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7 Examples
0
View Source File : visualization.py
License : GNU Affero General Public License v3.0
Project Creator : iza-institute-of-labor-economics
License : GNU Affero General Public License v3.0
Project Creator : iza-institute-of-labor-economics
def plot_dag(
functions,
targets=None,
columns_overriding_functions=None,
check_minimal_specification="ignore",
selectors=None,
labels=True,
tooltips=False,
plot_kwargs=None,
arrow_kwargs=None,
edge_kwargs=None,
label_kwargs=None,
node_kwargs=None,
):
"""Plot the dag of the tax and transfer system.
Parameters
----------
functions : str, pathlib.Path, callable, module, imports statements, dict
Functions can be anything of the specified types and a list of the same objects.
If the object is a dictionary, the keys of the dictionary are used as a name
instead of the function name. For all other objects, the name is inferred from
the function name.
targets : str, list of str
String or list of strings with names of functions whose output is actually
needed by the user.
columns_overriding_functions : str list of str
Names of columns in the data which are preferred over function defined in the
tax and transfer system.
check_minimal_specification : {"ignore", "warn", "raise"}, default "ignore"
Indicator for whether checks which ensure the most minimal configuration should
be silenced, emitted as warnings or errors.
selectors : str or list of str or dict or list of dict or list of str and dict
Selectors allow to you to select and de-select nodes in the graph for
visualization. For the full list of options, see the tutorial about
`visualization < ../docs/tutorials/visualize.ipynb>`_. By default, all nodes are
shown.
labels : bool, default True
Annotate nodes with labels.
tooltips : bool, default False
Experimental feature which makes the source code of the functions accessible as
a tooltip. Sometimes, the tooltip is not properly displayed.
plot_kwargs : dict
Additional keyword arguments passed to :class:`bokeh.models.Plot`.
arrow_kwargs : dict
Additional keyword arguments passed to :class:`bokeh.models.Arrow`. For example,
change the size of the head with ``{"size": 10}``.
edge_kwargs : dict
Additional keyword arguments passed to :class:`bokeh.models.MultiLine`. For
example, change the color with ``{"fill_color": "green"}``.
label_kwargs : dict
Additional keyword arguments passed to :class:`bokeh.models.LabelSet`. For
example, change the fontsize with ``{"text_font_size": "12px"}``.
node_kwargs : dict
Additional keyword arguments passed to :class:`bokeh.models.Circle`. For
example, change the color with ``{"fill_color": "orange"}``.
"""
targets = DEFAULT_TARGETS if targets is None else targets
targets = parse_to_list_of_strings(targets, "targets")
columns_overriding_functions = parse_to_list_of_strings(
columns_overriding_functions, "columns_overriding_functions"
)
# Load functions and perform checks.
functions, internal_functions = load_user_and_internal_functions(functions)
# Create one dictionary of functions and perform check.
functions = {**internal_functions, **functions}
functions = {
k: v for k, v in functions.items() if k not in columns_overriding_functions
}
_fail_if_targets_not_in_functions(functions, targets)
# Partial parameters to functions such that they disappear in the DAG.
functions = _mock_parameters_arguments(functions)
dag = create_dag(
functions, targets, columns_overriding_functions, check_minimal_specification
)
selectors = [] if selectors is None else _to_list(selectors)
plot_kwargs = {} if plot_kwargs is None else plot_kwargs
arrow_kwargs = {} if arrow_kwargs is None else arrow_kwargs
edge_kwargs = {} if edge_kwargs is None else edge_kwargs
label_kwargs = {} if label_kwargs is None else label_kwargs
node_kwargs = {} if node_kwargs is None else node_kwargs
dag = _select_nodes_in_dag(dag, selectors)
dag = _add_url_to_dag(dag)
# Even if we do not use the source codes as tooltips, we need to remove the
# functions.
dag = _replace_functions_with_source_code(dag)
plot_kwargs["title"] = _to_bokeh_title(
plot_kwargs.get("title", "Tax and Transfer System")
)
plot = Plot(**{**PLOT_KWARGS_DEFAULTS, **plot_kwargs})
layout = _create_pydot_layout(dag)
graph_renderer = from_networkx(dag, layout, scale=1, center=(0, 0))
graph_renderer.node_renderer.glyph = Circle(
**{**NODE_KWARGS_DEFAULTS, **node_kwargs}
)
graph_renderer.edge_renderer.visible = False
for (
_,
(start_node, end_node),
) in graph_renderer.edge_renderer.data_source.to_df().iterrows():
(x_start, y_start), (x_end, y_end) = _compute_arrow_coordinates(
layout[start_node], layout[end_node]
)
plot.add_layout(
Arrow(
end=NormalHead(**{**ARROW_KWARGS_DEFAULTS, **arrow_kwargs}),
x_start=x_start,
y_start=y_start,
x_end=x_end,
y_end=y_end,
**{**EDGE_KWARGS_DEFAULTS, **edge_kwargs},
)
)
plot.renderers.append(graph_renderer)
tools = [BoxZoomTool(), ResetTool()]
tools.append(TapTool(callback=OpenURL(url="@url")))
if tooltips:
tools.append(HoverTool(tooltips=TOOLTIPS))
plot.add_tools(*tools)
if labels:
source = ColumnDataSource(
pd.DataFrame(layout).T.rename(columns={0: "x", 1: "y"})
)
labels = LabelSet(
x="x",
y="y",
text="index",
source=source,
**{**LABEL_KWARGS_DEFAULT, **label_kwargs},
)
plot.add_layout(labels)
output_notebook()
show(plot)
return plot
def _mock_parameters_arguments(functions):
0
View Source File : metainfo_plots.py
License : MIT License
Project Creator : skiniry
License : MIT License
Project Creator : skiniry
def single_tran_de(single_tran_de_transcript, sorted_master_list, study_master_list, organism, transcriptome,single_tran_de_study):
xvals = []
yvals = []
labels = []
range1counts = []
file_ids = []
mapped_reads = []
file_descs = []
study_names = []
if single_tran_de_study == False:
for tup in sorted_master_list:
file_id = tup[0]
filename = tup[1]
r1 = float(tup[2])
r2 = float(tup[3])
file_desc = tup[4]
study = tup[5]
ratio = r1/r2
avg = (r1+r2)/2
xvals.append(avg)
yvals.append(ratio)
labels.append(filename)
range1counts.append(r1)
file_ids.append(file_id)
mapped_reads.append(r2)
file_descs.append(file_desc)
study_names.append(study)
else:
for tup in study_master_list:
file_id = tup[0]
study = tup[1]
r1 = float(tup[2])
r2 = float(tup[3])
ratio = log(r1/r2,2)
avg = (r1+r2)/2
xvals.append(avg)
yvals.append(ratio)
labels.append(study)
range1counts.append(r1)
file_ids.append(file_id)
mapped_reads.append(r2)
file_descs.append(study)
study_names.append(study)
full_title = "ORF TPMs ({})"
x_lab = ''
y_lab = 'TPM'
p = figure(plot_width=1300, plot_height=750,x_axis_label=x_lab, y_axis_label=y_lab,title= full_title,toolbar_location="below",
