update figure styles (#86)

maserol/datasets.py

@@ -3,6 +3,7 @@
 import re
 import xarray as xr
 import tensordata
+
 from tensordata.zohar import data as zohar
 from tensordata.kaplonek import MGH4D, load_file as load_file_kaplonek
 from tensordata.alter import data as alter, load_file as load_file_alter

maserol/figures/figure_2.py

@@ -8,6 +8,7 @@
 from maserol.figures.common import CACHE_DIR, Multiplot
 from maserol.forward_backward import forward_backward
 
+
 N_ITER_2D = 3
 STEPS_2D = 4
 N_ITER_2E = 3
@@ -19,7 +20,7 @@
 def makeFigure():
     plot = Multiplot(
         (3, 2),
-        (3, 2.5),
+        fig_size=(7.5, 7.5 * 2 / 3),
         subplot_specs=[
             (0, 2, 0, 1),
             (2, 1, 0, 1),
@@ -28,13 +29,13 @@ def makeFigure():
             (2, 1, 1, 1),
         ],
     )
-    plot.add_subplot_labels()
+    plot.add_subplot_labels(ax_relative=True)
     axes, fig = plot.axes, plot.fig
     figure_2b(axes[1])
     figure_2c(axes[2])
     figure_2d(axes[3])
     figure_2e(axes[4])
-    fig.tight_layout()
+    fig.tight_layout(pad=0.01, w_pad=0, h_pad=0.2)
     return fig
 
 
@@ -66,7 +67,7 @@ def figure_2c(ax):
     sns.scatterplot(
         x=np.log10(Rtot_inferred[:, 0]), y=np.log10(Rtot[:, 0]), alpha=0.6, ax=ax
     )
-    ax.set_title("Actual vs predicted abundance (30% noise)")
+    ax.set_title("Actual vs predicted (30% noise)")
     ax.set_xlabel("$\mathrm{log_{10}}$ Inferred IgG1")
     ax.set_ylabel("$\mathrm{log_{10}}$ Actual IgG1")
 
@@ -104,6 +105,7 @@ def figure_2d(ax):
     ax.set_title("Prediction performance vs detection noise")
     ax.set_xlabel("Noise σ")
     ax.set_ylabel("$R^2$")
+    ax.set_ylim(-0.01, 1.01)
 
 
 def figure_2e(ax):
@@ -141,3 +143,4 @@ def figure_2e(ax):
     ax.set_title("Prediction performance vs $K_{a}$ noise")
     ax.set_xlabel("Noise σ")
     ax.set_ylabel("$R^2$")
+    ax.set_ylim(-0.01, 1.01)

maserol/figures/figure_3.py

@@ -22,7 +22,7 @@
 def makeFigure():
     plot = Multiplot(
         (3, 2),
-        (3, 2.5),
+        fig_size=(7.5, 7.5 * 2 / 3),
         subplot_specs=[
             (0, 2, 0, 1),
             (2, 1, 0, 1),
@@ -34,7 +34,7 @@ def makeFigure():
     figure_3b(plot.axes[1])
     figure_3cd(plot.axes[2], plot.axes[3])
     figure_3e(plot.axes[4])
-    plot.fig.tight_layout()
+    plot.fig.tight_layout(pad=0.01, w_pad=0, h_pad=0.2)
     plot.add_subplot_labels(ax_relative=True)
     return plot.fig
 

maserol/figures/figure_4.py

@@ -16,23 +16,23 @@
 def makeFigure():
     plot = Multiplot(
         (3, 2),
-        (3, 2.5),
+        fig_size=(7.5, 7.5 * 2 / 3 + 0.1),
         subplot_specs=[
-            (0, 1, 0, 2),
+            (0, 1, 0, 1),
             (1, 1, 0, 1),
             (2, 1, 0, 1),
-            (1, 1, 1, 1),
+            (0, 2, 1, 1),
             (2, 1, 1, 1),
         ],
     )
-    figure_4b(plot.axes[1])
-    figure_4cde(plot.axes[2], plot.axes[3], plot.axes[4])
-    plot.add_subplot_labels()
-    plot.fig.tight_layout()
+    figure_mechanistic_relate(plot.axes[0], plot.axes[1], plot.axes[2])
+    figure_CE(plot.axes[4])
+    plot.add_subplot_labels(ax_relative=True)
+    plot.fig.tight_layout(pad=0, w_pad=0.2, h_pad=1)
     return plot.fig
 
 
-def figure_4b(ax):
+def figure_CE(ax):
     fucose_ce = Alter().get_fucose_data()
     if UPDATE_CACHE:
         detection_signal = Alter().get_detection_signal()
@@ -56,15 +56,15 @@ def figure_4b(ax):
     r, p = pearsonr(fucose_compare["fucose_ce"], fucose_compare["fucose_inferred"])
     ax.set_title("Model Inferences vs CE Measurements")
     ax.text(
-        0.7,
+        0.6,
         0.06,
         r"r = " + str(round(r, 2)),
         verticalalignment="bottom",
         horizontalalignment="left",
         transform=ax.transAxes,
     )
     ax.text(
-        0.7,
+        0.6,
         0.01,
         r"p = " + "{:.2e}".format(p),
         verticalalignment="bottom",
@@ -82,7 +82,7 @@ def figure_4b(ax):
     ax.plot(lims, lims, linestyle="--", color="gray", alpha=0.75)
 
 
-def figure_4cde(ax_c, ax_d, ax_e):
+def figure_mechanistic_relate(ax_0, ax_1, ax_2):
     zohar = Zohar()
     if UPDATE_CACHE:
         detection_signal = zohar.get_detection_signal()
@@ -104,50 +104,50 @@ def figure_4cde(ax_c, ax_d, ax_e):
     sns.scatterplot(
         y=y,
         x=df["fucose_inferred"],
-        ax=ax_c,
+        ax=ax_0,
         alpha=ALPHA,
     )
-    ax_c.set_xlabel("Inferred IgG Fucosylation (%)")
-    ax_c.set_ylabel(
+    ax_0.set_xlabel("Inferred IgG Fucosylation (%)")
+    ax_0.set_ylabel(
         r"$\mathrm{log_{10}}$"
         + f"({DETECTION_DISPLAY_NAMES['FcR3A']} / {DETECTION_DISPLAY_NAMES['FcR2A']})"
     )
     r, p = pearsonr(df["fucose_inferred"], y)
-    ax_c.text(
-        0.75,
+    ax_0.text(
+        0.05,
         0.06,
         r"r = " + str(round(r, 2)),
         verticalalignment="bottom",
         horizontalalignment="left",
-        transform=ax_c.transAxes,
+        transform=ax_0.transAxes,
     )
 
     sns.scatterplot(
-        data=df, y="ADNKA_CD107a_S", x="fucose_inferred", ax=ax_d, alpha=ALPHA
+        data=df, y="ADNKA_CD107a_S", x="fucose_inferred", ax=ax_1, alpha=ALPHA
     )
-    ax_d.set_xlabel("Inferred IgG Fucosylation (%)")
-    ax_d.set_ylabel(r"ADCC (CD107a)")
+    ax_1.set_xlabel("Inferred IgG Fucosylation (%)")
+    ax_1.set_ylabel(r"ADCC (CD107a)")
     r, p = pearsonr(df["fucose_inferred"], df["ADNKA_CD107a_S"])
-    ax_d.text(
+    ax_1.text(
         0.05,
         0.86,
         r"r = " + str(round(r, 2)),
         verticalalignment="bottom",
         horizontalalignment="left",
-        transform=ax_d.transAxes,
+        transform=ax_1.transAxes,
     )
 
     sns.scatterplot(
-        data=df, y="ADNKA_MIP1b_S", x="fucose_inferred", ax=ax_e, alpha=ALPHA
+        data=df, y="ADNKA_MIP1b_S", x="fucose_inferred", ax=ax_2, alpha=ALPHA
     )
-    ax_e.set_xlabel("Inferred IgG Fucosylation (%)")
-    ax_e.set_ylabel(r"ADCC (MIP1b)")
+    ax_2.set_xlabel("Inferred IgG Fucosylation (%)")
+    ax_2.set_ylabel(r"ADCC (MIP1b)")
     r, p = pearsonr(df["fucose_inferred"], df["ADNKA_MIP1b_S"])
-    ax_e.text(
+    ax_2.text(
         0.05,
         0.86,
         r"r = " + str(round(r, 2)),
         verticalalignment="bottom",
         horizontalalignment="left",
-        transform=ax_e.transAxes,
+        transform=ax_2.transAxes,
     )

