Summary stats¶

In [1]:

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import os
import tempfile
cache_dir = tempfile.mkdtemp()
os.environ['XDG_CACHE_HOME'] = cache_dir

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
import tempfile
cache_dir = tempfile.mkdtemp()
os.environ['XDG_CACHE_HOME'] = cache_dir

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

In [2]:

parameters

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metadata_csv = "../../gc_metadata.csv"
cell_table_csv = "../results/observed-seqs.csv"
ranking_subdir = "default"
nds_lb_summary_csv = "../results/notebooks/NDS_LB/default/data.csv"
outbase = "_ignore/cell-summaries"
workflow_env_exec = False
metadata_csv = "../../gc_metadata.csv"
cell_table_csv = "../results/observed-seqs.csv"
ranking_subdir = "default"
nds_lb_summary_csv = "../results/notebooks/NDS_LB/default/data.csv"
outbase = "_ignore/cell-summaries"
workflow_env_exec = False

In [3]:

injected-parameters

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# Parameters
metadata_csv = "gc_metadata.csv"
cell_table_csv = "observed-seqs.csv"
ranking_subdir = "default"
nds_lb_summary_csv = "data.csv"
outbase = "."
workflow_env_exec = True
# Parameters
metadata_csv = "gc_metadata.csv"
cell_table_csv = "observed-seqs.csv"
ranking_subdir = "default"
nds_lb_summary_csv = "data.csv"
outbase = "."
workflow_env_exec = True

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output_dir = f"{outbase}/{ranking_subdir}"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)
output_dir = f"{outbase}/{ranking_subdir}"
if not os.path.exists(output_dir):
    os.makedirs(output_dir)

In [5]:

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metadata = pd.read_csv(metadata_csv, index_col=0).query("cell_type == 'GC' and strain == 'wt'")
metadata
metadata = pd.read_csv(metadata_csv, index_col=0).query("cell_type == 'GC' and strain == 'wt'")
metadata

Out[5]:

	ngs_id	imm_duration	mouse	gc	strain	node	cell_type	plate	hc_barcode	lc_barcode	row	col
uid
D15_M1_GC1	PR-2-01	d15	1	1	wt	RP	GC	2	9	9	A.B.C.D.E.F.G.H	1.2.3.4.5.6.7.8.9.10.11.12
D15_M1_GC2	PR-2-01	d15	1	2	wt	RI	GC	3	2	1	A.B.C.D.E.F.G.H	1.2.3.4.5.6.7.8.9.10.11.12
D15_M1_GC3	PR-2-01	d15	1	3	wt	LI	GC	4	14	2	A.B.C.D.E.F.G.H	1.2.3.4.5.6.7.8.9.10.11.12
D15_M2_GC4	PR-2-01	d15	2	4	wt	RP	GC	5	10	11	A.B.C.D.E.F.G.H	1.2.3.4.5.6.7.8.9.10.11.12
D15_M3_GC5	PR-2-01	d15	3	5	wt	RP	GC	6	7	4	A.B.C.D.E.F.G.H	1.2.3.4.5.6.7.8.9.10.11.12
...	...	...	...	...	...	...	...	...	...	...	...	...
D20_M24_GC115	PR-1-04	d20	24	115	wt	RP	GC	72	16	16	A.B.C.D.E.F.G.H	1.2.3.4.5.6.7.8.9.10.11.12
D20_M25_GC116	PR-1-02	d20	25	116	wt	RP	GC	65	8	8	A.B.C.D.E.F.G.H	1.2.3.4.5.6.7.8.9.10.11.12
D20_M25_GC117	PR-1-03	d20	25	117	wt	RP	GC	68	9	9	A.B.C.D.E.F.G.H	1.2.3.4.5.6.7.8.9.10.11.12
D20_M25_GC118	PR-1-08	d20	25	118	wt	RP	GC	66	15	15	A.B.C.D.E.F.G.H	1.2.3.4.5.6.7.8.9.10.11.12
D20_M25_GC119	PR-1-08	d20	25	119	wt	RP	GC	67	16	16	A.B.C.D.E.F.G.H	1.2.3.4.5.6.7.8.9.10.11.12

119 rows × 12 columns

Cell-wise stats¶

In [6]:

