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singlecell.py
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singlecell.py
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import os
import warnings
from copy import deepcopy
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import requests
import scipy.stats as st
import seaborn as sns
from IPython.display import display
from ipywidgets import (HTML, Accordion, Button, Dropdown, FloatProgress, HBox,
IntSlider, Layout, Output, SelectionSlider, Tab, Text,
VBox)
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.ticker import MaxNLocator
from statsmodels.sandbox.stats.multicomp import multipletests
import plotly.offline as py
import plotly.tools as tls
import scanpy.api as sc
from beakerx import TableDisplay, TableDisplayCellHighlighter
py.init_notebook_mode()
warnings.simplefilter('ignore', UserWarning)
# -------------------- HELPERS --------------------
_CLUSTERS_CMAP = 'tab20'
_EXPRESSION_CMAP = LinearSegmentedColormap.from_list(
'name', ['lightgrey', 'orangered', 'red'])
_LINE_HEIGHT = '20px'
def _create_progress_bar():
style = '''
<style>
@-webkit-keyframes container-rotate {
to {
-webkit-transform: rotate(360deg)
}
}
@keyframes container-rotate {
to {
-webkit-transform: rotate(360deg);
transform: rotate(360deg)
}
}
@-webkit-keyframes fill-unfill-rotate {
12.5% {
-webkit-transform: rotate(135deg)
}
25% {
-webkit-transform: rotate(270deg)
}
37.5% {
-webkit-transform: rotate(405deg)
}
50% {
-webkit-transform: rotate(540deg)
}
62.5% {
-webkit-transform: rotate(675deg)
}
75% {
-webkit-transform: rotate(810deg)
}
87.5% {
-webkit-transform: rotate(945deg)
}
to {
-webkit-transform: rotate(1080deg)
}
}
@keyframes fill-unfill-rotate {
12.5% {
-webkit-transform: rotate(135deg);
transform: rotate(135deg)
}
25% {
-webkit-transform: rotate(270deg);
transform: rotate(270deg)
}
37.5% {
-webkit-transform: rotate(405deg);
transform: rotate(405deg)
}
50% {
-webkit-transform: rotate(540deg);
transform: rotate(540deg)
}
62.5% {
-webkit-transform: rotate(675deg);
transform: rotate(675deg)
}
75% {
-webkit-transform: rotate(810deg);
transform: rotate(810deg)
}
87.5% {
-webkit-transform: rotate(945deg);
transform: rotate(945deg)
}
to {
-webkit-transform: rotate(1080deg);
transform: rotate(1080deg)
}
}
.preloader-wrapper.active {
-webkit-animation: container-rotate 1568ms linear infinite;
animation: container-rotate 1568ms linear infinite;
} .preloader-wrapper {
display: inline-block;
position: relative;
width: 50px;
height: 50px;
}
.spinner-layer {
position: absolute;
width: 100%;
height: 100%;
opacity: 0;
border-color: #26a69a;
}
.active .spinner-layer {
border-color: #4285f4;
opacity: 1;
-webkit-animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both;
animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both;
}
.circle-clipper {
display: inline-block;
position: relative;
width: 50%;
height: 100%;
overflow: hidden;
border-color: inherit;
}
.circle-clipper.left {
float: left !important;
}
.active .circle-clipper.left .circle {
-webkit-animation: left-spin 1333ms cubic-bezier(0.4, 0, 0.2, 1) infinite both;
