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panel.py
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panel.py
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#@title Load packages
import subprocess
import gc
import pandas as pd
import multiprocessing as mp
import datashader as ds
import panel as pn
import param
from holoviews.operation import decimate
from holoviews.operation.datashader import datashade, rasterize, dynspread
from holoviews import dim, opts
from holoviews.selection import link_selections
from holoviews.streams import Selection1D
import holoviews as hv
import matplotlib.pyplot as plt
import bokeh
import colorcet as cc
from colorcet.plotting import swatch
from matplotlib import cm, colors
from bokeh.settings import settings
from bokeh.models import HoverTool
from urllib import request
import numpy as np
np.random.seed(42)
from io import StringIO
from itertools import chain, combinations
from collections import defaultdict
hv.extension('bokeh', logo=False)
pn.extension(design="bootstrap")
from collections import Counter
from Bio.Blast.NCBIWWW import qblast
from Bio.Blast import NCBIXML
#@title Functions to read in dataset, set up plotting
def drop_samples(ix, max_reads):
"""Return indices of random samples from a batch of reads"""
if len(ix) > max_reads:
ix = np.random.choice(ix, size=max_reads, replace=False)
return ix
def downsample(a, max_reads=50, nbins=250):
"""Drop samples from dataset in regions of the 2D histogram that exceed a threshold (max_reads)"""
#nbins = 500
h = np.histogram2d(a[:,0], a[:,1], bins=nbins)
bins_y = np.searchsorted(h[2], a[:,1])
bins_x = np.searchsorted(h[1], a[:,0])
d = defaultdict(list)
for i in range(len(a)):
d[(bins_x[i], bins_y[i])].append(i)
return np.array(list(chain.from_iterable(drop_samples(v,max_reads) for v in d.values())))
def remove_ids(df, rm_list):
return df[~df['id'].isin(rm_list)]
def get_category_labels(read_ids, label_files):
"""Assign labels to reads based on files with lists. Reads that do not appear in a list are labelled 0.
All others are assigned integer labels corresponding to the order in which the files are listed - unless
they are present in more than one set, in which case they are assigned to an additional category."""
mask = np.array(len(read_ids)*[0], dtype="int32")
if len(label_files) == 0:
print("Nothing to label.")
return mask
# iterate over lists of classified reads and assign integer labels
all_sets = []
for i, cat in enumerate(label_files):
seq_set = {j.strip("\n") for j in open(cat)}
all_sets.append(seq_set)
np.put(mask, np.where([seqid in seq_set for seqid in read_ids]), [i+1])
#check if lists overlap
print(f"{i+1} class(es).")
nt = lambda a, b: all_sets[a].intersection(all_sets[b])
if in_multiple := set().union(
*[nt(*j) for j in combinations(range(i + 1), 2)]
):
np.put(mask, np.where([seqid in in_multiple for seqid in read_ids]), [i+2])
print(f"Adding extra bin containing intersect of sets: {i + 2}")
return mask
def load_df(data, samples_bin=50, max_reads=50000000):
"""Determine whether to downsample the dataset and fetch indices, then load into dataframe"""
if len(data['vae']) > max_reads:
print("Downsampling data.")
idxs = downsample(data["vae"], samples_bin)
print("Downsampled.")
