A multistage and multiple response optimization approach for serial manufacturing system

An ellipsoidal distance-based search strategy of ants for nonlinear single and multiple response optimization problems

Multiple parameter optimization methodology by integrating a game theory principle into priority-based decision making

Multiple response optimisation design can effectively improve the quality of the products, and generate huge economic benefits. For multiple response problem in the process of industrial production, owing to the control variables, and because the relationship between the control variable is relatively complex, the traditional multiple response optimisation method will not be able to improve the fitting model. On the basis of the theory of ANN, this paper builds a multiple response optimisation model of the injection moulding process, using Principal Component Analysis (PCA) to deal with multiple correlation between response factor, through the TOPSIS method to obtain the optimal level of factor combination, combined with the enterprise product injection instances, and effectively solve the complicated multiple response optimisation problems in moulding process, with a certain referential significance.

In the real problems, there are many cases which have correlated quality characteristics so multiple response optimization can be more realistic if we can consider correlation structure of responses. In this study we propose a new method which uses multivariate normal probability to find the optimal treatment in an experimental design. Moreover, a heuristic method is used to find better factors’ level in all possible combinations in the designs with large number of controllable factors and their levels. Some simulated numerical examples and a real case were studied by the proposed approach and the comparison of the results with previous methods show efficiency of the proposed method.

In production design processes, multiple correlated responses with different distributions are often encountered. The existing literature usually assumes that they follow normal distributions for computational convenience, and then analyzes these responses using traditional parametric methods. A few research papers assume that they follow the same type of distribution, such as the t-distribution, and then use a multivariate joint distribution to deal with the correlation. However, these methods give a poor approximation to the actual problem and may lead to the recommended settings that yield substandard products. In this article, we propose a new method for the robust parameter design that can solve the above problems. Specifically, a semiparametric model is used to estimate the margins, and then a joint distribution function is constructed using a multivariate copula function. Finally, the probability that the responses meet the specifications simultaneously is used to obtain the optimal settings. The advantages of the proposed method lie in the consideration of multiple correlation patterns among responses, the absence of restrictions on the response distributions, and the use of nonparametric smoothing to reduce the risk of model misspecification. The results of the case study and the simulation study validate the effectiveness of the proposed method.

The problems of M-machine, J-product, N-time point preemptive scheduling in parallel and serial production systems are the focus of this paper. The objective is to minimize the sum of the costs related to inventory level and production rate along a planning horizon. Although the problem is NP-hard, the application of the maximum principle reduces it into a well-tractable type of the two-point boundary value problem. As a result, algorithms of O(NMJk(N-L)+1) and O(N(MJ)k(N-L)+1) time complexities are developed for parallel and serial production systems, respectively, where L is the time point when the demand starts and k is the ratio of backlog cost MN over the inventory cost. This compares favorably with the time complexity O((J+1MN) of a naive enumeration algorithm.

When multiple responses are considered in process optimization, the degree to which they can be simultaneously optimized depends on the optimization objectives and the amount of trade‐offs between the responses. The normalized hypervolume of the Pareto front is a useful summary to quantify the amount of trade‐offs required to balance performance across the multiple responses. To quantify the impact of uncertainty of the estimated response surfaces and add realism to what future data to expect, 2 versions of the scaled normalized hypervolume of the Pareto front are presented. To demonstrate the variation of the hypervolume distributions, we explore a case study for a chemical process involving 3 responses, each with a different type of optimization goal. Results show that the global normalized hypervolume characterizes the proximity to the ideal results possible, while the instance‐specific summary considers the richness of the front and the severity of trade‐offs between alternatives. The 2 scaling schemes complement each other and highlight different features of the Pareto front and hence are useful to quantify what solutions are possible for simultaneous optimization of multiple responses.

In a multi-stage production/inventory system, customer service level is a function of the service levels at individual stages. The objective of a multi-stage system can be defined in terms of determining the stage-specific service levels, which minimize the total cost of the system subject to achieving a desired customer service level. The literature available so far discusses the service level perspective of multi-stage systems under the continuous review and periodic review policy. In this paper, we consider systems operating under the base stock policy, and derive service level relationships in multi-stage serial and assembly systems. First, we develop models for a two-stage serial system, and illustrate the models with the help of a numerical example. Finally, we propose an extension of the models for multi-stage serial and assembly systems.