animation: left-spin 1333ms cubic-bezier(0.4, 0, 0.2, 1) infinite both;
}
.circle-clipper.left .circle {
left: 0;
border-right-color: transparent !important;
-webkit-transform: rotate(129deg);
transform: rotate(129deg);
}
.circle-clipper .circle {
width: 200%;
height: 100%;
border-width: 3px;
border-style: solid;
border-color: inherit;
border-bottom-color: transparent !important;
border-radius: 50%;
-webkit-animation: none;
animation: none;
position: absolute;
top: 0;
right: 0;
bottom: 0;
}
.circle {
border-radius: 50%;
}
.gap-patch {
position: absolute;
top: 0;
left: 45%;
width: 10%;
height: 100%;
overflow: hidden;
border-color: inherit;
}
.gap-patch .circle {
width: 1000%;
left: -450%;
}
.circle-clipper {
display: inline-block;
position: relative;
width: 50%;
height: 100%;
overflow: hidden;
border-color: inherit;
}
.circle-clipper.right {
float: right !important;
}
.active .circle-clipper.right .circle {
-webkit-animation: right-spin 1333ms cubic-bezier(0.4, 0, 0.2, 1) infinite both;
animation: right-spin 1333ms cubic-bezier(0.4, 0, 0.2, 1) infinite both;
}
.circle-clipper.right .circle {
left: -100%;
border-left-color: transparent !important;
-webkit-transform: rotate(-129deg);
transform: rotate(-129deg);
}
@-webkit-keyframes left-spin {
from {
-webkit-transform: rotate(130deg)
}
50% {
-webkit-transform: rotate(-5deg)
}
to {
-webkit-transform: rotate(130deg)
}
}
@keyframes left-spin {
from {
-webkit-transform: rotate(130deg);
transform: rotate(130deg)
}
50% {
-webkit-transform: rotate(-5deg);
transform: rotate(-5deg)
}
to {
-webkit-transform: rotate(130deg);
transform: rotate(130deg)
}
}
@-webkit-keyframes right-spin {
from {
-webkit-transform: rotate(-130deg)
}
50% {
-webkit-transform: rotate(5deg)
}
to {
-webkit-transform: rotate(-130deg)
}
}
@keyframes right-spin {
from {
-webkit-transform: rotate(-130deg);
transform: rotate(-130deg)
}
50% {
-webkit-transform: rotate(5deg);
transform: rotate(5deg)
}
to {
-webkit-transform: rotate(-130deg);
transform: rotate(-130deg)
}
}
</style>
'''
progress_bar = HTML(
'''{}
<div class="preloader-wrapper active">
<div class="spinner-layer spinner-blue-only">
<div class="circle-clipper left">
<div class="circle"></div>
</div><div class="gap-patch">
<div class="circle"></div>
</div><div class="circle-clipper right">
<div class="circle"></div>
</div>
</div>
</div>
'''.format(style),
layout=Layout(height='150px', width='100%'))
return progress_bar
def _create_placeholder(kind):
if kind == 'plot':
word = 'Plot'
elif kind == 'table':
word = 'Table'
placeholder_html = '<p>{} will display here.</p>'.format(word)
return HTML(placeholder_html, layout=Layout(padding='28px'))
def _info_message(message):
return HTML(
'<div class="alert alert-info" style="font-size:14px; line-height:20px;"><p><b>NOTE:</b> {}</p></div>'.
format(message))
def _output_message(message):
return HTML(
'<div class="well well-sm" style="font-size:14px; line-height:20px; padding: 15px;">{}</div>'.
format(message))
def _warning_message(message):
return HTML(
'<div class="alert alert-warning" style="font-size:14px; line-height:20px;">{}</div>'.
