Diagonal Linear Discriminant Analysis Research Articles

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution.

Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children. We used novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we showed that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and showed widespread genetic regulation of the transcriptional dynamics of the gene expression response.

A robust and efficient EEG-based drowsiness detection system using different machine learning algorithms

Vehicle accidents on long routes around the world are frequently caused by drowsy drivers. It is mainly because there is no system that measures alertness. The driver will be notified to interrupt his/her travel if an accurate and robust fatigue detection system is available. Dealing with this approach will help the driver avoid accidents and make the right decisions. This paper aims to detect drivers’ sleepiness using a powerful software tool. It was initially developed by capturing electroencephalography (EEG) signals and processing them. In this research, different machine learning algorithms were applied to the EEG signals of twelve subjects to measure their performance. In the first step, all recorded data for all subjects were segmented into second epochs. Brain signals were labeled alert or drowsy for each epoch. Before applying the machine learning algorithms to the epoched signal, a preprocessing step is introduced to extract the relevant features. The applied algorithms are: Naive Bayes (Diagonal Linear Discriminant Analysis), Support Vector Machines (Linear and Radial Basis Functions), K-Nearest Neighbor (KNN), and Random Forest Analysis (RFA). By capturing signals from only three electrodes, it was found that utilizing more than one classifier led to the highest accuracy of 100% for all subjects considered in this study. In general, this developed EEG-based system detects drowsiness and loss of focus of drivers in real-time with high accuracy, making it a practicable and reliable option for real-time applications.

Role of Deep Learning in Improving the Performance of Driver Fatigue Alert System

Driver fatigue detection system aims to monitor the driver state. When detecting a fatigue caused by different attitudes other than normal driving habit, the system warns the driver that traveling should be interrupted. In this way, it helps the driver to make the right decision. The aim of this study is to prevent traffic accidents. The system analyzes any changes in the driver's eyes and mouth features in real time and warns the driver when necessary. The proposed system contains several stages to detect the driver's fatigue. First, the preprocessing stage; enhancement of the frames, determining the face, and cropping eyes and mouth of the driver was done. Then, dealing with feature extraction stage; the features concerning each frame was processed. Finally, two classification approaches were presented and a comparison between them was addressed. In the first approach, four traditional classifiers were applied; Diagonal Linear Discriminant Analysis (DiagLDA), Linear Support Vector Machine (LSVM), K-Nearest Neighbor (KNN), and Random Forest Classifier (RFC). The results show that two classifiers; KNN and RFC yield the highest average accuracy of 91.94% for all subjects presented in this paper. In the second approach, one model of deep learning neural network (CNN) was applied; "Resnet-50" model. The results also show that the proposed deep learning model yields a high average accuracy of 96.3889% for the same data. In general, the drowsiness and lost focus of drivers with high accuracy have been detected with the developed image processing based system, which makes it practicable and reliable for real-time applications.

The Exploring of Student’s School Majoring Data at Madrasah Aliyah Negeri in Samarinda Using Linear Discriminant Analysis Models

Curriculum 2013, which applied to Madrasah Aliyah Negeri (MAN) requires that majors are implemented since the 10th grade The majors is supposed to relate to the study plan when they study at University. The aims of this research are to describe correspondence between current majors and desired future majors, to observe distribution pattern of student exam data based on their boxplot, to classify student majors data using the Linear Discriminant Analysis (LDA) models, and to get the best LDA model to describe the characteristics of majors in MAN. The LDA models used in this research were Fisher’s Linear Discriminant Analysis (FLDA), Diagonal Linear Discriminant Analysis (DLDA), Shrunken Linear Discriminant Analysis (SLDA), Maximum-uncertainty Linear Discriminant Analysis (MLDA), and Factor-model Linear Discriminant Analysis (RFLDA). The best LDA model was chosen based on the classification accuracy produced by resampling with n = 1000 and n = 5000 for training data and testing data with the probability of each data being drawn were 60:40, 70:30, 80:20, and 90:10.

