Taxol

lineage/Lineage_collections.py

@@ -1,7 +1,7 @@
 """ This file is to collect those lineages that have the same condition, and have 3 or greater number of cells in their lineages. """
 
 from .LineageInputOutput import import_exp_data
-from .import_lineage import MCF10A
+from .import_lineage import MCF10A, import_taxol
 from .states.StateDistributionGaPhs import StateDistribution
 from .states.StateDistributionGamma import StateDistribution as StDist
 from .LineageTree import LineageTree
@@ -217,3 +217,15 @@ def popout_single_lineages(lineages):
 AllLapatinib = [Lapatinib_Control + Gemcitabine_Control, Lapt25uM, Lapt50uM, Lap250uM]
 AllGemcitabine = [Lapatinib_Control + Gemcitabine_Control, Gem5uM, Gem10uM, Gem30uM]
 GFs = [pbs, egf, hgf, osm]
+
+##########################################################
+# NEW PACLITAXEL DATA
+untreated, taxol_05, taxol_1, taxol_15, taxol_2, taxol_25, taxol_3, taxol_4 = import_taxol()
+# 555, 1119, 486, 382, 272, 365, 246, 266
+untreated_taxol = [LineageTree(list_cells, E) for list_cells in untreated] # 555 cells
+Taxol_1 = [LineageTree(list_cells, E) for list_cells in taxol_1] # 486 cells
+Taxol_2 = [LineageTree(list_cells, E) for list_cells in taxol_2] # 272 cells
+Taxol_3 = [LineageTree(list_cells, E) for list_cells in taxol_3] # 246 cells
+Taxol_4 = [LineageTree(list_cells, E) for list_cells in taxol_4] # 266 cells
+
+taxols = [untreated_taxol, Taxol_1, Taxol_2, Taxol_4]

lineage/figures/figureS18.py

@@ -0,0 +1,45 @@
+""" In this file we plot the raw data before fitting from paclitaxel treated HCC1143 cells """
+# from ..Lineage_collections import taxols as Taxol_lin_list
+import numpy as np
+from ..import_lineage import import_taxol_file, trim_taxol
+from ..plotTree import plotLineage
+from .common import getSetup
+from string import ascii_lowercase
+from ..states.StateDistributionGaPhs import StateDistribution
+from ..LineageTree import LineageTree
+
+desired_num_states = 2
+E = [StateDistribution() for _ in range(desired_num_states)]
+untreated_t = import_taxol_file()
+untreated_taxol = [LineageTree(list_cells, E) for list_cells in untreated_t]
+c = 0
+for lins in untreated_t:
+    for cell in lins:
+        c += 1
+# counts = len(untreated_taxol)
+counts = 100
+print("number of lineages", len(untreated_taxol), " and the number of total cells: ", c)
+
+def makeFigure():
+    """
+    Makes figure S18.
+    """
+    titles = ["Untreated", "Taxol 1 nM", "Taxol 2 nM", "Taxol 3 nM", "Taxol 4 nM"]
+    ax, f = getSetup((17, 35), (counts, 4))
+
+    for i in range(counts):
+        ax[4 * i].axis('off')
+        # ax[4 * i + 1].axis('off')
+        # ax[4 * i + 2].axis('off')
+        # ax[4 * i + 3].axis('off')
+        plotLineage(untreated_taxol[i], ax[4 * i], color=False)
+        # plotLineage(Taxol_lin_list[1][i], ax[4 * i + 1], color=False)
+        # plotLineage(Taxol_lin_list[2][i], ax[4 * i + 2], color=False)
+        # plotLineage(Taxol_lin_list[3][i], ax[4 * i + 3], color=False)
+
+    for i in range(4):
+        ax[i].axis('off')
+        ax[i].text(-0.2, 1.55, ascii_lowercase[i], transform=ax[i].transAxes, fontsize=20, fontweight="bold", va="top")
+        ax[i].text(0.0, 1.55, titles[i], transform=ax[i].transAxes, fontsize=20, va="top")
+
+    return f

lineage/figures/figureS19.py

@@ -0,0 +1,35 @@
+""" BIC for Taxol data."""
+from ..Lineage_collections import taxols as Taxol_lin_list
+from ..Analyze import run_Analyze_over, Analyze_list
+from .common import getSetup
+from .figure9 import find_BIC
+
+import pickle
+import numpy as np
+from matplotlib.ticker import MaxNLocator
+
+desired_num_states = np.arange(1, 8)
+
+def makeFigure():
+    """
+    Makes figure 9.
+    """
+    ax, f = getSetup((14, 4), (1, 3))
+
+    BIC, Obj = find_BIC(Taxol_lin_list, desired_num_states, num_cells=1041)
+
+    # create a pickle file
+    pik1 = open("taxols.pkl", "wb")
+    for taxol_tHMMobj_list in Obj:
+        for laps in taxol_tHMMobj_list:
+            pickle.dump(laps, pik1)
+    pik1.close()
+
+    i = 0
+    ax[i].set_xlabel("Number of States Predicted")
+    ax[i].set_ylabel("Normalized BIC")
+    ax[i].xaxis.set_major_locator(MaxNLocator(integer=True))
+
+    ax[0].set_title("Taxol Treated Populations")
+
+    return f