REBET: a method to determine the number of cell clusters based on batch effect removal.

In single-cell RNA-seq (scRNA-seq) data analysis, a fundamental problem is to determine the number of cell clusters based on the gene expression profiles. However, the performance of current methods is still far from satisfactory, presumably due to their limitations in capturing the expression variability among cell clusters. Batch effects represent the undesired variability between data measured in different batches. When data are obtained from different labs or protocols batch effects occur. Motivated by the practice of batch effect removal, we considered cell clusters as batches. We hypothesized that the number of cell clusters (i.e. batches) could be correctly determined if the variances among clusters (i.e. batch effects) were removed. We developed a new method, namely, removal of batch effect and testing (REBET), for determining the number of cell clusters. In this method, cells are first partitioned into k clusters. Second, the batch effects among these k clusters are then removed. Third, the quality of batch effect removal is evaluated with the average range of normalized mutual information (ARNMI), which measures how uniformly the cells with batch-effects-removal are mixed. By testing a range of k values, the k value that corresponds to the lowest ARNMI is determined to be the optimal number of clusters. We compared REBET with state-of-the-art methods on 32 simulated datasets and 14 published scRNA-seq datasets. The results show that REBET can accurately and robustly estimate the number of cell clusters and outperform existing methods. Contact: H.D.L. (hongdong@csu.edu.cn) or Q.S.X. (qsxu@csu.edu.cn).

Transarterial embolization with hepatectomy for ruptured hepatocellular carcinoma: a meta-analysis.

PURPOSE: To compare the clinical effectiveness between transarterial embolization (TAE) with staged hepatectomy (SH) and emergency hepatectomy (EH) for ruptured hepatocellular carcinoma (HCC). MATERIAL AND METHODS: Pubmed, Embase, and Cochrane Library databases were screened for eligible publications from the inception of the databases till February 2021. RESULTS: This meta-analysis included seven studies comprising 162 patients who underwent TAE with SH and 266 patients who underwent EH. The pooled intraoperative blood loss was less in the TAE with SH cohort, as compared to the EH cohort without significant difference (p = .20). The pooled blood transfer rate (p<.00001), blood transfer volume (p = .002), and 30-day patient death (p = .04) were all markedly reduced in the TAE with SH cohort versus the EH cohort. No significant differences in surgery duration (p = .27), hospital stay period (p = .81), complication rate (p = 0.92), disease-free survival (DFS) (p = .79), and overall survival (OS) (p = 0.28) were found between the two groups. CONCLUSIONS: Compared with EH for ruptured HCC, TAE with SH could effectively decrease intraoperative blood loss and 30-day mortality. However, the long-term DFS and OS might not be beneficial to preoperative TAE.

Kernel density-based likelihood ratio tests for linear regression models.

In this article, we develop a so-called profile likelihood ratio test (PLRT) based on the estimated error density for the multiple linear regression model. Unlike the existing likelihood ratio test (LRT), our proposed PLRT does not require any specification on the error distribution. The asymptotic properties are developed and the Wilks phenomenon is studied. Simulation studies are conducted to examine the performance of the PLRT. It is observed that our proposed PLRT generally outperforms the existing LRT, empirical likelihood ratio test and the weighted profile likelihood ratio test in sense that (i) its type I error rates are closer to the prespecified nominal level; (ii) it generally has higher powers; (iii) it performs satisfactorily when moments of the error do not exist (eg, Cauchy distribution); and (iv) it has higher probability of correctly selecting the correct model in the multiple testing problem. A mammalian eye gene expression dataset and a concrete compressive strength dataset are analyzed to illustrate our methodologies.

Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules.

BACKGROUND: There is a lack of clinical-radiological predictive models for the small (≤ 20 mm) solitary pulmonary nodules (SPNs). We aim to establish a clinical-radiological predictive model for differentiating malignant and benign small SPNs. MATERIALS AND METHODS: Between January 2013 and December 2018, a retrospective cohort of 250 patients with small SPNs was used to construct the predictive model. A second retrospective cohort of 101 patients treated between January 2019 and December 2020 was used to independently test the model. The model was also compared to two other models that had previously been identified. RESULTS: In the training group, 250 patients with small SPNs including 156 (62.4%) malignant SPNs and 94 (37.6%) benign SPNs patients were included. Multivariate logistic regression analysis indicated that older age, pleural retraction sign, CT bronchus sign, and higher CEA level were the risk factors of malignant small SPNs. The predictive model was established as: X = - 10.111 + [0.129 × age (y)] + [1.214 × pleural retraction sign (present = 1; no present = 0)] + [0.985 × CT bronchus sign (present = 1; no present = 0)] + [0.21 × CEA level (ug/L)]. Our model had a significantly higher region under the receiver operating characteristic (ROC) curve (0.870; 50% CI: 0.828-0.913) than the other two models. CONCLUSIONS: We established and validated a predictive model for estimating the pre-test probability of malignant small SPNs, that can help physicians to choose and interpret the outcomes of subsequent diagnostic tests.

