) 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])