, we employed cluster analysis, correlation analysis, and principal component analysis (PCA) to test if there were systematic biases among the samples processed differently.As the first step towards QC assessment of the data, boxplots with data before normalization were made to test if there were any outliers that were significantly different from the other samples tested. As shown in To have a statistical evaluation of variation due to different biological samples vs. variation due to a system's random noise, we employed a mixed-model two-way ANOVA to test if the small variations among the 5 biological samples are indeed too minor to be detected by the NanoString platform . As a poAffymetrix CEL files were processed using the BioConductor mas5 package for background correction and data normalization. The quality of all the 60 samples from the Affymetrix experiment was assessed using BioConductor Affy package with histogram and boxplot as well as RNA degradation plot, and no sample was found to be significantly different from others (data not shown). The Affymetrix results from the 30 ER+ samples  were oncWe calculated pairwise Pearson correlation coefficients among the 29 TNB samples and 29 ER+ samples that passed NanoString nCounter QC and visualized the data in the heatmap view Figures . As demoThe correlation heatmap for the 29 ER+ samples further validated the finding from cluster and PCA (data not shown) that the samples from cases 1-3 (the red block on the top right)  were difA large number of publications have shown that the TNB breast cancer subtype is significantly different from the ER+ breast cancer subtype at both the pathological level and the molecular level , 19. ForR = 0.995 for Affymetrix . We then extracted the corresponding expression values for the sorted genes from the Affymetrix experiment, aligned them together, and viewed the aligned data in a heatmap view (data not shown).We also calculated the pairwise Pearson correlation coefficients among the samples measured by the NanoString experiment vs. samples measured by the Affymetrix experiment. While the correlation among samples within the sample platform was quite high (range from 0.90 to 0.99), the correlation among samples between the two different platforms was poor (range from 0.4 to 0.5). From the RNA samples of DLBCL and using NanoString platform, Veldman-Jones et al.  have fouOne potential issue of directly comparing expression levels from two different platforms is whether or not a gene's endogenous expression level always correlates with the signal intensities measured by the two platforms. Woo et al.  suggesteAs already been discussed, a direct comparison between absolute signal intensities did not yield a good correlation between the gene expression measurements with the Affymetrix platform and those with the NanoString platform , 14, 21.We tested whether the correlation between the two platforms can be further improved with a larger sample size. The analysis was still done independently for the two different laboratories. This time, the ratio was done between the mean expression values from the 15 ER+ samples from one particular lab  over the mean values from the 15 TNB samples from the same lab for all the 332 genes which are present in the NanoString and Affymetrix experiments. The log2 ratios were then plotted and the correlation between the two platforms had significantly improved when pooling a larger number of samples together. The Pearson correlation coefficients were 0.81 and 0.83, for samples prepared by Laboratory 1 and Laboratory 2, respectively.Finally, we took the ratio between the mean expression values from all the 30 ER+ samples  over the mean values from all the 30 TNB samples for all the 332 genes which are present in the NanoString and Affymetrix experiments. The log2 ratios were then plotted and the results are shown in We then defined the concordant genes to be the ones that have changes less than lm predicted values ± 2 standard deviation at 95% confidence interval (gray dots in The reproducibility among the technical replicates for both the NanoString platform and the Affymetrix platform was high, as demonstrated by heatmaps, cluster analyses, and correlation analysis. A high similarity was observed among the 5 unique TNB samples included in this study using the genes we examined, as shown by all 6 technical replicates for all 5 unique samples forming a single cluster. The ER+ samples presented a differential expression profile for two samples, but the replication grouping was the same, demonstrating that intratumoral heterogeneity could exist between tumors of the same molecular subtype. Although the correlation between NanoString and Affymetrix platforms was not good when directly comparing absolute signal intensities, there appears a moderate concordance between these two platforms when using changes in gene expression. Further, the concordance can be improved significantly when pooling a larger number of samples. We should consider that the degree of degradation of RNA derived from FFPE samples is still a challenge for transcriptome analysis, and further studies should be conducted for comparison between samples and expression analysis platforms.