format(message))
def _create_export_button(figure, fname):
# Default png
filetype_dropdown = Dropdown(
options=['png', 'svg', 'pdf'],
value='png',
layout=Layout(width='75px'))
if not os.path.isdir('figures'):
os.mkdir('figures')
# Default filename value
filename = 'figures/{}.{}'.format(fname, filetype_dropdown.value)
figure.savefig(
filename, bbox_inches='tight', format=filetype_dropdown.value)
# HTML button opens local link on click
a_style = '''text-decoration: none;
color: black;
border: 0px white solid !important;
'''
button_style = "width:100px; height:28px; margin:0px;"
button_classes = "p-Widget jupyter-widgets jupyter-button widget-button"
button_html = '<button class="{}" style="{}"><a href="{}" target="_blank" style="{}">Save Plot</a>'.format(
button_classes, button_style, filename, a_style)
export_button = HTML(button_html)
# Download file locally
def save_fig(value_info):
if not os.path.isdir('figures'):
os.mkdir('figures')
filename = 'figures/{}.{}'.format(fname, value_info['new'])
# Disable button until file is properly saved
export_button.value = '<button class="{}" style="{}" disabled><a href="{}" target="_blank" style="{}">Wait...</a>'.format(
button_classes, button_style, filename, a_style)
figure.savefig(filename, bbox_inches='tight', format=value_info['new'])
export_button.value = '<button class="{}" style="{}"><a href="{}" target="_blank" style="{}">Save Plot</a>'.format(
button_classes, button_style, filename, a_style)
filetype_dropdown.observe(save_fig, names='value')
return HBox(
[filetype_dropdown, export_button], justify_content='flex-start')
def _download_text_file(url):
'''
Downloads file assuming simple text file. Returns name of file.
'''
filename = url.split('/')[-1]
r = requests.get(url, stream=True)
file_size = int(r.headers['Content-Length'])
chunk_size = int(file_size / 50)
progress_bar = FloatProgress(
value=0,
min=0,
max=50,
step=1,
description='Loading...',
bar_style='info',
orientation='horizontal')
display(progress_bar)
f = open(filename, 'wb')
for chunk in r.iter_content(chunk_size=chunk_size):
f.write(chunk)
progress_bar.value += 1
f.close()
progress_bar.close()
display(HTML('<p>Downloaded file: <code>{}</code>.</p>'.format(filename)))
return filename
class SingleCellAnalysis:
"""docstring for SingleCellAnalysis."""
def __init__(self, verbose=False):
self.data = ''
self.verbose = verbose
mpl.rcParams['figure.dpi'] = 80
# -------------------- SETUP ANALYSIS --------------------
def setup_analysis(self, matrix_filepath):
'''
Load a raw count matrix for a single-cell RNA-seq experiment.
'''
# Hide FutureWarnings.
warnings.simplefilter('ignore',
FutureWarning) if self.verbose else None
self._setup_analysis(matrix_filepath)
self._setup_analysis_ui()
# Revert to default settings to show FutureWarnings.
warnings.simplefilter('default',
FutureWarning) if self.verbose else None
def _setup_analysis(self, matrix_filepath):
# Downloads matrix if detects URL
local_matrix_filepath = matrix_filepath
if matrix_filepath.startswith('http'):
local_matrix_filepath = _download_text_file(matrix_filepath)
data = sc.read(local_matrix_filepath, cache=True).transpose()
# Download genes.tsv and barcodes.tsv if matrix_filepath is URL
if matrix_filepath.endswith('.mtx'):
base_url = '/'.join(matrix_filepath.split('/')[:-1])
# base_url refers to current directory if empty
if base_url == '':
base_url = '.'
barcodes_filepath = '/'.join([base_url, 'barcodes.tsv'])
genes_filepath = '/'.join([base_url, 'genes.tsv'])
# Download as necessary
if matrix_filepath.startswith('http'):
barcodes_filepath = _download_text_file(barcodes_filepath)
genes_filepath = _download_text_file(genes_filepath)
data.obs_names = np.genfromtxt(barcodes_filepath, dtype=str)
data.var_names = np.genfromtxt(genes_filepath, dtype=str)[:, 1]