Computed tomography-guided lung biopsy: a randomized controlled trial of low-dose versus standard-dose protocol.

OBJECTIVES: To assess the relative diagnostic utility of low- and standard-dose computed tomography (CT)-guided lung biopsy. METHODS: In this single-center, single-blind, prospective, randomized controlled trial, patients were enrolled between November 2016 and June 2017. Enrolled study participants were randomly selected to undergo either low- or standard-dose CT-guided lung biopsy. Diagnostic accuracy was the primary study endpoint, whereas technical success, radiation dose, and associated complications were secondary study endpoints. RESULTS: In total, 280 patients underwent study enrollment and randomization, with 271 (low-dose group, 135; standard-dose group, 136) receiving the assigned interventions. Both groups had a 100% technical success rate for CT-guided lung biopsy, and complication rates were similar between groups (p > 0.05). The mean dose-length product (36.0 ± 14.1 mGy cm vs. 361.8 ± 108.0 mGy cm, p < 0.001) and effective dose (0.5 ± 0.2 mSv vs. 5.1 ± 1.5 mSv, p < 0.001) were significantly reduced in the low-dose group participants. Sensitivity, specificity, and overall diagnostic accuracy rates in the low-dose group were 91.8%, 100%, and 94.6%, respectively, whereas in the standard-dose group, the corresponding values were 89.6%, 100%, and 92.4%, respectively. These results indicated that diagnostic performance did not differ significantly between the 2 groups. Using univariate and multivariate analyses, we found larger lesion size (p = 0.038) and procedure-related pneumothorax (p = 0.033) to both be independent predictors of diagnostic failure. CONCLUSIONS: Our results demonstrate that low-dose CT-guided lung biopsy can yield comparable diagnostic accuracy to standard-dose CT guidance, while significantly reducing the radiation dose delivered to patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT02971176 KEY POINTS: • Low-dose CT-guided lung biopsy is a safe and simple method for diagnosis of lung lesions. • Low-dose CT-guided lung biopsy can yield comparable diagnostic accuracy to standard-dose CT guidance. • Low-dose CT-guided lung biopsy can achieve a 90% reduction in radiation exposure when compared with standard-dose CT guidance.

Rcpi: R/Bioconductor package to generate various descriptors of proteins, compounds and their interactions.

UNLABELLED: In chemoinformatics and bioinformatics fields, one of the main computational challenges in various predictive modeling is to find a suitable way to effectively represent the molecules under investigation, such as small molecules, proteins and even complex interactions. To solve this problem, we developed a freely available R/Bioconductor package, called Compound-Protein Interaction with R (Rcpi), for complex molecular representation from drugs, proteins and more complex interactions, including protein-protein and compound-protein interactions. Rcpi could calculate a large number of structural and physicochemical features of proteins and peptides from amino acid sequences, molecular descriptors of small molecules from their topology and protein-protein interaction and compound-protein interaction descriptors. In addition to main functionalities, Rcpi could also provide a number of useful auxiliary utilities to facilitate the user's need. With the descriptors calculated by this package, the users could conveniently apply various statistical machine learning methods in R to solve various biological and drug research questions in computational biology and drug discovery. AVAILABILITY AND IMPLEMENTATION: Rcpi is freely available from the Bioconductor site (http://bioconductor.org/packages/release/bioc/html/Rcpi.html).

protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences.

UNLABELLED: Amino acid sequence-derived structural and physiochemical descriptors are extensively utilized for the research of structural, functional, expression and interaction profiles of proteins and peptides. We developed protr, a comprehensive R package for generating various numerical representation schemes of proteins and peptides from amino acid sequence. The package calculates eight descriptor groups composed of 22 types of commonly used descriptors that include about 22 700 descriptor values. It allows users to select amino acid properties from the AAindex database, and use self-defined properties to construct customized descriptors. For proteochemometric modeling, it calculates six types of scales-based descriptors derived by various dimensionality reduction methods. The protr package also integrates the functionality of similarity score computation derived by protein sequence alignment and Gene Ontology semantic similarity measures within a list of proteins, and calculates profile-based protein features based on position-specific scoring matrix. We also developed ProtrWeb, a user-friendly web server for calculating descriptors presented in the protr package. AVAILABILITY AND IMPLEMENTATION: The protr package is freely available from CRAN: http://cran.r-project.org/package=protr, ProtrWeb, is freely available at http://protrweb.scbdd.com/.

