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- rename tutorials from `plot_` to `tutorial_` - add tutorial on plotting cortical cell types - fix some broken links
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docs/examples/1_plotting/tutorial_plotting_06_cortex.py
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| """ | ||
| Cortical Neurons | ||
| ================ | ||
| This tutorial demonstrates how to plot cortical neurons. | ||
| In this exercise we will visualize morphological data from ["Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells"](https://www.cell.com/cell/pdf/S0092-8674(20)31254-X.pdf) | ||
| by Gouwens, Sorensen _et al._, Cell (2020). Specifically, we will re-create a plot similar to their | ||
| [Figure 4A](https://www.cell.com/cms/10.1016/j.cell.2020.09.057/asset/c684cc3f-ee17-4a36-98c9-e464a7ce8063/main.assets/gr4_lrg.jpg). | ||
| For brevity, we will use some fixed cell IDs and properties from the dataset. These were taken from the | ||
| [`20200711_patchseq_metadata_mouse.csv`](https://brainmapportal-live-4cc80a57cd6e400d854-f7fdcae.divio-media.net/filer_public/5e/2a/5e2a5936-61da-4e09-b6da-74ab97ce1b02/20200711_patchseq_metadata_mouse.csv) | ||
| file provided alongside the supplementary material of the paper: | ||
| """ | ||
|
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| # %% | ||
|
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| # The cell IDs we will use (it's the first 5 in the meta data file) | ||
| ids = [601506507, 601790961, 601803754, 601808698, 601810307] | ||
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| # The normalized soma depths for these cells (also from the meta data file) | ||
| soma_depths = [0.36101451, 0.62182935, 0.16423996, 0.48303029, 0.2956563] | ||
|
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| # %% | ||
| # ## Part I: Loading and Aligning Neurons | ||
| # | ||
| # First we need to load the neurons. Here, we will take them straight from their FTP server but you can of course download them first | ||
| # and load then from disk! | ||
|
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| import navis | ||
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| nl = navis.read_swc( | ||
| "ftp://download.brainlib.org:8811/biccn/zeng/pseq/morph/200526/", | ||
| limit=[f"{i}_transformed.swc" for i in ids], # Load only the files we need | ||
| fmt="{name,id:int}_transformed.swc", # Parse the name and id from the file name | ||
| ) | ||
|
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| # %% | ||
| # To make our lives a bit easier, we will attach the soma depth to the neurons as metadata: | ||
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| nl.set_neuron_attributes( | ||
| soma_depths, | ||
| name="cell_soma_normalized_depth", | ||
| register=True | ||
| ) | ||
|
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| nl | ||
|
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| # %% | ||
| # Next, we need to align the neurons according to their soma depth! The normalized `cell_soma_normalized_depth` should | ||
| # map to a physical range of `0` to `922.5861720311` microns. | ||
| # | ||
| # Let's demo with one neuron before we run this for all neurons: | ||
|
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| # Grab one of the neurons | ||
| n = nl[0] | ||
|
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| # This is the normalized soma depth: | ||
| print(f"Normalized soma depth: {n.cell_soma_normalized_depth}") | ||
|
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| # %% | ||
| # The physical soma depth is simply the normalized depth multiplied by the total depth of the cortex. | ||
| # Note that we're positioning from the bottom - i.e. 922.586 will be at the surface and 0 at the bottom! | ||
| # This is to make our lifes easier when it comes to plotting: the origin in `matplotlib` | ||
| # figures is in the bottom left corner. | ||
|
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| phys_y = (1 - n.cell_soma_normalized_depth) * 922.5861720311 | ||
| print(f"Physical soma depth: {phys_y}") | ||
|
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| # Current soma | ||
| print(f"Current soma coordinates: {n.soma_pos[0]}") | ||
|
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| # %% | ||
| # We will now offset the neuron such that the soma is at `(0, 589.519, 0)`: | ||
|
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| # %% | ||
| offset = [0, phys_y, 0] - n.soma_pos[0] | ||
| offset | ||
|
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| # %% | ||
| # Moving or scaling neurons in {{ navis }} is super straight forward: adding, subtracting, dividing or multiplying neurons by a number or an | ||
| # `[x, y, z]` vector will change their coordinates: | ||
|
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| # Move the neuron to the new centered position | ||
| n += offset | ||
|
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| # Check the that the soma is now in the correct position | ||
| n.soma_pos[0] | ||
|
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| # %% | ||
| # That looks good! Let's do it for all neurons: | ||
|
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| # %% | ||
| for n in nl: | ||
| phys_y = (1 - n.cell_soma_normalized_depth) * 922.5861720311 | ||
| offset = [0, phys_y, 0] - n.soma_pos[0] | ||
| n += offset | ||
|
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| # %% | ||
| # Check that all soma positions are correct: | ||
| nl.soma_pos.reshape(-1, 3) | ||
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| # %% [markdown] | ||
| # ## Part II: Plotting | ||
| # | ||
| # Now that we have loaded and aligned the neurons, let's recreate a plot similar to those in Figure 4A: | ||
|
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||