Circular RNAs (circRNAs) are widely expressed in human cells and tissues and can form a covalently closed exon circularization, which have stable patterns and play important regulatory roles in physiological or pathological process. There is still lack of a comprehensively disease-related knowledge base for in-depth analysis of circRNAs. In this paper, a cancer circRNAs-related database (CCRDB) was established. The CCRDB’s initial circRNAs data were collected by sequencing experimental data of 10 samples from 5 patients with hepatocellular carcinoma (HCC), where a total of 11 501 circRNAs were found and can easily be expanded by collecting and analyzing external data sources such as circBASE , 3, ASPH This feature confers numerous properties to circRNAs, many of which have only been identified recently. Some circRNAs can act as microRNA sponges to block the function of microRNAs, thereby affecting gene regulation and expression, and are widely involved in life activities and play important regulatory roles in tumorigenesis and development . For exa (ceRNA) . Guarner  is one of the most common malignancies and the sixth largest cancer killer in the world. Most HCC is caused by chronic hepatitis B virus infection and subsequent cirrhosis . It has  samples, 23. Li  for HCC . Huang e studies . Fu et a_0353570 . Chen fo_0353570 .In recent years, researchers have paid more and more attention to the study of circRNAs, and many circRNAs-related databases have been published, such as circBase, circNet and database for cancer-specific circRNAs(CSCD) , 30–34. The CCRDB data sources include our experimental data and external data from other author’s literatures. Several individuals were selected to conduct the experiment. Five pairs of circRNAs differentially expressed in HCC cells and normal tissues adjacent to the cancer were screened. We divided them into two groups: group B was normal cells and group C was HCC cells.Combining with the published circRNAs database circBASE, we annotated the circRNAs in the samples according to the source region. In our experiment, a total of 11 501 circRNAs were found and listed in Homo sapiens cells, which are published from literatures , circBASE_ID, gene_id and more to get more intuitive information . All theWe innovatively provide a comparative analysis platform to provide data analysis functions by importing different samples of circRNAs data from different organs. The comparison of two groups of circRNAs data can come from different sources, which is flexible and suitable for various comparative analyses.The number of junction reads that supports the connection between the head and tail of the circRNAs is used as a comparison criterion to measure the strength of circRNAs signal. The corresponding circRNAs in the sample selected by the user will get the relevant tabular data or up-and-down column analysis diagram  and D unWe can use the upload function to import data to be analyzed, and its semaphore is based on junction reads. In analysis application, select the circRNAs data of the comparison group to be compared to carry on a pairwise comparison by choosing the result condition (FDR and log2Ratio) . You canWe compare horizontally with other circRNA databases , or they can be chosen from different person’s (organ’s) sample. A comparison group selection method is that circRNAs are obtained from the same person’s circRNAs sequencing data to avoid background effects such as genetic differences among people. By using the circRNAs comparative analysis application, we compare the results between the circRNAs of the human cancer cells and the circRNAs of the same human’s adjacent normal cells.Semaphore of the comparison group must be chosen for the comparative signal strength. The main principle of the circRNAs comparative analysis application is to compare the signal expression of the samples, which is the number of junction reads that supports circRNAs’ head to tail connections. It is the field name of ‘#junction_reads’ in the circRNAs information listed in P-value method is calculated in hypothesis test.The formula of P-value is shown below, where x and y are expressions of the two samples’ circRNAs in the comparison group, N1 and N2 are the summary expressions of the samples’ circRNAs in the comparison group.The P-value. Usually |log2Ratio| is set to be greater than or equal to 1, and FDR is less than 0.001. These two parameters can be set according to actual needs.There are two major parameters, FDR and |log2Ratio|. log2Ratio| is the ratio of the semaphores when two samples are compared with log2. FDR is the false discovery rate of Using the comparative analysis application, we select the same person (organ) as the comparison group samples, of which sample B was normal cells and sample C showed hepatoma cells. . We labeled them 1B&1C, 2B&2C, … 5B&5C, respectively. The circRNAs expressed in the same organ (liver) of several groups of people were identified. The numbers of differences found in circRNAs between samples B and C were 6808, 4652, 4365, 3102 and 3534, respectively, compared with five different comparison groups. The numbers of significant differences were 111, 44, 21, 47 and 25, respectively. These