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cell_table = pd.read_csv(cell_table_csv).query("uid in @metadata.index")
cell_table.to_csv(f"{output_dir}/cell_table.csv", index=False)
cell_table
cell_table = pd.read_csv(cell_table_csv).query("uid in @metadata.index")
cell_table.to_csv(f"{output_dir}/cell_table.csv", index=False)
cell_table

Out[6]:

	ID_HK	well	uid	ngs_id	mouse	gc	node	cell_type	imm_duration	aa_substitutions_IMGT	...	fasta_header_HC	fasta_header_LC	fasta_seq_HC	fasta_seq_LC	partis_sequence_HC	partis_sequence_LC	seq_aa_HC	seq_aa_LC	seq_nt_HC	seq_nt_LC
0	230512P15A02HK	A02	D15_M6_GC11	PR-2-01	6	11	RP	GC	d15	P9(H)L S11(H)G I42(H)L S64(H)C R106(H)W R67(L)...	...	PR-2-01.230512.P15.A02.H.R.1-51	PR-2-01.230512.P14.A02.K.R.1-442	GAGGTGCAGCTTCAGGAGTCAGGACTTGGCCTCGTGAAACCTTCTC...	GTGTTGATGGAGACATTGTGATGACTCAGTCTCAAAAATTCATGTC...	GAGGTGCAGCTTCAGGAGTCAGGACTTGGCCTCGTGAAACCTTCTC...	GACATTGTGATGACTCAGTCTCAAAAATTCATGTCCACATCAGTAG...	EVQLQESGLGLVKPSQTLSLTCSVTGDSITSGYWNWLRKFPGNKLE...	DIVMTQSQKFMSTSVGDRVSVTCKASQNVGTNVAWYQQKPGQSPKA...	gaggtgcagcttcaggagtcaggacttggcctcgtgaaaccttctc...	gacattgtgatgactcagtctcaaaaattcatgtccacatcagtag...
1	230512P15A03HK	A03	D15_M6_GC11	PR-2-01	6	11	RP	GC	d15	M53(H)V S57(H)N Y67(H)S K51(L)R A52(L)S N93(L)...	...	PR-2-01.230512.P15.A03.H.R.1-211	PR-2-01.230512.P14.A03.K.R.1-332	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GTGTTGATGGAGACATTGTGATGACTCAGTCTCAAAAATTCATGTC...	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GACATTGTGATGACTCAGTCTCAAAAATTCATGTCCACATCAGTAG...	EVQLQESGPSLVKPSQTLSLTCSVTGDSITSGYWNWIRKFPGNKLE...	DIVMTQSQKFMSTSVGDRVSVTCKASQNVGTNVAWYQQKPGQSPRS...	gaggtgcagcttcaggagtcaggacctagcctcgtgaaaccttctc...	gacattgtgatgactcagtctcaaaaattcatgtccacatcagtag...
2	230512P15A04HK	A04	D15_M6_GC11	PR-2-01	6	11	RP	GC	d15	T96(H)N Y103(H)* A105(H)G	...	PR-2-01.230512.P15.A04.H.R.1-67	PR-2-01.230512.P14.A04.K.R.1-364	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GTGTTGATGGAGACATTGTGATGACTCAGTCTCAAAAATTCATGTC...	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GACATTGTGATGACTCAGTCTCAAAAATTCATGTCCACATCAGTAG...	EVQLQESGPSLVKPSQTLSLTCSVTGDSITSGYWNWIRKFPGNKLE...	DIVMTQSQKFMSTSVGDRVSVTCKASQNVGTNVAWYQQKPGQSPKA...	gaggtgcagcttcaggagtcaggacctagcctcgtgaaaccttctc...	gacattgtgatgactcagtctcaaaaattcatgtccacatcagtag...
3	230512P15A05HK	A05	D15_M6_GC11	PR-2-01	6	11	RP	GC	d15	S64(H)G K84(H)Q N92(H)K A105(H)G N108(L)K T117...	...	PR-2-01.230512.P15.A05.H.R.1-54	PR-2-01.230512.P14.A05.K.R.1-453	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GTGTTGATGGAGACATTGTGATGACTCAGTCTCAAAAATTCATGTC...	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GACATTGTGATGACTCAGTCTCAAAAATTCATGTCCACATCAGTAG...	EVQLQESGPSLVKPSQTLSLTCSVTGDSITSGYWNWIRKFPGNKLE...	DIVMTQSQKFMSTSVGDRVSVTCKASQNVGTNVAWYQQKPGQSPKA...	gaggtgcagcttcaggagtcaggacctagcctcgtgaaaccttctc...	gacattgtgatgactcagtctcaaaaattcatgtccacatcagtag...
