(drespo.T < dmax[drespo.index]).sum().rename("nsamples"), drespo.T[drespo.T < dmax[drespo.index]].median().rename("dependency"), ml_scores, ], axis=1, ) drug_selective["name"] = [i.split(";")[1] for i in drug_selective.index] drug_selective_set = set(drug_selective.query("skew < -2").index) # Scatter grid = GIPlot.gi_regression( "skew", "median", drug_selective, size="dependency", size_inverse=True, size_legend_title="Median IC50", plot_reg=False, plot_annot=False, ) grid.ax_joint.axvline(-1, c=GIPlot.PAL_DTRACE[1], lw=0.3, ls="--") g_highlight_df = drug_selective.query("skew < -1").sort_values("skew").head(5) labels = [ grid.ax_joint.text( row["skew"], row["median"], row["name"], color="k", fontsize=4 ) for _, row in g_highlight_df.iterrows() ] adjust_text( labels,
drug_pca_df.corr(), cmap="Spectral", annot=True, center=0, fmt=".2f", annot_kws=dict(size=4), lw=0.05, figsize=(3, 3), ) plt.savefig(f"{RPATH}/drug_pca_clustermap.pdf", bbox_inches="tight", dpi=600) plt.close("all") # y_var = "PC1" g = GIPlot.gi_regression("growth", y_var, drug_pca_df, lowess=True) g.set_axis_labels("Growth rate", f"{y_var} ({drug_vexp[y_var]*100:.1f}%)") plt.savefig(f"{RPATH}/drug_pca_regression_growth.pdf", bbox_inches="tight", dpi=600) plt.close("all") # Covariates # # CRISPR covs_crispr = LMModels.define_covariates( institute=crispr_obj.merged_institute, medium=True, cancertype=False, tissuetype=False, mburden=False, ploidy=True,
crispr.apply(skew, axis=1).rename("skew"), crispr.median(1).rename("median"), (crispr < -0.5).sum(1).rename("nsamples"), crispr[crispr < -0.5].median(1).rename("dependency").abs(), ml_scores, ], axis=1, ) crispr_selective_set = set(crispr_selective.query("skew < -3").index) # Scatter grid = GIPlot.gi_regression( "skew", "median", crispr_selective, size="dependency", plot_reg=False, plot_annot=False, ) grid.ax_joint.axvline(-3, c=GIPlot.PAL_DTRACE[1], lw=0.3, ls="--") g_highlight_df = crispr_selective.query("skew < -3").sort_values( "skew").head(15) labels = [ grid.ax_joint.text(row["skew"], row["median"], i, color="k", fontsize=4) for i, row in g_highlight_df.iterrows() ] adjust_text(