Nowadays, micro-machining processes are playing an important role because of the technological developments in the MEMS, electrical, medical and aerospace industries. In this research work, the principal component analysis (PCA) technique is used to examine and explains the influences of three process parameters (spindle speed, feed rate and depth of cut) on the output responses such as MRR and Ra in micro-end milling process. Also this approach is used for the optimisation of multiple responses in micro-end milling operation to achieve maximum metal removal rate (MRR) and minimum surface roughness (Ra). Further, the optimised machining parameters were determined considering the multiple response objective using multiple response performance index value. The predicted value of multiple response performance index (Z) is 0.6412 which is calculated by using additive model. The measured value of multiple response performance index is 0.707, which shows good prediction accuracy. From the experimental result, it was concluded that, the PCA technique is suitable for the optimisation of multi-response problem.

One of the most challenging tasks of companies with complex and discrete production systems is to determine an appropriate production control strategy (PCS). The PCS is crucial, as it influences inventory, costs, and service. We propose a framework to address the most important questions of PCS engineering: limiting work in process, positioning the order penetration point (OPP), and coping with demand uncertainty upstream the OPP. Thereby, uncertain advance demand information in the form of forecasts is considered and desirable characteristics of push and pull strategies are combined. A serial manufacturing system with multiple products, variable processing times, and uncertain advance demand information is examined. The model is formulated as a queuing network model and solved numerically by discrete-event simulation. The framework and a resulting hybrid control strategy are applied to a case study from electronics manufacturing, where a significant improvement potential is identified.

We study the problem of dynamically allocating flexible workers to stations in tandem or serial manufacturing systems. Workers are trained to do a subset of consecutive tasks. We show that the optimal policy is often LBFS (last buffer first-served) or FBFS (first buffer first-served). These results generalize earlier results on the optimality of the pick-and-run, expedite, and bucket brigade-type policies. We also show that, for exponential processing times and general manufacturing networks, the optimal policy will tend to have several workers assigned to the same station.

In a manufacturing system, the machines have many idle statuses which consumed large quantity energy but with non-value added activities. Real time idle status control is important and challenging for the energy efficient manufacturing. With the sensible ability improved, more and more data can be collected from the manufacturing system. A fuzzy decision method is proposed to decrease the idle status by switching on-off the machine based on the real time data of the system. The serial manufacturing system is decomposed to basic control modules. A fuzzy controller with a fuzzy logic model and an on-off decision model is design based on the real time data of buffers and machines in order to switch the machine status. The fuzzy rules are described by analyzing the decision intention according to the human knowledge. After a series of simulation experiments, it has been observed that the proposed approach can be a simple, practical way to energy saving operation with accepted throughput loss.

When optimizing a product or process with multiple responses, a two‐stage Pareto front approach is a useful strategy to evaluate and balance trade‐offs between different estimated responses to seek optimum input locations for achieving the best outcomes. After objectively eliminating non‐contenders in the first stage by looking for a Pareto front of superior solutions, graphical tools can be used to identify a final solution in the second subjective stage to compare options and match with user priorities. Until now, there have been limitations on the number of response variables and input factors that could effectively be visualized with existing graphical summaries. We present novel graphical tools that can be more easily scaled to higher dimensions, in both the input and response spaces, to facilitate informed decision making when simultaneously optimizing multiple responses. A key aspect of these graphics is that the potential solutions can be flexibly sorted to investigate specific queries, and that multiple aspects of the solutions can be simultaneously considered. Recommendations are made about how to evaluate the impact of the uncertainty associated with the estimated response surfaces on decision making with higher dimensions. Copyright © 2016 John Wiley & Sons, Ltd.

Optimization and significance of fabrication parameters on the mechanical properties of 3D printed Chitosan/PLA scaffold

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Lung squamous cell carcinoma (LUSC) is a type of lung cancer with a dismal prognosis that lacks adequate therapies and actionable targets. This disease is characterized by a sequence of low- and high-grade preinvasive stages with increasing probability of malignant progression. Increasing our knowledge about the biology of these premalignant lesions (PMLs) is necessary to design new methods of early detection and prevention, and to identify the molecular processes that are key for malignant progression. To facilitate this research, we have designed XTABLE (Exploring Transcriptomes of Bronchial Lesions), an open-source application that integrates the most extensive transcriptomic databases of PMLs published so far. With this tool, users can stratify samples using multiple parameters and interrogate PML biology in multiple manners, such as two- and multiple-group comparisons, interrogation of genes of interests, and transcriptional signatures. Using XTABLE, we have carried out a comparative study of the potential role of chromosomal instability scores as biomarkers of PML progression and mapped the onset of the most relevant LUSC pathways to the sequence of LUSC developmental stages. XTABLE will critically facilitate new research for the identification of early detection biomarkers and acquire a better understanding of the LUSC precancerous stages. Editor's evaluation The authors have created a resource tool that is valuable in assessing precancerous lesions in the lung, which may serve as a tool for investigators working in this area, and as an example for additional similar resources. The accessibility of the tool is a concern but does not diminish the quality of the product. https://doi.org/10.7554/eLife.77507.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Lung squamous cell carcinoma (LUSC) is a type of non-small cell lung cancer that accounts for 20–30% of all lung cancer cases (Chen et al., 2014; Sung et al., 2021; Bray et al., 2021). Despite being the second most frequent type of lung cancer (Torre et al., 2016), our knowledge regarding the biology of this disease as well as the therapeutic modalities to treat it remain far behind the most frequent type of lung cancer, lung adenocarcinoma (LUAD) (Hirsch et al., 2016; Khuder, 2001). LUAD genetics is dominated by mutations (that are often druggable) that activate the RTK/RAS pathway, including EGFR and KRAS mutations (The Cancer Genome Atlas Research Network, 2012; Jamal-Hanjani et al., 2017). However, the genetic landscape of LUSC is more complex, with multiple pathways altered in subsets of patients and a lack of actionable mutations (Campbell et al., 2016; The Cancer Genome Atlas Research Network, 2012; Kim et al., 2014), precluding the development of new therapies. Hence, the only pharmacological therapies available to treat LUSC patients are immune checkpoint inhibitor monotherapy or in combination with chemotherapy (Mok et al., 2019; Paz-Ares et al., 2018; Weinberg and Gadgeel, 2019). Furthermore, the National Lung Cancer Matrix Trial and The Lung Master Protocol, the largest personalized medicine trials in lung cancer, have not shown clear therapeutic benefits with targeted agents in LUSC (; Middleton et al., 2020; Redman et al., 2020). LUSC also has a worse prognosis than LUAD independent of stage at detection, with a 5-year overall survival (OS) of 6.2% for patients diagnosed with distant metastasis (9.5% for LUAD). However, patients diagnosed with localized disease are eligible for curative surgery and the 5-year OS is 50% (National Cancer Institute and DCCPS, 2018). Therefore, early detection is currently the most valuable tool to prevent deaths by LUSC as evidenced by several ongoing programmes of lung cancer early detection (Crosbie et al., 2019a; Crosbie et al., 2019b; Aberle et al., 2011). These initiatives make use of low-dose CT scans in high-risk populations, but in spite of the frequent detection of localized lung cancer eligible for resection with curative intent, 40% of early diagnosed patients die within 5 years. LUSC progresses through a series of premalignant stages characterized by alterations of the normal bronchial epithelium (Figure 1; Ishizumi et al., 2010; Kadara et al., 2016; Thakrar et al., 2017; Pennycuick et al., 2020). These endobronchial premalignant lesions (PMLs) are classified as low-grade (squamous metaplasia, mild and moderate dysplasia) and high-grade (severe dysplasia, and carcinomas in situ [CIS]) (Figure 1). However, not all PMLs progress to LUSC. Although obvious differences exist between the multiple studies published on the topic, high-grade PMLs have a higher risk of progression than low grade, and high levels of chromosomal instability (CIN) are also predictive of progressive PMLs (Thakrar et al., 2017; van Boerdonk et al., 2014; Teixeira et al., 2019; Merrick et al., 2016). This potential role of CIN as biomarker of PMLs progression has been observed in low- and high-grade PMLs (van Boerdonk et al., 2014; Teixeira et al., 2019). These reports showed that high levels of copy number variations was the best predictor of progressive PMLs (van Boerdonk et al., 2014; Teixeira et al., 2019) and that immune response is the most