4	230512P15A06HK	A06	D15_M6_GC11	PR-2-01	6	11	RP	GC	d15	T18(H)P N40(H)S T65(H)N A100(H)V T122(H)M K51(...	...	PR-2-01.230512.P15.A06.H.R.1-46	PR-2-01.230512.P14.A06.K.R.1-194	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GTGTTGATGGAGACATTGTGATGACTCAGTCTCAAAAATTCATGTC...	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GACATTGTGATGACTCAGTCTCAAAAATTCATGTCCACATCAGTAG...	EVQLQESGPSLVKPSQPLSLTCSVTGDSITSGYWSWIRKFPGNKLE...	DIVMTQSQKFMSTSVGDRVSVTCKASQNVGTNVAWYQQKPGQSPRS...	gaggtgcagcttcaggagtcaggacctagcctcgtgaaaccttctc...	gacattgtgatgactcagtctcaaaaattcatgtccacatcagtag...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10424	211014P07H08HK	H08	D20_M22_GC88	PR-1-03	22	88	LP	GC	d20	T26(H)S Y55(H)F L116(L)P	...	PR-1-03.211014.P07.H08.H.R.1-72	PR-1-03.211014.P07.H08.K.R.1-22	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GTGTTGATGGAGACATTGTGATGACTCAGTCTCAAAAATTCATGTC...	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GACATTGTGATGACTCAGTCTCAAAAATTCATGTCCACATCAGTAG...	EVQLQESGPSLVKPSQTLSLTCSVSGDSITSGYWNWIRKFPGNKLE...	DIVMTQSQKFMSTSVGDRVSVTCKASQNVGTNVAWYQQKPGQSPKA...	gaggtgcagcttcaggagtcaggacctagcctcgtgaaaccttctc...	gacattgtgatgactcagtctcaaaaattcatgtccacatcagtag...
10425	211014P07H09HK	H09	D20_M22_GC88	PR-1-03	22	88	LP	GC	d20	S64(H)N A105(H)G T37(L)A Y68(L)F Q105(L)H	...	PR-1-03.211014.P07.H09.H.R.1-13	PR-1-03.211014.P07.H09.K.R.1-164	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GTGTGGATGGAGACATTGTGATGACTCAGTCTCAAAAATTCATGTC...	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GACATTGTGATGACTCAGTCTCAAAAATTCATGTCCACATCAGTAG...	EVQLQESGPSLVKPSQTLSLTCSVTGDSITSGYWNWIRKFPGNKLE...	DIVMTQSQKFMSTSVGDRVSVTCKASQNVGANVAWYQQKPGQSPKA...	gaggtgcagcttcaggagtcaggacctagcctcgtgaaaccttctc...	gacattgtgatgactcagtctcaaaaattcatgtccacatcagtag...
10426	211014P07H10HK	H10	D20_M22_GC88	PR-1-03	22	88	LP	GC	d20	S64(H)G T79(H)A N92(H)I A105(H)G T37(L)A N108(...	...	PR-1-03.211014.P07.H10.H.R.1-11	PR-1-03.211014.P07.H10.K.R.1-175	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GTGTTGATGGAGACATTGTGATGACTCAGTCTCAAAAATTCATGTC...	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GACATTGTGATGACTCAGTCTCAAAAATTCATGTCCACATCAGTAG...	EVQLQESGPSLVKPSQTLSLTCSVTGDSITSGYWNWIRKFPGNKLE...	DIVMTQSQKFMSTSVGDRVSVTCKASQNVGANVAWYQQKPGQSPKA...	gaggtgcagcttcaggagtcaggacctagcctcgtgaaaccttctc...	gacattgtgatgactcagtctcaaaaattcatgtccacatcagtag...
10427	211014P07H11HK	H11	D20_M22_GC88	PR-1-03	22	88	LP	GC	d20	D28(H)A S57(H)N N92(H)I A105(H)G T37(L)A A57(L...	...	PR-1-03.211014.P07.H11.H.R.1-14	PR-1-03.211014.P07.H11.K.R.1-188	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GTGTTGATGGAGACATTGTGATGACTCAGTCTCAAAAATTCATGTC...	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GACATTGTGATGACTCAGTCTCAAAAATTCATGTCCACATCAGTTG...	EVQLQESGPSLVKPSQTLSLTCSVTGASITSGYWNWIRKFPGNKLE...	DIVMTQSQKFMSTSVGDRVSVTCKASQNVGANVAWYQQKPGQSPKA...	gaggtgcagcttcaggagtcaggacctagcctcgtgaaaccttctc...	gacattgtgatgactcagtctcaaaaattcatgtccacatcagttg...
10428	211014P07H12HK	H12	D20_M22_GC88	PR-1-03	22	88	LP	GC	d20	D81(H)H N92(H)I A105(H)G T37(L)A S92(L)N Q105(L)H	...	PR-1-03.211014.P07.H12.H.R.1-9	PR-1-03.211014.P07.H12.K.R.1-235	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GTGTTGATGGAGACATTGTGATGACTCAGTCTCAAAAATTCATGTC...	GAGGTGCAGCTTCAGGAGTCAGGACCTAGCCTCGTGAAACCTTCTC...	GACATTGTGATGACTCAGTCTCAAAAATTCATGTCCACATCAGTAG...	EVQLQESGPSLVKPSQTLSLTCSVTGDSITSGYWNWIRKFPGNKLE...	DIVMTQSQKFMSTSVGDRVSVTCKASQNVGANVAWYQQKPGQSPKA...	gaggtgcagcttcaggagtcaggacctagcctcgtgaaaccttctc...	gacattgtgatgactcagtctcaaaaattcatgtccacatcagtag...