else:
idxs = np.array(range(len(data['vae'])))
df = pd.DataFrame(data=data['vae'][idxs,:], columns=['x', 'y'])
df['id'] = pd.Series(data["reads"][idxs])
df['hex'] = pd.Series(data['annot'][idxs])
df['fastk'] = pd.Series(data['slice'][idxs])
df['bin'] = 99
df['classes'] = pd.Categorical(data['classes'][idxs], ordered=True)
#print(df)
return df
clrs = ['#E8ECFB', '#D9CCE3', '#D1BBD7', '#CAACCB', '#BA8DB4',
'#AE76A3', '#AA6F9E', '#994F88', '#882E72', '#1965B0',
'#437DBF', '#5289C7', '#6195CF', '#7BAFDE', '#4EB265',
'#90C987', '#CAE0AB', '#F7F056', '#F7CB45', '#F6C141',
'#F4A736', '#F1932D', '#EE8026', '#E8601C', '#E65518',
'#DC050C', '#A5170E', '#72190E', '#42150A']
indexes = [[9], [9, 25], [9, 17, 25], [9, 14, 17, 25], [9, 13, 14, 17,
25], [9, 13, 14, 16, 17, 25], [8, 9, 13, 14, 16, 17, 25], [8,
9, 13, 14, 16, 17, 22, 25], [8, 9, 13, 14, 16, 17, 22, 25, 27],
[8, 9, 13, 14, 16, 17, 20, 23, 25, 27], [8, 9, 11, 13, 14, 16,
17, 20, 23, 25, 27], [2, 5, 8, 9, 11, 13, 14, 16, 17, 20, 23,
25], [2, 5, 8, 9, 11, 13, 14, 15, 16, 17, 20, 23, 25], [2, 5,
8, 9, 11, 13, 14, 15, 16, 17, 19, 21, 23, 25], [2, 5, 8, 9, 11,
13, 14, 15, 16, 17, 19, 21, 23, 25, 27], [2, 4, 6, 8, 9, 11,
13, 14, 15, 16, 17, 19, 21, 23, 25, 27], [2, 4, 6, 7, 8, 9, 11,
13, 14, 15, 16, 17, 19, 21, 23, 25, 27], [2, 4, 6, 7, 8, 9, 11,
13, 14, 15, 16, 17, 19, 21, 23, 25, 26, 27], [1, 3, 4, 6, 7, 8,
9, 11, 13, 14, 15, 16, 17, 19, 21, 23, 25, 26, 27], [1, 3, 4,
6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 21, 23, 25, 26,
27], [1, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 20,
22, 24, 25, 26, 27], [1, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15,
16, 17, 18, 20, 22, 24, 25, 26, 27, 28], [0, 1, 3, 4, 6, 7, 8,
9, 10, 12, 13, 14, 15, 16, 17, 18, 20, 22, 24, 25, 26, 27, 28]]
# Interactive plotting
class Scatter(param.Parameterized):
"""Build scatterplot for reads. Set up widgets to control display parameters, and
the number of discrete bins for the coding density annotation (num_bins) and the
coverage range displayed (upper, lower)."""
min_alpha = param.Integer(50, bounds=(10, 255), doc="Set the minimum alpha value for points.", label="Minimum alpha")
num_bins = param.Integer(5, bounds=(1, 10), doc="Select number of quantile bins for coding density (hex).", label="Number of bins")
upper = param.Integer(32767, doc="Maximum k-mer coverage to display.", label="Max k-mer coverage")
lower = param.Integer(0, doc="Minimum k-mer coverage to display.", label="Min k-mer coverage")
bg = param.Selector(["white", "grey", "black"], doc="Select the background colour for the plot.", label="Background colour")
show_class = param.ListSelector(default=[], objects=[], label='Select classes')
color_cat = param.Selector(["glasbey_hv", "colorblind_bokeh", "tol_rainbow"], label='Categorical colour scheme')
action = param.Action(lambda x: x.param.trigger('action'), label='Update histogram for current selection')
reverse_colours = param.Boolean(doc="Reverse colour map for binned annotations", label="Reverse colours")
#pn.config.throttled = True
def __init__(self, df_complete, sample_id, **kwargs):
super(Scatter, self).__init__(**kwargs)