differences and significant differences are analyzed, as shown in By setting the FDR and |log2Ratio| parameters, the results of the analysis with significant differences are obtained. The result of expression level 1B vs 1C is shown in We put all comparison groups together. The significant differences of the same category in all groups are compared. And the numbers of comparison groups are analyzed where their differences are in the same regulatory direction.All the significant differences between cancer cells and their adjacent normal cells of the same person were analyzed. In the comparison group of five persons, there were 31 circRNAs with two or more comparison groups, which their significant differences have the same regulatory directions, including 20 circRNAs with circBASE_ID data and 11 without circBASE_ID data, as they are newly found.There are three circRNAs with significant differences in the same direction of regulation that have been found in five comparison groups . There are five circRNAs with significant differences in the same direction of regulation that have been found in four comparison groups . There are five circRNAs with significant differences in the same direction of regulation that have been found in four comparison groups .The changes of circRNAs from normal cells to diseased cells in different comparison groups were generally consistent with the same regulatory directions (UP or DOWN). This helps us to find the corresponding regulatory or target genes from the significant variation of circRNAs, as shown in The circRNAs with same significant differences and same regulation directions, which occurred many times (comparison groups count) in the comparison groups through our analysis application, seem to strongly related to the disease. Corresponding candidate regulatory genes or target genes can be found from the circRNAs, as shown in the We have found that, Has_circ_0002130-related geneID C3 showed significant differences in five of five comparison groups (5/5), which is down-regulated in our experimental samples. According to the report of the papers, the gene C3, inhibiting cancer in HCC, was found to be the biomarker candidates for distinguishing early HCC from cirrhosis. Hsa_circ_0001445 , hsa_circ_0001727 , chr12:23998917| -24048958  and chr16:72090429|72093087 , were down-regulated, which was consistent with the results of related papers. Hsa_circ_0084615  and hsacirc0001361, were up-regulated, which was consistent with the results of related papers. Details are shown in We sequenced the circRNAs of hepatocytes and constructed a new database CCRDB. Using the new database CCRDB and its analyzing tools, we further studied circRNAs and the relationship between circRNAs and HCC. It is of great significance for researchers to further analyze the rules of circRNAs, to understand the causes of circRNAs in disease discovery and to search for target genes for therapeutic approaches. Researchers can easily add circRNA sequencing data from other organs to this database and use the comparative analysis tools to provide powerful analytical functions to facilitate the discovery of new knowledge.The future direction for development is to mine more circRNAs data from literatures and experiment to compile a more comprehensive database and offer a variety of analytical functions, including verification of analysis results, and intelligent tools by artificial intelligence technology.


Data normalization is a critical step in RNA sequencing (RNA-seq) analysis, aiming to remove systematic effects from the data to ensure that technical biases have minimal impact on the results. Analyzing numerous RNA-seq datasets, we detected a prevalent sample-specific length effect that leads to a strong association between gene length and fold-change estimates between samples. This stochastic sample-specific effect is not corrected by common normalization methods, including reads per kilobase of transcript length per million reads (RPKM), Trimmed Mean of M values (TMM), relative log expression (RLE), and quantile and upper-quartile normalization. Importantly, we demonstrate that this bias causes recurrent false positive calls by gene-set enrichment analysis (GSEA) methods, thereby leading to frequent functional misinterpretation of the data. Gene sets characterized by markedly short genes  or long genes  are particularly prone to such false calls. This sample-specific length bias is effectively removed by the conditional quantile normalization (cqn) and EDASeq methods, which allow the integration of gene length as a sample-specific covariate. Consequently, using these normalization methods led to substantial reduction in GSEA false results while retaining true ones. In addition, we found that application of gene-set tests that take into account gene–gene correlations attenuates false positive rates caused by the length bias, but statistical power is reduced as well. Our results advocate the inspection and correction of sample-specific length biases as default steps in RNA-seq analysis pipelines and reiterate the need to account for intergene correlations when performing gene-set enrichment tests to lessen false interpretation of