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plot_h5ad.py
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executable file
·691 lines (653 loc) · 21.9 KB
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import sys, json, re, time, warnings, math, os, contextlib, textwrap, traceback, distinctipy, resource
from datetime import timedelta
import pandas as pd
import seaborn as sns
import anndata as ad
import scanpy as sc
import matplotlib.pyplot as plt
import base64
from io import BytesIO
import fastcluster as fc
from scipy.cluster import hierarchy
from difflib import SequenceMatcher
import PyComplexHeatmap as pch
warnings.simplefilter("ignore", UserWarning)
verbose = False
def plot(data):
try:
html = distributeTask(data["plot"])(data)
return html
except Exception as e:
return msgPlot(traceback.format_exc(), data)
def main():
if len(sys.argv) == 1:
data = json.load(sys.stdin)
else:
with open(sys.argv[1], "r") as f:
data = json.load(f)
try:
html = distributeTask(data["plot"])(data)
return html
except Exception as e:
msgPlot(traceback.format_exc(), data)
if verbose:
print(
"Final main memory %.2fG<br>"
% (resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024**2)
)
def errorTask(data):
msgPlot("Error plot task (unknown %s)!" % data["plot"], data)
def errorCheck(data):
if data["plot"] in ["violin", "dotplot", "heatmap"]:
if len(data["genes"]) < 1:
msgPlot("Error: No matched gene!", data)
if len(data["groups"]) < 1:
msgPlot("Error: No matched annotation!", data)
if data["plot"] == "embedding":
if len(data["reductions"]) < 1:
msgPlot("Error: No matched embedding keys or genes or annotations)!", data)
if len(data["genes"]) < 1 and len(data["groups"]) < 1:
msgPlot("Error: No matched genes and annotations)!", data)
if data["plot"] == "stackbar":
if len(data["groups"]) < 2:
msgPlot("Error: At least two matched annotations are required!", data)
def msgPlot(msg, data):
fig = plt.figure(figsize=(4, 3))
if msg.startswith("Traceback"):
a = plt.text(
0.01,
0.5,
msg,
fontsize=4,
horizontalalignment="left",
verticalalignment="center",
)
else:
a = plt.text(
0.5,
0.5,
textwrap.fill(msg, 35),
fontsize=14,
horizontalalignment="center",
verticalalignment="center",
)
a = plt.axis("off")
return toHTML(fig, data)
def distributeTask(aTask):
return {
"violin": complexViolin,
"dotplot": twofactorDotplot,
"embedding": reductionPlot,
"stackbar": stackBar,
"heatmap": complexHeatmap,
}.get(aTask, errorTask)
def is_numeric(var):
try:
float(var) # Convert to float (handles integers and floats)
return True
except ValueError:
return False
def isOptionDefined(data, k):
return data["options"].get(k) is not None and (
is_numeric(data["options"][k]) or len(data["options"][k]) > 0
)
def get_n_distinct_colors(n, lightness=0.5, saturation=0.9, cName=None):
cmap = plt.get_cmap("Set3" if cName is None else cName)
if n < len(cmap.colors):
return [cmap(i) for i in range(n)]
else:
return distinctipy.get_colors(n, [cmap(i)[:3] for i in range(len(cmap.colors))])
# return [colorsys.hls_to_rgb(i/n, lightness, saturation) for i in range(n)]
def toHTML(fig, data):
st = time.time()
imgD = iostreamFig(fig, data["options"]["img_format"])
imgID = ""
if len(data["options"]["img_id"]) > 0:
imgID = 'id="%s" ' % data["options"]["img_id"]
imgFormat = re.sub("svg", "svg+xml", data["options"]["img_format"])
if data["options"]["img_html"]:
if verbose:
print("toHtml: %s<br>" % str(timedelta(seconds=time.time() - st)))
return(
'<html><body><img %s src="data:image/%s;base64,%s" width="100%%" height="auto"/></body></html>'
% (imgID, imgFormat, imgD)
)
else:
return("data:image/%s;base64,%s" % (imgFormat, imgD))
def iostreamFig(fig, img_format):
figD = BytesIO()
fig.savefig(figD, bbox_inches="tight", format=img_format, dpi=250)
imgD = base64.encodebytes(figD.getvalue()).decode("utf-8")
figD.close()
if "matplotlib" in str(type(fig)):
plt.close(fig) #'all'
return imgD
def getData(data, dataframe=True):
st = time.time()
errorCheck(data)
D = ad.read_h5ad(data["h5ad"], backed="r")
if verbose:
print("Read: %s<br>" % str(timedelta(seconds=time.time() - st)))
st = time.time()
data["options"]["img_format"] = (
data["options"]["img_format"]
if data["options"].get("img_format") in ["png", "svg"]
else "png"
)
data["options"]["img_width"] = (
6 if not isOptionDefined(data, "img_width") else data["options"]["img_width"]
)
data["options"]["img_height"] = (
4 if not isOptionDefined(data, "img_height") else data["options"]["img_height"]
)
data["options"]["cutoff"] = (
0 if not isOptionDefined(data, "cutoff") else data["options"]["cutoff"]
)
data["options"]["titlefontsize"] = (
6
if not isOptionDefined(data, "titlefontsize")
else data["options"]["titlefontsize"]
)
# checking existing genes/annotations/reduction keys
if len(data["var_col"]) > 0 and data["var_col"] in D.var.columns:
genes = {
(D.var_names[D.var[data["var_col"]] == k][0]): k for k in data["genes"]
}
else:
g = list(
D.var_names[
D.var_names.str.lower().isin([s.lower() for s in data["genes"]])
]
)
genes = dict(zip(g, g))
data["genes"] = list(genes.values())
data["groups"] = {
k: data["groups"][k] for k in data["groups"] if k in D.obs.columns
}
# only needs when plotting embedding
reduc = []
if data["plot"] == "embedding":
reducName = []
for one in data["reductions"]:
s = 0.5
selK = None
for k in D.obsm.keys():
if SequenceMatcher(None, one.lower(), k.lower()).ratio() > s:
s = SequenceMatcher(None, one.lower(), k.lower()).ratio()
selK = k
if selK is not None and not selK in reducName:
reducName += [selK]
reduc += [(selK, 0), (selK, 1)]
data["reductions"] = reducName
errorCheck(data)
if verbose:
print("Init: %s<br>" % str(timedelta(seconds=time.time() - st)))
st = time.time()
# filter cells by annotation selections
selC = [True] * D.shape[0]
for one in data["groups"]:
if len(data["groups"][one]) > 0:
delGrp = [
re.sub("^-", "", _) for _ in data["groups"][one] if _.startswith("-")
]
if len(delGrp) > 0:
selC = selC & ~D.obs[one].isin(delGrp)
else:
selC = selC & D.obs[one].isin(data["groups"][one])
if verbose:
print("Filter: %s<br>" % str(timedelta(seconds=time.time() - st)))
st = time.time()
if dataframe:
df = sc.get.obs_df(D, list(genes.keys()) + list(data["groups"].keys()))[
selC
].rename(columns=genes)
for k in data["groups"]:
df[k] = df[k].astype(str).astype("category")
if len(reduc) > 0:
df = df.merge(
sc.get.obs_df(D, obsm_keys=reduc),
how="left",
left_index=True,
right_index=True,
)
if verbose:
print("Get data: %s<br>" % str(timedelta(seconds=time.time() - st)))
return df
if verbose:
print("Get data: %s<br>" % str(timedelta(seconds=time.time() - st)))
return D, selC
def complexViolin(data):
df = getData(data)
st = time.time()
w = data["options"]["img_width"]
h = data["options"]["img_height"]
genes = data["genes"]
grps = list(data["groups"].keys())
gN = len(genes)
fig, axes = plt.subplots(gN, 1, figsize=(w, h * gN), sharey="row")
if gN == 1:
axes = [axes]
for i in range(gN):
subDF = df
strTitle = "Total of %d cells" % df.shape[0]
if data["options"]["cutoff"] > 0:
subDF = df[(df[genes[i]] > data["options"]["cutoff"]).values]
strTitle = (
"%d out of selected %d cells passed the expression filter %.2f"
% (subDF.shape[0], df.shape[0], data["options"]["cutoff"])
)
if subDF.shape[0] < 5:
msgPlot(
"Less than 5 cells are satisfied with cutoff %.3f"
% data["options"]["cutoff"],
data,
)
sns.violinplot(
x=grps[0],
y=genes[i],
ax=axes[i],
data=subDF,
cut=0,
palette=(
"bright"
if not isOptionDefined(data, "palette")
else data["options"]["palette"]
),
# fill=False,inner_kws={"alpha":0.5}, seaborn v0.13.0
hue=None if len(grps) < 2 else grps[1],
)
if isOptionDefined(data, "dotsize"):
dotColor = (
"#000"
if not isOptionDefined(data, "dotcolor")
else data["options"]["dotcolor"]
)
sns.stripplot(
x=grps[0],
y=genes[i],
ax=axes[i],
legend=False,
data=subDF,
size=data["options"]["dotsize"],
palette=(
[dotColor] if len(grps) < 2 else [dotColor] * df[grps[1]].nunique()
),
dodge=False if len(grps) < 2 else True,
hue=None if len(grps) < 2 else grps[1],
)
axes[i].set_title(
strTitle,
loc="left",
fontdict={"fontsize": data["options"]["titlefontsize"]},
)
if i < (len(genes) - 1):
axes[i].get_xaxis().set_visible(False)
else:
plt.setp(
axes[i].get_xticklabels(),
rotation=45,
ha="right",
rotation_mode="anchor",
)
if len(grps) > 1:
if i == 0:
axes[i].legend(
loc="lower right",
bbox_to_anchor=(1, 1),
ncol=1 if len(grps) < 2 else df[grps[1]].nunique(),
)
else:
axes[i].get_legend().remove()
if verbose:
print("complexViolin: %s<br>" % str(timedelta(seconds=time.time() - st)))
return toHTML(fig, data)
def twofactorDotplot(data):
# D,selC = getData(data,False)
# cellN = pd.DataFrame(selC).sum()[0]
df = getData(data)
st = time.time()
cellN = df.shape[0]
if verbose:
print(
"Dotplot Backed peak memory %.2fG %s<br>"
% (
resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024**2,
str(timedelta(seconds=time.time() - st)),
)
)
st = time.time()
w = data["options"]["img_width"]
h = data["options"]["img_height"]
grps = list(data["groups"].keys())
genes = data["genes"]
D = ad.AnnData(X=df[genes], obs=df[grps])
strGrp = grps[0]
if len(grps) > 1:
strGrp = "_".join(grps[:2])
D.obs[strGrp] = (
D.obs[grps[0]].astype(str) + "_" + D.obs[grps[1]].astype(str)
).astype(
"category"
) # D.obs.apply(lambda x: "_".join(x[grps[:2]]),axis=1)
if verbose:
print(
"Dotplot merge groups peak memory %.2fG %s<br>"
% (
resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024**2,
str(timedelta(seconds=time.time() - st)),
)
)
st = time.time()
strTitle = "%d selected cells" % cellN
if data["options"]["cutoff"] > 0:
strTitle = "%d selected cells with expression cutoff %.2f" % (
cellN,
data["options"]["cutoff"],
)
dp = sc.pl.dotplot(
D,
genes,
groupby=strGrp,
figsize=(w, h),
expression_cutoff=data["options"]["cutoff"],
mean_only_expressed=True,
return_fig=True,
)
if verbose:
print(
"Dotplot Plot1 peak memory %.2fG %s<br>"
% (
resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024**2,
str(timedelta(seconds=time.time() - st)),
)
)
st = time.time()
dp = (
dp.add_totals(size=1.2)
.legend(show_size_legend=True) # ,width=float(data['legendW'])
.style(
cmap=(
"Reds"
if not isOptionDefined(data, "color_map")
else data["options"]["color_map"]
),
dot_edge_color="black",
dot_edge_lw=0.5,
size_exponent=1.5,
)
)
if verbose:
print(
"Dotplot Plot2 peak memory %.2fG %s<br>"
% (
resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024**2,
str(timedelta(seconds=time.time() - st)),
)
)
st = time.time()
fig = dp.show(True)["mainplot_ax"].figure
if len(grps) > 1:
# n = df[selC][grps[1]].nunique()
n = df[grps[1]].nunique()
for i in range(df[grps[0]].nunique()): # [selC]
if i == 0:
fig.axes[0].set_title(
strTitle,
loc="left",
fontdict={"fontsize": data["options"]["titlefontsize"]},
)
else:
fig.axes[0].axhline(y=i * n, color="#0002", linestyle="--")
if verbose:
print(
"Dotplot Add line peak memory %.2fG %s <br>"
% (
resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024**2,
str(timedelta(seconds=time.time() - st)),
)
)
return toHTML(fig, data)
def reductionPlot(data):
df = getData(data)
st = time.time()
w = data["options"]["img_width"]
h = data["options"]["img_height"]
grps = list(data["groups"].keys())
genes = data["genes"]
obsm = {}
for one in data["reductions"]:
obsm[one] = df[["%s-0" % one, "%s-1" % one]].to_numpy()
dotsize = 120000 / df.shape[0]
subSize = 4
groupN = len(grps)
geneN = len(genes)
ncol = 4 if groupN < 2 else df[grps[1]].nunique()
nrow = (groupN + geneN) if groupN > 1 else (groupN + math.ceil(geneN / ncol))
fig = plt.figure(figsize=(ncol * subSize, subSize * nrow))
gs = fig.add_gridspec(nrow, ncol, wspace=0.2)
oneReduc = re.sub("^X_", "", data["reductions"][0])
D = ad.AnnData(
X=None if geneN == 0 else df[genes],
obs=None if groupN == 0 else df[grps],
obsm=obsm,
)
for i in range(groupN):
ix = groupN - i - 1
ax = sc.pl.embedding(
D,
oneReduc,
color=grps[ix],
ax=fig.add_subplot(gs[i, 0]),
show=False,
palette=(
None
if not isOptionDefined(data, "palette")
else data["options"]["palette"]
),
)
ax.legend(
ncol=math.ceil(df[grps[ix]].nunique() / 10),
loc=6,
bbox_to_anchor=(1, 0.5),
frameon=False,
fontsize=8 - df[grps[ix]].nunique() / 20,
)
ax.set_xlabel("%s 1" % oneReduc)
ax.set_ylabel("%s 2" % oneReduc)
if groupN < 2:
for i in range(geneN):
x = int(i / ncol) + groupN
y = i % ncol
ax = sc.pl.embedding(
D,
oneReduc,
color=genes[i],
ax=fig.add_subplot(gs[x, y]),
show=False,
size=dotsize,
)
ax.set_xlabel("%s 1" % oneReduc)
ax.set_ylabel("%s 2" % oneReduc)
else:
splitNames = list(df[grps[1]].unique())
for i in range(geneN):
for j in range(len(splitNames)):
x = groupN + i
y = j
ax = sc.pl.embedding(
D, oneReduc, ax=fig.add_subplot(gs[x, y]), show=False, size=dotsize
)
ax = sc.pl.embedding(
D[D.obs[grps[1]] == splitNames[j]],
oneReduc,
color=genes[i],
color_map=(
"viridis"
if not isOptionDefined(data, "color_map")
else data["options"]["color_map"]
),
vmin=df[genes[i]].min(),
vmax=df[genes[i]].max(),
ax=ax,
show=False,
size=dotsize,
title="{} in {}".format(genes[i], splitNames[j]),
)
ax.set_xlabel("%s 1" % oneReduc)
ax.set_ylabel("%s 2" % oneReduc)
fig.suptitle(
"%d selected cells" % df.shape[0],
x=0.9,
y=0.9,
ha="right",
va="top",
fontsize=data["options"]["titlefontsize"],
)
if verbose:
print("reductionPlot: %s<br>" % str(timedelta(seconds=time.time() - st)))
return toHTML(fig, data)
def stackBar(data):
df = getData(data)
st = time.time()
strTitle = "%d selected cells" % df.shape[0]
w = data["options"]["img_width"]
h = data["options"]["img_height"]
grps = list(data["groups"].keys())
x = (
list(df[grps[1]].unique())
if len(data["groups"][grps[1]]) == 0
else data["groups"][grps[1]]
)
df = (
df[grps[:2]]
.value_counts()
.to_frame("count")
.reset_index()
.pivot_table(index=grps[0], columns=grps[1], values="count")
)
fig = plt.figure(figsize=(w, h))
if (
data["options"].get("yscale") is not None
and data["options"]["yscale"] == "proportion"
):
df = df.apply(lambda x: x / x.sum())
plt.ylabel("Proportion")
else:
plt.ylabel("Count")
plt.xlabel(grps[1])
color = get_n_distinct_colors(
df.shape[0],
cName=data["options"]["palette"] if isOptionDefined(data, "palette") else None,
)
for i in range(df.shape[0]):
plt.bar(x, df.iloc[i, :][x], color=color[i], bottom=df.iloc[:i, :][x].sum())
plt.legend(
df.index,
loc=4,
bbox_to_anchor=(1, 1),
ncol=math.ceil(df.shape[0] / 10),
fontsize=max(2, 6 - df.shape[0] / 20),
)
fig.axes[0].set_title(
strTitle, loc="left", fontdict={"fontsize": data["options"]["titlefontsize"]}
)
if verbose:
print("stackBar: %s<br>" % str(timedelta(seconds=time.time() - st)))
return toHTML(plt, data)
def complexHeatmap(data):
df = getData(data)
st = time.time()
w = data["options"]["img_width"]
h = data["options"]["img_height"]
grps = list(data["groups"].keys())
genes = data["genes"]
selN = df.shape[0]
df = df[df[genes].apply(lambda x: max(x) > data["options"]["cutoff"], axis=1)]
if df.shape[0] < 5:
msgPlot(
"Less than 5 cells are satisfied with cutoff %.3f"
% data["options"]["cutoff"],
data,
)
heat_scale = None
heat_title = "Expression"
if (
data["options"].get("heat_scale") is not None
and data["options"]["heat_scale"] == "z-score"
):
heat_scale = 0
heat_title = "Row Z-score"
if (
data["options"].get("cell_order") is None
or data["options"]["cell_order"] == "groups"
):
df = df.sort_values(list(data["groups"].keys()))
elif data["options"]["cell_order"] == "expression":
ix = hierarchy.leaves_list(fc.linkage_vector(df[genes], method="ward"))
df = df.iloc[ix,]
colors = {
_: dict(
zip(
df[_].unique(),
get_n_distinct_colors(
df[_].nunique(),
cName=(
data["options"]["palette"]
if isOptionDefined(data, "palette")
else None
),
),
)
)
for _ in grps
}
fig = plt.figure(figsize=(w, h))
df.to_csv("test.csv")
with open(os.devnull, "w") as f, contextlib.redirect_stdout(f):
left_anno = pch.HeatmapAnnotation(df[grps], colors=colors, axis=0)
left_anno.plot_annotations()
plt.close()
plt.rc(
"legend",
fontsize=(
8
if not isOptionDefined(data, "heat_legend_fontsize")
else data["options"]["heat_legend_fontsize"]
),
)
cm = pch.ClusterMapPlotter(
data=df[genes],
z_score=heat_scale,
label=heat_title,
cmap=(
"jet"
if not isOptionDefined(data, "color_map")
else data["options"]["color_map"]
),
left_annotation=left_anno,
show_rownames=False,
show_colnames=True,
row_dendrogram=False,
col_dendrogram=False,
col_cluster=False,
row_cluster=False,
# row_cluster_method="complete",col_cluster_method="complete",
rasterized=True,
legend=True,
legend_anchor="ax_heatmap",
verbose=0,
)
# print(len(fig.axes))
fig.axes[1].set_title(
"%d of %d selected cells passed expression threshold %.2f"
% (df.shape[0], selN, data["options"]["cutoff"]),
loc="left",
fontdict={"fontsize": data["options"]["titlefontsize"]},
)
if verbose:
print("complexHeatmap: %s<br>" % str(timedelta(seconds=time.time() - st)))
return toHTML(plt, data)
# html = main()
# print(html)
# cat ../testVIP/violin.json | python -u plotH5ad.py
# python -u ./plotH5ad.py ../testVIP/violin.json