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Calculate kBET scores

calculate_kbet_sc.Rd

This function calculates the k-nearest neighbour batch-effect test (kBET). Briefly, the function leverages a Chi-Square statistic to calculate the differences in batch proportions observed in the neighbourhood of a given cell with the overall batch proportions. If the test is significant for that cell it indicates poor mixing for that cell specifically. Large number of positive tests indicate bad mixing overall. For more details, please see Büttner et al.

Usage

calculate_kbet_sc(object, batch_column, threshold = 0.05, .verbose = TRUE)

Arguments

object

SingleCells class.

batch_column

String. The column with the batch information in the obs data of the class.

threshold

Numeric. Number between 0 and 1. Below this threshold, the test is considered significant. Defaults to 0.05.

.verbose

Boolean. Controls the verbosity of the function.

Value

A KbetScores object with the following elements

  • kbet_score - Proportion of significant tests over all cells. 0 indicates perfect mixing, 1 indicates no mixing between batches.

  • significant_tests - Logical vector indicating for which cells the test was below the threshold.

  • p_values - The p-values from the Chi-Square test.

  • chi_square_stats - Per-cell Chi-Square statistics.

  • mean_chi_square - Mean Chi-Square statistic across all cells.

  • median_chi_square - Median Chi-Square statistic across all cells.

  • threshold - The significance threshold used.

  • n_batches - Number of batches in the data.

References

Büttner, et al., Nat. Methods, 2019