# This is needed to setup the "n_genes" column in data.obs.
sc.pp.filter_cells(data, min_genes=0)
# Plot some information about mitochondrial genes, important for quality control
mito_genes = [
name for name in data.var_names if name.startswith('MT-')
]
data.obs['percent_mito'] = np.sum(
data[:, mito_genes].X, axis=1) / np.sum(
data.X, axis=1)
# add the total counts per cell as observations-annotation to data
data.obs['n_counts'] = np.sum(data.X, axis=1)
data.is_log = False
self.data = data
def _setup_analysis_ui(self):
measures = [
self.data.obs['n_genes'], self.data.obs['n_counts'],
self.data.obs['percent_mito'] * 100
]
measure_names = ['# of Genes', 'Total Counts', '% Mitochondrial Genes']
# Pairplot of each variable
with sns.axes_style("whitegrid", {
'axes.edgecolor': 'black',
'grid.color': '0.9',
'xtick.color': 'black',
'ytick.color': 'black',
'xtick.direction': 'out',
'ytick.direction': 'out',
'xtick.major.size': '5',
'ytick.major.size': '5'
}):
g = sns.pairplot(
self.data.obs,
size=4,
diag_kind='kde',
plot_kws=dict(s=2, edgecolor="#1976D2", linewidth=0.5),
diag_kws=dict(shade=True, color='#1976D2'))
# Set limits to 0
for row in g.axes:
for ax in row:
ax.set_ylim(
0, )
ax.set_xlim(
0, )
# Set axes labels
xlabels, ylabels = [], []
for ax in g.axes[-1, :]:
xlabel = ax.xaxis.get_label_text()
xlabels.append(xlabel)
for ax in g.axes[:, 0]:
ylabel = ax.yaxis.get_label_text()
ylabels.append(ylabel)
measure_names = {
'n_genes': '# of Genes',
'n_counts': 'Total Counts',
'percent_mito': '% Mitochondrial Genes'
}
for i in range(len(xlabels)):
for j in range(len(ylabels)):
g.axes[j, i].xaxis.set_label_text(measure_names[xlabels[i]])
g.axes[j, i].yaxis.set_label_text(measure_names[ylabels[j]])
fig1 = g.fig
plt.close()
fig1_out = Output()
with fig1_out:
display(
_create_export_button(fig1,
'1_setup_analysis_single_qc_plots'))
display(fig1)
# Descriptive text
header = _output_message('''<h3>Results</h3>
<p>Loaded <code>{}</code> cells and <code>{}</code> total genes.</p>
<h3>QC Metrics</h3>
<p>Use the displayed quality metrics to detect outliers cells and filter unwanted cells below in
<b>Step 2</b>.
An abnormally high number of genes or counts in a cell suggests a higher probability of a doublet.
High levels of mitochondrial genes is characteristic of broken/low quality cells.<br><br>
Some sensible ranges for this example dataset are:
<ol>
<li><code>0 to 2500</code> # of genes per cell</li>
<li><code>0 to 15000</code> counts per cell</li>
<li><code>0 to 15%</code> mitochondrial genes per cell</li>
</p>'''.format(len(measures[0]), len(self.data.var_names)))
display(header, fig1_out)
# -------------------- PREPROCESS COUNTS --------------------
def preprocess_counts(self,
min_n_cells=0,
min_n_genes=0,
max_n_genes='inf',
min_n_counts=0,
max_n_counts='inf',
min_percent_mito=0,
max_percent_mito='inf',
normalization_method='LogNormalize'):
'''
Perform cell quality control by evaluating quality metrics, normalizing counts, scaling, and correcting for effects of total counts per cell and the percentage of mitochondrial genes expressed. Also detect highly variable genes and perform linear dimensional reduction (PCA).
