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Fix and improve plotting pairwise average fst #579

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Aug 9, 2024
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Fix and improve plotting pairwise average fst #579

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3aa477e
fix and improve plotting pairwise average fst
alimanfoo Aug 9, 2024
6e09ad4
Merge branch 'master' into fst-bug-alimanfoo-2024-08-09
alimanfoo Aug 9, 2024
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59 changes: 40 additions & 19 deletions malariagen_data/anoph/fst.py
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Original file line number Diff line number Diff line change
Expand Up @@ -485,32 +485,49 @@ def plot_pairwise_average_fst(
zmax: Optional[plotly_params.zmax] = None,
text_auto: plotly_params.text_auto = ".3f",
color_continuous_scale: plotly_params.color_continuous_scale = "gray_r",
width: plotly_params.fig_width = 700,
height: plotly_params.fig_height = 600,
width: plotly_params.fig_width = None,
height: plotly_params.fig_height = None,
row_height: plotly_params.height = 40,
col_width: plotly_params.width = 50,
show: plotly_params.show = True,
renderer: plotly_params.renderer = None,
**kwargs,
):
# setup df
cohort_list = np.unique(fst_df[["cohort1", "cohort2"]].values)
# df to fill
fig_df = pd.DataFrame(columns=cohort_list, index=cohort_list)
# fill df from fst_df
for index_key in range(len(fst_df)):
index = fst_df.iloc[index_key]["cohort1"]
col = fst_df.iloc[index_key]["cohort2"]
fst = fst_df.iloc[index_key]["fst"]
fig_df[index][col] = fst
# Obtain a list of all cohorts analysed. N.B., preserve the order in
# which the cohorts are provided in the input dataframe.
cohorts = pd.unique(fst_df[["cohort1", "cohort2"]].values.flatten())

# Create a dataframe to visualise as a heatmap.
fig_df = pd.DataFrame(columns=cohorts, index=cohorts)

# Set up plot title.
title = "<i>F</i><sub>ST</sub>"
if annotation is not None:
title += " ⧅ " + annotation

# Fill the figure dataframe from the Fst dataframe.
for cohort1, cohort2, fst, se in fst_df.itertuples(index=False):
fig_df.loc[cohort2, cohort1] = fst
if annotation == "standard error":
se = fst_df.iloc[index_key]["se"]
fig_df[col][index] = se
fig_df.loc[cohort1, cohort2] = se
elif annotation == "Z score":
zs = fst_df.iloc[index_key]["fst"] / fst_df.iloc[index_key]["se"]
fig_df[col][index] = zs
zs = fst / se
fig_df.loc[cohort1, cohort2] = zs
else:
fig_df.loc[index, col] = fst
fig_df.loc[cohort1, cohort2] = fst

# Don't colour the plot if the upper triangle is SE or Z score,
# as the colouring doesn't really make sense.
if annotation is not None and zmax is None:
zmax = 1e9

# create plot
# Dynamically size the figure based on number of cohorts.
if height is None:
height = 100 + len(cohorts) * row_height
if width is None:
width = 200 + len(cohorts) * col_width

# Create plot.
with np.errstate(invalid="ignore"):
fig = px.imshow(
img=fig_df,
Expand All @@ -523,7 +540,11 @@ def plot_pairwise_average_fst(
aspect="auto",
**kwargs,
)
fig.update_layout(plot_bgcolor="rgba(0,0,0,0)")
fig.update_layout(
plot_bgcolor="rgba(0,0,0,0)",
coloraxis_showscale=False,
title=title,
)
fig.update_yaxes(showgrid=False, linecolor="black")
fig.update_xaxes(showgrid=False, linecolor="black")

Expand Down
7 changes: 6 additions & 1 deletion malariagen_data/anoph/plotly_params.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,12 @@

fig_height: TypeAlias = Annotated[
Optional[int],
"Figure weight in pixels (px).",
"Figure height in pixels (px).",
]

width: TypeAlias = Annotated[
int,
"Width in pixels (px).",
]

height: TypeAlias = Annotated[
Expand Down
114 changes: 108 additions & 6 deletions notebooks/plot_pairwise_average_fst.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@
"ag3 = malariagen_data.Ag3(\n",
" \"simplecache::gs://vo_agam_release\",\n",
" simplecache=dict(cache_storage=\"../gcs_cache\"),\n",
" cohorts_analysis=\"20230516\",\n",
" cohorts_analysis=\"20240717\",\n",
" results_cache=\"results_cache\",\n",
")\n",
"ag3"
Expand Down Expand Up @@ -87,6 +87,16 @@
"pairwise_fst_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "35959f42-732e-40fa-86ad-16d95fb6e740",
"metadata": {},
"outputs": [],
"source": [
"ag3.plot_pairwise_average_fst(pairwise_fst_df)"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -110,21 +120,23 @@
{
"cell_type": "code",
"execution_count": null,
"id": "35959f42-732e-40fa-86ad-16d95fb6e740",
"id": "c16035d2-b5d3-4441-87d9-eda953079161",
"metadata": {},
"outputs": [],
"source": [
"ag3.plot_pairwise_average_fst(pairwise_fst_df, zmax=0.1)"
"ag3.plot_pairwise_average_fst(pairwise_fst_df, zmax=0.1, width=400, height=300)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c16035d2-b5d3-4441-87d9-eda953079161",
"id": "13afd6ed-7962-4935-b1f1-ed12f5e2ceed",
"metadata": {},
"outputs": [],
"source": [
"ag3.plot_pairwise_average_fst(pairwise_fst_df, zmax=0.1, width=400, height=300)"
"region=\"3L:15,000,000-16,000,000\"\n",
"site_mask='arab'\n",
"n_jack=200"
]
},
{
Expand All @@ -133,6 +145,96 @@
"id": "48a52211-2ef9-47da-b715-d0d74a69a4ad",
"metadata": {},
"outputs": [],
"source": [
"pairwise_fst_df = ag3.pairwise_average_fst(\n",
" region=region,\n",
" cohorts=\"cohort_admin1_year\",\n",
" sample_query=\"country == 'Kenya' and taxon == 'arabiensis'\",\n",
" sample_sets=[\"AG1000G-KE\", \"1274-VO-KE-KAMAU-VMF00246\"],\n",
" n_jack=n_jack,\n",
" site_mask=site_mask,\n",
" min_cohort_size=10,\n",
")\n",
"pairwise_fst_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "09cc1824-e6f4-4799-a8f4-cf61e472eaf8",
"metadata": {},
"outputs": [],
"source": [
"ag3.plot_pairwise_average_fst(pairwise_fst_df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "95f112d2-fe76-4091-b73b-d7a17a61cc18",
"metadata": {},
"outputs": [],
"source": [
"ag3.plot_pairwise_average_fst(pairwise_fst_df, annotation=\"standard error\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "908b08c8-290f-49d1-b3d3-cb8f9e7d2d21",
"metadata": {},
"outputs": [],
"source": [
"ag3.plot_pairwise_average_fst(pairwise_fst_df, annotation=\"Z score\", zmax=0.03)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "818fdac7-dcb4-4da2-8a52-f16ce51a2c0d",
"metadata": {},
"outputs": [],
"source": [
"wild_cohorts = {\n",
" \"Mwea_2007\": \"taxon == 'arabiensis' and location == 'Mwea' and year == 2007\",\n",
" \"Mwea_2014\": \"taxon == 'arabiensis' and location == 'Mwea' and year == 2014\",\n",
" \"Teso_2013\": \"taxon == 'arabiensis' and location == 'Teso'\",\n",
" \"Turkana_2006\": \"taxon == 'arabiensis' and location == 'Turkana'\",\n",
" \"Kilifi_2012\": \"taxon == 'arabiensis' and location == 'Kilifi' and year == 2012\",\n",
"}\n",
"fst_df = ag3.pairwise_average_fst(\n",
" region=\"3L:15,000,000-16,000,000\", \n",
" cohorts=wild_cohorts,\n",
" min_cohort_size=10,\n",
" site_mask=\"arab\",\n",
")\n",
"fst_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "65c1bdd5-f1d2-4814-93e5-ddb1d9c42258",
"metadata": {},
"outputs": [],
"source": [
"ag3.plot_pairwise_average_fst(fst_df, annotation=\"Z score\", zmax=0.4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5ec8cfe3-336c-4362-9361-78d40ffa0a8e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d3236a2-842d-4c13-92ec-9a4338750aca",
"metadata": {},
"outputs": [],
"source": []
}
],
Expand All @@ -152,7 +254,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.18"
"version": "3.10.12"
}
},
"nbformat": 4,
Expand Down
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