)
ax.set_title(f"Mann-Whitney rank test p-value={p:.2e}")
ax.grid(axis="x", lw=0.1, color="#e1e1e1", zorder=0)
plt.savefig(
f"{RPATH}/ProteinTranscript_cyclops_boxplot.pdf",
bbox_inches="tight",
transparent=True,
)
plt.close("all")
#
g = "PSMC2"
plot_df = pd.concat([
gexp.loc[g].rename("gexp"),
prot.loc[g].rename("prot"),
cnv.loc[g].rename("cnv"),
], axis=1).sort_values("cnv", ascending=False)
_, ax = plt.subplots(1, 1, figsize=(2.5, 2.0), dpi=600)
GIPlot.gi_continuous_plot("gexp", "prot", "cnv", plot_df, ax=ax, mid_point_norm=False)
plt.savefig(
f"{RPATH}/ProteinTranscript_cyclops_omics_{g}.pdf",
bbox_inches="tight",
transparent=True,
)
plt.close("all")
plot_reg=False,
pal=PALETTE_TTYPE)
ax.set_xlabel(f"Factor {f_x[1:]}")
ax.set_ylabel(f"Factor {f_y[1:]}")
plt.savefig(f"{RPATH}/MultiOmics_{f_x}_{f_y}_tissue_plot.pdf",
bbox_inches="tight")
plt.savefig(f"{RPATH}/MultiOmics_{f_x}_{f_y}_tissue_plot.png",
bbox_inches="tight",
dpi=600)
plt.close("all")
# Continous annotation
for z in ["VIM_proteomics", "CDH1_proteomics"]:
ax = GIPlot.gi_continuous_plot(f_x,
f_y,
z,
plot_df,
cbar_label=z.replace("_", " "))
ax.set_xlabel(f"Factor {f_x[1:]}")
ax.set_ylabel(f"Factor {f_y[1:]}")
plt.savefig(f"{RPATH}/MultiOmics_{f_x}_{f_y}_continous_{z}.pdf",
bbox_inches="tight")
plt.savefig(
f"{RPATH}/MultiOmics_{f_x}_{f_y}_continous_{z}.png",
bbox_inches="tight",
dpi=600,
)
plt.close("all")
# Export matrix
plot_df = pd.concat(
cbar_pos=None,
figsize=np.array(plot_df.shape) * 0.275,
)
plt.savefig(
f"{RPATH}/ProteinTranscriptSample_cfeatures_clustermap.pdf",
bbox_inches="tight",
transparent=True,
)
plt.close("all")
#
for z_var in ["CopyNumberInstability", "ploidy"]:
ax = GIPlot.gi_continuous_plot("prot_corr",
"gexp_prot_corr",
z_var,
satt_corr,
mid_point_norm=False)
ax.set_xlabel("Sanger&CMRI\nProtein ~ Copy number (Pearson's R)")
ax.set_ylabel("Sanger&CMRI\nProtein ~ Transcript (Pearson's R)")
plt.savefig(
f"{RPATH}/ProteinTranscriptSample_prot_gexp_regression_{z_var}.pdf",
bbox_inches="tight",
)
plt.close("all")
#
x_var, y_var, z_var = "ploidy", "CopyNumberInstability", "size"
ax = GIPlot.gi_continuous_plot(x_var,
y_var,
z_var,
dtype_df_lm = sl_lm_fimpor[dtype]
# Correlate feature weights
fimpor_corr = pd.Series({
p: two_vars_correlation(dtype_df[p], dtype_df_lm[p].abs())["corr"]
for p in set(dtype_df_lm).intersection(dtype_df)
})
# Plot
nassoc_df_fimpo = pd.concat(
[nassoc_df, fimpor_corr.rename("feature")], axis=1)
g = GIPlot.gi_continuous_plot(
"nassoc",
dtype,
"feature",
nassoc_df_fimpo,
lowess=True,
cbar_label="Feature importance correlation",
)
g.set_xlabel("Number of associations (LM)")
g.set_ylabel(f"Score (ML {dtype})")
plt.savefig(f"{RPATH}/SLinteractions_ml_nassoc_{dtype}_fimpo.pdf",
bbox_inches="tight")
plt.close("all")
# Volcano
#
plot_df = sl_lm.query("fdr < .1")
s_transform = MinMaxScaler(feature_range=[1, 10])