'''
# Hide FutureWarnings.
warnings.simplefilter('ignore',
FutureWarning) if self.verbose else None
if min_n_cells == '':
min_n_cells = 0
if min_n_genes == '':
min_n_genes = 0
if max_n_genes == '':
max_n_genes = 'inf'
if min_n_counts == '':
min_n_counts = 0
if max_n_counts == '':
max_n_counts = 'inf'
if min_percent_mito == '':
min_percent_mito = 0
if max_percent_mito == '':
max_percent_mito = 'inf'
# Sanitize input
min_n_cells = float(min_n_cells)
n_genes_range = [float(min_n_genes), float(max_n_genes)]
n_counts_range = [float(min_n_counts), float(max_n_counts)]
percent_mito_range = [float(min_percent_mito), float(max_percent_mito)]
orig_n_cells = len(self.data.obs_names)
orig_n_genes = len(self.data.var_names)
# Perform filtering on genes and cells
success_run = self._preprocess_counts(
min_n_cells, n_genes_range, n_counts_range, percent_mito_range,
normalization_method)
# Build UI output
if success_run:
self._preprocess_counts_ui(orig_n_cells, orig_n_genes)
# Revert to default settings to show FutureWarnings.
warnings.simplefilter('default',
FutureWarning) if self.verbose else None
def _preprocess_counts(self, min_n_cells, n_genes_range, n_counts_range,
percent_mito_range, normalization_method):
if self.data.raw:
display(
_warning_message(
'This data has already been preprocessed. Please run <a href="#Step-1:-Setup-Analysis">Step 1: Setup Analysis</a> again if you would like to perform preprocessing again.</div>'
))
return False
# Gene filtering
sc.pp.filter_genes(self.data, min_cells=min_n_cells)
# Filter cells within a range of # of genes and # of counts.
sc.pp.filter_cells(self.data, min_genes=n_genes_range[0])
sc.pp.filter_cells(self.data, max_genes=n_genes_range[1])
sc.pp.filter_cells(self.data, min_counts=n_counts_range[0])
sc.pp.filter_cells(self.data, max_counts=n_counts_range[1])
# Remove cells that have too many mitochondrial genes expressed.
percent_mito_filter = (
self.data.obs['percent_mito'] * 100 >= percent_mito_range[0]) & (
self.data.obs['percent_mito'] * 100 < percent_mito_range[1])
if not percent_mito_filter.any():
self.data = self.data[percent_mito_filter, :]
# Set the `.raw` attribute of AnnData object to the logarithmized raw gene expression for later use in
# differential testing and visualizations of gene expression. This simply freezes the state of the data stored
# in `data_raw`.
if normalization_method == 'LogNormalize' and self.data.is_log is False:
data_raw = sc.pp.log1p(self.data, copy=True)
self.data.raw = self.data
# Per-cell scaling.
sc.pp.normalize_per_cell(self.data, counts_per_cell_after=1e4)
# Identify highly-variable genes.
sc.pp.filter_genes_dispersion(
self.data, min_mean=0.0125, max_mean=3, min_disp=0.5)
# Logarithmize the data.
if normalization_method == 'LogNormalize' and self.data.is_log is False:
sc.pp.log1p(self.data)
self.data.is_log = True
# Regress out effects of total counts per cell and the percentage of mitochondrial genes expressed.
sc.pp.regress_out(self.data, ['n_counts', 'percent_mito'])
# Scale the data to unit variance and zero mean. Clips to max of 10.
sc.pp.scale(self.data, max_value=10)
# Calculate PCA
sc.tl.pca(self.data, n_comps=30)
# Successfully ran
return True
def _preprocess_counts_ui(self, orig_n_cells, orig_n_genes):
cell_text = '<p><code>{}/{}</code> cells passed filtering.</p>'.format(
len(self.data.obs_names), orig_n_cells)
genes_text = '<p><code>{}/{}</code> genes passed filtering.</p>'.format(
len(self.data.raw.var_names), orig_n_genes)
v_genes_text = '<p><code>{}/{}</code> genes detected as variable genes.</p>'.format(
len(self.data.var_names), len(self.data.raw.var_names))
if self.data.is_log:
log_text = '<p>Data is log normalized.</p>'
else:
log_text = '<p>Data is not normalized.</p>'
regress_text = '''<p>Perform linear regression to remove unwanted sources of variation including:</p><ol><li># of detected molecules per cell</li>
<li>% mitochondrial gene content</li></ol>'''
pca_help_text = '''<h3>Dimensional Reduction: Principal Components</h3>
<p>Use the following plot showing the standard deviations of the principal components to determine the number of relevant components to use downstream.</p>'''
output_div = _output_message(
'''<h3 style="position: relative; top: -10px">Results</h3>{}{}{}{}{}{}'''.