maserol/figures/figure_5.py

@@ -1,7 +1,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import pingouin as pg
 import seaborn as sns
+from scipy.stats import pearsonr
 from statannotations.Annotator import Annotator
 
 from maserol.core import optimize_loss
@@ -22,19 +24,19 @@
 
 def makeFigure():
     plot = Multiplot(
-        (3, 2),
-        (3, 2.5),
+        (10, 2),
+        fig_size=(7.5, 7.5 * 2 / 3),
         subplot_specs=[
-            (0, 1, 0, 1),
-            (1, 1, 0, 1),
-            (2, 1, 0, 1),
-            (0, 3, 1, 1),
+            (0, 3, 0, 1),
+            (3, 3, 0, 1),
+            (6, 4, 0, 1),
+            (0, 10, 1, 1),
         ],
     )
     figure_5abc(plot.axes[0], plot.axes[1], plot.axes[2])
     figure_5d(plot.axes[3])
     plot.add_subplot_labels()
-    plot.fig.tight_layout()
+    plot.fig.tight_layout(pad=0, w_pad=0.2, h_pad=1)
     return plot.fig
 
 
@@ -68,7 +70,6 @@ def figure_5abc(ax_a, ax_b, ax_c):
         showfliers=False,
     )
     ax.set_ylabel("anti-S IgG Fucosylation (%)")
-    ax.set_ylim(30, 100)
     pairs = (("Yes", "No"),)
     annotator = Annotator(ax, pairs, data=df_merged, x="ARDS", y="fucose_inferred")
     annotate_mann_whitney(annotator)
@@ -82,25 +83,54 @@ def figure_5abc(ax_a, ax_b, ax_c):
         order=[0, 1],
         showfliers=False,
     )
-    ax.set_ylim(30, 100)
-    ax.set_ylabel("anti-S IgG Fucosylation (%)")
+    # ax.set_ylabel("anti-S IgG Fucosylation (%)")
+    ax.set_ylabel(None)
     ax.set_xlabel("Immunosuppressed")
     ax.set_xticklabels(["No", "Yes"])
     pairs = ((1, 0),)
     annotator = Annotator(ax, pairs, data=df_merged, x="immunosup", y="fucose_inferred")
     annotate_mann_whitney(annotator)
 
     ax = ax_c
+    fucose_over_time = pd.merge(
+        fucose_inferred, metadata[["days", "patient_ID"]], how="inner", on="Sample"
+    )
+    fucose_over_time.dropna(inplace=True)
+    fucose_over_time = fucose_over_time.groupby("patient_ID").filter(
+        lambda x: x["days"].nunique() >= 2
+    )
+    rmcorr_result = pg.rm_corr(
+        data=fucose_over_time, x="days", y="fucose_inferred", subject="patient_ID"
+    )
+    r, p = rmcorr_result.r.iloc[0], rmcorr_result.pval.iloc[0]
+    fucose_over_time_binned = pd.merge(
+        fucose_inferred, zohar.get_days_binned(), how="inner", on="Sample"
+    )
     sns.lineplot(
-        data=pd.merge(
-            fucose_inferred, zohar.get_days_binned(), how="inner", on="Sample"
-        ),
+        data=fucose_over_time_binned,
         x="days",
         y="fucose_inferred",
         ax=ax,
     )
+    ax.text(
+        0.7,
+        0.8,
+        f"r = " + str(round(r, 2)),
+        verticalalignment="bottom",
+        horizontalalignment="left",
+        transform=ax.transAxes,
+    )
+    ax.text(
+        0.7,
+        0.72,
+        r"p = " + "{:.2e}".format(p),
+        verticalalignment="bottom",
+        horizontalalignment="left",
+        transform=ax.transAxes,
+    )
     ax.set_xlabel("Days following symptom onset")
-    ax.set_ylabel("anti-S IgG Fucosylation (%)")
+    # ax.set_ylabel("anti-S IgG Fucosylation (%)")
+    ax.set_ylabel(None)
     ax.set_xlim(0, 30)
 
 
@@ -151,7 +181,7 @@ def figure_5d(ax):
     )
     ax.set_xticklabels(ax.get_xticklabels(), rotation=30, fontsize="small")
     ax.set_ylabel("IgG Fucosylation (%)")
-    ax.set_ylim(0, 100)
+    ax.set_ylim(-2, 102)
     ax.set_xlabel("Antigen")
     pairs = [((ag, "control"), (ag, "case")) for ag in df_compare.Antigen.unique()]
     annotator = Annotator(
@@ -166,6 +196,5 @@ def figure_5d(ax):
 
     handles, labels = ax.get_legend_handles_labels()
     new_labels = ["Avoided infection", "Infected"]
-    ax.legend(handles, new_labels, framealpha=1, title=None)
-
-    sns.move_legend(ax, "lower right")
+    legend = ax.legend(handles, new_labels, title=None, loc="lower right")
+    legend.get_frame().set_alpha(1)

maserol/figures/figure_6.py

@@ -33,12 +33,12 @@ def makeFigure():
     df_compare.replace("IIIb.pr55.Gag", "pr55.Gag.IIIb", inplace=True)
 
     plot = Multiplot(
-        (5, 2),
-        fig_size=(9, 5),
+        (7, 2),
+        fig_size=(7.5, 5),
         subplot_specs=[
-            (0, 5, 0, 1),
-            (0, 4, 1, 1),
-            (4, 1, 1, 1),
+            (0, 7, 0, 1),
+            (0, 6, 1, 1),
+            (6, 1, 1, 1),
         ],
     )
 
@@ -67,8 +67,8 @@ def makeFigure():
         "Treated progressor",
         "Untreated progressor",
     ]
-    ax.legend(handles, new_labels)
-    sns.move_legend(ax, "lower right")
+    legend = ax.legend(handles, new_labels, loc="lower right")
+    legend.get_frame().set_alpha(1)
 
     # b
     ax = plot.axes[1]
@@ -128,6 +128,7 @@ def makeFigure():
     ax.set_xticklabels(
         ax.get_xticklabels(), rotation=X_LABEL_ROTATION, fontsize="small"
     )
+    ax.set_yticklabels([])
 
     pairs = [("Env trimer", "p24"), ("Env trimer", "pr55.Gag")]
     annotator = Annotator(
@@ -136,5 +137,6 @@ def makeFigure():
     annotate_mann_whitney(annotator)
 
     plot.add_subplot_labels()
+    plot.fig.tight_layout(pad=0, w_pad=0.2, h_pad=1)
 
     return plot.fig

maserol/figures/figure_7.py

@@ -22,11 +22,11 @@
 }
 
 
-TITLE_FONT_SIZE = 13
+TITLE_FONT_SIZE = 12.5
 
 
 def makeFigure():
-    plot = Multiplot((2, 2), (4.5, 3))
+    plot = Multiplot((2, 2), fig_size=(7.5, 5))
 
     if UPDATE_CACHE:
         update_cache(2)
@@ -45,8 +45,10 @@ def makeFigure():
     plot_combinations(df_3, "r2", plot.axes[3], ylim=(-1, 1))
     plot.axes[0].set_title("2 Missing Detections", fontsize=TITLE_FONT_SIZE)
     plot.axes[1].set_title("3 Missing Detections", fontsize=TITLE_FONT_SIZE)
+    plot.axes[1].set_yticklabels([])
     plot.axes[1].set_ylabel(None)
     plot.axes[3].set_ylabel(None)
+    plot.axes[3].set_yticklabels([])
 
     plot.axes[0].set_ylabel(
         f"Imputation Accuracy ({METRIC_LABEL_MAPPINGS['r']})", fontsize=TITLE_FONT_SIZE