| def plot_neurons(to_plot, color="purple", axon_color="magenta", offset=500): | ||
| """Plot all neurons of a given transcriptomic type. | ||
| Parameters | ||
| ---------- | ||
| neurons : NeuronList | ||
| The aligned neurons to plot. | ||
| color : str | ||
| The color of the dendrites. | ||
| axon_color : str | ||
| The color of the axon. | ||
| offset : int | ||
| The offset between neurons along the x-axis. | ||
| Returns | ||
| ------- | ||
| fig, ax | ||
| The matplotlib figure and axis. | ||
| """ | ||
| # Offset the neurons along the x-axis so that they don't overlap | ||
| to_plot = [n + [offset * i, 0, 0] for i, n in enumerate(to_plot)] | ||
|
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| # The SWC files for this dataset include a `label` column which | ||
| # indicates the compartment type: | ||
| # 1 = soma | ||
| # 2 = axon | ||
| # 3 = dendrites | ||
| # We will use this `label` to color the neurons' compartments. | ||
|
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| # Here we define a color palette for the compartments: | ||
| compartment_palette = {1: color, 2: axon_color, 3: color} | ||
|
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| # Plot the neuron | ||
| fig, ax = navis.plot2d( | ||
| to_plot, | ||
| radius=False, | ||
| lw=1.5, | ||
| soma=dict( | ||
| fc="black", # soma fill color | ||
| ec="white", # highlight the soma with a white outline | ||
| radius=10, # override the default soma radius | ||
| ), | ||
| color_by="label", # color by `label` column in node table | ||
| palette=compartment_palette, | ||
| figsize=( | ||
| len(to_plot) * 2, | ||
| 10, | ||
| ), # scale the figure size with the number of neurons | ||
| method="2d", | ||
| ) | ||
|
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| # Add the layer boundaries (top bound for each layer in microns) | ||
| layer_bounds = { | ||
| "L1": 0, | ||
| "L2/3": 115.1112491335, | ||
| "L4": 333.4658190171, | ||
| "L5": 453.6227158132, | ||
| "L6": 687.6482650269, | ||
| "L6b": 883.1308910545, | ||
| } | ||
|
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| for layer, y in layer_bounds.items(): | ||
| y = 922.5861720311 - y # flip the y-axis | ||
| # Add a dashed line | ||
| ax.axhline(y, color="gray", ls="--", lw=1) | ||
| # Add the layer name | ||
| ax.text(-300, y - 25, layer, color="gray", va="center", size=10) | ||
| # Add the bottom bound | ||
| ax.axhline(0, color="gray", ls="--", lw=1) | ||
|
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| # Set the axis y limits according to the layers | ||
| ax.set_ylim(-10, 930) | ||
|
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| # Hide axes | ||
| ax.axis("off") | ||
|
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| return fig, ax | ||
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| fig, ax = plot_neurons(nl) | ||
|
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| # %% [markdown] | ||
| # Next, let's calculate the distribution of cable lengths: | ||
| # | ||
| # We're going to be cheap here and simply generate a histogram over the node positions. | ||
| # To make this representative, we should make sure that the number of nodes per unit of cable | ||
| # is homogeneous across neurons. For that we will resample the neurons: | ||
| print( | ||
| f"Sampling rate (nodes per micron of cable) before resampling: {nl.sampling_resolution.mean():.2f}" | ||
| ) | ||
|
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| # Resample to 2 nodes per micron | ||
| resampled = navis.resample_skeleton( | ||
| nl, | ||
| resample_to=0.5, | ||
| map_columns="label", # make sure label column is carried over | ||
| ) | ||
|
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| print( | ||
| f"Sampling rate (nodes per micron of cable) after resampling: {resampled.sampling_resolution.mean():.2f}" | ||
| ) | ||
|
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| # %% | ||
| # Get the combined nodes table: | ||
| nodes = resampled.nodes | ||
| nodes.head() | ||
|
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| # %% | ||
| # Now we can plot the distribution of cable lengths for the neurons of the type we're interested in: | ||
|
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| import seaborn as sns | ||
| from mpl_toolkits.axes_grid1 import make_axes_locatable | ||
|
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| # Plot the neurons using the function we defined earlier | ||
| fig, ax = plot_neurons(nl) | ||
|
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| # Add a new axis to the right of the main plot | ||
| divider = make_axes_locatable(ax) | ||
| ax_hist = divider.append_axes("right", size=0.75, pad=0.05) | ||
|
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| # Add histograms | ||
| # For axon: | ||
| sns.kdeplot( | ||
| data=nodes[nodes.label == 2], y="y", ax=ax_hist, color="purple", linewidth=1.5 | ||
| ) | ||
| # For the rest: | ||
| sns.kdeplot( | ||
| data=nodes[nodes.label != 2], y="y", ax=ax_hist, color="magenta", linewidth=1.5 | ||
| ) | ||
|
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| # Add soma positions | ||
| soma_pos = nl.soma_pos.reshape(-1, 3) | ||
| ax_hist.scatter([0] * len(soma_pos), soma_pos[:, 1], color="black", s=10, clip_on=False) | ||
|
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| # Set same axis limits as the main plot | ||
| ax_hist.set_ylim(-10, 930) | ||
|
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| # Hide axes | ||
| ax_hist.set_axis_off() | ||
|
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| # %% | ||
| # ## Acknowledgements | ||
| # | ||
| # We thank Staci Sorensen and Casey Schneider-Mizell from the Allen Institute for Brain Science | ||
| # for helping with extra information and data for this tutorial! |
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