8744 rows × 64 columns

In [7]:

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plt.figure(figsize=(13, 5))
sns.pointplot(
    data=cell_table,
    hue="imm_duration",
    x="gc", order=cell_table.groupby("gc").agg(dict(imm_duration="first", delta_bind="median")).sort_values(["imm_duration", "delta_bind"]).index,
    y="delta_bind",
    estimator="median",
    errorbar=("pi", 50),
    )
plt.axhline(0, color="black", linestyle="--")
plt.xticks(rotation=90)
plt.tight_layout()
plt.savefig(f"{output_dir}/delta_bind.pdf")
plt.show()
plt.figure(figsize=(13, 5))
sns.pointplot(
    data=cell_table,
    hue="imm_duration",
    x="gc", order=cell_table.groupby("gc").agg(dict(imm_duration="first", delta_bind="median")).sort_values(["imm_duration", "delta_bind"]).index,
    y="delta_bind",
    estimator="median",
    errorbar=("pi", 50),
    )
plt.axhline(0, color="black", linestyle="--")
plt.xticks(rotation=90)
plt.tight_layout()
plt.savefig(f"{output_dir}/delta_bind.pdf")
plt.show()

No description has been provided for this image

In [8]:

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plt.figure(figsize=(13, 5))
sns.pointplot(
    data=cell_table,
    hue="imm_duration",
    x="gc", order=cell_table.groupby("gc").agg(dict(imm_duration="first", delta_expr="median")).sort_values(["imm_duration", "delta_expr"]).index,
    y="delta_expr",
    estimator="median",
    errorbar=("pi", 50),
    )
plt.axhline(0, color="black", linestyle="--")
plt.xticks(rotation=90)
plt.tight_layout()
plt.savefig(f"{output_dir}/delta_expr.pdf")
plt.show()
plt.figure(figsize=(13, 5))
sns.pointplot(
    data=cell_table,
    hue="imm_duration",
    x="gc", order=cell_table.groupby("gc").agg(dict(imm_duration="first", delta_expr="median")).sort_values(["imm_duration", "delta_expr"]).index,
    y="delta_expr",
    estimator="median",
    errorbar=("pi", 50),
    )
plt.axhline(0, color="black", linestyle="--")
plt.xticks(rotation=90)
plt.tight_layout()
plt.savefig(f"{output_dir}/delta_expr.pdf")
plt.show()

In [9]:

