Pseudo-time analysis is an approach used primarily in single-cell RNA sequencing (scRNA-seq) data to study the progression of cellular states over time, without the need for direct time-course experiments. By organizing cells along a "pseudo-time" trajectory, researchers can infer the order of events and identify changes in gene expression that occur as cells transition through different stages. This method is particularly useful for understanding dynamic processes such as cell differentiation, disease progression, or response to external stimuli, such as drug treatment.

🗯️MATLAB snippet

Pseudo-time analysis is a powerful method for studying the progression of biological processes by ordering cells or observations along a hypothetical timeline that represents the progression of a process, such as cell differentiation or response to a perturbation. This analysis can be particularly useful for detecting and analyzing the effects of selective perturbations in time-course experiments or single-cell RNA sequencing (scRNA-seq) data. MATLAB, with its extensive toolbox and data visualization capabilities, is a suitable platform for performing such analyses. Here's a guide on how to perform pseudo-time analysis with MATLAB, focusing on detecting and analyzing selective perturbation effects:

Steps to Perform Pseudo-Time Analysis in MATLAB

1. Preprocess the Data:

   • Import your gene expression data (e.g., scRNA-seq matrix).
   • Normalize the data (e.g., log-normalization or scaling).
   • Filter out low-quality cells or genes to ensure clean input data.

    % Load data matrix
    data = readmatrix('gene_expression_data.csv'); % Replace with your file
    % Normalization step
    data_normalized = log1p(data);
   

2. Dimensionality Reduction:

   • Use PCA or t-SNE/UMAP to reduce the dimensionality of the data and highlight relationships between cells.

    [coeff, score, ~] = pca(data_normalized);
    % Visualize the first two principal components
    scatter(score(:,1), score(:,2));
    title('PCA of Gene Expression Data');
    xlabel('PC1');
    ylabel('PC2');
   

3. Construct a Trajectory Using Pseudo-time Algorithms:

   • Implement or use pre-built algorithms to infer the trajectory. MATLAB may not have direct packages for advanced trajectory inference like Monocle or Slingshot (available in R), but custom approaches using graph theory or available toolboxes can be implemented.

    % For custom trajectory inference, build a k-nearest neighbor graph
    % Example with k = 5
    knnGraph = knnsearch(score(:, 1:2), score(:, 1:2), 'K', 5);
    ​
    % Further process this graph to define a pseudo-time path
   

4. Order Cells Along the Pseudo-time:

   • Use a root cell (start point) to order cells in pseudo-time. This step involves finding a starting cell (e.g., the one most similar to a known early stage) and performing shortest-path analysis along the constructed graph.

    % Root cell index (manually chosen or identified by specific criteria)
    root_idx = 1;  % Example index
    distances = graphshortestpath(knnGraph, root_idx);
    ​
    % Order cells based on distances
    [~, pseudo_time_order] = sort(distances);
   

5. Visualize Pseudo-time Progression:

   • Plot cells colored by pseudo-time to visualize the progression.

    scatter(score(pseudo_time_order,1), score(pseudo_time_order,2), [], distances, 'filled');
    colorbar;
    title('Pseudo-time Analysis');
   

6. Detect and Analyze Selective Perturbation Effects:

   • Overlay perturbation conditions on the pseudo-time analysis to see how they affect cell states along the trajectory.

    % Assuming `condition` is a vector indicating perturbation states (0 for control, 1 for perturbed)
    scatter(score(:,1), score(:,2), 50, condition, 'filled');
    colorbar;
    title('Perturbation Overlay on Pseudo-time');
   

7. Quantify Differential Expression Along Pseudo-time:

   • Analyze specific genes for their differential expression along the pseudo-time and between perturbed and control conditions.

    % Select a gene of interest and plot its expression along pseudo-time
    gene_idx = 10;  % Example gene index
    gene_expression = data_normalized(:, gene_idx);
    ​
    figure;
    plot(distances, gene_expression, '.');
    xlabel('Pseudo-time');
    ylabel('Gene Expression');
    title(['Expression of Gene ', num2str(gene_idx), ' Along Pseudo-time']);
   

Tips for Enhancing the Analysis

• Use External Libraries: For more advanced trajectory inference (e.g., diffusion maps), consider integrating MATLAB with external libraries like Python's scanpy.
• Bootstrap Analysis: Conduct bootstrap analysis to confirm the robustness of the pseudo-time results.
• Functional Enrichment: Analyze differentially expressed genes for functional enrichment to understand biological implications.

Conclusion

MATLAB's data processing and visualization tools, combined with customized scripts and algorithms, can facilitate pseudo-time analysis for detecting and analyzing perturbation effects in biological data. For more complex needs, integrating MATLAB with Python or R can offer additional capabilities, such as using Monocle or other specialized trajectory analysis tools.

🗯️Python snippet

Pseudo-time analysis is a computational method primarily used in single-cell RNA sequencing data to infer the trajectory of cellular processes, such as differentiation or response to perturbations, across time. This approach allows researchers to analyze how gene expression changes continuously over a "pseudo-time" axis, which represents a developmental or response process inferred from data rather than actual time.

To detect and analyze selective perturbation effects using pseudo-time analysis with Python, follow these main steps: