Running example on how to use wot on simulated data

These examples are also available as a python notebook

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Generating data

All the data included in the downloadable archive from the documentation can be reproduced from scratch. This example shows how to produce a dataset from gaussian noise around several paths.


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Plotting cell sets

It is very easy to generate and parse cell sets in python and color each cell set for plotting. This example shows how to parse cell sets files and quickly plot 2 genes from a dataset, coloring every cell set.


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Computing transport maps

This example shows how to compute transport maps from the command line, choosing the OT parameters that best suit your needs before using those maps to compute ancestors and trajectories.


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Plotting ancestors

The time-course data about the cells is then used to infer the evolution of cells over time in gene-expression space. This example shows how to plot infered ancestors and descendants of a given cell set, to show its trajectory.


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Plotting shared ancestry

Knowing the trajectory of each cell set, one can then identify the shared ancestors between two cell sets. This example shows how to plot shared ancestry between several cell sets.


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Plotting trajectory trends

While two-dimensional embeddings are fine for vizualizing a whole population, the evolution of each gene can also be more closely monitored over the evolution of a cell set. This example shows how to plot the expression of each gene over time in a cell set.


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Plotting ancestor census

Several files generated by wot can be viewed and plotted as datasets. This example shows how to parse and plot a census file to generate an ancestor census plot. This method can be easily extended to all kinds of dataset files.


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Plotting validation summary

After using the optimal transport validation tool, a validation summary file is generated. This example show how to parse it and plot the average and standard deviation of the distances between each type of populations (real, random, interpolated) over time.


Running example on how to use wot on real data
(currently unavailable)

These examples are also available as a python notebook

Generic placeholder image

Plotting cell sets

It is very easy to generate and parse cell sets in python and color each cell set for plotting. This example shows how to parse cell sets files and quickly plot 2 genes from a dataset, coloring every cell set.


Generic placeholder image

Computing transport maps

This example shows how to compute transport maps from the command line, choosing the OT parameters that best suit your needs before using those maps to compute ancestors and trajectories.


Generic placeholder image

Plotting ancestors

The time-course data about the cells is then used to infer the evolution of cells over time in gene-expression space. This example shows how to plot infered ancestors and descendants of a given cell set, to show its trajectory.


Generic placeholder image

Plotting shared ancestry

Knowing the trajectory of each cell set, one can then identify the shared ancestors between two cell sets. This example shows how to plot shared ancestry between several cell sets.


Generic placeholder image

Plotting trajectory trends

While two-dimensional embeddings are fine for vizualizing a whole population, the evolution of each gene can also be more closely monitored over the evolution of a cell set. This example shows how to plot the expression of each gene over time in a cell set.


Generic placeholder image

Plotting ancestor census

Several files generated by wot can be viewed and plotted as datasets. This example shows how to parse and plot a census file to generate an ancestor census plot. This method can be easily extended to all kinds of dataset files.


Generic placeholder image

Plotting validation summary

After using the optimal transport validation tool, a validation summary file is generated. This example show how to parse it and plot the average and standard deviation of the distances between each type of populations (real, random, interpolated) over time.