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g = sns.JointGrid(data=cell_table, x="delta_bind", y="delta_expr", hue="imm_duration")
g.plot_joint(sns.scatterplot)
g.plot_marginals(sns.kdeplot, common_norm=False, fill=True, alpha=0.1)
g.ax_joint.axvline(0, color="black", linestyle="--")
g.ax_joint.axhline(0, color="black", linestyle="--")
g.ax_marg_x.axvline(0, color="black", linestyle="--")
g.ax_marg_y.axhline(0, color="black", linestyle="--")
plt.tight_layout()
plt.savefig(f"{output_dir}/joint_cells.pdf")
plt.show()

g = sns.JointGrid(data=cell_table, x="delta_bind", y="delta_expr", hue="imm_duration")
g.plot_joint(sns.scatterplot)
g.plot_marginals(sns.kdeplot, common_norm=False, fill=True, alpha=0.1)
g.ax_joint.axvline(0, color="black", linestyle="--")
g.ax_joint.axhline(0, color="black", linestyle="--")
g.ax_marg_x.axvline(0, color="black", linestyle="--")
g.ax_marg_y.axhline(0, color="black", linestyle="--")
plt.tight_layout()
plt.savefig(f"{output_dir}/joint_cells.pdf")
plt.show()

GC-wise stats¶

In [10]:

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gc_summary = (
    (
        cell_table.groupby("gc").agg(
            uid = pd.NamedAgg(column="uid", aggfunc="first"),
            mouse = pd.NamedAgg(column="mouse", aggfunc="first"),
            ngs_id = pd.NamedAgg(column="ngs_id", aggfunc="first"),
            sampling_time=pd.NamedAgg(column="imm_duration", aggfunc="first"),
            cells_sampled=pd.NamedAgg(column="uid", aggfunc="count"),
            median_delta_bind=pd.NamedAgg(column="delta_bind", aggfunc="median"),
            median_delta_expr=pd.NamedAgg(column="delta_expr", aggfunc="median")
        )
    )
    .reset_index()
    .set_index("uid")
    .rename(
        columns={
            'median_delta_bind': 'median Δaffinity',
            'median_delta_expr': 'median Δexpression'
        }
    )
)

gc_summary
gc_summary = (
    (
        cell_table.groupby("gc").agg(
            uid = pd.NamedAgg(column="uid", aggfunc="first"),
            mouse = pd.NamedAgg(column="mouse", aggfunc="first"),
            ngs_id = pd.NamedAgg(column="ngs_id", aggfunc="first"),
            sampling_time=pd.NamedAgg(column="imm_duration", aggfunc="first"),
            cells_sampled=pd.NamedAgg(column="uid", aggfunc="count"),
            median_delta_bind=pd.NamedAgg(column="delta_bind", aggfunc="median"),
            median_delta_expr=pd.NamedAgg(column="delta_expr", aggfunc="median")
        )
    )
    .reset_index()
    .set_index("uid")
    .rename(
        columns={
            'median_delta_bind': 'median Δaffinity',
            'median_delta_expr': 'median Δexpression'
        }
    )
)

gc_summary

Out[10]:

	gc	mouse	ngs_id	sampling_time	cells_sampled	median Δaffinity	median Δexpression
uid
D15_M1_GC1	1	1	PR-2-01	d15	67	0.773400	-0.05788
D15_M1_GC2	2	1	PR-2-01	d15	79	0.397360	-0.03688
D15_M1_GC3	3	1	PR-2-01	d15	73	0.535100	-0.06153
D15_M2_GC4	4	2	PR-2-01	d15	44	0.287630	-0.08646
D15_M3_GC5	5	3	PR-2-01	d15	78	0.000000	-0.08452
...	...	...	...	...	...	...	...
D20_M24_GC115	115	24	PR-1-04	d20	85	1.046480	-0.17240
D20_M25_GC116	116	25	PR-1-02	d20	78	1.105020	-0.03121
D20_M25_GC117	117	25	PR-1-03	d20	76	0.608315	-0.51338
D20_M25_GC118	118	25	PR-1-08	d20	70	0.492355	-0.02191
D20_M25_GC119	119	25	PR-1-08	d20	81	0.700545	-0.26017

119 rows × 7 columns

In [11]:

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cols_to_keep = [
    # 'cells sampled',
    'normalized dominance score',
    'maximum REI',
    'max Δaffinity',
    '95th percentile Δaffinity',
    # 'mean Δaffinity',
    # 'median Δaffinity', 
    'id of max REI node', 
    'mutations in max REI node', 
    'Δaffinity of max REI node',
    'ΔΔaffinity of max REI node',
    'max REI node is root'
]
nds_lb = pd.read_csv(nds_lb_summary_csv, index_col="uid")[cols_to_keep]
nds_lb.info()
cols_to_keep = [
    # 'cells sampled',
    'normalized dominance score',
    'maximum REI',
    'max Δaffinity',
    '95th percentile Δaffinity',
    # 'mean Δaffinity',
    # 'median Δaffinity', 
    'id of max REI node', 
    'mutations in max REI node', 
    'Δaffinity of max REI node',
    'ΔΔaffinity of max REI node',
    'max REI node is root'
]
nds_lb = pd.read_csv(nds_lb_summary_csv, index_col="uid")[cols_to_keep]
nds_lb.info()

