mmc.stacked_density_plots()
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mmochi.landmarking.stacked_density_plots(adata, marker_list, batch_key=utils.BATCH_KEY, data_key=['protein', 'landmark_protein'], data_key_colors=['b', 'r'], aspect=3, height=.85, save_fig=None, subsample=1, bw_adjust=0) Method to plot multiple density plots of positive and negative peaks for batches and [data_keys]s with properly placed labels. Method will create density plots for all items in a batch to help visualize the process of realignment (landmark registration)
Code adapted from https://python.plainenglish.io/ridge-plots-with-pythons-seaborn-4de5725881af
Parameters: - adata (
AnnData) – AnnData containing [batch] labels in obs, [data_key] in obsm, and contains all items in [markers_list] to plot found using mmc.utils.get_data - marker_list (
Union[DataFrame,list,tuple]) – List of markers to be plotted for each [batch], uses mmc.utils.get_data to search for marker info - batch_key (
str(default:utils.BATCH_KEY)) – Chosen labels to compare. Each item in batch will be its own row in the stacked plot - data_key (
List[str] (default:['protein','landmark_protein'])) – List of labels to on which to compare [batch]es. Uses mmc.utils.get_data to find labels in data_key in [adata] - data_key_colors (
Union[List[str],List[Tuple[float]]] (default:['b','r'])) – Colors to use for text labels for [data_key]s - aspect (
Union[float,int] (default:3)) – Aspect value passed to seaborne.FacetGrid function - height (
Union[float,int] (default:.85)) – Height of the plots. Passed to seaborne.FacetGrid and matplotlib.plt.text functions - save_fig (
Optional[str] (default:None)) – Filepath to save figure to. If none, will not save figure - subsample (
Union[float,int] (default:1)) – Fraction of adata data to use for plotting. If less than 1 that fraction will be chosen randomly - bw_adjust (
Union[float,int] (default:0)) – Scalar to multiply bandwidth smoothing method used by seaborn.kdeplot. See seaborne.kdeplot for more details
Return type: None. Plots labeled stacked density plots of positive and negative peaks to compare [batch]es across [data_key]s
- adata (