tools = "reset,pan,box_zoom,hover,tap",logo=None)
p.title.align="center"
p.xgrid.grid_line_color = "white"
p.ygrid.grid_line_color = "white"
hover = p.select(dict(type=HoverTool))
hover.tooltips = [("Ratio", "@y"),("Max count","@x"),("File name","@labels"),("Range 1 count","@range1counts"),("Mapped reads","@mapped_reads"),("Description","@file_descs"),("Study","@study")]
source = ColumnDataSource({'x':xvals,'y':yvals,'labels':labels,"range1counts":range1counts,"file_id":file_ids,"mapped_reads":mapped_reads,"file_descs":file_descs,"study":study_names})
p.scatter('x','y',source=source, alpha=1,color="grey",size=9)
output_file("scatter10k.html", title="Single transcript differential translation")
hover = p.select(dict(type=HoverTool))
hover.mode = 'mouse'
#/saccharomyces_cerevisiae/Gencode_v24/comparison/?files=227,%23ff1f00_228,%233BFF00_231,%23ffffff_232,%23000000_&transcript=YLR162W&normalize=F&cov=T&ambig=F&minread=25&maxread=100
#/saccharomyces_cerevisiae/Gencode_v24/comparison/?files=227,228,229,%23ff1f00_230,%233BFF00&transcript=YDR003W&normalize=T&cov=T&ambig=T&minread=18&maxread=45
#url = "http://143.239.109.139/tripsviz/{}/{}/interactive_plot/[email protected]".format(organism, transcriptome)
#file_string = ""
#label_string="&labels=RIBO-Seq Cond 1,%23007a02_RIBO-Seq Cond 2,%23960000_mRNA-Seq Cond 1,%2374ed76_mRNA-seq Cond 2,%23ff6d6d"
# remove the trailing _ in file_string if it's been populated
#if file_string:
# file_string = file_string[:len(file_string)-1]
url = "http://trips.ucc.ie/{}/{}/interactive_plot/[email protected]_id&tran={}&ambig=F&minread=25&maxread=150".format(organism, transcriptome,single_tran_de_transcript)
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
#TODO FIX HARDCODED TMP FILE LINK
#graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://trips.ucc.ie/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < a href='https://trips.ucc.ie/static/tmp/{1}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Download results as csv file < /b> < /button> < /a> < /div>".format(short_code,filename)
#graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://trips.ucc.ie/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < /div>".format(short_code)
graph = file_html(p,CDN)
return graph
def tran_corr(tran_corr_transcript1, tran_corr_transcript2,sorted_master_list,organism, transcriptome):
0
View Source File : metainfo_plots.py
License : MIT License
Project Creator : skiniry
License : MIT License
Project Creator : skiniry
def tran_corr(tran_corr_transcript1, tran_corr_transcript2,sorted_master_list,organism, transcriptome):
xvals = []
yvals = []
labels = []
range1counts = []
range2counts = []
file_ids = []
for tup in sorted_master_list:
xvals.append(tup[2])
yvals.append(tup[3])
labels.append(tup[1])
range1counts.append(tup[4])
range2counts.append(tup[3])
file_ids.append(tup[0])
full_title = "Transcript correllation ({})"
x_lab = '{} count (log2)'.format(tran_corr_transcript1)
y_lab = '{} count (log2)'.format(tran_corr_transcript2)
p = figure(plot_width=2000, plot_height=1000,x_axis_label=x_lab, y_axis_label=y_lab,title= full_title,toolbar_location="below",
tools = "reset,pan,box_zoom,hover,tap",logo=None)
p.title.align="center"
p.xgrid.grid_line_color = "white"
p.ygrid.grid_line_color = "white"
hover = p.select(dict(type=HoverTool))
hover.tooltips = [("Ratio", "@y"),("Max count","@x"),("File name","@labels"),("Range 1 count","@range1counts"),("Range 2 count","@range2counts")]
source = ColumnDataSource({'x':xvals,'y':yvals,'labels':labels,"range1counts":range1counts,"range2counts":range2counts,"file_id":file_ids})
p.scatter('x','y',source=source, alpha=1,color="grey",size=12)
output_file("scatter10k.html", title="Single transcript differential translation")
hover = p.select(dict(type=HoverTool))
hover.mode = 'mouse'
#/saccharomyces_cerevisiae/Gencode_v24/comparison/?files=227,%23ff1f00_228,%233BFF00_231,%23ffffff_232,%23000000_&transcript=YLR162W&normalize=F&cov=T&ambig=F&minread=25&maxread=100
#/saccharomyces_cerevisiae/Gencode_v24/comparison/?files=227,228,229,%23ff1f00_230,%233BFF00&transcript=YDR003W&normalize=T&cov=T&ambig=T&minread=18&maxread=45
#url = "http://143.239.109.139/tripsviz/{}/{}/interactive_plot/[email protected]".format(organism, transcriptome)
#file_string = ""
#label_string="&labels=RIBO-Seq Cond 1,%23007a02_RIBO-Seq Cond 2,%23960000_mRNA-Seq Cond 1,%2374ed76_mRNA-seq Cond 2,%23ff6d6d"
# remove the trailing _ in file_string if it's been populated
#if file_string:
# file_string = file_string[:len(file_string)-1]
url = "http://trips.ucc.ie/{}/{}/interactive_plot/[email protected]e_id&tran={}&ambig=F&minread=25&maxread=150".format(organism, transcriptome,tran_corr_transcript1)
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
#TODO FIX HARDCODED TMP FILE LINK
#graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://trips.ucc.ie/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < a href='https://trips.ucc.ie/static/tmp/{1}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Download results as csv file < /b> < /button> < /a> < /div>".format(short_code,filename)
#graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://trips.ucc.ie/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < /div>".format(short_code)
graph = file_html(p,CDN)
return graph
def explore_offsets(f0_counts, f1_counts, f2_counts, labels,short_code,background_col,title_size, axis_label_size, subheading_size,marker_size):
0
View Source File : riboflask_diff.py
License : MIT License
Project Creator : skiniry
License : MIT License
Project Creator : skiniry
def generate_plot(sorted_min_exp_list,bin_list,organism, label,transcriptome,riboseq1,riboseq2,rnaseq1,rnaseq2,background_color, short_code,normalized,filename,no_groups,title_size, axis_label_size, subheading_size,marker_size,ambiguous,gene_list):
#Convert gene_list from string to list
logging.debug("generate plot called")
#logging.debug("sorted_min_exp_list: {}".format(sorted_min_exp_list))
logging.debug("bin_list: {}".format(bin_list))
if gene_list != "":
gene_list = gene_list.replace(","," ").replace("\t"," ")
split_list = gene_list.split(" ")
gene_list = []
for item in split_list:
gene_list.append(item.strip(" ").upper())
posallxvals = []
posallyvals = []
posalllabels = []
posallgenes = []
poszscores = []
negallxvals = []
negallyvals = []
negalllabels = []
negallgenes = []
negzscores = []
nonde_xvals = []
nonde_yvals = []
nonde_labels = []
nonde_allgenes = []
nonde_zscores = []
upper_thresholds_y = [bin_list[0][2]]
upper_thresholds_x = [(sorted_min_exp_list[0][1])*0.99]