MLSeq: Machine learning interface for RNA-sequencing data

Background and ObjectiveIn the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data. Although there are many algorithms proposed for microarray data, the number of available algorithms and programs are limited for classification of RNA-sequencing data. For this reason, we developed MLSeq, to bring not only frequently used classification algorithms but also novel approaches together and make them available to be used for classification of RNA sequencing data. This package is developed using R language environment and distributed through BIOCONDUCTOR network. MethodsClassification of RNA-sequencing data is not straightforward since raw data should be preprocessed before downstream analysis. With MLSeq package, researchers can easily preprocess (normalization, filtering, transformation etc.) and classify raw RNA-sequencing data using two strategies: (i) to perform algorithms which are directly proposed for RNA-sequencing data structure or (ii) to transform RNA-sequencing data in order to bring it distributionally closer to microarray data structure, and perform algorithms which are developed for microarray data. Moreover, we proposed novel algorithms such as voom (an acronym for variance modelling at observational level) based nearest shrunken centroids (voomNSC), diagonal linear discriminant analysis (voomDLDA), etc. through MLSeq. MaterialsThree real RNA-sequencing datasets (i.e cervical cancer, lung cancer and aging datasets) were used to evalute model performances. Poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA) were selected as algorithms based on dicrete distributions, and voomNSC, nearest shrunken centroids (NSC) and support vector machines (SVM) were selected as algorithms based on continuous distributions for model comparisons. Each algorithm is compared using classification accuracies and sparsities on an independent test set. ResultsThe algorithms which are based on discrete distributions performed better in cervical cancer and aging data with accuracies above 0.92. In lung cancer data, the most of algorithms performed similar with accuracies of 0.88 except that SVM achieved 0.94 of accuracy. Our voomNSC algorithm was the most sparse algorithm, and able to select 2.2% and 6.6% of all features for cervical cancer and lung cancer datasets respectively. However, in aging data, sparse classifiers were not able to select an optimal subset of all features. ConclusionMLSeq is comprehensive and easy-to-use interface for classification of gene expression data. It allows researchers perform both preprocessing and classification tasks through single platform. With this property, MLSeq can be considered as a pipeline for the classification of RNA-sequencing data.

Biomarkers from circulating neutrophil transcriptomes have potential to detect unruptured intracranial aneurysms

BackgroundIntracranial aneurysms (IAs) are dangerous because of their potential to rupture and cause deadly subarachnoid hemorrhages. Previously, we found significant RNA expression differences in circulating neutrophils between patients with unruptured IAs and aneurysm-free controls. Searching for circulating biomarkers for unruptured IAs, we tested the feasibility of developing classification algorithms that use neutrophil RNA expression levels from blood samples to predict the presence of an IA.MethodsNeutrophil RNA extracted from blood samples from 40 patients (20 with angiography-confirmed unruptured IA, 20 angiography-confirmed IA-free controls) was subjected to next-generation RNA sequencing to obtain neutrophil transcriptomes. In a randomly-selected training cohort of 30 of the 40 samples (15 with IA, 15 controls), we performed differential expression analysis. Significantly differentially expressed transcripts (false discovery rate < 0.05, fold change ≥ 1.5) were used to construct prediction models for IA using four well-known supervised machine-learning approaches (diagonal linear discriminant analysis, cosine nearest neighbors, nearest shrunken centroids, and support vector machines). These models were tested in a testing cohort of the remaining 10 neutrophil samples from the 40 patients (5 with IA, 5 controls), and model performance was assessed by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (PCR) was used to corroborate expression differences of a subset of model transcripts in neutrophil samples from a new, separate validation cohort of 10 patients (5 with IA, 5 controls).ResultsThe training cohort yielded 26 highly significantly differentially expressed neutrophil transcripts. Models using these transcripts identified IA patients in the testing cohort with accuracy ranging from 0.60 to 0.90. The best performing model was the diagonal linear discriminant analysis classifier (area under the ROC curve = 0.80 and accuracy = 0.90). Six of seven differentially expressed genes we tested were confirmed by quantitative PCR using isolated neutrophils from the separate validation cohort.ConclusionsOur findings demonstrate the potential of machine-learning methods to classify IA cases and create predictive models for unruptured IAs using circulating neutrophil transcriptome data. Future studies are needed to replicate these findings in larger cohorts.