<class 'pandas.core.frame.DataFrame'>
Index: 119 entries, D20_M22_GC96 to D15_M13_GC31
Data columns (total 9 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   normalized dominance score  119 non-null    float64
 1   maximum REI                 119 non-null    float64
 2   max Δaffinity               119 non-null    float64
 3   95th percentile Δaffinity   119 non-null    float64
 4   id of max REI node          119 non-null    object 
 5   mutations in max REI node   113 non-null    object 
 6   Δaffinity of max REI node   119 non-null    float64
 7   ΔΔaffinity of max REI node  119 non-null    float64
 8   max REI node is root        119 non-null    bool   
dtypes: bool(1), float64(6), object(2)
memory usage: 8.5+ KB

In [12]:

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gc_summary = gc_summary.join(nds_lb)
gc_summary.to_csv(f"{output_dir}/gc_summary.csv")
gc_summary = gc_summary.join(nds_lb)
gc_summary.to_csv(f"{output_dir}/gc_summary.csv")

In [13]:

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gc_summary.info()
gc_summary.info()

<class 'pandas.core.frame.DataFrame'>
Index: 119 entries, D15_M1_GC1 to D20_M25_GC119
Data columns (total 16 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   gc                          119 non-null    int64  
 1   mouse                       119 non-null    int64  
 2   ngs_id                      119 non-null    object 
 3   sampling_time               119 non-null    object 
 4   cells_sampled               119 non-null    int64  
 5   median Δaffinity            119 non-null    float64
 6   median Δexpression          119 non-null    float64
 7   normalized dominance score  119 non-null    float64
 8   maximum REI                 119 non-null    float64
 9   max Δaffinity               119 non-null    float64
 10  95th percentile Δaffinity   119 non-null    float64
 11  id of max REI node          119 non-null    object 
 12  mutations in max REI node   113 non-null    object 
 13  Δaffinity of max REI node   119 non-null    float64
 14  ΔΔaffinity of max REI node  119 non-null    float64
 15  max REI node is root        119 non-null    bool   
dtypes: bool(1), float64(8), int64(3), object(4)
memory usage: 19.1+ KB

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g = sns.JointGrid(data=gc_summary, x="median Δaffinity", y="median Δexpression", hue="sampling_time")
g.plot_joint(sns.scatterplot, size=gc_summary.cells_sampled)
g.plot_marginals(sns.kdeplot, common_norm=False)
g.ax_joint.axvline(0, color="black", linestyle="--")
g.ax_joint.axhline(0, color="black", linestyle="--")
g.ax_marg_x.axvline(0, color="black", linestyle="--")
g.ax_marg_y.axhline(0, color="black", linestyle="--")
plt.tight_layout()
plt.savefig(f"{output_dir}/joint_gc_medians.pdf")
plt.show()
g = sns.JointGrid(data=gc_summary, x="median Δaffinity", y="median Δexpression", hue="sampling_time")
g.plot_joint(sns.scatterplot, size=gc_summary.cells_sampled)
g.plot_marginals(sns.kdeplot, common_norm=False)
g.ax_joint.axvline(0, color="black", linestyle="--")
g.ax_joint.axhline(0, color="black", linestyle="--")
g.ax_marg_x.axvline(0, color="black", linestyle="--")
g.ax_marg_y.axhline(0, color="black", linestyle="--")
plt.tight_layout()
plt.savefig(f"{output_dir}/joint_gc_medians.pdf")
plt.show()

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sns.boxenplot(data=gc_summary, y="median Δaffinity", hue="sampling_time")
plt.show()
sns.boxenplot(data=gc_summary, y="median Δexpression", hue="sampling_time")
plt.show()
sns.boxenplot(data=gc_summary, y="median Δaffinity", hue="sampling_time")
plt.show()
sns.boxenplot(data=gc_summary, y="median Δexpression", hue="sampling_time")
plt.show()