dynamo.pl.scatters(adata, basis='umap', x=0, y=1, z=2, color='ntr', layer='X', highlights=None, labels=None, values=None, theme=None, cmap=None, color_key=None, color_key_cmap=None, background=None, ncols=4, pointsize=None, figsize=(6, 4), show_legend='on data', use_smoothed=True, aggregate=None, show_arrowed_spines=False, ax=None, sort='raw', save_show_or_return='show', save_kwargs={}, return_all=False, add_gamma_fit=False, frontier=False, contour=False, ccmap=None, alpha=0.1, calpha=0.4, sym_c=False, smooth=False, dpi=100, inset_dict={}, marker=None, group=None, add_group_gamma_fit=False, affine_transform_degree=None, affine_transform_A=None, affine_transform_b=None, stack_colors=False, stack_colors_threshold=0.001, stack_colors_title='stacked colors', stack_colors_legend_size=2, stack_colors_cmaps=None, despline=True, deaxis=True, despline_sides=None, projection='2d', **kwargs)[source]

Plot an embedding as points. Currently this only works for 2D embeddings. While there are many optional parameters to further control and tailor the plotting, you need only pass in the trained/fit umap model to get results. This plot utility will attempt to do the hard work of avoiding overplotting issues, and make it easy to automatically colour points by a categorical labelling or numeric values. This method is intended to be used within a Jupyter notebook with %matplotlib inline.

  • adata (AnnData) – an Annodata object

  • basis (str) – The reduced dimension stored in adata.obsm. The specific basis key will be constructed in the following priority if exits: 1) specific layer input + basis 2) X_ + basis 3) basis. E.g. if basis is PCA, scatters is going to look for 1) if specific layer is spliced, spliced_pca 2) X_pca (dynamo convention) 3) pca

  • x (int (default: 0)) – The column index of the low dimensional embedding for the x-axis.

  • y (int (default: 1)) – The column index of the low dimensional embedding for the y-axis.

  • color (string (default: ntr)) – Any column names or gene expression, etc. that will be used for coloring cells.

  • layer (str (default: X)) – The layer of data to use for the scatter plot.

  • highlights (list (default: None)) – Which color group will be highlighted. if highligts is a list of lists - each list is relate to each color element.

  • labels (array, shape (n_samples,) (optional, default None)) – An array of labels (assumed integer or categorical), one for each data sample. This will be used for coloring the points in the plot according to their label. Note that this option is mutually exclusive to the values option.

  • values (array, shape (n_samples,) (optional, default None)) – An array of values (assumed float or continuous), one for each sample. This will be used for coloring the points in the plot according to a colorscale associated to the total range of values. Note that this option is mutually exclusive to the labels option.

  • theme (string (optional, default None)) –

    A color theme to use for plotting. A small set of predefined themes are provided which have relatively good aesthetics. Available themes are:

    • ’blue’

    • ’red’

    • ’green’

    • ’inferno’

    • ’fire’

    • ’viridis’

    • ’darkblue’

    • ’darkred’

    • ’darkgreen’

  • cmap (string (optional, default 'Blues')) – The name of a matplotlib colormap to use for coloring or shading points. If no labels or values are passed this will be used for shading points according to density (largely only of relevance for very large datasets). If values are passed this will be used for shading according the value. Note that if theme is passed then this value will be overridden by the corresponding option of the theme.

  • color_key (dict or array, shape (n_categories) (optional, default None)) – A way to assign colors to categoricals. This can either be an explicit dict mapping labels to colors (as strings of form ‘#RRGGBB’), or an array like object providing one color for each distinct category being provided in labels. Either way this mapping will be used to color points according to the label. Note that if theme is passed then this value will be overridden by the corresponding option of the theme.

  • color_key_cmap (Optional[str]) – The name of a matplotlib colormap to use for categorical coloring. If an explicit color_key is not given a color mapping for categories can be generated from the label list and selecting a matching list of colors from the given colormap. Note that if theme is passed then this value will be overridden by the corresponding option of the theme.

  • background (string or None (optional, default 'None`)) – The color of the background. Usually this will be either ‘white’ or ‘black’, but any color name will work. Ideally one wants to match this appropriately to the colors being used for points etc. This is one of the things that themes handle for you. Note that if theme is passed then this value will be overridden by the corresponding option of the theme.

  • ncols (int (optional, default 4)) – Number of columns for the figure.

  • pointsize (None or float (default: None)) – The scale of the point size. Actual point cell size is calculated as 500.0 / np.sqrt(adata.shape[0]) * pointsize

  • figsize (None or [float, float] (default: None)) – The width and height of a figure.

  • show_legend (bool (optional, default True)) – Whether to display a legend of the labels

  • use_smoothed (bool (optional, default True)) – Whether to use smoothed values (i.e. M_s / M_u instead of spliced / unspliced, etc.).

  • aggregate (str or None (default: None)) – The column in adata.obs that will be used to aggregate data points.

  • show_arrowed_spines (bool (optional, default False)) – Whether to show a pair of arrowed spines representing the basis of the scatter is currently using.

  • ax (matplotlib.Axis (optional, default None)) – The matplotlib axes object where new plots will be added to. Only applicable to drawing a single component.

  • sort (str (optional, default raw)) – The method to reorder data so that high values points will be on top of background points. Can be one of {‘raw’, ‘abs’, ‘neg’}, i.e. sorted by raw data, sort by absolute values or sort by negative values.

  • save_show_or_return (str {‘save’, ‘show’, ‘return’} (default: show)) – Whether to save, show or return the figure. If “both”, it will save and plot the figure at the same time. If “all”, the figure will be saved, displayed and the associated axis and other object will be return.