likely cause of regression as these lesions contained higher levels of immune infiltration (Pennycuick et al., 2020; Beane et al., 2019). These results are supported by transcriptomic analysis of PMLs that indicate immune evasion in the transition to invasiveness (Mascaux et al., 2019). Figure 1 Download asset Open asset Developmental stages of lung squamous cell carcinoma (LUSC) premalignant lesions (PMLs) with representative histological images for each stage (haematoxilyn-eosin) and a summary of the four studies included in XTABLE (Exploring Transcriptomes of Bronchial Lesions). PMLs are typically classified as normal epithelium (including hyperplasia), low-grade and high-grade. Two studies (Mascaux et al., 2019, and Beane et al., 2019) carried out gene expression analysis of multiple developmental stages, whereas Merrick et al., 2018, and Teixeira et al., 2019, focused on dysplasias (blue boxes) and carcinomas in situ (CIS) (pink boxes), respectively. The most relevant findings of each article are summarized in the figure. Error bars=50 µm. The detection of PMLs cannot be carried out by routine patient imaging techniques such as CT or PET scans as the morphological change that they cause in the airway does not result in radiological contrast. Alternatively, ablation of high-grade PMLs detected by autofluorescence bronchoscopy using minimally invasive endoscopic procedures in high-risk populations is an innovative and interesting strategy to prevent LUSC (Guibert et al., 2016). Nonetheless autofluorescence bronchoscopy is an expensive and complex technique of limited use in large screening programmes. Therefore, simpler, more cost effective, and scalable methods of high-grade PML detection are needed to prevent deaths by LUSC. Improving the detection of PMLs requires a better understanding of their biology and the validation of adequate biomarkers of progressive lesions that can be translated into new technologies for large screening initiatives. Cell surface proteins, metabolites, nasal-based biomarkers, blood and sputum/bronchoalveolar lavage biomarkers are examples of biomarkers that can be used to improve and/or complement current diagnostic techniques (such as CT and PET scans) or develop new ones. A user-friendly application to interrogate gene expression from studies focusing on precancerous LUSC stages will enhance the advances in PML biology. However, such application does not exist. Recently, scientific interest in the biology of preinvasive LUSC stages has motivated the publication of several articles characterizing PML transcriptomes from various perspectives (Figure 1). Two reports published by Beane et al., 2019, and Mascaux et al., 2019, showed stage-by-stage gene expression analyses of PMLs. These studies provided the most detailed transcriptomic characterization of all PML stages so far and identified changes in the immune microenvironment associated with invasive transformation. Additionally, longitudinal studies by Beane et al., 2019, Merrick et al., 2018, and Teixeira et al., 2019, contained samples with known progression potential. Beane et al., 2019, identified molecular subtypes with specific biological traits whereas articles published by Merrick et al., 2018, and Teixeira et al., 2019 focused on gene expression of specific preinvasive stages (dysplasias and CIS, respectively) with the objective of identifying predictors of PML progression and the biomolecular processes involved (Figure 1). These studies provide a valuable source of gene expression data to identify candidate biomarkers for the detection of high-risk PMLs and/or early stage LUSC as well as to investigate the biology of premalignant LUSC progression. Transparent and straightforward accessibility to transcriptomic databases is a key requirement for the open science philosophy. Applications that integrate multiple databases focusing on the same biological and clinical problem, such as camcAPP (Dunning et al., 2017), allow cross-comparisons between independent studies, strengthen the robustness of results obtained, and allow the selection of high-confidence data. In this report, we provide an overall description of XTABLE (Exploring Transcriptomes of Bronchial Lesions) and provide examples of its functions. XTABLE is a new open-source application that will enable scientists to interrogate currently four LUSC PML transcriptomic datasets in a versatile manner that can be adapted to the needs of each researcher. Specifically, LUSC prevention and diagnosis are the main areas that can benefit the most from XTABLE, but its versatility and multiple functions lend themselves to the exploration of a variety of research questions. Without XTABLE, researchers would have to put together all the packages, data processing steps themselves for each analysis they wished to run. In this report, we provide an overall explanation of all the functions of XTABLE as well as a detailed description of the most important analysis modules, including two-group comparisons, gene-of-interest analyses, and interrogation of transcriptional signatures. Additionally, we explored the use of CIN-related signatures as a biomarker of progressive PMLs and mapped the onset of the most important LUSC pathways to its developmental