Abstract P4-02-06: Intratumoral and peritumoral MRI signatures of HER2-enriched subtype also predict pathological response to neoadjuvant chemotherapy in HER2+ breast cancers

Abstract Background: Applying the PAM50 classifier to targeted RNA-Sequencing data allows HER2+ tumors to be sub-categorized into intrinsic breast cancer subtypes. HER2+ breast cancers belonging to the HER2-enriched [HER2-E] subtype exhibit the highest rate of response to neoadjuvant therapy with combination of HER2-blockade and chemotherapy, as well as dual-HER2 blockade alone. A non-invasive predictor of PAM50 subtype from clinical dynamic contrast-enhanced MRI [DCE-MRI] could provide valuable clinical guidance in the treatment of HER2+ breast cancer. In this work, we identify a set of computer-extracted heterogeneity features computed within the lesion and its surrounding peritumoral region capable of distinguishing HER2-E from other HER2+ breast cancers [Non-HER2-E]. We then demonstrate that this imaging signature of HER2-E is also predictive of pathological complete response [pCR] in an independent HER2+ testing set, consistent with the HER2-E subtype's elevated response to HER2-targeted therapy. Methods: The training set consisted of 42 HER2+ patients with both 1.5 or 3 T DCE-MRI and targeted RNA sequencing collected prior to neoadjuvant treatment from a multicenter trial [BrUOG 211B, n=35] and The Cancer Genome Atlas-Breast Cancer project [TCGA-BRCA, n=7]. Intrinsic subtypes were assigned by unsupervised hierarchical clustering of the PAM50 gene set. 19 patients were determined to belong to the HER2-E subtype, while the remaining 23 represented non-HER2-E subtypes [19 HER2-Luminal, 4 HER2-basal]. Lesion boundaries were annotated by an expertly trained radiologist and expanded to 5 annular peritumoral regions in 3 mm increments out to a maximum radius of 15 mm. Computer-extracted heterogeneity features were computed voxelwise within intratumoral and peritumoral regions by first order statistics. A top HER2-E-associated feature from each region was identified by Wilcoxon feature selection and used to train a diagonal linear discriminant analysis [DLDA] classifier to predict HER2-E in a 3-fold cross-validation setting. This classifier was then applied to pCR prediction from DCE-MRI in a testing set of 28 HER2+ patients with available post neoadjuvant chemotherapy surgical specimens at one institution. 16 patients achieved pCR (ypT0/is), while the remainder had partial or no response (non-pCR). Results: A combination of heterogeneity features within the intratumoral region and annular peritumoral regions out to 12 mm from the tumor yielded optimal results within the training set, with an average HER2-E prediction AUC of .77 +/- .03. When applied to response prediction in an independent testing set, this HER2-E classifier was predictive of pCR (AUC = .72). Conclusions: Computer-extracted heterogeneity features calculated within the tumor and the surrounding peritumoral environment on DCE-MRI were able to distinguish the HER2-E PAM50 intrinsic subtype from other HER2+ breast cancers. HER2-E was characterized by elevated expression of intratumoral and peritumoral heterogeneity features, indicating a more disordered imaging phenotype within and around the tumor. Additional independent validation of these findings is needed. Citation Format: Braman N, Prasanna P, Singh S, Beig N, Gilmore H, Etesami M, Bates D, Gallagher K, Bloch BN, Somlo G, Sikov W, Harris L, Plecha D, Varadan V, Madabhushi A. Intratumoral and peritumoral MRI signatures of HER2-enriched subtype also predict pathological response to neoadjuvant chemotherapy in HER2+ breast cancers [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-02-06.

A 14-Protein Signature for Rapid Identification of Poor Prognosis Stage III Metastatic Melanoma.

