scholarly journals New interpretable machine learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy

2019 ◽  
Author(s):  
Evan Greene ◽  
Greg Finak ◽  
Leonard A. D’Amico ◽  
Nina Bhardwaj ◽  
Candice D. Church ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
T Cell ◽  
Single Cell ◽  
Cancer Immunotherapy ◽  
Effector Memory ◽  
Machine Learning Method ◽  
Learning Method ◽  
Modeling Framework ◽  
Interpretable Machine Learning

AbstractHigh-dimensional single-cell cytometry is routinely used to characterize patient responses to cancer immunotherapy and other treatments. This has produced a wealth of datasets ripe for exploration but whose biological and technical heterogeneity make them difficult to analyze with current tools. We introduce a new interpretable machine learning method for single-cell mass and flow cytometry studies, FAUST, that robustly performs unbiased cell population discovery and annotation. FAUST processes data on a per-sample basis and returns biologically interpretable cell phenotypes that can be compared across studies, making it well-suited for the analysis and integration of complex datasets. We demonstrate how FAUST can be used for candidate biomarker discovery and validation by applying it to a flow cytometry dataset from a Merkel cell carcinoma anti-PD-1 trial and discover new CD4+ and CD8+ effector-memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. We then use FAUST to validate these correlates in an independent CyTOF dataset from a published metastatic melanoma trial. Importantly, existing state-of-the-art computational discovery approaches as well as prior manual analysis did not detect these or any other statistically significant T cell sub-populations associated with anti-PD-1 treatment in either data set. We further validate our methodology by using FAUST to replicate the discovery of a previously reported myeloid correlate in a different published melanoma trial, and validate the correlate by identifying it de novo in two additional independent trials. FAUST’s phenotypic annotations can be used to perform cross-study data integration in the presence of heterogeneous data and diverse immunophenotyping staining panels, enabling hypothesis-driven inference about cell sub-population abundance through a multivariate modeling framework we call Phenotypic and Functional Differential Abundance (PFDA). We demonstrate this approach on data from myeloid and T cell panels across multiple trials. Together, these results establish FAUST as a powerful and versatile new approach for unbiased discovery in single-cell cytometry.

scholarly journals An interpretable machine learning method for detecting novel pathogens

2020 ◽  
Author(s):  
Xiaoyong Zhao ◽  
Ningning Wang
Keyword(s):  
Machine Learning ◽  
Infectious Diseases ◽  
Bacterial Pathogen ◽  
World Health ◽  
Machine Learning Method ◽  
Learning Method ◽  
Human Pathogens ◽  
Healthcare Applications ◽  
Interpretable Machine Learning ◽  
Shapley Values

Abstract Background: According to the World Health Organization (WHO), infectious diseases continue to one of the leading causes of death worldwide. Since the core microbiota flora of humans is largely diverse and horizontal gene transfer (HGT), it is very challenging to determine whether a particular bacterial strain is commensal or pathogenic to humans. With the latest advances in next-generation sequencing (NGS) technology, bioinformatics tools and techniques using NGS data have increasingly been used for the diagnosis and monitoring of infectious diseases. Even if the biological background is not available, the machine learning method can still infer the pathogenic phenotype from the NGS readings, independent of the database of known organisms, and being studied intensively.However, previous methods have not considered opportunistic pathogenic and interpretability of black box model, are not well suited for clinical requirements. Results:In this study, we proposed a novel interpretable machine learning approach (IMLA) to identify the pathogenicity of bacterial genomes: human pathogens (HP), opportunistic pathogenicity (OHP) or non-pathogenicity(NHP), then use the following model-agnostic interpretation methods to interpret model: feature importance, accumulated local effects and Shapley values, due to the model interpretability is essential for healthcare applications. To our knowledge, our paper is the first attempt to infer opportunistic pathogenicity and explain the model. Conclusions: According to the simulation results, our approach IMLA can be a great addition to detect novel pathogens. Keywords: interpretable; machine learning; bacterial pathogen;

scholarly journals An interpretable machine learning method for detecting novel pathogens

2020 ◽  
Author(s):  
Xiaoyong Zhao ◽  
Ningning Wang
Keyword(s):  
Machine Learning ◽  
Infectious Diseases ◽  
World Health ◽  
Machine Learning Method ◽  
Learning Method ◽  
Human Pathogens ◽  
Healthcare Applications ◽  
Interpretable Machine Learning ◽  
Shapley Values ◽  
Opportunistic Pathogenic

Abstract Background: According to the World Health Organization (WHO), infectious diseases continue to one of the leading causes of death worldwide. Since the core microbiota flora of humans is largely diverse and horizontal gene transfer (HGT), it is very challenging to determine whether a particular bacterial strain is commensal or pathogenic to humans. With the latest advances in next-generation sequencing (NGS) technology, bioinformatics tools and techniques using NGS data have increasingly been used for the diagnosis and monitoring of infectious diseases. Even if the biological background is not available, the machine learning method can still infer the pathogenic phenotype from the NGS readings, independent of the database of known organisms, and being studied intensively.However, previous methods have not considered opportunistic pathogenic and interpretability of black box model, are not well suited for clinical requirements. Results :In this study, we proposed a novel interpretable machine learning approach (IMLA) to identify the pathogenicity of bacterial genomes: human pathogens (HP), opportunistic pathogenicity (OHP) or non-pathogenicity(NHP), then use the following model-agnostic interpretation methods to interpret model: feature importance, accumulated local effects and Shapley values, due to the model interpretability is essential for healthcare applications. To our knowledge, our paper is the first attempt to infer opportunistic pathogenicity and explain the model. Conclusions: According to the simulation results, our approach IMLA can be a great addition to detect novel pathogens.

An interpretable machine learning method for forecasting the SYM-H index

2021 ◽  
Author(s):  
Daniel Iong ◽  
Yang Chen ◽  
Gabor Toth ◽  
Shasha Zou ◽  
Tuija I. Pulkkinen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
H Index ◽  
Interpretable Machine Learning

scholarly journals Explainable t-SNE for single-cell RNA-seq data analysis

2022 ◽  
Author(s):  
Henry Han ◽  
Tianyu Zhang ◽  
Mary Lauren Benton ◽  
Chun Li ◽  
Juan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Data Analysis ◽  
Dimension Reduction ◽  
Single Cell ◽  
Method Development ◽  
Robustness Analysis ◽  
High Dimensional ◽  
Machine Learning Method ◽  
Learning Method

Single-cell RNA (scRNA-seq) sequencing technologies trigger the study of individual cell gene expression and reveal the diversity within cell populations. To measure cell-to-cell similarity based on their transcription and gene expression, many dimension reduction methods are employed to retrieve the corresponding low-dimensional embeddings of input scRNA-seq data to conduct clustering. However, the methods lack explainability and may not perform well with scRNA-seq data because they are often migrated from other fields and not customized for high-dimensional sparse scRNA-seq data. In this study, we propose an explainable t-SNE: cell-driven t-SNE (c-TSNE) that fuses the cell differences reflected from biologically meaningful distance metrics for input scRNA-seq data. Our study shows that the proposed method not only enhances the interpretation of the original t-SNE visualization for scRNA-seq data but also demonstrates favorable single cell segregation performance on benchmark datasets compared to the state-of-the-art peers. The robustness analysis shows that the proposed cell-driven t-SNE demonstrates robustness to dropout and noise in dimension reduction and clustering. It provides a novel and practical way to investigate the interpretability of t-SNE in scRNA-seq data analysis. Unlike the general assumption that the explainanbility of a machine learning method needs to compromise with the learning efficiency, the proposed explainable t-SNE improves both clustering efficiency and explainanbility in scRNA-seq analysis. More importantly, our work suggests that widely used t-SNE can be easily misused in the existing scRNA-seq analysis, because its default Euclidean distance can bring biases or meaningless results in cell difference evaluation for high-dimensional sparse scRNA-seq data. To the best of our knowledge, it is the first explainable t-SNE proposed in scRNA-seq analysis and will inspire other explainable machine learning method development in the field.

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