lower_thresholds_y = [bin_list[0][3]]
lower_thresholds_x = [(sorted_min_exp_list[0][1])*0.99]
# Easiest way to plot threshold lines is to just plot the threshold and min_exp for each bin, but this will result in slanted lines, i.ie if my first bin threshold is 8 and my second is 4
# there would be a slanted line between 8 and 4, but this makes no sense as the threshold is the same across an entire bin, so instead we plot two y values at every step (except first and last)
#d((transcript, log(min_exp,2), fold_change,gene))
cur_count = 0
bin_count = 0
for i in range(0,len(sorted_min_exp_list)):
cur_count+=1
if cur_count == 300:
#To x we add the log2(min exp) of the 300th (or multiple of) item in min exp list
bin_count += 1
try:
tst = bin_list[bin_count]
except:
bin_count -= 1
cur_count = 0
upper_thresholds_x.append(sorted_min_exp_list[(bin_count)*300][1])
upper_thresholds_x.append(sorted_min_exp_list[(bin_count)*300][1])
upper_thresholds_y.append(bin_list[bin_count-1][2])
upper_thresholds_y.append(bin_list[bin_count][2])
lower_thresholds_x.append(sorted_min_exp_list[(bin_count)*300][1])
lower_thresholds_x.append(sorted_min_exp_list[(bin_count)*300][1])
lower_thresholds_y.append(bin_list[bin_count-1][3])
lower_thresholds_y.append(bin_list[bin_count][3])
if bin_list[bin_count][1] == 0.0:
continue
z_score = (sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1])
if gene_list == "":
if sorted_min_exp_list[i][2] < = bin_list[bin_count][2] and sorted_min_exp_list[i][2] >= bin_list[bin_count][3]:
nonde_xvals.append(sorted_min_exp_list[i][1])
nonde_yvals.append(sorted_min_exp_list[i][2])
nonde_labels.append(sorted_min_exp_list[i][0])
nonde_allgenes.append(sorted_min_exp_list[i][3])
nonde_zscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
elif sorted_min_exp_list[i][2] > bin_list[bin_count][2]:
posallxvals.append(sorted_min_exp_list[i][1])
posallyvals.append(sorted_min_exp_list[i][2])
posalllabels.append(sorted_min_exp_list[i][0])
posallgenes.append(sorted_min_exp_list[i][3])
poszscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
elif sorted_min_exp_list[i][2] < (bin_list[bin_count][3]):
negallxvals.append(sorted_min_exp_list[i][1])
negallyvals.append(sorted_min_exp_list[i][2])
negalllabels.append(sorted_min_exp_list[i][0])
negallgenes.append(sorted_min_exp_list[i][3])
negzscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
else:
#If the user passes a gene list only highlight those genes red/green depending on if fold changes is >/ < 0
if sorted_min_exp_list[i][3] not in gene_list:
nonde_xvals.append(sorted_min_exp_list[i][1])
nonde_yvals.append(sorted_min_exp_list[i][2])
nonde_labels.append(sorted_min_exp_list[i][0])
nonde_allgenes.append(sorted_min_exp_list[i][3])
nonde_zscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
elif sorted_min_exp_list[i][3] in gene_list and sorted_min_exp_list[i][2] > 0:
posallxvals.append(sorted_min_exp_list[i][1])
posallyvals.append(sorted_min_exp_list[i][2])
posalllabels.append(sorted_min_exp_list[i][0])
posallgenes.append(sorted_min_exp_list[i][3])
poszscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
elif sorted_min_exp_list[i][3] in gene_list and sorted_min_exp_list[i][2] < 0:
negallxvals.append(sorted_min_exp_list[i][1])
negallyvals.append(sorted_min_exp_list[i][2])
negalllabels.append(sorted_min_exp_list[i][0])
negallgenes.append(sorted_min_exp_list[i][3])
negzscores.append((sorted_min_exp_list[i][2]-bin_list[bin_count][0])/(bin_list[bin_count][1]))
# Add z score thresholds for the last bin, which wouldn't have been covered in the above loop
upper_thresholds_x.append((sorted_min_exp_list[-1][1])*0.99)
upper_thresholds_y.append(bin_list[bin_count][2])
lower_thresholds_x.append((sorted_min_exp_list[-1][1])*0.99)
lower_thresholds_y.append(bin_list[bin_count][3])
full_title = "Differential translation ({})".format(short_code)
if no_groups == 1:
x_lab = 'Geometric mean (log2)'
y_lab = 'Fold change (log2)'
else:
x_lab = 'Average Geometric mean (log2)'
y_lab = 'Average Fold change (log2)'
p = figure(plot_width=1300, plot_height=750,x_axis_label=x_lab, y_axis_label=y_lab,title= full_title,toolbar_location="below",
tools = "reset,pan,box_zoom,hover,tap")
p.title.align="center"
p.title.text_font_size = title_size
p.xaxis.axis_label_text_font_size = axis_label_size
p.xaxis.major_label_text_font_size = marker_size
p.yaxis.axis_label_text_font_size = axis_label_size
p.yaxis.major_label_text_font_size = marker_size
p.background_fill_color = background_color
p.xgrid.grid_line_color = "white"
p.ygrid.grid_line_color = "white"
if gene_list == "":
p.line(upper_thresholds_x, upper_thresholds_y, color="yellow",line_width=3)
p.line(lower_thresholds_x, lower_thresholds_y, color="yellow",line_width=3)
source = ColumnDataSource({'x': nonde_xvals,'y':nonde_yvals,'labels':nonde_labels, 'genes':nonde_allgenes})
sct = p.scatter('x','y',source=source, alpha=1,color="grey",size=5)
#source = ColumnDataSource({'x': posallxvals,'y':posallyvals,'labels':posalllabels, 'genes':posallgenes,"zscores":poszscores})
source = ColumnDataSource({'x': posallxvals,'y':posallyvals,'labels':posalllabels, 'genes':posallgenes})
p.scatter('x','y',source=source, alpha=1,color="green",size=10)
hover = p.select(dict(type=HoverTool))
#hover.tooltips = [("Fold change", "@y"),("Geometric mean","@x"),("Transcript","@labels"),("Genes","@genes"), ("Z score","@zscores")]
hover.tooltips = [("Fold change", "@y"),("Geometric mean","@x"),("Transcript","@labels"),("Genes","@genes")]
#source = ColumnDataSource({'x': negallxvals,'y':negallyvals,'labels':negalllabels, 'genes':negallgenes,"zscores":negzscores})
source = ColumnDataSource({'x': negallxvals,'y':negallyvals,'labels':negalllabels, 'genes':negallgenes})
p.scatter('x','y',source=source, alpha=1,color="red",size=10)
#source = ColumnDataSource({'x': nonde_xvals,'y':nonde_yvals,'labels':nonde_labels, 'genes':nonde_allgenes,"zscores":nonde_zscores})
output_file("scatter10k.html", title="Differential translation")
hover = p.select(dict(type=HoverTool))
hover.mode = 'mouse'
#/saccharomyces_cerevisiae/Gencode_v24/comparison/?files=227,%23ff1f00_228,%233BFF00_231,%23ffffff_232,%23000000_&transcript=YLR162W&normalize=F&cov=T&ambig=F&minread=25&maxread=100
#/saccharomyces_cerevisiae/Gencode_v24/comparison/?files=227,228,229,%23ff1f00_230,%233BFF00&transcript=YDR003W&normalize=T&cov=T&ambig=T&minread=18&maxread=45
file_string = ""
label_string="&labels=RIBO-Seq Cond 1,%23007a02_RIBO-Seq Cond 2,%23960000_mRNA-Seq Cond 1,%2374ed76_mRNA-seq Cond 2,%23ff6d6d"
if riboseq1:
if riboseq1[0] != "":
for file_id in riboseq1:
file_string += ("{},".format(file_id))
file_string += ("%23007a02_")
if riboseq2:
if riboseq2[0] != "":
for file_id in riboseq2:
file_string += ("{},".format(file_id))
file_string += ("%23960000_")
if rnaseq1:
if rnaseq1[0] != "":
for file_id in rnaseq1:
file_string += ("{},".format(file_id))
file_string += ("%2374ed76_")
if rnaseq2:
if rnaseq2[0] != "":
for file_id in rnaseq2:
file_string += ("{},".format(file_id))
file_string += ("%23ff6d6d_")
# remove the trailing _ in file_string if it's been populated
if file_string:
file_string = file_string[:len(file_string)-1]
if ambiguous == True:
ambig = "T"
else:
ambig = "F"
url = "http://0.0.0.0:5000/{}/{}/comparison/?files={}{}&[email protected]&normalize={}&cov=T&ambig={}&minread=25&maxread=150".format(organism, transcriptome,file_string,label_string,str(normalized)[0],ambig)
'''
hili_gene = CustomJS(args=dict(sct=sct,source=source), code="""
console.log("Called hili gene");
var f = cb_obj['value']
var x = source["genes"][f]
console.log("x is" + x);
console.log("F is "+f);
sct.glyph.fill_color = '#4286f4';
""")
'''
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
#text_input = TextInput(value="B2M",title="Gene",callback=hili_gene)
#TODO FIX HARDCODED TMP FILE LINK
graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://0.0.0.0:5000/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < a href='https://0.0.0.0:5000/static/tmp/{1}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Download results as csv file < /b> < /button> < /a> < /div>".format(short_code,filename)
#graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://0.0.0.0:5000/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < /div>".format(short_code)
#layout = column(text_input, p)
graph += file_html(p,CDN)
logging.debug("Returning plot")
return graph
def ribo_vs_rna(ribo_rna_dict,organism,transcriptome,riboseq1,riboseq2,rnaseq1,rnaseq2,background_col,short_code,normalized,filename,no_groups,title_size, axis_label_size, subheading_size,marker_size,ambiguous,gene_list,label):
0
View Source File : riboflask_diff.py
License : MIT License
Project Creator : skiniry
License : MIT License
Project Creator : skiniry
def ribo_vs_rna(ribo_rna_dict,organism,transcriptome,riboseq1,riboseq2,rnaseq1,rnaseq2,background_col,short_code,normalized,filename,no_groups,title_size, axis_label_size, subheading_size,marker_size,ambiguous,gene_list,label):
#Convert gene_list from string to list
if gene_list != "":
gene_list = gene_list.replace(","," ").replace("\t"," ")
split_list = gene_list.split(" ")
gene_list = []
for item in split_list:
gene_list.append(item.strip(" ").upper())
x_values = []
y_values = []
genes = []
trans = []
hili_x_values = []
hili_y_values = []
hili_genes = []
hili_trans = []
for gene in ribo_rna_dict:
if label == "TE":
if gene not in gene_list:
y_values.append(log(ribo_rna_dict[gene]["ribo2"]/ribo_rna_dict[gene]["ribo1"],2))
x_values.append(log(ribo_rna_dict[gene]["rna2"]/ribo_rna_dict[gene]["rna1"],2))
genes.append(gene)
trans.append(ribo_rna_dict[gene]["tran"])
else:
hili_y_values.append(log(ribo_rna_dict[gene]["ribo2"]/ribo_rna_dict[gene]["ribo1"],2))
hili_x_values.append(log(ribo_rna_dict[gene]["rna2"]/ribo_rna_dict[gene]["rna1"],2))
hili_genes.append(gene)
hili_trans.append(ribo_rna_dict[gene]["tran"])
elif label == "Riboseq":
if gene not in gene_list:
if ribo_rna_dict[gene]["ribo1"] >= 1:
y_values.append(log(ribo_rna_dict[gene]["ribo1"],2))
else:
y_values.append(0)
if ribo_rna_dict[gene]["ribo2"] >= 1:
x_values.append(log(ribo_rna_dict[gene]["ribo2"],2))
else:
x_values.append(0)
genes.append(gene)
trans.append(ribo_rna_dict[gene]["tran"])
else:
if ribo_rna_dict[gene]["ribo1"] >= 1:
hili_y_values.append(log(ribo_rna_dict[gene]["ribo1"],2))
else:
hili_y_values.append(0)
if ribo_rna_dict[gene]["ribo2"] >= 1:
hili_x_values.append(log(ribo_rna_dict[gene]["ribo2"],2))
else:
hili_x_values.append(0)
hili_genes.append(gene)
hili_trans.append(ribo_rna_dict[gene]["tran"])
elif label == "Rnaseq":
if gene not in gene_list:
if ribo_rna_dict[gene]["rna1"] != 0:
y_values.append(log(ribo_rna_dict[gene]["rna1"],2))
else:
y_values.append(0)
if ribo_rna_dict[gene]["rna2"] != 0:
x_values.append(log(ribo_rna_dict[gene]["rna2"],2))
else:
x_values.append(0)
genes.append(gene)
trans.append(ribo_rna_dict[gene]["tran"])
else:
if ribo_rna_dict[gene]["rna1"] != 0:
hili_y_values.append(log(ribo_rna_dict[gene]["rna1"],2))
else:
hili_y_values.append(0)
if ribo_rna_dict[gene]["rna2"] != 0:
hili_x_values.append(log(ribo_rna_dict[gene]["rna2"],2))
else:
hili_x_values.append(0)
hili_genes.append(gene)
hili_trans.append(ribo_rna_dict[gene]["tran"])
source = ColumnDataSource({'x': x_values,'y':y_values,'trans':trans, 'genes':genes})
if label == "TE":
p = figure(plot_width=1300, plot_height=1300,x_axis_label="RNA-Seq FC (log2)", y_axis_label='Ribo-Seq FC (log2)',title="Ribo-Seq FC vs RNA-Seq FC ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
elif label == "Riboseq":
p = figure(plot_width=1300, plot_height=1300,x_axis_label="Ribo-Seq Cond2 count (log2)", y_axis_label='Ribo-Seq Cond1 count (log2)',title="Ribo-Seq correlation ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
elif label == "Rnaseq":
p = figure(plot_width=1300, plot_height=1300,x_axis_label="RNA-Seq Cond2 count (log2)", y_axis_label='RNA-Seq Cond1 count (log2)',title="RNA-Seq correlation ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
p.title.align="center"
p.title.text_font_size = title_size
p.xaxis.axis_label_text_font_size = axis_label_size
p.xaxis.major_label_text_font_size = marker_size
p.yaxis.axis_label_text_font_size = axis_label_size
p.yaxis.major_label_text_font_size = marker_size
p.background_fill_color = background_col
p.xgrid.grid_line_color = "#cccccc"
p.ygrid.grid_line_color = "#cccccc"
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='grey')
source = ColumnDataSource({'x':hili_x_values, 'y':hili_y_values, 'trans':hili_trans,'genes':hili_genes})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#4286f4')
hover = p.select(dict(type=HoverTool))
hover.mode = 'mouse'
if label == "TE":
p.line([-8,8], [-8,8], color="#cccccc",line_width=1)
hover.tooltips = [("Ribo fc", "@y"),("RNA fc","@x"),("Genes","@genes"),("Transcript","@trans")]
elif label == "Riboseq":
p.line([0,16], [0,16], color="#cccccc",line_width=1)
hover.tooltips = [("Ribo Cond 1 count (log2)", "@y"),("Ribo Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans")]
#corr = spearmanr(x_values, y_values)
pearson_corr = pearsonr(x_values, y_values)
mytext = Label(x=0.1,y=max(y_values),text="Pearson correlation: {}".format(round(pearson_corr[0],2)),background_fill_color="white",text_font_size="13pt")
p.add_layout(mytext)
else:
p.line([0,16], [0,16], color="#cccccc",line_width=1)
hover.tooltips = [("Rna-seq Cond 1 count (log2)", "@y"),("Rna-seq Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans")]
hover.tooltips = [("Ribo Cond 1 count (log2)", "@y"),("Ribo Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans")]
#corr = spearmanr(x_values, y_values)
pearson_corr = pearsonr(x_values, y_values)
mytext = Label(x=0.1,y=max(y_values),text="Pearson correlation: {}".format(round(pearson_corr[0],2)),background_fill_color="white",text_font_size="13pt")
p.add_layout(mytext)
file_string = ""
label_string="&labels=RIBO-Seq Cond 1,%23007a02_RIBO-Seq Cond 2,%23960000_mRNA-Seq Cond 1,%2374ed76_mRNA-seq Cond 2,%23ff6d6d"
if riboseq1:
if riboseq1[0] != "":
for file_id in riboseq1:
file_string += ("{},".format(file_id))
file_string += ("%23007a02_")
if riboseq2:
if riboseq2[0] != "":
for file_id in riboseq2:
file_string += ("{},".format(file_id))
file_string += ("%23960000_")
if rnaseq1:
if rnaseq1[0] != "":
for file_id in rnaseq1:
file_string += ("{},".format(file_id))
file_string += ("%2374ed76_")
if rnaseq2:
if rnaseq2[0] != "":
for file_id in rnaseq2:
file_string += ("{},".format(file_id))
file_string += ("%23ff6d6d_")
# remove the trailing _ in file_string if it's been populated
if file_string:
file_string = file_string[:len(file_string)-1]
if ambiguous == True:
ambig = "T"
else:
ambig = "F"
url = "http://0.0.0.0:5000/{}/{}/comparison/?files={}{}&[email protected]&normalize={}&cov=T&ambig={}&minread=25&maxread=150".format(organism, transcriptome,file_string,label_string,str(normalized)[0],ambig)
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://0.0.0.0:5000/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < a href='https://0.0.0.0:5000/static/tmp/{1}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Download results as csv file < /b> < /button> < /a> < /div>".format(short_code,filename)
#graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://0.0.0.0:5000/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < /div>".format(short_code)
#layout = column(text_input, p)
graph += file_html(p,CDN)
return graph
def deseq2_plot(ribo_rna_dict,organism,transcriptome,riboseq1,riboseq2,rnaseq1,rnaseq2,background_col,short_code,normalized,filename,no_groups,title_size, axis_label_size, subheading_size,marker_size,ambiguous,gene_list,label,minzscore):
0
View Source File : riboflask_diff.py
License : MIT License
Project Creator : skiniry
License : MIT License
Project Creator : skiniry
def deseq2_plot(ribo_rna_dict,organism,transcriptome,riboseq1,riboseq2,rnaseq1,rnaseq2,background_col,short_code,normalized,filename,no_groups,title_size, axis_label_size, subheading_size,marker_size,ambiguous,gene_list,label,minzscore):
#Convert gene_list from string to list
if gene_list != "":
gene_list = gene_list.replace(","," ").replace("\t"," ")
split_list = gene_list.split(" ")
gene_list = []
for item in split_list:
gene_list.append(item.strip(" ").upper())
x_values = []
y_values = []
basemeans = []
lfcses = []
genes = []
trans = []
highlight_x_values = []
highlight_y_values = []
highlight_basemeans = []
highlight_lfcses = []
highlight_genes = []
highlight_trans = []
teup_x_values = []
teup_y_values = []
teup_basemeans = []
teup_lfcses = []
teup_genes = []
teup_trans = []
tedown_x_values = []
tedown_y_values = []
tedown_basemeans = []
tedown_lfcses = []
tedown_genes = []
tedown_trans = []
rnaup_x_values = []
rnaup_y_values = []
rnaup_basemeans = []
rnaup_lfcses = []
rnaup_genes = []
rnaup_trans = []
rnadown_x_values = []
rnadown_y_values = []
rnadown_basemeans = []
rnadown_lfcses = []
rnadown_genes = []
rnadown_trans = []
largest_fc = 0
for gene in ribo_rna_dict:
if label == "TE":
x = ribo_rna_dict[gene]["rna_fc"]
y = ribo_rna_dict[gene]["ribo_fc"]
basemean = ribo_rna_dict[gene]["te_basemean"]
lfcSE = ribo_rna_dict[gene]["te_lfcSE"]
te_padj = ribo_rna_dict[gene]["te_padj"]
ribo_padj = ribo_rna_dict[gene]["ribo_padj"]
rna_padj = ribo_rna_dict[gene]["rna_padj"]
if x != "NA" and y != "NA":
x = float(x)
y = float(y)
if abs(x) > largest_fc:
largest_fc = abs(x)
if abs(y) > largest_fc:
largest_fc = abs(y)
if "NA" in te_padj:
te_padj = 1
if "NA" in ribo_padj:
ribo_padj = 1
if "NA" in rna_padj:
rna_padj = 1
te_padj = float(te_padj)
ribo_padj = float(ribo_padj)
rna_padj = float(rna_padj)
if gene not in gene_list:
if te_padj > (minzscore/100) and ribo_padj > (minzscore/100) and rna_padj > (minzscore/100):
y_values.append(y)
x_values.append(x)
basemeans.append(basemean)
lfcses.append(lfcSE)
genes.append(gene)
trans.append(ribo_rna_dict[gene]["tran"])
elif te_padj < (minzscore/100) and x < y :
teup_y_values.append(y)
teup_x_values.append(x)
teup_basemeans.append(basemean)
teup_lfcses.append(lfcSE)
teup_genes.append(gene)
teup_trans.append(ribo_rna_dict[gene]["tran"])
elif te_padj < (minzscore/100) and y < x :
tedown_y_values.append(y)
tedown_x_values.append(x)
tedown_basemeans.append(basemean)
tedown_lfcses.append(lfcSE)
tedown_genes.append(gene)
tedown_trans.append(ribo_rna_dict[gene]["tran"])
elif rna_padj < (minzscore/100):
if x > 0:
rnaup_y_values.append(y)
rnaup_x_values.append(x)
rnaup_basemeans.append(basemean)
rnaup_lfcses.append(lfcSE)
rnaup_genes.append(gene)
rnaup_trans.append(ribo_rna_dict[gene]["tran"])
elif x < 0 :
rnadown_y_values.append(y)
rnadown_x_values.append(x)
rnadown_basemeans.append(basemean)
rnadown_lfcses.append(lfcSE)
rnadown_genes.append(gene)
rnadown_trans.append(ribo_rna_dict[gene]["tran"])
else:
highlight_y_values.append(y)
highlight_x_values.append(x)
highlight_basemeans.append(basemean)
highlight_lfcses.append(lfcSE)
highlight_genes.append(gene)
highlight_trans.append(ribo_rna_dict[gene]["tran"])
elif label == "Riboseq":
ribo_padj = ribo_rna_dict[gene]["ribo_padj"]
basemean = ribo_rna_dict[gene]["ribo_basemean"]
lfcSE = ribo_rna_dict[gene]["ribo_lfcSE"]
if "NA" in ribo_padj:
ribo_padj = 1
ribo_padj = float(ribo_padj)
if gene not in gene_list:
if ribo_padj > (minzscore/100):
if ribo_rna_dict[gene]["ribo1"] != 0:
y_values.append(log(ribo_rna_dict[gene]["ribo1"],2))
else:
y_values.append(0)
if ribo_rna_dict[gene]["ribo2"] != 0:
x_values.append(log(ribo_rna_dict[gene]["ribo2"],2))
else:
x_values.append(0)
genes.append(gene)
trans.append(ribo_rna_dict[gene]["tran"])
basemeans.append(basemean)
lfcses.append(lfcSE)
else:
if ribo_rna_dict[gene]["ribo1"] != 0:
teup_y_values.append(log(ribo_rna_dict[gene]["ribo1"],2))
else:
teup_y_values.append(0)
if ribo_rna_dict[gene]["ribo2"] != 0:
teup_x_values.append(log(ribo_rna_dict[gene]["ribo2"],2))
else:
teup_x_values.append(0)
teup_genes.append(gene)
teup_trans.append(ribo_rna_dict[gene]["tran"])
teup_basemeans.append(basemean)
teup_lfcses.append(lfcSE)
else:
if ribo_rna_dict[gene]["ribo1"] != 0:
highlight_y_values.append(log(ribo_rna_dict[gene]["ribo1"],2))
else:
highlight_y_values.append(0)
if ribo_rna_dict[gene]["ribo2"] != 0:
highlight_x_values.append(log(ribo_rna_dict[gene]["ribo2"],2))
else:
highlight_x_values.append(0)
highlight_basemeans.append(basemean)
highlight_lfcses.append(lfcSE)
highlight_genes.append(gene)
highlight_trans.append(ribo_rna_dict[gene]["tran"])
elif label == "Rnaseq":
rna_padj = ribo_rna_dict[gene]["rna_padj"]
basemean = ribo_rna_dict[gene]["rna_basemean"]
lfcSE = ribo_rna_dict[gene]["rna_lfcSE"]
if "NA" in rna_padj:
rna_padj = 1
rna_padj = float(rna_padj)
if gene not in gene_list:
if rna_padj > (minzscore/100):
basemeans.append(basemean)
lfcses.append(lfcSE)
if ribo_rna_dict[gene]["rna1"] != 0:
y_values.append(log(ribo_rna_dict[gene]["rna1"],2))
else:
y_values.append(0)
if ribo_rna_dict[gene]["rna2"] != 0:
x_values.append(log(ribo_rna_dict[gene]["rna2"],2))
else:
x_values.append(0)
genes.append(gene)
trans.append(ribo_rna_dict[gene]["tran"])
else:
if ribo_rna_dict[gene]["rna1"] != 0:
teup_y_values.append(log(ribo_rna_dict[gene]["rna1"],2))
else:
teup_y_values.append(0)
if ribo_rna_dict[gene]["rna2"] != 0:
teup_x_values.append(log(ribo_rna_dict[gene]["rna2"],2))
else:
teup_x_values.append(ribo_rna_dict[gene]["rna2"])
teup_genes.append(gene)
teup_trans.append(ribo_rna_dict[gene]["tran"])
teup_basemeans.append(basemean)
teup_lfcses.append(lfcSE)
else:
if ribo_rna_dict[gene]["rna1"] != 0:
highlight_y_values.append(log(ribo_rna_dict[gene]["rna1"],2))
else:
highlight_y_values.append(0)
if ribo_rna_dict[gene]["rna2"] != 0:
highlight_x_values.append(log(ribo_rna_dict[gene]["rna2"],2))
else:
highlight_x_values.append(0)
highlight_basemeans.append(basemean)
highlight_lfcses.append(lfcSE)
highlight_genes.append(gene)
highlight_trans.append(ribo_rna_dict[gene]["tran"])
source = ColumnDataSource({'x': x_values,'y':y_values,'trans':trans, 'genes':genes,'basemeans':basemeans,'lfcses':lfcses})
if label == "TE":
p = figure(plot_width=1300, plot_height=1300,x_axis_label="RNA-Seq FC (log2)", y_axis_label='Ribo-Seq FC (log2)',title="Ribo-Seq FC vs RNA-Seq FC ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
elif label == "Riboseq":
p = figure(plot_width=1300, plot_height=1300,x_axis_label="Ribo-Seq Cond2 count (log2)", y_axis_label='Ribo-Seq Cond1 count (log2)',title="Ribo-Seq correlation ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
elif label == "Rnaseq":
p = figure(plot_width=1300, plot_height=1300,x_axis_label="RNA-Seq Cond2 count (log2)", y_axis_label='RNA-Seq Cond1 count (log2)',title="RNA-Seq correlation ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
p.title.align="center"
p.title.text_font_size = title_size
p.xaxis.axis_label_text_font_size = axis_label_size
p.xaxis.major_label_text_font_size = marker_size
p.yaxis.axis_label_text_font_size = axis_label_size
p.yaxis.major_label_text_font_size = marker_size
p.background_fill_color = background_col
p.xgrid.grid_line_color = "#cccccc"
p.ygrid.grid_line_color = "#cccccc"
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='gray')
if highlight_genes != []:
source = ColumnDataSource({'x':highlight_x_values, 'y':highlight_y_values, 'trans':highlight_trans,'genes':highlight_genes,'basemeans':highlight_basemeans,'lfcses':highlight_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#000000',legend="Highlighted Genes")
if label == "TE":
source = ColumnDataSource({'x':teup_x_values, 'y':teup_y_values, 'trans':teup_trans,'genes':teup_genes,'basemeans':teup_basemeans,'lfcses':teup_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#00ff99',legend="Translation up")
source = ColumnDataSource({'x':tedown_x_values, 'y':tedown_y_values, 'trans':tedown_trans,'genes':tedown_genes,'basemeans':tedown_basemeans,'lfcses':tedown_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#ff5050',legend="Translation down")
source = ColumnDataSource({'x':rnaup_x_values, 'y':rnaup_y_values, 'trans':rnaup_trans,'genes':rnaup_genes,'basemeans':rnaup_basemeans,'lfcses':rnaup_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#cc0099',legend="mRNA up")
source = ColumnDataSource({'x':rnadown_x_values, 'y':rnadown_y_values, 'trans':rnadown_trans,'genes':rnadown_genes,'basemeans':rnadown_basemeans,'lfcses':rnadown_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#ffff00',legend="mRNA down")
elif label == "Riboseq":
source = ColumnDataSource({'x':teup_x_values, 'y':teup_y_values, 'trans':teup_trans,'genes':teup_genes,'basemeans':teup_basemeans,'lfcses':teup_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#00ff99',legend="Significant genes")
source = ColumnDataSource({'x':tedown_x_values, 'y':tedown_y_values, 'trans':tedown_trans,'genes':tedown_genes,'basemeans':tedown_basemeans,'lfcses':tedown_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#00ff99',legend="Significant genes")
elif label == "Rnaseq":
source = ColumnDataSource({'x':teup_x_values, 'y':teup_y_values, 'trans':teup_trans,'genes':teup_genes,'basemeans':teup_basemeans,'lfcses':teup_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#00ff99',legend="Significant genes")
source = ColumnDataSource({'x':rnadown_x_values, 'y':rnadown_y_values, 'trans':rnadown_trans,'genes':rnadown_genes,'basemeans':rnadown_basemeans,'lfcses':rnadown_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#00ff99',legend="Significant genes")
p.legend.location = "top_left"
p.legend.click_policy="hide"
p.legend.label_text_font_size = "28px"
hover = p.select(dict(type=HoverTool))
hover.mode = 'mouse'
plot_limit = largest_fc*1.1
if label == "TE":
p.line([plot_limit*-1,plot_limit], [plot_limit*-1,plot_limit], color="#cccccc",line_width=1)
hover.tooltips = [("Ribo fc", "@y"),("RNA fc","@x"),("Genes","@genes"),("Transcript","@trans"),("Basemean","@basemeans"),("lfcSE","@lfcses")]
elif label == "Riboseq":
p.line([0,16], [0,16], color="#cccccc",line_width=1)
hover.tooltips = [("Ribo Cond 1 count (log2)", "@y"),("Ribo Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans"),("Basemean","@basemeans"),("lfcSE","@lfcses")]
#corr = spearmanr(x_values, y_values)
#pearson_corr = pearsonr(x_values, y_values)
#mytext = Label(x=0.1,y=max(y_values),text="Pearson correlation: {}".format(round(pearson_corr[0],2)),background_fill_color="white",text_font_size="13pt")
#p.add_layout(mytext)
else:
p.line([0,16], [0,16], color="#cccccc",line_width=1)
hover.tooltips = [("Rna-seq Cond 1 count (log2)", "@y"),("Rna-seq Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans"),("Basemean","@basemeans"),("lfcSE","@lfcses")]
hover.tooltips = [("Ribo Cond 1 count (log2)", "@y"),("Ribo Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans"),("Basemean","@basemeans"),("lfcSE","@lfcses")]
#corr = spearmanr(x_values, y_values)
#pearson_corr = pearsonr(x_values, y_values)
#mytext = Label(x=0.1,y=max(y_values),text="Pearson correlation: {}".format(round(pearson_corr[0],2)),background_fill_color="white",text_font_size="13pt")
#p.add_layout(mytext)
file_string = ""
label_string="&labels=RIBO-Seq Cond 1,%23007a02_RIBO-Seq Cond 2,%23960000_mRNA-Seq Cond 1,%2374ed76_mRNA-seq Cond 2,%23ff6d6d"
if riboseq1:
if riboseq1[0] != "":
for file_id in riboseq1:
file_string += ("{},".format(file_id))
file_string += ("%23007a02_")
if riboseq2:
if riboseq2[0] != "":
for file_id in riboseq2:
file_string += ("{},".format(file_id))
file_string += ("%23960000_")
if rnaseq1:
if rnaseq1[0] != "":
for file_id in rnaseq1:
file_string += ("{},".format(file_id))
file_string += ("%2374ed76_")
if rnaseq2:
if rnaseq2[0] != "":
for file_id in rnaseq2:
file_string += ("{},".format(file_id))
file_string += ("%23ff6d6d_")
# remove the trailing _ in file_string if it's been populated
if file_string:
file_string = file_string[:len(file_string)-1]
if ambiguous == True:
ambig = "T"
else:
ambig = "F"
url = "http://0.0.0.0:5000/{}/{}/comparison/?files={}{}&[email protected]&normalize={}&cov=T&ambig={}&minread=25&maxread=150".format(organism, transcriptome,file_string,label_string,str(normalized)[0],ambig)
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://0.0.0.0:5000/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < a href='https://0.0.0.0:5000/static/tmp/{1}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Download DESeq2 output files < /b> < /button> < /a> < /div>".format(short_code,filename)
#graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://0.0.0.0:5000/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < /div>".format(short_code)
#layout = column(text_input, p)
graph += file_html(p,CDN)
return graph
def anota2seq_plot(ribo_rna_dict,organism,transcriptome,riboseq1,riboseq2,rnaseq1,rnaseq2,background_col,short_code,normalized,filename,no_groups,title_size, axis_label_size, subheading_size,marker_size,ambiguous,gene_list,label,minzscore,sig_translated,sig_rna,sig_buffering):
0
View Source File : riboflask_diff.py
License : MIT License
Project Creator : skiniry
License : MIT License
Project Creator : skiniry
def anota2seq_plot(ribo_rna_dict,organism,transcriptome,riboseq1,riboseq2,rnaseq1,rnaseq2,background_col,short_code,normalized,filename,no_groups,title_size, axis_label_size, subheading_size,marker_size,ambiguous,gene_list,label,minzscore,sig_translated,sig_rna,sig_buffering):
#Convert gene_list from string to list
# ("gene list passed to deseq2_plot", gene_list)
if gene_list != "":
gene_list = gene_list.replace(","," ").replace("\t"," ")
split_list = gene_list.split(" ")
gene_list = []
for item in split_list:
gene_list.append(item.strip(" ").upper())
x_values = []
y_values = []
basemeans = []
lfcses = []
genes = []
trans = []
highlight_x_values = []
highlight_y_values = []
highlight_basemeans = []
highlight_lfcses = []
highlight_genes = []
highlight_trans = []
teup_x_values = []
teup_y_values = []
teup_basemeans = []
teup_lfcses = []
teup_genes = []
teup_trans = []
tedown_x_values = []
tedown_y_values = []
tedown_basemeans = []
tedown_lfcses = []
tedown_genes = []
tedown_trans = []
bufferedup_x_values = []
bufferedup_y_values = []
bufferedup_basemeans = []
bufferedup_lfcses = []
bufferedup_genes = []
bufferedup_trans = []
buffereddown_x_values = []
buffereddown_y_values = []
buffereddown_basemeans = []
buffereddown_lfcses = []
buffereddown_genes = []
buffereddown_trans = []
rnaup_x_values = []
rnaup_y_values = []
rnaup_basemeans = []
rnaup_lfcses = []
rnaup_genes = []
rnaup_trans = []
rnadown_x_values = []
rnadown_y_values = []
rnadown_basemeans = []
rnadown_lfcses = []
rnadown_genes = []
rnadown_trans = []
largest_fc = 0
for gene in ribo_rna_dict:
if label == "TE":
x = ribo_rna_dict[gene]["rna_fc"]
y = ribo_rna_dict[gene]["ribo_fc"]
basemean = ribo_rna_dict[gene]["te_basemean"]
lfcSE = ribo_rna_dict[gene]["te_lfcSE"]
te_padj = ribo_rna_dict[gene]["te_padj"]
ribo_padj = ribo_rna_dict[gene]["ribo_padj"]
rna_padj = ribo_rna_dict[gene]["rna_padj"]
if x != "NA" and y != "NA":
x = float(x)
y = float(y)
if abs(x) > largest_fc:
largest_fc = abs(x)
if abs(y) > largest_fc:
largest_fc = abs(y)
if "NA" in te_padj:
te_padj = 1
if "NA" in ribo_padj:
ribo_padj = 1
if "NA" in rna_padj:
rna_padj = 1
te_padj = float(te_padj)
ribo_padj = float(ribo_padj)
rna_padj = float(rna_padj)
if gene not in gene_list:
if gene not in sig_translated and gene not in sig_rna and gene not in sig_buffering:
y_values.append(y)
x_values.append(x)
basemeans.append(basemean)
lfcses.append(lfcSE)
genes.append(gene)
trans.append(ribo_rna_dict[gene]["tran"])
elif gene in sig_translated:
if x < y :
teup_y_values.append(y)
teup_x_values.append(x)
teup_basemeans.append(basemean)
teup_lfcses.append(lfcSE)
teup_genes.append(gene)
teup_trans.append(ribo_rna_dict[gene]["tran"])
elif y < x :
tedown_y_values.append(y)
tedown_x_values.append(x)
tedown_basemeans.append(basemean)
tedown_lfcses.append(lfcSE)
tedown_genes.append(gene)
tedown_trans.append(ribo_rna_dict[gene]["tran"])
elif gene in sig_rna:
if x > 0:
rnaup_y_values.append(y)
rnaup_x_values.append(x)
rnaup_basemeans.append(basemean)
rnaup_lfcses.append(lfcSE)
rnaup_genes.append(gene)
rnaup_trans.append(ribo_rna_dict[gene]["tran"])
elif x < 0 :
rnadown_y_values.append(y)
rnadown_x_values.append(x)
rnadown_basemeans.append(basemean)
rnadown_lfcses.append(lfcSE)
rnadown_genes.append(gene)
rnadown_trans.append(ribo_rna_dict[gene]["tran"])
elif gene in sig_buffering:
#If no change in ribo, then this is buffered
if x > 0:
bufferedup_y_values.append(y)
bufferedup_x_values.append(x)
bufferedup_basemeans.append(basemean)
bufferedup_lfcses.append(lfcSE)
bufferedup_genes.append(gene)
bufferedup_trans.append(ribo_rna_dict[gene]["tran"])
else:
buffereddown_y_values.append(y)
buffereddown_x_values.append(x)
buffereddown_basemeans.append(basemean)
buffereddown_lfcses.append(lfcSE)
buffereddown_genes.append(gene)
buffereddown_trans.append(ribo_rna_dict[gene]["tran"])
else:
highlight_y_values.append(y)
highlight_x_values.append(x)
highlight_basemeans.append(basemean)
highlight_lfcses.append(lfcSE)
highlight_genes.append(gene)
highlight_trans.append(ribo_rna_dict[gene]["tran"])
source = ColumnDataSource({'x': x_values,'y':y_values,'trans':trans, 'genes':genes,'basemeans':basemeans,'lfcses':lfcses})
if label == "TE":
p = figure(plot_width=1300, plot_height=1300,x_axis_label="RNA-Seq FC (log2)", y_axis_label='Ribo-Seq FC (log2)',title="Ribo-Seq FC vs RNA-Seq FC ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
elif label == "Riboseq":
p = figure(plot_width=1300, plot_height=1300,x_axis_label="Ribo-Seq Cond2 count (log2)", y_axis_label='Ribo-Seq Cond1 count (log2)',title="Ribo-Seq correlation ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
elif label == "Rnaseq":
p = figure(plot_width=1300, plot_height=1300,x_axis_label="RNA-Seq Cond2 count (log2)", y_axis_label='RNA-Seq Cond1 count (log2)',title="RNA-Seq correlation ({})".format(short_code),toolbar_location="below",
tools = "reset,pan,box_zoom,save,hover,tap")
p.title.align="center"
p.title.text_font_size = title_size
p.xaxis.axis_label_text_font_size = axis_label_size
p.xaxis.major_label_text_font_size = marker_size
p.yaxis.axis_label_text_font_size = axis_label_size
p.yaxis.major_label_text_font_size = marker_size
p.background_fill_color = background_col
p.xgrid.grid_line_color = "#cccccc"
p.ygrid.grid_line_color = "#cccccc"
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='gray')
if highlight_genes != []:
source = ColumnDataSource({'x':highlight_x_values, 'y':highlight_y_values, 'trans':highlight_trans,'genes':highlight_genes,'basemeans':highlight_basemeans,'lfcses':highlight_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#000000',legend="Highlighted Genes")
source = ColumnDataSource({'x':teup_x_values, 'y':teup_y_values, 'trans':teup_trans,'genes':teup_genes,'basemeans':teup_basemeans,'lfcses':teup_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#00ff99',legend="Translation up")
source = ColumnDataSource({'x':tedown_x_values, 'y':tedown_y_values, 'trans':tedown_trans,'genes':tedown_genes,'basemeans':tedown_basemeans,'lfcses':tedown_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#ff5050',legend="Translation down")
source = ColumnDataSource({'x':bufferedup_x_values, 'y':bufferedup_y_values, 'trans':bufferedup_trans,'genes':bufferedup_genes,'basemeans':bufferedup_basemeans,'lfcses':bufferedup_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#6699ff',legend="Buffered up")
source = ColumnDataSource({'x':buffereddown_x_values, 'y':buffereddown_y_values, 'trans':buffereddown_trans,'genes':buffereddown_genes,'basemeans':buffereddown_basemeans,'lfcses':buffereddown_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#ffcc66',legend="Buffered down")
source = ColumnDataSource({'x':rnaup_x_values, 'y':rnaup_y_values, 'trans':rnaup_trans,'genes':rnaup_genes,'basemeans':rnaup_basemeans,'lfcses':rnaup_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#cc0099',legend="mRNA abundance up")
source = ColumnDataSource({'x':rnadown_x_values, 'y':rnadown_y_values, 'trans':rnadown_trans,'genes':rnadown_genes,'basemeans':rnadown_basemeans,'lfcses':rnadown_lfcses})
p.scatter('x','y', alpha=0.2,color="black",fill_alpha=1,size=12,source=source,fill_color='#ffff00',legend="mRNA abundance down")
#legend_label=name)
p.legend.location = "top_left"
p.legend.click_policy="hide"
p.legend.label_text_font_size = "28px"
hover = p.select(dict(type=HoverTool))
hover.mode = 'mouse'
plot_limit = largest_fc*1.1
if label == "TE":
p.line([plot_limit*-1,plot_limit], [plot_limit*-1,plot_limit], color="#cccccc",line_width=1)
hover.tooltips = [("Ribo fc", "@y"),("RNA fc","@x"),("Genes","@genes"),("Transcript","@trans"),("Basemean","@basemeans"),("lfcSE","@lfcses")]
elif label == "Riboseq":
p.line([0,16], [0,16], color="#cccccc",line_width=1)
hover.tooltips = [("Ribo Cond 1 count (log2)", "@y"),("Ribo Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans"),("Basemean","@basemeans"),("lfcSE","@lfcses")]
#corr = spearmanr(x_values, y_values)
#pearson_corr = pearsonr(x_values, y_values)
#mytext = Label(x=0.1,y=max(y_values),text="Pearson correlation: {}".format(round(pearson_corr[0],2)),background_fill_color="white",text_font_size="13pt")
#p.add_layout(mytext)
else:
p.line([0,16], [0,16], color="#cccccc",line_width=1)
hover.tooltips = [("Rna-seq Cond 1 count (log2)", "@y"),("Rna-seq Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans"),("Basemean","@basemeans"),("lfcSE","@lfcses")]
hover.tooltips = [("Ribo Cond 1 count (log2)", "@y"),("Ribo Cond 2 count (log2)","@x"),("Genes","@genes"),("Transcript","@trans"),("Basemean","@basemeans"),("lfcSE","@lfcses")]
#corr = spearmanr(x_values, y_values)
#pearson_corr = pearsonr(x_values, y_values)
#mytext = Label(x=0.1,y=max(y_values),text="Pearson correlation: {}".format(round(pearson_corr[0],2)),background_fill_color="white",text_font_size="13pt")
#p.add_layout(mytext)
file_string = ""
label_string="&labels=RIBO-Seq Cond 1,%23007a02_RIBO-Seq Cond 2,%23960000_mRNA-Seq Cond 1,%2374ed76_mRNA-seq Cond 2,%23ff6d6d"
if riboseq1:
if riboseq1[0] != "":
for file_id in riboseq1:
file_string += ("{},".format(file_id))
file_string += ("%23007a02_")
if riboseq2:
if riboseq2[0] != "":
for file_id in riboseq2:
file_string += ("{},".format(file_id))
file_string += ("%23960000_")
if rnaseq1:
if rnaseq1[0] != "":
for file_id in rnaseq1:
file_string += ("{},".format(file_id))
file_string += ("%2374ed76_")
if rnaseq2:
if rnaseq2[0] != "":
for file_id in rnaseq2:
file_string += ("{},".format(file_id))
file_string += ("%23ff6d6d_")
# remove the trailing _ in file_string if it's been populated
if file_string:
file_string = file_string[:len(file_string)-1]
if ambiguous == True:
ambig = "T"
else:
ambig = "F"
url = "http://0.0.0.0:5000/{}/{}/comparison/?files={}{}&[email protected]&normalize={}&cov=T&ambig={}&minread=25&maxread=150".format(organism, transcriptome,file_string,label_string,str(normalized)[0],ambig)
taptool = p.select(type=TapTool)
taptool.callback = OpenURL(url=url)
graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://0.0.0.0:5000/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < a href='https://0.0.0.0:5000/static/tmp/{1}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Download DESeq2 output files < /b> < /button> < /a> < /div>".format(short_code,filename)
#graph = " < div style='padding-left: 55px;padding-top: 22px;'> < a href='https://0.0.0.0:5000/short/{0}' target='_blank' > < button class='button centerbutton' type='submit'> < b>Direct link to this plot < /b> < /button> < /a> < br> < /div>".format(short_code)
#layout = column(text_input, p)
graph += file_html(p,CDN)
return graph