transcriptomic data. Analysis of numerous RNA-seq datasets reveals a recurrent sample-specific length bias that causes frequent false positive calls by gene-set enrichment analyses, leading to functional misinterpretation of the data. Its removal requires methods that allow the integration of gene length as sample-specific covariate. The ability to profile entire cellular transcriptomes, formerly by expression microarrays and subsequently by RNA sequencing (RNA-seq), has transformed biological research over the last two decades by turning the paradigm of systems-level analysis from a formidable task to one that is readily accessible to most experimental laboratories . ConsequData normalization is a critical component of RNA-seq processing pipelines, allowing for accurate estimation and detection of differential expression. The aim of normalization is to remove systematic effects that occur in the data to ensure that technical bias has minimal impact on the results –6. AttesA well-known inherent technical effect in RNA-seq experiments relates to gene length and stems from the fact that in standard RNA-seq protocols, RNA (or cDNA) molecules are fragmented prior to sequencing in such a way that longer transcripts are sheared into more fragments than shorter ones are. Therefore, the number of reads for a given transcript is proportional not only to its expression level but also to its length. Thus, one of the most basic RNA-seq normalization methods, RPKM, divides gene counts by gene length (in addition to library size), aiming to adjust expression estimates for this length effect. A well-known consequence of the fact that longer genes tend to get more counts than equally expressed shorter genes is overrepresentation of long genes among the ones that pass statistical tests for differential expression (termed “length bias”), because of the increased statistical power ,13.Importantly, the way RPKM normalization handles the length effect is based on the assumption that this effect is the same for all samples. However, previous studies indicated that in addition to this universal length effect, gene length can affect expression measurement in a sample-specific manner. Importantly, removal of sample-specific technical effects requires normalization methods that allow for correction of sample-specific covariates. Two such methods are conditional quantile normalization (cqn)  and EDASGene-set enrichment analyses (GSEAs) are among the most vastly used techniques for functional interpretation of gene-expression data, and numerous statistical methods were developed over the last two decades for this task –19. NotaIn this study, analyzing numerous publicly available RNA-seq datasets, we found that sample-specific length effects have greater impact on expression measurements than currently appreciated. If left uncorrected, sample-specific length effects make the comparison of expression level of a gene between samples problematic and distort fold-change (FC) estimates. We found that the coupling between FC estimates and genes' length, caused by sample-specific length effects, recurrently prompt false results by gene-set enrichment tests that assume gene independence. In addition, we observed that gene sets characterized by exceptionally short genes  or long genes  are particularly prone to such false calls. Allowing for the integration of gene length as sample-specific covariate, cqn and EDASeq effectively remove this length bias and thus substantially reduce false results while retaining true ones. Notably, false calls were also attenuated when we applied gene-set methods that account for intergene correlation.p < 10−8) between gene length and FC in treated versus control samples, across the vast majority of the datasets . The magnitude of the coupling varied considerably over these 30 datasets, with some showing very strong bias, whereas others showed only a subtle one. The median Spearman's correlation between gene length and (log) FC was 0.18 (range: 0.05–0.43)  . To rule05–0.43) . Importa05–0.43) . We alsop < 10−8) relationship between gene length and FC in comparisons between replicate samples . Notably, virtually all 35 datasets showed a significant , followed by their functional characterization, commonly through identification of functional categories  that the DEG sets are enriched for . Howeverr < 0.05 after normalization) in 26 of them  treatment, although cqn canceled the false detection of “mitochondrial protein complex” (containing markedly short genes), it did not compromise the call of the true gene-set “inflammatory response” . We confLast, we sought to examine the effect of cqn correction in a more challenging test case, in which the true biological response is genuinely coupled to gene length. For this task, we analyzed an RNA-seq dataset that recorded gene-expression profiles in the transition of cells from epithelial to mesenchymal states (EMT). This physiological transition is known to involve drastic changes in the expression of the markedly long genes that encode ECM proteins . Importantly, although in this test case too did cqn remove false positive calls caused by sample-specific length effects, it did not compromise at all the