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 To bin or not to bin: analyzing single-cell growth data

2021 ◽  
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 ◽  
Growth Data ◽  
Biological Mechanisms ◽  
E Coli ◽  
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 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 The role of incoherent feedforward circuits in regulating precision of event timing

2020 ◽  
Author(s):  
Supravat Dey ◽  
Sherin Kannoly ◽  
Pavol Bokes ◽  
John J Dennehy ◽  
Abhyudai Singh
Keyword(s):  
Single Cells ◽  
Threshold Level ◽  
Cellular Growth ◽  
Critical Threshold ◽  
E Coli ◽  
Cellular Events ◽  
Event Timing ◽  
Simple Model System ◽  
Cell Data

AbstractTriggering of cellular events often relies on the level of a key gene product crossing a critical threshold. Achieving precision in event timing in spite of noisy gene expression facilitates high-fidelity functioning of diverse processes from biomolecular clocks, apoptosis and cellular differentiation. Here we investigate the role of an incoherent feedforward circuit in regulating the time taken by a bacterial virus (bacteriophage lambda) to lyse an infected Escherichia coli cell. Lysis timing is the result of expression and accumulation of a single lambda protein (holin) in the E. coli cell membrane up to a critical threshold level, which triggers the formation of membrane lesions. This easily visualized process provides a simple model system for characterizing event-timing stochasticity in single cells. Intriguingly, lambda’s lytic pathway synthesizes two functionally opposite proteins: holin and antiholin from the same mRNA in a 2:1 ratio. Antiholin sequesters holin and inhibits the formation of lethal membrane lesions, thus creating an incoherent feedforward circuit. We develop and analyze a stochastic model for this feedforward circuit that considers correlated bursty expression of holin/antiholin, and their concentrations are diluted from cellular growth. Interestingly, our analysis shows the noise in timing is minimized when both proteins are expressed at an optimal ratio, hence revealing an important regulatory role for antiholin. These results are in agreement with single cell data, where removal of antiholin results in enhanced stochasticity in lysis timing.

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 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 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.

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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