Single-Cell Trajectory Analysis

Fig 2: Automated detection of patterned single-cells within hydrogel using deep learning

At CD ComputaBio, we offer cutting-edge AI-aided single-cell trajectory analysis services to provide invaluable insights into cellular dynamics and development. By leveraging advanced computational techniques and machine learning algorithms, our experts can help you unravel the complex trajectory of individual cells, leading to a deeper understanding of cellular behavior and function. With our comprehensive and customizable services, we aim to empower researchers and scientists in various fields, including cell biology, developmental biology, immunology, and oncology, to make significant breakthroughs in their research endeavors.

What is Single-Cell Trajectory Analysis?

Single-cell trajectory analysis is a powerful tool that allows researchers to investigate the developmental lineage and differentiation paths of individual cells over time. This approach enables the reconstruction of cell differentiation processes and the identification of critical molecular events that govern cellular fate decisions. By understanding the dynamics of cell trajectories, researchers can gain insights into various biological processes, such as embryonic development, tissue regeneration, immune response, and disease progression. By harnessing the power of AI, researchers can overcome the challenges posed by the high-dimensional and complex nature of single-cell data, leading to more precise trajectory reconstructions and the discovery of novel cellular states and transitional intermediates.

Fig 1: Stem cell fate in cancer growth, progression and therapy resistanceFig 1. Stem cell fate in cancer growth, progression and therapy resistance. (Lytle N K, et al., 2018)

Our Services

Our Analysis Methods

Probabilistic Modeling of Cellular Dynamics

Our experts leverage probabilistic modeling approaches, such as Gaussian processes and hidden Markov models, to characterize the stochastic nature of cellular differentiation and predict probabilistic lineage transitions with high confidence.

Transfer Learning for Cell Fate Prediction

We apply transfer learning techniques to integrate prior knowledge from related cellular systems and leverage pre-trained models for cell fate prediction, enhancing the predictive power and generalizability of our analysis.

Ensemble Modeling for Robust Differential Expression Analysis

We utilize ensemble learning methods, such as random forests and gradient boosting, to perform robust and comprehensive differential expression analysis, accounting for the variability and noise inherent in single-cell gene expression data.

At CD ComputaBio, we offer a comprehensive range of AI-aided single-cell trajectory analysis services to meet the diverse needs of our clients. Our services are tailored to accommodate various research objectives and experimental designs, ensuring that researchers can extract meaningful and actionable insights from their single-cell data. If you are interested in our services or have any questions, please feel free to contact us.


  • Lytle N K, Barber A G, Reya T. Stem cell fate in cancer growth, progression and therapy resistance[J]. Nature Reviews Cancer, 2018, 18(11): 669-680.
  • Debnath T, Hattori R, Okamoto S, et al. Automated detection of patterned single-cells within hydrogel using deep learning[J]. Scientific Reports, 2022, 12(1): 18343.


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