detection of true ECM-related gene sets .p < 0.05), they did not pass significance threshold after correcting for multiple testing . This observation lends support to the hypothesis that sets with very short genes  or very long genes  also feature exceptionally tight co-regulation that is not related to any specific biological condition and that this  coupling between gene length and extent of gene–gene correlation contributes to the gene length–FC link that we recurrently observed in RNA-seq datasets.We still do not understand the exact factors that cause the sample-specific length effect. In our analyses, the correlation between gene length and FC was recurrently highly significant . SimulatTaken together, our study reports on a prevalent sample-specific length effect in RNA-seq data. We therefore recommend inspection for this bias and the usage of normalization methods that support gene-level sample-specific covariates as default steps in RNA-seq data analysis pipelines. In addition, our results reiterate the need to account for intergene correlations when performing gene-set enrichment tests.We analyzed 35 publicly available human or mouse RNA-seq datasets from GEO  S1 Tabl. We soughttps://github.com/ElkonLab/RNA-seq_length_bias.Our R script and raw counts data for the RNA-seq datasets analyzed in this study are provided at The GSEAs that we carried out in this study used the pre-ranked GSEA method . We ran CAMERA, implemented in the limma package (v3.38.3) , using eS1 Fighttps://github.com/ElkonLab/RNA-seq_length_bias/. FC, fold change; RNA-seq, RNA sequencing; RPKM, reads per kilobase of transcript length per million reads.Relationship between gene-expression FC and length in 35 publicly available RNA-seq datasets. For each dataset, normalized expression levels were averaged over replicate samples, and FC was calculated as (log2) ratio of these means (the number of replicates per condition in each dataset is given in (PDF)Click here for additional data file.S2 Fighttps://github.com/ElkonLab/RNA-seq_length_bias/. FC, fold change; RNA-seq, RNA sequencing; RPKM, reads per kilobase of transcript length per million reads.Relationship between gene length and expression FC in comparisons between individual replicate samples from 35 publicly available RNA-seq datasets. For each dataset, the replicates pair that showed the strongest length–FC coupling is shown. Results shown here are based on RPKM normalization. Very similar results were obtained using five alternative normalization methods . Data un(PDF)Click here for additional data file.S3 Fighttps://github.com/ElkonLab/RNA-seq_length_bias/. cqn, conditional quantile normalization; FC, fold change; GEO, Gene Expression Omnibus; GSEA, gene-set enrichment analysis.(A-N) All items in this figure show results for comparisons between replicate samples of the same biological condition. Click here for additional data file.S4 Fighttps://github.com/ElkonLab/RNA-seq_length_bias/. cqn, conditional quantile normalization; FC, fold change; FQ, full quantile; RNA-seq, RNA sequencing; TNFa, tumor necrosis alpha.(A) (Top left) Sample-specific length bias in the TNFa (GSE64233) dataset. This figure is the same as (PDF)Click here for additional data file.S5 Fighttps://github.com/ElkonLab/RNA-seq_length_bias/. cqn, conditional quantile normalization; GSEA, gene-set enrichment analysis.(A-G). Each item shows GSEA results on the original dataset (left) and after the application of cqn (right). The enrichment of the upper gene set is biologically genuine, whereas the enrichment of the bottom gene set is an artifact caused by sample-specific length bias and is canceled by cqn. Data underlying the results presented in this figure are provided in (PDF)Click here for additional data file.S1 TableFC, fold change; RNA-seq, RNA sequencing.(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 TableRNA-seq, RNA sequencing.(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S1 DataTNFα, tumor necrosis factor alpha.(XLSX)Click here for additional data file.S2 DataFC, fold change; TNFα, tumor necrosis factor alpha.(XLSX)Click here for additional data file.S3 Datacqn, conditional normalized quantile; EMT, epithelial–mesenchymal transition; FC, fold change.(XLSX)Click here for additional data file.


Partial least squares-discriminant analysis (PLS-DA) and PLS chemometric methods were built for qualitative and quantitative analysis of pekmez samples, respectively. PLS-DA models were successfully used for the discrimination of pure pekmez samples and the adulterated pekmez samples with glucose syrup. Sensitivity and specificity of 100%, and model efficiency of 100% were obtained in PLS-DA models for all pekmez groups. Detection of the adulteration ratio of pekmez samples was also accomplished using ATR-FTIR spectroscopy in combination with PLS. As a result, it was shown that ATR-FTIR spectroscopy along with chemometric methods had a great potential for determination of pekmez adulteration with glucose syrup.Pekmez, a traditional Turkish food generally produced by concentration of fruit juices, is subjected to fraudulent activities like many other foodstuffs. This study reports the use of Fourier transform infrared spectroscopy (FTIR) in combination with chemometric methods for the detection of fraudulent addition of glucose syrup to traditional grape, carob and mulberry pekmez. FTIR spectra of samples were taken in mid-infrared (MIR) range of 400–4000 cm Pekmez is a traditional Turkish food, generally produced by concentration of fruit juices in open or vacuum vessels with or without deacidification. Grape is the most widely used fruit and other fruits such as mulberry, fig and carob are also used as raw materials for the production of pekmez . BecauseThe fruit used for the production of pekmez is of great importance due to the nutritional and technological aspects. Because of the high prices or difficulties in the supply of some fruits, as well as the high production inputs, traditional pekmez is subjected to fraudulent activities like many other foodstuffs . Food prNowadays, studies about the determination of adulterations in foodstuffs using spectroscopic methods have been extensively carried out  includinFourier transform infrared spectroscopy (FTIR) is one of the spectroscopic methods employed for quality analysis, detection of adulteration and discrimination of food ,14,15. ISpectroscopic techniques are generally integrated with multivariate data analysis methods by several researchers for the determination of sugar profile ,20, deteThe samples constituted three groups, which belong to three pekmez types including grape, carob and mulberry pekmez. A total of 52 original pekmez samples  produced traditionally were obtained directly from 30 different production plants located in Malatya, Turkey. Synthetically adulterated samples with the known adulteration levels were prepared in laboratory conditions as explained in the following subsection. Additionally, six different adulterated carob pekmez samples as test samples were obtained from the pekmez production plants in order to test the method using the real adulterated samples found in the market.The pekmez samples were placed into amber glass bottles and kept refrigerated at 4 °C until analysis within one week.A total of 60 synthetically adulterated samples were prepared by the addition of commercial glucose syrup to original pekmez samples under laboratory conditions. Glucose syrup was selected as the adulterant due to its low price and availability.w/w) with an increment of 2.5%. This corresponded to a total of 20 adulterated samples for each pekmez type, and a total of 60 synthetically adulterated sample. The concentration range was wide to imitate the possible real-life adulteration practices.Firstly, a master sample for each pekmez type was obtained by mixing an equal amount of original samples. Then, glucose syrup was heated up to 70 °C and mixed with the master sample at different ratios. The mass percentages used for the preparation of these adulterated samples were in the range from 2.5 to 50%  with distilled water before FTIR measurements. The spectra of the pure and adulterated samples were utilized to build PLS-DA and PLS models for the qualitative and quantitative determination of adulteration for each pekmez type.The spectra of the samples were recorded using ATR-FTIR spectrometer  equipped with a single bounce diamond crystal and a deuterated triglycine sulfate detector.−1 with a resolution of 4 cm−1. Each spectrum was collected from 50 scans in the absorbance mode. Triplicate measurements were made and the mean values were used. In order to obtain the FTIR spectra, the intensity values were plotted (y-axis) as a function of wave number (x-axis). OriginPro 7.5  were used for drawing the spectra.The FTIR spectra of samples were determined to be in the MIR range of 400–4000 cmPLS-DA and PLS  were used for chemometric analyses of the FTIR-ATR data using optimal spectral data preprocessing techniques. PLS-DA was used for discrimination of pure and adulterated grape, carob and mulberry pekmez samples. After PLS-DA discrimination, PLS technique was utilized in order to determine the glucose syrup addition to various pekmez samples quantitatively.In order to generate PLS-DA model for discrimination of pure and adulterated grape pekmez samples, 24  and 14 grape pekmez samples  were used for calibration and validation, respectively. For carob pekmez, 22 samples  were used for calibration and 14 samples  were used for validation. For the discrimination of pure and adulterated mulberry pekmez, 24  and 14 samples  were used for calibration and validation, respectively. In order to test the method for the real samples, the spectral data of six different carob pekmez samples obtained from the market were also added to the validation data set of carob pekmez samples.In addition, sensitivity rate , specificity rate  and model efficiency rate  were calculated to determine the performance of the classification .STR = TPLS models were generated in order to determine the adulteration ratio of pekmez samples. 14 grape, 14 carob, 12 mulberry pekmez samples and seven grape, seven carob, seven mulberry pekmez samples were used for calibration and validation data sets, respectively. Pekmez samples were randomly divided into two groups to obtain calibration and validation data sets.2), root mean square error of cross-validation (RMSECV), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) values. For the PLS study, limit of detection value (LOD) and limit of quantification (LOQ) were calculated based on the standard deviation of the response and the slope of the calibration graph [The performance of the models was evaluated using coefficient of determination . The total soluble solids values of the samples were adjusted to 50 °Brix before spectral analysis.−1) of the representative grape, carob and mulberry pekmez samples are given in −1) as expected. However, there were some intensity differences among the pekmez types, possibly because of the differences in the composition of the fruits used in the pekmez production. The broad band located around 3292 cm−1 and the band at 1626 cm−1 were associated with O-H stretching and O-H deformation, respectively [−1 was associated with C-H stretching of carboxylic acid [−1. It was reported that the spectral region between n 800 and 1500 cm−1 would cover most of characteristic absorption bands relevant to major sugars [−1 were reported to be associated with the deformation of -CH2, and angular deformation of C-C-H and H-C-O linkages [−1 were associated to C-O and C-C stretching modes of carbohydrates [The ATR-FTIR spectra of adulterated and pure grape, carob and mulberry pekmez samples were collected with FTIR spectrometer equipped with an ATR sample accessory. The FTIR spectra . The absorption of peak at 1027 cm−1 changed with the change in adulteration level. This peak at 1027 cm−1 was assigned to glucose. This finding is consistent with the findings of Se et al. [−1 and 1029 cm−1, respectively. Glucose is present in the pekmez samples and also in the adulterant, i.e., glucose syrup in which glucose is the main sugar [e et al.  and Melle et al.  who repoin sugar .In order to discriminate the adulterated pekmez samples, FTIR spectral data were analyzed with PLS-DA which is one of the chemometric methods. The pretreatment method having the highest performance of discrimination was determined after different pretreatments applied to the data.−1 were used for the discrimination of pekmez samples. As it can be seen from For PLS-DA analysis, spectral region of 600–4000 cmw/w) from the original samples. The classification parameters for the three different pekmez types were similar to each other. Hence it could be reported that ATR-FTIR technique along with PLS-DA worked well in detection of adulteration in grape, carob and mulberry pekmez samples.PLS-DA classification parameters for different pekmez types are seen in 2 values for the calibration and validation data sets are shown in The ATR-FTIR spectral data were used for determination of adulteration ratio of grape, carob and mulberry pekmez samples. The PLS calibration models were generated between the ATR-FTIR spectral data and the level of glucose syrup addition. The preprocessing method which enabled the reduction of noise in the system measurements while keeping useful variation was determined as autoscaling. Correlations between the spectral data and the adulteration level of the pekmez samples for each pekmez type were calculated. R2 values for calibration and validation data curves except the validation data curve of mulberry pekmez were higher than 0.95 indicating a good correlation between the actual values of adulteration levels and the estimated values using PLS regression for both data sets. Rw/w) and cane sugar over 2% (w/w) using ATR-FTIR spectroscopy together with chemometrics. In the literature, detection of adulteration in foodstuffs below 10% is generally found satisfactory [LOD and LOQ values are shown in sfactory ,30.The prediction results for pekmez adulteration levels using PLS models are also seen in 2) value [2 values demonstrate satisfactory prediction results of the models. The parameters of PLS models generated can be seen in In order to assess the predictive ability of the models, RMSEC, RMSECV and RMSEP values are important as well as the coefficient of determination (R2) value . The lowAdulteration of pure grape, carob and mulberry pekmez samples with glucose syrup was evaluated using ATR-FTIR spectroscopy. PLS-DA models were built in order to discriminate the pure and adulterated pekmez samples. Sensitivity and specificity of 100%, and model efficiency of 100%, were obtained in PLS-DA models for all pekmez types. For the quantitative analyses of pekmez adulteration, PLS models were generated. The predictive ability of the PLS models for detection of the adulteration levels in grape and carob pekmez were also good enough, however there were some difficulties in quantification of adulteration in mulberry pekmez. Nevertheless it can be concluded that ATR-FTIR spectroscopy in combination with chemometrics has a great potential to be used as a simple and fast method for the determination of adulteration of pekmez samples with glucose syrup. Further studies having larger sample sizes could be conducted in order to implement this method for routine analysis. The studies about the simultaneous determination of possible adulterants in various pekmez types using ATR-FTIR technique can also be the subject of further studies.