To validate differences in protein levels between good and poor prognosis American Joint Committee on Cancer (AJCC) stage III melanoma patients and compile a protein panel to stratify patient risk. Protein extracts from melanoma metastases within lymph nodes in patients with stage III disease with good (n=16, >4years survival) and poor survival (n=14, <2years survival) were analyzed by selected reaction monitoring (SRM). Diagonal Linear Discriminant Analysis (DLDA) was performed to generate a protein biomarker panel. SRM analysis identified ten proteins that were differentially abundant between good and poor prognosis stage III melanoma patients. The ten differential proteins were combined with 22 proteins identified in our previous work. A panel of 14 proteins was selected by DLDA that was able to accurately classify patients into prognostic groups based on levels of these proteins. The ten differential proteins identified by SRM have biological significance in cancer progression. The final signature of 14 proteins identified by SRM could be used to identify AJCC stage III melanoma patients likely to have poor outcomes who may benefit from adjuvant systemic therapy.

Serum microRNA panel excavated by machine learning as a potential biomarker for the detection of gastric cancer.

Early detection of gastric cancer (GC) is crucial to improve the therapeutic effect and prolong the survival of patients. MicroRNAs (miRNAs) are a group of small non-protein-coding RNAs that function as repressors of diverse genes. We aimed to identify a microRNA panel in the serum of patients to predict GC non-invasively with high accuracy and sensitivity. Using six types of classifiers, we selected three markers (miR‑21-5p, miR-22-3p and miR-29c-3p) from a published miRNA profiling study (GSE23739) which was treated as a training set. The values of the area under the receiver operating characteristic (ROC) curves (AUCs) were 0.9437, 0.9456 and 0.9563 in the three classifiers [Compound covariate classifier, Diagonal linear discriminant analysis (DLDA) classifier and Support vector machine classifier], respectively. Then the panel was validated further in another two miRNA profiles in GEO (Gene Expression Omnibus) databases (GSE26595, GSE28700) with high AUC values as well. Next, we found that the serum levels of miR-21 were significantly higher in GC patients than levels in healthy controls by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) for confirmation, which was opposite to the serum levels of miR-22 and miR-29c (all P<0.0001). Finally, using bioinformatic tools, their biological mechanisms were elucidated by their predicted targets: Sp1 (miR-21) and PTEN (miR-22 and miR-29c). This miRNA panel is a non‑invasive and potential biomarker for GC.

Prognostic Classifier Based on Genome-Wide DNA Methylation Profiling in Well-Differentiated Thyroid Tumors.

Context:Even though the majority of well-differentiated thyroid carcinoma (WDTC) is indolent, a number of cases display an aggressive behavior. Cumulative evidence suggests that the deregulation of DNA methylation has the potential to point out molecular markers associated with worse prognosis.Objective:To identify a prognostic epigenetic signature in thyroid cancer.Design:Genome-wide DNA methylation assays (450k platform, Illumina) were performed in a cohort of 50 nonneoplastic thyroid tissues (NTs), 17 benign thyroid lesions (BTLs), and 74 thyroid carcinomas (60 papillary, 8 follicular, 2 Hürthle cell, 1 poorly differentiated, and 3 anaplastic). A prognostic classifier for WDTC was developed via diagonal linear discriminant analysis. The results were compared with The Cancer Genome Atlas (TCGA) database.Results:A specific epigenetic profile was detected according to each histological subtype. BTLs and follicular carcinomas showed a greater number of methylated CpG in comparison with NTs, whereas hypomethylation was predominant in papillary and undifferentiated carcinomas. A prognostic classifier based on 21 DNA methylation probes was able to predict poor outcome in patients with WDTC (sensitivity 63%, specificity 92% for internal data; sensitivity 64%, specificity 88% for TCGA data). High-risk score based on the classifier was considered an independent factor of poor outcome (Cox regression, P < 0.001).Conclusions:The methylation profile of thyroid lesions exhibited a specific signature according to the histological subtype. A meaningful algorithm composed of 21 probes was capable of predicting the recurrence in WDTC.

