ScPace: Timestamp Calibration for time-series ScRNA-seq expression data

We present ScPace, a novel approach for timestamp calibration in time-series single-cell RNA sequencing (scRNA-seq) data. This method tackles the significant issue of noisy timestamps in time-series ScRNA-seq data, which can undermine the accuracy of timestamp automatic annotation (TAA) and interfere with downstream analyses, such as supervised pseudotime analysis. ScPace leverages a latent variable indicator within a support vector machine (SVM) framework to efficiently identify and correct mislabeled samples, improving the robustness and reliability of results across a range of time-series ScRNA-seq datasets.

Files

• ScPace/ScPace: Main code for ScPace.
• ScPace/Hinge_Loss: Computes Hinge Loss for each Samples
• Simulated Datasets: Simulated datasets using Splatter

Input

• data: The input time-series ScRNA-seq expression matrix.
• labels: Time-labels for each cells(Numpy Ndarray)
• C: The regularization parameter for SVM
• num_iteration: Numbers of iterations to perform ScPace
• p: The C-Growing Parameter
• lam: Threshold for updating the latent variables
• methods: reclassify/deletion

Paper

Timestamp calibration for time-series single cell RNA-seq expression data