Since the turn of the century, researchers have sought to diagnose cancer based on gene expression signatures measured from the blood or biopsy as biomarkers. This task, known as classification, is typically solved using a suite of algorithms that learn a mathematical rule capable of discriminating one group (“cases”) from another (“controls”). However, discriminatory methods can only identify cancerous samples that resemble those that the algorithm already saw during training. As such, discriminatory methods may be ill-suited for the classification of cancer: because the possibility space of cancer is definitively large, the existence of a one-of-a-kind gene expression signature is likely. Instead, we propose using an established surveillance method that detects anomalous samples based on their deviation from a learned normal steady-state structure. By transferring this method to transcriptomic data, we can create an anomaly detector for tissue transcriptomes, a “tissue detector,” that is capable of identifying cancer without ever seeing a single cancer example. As a proof-of-concept, we train a “tissue detector” on normal GTEx samples that can classify TCGA samples with >90% AUC for 3 out of 6 tissues. Importantly, we find that the classification accuracy is improved simply by adding more healthy samples. We conclude this report by emphasizing the conceptual advantages of anomaly detection and by highlighting future directions for this field of study. Cancer is a collection of complex heterogeneous diseases with known genetic and environmental risk factors. Physicians diagnose cancer by carefully weighing evidence collected from patient history, physical examination, laboratory testing, clinical imaging, and biopsy. Computers can aid diagnosis and improve outcomes by mitigating diagnostic errors. Indeed, this objective is actively researched, where studies have shown that computers can reduce the reading errors of mammography , we have a predictive rule that decides whether to reject the null hypothesis and call that sample an anomaly . To train the “tissue detector,” we z-score standardized each gene within the GTEx training set, then performed a residual analysis of the GTEx training set. Residual analysis is based on the principle that most data have an underlying structure that can be largely reconstructed using a subset of the principal components, whereby the difference between the reduced representation and the original observations are termed the residues. Residual analysis uses the squared value of the residue as a proven way to measure the degree to which an observation is an outlier. For normally distributed data, the squared value of the residues follows a non-central χAfter training each model on the GTEx data, we evaluated its performance on the respective TCGA data. For each sample in the test set, we calculated an anomaly score based on the distance between that sample and the model reference. We did this by projecting the sample to the principal component space and measuring its residue, where higher residue scores indicate that the sample is more anomalous. If the anomaly score is larger than the anomaly detection threshold, the sample is called abnormal . Otherwise, the sample is called normal . This allows us to differentiate between normal and cancerous TCGA samples without ever seeing a single cancer example. We repeated this procedure for increasingly smaller subsets of the training data, with specificity averaged across ten bootstraps each.By using the Wang et al. data, we can evaluate the utility of the anomaly detection method with all batch effects already removed. Nevertheless, we chose to use the GTEx data as the “normal” training set so that any residual batch effects between the GTEx and TCGA data would cause the “tissue detector” to call false positives . For a robust and conservative estimate of performance, we focus our discussion on specificity .For this study, we trained a “tissue detector” on each of the six tissues described in We hypothesized that increasing the number of normal samples shown to the “tissue detector” during model training would improve its specificity, especially for the poorly performing prostate and stomach detectors. To test this hypothesis, we measured the specificity of each “tissue detector” as trained on increasingly smaller subsets of the GTEx data. In this study, we used normal GTEx samples to train a model that could classify TCGA samples. We acknowledge that there is no direct clinical application for this experiment, since it is trivial to differentiate between cancer and non-cancer tissue using simple microscopy. As a proof-of-concept, we chose to use these data to demonstrate tissue anomaly detection because the data set is sufficiently large and publicly available. However, anomaly detection could suit many health surveillance applications. By changing the class of samples used in the training set, the meaning of “anomaly” changes. For example, if we include only benign tumors in the training set, then an anomaly detector might identify whether a biopsied tumor is potentially malignant . Likewise, using a training set of blood biomarkers for patients with surgically resected tumors might yield an anomaly detector that can identify whether a primary tumor has recurred. Other novel applications might include training a “tissue detector” on healthy lymphatic tissue to screen for lymphatic metastasis or on chemotherapy-sensitive tumor biopsies to screen for emerging drug resistance. Whatever the application, anomaly detection is unique in that it only requires that there exist data for the null state that is under surveillance: it is not necessary that researchers have characterized the full spectrum of the undesired outcome.One challenge faced in the detection of anomalous gene expression signatures is the limited amount of data available for training and testing. Even as data sets get larger, anomaly detection will still benefit from the combination of multiple data sets, known as horizontal data integration , as done by Weighted Gene Correlation Network Analysis (Langfelder and Horvath, Technological advances have made it possible to measure the global gene expression signature of any biological sample at little cost. Already, there is a growing body of evidence that gene expression signatures can be used as biomarkers to diagnose cancer (Kourou et al., www.doi.org/10.1038/sdata.2018.61.Publicly available datasets were analyzed in this study. This data can be found here: TQ prepared the figures and drafted the manuscript. TN performed the primary analyses. SL pre-processed the data and supported primary analyses. SV supervised the project. All authors helped design the project and revise 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.