Abstract 3358: Epigenetic signatures associated with patient outcome in thyroid carcinoma

Abstract BACKGROUND: Thyroid cancer (TC) is the most frequent endocrine neoplasia composed essentially by well-differentiated tumors (90%). These tumors generally are indolent and the patients show a favorable outcome. However, a set of TC patients presents aggressive outcome. Global deregulation of DNA methylation has been described as involved in thyroid cancer (TC) development. In this study, DNA methylation profile was performed aiming to identify a prognostic signature in TC. PATIENTS AND METHODS: The methylation profile of 50 non-neoplastic thyroid tissues (NT), 17 benign thyroid lesions and 74 TC (60 papillary, 8 follicular, 2 Hürthle cell, 1 poorly differentiated and 3 anaplastic thyroid carcinomas) were investigated using the Methylation 450 Human Infinium®BeadChip platform (Illumina). The data were normalized and analyzed using SVA, wateRmelon and LIMMA packages. The threshold delta beta of 0.2 and adjusted p-value &amp;lt;0.05 were used to identify differentially methylated probes among the histological subtypes. The delta beta of 0.1 and adjusted p-value &amp;lt;0.05 were used in the prognostic features analysis in WDTC (well differentiated thyroid cancer) cases. An epigenetic classifier according to WDTC was identified using diagonal linear discriminant analysis. The results were compared with The Cancer Genome Atlas (TCGA) database. RESULTS: Methylation analyses revealed a specific epigenetic profile according to the histological subtypes. A global hypermethylation was observed in benign lesions and follicular carcinomas, while papillary and undifferentiated carcinomas were widely hypomethylated compared with NT. An epigenetic signature comprising 21 probes differentially methylated (delta beta 0.1) was able to predict poor outcome in WDTC patients. This classifier reveled 63% of sensitivity and 92% of specificity, which was confirmed by TCGA database (64% of sensitivity and 88% of specificity). Using the established signature, we were able to confirm the involvement of poor prognosis markers with high-risk scores (multivariable analysis; P&amp;lt;0.001). CONCLUSION: Thyroid tumors showed different methylation profile according to the histological subtypes. Genes regulated by methylation in TC and associated with the tumor development were identified and confirmed by TCGA. In addition, a meaningful algorithm was designed and confirmed as capable to predict recurrence in WDTC. FINANCIAL SUPPORT: FAPESP (2015/20548-5) and National Institute of Science and Technology in Oncogenomics (FAPESP 2008/57887-9 and CNPq 573589/08-9), CNPq (302606/2011-4). Note: This abstract was not presented at the meeting. Citation Format: Mariana Bisarro dos Reis, Caroline Moraes Beltrami, Mateus Camargo Barros-Filho, Fabio Albuquerque Marchi, Hellen Kuasne, Skirant Ambatipudi, Zdenko Herceg, Luiz Paulo Kowalski, Silvia Regina Rogatto. Epigenetic signatures associated with patient outcome in thyroid carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3358. doi:10.1158/1538-7445.AM2017-3358

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

BackgroundIn this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).MethodsA total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2−) and triple-negative or HER2+ (TN/HER2+) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance.ResultsAmong all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR+, HER2− group using DLDA and 0.93 ± 0.018 within the TN/HER2+ group using a naive Bayes classifier. In HR+, HER2− breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2+ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors.ConclusionsThrough a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.

Subgroup identification for treatment selection in biomarker adaptive design.

BackgroundAdvances in molecular technology have shifted new drug development toward targeted therapy for treatments expected to benefit subpopulations of patients. Adaptive signature design (ASD) has been proposed to identify the most suitable target patient subgroup to enhance efficacy of treatment effect. There are two essential aspects in the development of biomarker adaptive designs: 1) an accurate classifier to identify the most appropriate treatment for patients, and 2) statistical tests to detect treatment effect in the relevant population and subpopulations. We propose utilization of classification methods to identity patient subgroups and present a statistical testing strategy to detect treatment effects.MethodsThe diagonal linear discriminant analysis (DLDA) is used to identify targeted and non-targeted subgroups. For binary endpoints, DLDA is directly applied to classify patient into two subgroups; for continuous endpoints, a two-step procedure involving model fitting and determination of a cutoff-point is used for subgroup classification. The proposed strategy includes tests for treatment effect in all patients and in a marker-positive subgroup, with a possible follow-up estimation of treatment effect in the marker-negative subgroup. The proposed method is compared to the ASD classification method using simulated datasets and two publically available cancer datasets.ResultsThe DLDA-based classifier performs well in terms of sensitivity, specificity, positive and negative predictive values, and accuracy in the simulation data and the two cancer datasets, with superior accuracy compared to the ASD method. The subgroup testing strategy is shown to be useful in detecting treatment effect in terms of power and control of study-wise error.ConclusionAccuracy of a classifier is essential for adaptive designs. A poor classifier not only assigns patients to inappropriate treatments, but also reduces the power of the test, resulting in incorrect conclusions. The proposed procedure provides an effective approach for subgroup identification and subgroup analysis.

