def plot_counts(counts: pd.DataFrame):
counts.bc_l = pd.Categorical(counts.bc_l, bc_range(counts.bc_l))
counts.bc_r = pd.Categorical(counts.bc_r, bc_range(counts.bc_r))
log = np.log
return (
ggplot(counts, aes('bc_r', 'bc_l', fill='log(Count)'))
+ geom_tile()
# + scale_y_reverse()
+ coord_fixed()
+ scale_fill_cmap('inferno')
+ theme(axis_text_x=element_text(angle=90, vjust=.5))
+ labs(x='Right Barcode', y='Left Barcode')
)
def user_theme(
plot: p9.ggplot,
x_axis_label: str = "",
y_axis_label: str = "",
title: str = "",
**plot_theme,
) -> p9.ggplot:
"""Render the specified plot in the current theme with common attributes to all plots eg. legend_position etc. The themed plot is
returned. Saves code in each plot by handled all the standard stuff here."""
theme_args = { # TODO FIXME... make defaults chosen from user profile
"axis_text_x": p9.element_text(size=7),
"axis_text_y": p9.element_text(size=7),
"figure_size": (12, 6),
"legend_position": "none",
}
theme_args.update(**plot_theme)
# remove asxtrade kwargs
want_cmap_d = theme_args.pop("asxtrade_want_cmap_d", True)
want_fill_d = theme_args.pop(
"asxtrade_want_fill_d",
False) # most graphs dont fill, so False by default
want_fill_continuous = theme_args.pop("asxtrade_want_fill_continuous",
False)
plot = (
plot +
p9.theme_bw() # TODO FIXME... make chosen theme from user profile
+ p9.labs(x=x_axis_label, y=y_axis_label, title=title) +
p9.theme(**theme_args))
if want_cmap_d:
plot += p9.scale_colour_cmap_d()
if want_fill_d:
plot += p9.scale_fill_cmap_d()
elif want_fill_continuous:
plot += p9.scale_fill_cmap()
return plot
def log_HR_plot(inFile, label_unit=10, log_scale_color=True):
par = get_params(inFile)
pca_components = par["means"]["logHR"].shape[0] >> 1
components = range(pca_components)
tf_components = slice(pca_components, 2 * pca_components)
logHR_df = pd.DataFrame(index=[f"{i+1}" for i in components])
logHR_df["tumor logHR"] = par["means"]["logHR"][components, 0]
logHR_df["non-tumor logHR"] = par["means"]["logHR"][tf_components, 0]
logHR_df["component"] = components
logHR_df["label"] = [
logHR_df.index[i] if i <= label_unit else "" for i in components
]
logHR_df["tumor logHR sd"] = par["stds"]["logHR"][components, 0]
logHR_df["non-tumor logHR sd"] = par["stds"]["logHR"][tf_components, 0]
logHR_df["tumor Z"] = logHR_df["tumor logHR"] / logHR_df["tumor logHR sd"]
logHR_df["non-tumor Z"] = (logHR_df["non-tumor logHR"] /
logHR_df["tumor logHR sd"])
logHR_df["tumor p-value"] = norm.sf(abs(logHR_df["tumor Z"])) * 2
logHR_df["non-tumor p-value"] = norm.sf(abs(logHR_df["non-tumor Z"])) * 2
logHR_df["tumor -log10(p-value)"] = -np.log10(logHR_df["tumor p-value"])
logHR_df["non-tumor -log10(p-value)"] = -np.log10(
logHR_df["non-tumor p-value"])
lb = min(logHR_df["non-tumor logHR"].min(), logHR_df["tumor logHR"].min())
ub = max(logHR_df["non-tumor logHR"].max(), logHR_df["tumor logHR"].max())
pl = (pn.ggplot(
pn.aes(
"non-tumor logHR",
"tumor logHR",
color="non-tumor p-value",
fill="tumor p-value",
label="label",
),
logHR_df,
) + pn.xlim(lb, ub) + pn.ylim(lb, ub) + pn.geom_abline() +
pn.geom_point() + pn.theme_minimal() +
pn.geom_text(ha="left", va="bottom", color="black"))
if log_scale_color:
pl += pn.scale_color_cmap(trans="log")
pl += pn.scale_fill_cmap(trans="log")
lb = min(
logHR_df["non-tumor -log10(p-value)"].min(),
logHR_df["tumor -log10(p-value)"].min(),
)
ub = max(
logHR_df["non-tumor -log10(p-value)"].max(),
logHR_df["tumor -log10(p-value)"].max(),
)
pl_p = (pn.ggplot(
pn.aes(
"non-tumor -log10(p-value)",
"tumor -log10(p-value)",
color="component",
label="label",
),
logHR_df,
) + pn.geom_point() + pn.xlim(lb, ub) + pn.ylim(lb, ub) +
pn.theme_minimal() +
pn.geom_text(ha="left", va="bottom", color="black"))
return pl, pl_p, logHR_df
["x_loc", "y_loc", "well", "site_location",
"site"])["total_cell_count"].mean().reset_index())
plate = cell_count_df["plate"].unique()[0]
os.makedirs(output_figuresdir, exist_ok=True)
by_well_gg = (
gg.ggplot(cell_count_totalcells_df, gg.aes(x="x_loc", y="y_loc")) +
gg.geom_point(gg.aes(fill="total_cell_count"), size=10) +
gg.geom_text(gg.aes(label="site_location"), color="lightgrey") +
gg.facet_wrap("~well") + gg.coord_fixed() + gg.theme_bw() +
gg.ggtitle(f"Total Cells/Well\n{plate}") + gg.theme(
axis_text=gg.element_blank(),
axis_title=gg.element_blank(),
strip_background=gg.element_rect(colour="black", fill="#fdfff4"),
) + gg.labs(fill="Cells") + gg.scale_fill_cmap(name="magma"))
output_file = pathlib.Path(output_figuresdir,
"plate_layout_cells_count_per_well.png")
if check_if_write(output_file, force, throw_warning=True):
by_well_gg.save(output_file, dpi=300, verbose=False)
# Plot cell category ratios per well
ratio_df = pd.pivot_table(
cell_count_df,
values="cell_count",
index=["site", "plate", "well", "site_location", "x_loc", "y_loc"],
columns=["Cell_Quality"],
)
ratio_df = ratio_df.assign(Sum=ratio_df.sum(axis=1),
Pass_Filter=ratio_df[cell_filter].sum(axis=1))
os.makedirs(output_figuresdir, exist_ok=True)
by_well_gg = (
gg.ggplot(cell_count_totalcells_df, gg.aes(x="x_loc", y="y_loc"))
+ gg.geom_point(gg.aes(fill="total_cell_count"), size=10)
+ gg.geom_text(gg.aes(label="site_location"), color="lightgrey")
+ gg.facet_wrap("~well")
+ gg.coord_fixed()
+ gg.theme_bw()
+ gg.ggtitle(f"Total Cells/Well\n{plate}")
+ gg.theme(
axis_text=gg.element_blank(),
axis_title=gg.element_blank(),
strip_background=gg.element_rect(colour="black", fill="#fdfff4"),
)
+ gg.labs(fill="Cells")
+ gg.scale_fill_cmap(name="magma")
)
output_file = pathlib.Path(output_figuresdir, "plate_layout_cells_count_per_well.png")
if check_if_write(output_file, force, throw_warning=True):
by_well_gg.save(output_file, dpi=300, verbose=False)
# Plot cell category ratios per well
ratio_df = pd.pivot_table(
cell_count_df,
values="cell_count",
index=["site", "plate", "well", "site_location", "x_loc", "y_loc"],
columns=["Cell_Quality"],
)
ratio_df = ratio_df.assign(
Sum=ratio_df.sum(axis=1), Pass_Filter=ratio_df[cell_filter].sum(axis=1)
],
gg.aes(x="x_loc", y="y_loc"),
)
+ gg.geom_point(gg.aes(fill="total_cell_count"), shape="s", size=6)
+ gg.geom_text(gg.aes(label="site_location"), color="lightgrey", size=6)
+ gg.facet_wrap("~well")
+ gg.coord_fixed()
+ gg.theme_bw()
+ gg.ggtitle(f"Total Cells/Well\n{plate}")
+ gg.theme(
axis_text=gg.element_blank(),
axis_title=gg.element_blank(),
strip_background=gg.element_rect(colour="black", fill="#fdfff4"),
)
+ gg.labs(fill="Cells")
+ gg.scale_fill_cmap(name="Number of Cells")
)
output_file = pathlib.Path(
output_figuresdir, f"plate_layout_cells_count_per_well_{plate}.png"
)
if check_if_write(output_file, force, throw_warning=True):
by_well_gg.save(output_file, dpi=300, verbose=False)
# Plot cell category ratios per well and empty cells per well
ratio_df = pd.pivot_table(
cell_count_df,
values="cell_count",
index=["site", "plate", "well", "site_location", "x_loc", "y_loc"],
columns=["Cell_Quality"],
)