scholarly journals Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery

2019 ◽  
pp. 309-332
Author(s):  
Hannah G. Polikowsky ◽  
Katherine A. Drake
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Biomarker Discovery ◽  
Supervised Machine Learning

scholarly journals Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning

2020 ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Florent Ginhoux ◽  
Evan W. Newell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Low Cost ◽  
Expression Patterns ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Supervised Machine Learning ◽  
Melanoma Metastasis ◽  
Immunologic Research

AbstractModern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.

scholarly journals Disease-relevant single cell photonic signatures identify S100β stem cells and their myogenic progeny in vascular lesions

2020 ◽  
Author(s):  
Claire Molony ◽  
Damien King ◽  
Mariana Di Luca ◽  
Abidemi Olayinka ◽  
Roya Hakimjavadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stem Cells ◽  
Single Cell ◽  
Ex Vivo ◽  
Lineage Tracing ◽  
Supervised Machine Learning ◽  
Vascular Lesions ◽  
Cell Phenotype ◽  
Genetic Lineage ◽  
Collagen Iii

AbstractA hallmark of subclinical atherosclerosis is the accumulation of vascular smooth muscle cell (SMC)-like cells leading to intimal thickening and lesion formation. While medial SMCs contribute to vascular lesions, the involvement of resident vascular stem cells (vSCs) remains unclear. We evaluated single cell photonics as a discriminator of cell phenotype in vitro before the presence of vSC within vascular lesions was assessed ex vivo using supervised machine learning and further validated using lineage tracing analysis. Using a novel lab-on-a-Disk (Load) platform, label-free single cell photonic emissions from normal and injured vessels ex vivo were interrogated and compared to freshly isolated aortic SMCs, cultured Movas SMCs, macrophages, B-cells, S100β+ mVSc, bone marrow derived mesenchymal stem cells (MSC) and their respective myogenic progeny across five broadband light wavelengths (λ465 - λ670 ± 20 nm). We found that profiles were of sufficient coverage, specificity, and quality to clearly distinguish medial SMCs from different vascular beds (carotid vs aorta), discriminate normal carotid medial SMCs from lesional SMC-like cells ex vivo following flow restriction, and identify SMC differentiation of a series of multipotent stem cells following treatment with transforming growth factor beta 1 (TGF-β1), the Notch ligand Jagged1, and Sonic Hedgehog using multivariate analysis, in part, due to photonic emissions from enhanced collagen III and elastin expression. Supervised machine learning supported genetic lineage tracing analysis of S100β+ vSCs and identified the presence of S100β+ vSC-derived myogenic progeny within vascular lesions. We conclude disease-relevant photonic signatures may have predictive value for vascular disease.

scholarly journals A Systematic Evaluation of Methods for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Xiaowen Cao ◽  
Li Xing ◽  
Elham Majd ◽  
Hua He ◽  
Junhua Gu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Rna Sequencing ◽  
Simulated Data ◽  
Supervised Machine Learning ◽  
Cell Phenotype ◽  
Data Sets ◽  
Phenotype Classification ◽  
Single Cell Rna Sequencing ◽  
Cell Phenotypes

Abstract Background: Single-cell RNA sequencing (scRNA-seq) yields valuable insights about gene expression and gives critical information about complex tissue cellular composition. In the analysis of single-cell RNA sequencing, the annotations of cell subtypes are often done manually, which is time-consuming and irreproducible. Garnett is a cell-type annotation software based the on elastic net method. Beside cell-type annotation, supervised machine learning methods can also be applied to predict other cell phenotypes from genomic data. Despite the popularity of such applications, there is no existing study to systematically investigate the performance of those supervised algorithms in various sizes of scRNA-seq data sets. Methods and Results: This study evaluates 13 popular supervised machine learning algorithms to classify cell phenotypes, using published real and simulated data sets with diverse cell sizes. The benchmark contained two parts. In the first part, we used real data sets to assess the popular supervised algorithms’ computing speed and cell phenotype classification performance. The classification performances were evaluated using AUC statistics, F1-score, precision, recall, and false-positive rate. In the second part, we evaluated gene selection performance using published simulated data sets with a known list of real genes. Conclusion: The study outcomes showed that ElasticNet with interactions performed best in small and medium data sets. NB was another appropriate method for medium data sets. In large data sets, XGB works excellent. Ensemble algorithms were not significantly superior to individual machine learning methods. Adding interactions to ElasticNet can help, and the improvement was significant in small data sets.

scholarly journals Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research

2020 ◽  
Author(s):  
Sophia Clara Mädler ◽  
Alice Julien-Laferriere ◽  
Luis Wyss ◽  
Miroslav Phan ◽  
Albert S. W. Kang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Data Analysis ◽  
Best Practices ◽  
Translational Research ◽  
Single Cell ◽  
Supervised Machine Learning ◽  
Cell Type ◽  
Disease Biology ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA sequencing (scRNA-seq) revolutionised our understanding of disease biology and presented the promise of transforming translational research. We developed Besca, a toolkit that streamlines scRNA-seq analyses according to current best practices. A standard workflow covers quality control, filtering, and clustering. Two complementary Besca modules, utilizing hierarchical cell signatures or supervised machine learning, automate cell annotation and provide harmonised nomenclatures across studies. Subsequently, Besca enables estimation of cell type proportions in bulk transcriptomics studies. Using multiple heterogeneous scRNA-seq datasets we show how Besca aids acceleration, interoperability, reusability, and interpretability of scRNA-seq data analysis, crucial aspects in translational research and beyond.