stages. Results Description of the studies included in XTABLE Four datasets originating from four independent PML transcriptomic studies have been included in the XTABLE application (Figure 1, Table 1). Two datasets, GSE109743 (Beane et al., 2019) and GSE33479 (Mascaux et al., 2019), provide gene expression data of the developmental LUSC stages (with and without progression status information, respectively), whereas the remaining studies, GSE114489 (Merrick et al., 2018) and GSE108124 (Teixeira et al., 2019), focus on analysing specific PML stages (dysplasias and CIS, respectively) that have been followed up to establish their progressive, persistent, or regressive potential. Table 1 Description of the four cohorts included in XTABLE (Exploring Transcriptomes of Bronchial Lesions). GEO accessionPMIDStagesProgression status knownNumber of samplesSample typeTranscriptomeGSE3347931243362MultipleNo122Whole biopsiesMicroarrayGSE10974331015447Multiple*Yes448Discovery cohort (197 Bx, 91 Br)Validation cohort (111Bx, 49 Br)Whole biopsies and brushingsRNAseqGSE11448929997230Dysplasias normalYes63Whole biopsiesMicroarrayGSE10812430664780CISYes33MicrodissectedMicroarray Bx: biopsies; Br: brushings. * This cohort includes neither carcinomas in situ (CIS) nor invasive carcinomas. Dataset GSE33479 (Mascaux et al., 2019) comprises expression microarray analyses of 122 endobronchial biopsies of unknown progression status. Samples were obtained following autofluorescence bronchoscopy and as no enrichment or microdissection of the biopsy epithelium was carried out, variable levels of stromal component are present in samples. Although this results in dilution of epithelial signals, it has the advantage of providing information about the microenvironment as well as insights into the infiltrated immune cells. Dataset GSE109743 (Beane et al., 2019) consists of a transcriptomic analysis of whole PML biopsies with no purification of the epithelial compartment as well as bronchial brushings obtained from adjacent normal regions of the bronchial mucosa. Unlike GSE33479, GSE109743 used RNAseq and more importantly, includes the progression status for some lesions established by serial biopsies. Samples were classified as ‘normal-stable’ when they changed between normal, hyperplasia, and metaplasia, ‘regressive’ when they regress from dysplasia to a less severe dysplastic grade, or from dysplasia to normal/hyperplasia/metaplasia. Remaining samples were considered persistent/progressive. One advantage of this dataset is the large number of samples divided into a discovery and a validation cohort (Figure 1, Table 1). However, the representation of each PML stage is not homogeneous and CIS samples are not included. Datasets GSE114489 (Merrick et al., 2018) and GSE108124 (Teixeira et al., 2019) constitute a different type of study. Both make use of sequential biopsies to classify lesions according to their progression potential, but they differ in the stages included, the classification of progression status and sample processing. In GSE114489, the authors collected 63 baseline bronchial biopsies (with corresponding follow-up biopsies) and classified samples in four groups: 23 persistent dysplasias (dysplastic lesions with the same or higher severity scores in follow-up biopsies), 15 regressive dysplasias (dysplasias progressing to lower severity scores), 9 progressive non-dysplasias (biopsies with normal or hyperplastic morphologies that progress to more severe morphologies), and 16 stable non-dysplasias (normal or hyperplastic pathology that remains stable in follow-up biopsies). Microarray analysis of gene expression was performed on whole biopsies. The study published by Teixeira et al., 2019 (GSE108124) also makes use of follow-up biopsies to classify the progression potential of PMLs, but unlike GSE114489, GSE108124 focuses on CIS and progression is defined as the transition to invasive carcinomas in follow-up biopsies. RNA for gene expression microarray analysis was extracted from microdissected FFPE samples to enrich the epithelial component. XTABLE access, interface, and functions XTABLE download can be carried out from https://gitlab.com/cruk-mi/xtable (copy archived at Roberts, 2022) following the instructions in the Materials and methods section and Video 1. XTABLE has been designed using the shiny app interface (see Materials and methods section) and its functions have been divided into 11 interrelated tabs that contain a specific function to interrogate each dataset separately (Figure 2A). Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Step-by-step instructions to install XTABLE (Exploring Transcriptomes of Bronchial Lesions) using RStudio. Figure 2 with 3 supplements see all Download asset Open asset Overall organization of XTABLE (Exploring Transcriptomes of Bronchial Lesions) functions and use of the DEG function. (A) Organization of all the functions in the XTABLE interface. The functions are interrelated and completing certain analyses requires the use of several functions. For instance, the GSEA and PA functions operate with gene lists obtained with the DEG function. (B) Workflow to obtain differentially expressed genes between two groups using the DEG function. The example shows groups of samples arranged by developmental stage to compare low-grade and high-grade premalignant lesions (PMLs) in the GSE33479 cohort. (C and D) Workflow to obtain differentially expressed genes between two groups using the DEG function. The two groups have been arranged by progression status using in the GSE114489 and GSE108124 cohorts, respectively. Introduction Detailed description of the four studies, analyses performed in each tab, output formats, links to the four articles used in the application, bioinformatic packages used in the different functions and additional references. Dataset tab Explanation of the differences in methodology and progression status definitions between the four studies. Load tab In this tab, the user selects the database (GSE114489, GSE108124, GSE109743, or GSE33479) for subsequent interrogation. Due to specific differences between the four studies, only one dataset can be interrogated at a time. Since dataset GSE109743 contains a discovery and a validation cohort, as well as biopsies and brushings, preselection of the cohort and type of sample has to be carried out (Figure 2—figure supplement 1). CIN-score tab CIN has been identified as a good predictor of PML progression. Except for Teixeira et al., 2019, the other three studies do not provide genomic analyses that provide an estimate of the level of chromosomal alterations in the samples. Several gene expression signatures that correlate with CIN (CIN-scores) have been described in the literature (Teixeira et al., 2019; Carter et al., 2006). This CIN-score function returns a list of several CIN-scores (CIN70, CIN25, and CIN5, depending on the number of genes included in the signature) for all samples included in the study and a graph depicting CIN-scores in different sample types classified according to the sample classification defined in the study. A line marking a selected CIN-score threshold can be added for visualization purpose and to help determine appropriate thresholds for additional CIN-related analyses (Figure 2—figure supplement 2). DEG tab Function to identify differentially expressed genes in comparisons of two groups of samples determined by the user. This function allows the selection of p-value and fold-change cutoffs for the analysis. Additionally, AUC is calculated for each gene to provide further confidence to the differentially expressed gene results and additional gene IDs are added at this stage that have been sourced from two separate datasets to maximize identification and discrepancies manually reviewed to improve downstream enrichment and pathway analyses. GSEA and PA In these two tabs, the user can carry out gene set and pathway enrichment analyses using multiple tools (goseq/ideal, fgsea/MSigDB, enrichR, gage/gageData, kegga/pathview, ReactomePA, Progeny, and Dorothea). This function operates with the list of differentially expressed genes obtained with the DEG function. Signature This function returns the gene expression values of a user-defined gene list. Lists can be manually entered or uploaded from a .csv file. Deconvolution Estimation of immune and stromal component in samples from gene expression data using the ESTIMATE tool. ROC This function returns receiver operating characteristic (ROC) curves in a user-defined comparison of two groups of samples stratified by the expression of a gene or by CIN-scores. Heatmap Returns a gene expression heatmap for genes selected by variance, user-defined gene signatures, and differentially expressed genes in the DEG tab. PCA Principal component analysis (PCA) of all the samples included in each study. Several sample characteristics can be highlighted in the PCA plot including CIN signatures, progression status, and PML analysis of expression of genes selected by the user. Several for sample are expression analysis The discovery of candidate biomarkers for the detection of PMLs at high risk of malignant progression and the interrogation of PML biology on the comparison of gene expression between lesions with known progressive or regressive potential. The four databases included in XTABLE contain different types of information which users can interrogate these To facilitate the interrogation of the four transcriptomic databases in a manner that allows the versatile of we have designed a in XTABLE DEG (Figure that returns the differentially expressed genes in two user-defined groups of samples stratified by PML stage and by known progression status GSE114489, and or by CIN-score In XTABLE, we have included three et al., and (Teixeira et al., 2019). and have been in the with the the number of genes for interrogation. is from the used by Teixeira et al., 2019, and is to a good with progression in CIS groups by stage using DEG allows the comparison of two PML stages or the of stages into two For instance, to compare the expression between low-grade and high-grade PMLs, we can two one including metaplasia, low and moderate dysplasia and one including severe dysplasia and CIS using cohort GSE33479 (Figure selection of the cohort and up the groups (Figure the application returns a list of differentially expressed genes with associated information (Figure including AUC by ROC analysis. ROC curves associated with each gene in a two-group for