Modified linear discriminant analysis using block covariance matrix in high-dimensional data

ABSTRACTAmong many classification methods, linear discriminant analysis (LDA) is a favored tool due to its simplicity, robustness, and predictive accuracy but when the number of genes is larger than the number of observations, it cannot be applied directly because the within-class covariance matrix is singular. Also, diagonal LDA (DLDA) is a simpler model compared to LDA and has better performance in some cases. However, in reality, DLDA requires a strong assumption based on mutual independence. In this article, we propose the modified LDA (MLDA). MLDA is based on independence, but uses the information that has an effect on classification performance with the dependence structure. We suggest two approaches. One is the case of using gene rank. The other involves no use of gene rank. We found that MLDA has better performance than LDA, DLDA, or K-nearest neighborhood and is comparable with support vector machines in real data analysis and the simulation study.

Cross platform classification of microarrays by rank comparison

Mining the microarray data accumulated in the public data repositories can save experimental cost and time and provide valuable biomedical information. Big data analysis pooling multiple data sets increases statistical power, improves the reliability of the results, and reduces the specific bias of the individual study. However, integrating several data sets from different studies is needed to deal with many problems. In this study, I limited the focus to the cross platform classification that the platform of a testing sample is different from the platform of a training set, and suggested a simple classification method based on rank. This method is compared with the diagonal linear discriminant analysis, k nearest neighbor method and support vector machine using the cross platform real example data sets of two cancers.

Computing molecular signatures as optima of a bi-objective function: method and application to prediction in oncogenomics.

BACKGROUNDFilter feature selection methods compute molecular signatures by selecting subsets of genes in the ranking of a valuation function. The motivations of the valuation functions choice are almost always clearly stated, but those for selecting the genes according to their ranking are hardly ever explicit.METHODWe addressed the computation of molecular signatures by searching the optima of a bi-objective function whose solution space was the set of all possible molecular signatures, ie, the set of subsets of genes. The two objectives were the size of the signature–to be minimized–and the interclass distance induced by the signature–to be maximized–.RESULTSWe showed that: 1) the convex combination of the two objectives had exactly n optimal non empty signatures where n was the number of genes, 2) the n optimal signatures were nested, and 3) the optimal signature of size k was the subset of k top ranked genes that contributed the most to the interclass distance. We applied our feature selection method on five public datasets in oncology, and assessed the prediction performances of the optimal signatures as input to the diagonal linear discriminant analysis (DLDA) classifier. They were at the same level or better than the best-reported ones. The predictions were robust, and the signatures were almost always significantly smaller. We studied in more details the performances of our predictive modeling on two breast cancer datasets to predict the response to a preoperative chemotherapy: the performances were higher than the previously reported ones, the signatures were three times smaller (11 versus 30 gene signatures), and the genes member of the signature were known to be involved in the response to chemotherapy.CONCLUSIONSDefining molecular signatures as the optima of a bi-objective function that combined the signature size and the interclass distance was well founded and efficient for prediction in oncogenomics. The complexity of the computation was very low because the optimal signatures were the sets of genes in the ranking of their valuation. Software can be freely downloaded from http://gardeux-vincent.eu/DeltaRanking.php

Molecular Signature in the Peripheral Blood for Sub-Clinical Acute Kidney Rejection.