scholarly journals ELeFHAnt: A supervised machine learning approach for label harmonization and annotation of single cell RNA-seq data

2021 ◽  
Author(s):  
Konrad Thorner ◽  
Aaron M. Zorn ◽  
Praneet Chaturvedi
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
A Priori ◽  
Cell Types ◽  
R Package ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Rna Seq

AbstractAnnotation of single cells has become an important step in the single cell analysis framework. With advances in sequencing technology thousands to millions of cells can be processed to understand the intricacies of the biological system in question. Annotation through manual curation of markers based on a priori knowledge is cumbersome given this exponential growth. There are currently ~200 computational tools available to help researchers automatically annotate single cells using supervised/unsupervised machine learning, cell type markers, or tissue-based markers from bulk RNA-seq. But with the expansion of publicly available data there is also a need for a tool which can help integrate multiple references into a unified atlas and understand how annotations between datasets compare. Here we present ELeFHAnt: Ensemble learning for harmonization and annotation of single cells. ELeFHAnt is an easy-to-use R package that employs support vector machine and random forest algorithms together to perform three main functions: 1) CelltypeAnnotation 2) LabelHarmonization 3) DeduceRelationship. CelltypeAnnotation is a function to annotate cells in a query Seurat object using a reference Seurat object with annotated cell types. LabelHarmonization can be utilized to integrate multiple cell atlases (references) into a unified cellular atlas with harmonized cell types. Finally, DeduceRelationship is a function that compares cell types between two scRNA-seq datasets. ELeFHAnt can be accessed from GitHub at https://github.com/praneet1988/ELeFHAnt.

scholarly journals A machine learning approach for single cell interphase cell cycle staging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hemaxi Narotamo ◽  
Maria Sofia Fernandes ◽  
Ana Margarida Moreira ◽  
Soraia Melo ◽  
Raquel Seruca ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Function ◽  
Ground Truth ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Svm Classifier ◽  
Interphase Cell

AbstractThe cell nucleus is a tightly regulated organelle and its architectural structure is dynamically orchestrated to maintain normal cell function. Indeed, fluctuations in nuclear size and shape are known to occur during the cell cycle and alterations in nuclear morphology are also hallmarks of many diseases including cancer. Regrettably, automated reliable tools for cell cycle staging at single cell level using in situ images are still limited. It is therefore urgent to establish accurate strategies combining bioimaging with high-content image analysis for a bona fide classification. In this study we developed a supervised machine learning method for interphase cell cycle staging of individual adherent cells using in situ fluorescence images of nuclei stained with DAPI. A Support Vector Machine (SVM) classifier operated over normalized nuclear features using more than 3500 DAPI stained nuclei. Molecular ground truth labels were obtained by automatic image processing using fluorescent ubiquitination-based cell cycle indicator (Fucci) technology. An average F1-Score of 87.7% was achieved with this framework. Furthermore, the method was validated on distinct cell types reaching recall values higher than 89%. Our method is a robust approach to identify cells in G1 or S/G2 at the individual level, with implications in research and clinical applications.

scholarly journals Disease-Relevant Single Cell Photonic Signatures Identify S100β Stem Cells and their Myogenic Progeny in Vascular Lesions

2021 ◽  
Author(s):  
Claire Molony ◽  
Damien King ◽  
Mariana Di Luca ◽  
Michael Kitching ◽  
Abidemi Olayinka ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stem Cells ◽  
Single Cell ◽  
Ex Vivo ◽  
Lineage Tracing ◽  
Supervised Machine Learning ◽  
Vascular Lesions ◽  
Cell Phenotype ◽  
Genetic Lineage ◽  
Collagen Iii

AbstractA hallmark of subclinical atherosclerosis is the accumulation of vascular smooth muscle cell (SMC)-like cells leading to intimal thickening and lesion formation. While medial SMCs contribute to vascular lesions, the involvement of resident vascular stem cells (vSCs) remains unclear. We evaluated single cell photonics as a discriminator of cell phenotype in vitro before the presence of vSC within vascular lesions was assessed ex vivo using supervised machine learning and further validated using lineage tracing analysis. Using a novel lab-on-a-Disk(Load) platform, label-free single cell photonic emissions from normal and injured vessels ex vivo were interrogated and compared to freshly isolated aortic SMCs, cultured Movas SMCs, macrophages, B-cells, S100β+ mVSc, bone marrow derived mesenchymal stem cells (MSC) and their respective myogenic progeny across five broadband light wavelengths (λ465 - λ670 ± 20 nm). We found that profiles were of sufficient coverage, specificity, and quality to clearly distinguish medial SMCs from different vascular beds (carotid vs aorta), discriminate normal carotid medial SMCs from lesional SMC-like cells ex vivo following flow restriction, and identify SMC differentiation of a series of multipotent stem cells following treatment with transforming growth factor beta 1 (TGF- β1), the Notch ligand Jagged1, and Sonic Hedgehog using multivariate analysis, in part, due to photonic emissions from enhanced collagen III and elastin expression. Supervised machine learning supported genetic lineage tracing analysis of S100β+ vSCs and identified the presence of S100β+vSC-derived myogenic progeny within vascular lesions. We conclude disease-relevant photonic signatures may have predictive value for vascular disease. Graphical abstract

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

Sign in / Sign up

Export Citation Format

Share Document