format(cell_text, genes_text, v_genes_text, log_text, regress_text,
pca_help_text))
display(output_div)
display(_info_message('Hover over the plot to interact.'))
pca_fig, pca_py_fig = self._plot_pca()
display(
_create_export_button(
pca_fig, '2_preprocess_counts_pca_variance_ratio_plot'))
pca_plot_box = Output(layout=Layout(
display='flex',
align_items='center',
justify_content='center',
margin='0 0 0 -50px'))
display(pca_plot_box)
with pca_plot_box:
py.iplot(pca_py_fig, show_link=False)
def _plot_pca(self):
# mpl figure
fig_elbow_plot = plt.figure(figsize=(6, 5))
pc_std = self.data.obsm['X_pca'].std(axis=0).tolist()
pc_std = pd.Series(
pc_std, index=[x + 1 for x in list(range(len(pc_std)))])
pc_std = pc_std.iloc[:min(len(pc_std), 30)]
plt.plot(pc_std, 'o')
ax = fig_elbow_plot.gca()
ax.set_xlim(left=0)
ax.get_xaxis().set_major_locator(MaxNLocator(integer=True))
ax.get_xaxis().set_minor_locator(MaxNLocator(integer=True))
ax.set_xlabel('Principal Component', size=16)
ax.set_ylabel('Variance of PC', size=16)
plt.close()
# plot interactive
py_fig = tls.mpl_to_plotly(fig_elbow_plot)
py_fig['layout']['margin'] = {'l': 80, 'r': 14, 't': 10, 'b': 45}
return fig_elbow_plot, py_fig
# -------------------- Cluster Cells --------------------
def cluster_cells(self, pcs=10, resolution=1.2, perplexity=30):
# Hide FutureWarnings.
warnings.simplefilter('ignore',
FutureWarning) if self.verbose else None
# -------------------- tSNE PLOT --------------------
pc_sdev = pd.Series(np.std(self.data.obsm['X_pca'], axis=0))
pc_sdev.index = pc_sdev.index + 1
# Parameter values
pc_range = range(2, 31)
res_range = [
float('{:0.1f}'.format(x)) for x in list(np.arange(.5, 2.1, 0.1))
]
perp_range = range(5, min(51, len(self.data.obs_names)))
# Parameter slider widgets
pc_slider = SelectionSlider(
options=pc_range,
value=pcs,
description="# of PCs",
continuous_update=False)
res_slider = SelectionSlider(
options=res_range,
value=resolution,
description="Resolution",
continuous_update=False)
perp_slider = SelectionSlider(
options=perp_range,
value=perplexity,
description="Perplexity",
continuous_update=False)
# Output widget
plot_output = Output(layout=Layout(
height='700px',
display='flex',
align_items='center',
justify_content='center'))
with plot_output:
display(_create_placeholder('plot'))
# "Go" button to plot on click
def plot_tsne_callback(button=None):
plot_output.clear_output()
progress_bar = _create_progress_bar()
with plot_output:
# show progress bar
display(progress_bar)
# perform tSNE calculation and plot
self._run_tsne(pc_slider.value, res_slider.value,
perp_slider.value)
tsne_fig, py_tsne_fig = self._plot_tsne(figsize=(10, 8))
display(
_create_export_button(
tsne_fig, '3_perform_clustering_analysis_tsne_plot'))
py.iplot(py_tsne_fig, show_link=False)
# close progress bar
progress_bar.close()
# Button widget
go_button = Button(description='Plot', button_style='info')
go_button.on_click(plot_tsne_callback)
# Parameter descriptions
param_info = _output_message('''
<h3 style="position: relative; top: -10px">Clustering Parameters</h3>
<p>
<h4>Number of PCs (Principal Components)</h4>The number of principal components to use in clustering.<br><br>
<h4>Resolution</h4>Higher resolution means more and smaller clusters. We find that values 0.6-1.2 typically
returns good results for single cell datasets of around 3K cells. Optimal resolution often increases for
larger datasets.<br><br>
<h4>Perplexity</h4>The perplexity parameter loosely models the number of close neighbors each point has.