####
#FIXME
def column_width(plot, element):
"""Manually override column widths"""
plot.handles['table'].columns[2].width = 205
for i in [0, 1, 3, 4, 5, 6]:
plot.handles['table'].columns[i].width = 50
#plot.handles['table'].autosize_mode = "none"
# Initialise dataframe
self.df_complete = df_complete
self.sample_id = sample_id
self.df = self.make_bins(self.num_bins)
# Get points, selection box, and summary t6able
self.points = hv.Points(data=self.df, kdims=['x','y'],vdims=['id'])
self.box = hv.streams.BoundsXY(source=self.points, bounds=(-0.5, -0.5, 0.5, 0.5))
#self.bounds, self.dmap = self.selections()
self.bounds = hv.DynamicMap(lambda bounds: hv.Bounds(bounds), streams=[self.box])
self.dmap = hv.DynamicMap(lambda bounds: hv.Table(self.df[(self.df['x'] > bounds[0]) & (self.df['x'] < bounds[2]) & \
(self.df['y'] > bounds[1]) & (self.df['y'] < bounds[3])].head(n=5000).round(2)).opts(editable=True, width=600), \
streams=[self.box])
# Set up colours for classes
self.n_classes = df_complete['classes'].nunique()
class_list = list(range(self.n_classes))
self.param.show_class.objects = class_list
self.show_class = class_list
self.cat_maps = {'glasbey_hv': cc.b_glasbey_hv,
'colorblind_bokeh': list(hv.Cycle.default_cycles["Colorblind"]),
}
# Drop maps that are too short
if self.n_classes > 23:
self.param.color_cat.objects = ["glasbey_hv"]
else:
self.cat_maps["tol_rainbow"] = [clrs[i] for i in indexes[self.n_classes]]
if self.n_classes > 9:
self.param.color_cat.objects = ["glasbey_hv", "tol_rainbow"]
@pn.depends('action')
def hist_coverage(self):
if () in self.dmap.data:
max_val = self.dmap.data[()]["fastk"].max()
min_val = self.dmap.data[()]["fastk"].min()
h_counts, h_bins = np.histogram(self.dmap.data[()]["fastk"], bins=min(50, max_val-min_val), range=(max(1, min_val),min(10000, max_val)))
else:
h_counts, h_bins = np.histogram(self.df["fastk"], bins=50, range=(1,10000))
return hv.Histogram((np.log1p(h_counts), h_bins)).opts(width=600, height=150, shared_axes=False, ylabel="log(Frequency)", xlabel="fastk")
@pn.depends('action')
def hist_hexamer(self):
if () in self.dmap.data:
#max_val = self.dmap.data[()]["hex"].max()
#min_val = self.dmap.data[()]["hex"].min()
h_counts, h_bins = np.histogram(self.dmap.data[()]["hex"], bins=50)
#h_counts_base, h_bins_base = np.histogram(self.df["hex"], bins=50)
return hv.Histogram((np.log1p(h_counts), h_bins)).opts(width=600, height=150, shared_axes=False, ylabel="log(Frequency)", xlabel="hexamer")
# hv.Histogram((np.log1p(h_counts_base), h_bins_base)).opts(width=400, shared_axes=False, ylabel="log(Frequency)", xlabel="hexamer") +
else:
h_counts, h_bins = np.histogram(self.df["hex"], bins=50)
return hv.Histogram((np.log1p(h_counts), h_bins)).opts(width=600, height=150, shared_axes=False, ylabel="log(Frequency)", xlabel="hexamer")
#@pn.depends('num_bins', watch=True)
def make_bins(self, num_bins):
"""Bin coding density into quantiles, where num_bins is the number of bins. Then, call function to filter rows by coverage"""
# If no data provided, fill with 0
if self.df_complete['hex'].min() == self.df_complete['hex'].max():
self.df_complete['bin'] = 0
else:
bins = pd.Categorical(pd.qcut(self.df_complete['hex'], num_bins, labels=False, duplicates='drop'))
self.df_complete['bin'] = bins
self.df = self.filter_df()
return self.df
def filter_df(self):
"""Get rows within specified coverage range, lower <= coverage <= upper. Filter selected classes"""
if self.lower <= self.upper:
self.df = self.df_complete.loc[(self.df_complete['fastk'] <= self.upper) & (self.df_complete['fastk'] >= self.lower)]
else:
self.df = self.df_complete
if (self.df_complete['classes'].nunique() > 1) & (len(self.show_class) > 0):
self.df = self.df[self.df['classes'].isin(self.show_class)]
return self.df
def colormap(self):
"""Set up colourmap using viridis, generate legend labels"""
v = cm.get_cmap('viridis')
colors_hex = [colors.rgb2hex(i)
for i in v(np.linspace(0, 1, self.num_bins))]
if self.reverse_colours is True:
colors_hex = colors_hex[::-1]
legend_labels = dict(
zip(
list(range(self.num_bins)),
[f"bin {i}" for i in range(self.num_bins)],
)
)
return colors_hex, legend_labels
def colormap_classes(self):
"""Set up categorical colourmap based on selection, generate legend labels"""
colors_hex = self.cat_maps[self.color_cat][:self.n_classes]
legend_labels = dict(
zip(
list(range(self.n_classes)),
[f"class {i}" for i in range(self.n_classes)],
)
)
return colors_hex, legend_labels
@pn.depends('num_bins', 'upper', 'lower', 'show_class')
def update_points(self):
self.df = self.make_bins(self.num_bins)
self.points = hv.Points(data=self.df, kdims=['x','y'],vdims=['id', 'bin', 'classes'])
return 0
@pn.depends('min_alpha', 'num_bins', 'upper', 'lower', 'bg', 'show_class', 'reverse_colours')
def draw_scatter_table(self):
self.update_points()
colors_hex, legend_labels = self.colormap()
return self.draw_shaded(colors_hex, legend_labels, "bin")
@pn.depends('min_alpha', 'num_bins', 'upper', 'lower', 'bg', 'show_class', 'color_cat')
def draw_scatter_table_classes(self):
self.update_points()
colors_hex, legend_labels = self.colormap_classes()
return self.draw_shaded(colors_hex, legend_labels, "classes")
def draw_shaded(self, colors_hex, legend_labels, col):
shaded = datashade(
self.points,
aggregator=ds.count_cat(col),
color_key=colors_hex,
min_alpha=self.min_alpha,
).opts(
bgcolor=self.bg,
width=700,
height=700,
show_grid=True,
tools=["box_select"],
default_tools=[],
legend_labels=legend_labels,
legend_position='top_right',
legend_offset=(0, 0),
title=f'Reads for {self.sample_id}',
)
return hv.Overlay([dynspread(shaded, threshold=0.7)]).collate() * self.bounds.clone()
def get_seq_file(identifier, fasta, seq_preview):
seq_cmd = "{0} -A1 -m1 \"{1}\" {2}".format("zgrep", identifier, fasta)
seq = subprocess.run(seq_cmd, capture_output=True, shell=True)
if seq.returncode != 0:
return 1
seqrecord = seq.stdout.decode("utf-8")
seq_preview.object = "{}".format(seqrecord)
return seqrecord
def box_selected_data_dl(box, df):
"""Select rows in table corresponding to selected points"""
return df[(df['x'] > box.bounds[0]) & (df['x'] < box.bounds[2]) & \
(df['y'] > box.bounds[1]) & (df['y'] < box.bounds[3])]
def blast_function(seq):
""" If you would like to use a custom command to run blast, replace this function.
For example. on local blast server:
blast_cmd = "timeout 300s curl -T temp.fa http://172.27.25.136:35227 | head -n5"
blast = subprocess.run(blast_cmd, capture_output=True, shell=True)
if blast.returncode == 0:
blast_pane.object = '{0}'.format(blast.stdout.decode('utf-8'))
else:
#text_blast.value
blast_pane.object = 'Non-zero return code {} {}'.format(blast, blast_cmd)
"""
#seq = "{}".format(open("temp.fa", "r").read())
result_handle = qblast("blastn", "nt", seq, megablast=True)
blast_record = NCBIXML.read(result_handle)
if len(blast_record.alignments) > 0:
return "\n".join([t.title for t in blast_record.alignments[:5]])#blast_record.alignments[0].title
else:
return "No result"
def make_panel(scatter, fasta):
def button_readid_click(event):
text_readid.value = '{0}'.format(scatter.dmap.data[()]['id'][0])
def button_click_seq(event):
if text_readid.value == "...":
text_readid.value = '{0}'.format(scatter.dmap.data[()]['id'][0])
if get_seq_file(text_readid.value, fasta, seq_preview) == 0:
seq = "{}".format(open("temp.fa", "r").read())
def button_click_blast(event):
if text_readid.value == "...":
text_readid.value = '{0}'.format(scatter.dmap.data[()]['id'][0])
blast_pane.object = "...running..."
idle.value = True
seq = get_seq_file(text_readid.value, fasta, seq_preview)
if seq != 1:
#seq = get_seq_file(text_readid.value, fasta, seq_preview) #"{}".format(open("temp.fa", "r").read())
blast_pane.object = blast_function(seq)
else:
blast_pane.object == 'Failed to get fasta'
idle.value = False
def find_read(event):
if text_readid.value == "...":
text_readid.value = '{0}'.format(scatter.dmap.data[()]['id'][0])
coord.value = ""
locate = scatter.df.loc[scatter.df['id'] == text_readid.value]
coord.value = "X: {:.4g}, Y: {:.4g}".format(
locate['x'].values[0], locate['y'].values[0])
def download_csv():
reads = box_selected_data_dl(scatter.box, scatter.df)
sio = StringIO()
reads.to_csv(sio)
sio.seek(0)
sio.flush()
return sio
# Define buttons, panes, and actions
button_readid = pn.widgets.Button(
name='Get first sequence in selection', button_type='primary')
text_readid = pn.widgets.TextInput(value='...')
button_readid.on_click(button_readid_click)
idle = pn.indicators.LoadingSpinner(value=False, width=50, height=50)
button_blast = pn.widgets.Button(
name='blastn selected sequence', button_type='primary')
button_seq = pn.widgets.Button(name="Get sequence", button_type="primary")
blast_pane = pn.pane.HTML("""Do megablast""", styles={'background-color': '#fcfcfc', 'border': '1px solid black',
'padding': '5px', 'overflow': 'scroll', 'width': '310px', 'height': '100px'})
button_blast.on_click(button_click_blast)
button_seq.on_click(button_click_seq)
coord = pn.widgets.StaticText(value="")
button_find = pn.widgets.Button(
name='Find read coordinates', button_type='primary')
button_find.on_click(find_read)
seq_preview = pn.pane.HTML(""" """, styles={'background-color': '#fcfcfc', 'border': '1px solid black',
'padding': '5px', 'overflow': 'scroll', 'width': '700px', 'height': '50px'})
file_download = pn.widgets.FileDownload(callback=download_csv, filename='reads.txt',
label='Download selected reads', button_type='success', auto=True, embed=False)
def lay_out_elements():
# Show class tab if multiple classes present
if scatter.n_classes > 1:
tabs = pn.Tabs(('Hexamer', scatter.draw_scatter_table),
('Classes', scatter.draw_scatter_table_classes), dynamic=True)
else:
tabs = pn.Tabs(('Hexamer', scatter.draw_scatter_table))
#tabs = scatter.draw_scatter_table
# Widgets for blast, displaying sequence
widgets_read_selection = pn.WidgetBox(pn.Row(text_readid), pn.Row(button_readid), pn.Row(pn.widgets.StaticText(
name='Note', value='Click to update after drawing new selection')), pn.Row(button_seq), pn.Row(button_find), pn.Row(coord))
widgets_blast = pn.WidgetBox(
pn.Row(blast_pane), pn.Row(button_blast), idle,)
# Configure class selection widget
if len(scatter.param.show_class.objects) <= 11:
multi_select = pn.widgets.CheckButtonGroup.from_param(
scatter.param.show_class)
else:
multi_select = pn.widgets.MultiChoice.from_param(
scatter.param.show_class)
# Lay out widgets for parameter settings
param_layout = pn.WidgetBox(scatter.param.min_alpha, scatter.param.num_bins, scatter.param.reverse_colours, scatter.param.upper, scatter.param.lower,
'Background colour', pn.widgets.RadioButtonGroup.from_param(
scatter.param.bg, name="Background colour"),
)
# Add elements to right column, depending on available annotations
right_col = pn.Column(scatter.dmap)
# Add histograms
if plain_scatter != "True":
for el in [scatter.hist_coverage, scatter.hist_hexamer, scatter.param.action]:
right_col.append(el)
else:
scatter.param.num_bins.constant = True
scatter.param.upper.constant = True
scatter.param.lower.constant = True
# Add widgets to filter by class
if scatter.n_classes > 1:
param_layout_classes = pn.WidgetBox(
"Filter classified sequences", scatter.param.color_cat, multi_select, width=600)
right_col.append(param_layout_classes)
filtered_view = pn.Row(
pn.Column(param_layout,
widgets_read_selection,
widgets_blast,
pn.Row(file_download)),
pn.Column(pn.panel(tabs), pn.Row(seq_preview)),
right_col
)
return filtered_view
return lay_out_elements()
data_path = ""
request.urlretrieve("https://cobiontid.github.io/examples/ilCarKade1_204_downsampled.npz", "ilCarKade1_204_downsampled.npz")
request.urlretrieve("https://cobiontid.github.io/examples/Trypanosomatidae.finalreads", "Trypanosomatidae.finalreads")
request.urlretrieve("https://vae.cog.sanger.ac.uk/downsampled.fa.gz", "downsampled.fa.gz")
use_own_data = False #@param {type:"boolean"}
import sys
def ids_width(reads):
#""" Get max length for read ids to prevent truncation by np.loadtxt() """
wc = subprocess.run(["wc", "-L", reads], capture_output=True)
if wc.returncode == 0:
width = int(wc.stdout.decode('utf-8').split()[0])
return width
else:
sys.exit(1)
#@markdown Your sample's name.
sample_id = "my_species" #@param {type:"string"}
#@markdown Specify your file paths.
read_ids = "example.reads.ids.txt" #@param {type:"string"}
vae_file = "example.vae.out.2d.0" #@param {type:"string"}
coverage_file = "example.median_31mer.txt" #@param {type:"string"}
density_file = "example.reads.hexsum" #@param {type:"string"}
fasta = "reads.fa.gz" #@param {type:"string"}
labelled_reads = "example.reads" #@param {type:"string"}
if use_own_data == True:
width = ids_width(read_ids)
# load own data
data_dict = {"vae": np.loadtxt(vae_file, dtype="float32"),
"slice": np.loadtxt(coverage_file, dtype="int32"),
"annot": np.loadtxt(density_file, dtype="float32"),
"reads": np.loadtxt(read_ids, dtype="U{}".format(width))
}
data_dict["classes"] = get_category_labels(data_dict["reads"], [labelled_reads])
else:
fasta = "downsampled.fa.gz"
sample_id = "ilCarKade1"
data_dict = dict(np.load("ilCarKade1_204_downsampled.npz"))
data_dict["classes"] = get_category_labels(data_dict["reads"], ["Trypanosomatidae.finalreads"])
import os
import gc
assert os.path.isfile(fasta)
xy = load_df(data_dict)
scatter = Scatter(xy, sample_id)
plain_scatter = "False"
view = make_panel(scatter, fasta)
gc.collect()
hv.extension("bokeh")
pn.extension()
view.servable("Read VAE")