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DA-seq

HY Haidong Yi
AP Alec Plotkin
NS Natalie Stanley
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In DA-seq [3], a logistic regression classifier is used to compute a local DA score for each cell so that DA subpopulations can be identified. The logistic regression classifier creates feature vectors for each cell, which reflect the abundance of two biological conditions in the area around each cell at different scales. Using the labels of the samples from which the cells originated, DA-seq trains a logistic regression model. The fitted probability is then used as the DA score for each cell. In this case, the trained logistic regression model serves as a smoothing function that transforms a cell’s input feature vector to its corresponding soft DA score. Next, DA-seq uses a random permutation test to find statistically significant DA cells in the dataset. The upper and lower cut-off thresholds are based on the highest and lowest DA scores inferred under the null hypothesis that the condition labels are distributed randomly. In our experiments, we used the official DA-seq implementation, which can be accessed at https://github.com/KlugerLab/DAseq.

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