ChroMo, an Application for Unsupervised Analysis of Chromosome Movements in Meiosis
Abstract
:1. Introduction
2. Materials and Methods
2.1. ChroMo Platform
2.2. Type of Data Required as Input
2.3. Synthetic TimeSeries Generation
2.4. TimeSeries Behavioural Segmentation and Statistics
 
 Interaction of cluster factor, its starting and ending time;
 
 Interaction of time;
 
 Interaction of cluster factor and its proportion;
 
 Interaction of cluster factor;
 
 Interceptonly.
2.5. Global and PerSegment Motifs Discovery
2.6. Global Covariate and TimeSeries Causality Analysis
2.7. Limitations of ChroMo
2.7.1. Which Data Can Be Used?
2.7.2. Parameterization and Interpretation
2.7.3. Causality Graphs
2.8. Strains, Growth Conditions, and Meiosis Induction
2.9. Fluorescence Microscopy, Image Processing, and Analysis
3. Results
3.1. First Steps with ChroMo
3.2. ChroMo Provides More Detailed Information about Fission Yeast Meiotic Prophase
3.3. ChroMo Detects Causal Relations in CM TimeSeries
3.4. Tuning up ChroMo Analysis with a Synthetic Dataset
3.5. ChroMo Performs Segmented and TimeWise Analysis of Spectrum and Velocities
3.6. ChroMo Uses Motifs to Add a Complexity and Detail Layer to Behavioural Segments
3.7. ChroMo Finds Undisclosed Features on Known Strains
3.7.1. ChroMo Shows That the Oscillatory Movement Patterns Are Conserved in the hrs1Δ Strain
3.7.2. The rdh54 Deletion Leads to Different Oscillatory Movements
4. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix A.1. Strains Used throughout the Study
Strain  Feature  Genotype  Origin  References 

AFA226  wt  h90 ade6M210 his3D1 leu132 Pnda3mCherryAtb2:aur1 Sid4GFP:KanMX6 Hht1CFP:his3  Lab stock  – 
AFA824  hrs1Δ  h90 ade6M210 his3D1 leu1.32 Hht1CFP:his3 Pnda3mCherryAtb2:aur1  JCF10361  [70] 
AFA826  rdh54Δ  h90 ura4D18 taz1YFP:KanMX6 Hht1CFP:ura4 Sid4mCherry:NatMX6 mei4mCherry:NatMX6 rdh54::zeoCV  KT2475  [31] 
Appendix A.2. Definition of Synthetic TimeSeries
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Module  Function  Description  UseCases 

Segments  Discovery  Performs timeseries segmentation, per particle ID and per group, and clusters all discovered segments into a common pool, visualized as a clustered segment plot. Then, assesses if the cluster composition (location, abundance) is a good predictor of group using logistic regression models. 

Cluster Summary  Displays a tablelike summary of the discovered clusters, and the corresponding features used for clustering.    
Spectrum  Calculation of spectral characteristics of the main coordinate variable (only in 1D), per particle and group ID. Displays global and percluster spectral densities, as well as heatmaps of spectral density across time, global and per discovered cluster—needs prior segmentation of data with the function Discovery—. Provides a summary with statistics to assess whether the spectrum is significantly different between strains. 
 
Distribution  Calculates the empirical distribution (probability density) of any selected numerical variable (column of the dataset) and displays it on violin and density plots, for the whole timeseries, per group, or per group and discovered behavioural mode. Additionally, it calculates the 2D density of the selected feature over time. It is possible to set up a moving average to smooth the data. Provides a summary with statistics to assess whether the probability density is significantly different between strains. 
 
Displacement  Calculate the Mean Squared Displacement (MSD) of the data selected as coordinate variables, per group, and per discovered cluster. 
 
Individual  Tools to visualize the timeseries of each particle, as well as the individual spectrograms for any variable.    
Motifs  Global  After selecting a variable (1D) and groups to analyse, motifs (and discords) are calculated for the concatenated timeseries. It is possible to parametrise the windowlength for which motifs will be discovered, as well as other tuneable parameters (see tsmp reference guide on these [71]. 

Per cluster  Analogous to Global motif analysis, for each discovered behavioural cluster. Displays the distribution of discovered motifs across time. 
 
Causality  PCalg VLTE VLGC  Calculates a causality graph using the PCalgorithm, VariableLag Transfer Entropy, or VariableLag Granger Causality, for selected variables, per particle ID and per group. Then, it builds a global graph of all the observed relationships in all the particles. Configurable parameters are the significance threshold (pvalue) to consider a relationship, the maximum lags to explore, and the presence of connection, with respect to how many times a relationship must appear in the dataset to be considered positive. 

Correlation  Calculates a pairwise correlation between all selected variables and shows the corresponding scatterplots. 
 
Matrix  Shows the adjacency matrix for the calculated causality graphs   
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LeónPeriñán, D.; FernándezÁlvarez, A. ChroMo, an Application for Unsupervised Analysis of Chromosome Movements in Meiosis. Cells 2021, 10, 2013. https://doi.org/10.3390/cells10082013
LeónPeriñán D, FernándezÁlvarez A. ChroMo, an Application for Unsupervised Analysis of Chromosome Movements in Meiosis. Cells. 2021; 10(8):2013. https://doi.org/10.3390/cells10082013
Chicago/Turabian StyleLeónPeriñán, Daniel, and Alfonso FernándezÁlvarez. 2021. "ChroMo, an Application for Unsupervised Analysis of Chromosome Movements in Meiosis" Cells 10, no. 8: 2013. https://doi.org/10.3390/cells10082013