<a href="https://distill.pub/2016/misread-tsne/">More info on how perplexity matters here</a>.
</p>''')
help_message = '''Hover over the plot to interact. Click and drag to zoom. Click on the legend to hide or show
specific clusters; single-click hides/shows the cluster while double-click isolates the cluster.'''
sliders = HBox([pc_slider, res_slider, perp_slider])
ui = VBox(
[param_info, _info_message(help_message), sliders, go_button])
plot_box = VBox([ui, plot_output])
display(plot_box)
plot_tsne_callback()
# Revert to default settings to show FutureWarnings.
warnings.simplefilter('default',
FutureWarning) if self.verbose else None
def _run_tsne(self, pcs, resolution, perplexity):
sc.tl.tsne(
self.data,
n_pcs=pcs,
perplexity=perplexity,
learning_rate=1000,
n_jobs=8)
sc.tl.louvain(
self.data,
n_neighbors=10,
resolution=resolution,
recompute_graph=True)
def _plot_tsne(self, figsize):
# Clusters
cell_clusters = self.data.obs['louvain_groups'].astype(int)
cluster_names = np.unique(cell_clusters).tolist()
num_clusters = len(cluster_names)
# Coordinates with cluster assignments
tsne_coordinates = pd.DataFrame(
self.data.obsm['X_tsne'],
index=cell_clusters,
columns=['tSNE_1', 'tSNE_2'])
tsne_coordinates['colors'] = cell_clusters.tolist()
# Cluster color assignments
palette = sns.color_palette(_CLUSTERS_CMAP, num_clusters)
colors = dict(zip(cluster_names, palette))
# Plot each group as a separate trace
fig, ax = plt.subplots(1, 1, figsize=figsize)
for c, group in tsne_coordinates.groupby(by='colors'):
group.plot(
x='tSNE_1',
y='tSNE_2',
kind='scatter',
c=colors[c],
label=c,
alpha=0.7,
ax=ax,
legend=True)
plt.title('tSNE Visualization', size=16)
ax.set_xlabel(ax.get_xlabel(), size=12)
ax.set_ylabel(ax.get_ylabel(), size=12)
plt.tight_layout()
plt.close()
py_fig = tls.mpl_to_plotly(fig)
# Let plotly generate legend for plotly fig
py_fig['layout']['annotations'] = []
py_fig['layout']['showlegend'] = True
py_fig['layout']['legend'] = {'orientation': 'h'}
py_fig.update(data=[dict(name=c) for c in cluster_names])
return fig, py_fig
# -------------------- MARKER ANALYSIS --------------------
def visualize_markers(self):
# Hide FutureWarnings.
warnings.simplefilter('ignore',
FutureWarning) if self.verbose else None
# Commonly used data
cell_clusters = self.data.obs['louvain_groups'].astype(int)
cluster_names = np.unique(cell_clusters).tolist()
cluster_names.sort(key=int)
# Initialize output widgets here so they are in scope
marker_plot_tab_1_output = Output(layout=Layout(
height='600px',
display='flex',
justify_content='flex-start',
align_items='center'))
marker_plot_tab_2_output = Output(layout=Layout(
height='600px',
display='flex',
justify_content='flex-start',
align_items='center'))
violin_plot_output = Output(layout=Layout(
display='flex',
justify_content='center',
align_items='center',
height='425px',
width='100%'))
marker_table_output = Output(layout=Layout(
display='flex',
justify_content='flex-start',
align_items='flex-start',
height='800px',
width='100%',
padding='0',
overflow_y='auto'))
marker_heatmap_output = Output(
style='border: 1px solid green',
layout=Layout(
display='flex',
justify_content='flex-start',
align_items='flex-start',
height='1000px',
width='100%',
margin='0',
overflow_x='auto',
overflow_y='auto'))
# Create main container
main_box = Tab(layout=Layout(
padding='0 12px', flex='1', min_width='500px'))
# t-SNE marker plot container
main_header_box = VBox()
marker_plot_tab = Tab(
[marker_plot_tab_1_output, marker_plot_tab_2_output])
marker_plot_tab.set_title(0, 'Marker(s) Expression')
marker_plot_tab.set_title(1, 'Clusters Reference')
marker_plot_box = VBox(
[main_header_box, marker_plot_tab, violin_plot_output])
# Top markers heatmap container
heatmap_header_box = VBox()
heatmap_box = VBox([heatmap_header_box, marker_heatmap_output])
# Populate tabs
main_box.children = [heatmap_box, marker_plot_box]
# TODO name tabs
main_box.set_title(0, 'Heatmap')
main_box.set_title(1, 'tSNE Plot')
# Table
explore_markers_box = Accordion(layout=Layout(
max_width='425px', orientation='vertical'))
cluster_table_header_box = VBox()
explore_markers_box.children = [
VBox([cluster_table_header_box, marker_table_output])
]
explore_markers_box.set_title(0, 'Explore Markers')
# ------------------------- Output Placeholders -------------------------
with marker_plot_tab_1_output:
display(_create_placeholder('plot'))
with marker_plot_tab_2_output:
display(_create_placeholder('plot'))
with violin_plot_output:
display(_create_placeholder('plot'))
with marker_table_output:
display(_create_placeholder('table'))
with marker_heatmap_output:
display(_create_placeholder('plot'))
# Fill container elements
# ------------------------- Main header -------------------------
gene_input_description = HTML('''<h3>Visualize Markers</h3>
<p style="font-size:14px; line-height:{};">Visualize the expression of gene(s) in each cell projected on the t-SNE map and the distribution across identified clusters.
Provide any number of genes. If more than one gene is provided, the average expression of those genes will be shown.</p>
'''.format(_LINE_HEIGHT))
gene_input = Text()
update_button = Button(
description='Plot Expression', button_style='info')
gene_input_box = HBox([gene_input, update_button])
main_header_box.children = [gene_input_description, gene_input_box]
def check_gene_input(t):
'''Don't allow submission of empty input.'''
if gene_input.value == '':
update_button.disabled = True
else:
update_button.disabled = False
def update_query_plots(b):
# Format gene list. Split by comma, remove whitespace, then split by whitespace.
gene_list = str(gene_input.value).upper()
gene_list = gene_list.split(',')
gene_list = [s.split(' ') for s in gene_list]
gene_list = np.concatenate(gene_list).ravel().tolist()
gene_list = [gene for gene in gene_list if gene.strip()]
if len(gene_list) == 1:
gene_list = [gene_list[0]]
# Retrieve expression
gene_locs = []
for gene in gene_list:
if gene in self.data.raw.var_names:
gene_locs.append(self.data.raw.var_names.get_loc(gene))
else:
# Gene not found
marker_plot_tab_1_output.clear_output()
with marker_plot_tab_1_output:
display(
_warning_message(
'The gene <code>{}</code> was not found. Try again.'.
format(gene)))
return
if type(self.data.raw.X) in [np.array, np.ndarray]:
gene_values = pd.DataFrame(self.data.raw.X[:, gene_locs])
else:
gene_values = pd.DataFrame(
self.data.raw.X[:, gene_locs].toarray())
# Final values for plot
if len(gene_values.shape) > 1:
values = gene_values.mean(axis=1)
else: