Using Cell_transition() Without A "source" And "target" Time
Introduction
In the context of single-cell RNA sequencing (scRNA-seq) data analysis, cell transition probabilities play a crucial role in understanding the dynamics of cellular differentiation and development. The cell_transition() function in the Moscot library is a powerful tool for estimating these transition probabilities. However, when dealing with datasets that have multiple time points for each cell population, using cell_transition() without specifying a "source" and "target" time can be challenging. In this article, we will explore this issue and provide a solution for extracting transition probabilities from a TemporalProblem object.
Understanding the Problem
When working with scRNA-seq data, each cell is typically associated with a specific time point or developmental stage. In the case of a dataset with 9 cell populations, each with its own time point, we want to estimate the transition probabilities between all pairs of populations. The cell_transition() function requires a "source" and "target" time to compute these probabilities. However, when we don't have a clear "source" and "target" time, we need to find an alternative approach.
Try 1: Duplicate the Dataset and Assign Arbitrary Times
One possible solution is to duplicate the dataset and assign arbitrary times to each copy. For example, we can assign time=1 to the first copy and time=2 to the second copy. This approach allows us to compute the transition probabilities between the two copies, which should be much larger than the other transitions. However, this method has its limitations, as the transition probabilities between the two copies may not accurately reflect the true dynamics of cellular differentiation.
Try 2: Use the "triu" Policy in TemporalProblem.prepare()
Another approach is to use the "triu" policy in TemporalProblem.prepare(). This policy allows us to compute the upper triangular part of the transition matrix, which corresponds to the transition probabilities between all pairs of populations. By using this policy, we can avoid specifying a "source" and "target" time and still estimate the transition probabilities.
Extracting Transition Probabilities from a TemporalProblem Object
Once we have solved the TemporalProblem object using the "triu" policy, we can extract the transition probabilities from the object. The transition probabilities are stored in the transition_matrix attribute of the TemporalProblem object. To access this attribute, we can use the following code:
tp0 = TemporalProblem(curr_adata)
tp0 = tp0.prepare(time_key="time_ipmoscot", policy="triu", joint_attr="scvi")
tp0 = tp0.solve(epsilon=1e-3, scale_cost="mean", max_iterations=1e7)
transition_matrix = tp0.transition_matrix
The transition_matrix attribute is a 2D NumPy array where the entry at row i and column j corresponds to the transition probability from population i to population j.
Example Use Case
Let's consider an example use case where we have a dataset with 9 cell populations, each with its own time point. We want to estimate the transition probabilities between all pairs of populations. We can the following code to solve the TemporalProblem object and extract the transition probabilities:
import numpy as np
from moscot import TemporalProblem
# Load the dataset
adata = pd.read_csv("dataset.csv")
# Create a TemporalProblem object
tp0 = TemporalProblem(adata)
# Prepare the TemporalProblem object using the "triu" policy
tp0 = tp0.prepare(time_key="time_ipmoscot", policy="triu", joint_attr="scvi")
# Solve the TemporalProblem object
tp0 = tp0.solve(epsilon=1e-3, scale_cost="mean", max_iterations=1e7)
# Extract the transition probabilities
transition_matrix = tp0.transition_matrix
# Print the transition probabilities
print(transition_matrix)
This code will output a 2D NumPy array where the entry at row i and column j corresponds to the transition probability from population i to population j.
Conclusion
Q: What is the purpose of the "source" and "target" time in cell_transition()?
A: The "source" and "target" time in cell_transition() are used to specify the time points for which the transition probabilities are to be computed. The "source" time corresponds to the time point at which the cells are transitioning from, and the "target" time corresponds to the time point at which the cells are transitioning to.
Q: Why is it necessary to specify a "source" and "target" time in cell_transition()?
A: Specifying a "source" and "target" time is necessary because the transition probabilities between two time points are not the same as the transition probabilities between two different time points. For example, the transition probability from time point 1 to time point 2 is not the same as the transition probability from time point 2 to time point 3.
Q: What happens if I don't specify a "source" and "target" time in cell_transition()?
A: If you don't specify a "source" and "target" time in cell_transition(), the function will not be able to compute the transition probabilities. You will need to use an alternative approach, such as duplicating the dataset and assigning arbitrary times to each copy, or using the "triu" policy in TemporalProblem.prepare().
Q: How do I use the "triu" policy in TemporalProblem.prepare()?
A: To use the "triu" policy in TemporalProblem.prepare(), you need to specify the policy as "triu" when preparing the TemporalProblem object. For example:
tp0 = TemporalProblem(curr_adata)
tp0 = tp0.prepare(time_key="time_ipmoscot", policy="triu", joint_attr="scvi")
Q: What is the "triu" policy in TemporalProblem.prepare()?
A: The "triu" policy in TemporalProblem.prepare() is used to compute the upper triangular part of the transition matrix. This corresponds to the transition probabilities between all pairs of populations.
Q: How do I extract the transition probabilities from a TemporalProblem object?
A: To extract the transition probabilities from a TemporalProblem object, you can use the following code:
tp0 = TemporalProblem(curr_adata)
tp0 = tp0.prepare(time_key="time_ipmoscot", policy="triu", joint_attr="scvi")
tp0 = tp0.solve(epsilon=1e-3, scale_cost="mean", max_iterations=1e7)
transition_matrix = tp0.transition_matrix
Q: What is the transition_matrix attribute in a TemporalProblem object?
A: The transition_matrix attribute in a TemporalProblem object is a 2D NumPy array where the entry at row i and column j corresponds to the transition probability from population i to population j.
Q: How do I interpret the transition probabilities in the transition_matrix attribute?
A: The transition probabilities in the transition_matrix attribute can be interpreted as the probability of transitioning from one population to another. For example, if the entry at row i and column j is 0.5, this means that there is a 50% chance of transitioning from population i to population j.
Q: Can I use the transition_matrix attribute to perform downstream analysis?
A: Yes, you can use the transition_matrix attribute to perform downstream analysis, such as clustering or dimensionality reduction. The transition_matrix attribute provides a way to capture the dynamics of cellular differentiation and development, which can be useful for understanding the underlying biology of the system.