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:

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

     [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:

     % 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:

     % 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:

     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:

     % 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:

     % 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

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: