ViSCA: Overview of the ViSCA package

In vicstefanou/ViSCA: Cell-Movie Analysis and Visualization

Description

ViSCA is an R package for the statistical analysis and visualization of single-cell data derived from the analysis of time-lapse cell-movies. The package enables users to explore the spatiotemporal trends of single-cell attributes, discover possible epigenetic effects across generations and even identify and correct errors.

Details

ViSCA was initially developed to extend the BaSCA pipeline with analytics, visualization and error correction capabilities. However, most of these capabilities are general and independent. Therefore, the package can be used for data derived from any other software, such as SuperSegger and Oufti.

Data import

ViSCA supports various input file formats for the analyzed cell-movies.

Use import_basca, import_oufti or import_ss to import data exported by the named software. These functions automatically convert the input file(s) into a cell list (and colony list), containing all the cell instants (and colony instants) of the movie, respectively.

Such structures can be also directly imported from custom-made .json files, provided that they have the appropriate format. See import_json for details.

FLT/FDT representation of cell-movies

Once the cell list of the movie is loaded, use createFLT to transform it into a Forest of Lineage Trees (FLT) data structure. A lineage tree (LT) node represents a cell instant (i.e. cell at a specific frame/instant of its lifespan). A continuous segment (sequence) of LT nodes between two successive cell divisions represents the lifespan of a cell.

If one reduces LT cell segments down to a single node, he obtains the Forest of Division Trees (FDT) data structure, capturing only each cell’s division event and summarizing its lifespan. A division tree (DT) node represents a cell (i.e. cell at its full lifespan). See createFDT for details.

These tree data structures are objects of class "igraph" and are the core structures of the package.

Attributes

Single-cell attributes are divided into two broad categories:

• cell instant attributes, that may change value at each time point. These attributes are extracted by the software, loaded into the cell list and finally stored as node attributes in the FLT by createFLT.
  
  Some other values as also stored as node attributes in the FLT when add_attr_roc or createFDT are called. See the documentation of each function for more details.

• cell life attributes characterize a cell’s whole lifespan. These attributes are estimated by createFDT and are stored as node attributes in the FDT.
  
  Some other values as also stored as node attributes in the FDT when add_attr_growth_fit_pars is called. See the documentation of the function for more details.

Analytics

ViSCA allows users to perform statistical analysis of single-cell attributes at multiple levels of organization (whole community, sub-population, colonies, generations, subtrees of individual colonies, etc.). Analytics capabilities are categorized into:

• statistics (get_attr_stats, plot_hist_attr, plot_pdf_attr, plot_viobox_attr, plot_time_attr)

• scatterplots for correlating attributes (plot_dot_attr2, plot_dot_attr3, plot_dot_time_attr, plot_dot_attr2_gen2, plot_dot_attr_fam)

• estimation of growth curves (plot_baranyi, add_attr_growth_fit_pars, plot_growth_attr_fit, plot_growth_attr, plot_growth_attr_cell)

Visualization

ViSCA provides two different ways for visualization:

1. plot_tree for visualizing a lineage or generation tree

2. create_movie for animating the segmented cells by creating videos

Color can be used to map a cell instant/life attribute and capture its variability across cells, colonies, frames or generations.

The user can also monitor how the life of a selected cell evolves in the movie using create_cell_life.

Error correction

ViSCA allows users to correct

• tracking errors with extract_branch, get_cand_mother_cells and add_branch.

• segmantation errors with split_cell, get_cand_merge_cells and merge_cells. This capability is offered to BaSCA users only.

Notes

Some functions have prerequisites in order to be used. See the Prerequisites field of each function for more details.

References

A. Balomenos, P. Tsakanikas, Z. Aspridou, A. Tampakaki, K. Koutsoumanis and E. Manolakos, “Image analysis driven single-cell analytics for systems microbiology”, BMC Systems Biology, vol. 11, no. 1, 2017.

http://oufti.org/

http://mtshasta.phys.washington.edu/website/SuperSegger.php

vicstefanou/ViSCA documentation built on May 31, 2019, 10:50 p.m.