Background: Sub-clinical acute rejection (SCAR) is defined as histologic rejection with normal serum creatinine and is associated with worse long term graft survival. Protocol kidney biopsies (bx) are done at some centers to detect SCAR. Methods: We identified 23 whole blood samples from recipients with SCAR and profiled these using Affymetrix HG-U133 Peg microarrays. We compared these to 21 pts with clinical acute rejection (AR), and 25 with normal bx and a normal serum creatinine (TX). SCAR bx included 16 “Borderline” and 7 Banff 1A (Banff 07). 3-way ANOVA analysis was performed between Groups 1-3. Results: The arrays yielded over 6,000 differentially expressed probesets at p<0.001. Even setting a False Discovery Rate (FDR) cut-off of <10% gave us over 2,500 significantly differentially expressed probesets. For the diagnostic signature, we used only the top 200 differentially expressed probesets as ranked by p values. These 200 top probesets have FDR values of <0.05%. We used 3 different predictive algorithms: Nearest Centroid (NC), Support Vector Machines (SVM) and Diagonal Linear Discriminant Analysis (DLDA) to build predictive models. NC, SVM and DLDA picked classifier sets of 188, 192 and 200 probesets as the best classifiers, respectively. Table 1 shows the performance of the 3-way NC classifier using both one-level cross validation as well as Optimism Corrected Bootstrapping (1000 data sets) as described by Harrell. We also tested a 2-way prediction of SCAR vs. TX to further validate that a phenotype as potentially subtle clinically as SCAR can be truly distinguished from TX. At a p-value <0.001, there were 33 probesets whose expression signals highly differentiated SCAR and TX. When these 33 probesets were used in NC to predict SCAR and TX creating a 2-way classifier, the predictive accuracies with a one-level cross-validation was 96% and with the Harrell 1000 test optimism correction it was 94%. Table 1 shows NC algorithm values.Table: No Caption available.Conclusion: Peripheral blood gene expression profiling can distinguish SCAR, TX and AR and promises a minimally invasive method for monitoring KTx recipients. This signature is now being further validated. DISCLOSURE:Friedewald, J.: Grant/Research Support, Pfizer, Stockholder, TGI. Kurian, S.: Stockholder, TGI. Levitsky, J.: Stockholder, TGI. Abecassis, M.: Stockholder, TGI. Salomon, D.: Stockholder, TGI.

An ensemble of dissimilarity based classifiers for Mackerel gender determination

Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity.

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays

BackgroundThe abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification.ResultsExperimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data.ConclusionsFuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.

Statistical classification of multivariate flow cytometry data analyzed by manual gating: Stem, progenitor, and epithelial marker expression in nonsmall cell lung cancer and normal lung

The use of supervised classification to extract markers from primary flow cytometry data is an emerging field that has made significant progress, spurred by the growing complexity of multidimensional flow cytometry. Whether the markers are extracted without supervision or by conventional gate and region methods, the number of candidate variables identified is typically larger than the number of specimens (p > n) and many variables are highly intercorrelated. Thus, comparison across groups or treatments to determine which markers are significant is challenging. Here, we utilized a data set in which 86 variables were created by conventional manual analysis of individual listmode data files, and compared the application of five multivariate classification methods to discern subtle differences between the stem/progenitor content of 35 nonsmall cell lung cancer and adjacent normal lung specimens. The methods compared include elastic-net, lasso, random forest, diagonal linear discriminant analysis, and best single variable (best-1). We described a broadly applicable methodology consisting of: 1) variable transformation and standardization; 2) visualization and assessment of correlation between variables; 3) selection of significant variables and modeling; and 4) characterization of the quality and stability of the model. The analysis yielded both validating results (tumors are aneuploid and have higher light scatter properties than normal lung), as well as leads that require followup: Cytokeratin+ CD133+ progenitors are present in normal lung but reduced in lung cancer; diploid (or pseudo-diploid) CD117+CD44+ cells are more prevalent in tumor. We anticipate that the methods described here will be broadly applicable to a variety of multidimensional cytometry problems.