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singlepp
A C++ library for cell type classification
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Options for classify_single() and friends.
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#include <classify_single.hpp>
Public Attributes | |
| Float_ | quantile = 0.8 |
| Float_ | fine_tune_threshold = 0.05 |
| bool | fine_tune = true |
| int | num_threads = 1 |
Options for classify_single() and friends.
| Float_ | Floating-point type for the correlations and scores. |
| bool singlepp::ClassifySingleOptions< Float_ >::fine_tune = true |
Whether to perform fine-tuning. This can be disabled to improve speed at the cost of accuracy.
| Float_ singlepp::ClassifySingleOptions< Float_ >::fine_tune_threshold = 0.05 |
Score threshold to use to select the top-scoring subset of labels during fine-tuning. Larger values increase the chance of recovering the correct label at the cost of computational time.
This threshold should not be set to to a value that is too large. Otherwise, the first fine-tuning iteration would just contain all labels and there would be no reduction of the marker space.
| int singlepp::ClassifySingleOptions< Float_ >::num_threads = 1 |
Number of threads to use. The parallelization scheme is determined by tatami::parallelize().
| Float_ singlepp::ClassifySingleOptions< Float_ >::quantile = 0.8 |
Quantile to use to define a per-label score for each test cell, by applying it to the distribution of correlations between the test cell and that label's reference profiles. Values closer to 0.5 focus on the behavior of the majority of a label's reference profiles. Smaller values will be more sensitive to the presence of a subset of profiles that are more similar to the test cell, which can be useful when the reference profiles themselves are heterogeneous.
1.9.8