`msmu.tl.pca`

Perform principal component analysis (PCA) on the specified modality of the MuData object.

This uses the scikit-learn implementation described by Pedregosa et al. (2011). For background on PCA, see Maćkiewicz and Ratajczak (1993).

Parameters:

Name	Type	Description	Default
`mdata`	`MuData`	`MuData` object containing the data.	required
`modality`	`str`	The modality to perform PCA on.	required
`layer`	`str \| None`	Layer to use for quantification aggregation. If `None`, the default layer (`.X`) will be used. Defaults to `"scaled"`.	`None`
`n_components`	`int \| None`	Number of components to keep. if `n_components` is not set all components are kept:: `n_components == min(n_samples, n_features)` If `n_components == 'mle'` and `svd_solver == 'full'`, Minka's MLE is used to guess the dimension. Use of `n_components == 'mle'` will interpret `svd_solver == 'auto'` as `svd_solver == 'full'`. If `0 < n_components < 1` and `svd_solver == 'full'`, select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by `n_components`. If `svd_solver == 'arpack'`, the number of components must be strictly less than the minimum of `n_features` and `n_samples`. Hence, the `None` case results in: `n_components == min(n_samples, n_features) - 1`	`None`
`svd_solver`	`Literal['auto', 'full', 'arpack', 'randomized']`	`"auto"`: The solver is selected by a default `'auto'` policy is based on `X.shape` and `n_components`: if the input data has fewer than 1000 features and more than 10 times as many samples, then the `"covariance_eigh"` solver is used. Otherwise, if the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient `"randomized"` method is selected. Otherwise the exact `"full"` SVD is computed and optionally truncated afterward. `"full"` : Run exact full SVD calling the standard LAPACK solver via `scipy.linalg.svd` and select the components by postprocessing `"arpack"` : Run SVD truncated to `n_components` calling ARPACK solver via `scipy.sparse.linalg.svds`. It requires strictly `0 < n_components < min(X.shape)` `"randomized"` : Run randomized SVD by the method of Halko et al. (2011).	`'auto'`
`random_state`	`int \| None`	Used when the `'arpack'` or `'randomized'` solvers are used. Pass an `int` for reproducible results across multiple function calls.	`0`
`key_added`	`str`	Base key used for PCA outputs. Results are stored in: - `.obsm[key_added]` for component scores - `.varm[key_added]` for loadings - `.uns[key_added]` for explained variance metadata Defaults to `"X_pca"`.	`'X_pca'`
`**kwargs`	`Any`	Additional keyword arguments passed to the `PCA` constructor.	`{}`

Returns:

Type	Description
`MuData`	Updated `MuData` object with PCA results.

References

Pedregosa, F. et al. Scikit-learn: machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011). Full text.

Maćkiewicz, A. and Ratajczak, W. Principal components analysis (PCA). Computers & Geosciences 19, 303–342 (1993). https://doi.org/10.1016/0098-3004(93)90090-r.

Halko, N., Martinsson, P. G., and Tropp, J. A. Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review 53, 217–288 (2011). https://doi.org/10.1137/090771806.