cliu72
commented
1 week ago Hi @janinemelsen! Thanks for bringing this to our attention. It seems like the default behavior of seaborn is to only show a subset of labels when it thinks there are too many labels. To fix this, you can add the yticklabels=True argument to the seaborn function.
Here is a temporary fix (all I did was take our existing functions at https://github.com/angelolab/ark-analysis/blob/main/src/ark/phenotyping/weighted_channel_comp.py#L414-L498 and https://github.com/angelolab/ark-analysis/blob/main/src/ark/analysis/visualize.py#L72-L153, and add the yticklabels=True argument to sns.clustermap. You should be able to copy and paste this code into your jupyter notebook and run:
import scipy.stats as stats
import numpy as np
import seaborn as sns
import matplotlib.patches as patches
def temp_draw_heatmap(data, x_labels, y_labels, dpi=None, center_val=None, min_val=None, max_val=None,
cbar_ticks=None, colormap="vlag", row_colors=None, row_cluster=True,
col_colors=None, col_cluster=True, left_start=None, right_start=None,
w_spacing=None, h_spacing=None, save_dir=None, save_file=None):
"""Plots the z scores between all phenotypes as a clustermap.
Args:
data (numpy.ndarray):
The data array to visualize
x_labels (list):
List of names displayed on horizontal axis
y_labels (list):
List of all names displayed on vertical axis
dpi (float):
The resolution of the image to save, ignored if save_dir is None
center_val (float):
value at which to center the heatmap
min_val (float):
minimum value the heatmap should take
max_val (float):
maximum value the heatmap should take
cbar_ticks (int):
list of values containing tick labels for the heatmap colorbar
colormap (str):
color scheme for visualization
row_colors (list):
Include these values as an additional color-coded cluster bar for row values
row_cluster (bool):
Whether to include dendrogram clustering for the rows
col_colors (list):
Include these values as an additional color-coded cluster bar for column values
col_cluster (bool):
Whether to include dendrogram clustering for the columns
left_start (float):
The position to set the left edge of the figure to (from 0-1)
right_start (float):
The position to set the right edge of the figure to (from 0-1)
w_spacing (float):
The amount of spacing to put between the subplots width-wise (from 0-1)
h_spacing (float):
The amount of spacing to put between the subplots height-wise (from 0-1)
save_dir (str):
If specified, a directory where we will save the plot
save_file (str):
If save_dir specified, specify a file name you wish to save to.
Ignored if save_dir is None
"""
# Replace the NA's and inf values with 0s
data[np.isnan(data)] = 0
data[np.isinf(data)] = 0
# Assign numpy values respective phenotype labels
data_df = pd.DataFrame(data, index=x_labels, columns=y_labels)
sns.set(font_scale=.7)
heatmap = sns.clustermap(
data_df, cmap=colormap, center=center_val,
vmin=min_val, vmax=max_val, row_colors=row_colors, row_cluster=row_cluster,
col_colors=col_colors, col_cluster=col_cluster,
cbar_kws={'ticks': cbar_ticks},
yticklabels=True
)
# ensure the row color axis doesn't have a label attacked to it
if row_colors is not None:
_ = heatmap.ax_row_colors.xaxis.set_visible(False)
if col_colors is not None:
_ = heatmap.ax_col_colors.yaxis.set_visible(False)
# update the figure dimensions to accommodate Jupyter widget backend
_ = heatmap.gs.update(
left=left_start, right=right_start, wspace=w_spacing, hspace=h_spacing
)
# ensure the y-axis labels are horizontal, will be misaligned if vertical
_ = plt.setp(heatmap.ax_heatmap.get_yticklabels(), rotation=0)
plt.tight_layout()
if save_dir is not None:
misc_utils.save_figure(save_dir, save_file, dpi=dpi)
def temp_generate_weighted_channel_avg_heatmap(cell_cluster_channel_avg_path, cell_cluster_col,
channels, raw_cmap, renamed_cmap,
center_val=0, min_val=-3, max_val=3):
"""Generates a z-scored heatmap of the average weighted channel expression per cell cluster
Args:
cell_cluster_channel_avg_path (str):
Path to the file containing the average weighted channel expression per cell cluster
cell_cluster_col (str):
The name of the cell cluster col,
needs to be either 'cell_som_cluster' or 'cell_meta_cluster_rename'
channels (str):
The list of channels to visualize
raw_cmap (dict):
Maps the raw meta cluster labels to their respective colors,
created by `generate_meta_cluster_colormap_dict`
renamed_cmap (dict):
Maps the renamed meta cluster labels to their respective colors,
created by `generate_meta_cluster_colormap_dict`
center_val (float):
value at which to center the heatmap
min_val (float):
minimum value the heatmap should take
max_val (float):
maximum value the heatmap should take
"""
# file path validation
io_utils.validate_paths([cell_cluster_channel_avg_path])
# verify the cell_cluster_col provided is valid
misc_utils.verify_in_list(
provided_cluster_col=[cell_cluster_col],
valid_cluster_cols=['cell_som_cluster', 'cell_meta_cluster_rename']
)
# read the channel average path
cell_cluster_channel_avgs = pd.read_csv(cell_cluster_channel_avg_path)
# assert the channels provided are valid
misc_utils.verify_in_list(
provided_channels=channels,
channel_avg_cols=cell_cluster_channel_avgs.columns.values
)
# sort the data by the meta cluster value
# this ensures the meta clusters are grouped together when the colormap is displayed
cell_cluster_channel_avgs = cell_cluster_channel_avgs.sort_values(
by='cell_meta_cluster_rename'
)
# map raw_cmap onto cell_cluster_channel_avgs for the heatmap to display the side color bar
meta_cluster_index = cell_cluster_channel_avgs[cell_cluster_col].values
meta_cluster_mapping = pd.Series(
cell_cluster_channel_avgs['cell_meta_cluster_rename']
).map(renamed_cmap)
meta_cluster_mapping.index = meta_cluster_index
# draw the heatmap
temp_draw_heatmap(
data=stats.zscore(cell_cluster_channel_avgs[channels].values),
x_labels=cell_cluster_channel_avgs[cell_cluster_col],
y_labels=channels,
center_val=center_val,
min_val=min_val,
max_val=max_val,
cbar_ticks=np.arange(-3, 4),
row_colors=meta_cluster_mapping,
row_cluster=False,
left_start=0.0,
right_start=0.85,
w_spacing=0.2,
colormap='vlag'
)
# add the legend
handles = [patches.Patch(facecolor=raw_cmap[mc]) for mc in raw_cmap]
_ = plt.legend(
handles,
renamed_cmap,
title='Meta cluster',
bbox_to_anchor=(1, 1),
bbox_transform=plt.gcf().transFigure,
loc='upper right'
)temp_generate_weighted_channel_avg_heatmap(
os.path.join(base_dir, cell_meta_cluster_channel_avg_name),
'cell_meta_cluster_rename',
channels,
raw_cmap,
renamed_cmap
)Let me know if this works!
Hi! in the cell clustering notebook I am trying to make a heatmap, but not all columns and rows are assigned a label:
Is there a way to adjust for instance the width and height of the heatmap, in order to visualize all labels?
Thanks!
Janine