Prediction of peptide fragment ion mass spectra by data mining techniques.

Accurate prediction of peptide fragment ion mass spectra is one of the critical factors to guarantee confident peptide identification by protein sequence database search in bottom-up proteomics. In an attempt to accurately and comprehensively predict this type of mass spectra, a framework named MS(2)PBPI is proposed. MS(2)PBPI first extracts fragment ions from large-scale MS/MS spectra data sets according to the peptide fragmentation pathways and uses binary trees to divide the obtained bulky data into tens to more than 1000 regions. For each adequate region, stochastic gradient boosting tree regression model is constructed. By constructing hundreds of these models, MS(2)PBPI is able to predict MS/MS spectra for unmodified and modified peptides with reasonable accuracy. Moreover, high consistency between predicted and experimental MS/MS spectra derived from different ion trap instruments with low and high resolving power is achieved. MS(2)PBPI outperforms existing algorithms MassAnalyzer and PeptideART.

propy: a tool to generate various modes of Chou's PseAAC.

SUMMARY: Sequence-derived structural and physiochemical features have been frequently used for analysing and predicting structural, functional, expression and interaction profiles of proteins and peptides. To facilitate extensive studies of proteins and peptides, we developed a freely available, open source python package called protein in python (propy) for calculating the widely used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes five feature groups composed of 13 features, including amino acid composition, dipeptide composition, tripeptide composition, normalized Moreau-Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors, composition, transition and distribution of various structural and physicochemical properties and two types of pseudo amino acid composition (PseAAC) descriptors. These features could be generally regarded as different Chou's PseAAC modes. In addition, it can also easily compute the previous descriptors based on user-defined properties, which are automatically available from the AAindex database. AVAILABILITY: The python package, propy, is freely available via http://code.google.com/p/protpy/downloads/list, and it runs on Linux and MS-Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ChemoPy: freely available python package for computational biology and chemoinformatics.

MOTIVATION: Molecular representation for small molecules has been routinely used in QSAR/SAR, virtual screening, database search, ranking, drug ADME/T prediction and other drug discovery processes. To facilitate extensive studies of drug molecules, we developed a freely available, open-source python package called chemoinformatics in python (ChemoPy) for calculating the commonly used structural and physicochemical features. It computes 16 drug feature groups composed of 19 descriptors that include 1135 descriptor values. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pairs fingerprints, topological torsion fingerprints and Morgan/circular fingerprints. By applying a semi-empirical quantum chemistry program MOPAC, ChemoPy can also compute a large number of 3D molecular descriptors conveniently. AVAILABILITY: The python package, ChemoPy, is freely available via http://code.google.com/p/pychem/downloads/list, and it runs on Linux and MS-Windows. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A perspective demonstration on the importance of variable selection in inverse calibration for complex analytical systems.

Classical calibration and inverse calibration are two kinds of multivariate calibration in chemical modeling. They use strategies of modeling in component spectral space and in measured variable space, respectively. However, the intrinsic difference between these two calibration models is not fully investigated. Besides, in the case of complex analytical systems, the net analyte signal (NAS) cannot be well defined in inverse calibration due to the existence of uninformative and/or interfering variables. Therefore, application of the NAS cannot improve the predictive performance for this kind of calibration, since it is essentially a technique based on the full-spectrum. From our perspective, variable selection can significantly improve the predictive performance through removing uninformative and/or interfering variables. Although the need for variable selection in the inverse calibration model has already been experimentally demonstrated, it has not aroused so much attention. In this study, we first clarify the intrinsic difference between these two calibration models and then use a new perspective to intrinsically prove the importance of variable selection in the inverse calibration model for complex analytical systems. In addition, we have experimentally validated our viewpoint through the use of one UV dataset and two generated near infrared (NIR) datasets.

Morphological weighted penalized least squares for background correction.

Backgrounds existing in the analytical signal always impair the effectiveness of signals and compromise selectivity and sensitivity of analytical methods. In order to perform further qualitative or quantitative analysis, the background should be corrected with a reasonable method. For this purpose, a new automatic method for background correction, which is based on morphological operations and weighted penalized least squares (MPLS), has been developed in this paper. It requires neither prior knowledge about the background nor an iteration procedure or manual selection of a suitable local minimum value. The method has been successfully applied to simulated datasets as well as experimental datasets from different instruments. The results show that the method is quite flexible and could handle different kinds of backgrounds. The proposed MPLS method is implemented and available as an open source package at http://code.google.com/p/mpls.

PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies.

The rapidly increasing amount of publicly available data in biology and chemistry enables researchers to revisit interaction problems by systematic integration and analysis of heterogeneous data. Herein, we developed a comprehensive python package to emphasize the integration of chemoinformatics and bioinformatics into a molecular informatics platform for drug discovery. PyDPI (drug-protein interaction with Python) is a powerful python toolkit for computing commonly used structural and physicochemical features of proteins and peptides from amino acid sequences, molecular descriptors of drug molecules from their topology, and protein-protein interaction and protein-ligand interaction descriptors. It computes 6 protein feature groups composed of 14 features that include 52 descriptor types and 9890 descriptors, 9 drug feature groups composed of 13 descriptor types that include 615 descriptors. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pair fingerprints, topological torsion fingerprints, and Morgan/circular fingerprints. By combining different types of descriptors from drugs and proteins in different ways, interaction descriptors representing protein-protein or drug-protein interactions could be conveniently generated. These computed descriptors can be widely used in various fields relevant to chemoinformatics, bioinformatics, and chemogenomics. PyDPI is freely available via https://sourceforge.net/projects/pydpicao/.

Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine.

Retention indices for frequently reported compounds of plant essential oils on three different stationary phases were investigated. Multivariate linear regression, partial least squares, and support vector machine combined with a new variable selection approach called random-frog recently proposed by our group, were employed to model quantitative structure-retention relationships. Internal and external validations were performed to ensure the stability and predictive ability. All the three methods could obtain an acceptable model, and the optimal results by support vector machine based on a small number of informative descriptors with the square of correlation coefficient for cross validation, values of 0.9726, 0.9759, and 0.9331 on the dimethylsilicone stationary phase, the dimethylsilicone phase with 5% phenyl groups, and the PEG stationary phase, respectively. The performances of two variable selection approaches, random-frog and genetic algorithm, are compared. The importance of the variables was found to be consistent when estimated from correlation coefficients in multivariate linear regression equations and selection probability in model spaces.

Pressor mechanism evaluation for phytochemical compounds using in silico compound-protein interaction prediction.

In this study, a method was applied to evaluate pressor mechanisms through compound-protein interactions. Our method assumed that the compounds with different pressor mechanisms should bind to different target proteins, and thereby these mechanisms could be differentiated using compound-protein interactions. Twenty-six phytochemical components and 46 tested target proteins related to blood pressure (BP) elevation were collected. Then, in silico compound-protein interactions prediction probabilities were calculated using a random forest model, which have been implemented in a web server, and the credibility was judged using related literature and other methods. Further, a heat map was constructed, it clearly showed different prediction probabilities accompanied with hierarchical clustering analysis results. Followed by a compound-protein interaction network was depicted according to the results, we can see the connectivity layout of phytochemical components with different target proteins within the BP elevation network, which guided the hypothesis generation of poly-pharmacology. Lastly, principal components analysis (PCA) was carried out upon the prediction probabilities, and pressor targets could be divided into three large classes: neurotransmitter receptors, hormones receptors and monoamine oxidases. In addition, steroid glycosides seem to be close to the region of hormone receptors, and a weak difference existed between them. This work explored the possibility for pharmacological or toxicological mechanism classification using compound-protein interactions. Such approaches could also be used to deduce pharmacological or toxicological mechanisms for uncharacterized compounds.

Chitosan oligosaccharides suppress production of nitric oxide in lipopolysaccharide-induced N9 murine microglial cells in vitro.

Chitosan oligosaccharides (COS) have been reported to exert many biological activities, such as antioxidant, antitumor and anti-inflammatory effects. In the present study, we examined the effect of COS on nitric oxide (NO) production in LPS induced N9 microglial cells. Pretreatment with COS (50~200 µg/ml) could markedly inhibit NO production by suppressing inducible nitric oxide synthase (iNOS) expression in activated microglial cells. Signal transduction studies showed that COS remarkably inhibited LPS-induced phosphorylation of p38 MAPK and ERK1/2. COS pretreatment could also inhibit the activation of both nuclear factor-κB (NF-κB) and activator protein-1 (AP-1). In conclusion, our results suggest that COS could suppress the production of NO in LPS-induced N9 microglial cells, mediated by p38 MAPK and ERK1/2 pathways.

Noise incorporated subwindow permutation analysis for informative gene selection using support vector machines.

Selecting a small subset of informative genes plays an important role in accurate prediction of clinical tumor samples. Based on model population analysis, a novel variable selection method, called noise incorporated subwindow permutation analysis (NISPA), is proposed in this study to work with support vector machines (SVMs). The essence of NISPA lies in the point that one noise variable is added into each sampled sub-dataset and then the distribution of variable importance of the added noise could be computed and serves as the common reference to evaluate the experimental variables. Further, by using the non-parametric Mann-Whitney U test, a P value can be assigned to each variable which describes to what extent the distributions of the gene variable and the noise variable are different. According to the computed P values, all the variables could be ranked and then a small subset of informative variables could be determined to build the model. Moreover, by NISPA, we are the first to distinguish the variables into a more detailed classification as informative, uninformative (noise) and interfering variables in comparison with other methods. In this study, two microarray datasets are employed to evaluate the performance of NISPA. The results show that the prediction errors of SVM classifiers could be significantly reduced by variable selection using NISPA. It is concluded that NISPA is a good alternative of variable selection algorithm.

A new strategy of exploring metabolomics data using Monte Carlo tree.

Large amounts of data from high-throughput metabolomics experiments have become commonly more and more complex, which brings a number of challenges to existing statistical modeling. Thus there is a need to develop a statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under investigation. In this work, we provide a new strategy based on Monte Carlo cross validation coupled with the classification tree algorithm, which is termed as the MCTree approach. The MCTree approach inherently provides a feasible way to uncover the predictive structure of metabolomics data by the establishment of many cross-predictive models. With the help of the sample proximity matrix such obtained, it seems to be able to give some interesting insights into metabolomics data. Simultaneously, informative metabolites or potential biomarkers can be successfully discovered by means of variable importance ranking in the MCTree approach. Two real metabolomics datasets are finally used to demonstrate the performance of the proposed approach.

Computed tomography-guided coil localization for scapula-blocked pulmonary nodules: A trans-scapular approach.

ABSTRACT: To evaluate the clinical efficiency, feasibility, and safety of computed tomography (CT)-guided trans-scapular coil localization (TSCL) approach to treating scapula-blocked pulmonary nodules (SBPNs).In total, 105 patients with pulmonary nodules underwent CT-guided CL and subsequent video-assisted thoracoscopic surgery (VATS)-guided wedge resection (WR) between January 2016 and July 2020. Six of these patients (5.7%) had SBPNs that led them to undergo CT-guided TSCL. Rates of technical success and localization-related complications were then recorded and analyzed.CT-guided TSCL was associated with a 100% technical success rate, with one coil being placed per patient. The median CT-guided TSCL duration was 15 min. No patients experienced any complications associated with this procedure, and subsequent VATS-guided WR of SBPNs was 100% technically successful. In two patients with invasive adenocarcinoma, additional lobectomy was performed. Median VATS duration and intraoperative blood loss were 120 min and 150 mL, respectively.In summary, these results indicate that CT-guided TSCL could be easily and safely implemented to achieve high success rate when performing the VATS-guided WR of SBPNs.

Coil localization assisted wedge resection for pulmonary nodules in patients with malignant history.

ABSTRACT: We describe the clinical efficacy of coil localization (CL) assisted video-assisted thoracoscopic surgery (VATS) wedge resection (WR) for pulmonary nodules (PNs) in patients having a history of malignancy.In a total of 16 patients having PNs and malignant history, treatment was carried out using computed tomography (CT)-guided CL and subsequent VATS-guided WR procedures from November 2015 to December 2019. Technical success of CL, WR, and long-term outcomes was analyzed.A total of 21 PNs were localized (1.3 PNs per patient). A 100% technical success rate was achieved in this study for CT-guided CL. Each PN was localized with 1 coil. Two and 2 patients experienced pneumothorax and hemoptysis, respectively. VATS-guided WR also achieved a 100% technical success rate. Additional lobectomy was performed in 2 patients due to the invasive adenocarcinoma. The final diagnoses of these 21 PNs were adenocarcinoma (T1N0M0, n = 8), adenocarcinoma in situ (n = 2), pre-cancerosis (n = 1), metastasis (n = 2), and benign (n = 8). All patients underwent CT follow-up for 6 to 48 months. All patients were alive during the follow-up. The cumulative 6-, 12, and 24-month disease-free survival rates were 100%, 92.9%, and 47.3%, respectively. The median disease-free survival was 27.9 months.Pre-operative CT-guided CL can be safely and conveniently used to facilitate a high success rate of VATS-guided WR for PNs in patients with a malignant history. Among the PNs in patients with malignant history, primary lung cancer also occupied approximately half of the PNs.