biomarker can be using the ROC tab (Figure 2—figure supplement of progressive and regressive samples can also be carried out by the in studies that provide this information (Figure and To carry out two-group comparison by CIN signatures, the CIN-score tab a visualization of the three CIN-scores for all samples to the selection of a CIN-score threshold for sample (Figure A .csv the CIN-scores of all samples can be The selected CIN threshold can be used in the DEG tab to stratify and low samples and a list of differentially expressed genes (Figure Figure 3 Download asset Open asset expression analysis between two groups of samples classified according to a chromosomal instability The CIN-score function allows the visualization of CIN-scores for all samples in a study. A CIN-score threshold selected by the user can be on the graph The CIN-score threshold selected by the user can be used in the DEG tab to the two-group the developmental stages of LUSC as described in Mascaux et al., 2019 CIN25, and can be used in the DEG tab. 1, and 2 and 3 and and the and largest within The list of differentially expressed genes obtained in the DEG tab can be used in the GSEA and PA tabs to carry out gene set enrichment and pathway analyses. The GSEA tab allows the user to the gene set enrichment tool fgsea/MSigDB, enrichR, and p-value and the gene to (Figure For instance, using the function to one of the three and molecular to for analysis (Figure with the tool, the user can the gene set of interest (Figure The PA tab operates in a similar manner using four pathway analysis tools ReactomePA, Progeny, and (Figure supplement 1). Figure with 1 supplement see all Download asset Open asset set enrichment analyses in a list differentially expressed genes using the GSEA tab. (A) set enrichment analysis using the tool of a list of differentially expressed genes obtained in the DEG tab. One of the three main can be selected for analysis at a time. selection of a XTABLE (Exploring Transcriptomes of Bronchial Lesions) returns a list of with associated (B) set enrichment analysis using the tool. This tool allows the selection of included in and returns a list of signatures with associated The example shows the selection of the in the analysis and user-defined transcriptional signatures can investigate their gene or of genes of interest in the To facilitate this type of analyses, we have included the and Signature functions in In the tab, the user can the expression of one gene of This tab is divided in three The tool returns a list of the multiple to the same gene expression values for the gene of interest in all samples (Figure The function returns the differentially expressed gene analysis results including fold-change and values for a gene in all groups of samples with a (Figure and on the study. The function to the function but allows of up to four groups of samples and the for analysis can be determined by the user. The example in Figure shows the of expression in four groups of samples that normal, low-grade PMLs, high-grade PMLs, and invasive carcinomas (Figure The and are the result with the comparison with the (normal normal and as the (Figure Figure 5 Download asset Open asset XTABLE (Exploring Transcriptomes of Bronchial Lesions) functions to analyses on genes and user defined gene signatures (A) The function the the expression values for a gene of interest in all samples. (B) The tool the expression of a gene of interest in groups of samples with a In the the function the expression of in all stages with the normal in The tool the of samples in up to four groups and comparison with a determined by the user. The example shows the analysis of expression in four groups of samples from the GSE33479 cohort and invasive The is set as for analysis. of the use of the Heatmap tab to interrogate to the expression of gene in premalignant lesions can be defined by the user in the and are shown using the stage classification and entered manually or from a .csv file. Alternatively, the heatmap can be from a list of differentially expressed genes from the DEG tab or a selected number of genes by The three can be selected in the In the example the heatmap shows all microarray associated to each gene calculated using To facilitate the interrogation of biological processes PML XTABLE also allows the interrogation of transcriptomic signatures using multiple functions. The Signature tab returns a list of expression values and a graph with the scores of expression for a gene set determined by the user in all samples of the selected study. The Heatmap tab returns a heatmap that the expression values in all samples in a selected study. The gene set shown in the heatmap can be selected using three from the With the the user can manually or a list of The example in Figure shows a heatmap from the GSE33479 dataset with transcriptional of an important LUSC The selects the list of genes differentially expressed in the DEG tab. the selects genes using a user-defined number of genes with the of the between CIN signatures and progression potential Two studies have highlighted the potential role of CIN as predictor of low-grade (van Boerdonk et al., and high-grade (Teixeira et al., 2019) PMLs progression in LUSC. XTABLE can be used to