scEvoNet: a gradient boosting-based method for prediction of cell state evolution - BMC Bioinformatics

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scEvoNet: a gradient boosting-based method for prediction of cell state evolution - BMC Bioinformatics
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An article published in BMCBioInformatics presents scEvoNet: a Python tool for predicting cell type evolution in cross-species or cancer-related single-cell RNA sequencing datasets.

]. Despite technical batch effects and biological batch effects , we have achieved a high AUC score of 0.95 for classifying zebrafish cells with our frog-based NC model []. Here, we have expanded this method: scEvoNet applies to a variety of applications, e.g., between different time points during a given organism’s development, between species, and when comparing primary tumor and metastasis.

Additionally, to reduce the effect of the different biological domains between datasets, and to reduce the effect of the scRNA-seq data sparsity, we apply a sigmoid function that smooths expression units in a more flexible manner than simple binarization, which has been shown to keep enough information for scRNA-seq data analysis []. For the model training, we use early stopping to avoid overfitting with 10 rounds which determines the actual number of estimators in the regressions.

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