scholarly journals Computational approaches for high‐throughput single‐cell data analysis

FEBS Journal ◽  
2018 ◽  
Vol 286 (8) ◽  
pp. 1451-1467 ◽  
Author(s):  
Helena Todorov ◽  
Yvan Saeys
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
High Throughput ◽  
Computational Approaches ◽  
Cell Data

scholarly journals High-throughput single cell data analysis – A tutorial

2021 ◽  
pp. 338872
Author(s):  
Gerjen H. Tinnevelt ◽  
Kristiaan Wouters ◽  
Geert J. Postma ◽  
Rita Folcarelli ◽  
Jeroen J. Jansen
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
High Throughput ◽  
Cell Data

scholarly journals Leveraging Novel Integrated Single-Cell Analyses to Define HIV-1 Latency Reversal

Viruses ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1197
Author(s):  
Suhui Zhao ◽  
Athe Tsibris
Keyword(s):  
Data Analysis ◽  
Antiretroviral Therapy ◽  
Single Cell ◽  
Recent Work ◽  
High Throughput ◽  
Computational Approaches ◽  
Integrative Data Analysis ◽  
Viral Genomes ◽  
Infection Models ◽  
Hiv 1

While suppressive antiretroviral therapy can effectively limit HIV-1 replication and evolution, it leaves behind a residual pool of integrated viral genomes that persist in a state of reversible nonproductive infection, referred to as the HIV-1 reservoir. HIV-1 infection models were established to investigate HIV-1 latency and its reversal; recent work began to probe the dynamics of HIV-1 latency reversal at single-cell resolution. Signals that establish HIV-1 latency and govern its reactivation are complex and may not be completely resolved at the cellular and regulatory levels by the aggregated measurements of bulk cellular-sequencing methods. High-throughput single-cell technologies that characterize and quantify changes to the epigenome, transcriptome, and proteome continue to rapidly evolve. Combinations of single-cell techniques, in conjunction with novel computational approaches to analyze these data, were developed and provide an opportunity to improve the resolution of the heterogeneity that may exist in HIV-1 reactivation. In this review, we summarize the published single-cell HIV-1 transcriptomic work and explore how cutting-edge advances in single-cell techniques and integrative data-analysis tools may be leveraged to define the mechanisms that control the reversal of HIV-1 latency.

scholarly journals Distinguishing different modes of growth using single-cell data

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Prathitha Kar ◽  
Sriram Tiruvadi-Krishnan ◽  
Jaana Männik ◽  
Jaan Männik ◽  
Ariel Amir
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Statistical Methods ◽  
Synthetic Data ◽  
Cellular Growth ◽  
Biological Mechanisms ◽  
E Coli ◽  
High Throughput Data ◽  
Consistent Method ◽  
Cell Data

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

scholarly journals SCOUT: Single-cell outlier analysis in cancer

2020 ◽  
Author(s):  
Giovana Ravizzoni Onzi ◽  
Juliano Luiz Faccioni ◽  
Alvaro G. Alvarado ◽  
Paula Andreghetto Bracco ◽  
Harley I. Kornblum ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Biological Markers ◽  
Rna Seq ◽  
Outlier Analysis ◽  
Mass Cytometry ◽  
Wide Range ◽  
Cell Data

Outliers are often ignored or even removed from data analysis. In cancer, however, single outlier cells can be of major importance, since they have uncommon characteristics that may confer capacity to invade, metastasize, or resist to therapy. Here we present the Single-Cell OUTlier analysis (SCOUT), a resource for single-cell data analysis focusing on outlier cells, and the SCOUT Selector (SCOUTS), an application to systematically apply SCOUT on a dataset over a wide range of biological markers. Using publicly available datasets of cancer samples obtained from mass cytometry and single-cell RNA-seq platforms, outlier cells for the expression of proteins or RNAs were identified and compared to their non-outlier counterparts among different samples. Our results show that analyzing single-cell data using SCOUT can uncover key information not easily observed in the analysis of the whole population.

scholarly journals scOrange—a tool for hands-on training of concepts from single-cell data analytics

2019 ◽  
Vol 35 (14) ◽  
pp. i4-i12 ◽  
Author(s):  
Martin Stražar ◽  
Lan Žagar ◽  
Jaka Kokošar ◽  
Vesna Tanko ◽  
Aleš Erjavec ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Data Analytics ◽  
Molecular Mechanisms ◽  
Teaching Style ◽  
Time Frame ◽  
Visual Programming ◽  
Training Methods ◽  
Hands On ◽  
Cell Data

Abstract Motivation Single-cell RNA sequencing allows us to simultaneously profile the transcriptomes of thousands of cells and to indulge in exploring cell diversity, development and discovery of new molecular mechanisms. Analysis of scRNA data involves a combination of non-trivial steps from statistics, data visualization, bioinformatics and machine learning. Training molecular biologists in single-cell data analysis and empowering them to review and analyze their data can be challenging, both because of the complexity of the methods and the steep learning curve. Results We propose a workshop-style training in single-cell data analytics that relies on an explorative data analysis toolbox and a hands-on teaching style. The training relies on scOrange, a newly developed extension of a data mining framework that features workflow design through visual programming and interactive visualizations. Workshops with scOrange can proceed much faster than similar training methods that rely on computer programming and analysis through scripting in R or Python, allowing the trainer to cover more ground in the same time-frame. We here review the design principles of the scOrange toolbox that support such workshops and propose a syllabus for the course. We also provide examples of data analysis workflows that instructors can use during the training. Availability and implementation scOrange is an open-source software. The software, documentation and an emerging set of educational videos are available at http://singlecell.biolab.si.

High-Throughput Single-Cell RNA Sequencing and Data Analysis

2018 ◽  
pp. 257-283 ◽  
Author(s):  
Sagar ◽  
Josip Stefan Herman ◽  
John Andrew Pospisilik ◽  
Dominic Grün
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Rna Sequencing ◽  
High Throughput ◽  
Single Cell Rna Sequencing

scholarly journals Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks

2017 ◽  
Vol 13 (12) ◽  
pp. e1005875 ◽  
Author(s):  
Ye Henry Li ◽  
Dangna Li ◽  
Nikolay Samusik ◽  
Xiaowei Wang ◽  
Leying Guan ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Multiple Alignments ◽  
Cell Data
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