  • save_kwargs (dict (default: {})) – A dictionary that will passed to the save_fig function. By default it is an empty dictionary and the save_fig function will use the {“path”: None, “prefix”: ‘scatter’, “dpi”: None, “ext”: ‘pdf’, “transparent”: True, “close”: True, “verbose”: True} as its parameters. Otherwise you can provide a dictionary that properly modify those keys according to your needs.

  • return_all (bool (default: False)) – Whether to return all the scatter related variables. Default is False.

  • add_gamma_fit (bool (default: False)) – Whether to add the line of the gamma fitting. This will automatically turn on if basis points to gene names and those genes have went through gamma fitting.

  • frontier (bool (default: False)) – Whether to add the frontier. Scatter plots can be enhanced by using transparency (alpha) in order to show area of high density and multiple scatter plots can be used to delineate a frontier. See matplotlib tips & tricks cheatsheet (https://github.com/matplotlib/cheatsheets). Originally inspired by figures from scEU-seq paper: https://science.sciencemag.org/content/367/6482/1151. If contour is set to be True, frontier will be ignored as contour also add an outlier for data points.

  • contour (bool (default: False)) – Whether to add an countor on top of scatter plots. We use tricontourf to plot contour for non-gridded data. The shapely package was used to create a polygon of the concave hull of the scatters. With the polygon we then check if the mean of the triangulated points are within the polygon and use this as our condition to form the mask to create the contour. We also add the polygon shape as a frontier of the data point (similar to when setting frontier = True). When the color of the data points is continuous, we will use the same cmap as for the scatter points by default, when color is categorical, no contour will be drawn but just the polygon. cmap can be set with ccmap argument. See below. This has recently changed to use seaborn’s kdeplot.

  • ccmap (str or None (default: None)) – The name of a matplotlib colormap to use for coloring or shading points the contour. See above.

  • calpha (float (default: 0.4)) – Contour alpha value passed into sns.kdeplot. The value should be inbetween [0, 1]

  • sym_c (bool (default: False)) – Whether do you want to make the limits of continuous color to be symmetric, normally this should be used for plotting velocity, jacobian, curl, divergence or other types of data with both positive or negative values.

  • smooth (bool or int (default: False)) – Whether do you want to further smooth data and how much smoothing do you want. If it is False, no smoothing will be applied. If True, smoothing based on one step diffusion of connectivity matrix (.uns[‘moment_cnn’] will be applied. If a number larger than 1, smoothing will based on `smooth steps of diffusion.

  • dpi (float, (default: 100.0)) – The resolution of the figure in dots-per-inch. Dots per inches (dpi) determines how many pixels the figure comprises. dpi is different from ppi or points per inches. Note that most elements like lines, markers, texts have a size given in points so you can convert the points to inches. Matplotlib figures use Points per inch (ppi) of 72. A line with thickness 1 point will be 1./72. inch wide. A text with fontsize 12 points will be 12./72. inch heigh. Of course if you change the figure size in inches, points will not change, so a larger figure in inches still has the same size of the elements.Changing the figure size is thus like taking a piece of paper of a different size. Doing so, would of course not change the width of the line drawn with the same pen. On the other hand, changing the dpi scales those elements. At 72 dpi, a line of 1 point size is one pixel strong. At 144 dpi, this line is 2 pixels strong. A larger dpi will therefore act like a magnifying glass. All elements are scaled by the magnifying power of the lens. see more details at answer 2 by @ImportanceOfBeingErnest: https://stackoverflow.com/questions/47633546/relationship-between-dpi-and-figure-size

  • inset_dict (dict (default: {})) – A dictionary of parameters in inset_ax. Example, something like {“width”: “5%”, “height”: “50%”, “loc”: ‘lower left’, “bbox_to_anchor”: (0.85, 0.90, 0.145, 0.145), “bbox_transform”: ax.transAxes, “borderpad”: 0} See more details at https://matplotlib.org/api/_as_gen/mpl_toolkits.axes_grid1.inset_locator.inset_axes.html or https://stackoverflow.com/questions/39803385/what-does-a-4-element-tuple-argument-for-bbox-to-anchor-mean -in-matplotlib

  • marker (str (default: None)) – The marker style. marker can be either an instance of the class or the text shorthand for a particular marker. See matplotlib.markers for more information about marker styles.

  • affine_transform_degree (Optional[int]) – Transform coordinates of points according to some degree.

  • affine_transform_A – Coefficients in affine transformation Ax + b. 2D for now.

  • affine_transform_b – Bias in affine transformation Ax + b.

  • stack_colors – Whether to stack all color on the same ax passed above. Currently only support 18 sequential matplotlib default cmaps assigning to different color groups. (#colors should be smaller than 18, reuse if #colors > 18. TODO generate cmaps according to #colors)

  • stack_colors_threshold – A threshold for filtering out points values < threshold when drawing each color. E.g. if you do not want points with values < 1 showing up on axis, set threshold to be 1

  • stack_colors_title – The title for the stack_color plot.

  • stack_colors_legend_size – Control the legend size in stack color plot.

  • stack_colors_cmaps – a list of cmaps that will be used to map values to color when stacking colors on the same subplot. The order corresponds to the order of color.

  • despline (bool) – Whether to remove splines of the figure.

  • despline_sides (Optional[List[str]]) – Which side of splines should be removed. Can be any combination of [“bottom”, “right”, “top”, “left”].

  • deaxis (bool) – Whether to remove axis ticks of the figure.

  • kwargs – Additional arguments passed to plt.scatters.


Either None or a matplotlib axis with the relevant plot displayed. If you are using a notbooks and have %matplotlib inline set then this will simply display inline.

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