Contents

Barplot of Samples by Country

Barplot of Samples by Country

Introduction

This notebook recreates Supplement Figure 1 from the Pf7 paper - a bar plot which shows the number of samples in the Pf7 release, broken down by country. Each bar also details the number of samples passing QC (or not) per country.

This notebook should take approximately 1 minute to run.

Setup

Install and import the malariagen Python package:

!pip install -q --no-warn-conflicts malariagen_data
import malariagen_data

Import required python libraries that are installed at colab by default.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import collections
from google.colab import drive

Access Pf7 Data

We use the malariagen data package to load the release data.

release_data = malariagen_data.Pf7()
sample_metadata = release_data.sample_metadata()

sample_metadata.head(3)
Sample Study Country Admin level 1 Country latitude Country longitude Admin level 1 latitude Admin level 1 longitude Year ENA All samples same case Population % callable QC pass Exclusion reason Sample type Sample was in Pf6
0 FP0008-C 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR1081237 FP0008-C AF-W 82.16 True Analysis_set gDNA True
1 FP0009-C 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR1081238 FP0009-C AF-W 88.85 True Analysis_set gDNA True
2 FP0010-CW 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR2889621 FP0010-CW AF-W 86.46 True Analysis_set sWGA False

We can start exploring the data by answering these questions:

  • How many samples with QC pass

  • How many samples in each country

# Calculate the total number of samples
total_sample_number = sample_metadata.Sample.count()

# Calculate the number of samples that passed QC
qc_pass_count = (sample_metadata['QC pass'] == True).sum()

# Calculate the number of samples that failed QC
qc_fail_count = (sample_metadata['QC pass'] == False).sum()

print(f"We see {total_sample_number} samples of which {qc_pass_count} QC-pass and {qc_fail_count} QC fail in the overall Pf7 dataset.")

We see 20864 samples of which 16203 QC-pass and 4661 QC fail in the overall Pf7 dataset.

# Calculate the number of samples in each country
sample_metadata['Country'].value_counts()

Ghana                               4090
Mali                                1804
Vietnam                             1733
Cambodia                            1723
Bangladesh                          1658
Myanmar                             1260
Gambia                              1247
Thailand                            1106
Laos                                1052
Kenya                                726
Tanzania                             697
Democratic Republic of the Congo     573
Malawi                               371
Benin                                334
India                                316
Cameroon                             294
Papua New Guinea                     251
Sudan                                203
Guinea                               199
Colombia                             159
Senegal                              155
Nigeria                              140
Indonesia                            133
Mauritania                           104
Mozambique                            91
Côte d'Ivoire                         71
Gabon                                 59
Burkina Faso                          58
Ethiopia                              34
Madagascar                            25
Peru                                  21
Uganda                                15
Venezuela                              2
Name: Country, dtype: int64

Figure preparation: Defining populations

Countries are grouped into ten major sub-populations based on their geographic and genetic characteristics.

The dataframe has a Population column that contains abbreviated names, for clarity, we want to display the full name in the figure.

# Define populations in an ordered dictionary
populations = collections.OrderedDict()
populations['SA'] = 'South America'
populations['AF-W'] = 'West Africa'
populations['AF-C'] = 'Central Africa'
populations['AF-NE'] = 'Northeast Africa'
populations['AF-E'] = 'East Africa'
populations['AS-S-E'] = 'Eastern South Asia'
populations['AS-S-FE'] = 'Far-eastern South Asia'
populations['AS-SE-W'] = 'Western Southeast Asia'
populations['AS-SE-E'] = 'Eastern Southeast Asia'
populations['OC-NG'] = 'Oceania'

# Map continent names into the df by using Population column and populations dictionary
sample_metadata['Continent'] = sample_metadata['Population'].map(populations)
sample_metadata.head(3)
Sample Study Country Admin level 1 Country latitude Country longitude Admin level 1 latitude Admin level 1 longitude Year ENA All samples same case Population % callable QC pass Exclusion reason Sample type Sample was in Pf6 Continent
0 FP0008-C 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR1081237 FP0008-C AF-W 82.16 True Analysis_set gDNA True West Africa
1 FP0009-C 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR1081238 FP0009-C AF-W 88.85 True Analysis_set gDNA True West Africa
2 FP0010-CW 1147-PF-MR-CONWAY Mauritania Hodh el Gharbi 20.265149 -10.337093 16.565426 -9.832345 2014.0 ERR2889621 FP0010-CW AF-W 86.46 True Analysis_set sWGA False West Africa 
# Create an ordered dictionary which maps the codes for major sub-populations -from west to east- to a colour code.
population_colours = collections.OrderedDict()
population_colours['SA']       = "#4daf4a"
population_colours['AF-W']     = "#e31a1c"
population_colours['AF-C']     = "#fd8d3c"
population_colours['AF-NE']    = "#bb8129"
population_colours['AF-E']     = "#fecc5c"
population_colours['AS-S-E']  = "#dfc0eb"
population_colours['AS-S-FE']  = "#984ea3"
population_colours['AS-SE-W'] = "#9ecae1"
population_colours['AS-SE-E'] = "#3182bd"
population_colours['OC-NG']    = "#f781bf"

# Map population colours into the df by using Population column and population_colours dictionary
sample_metadata['population_colour'] = sample_metadata['Population'].map(population_colours)

Figure preparation: Sort countries in geographic order

We want to sort the countries on the x-axis in geographic order, which means arranging them from left to right on the chart based on their geographical location, from west to east or by continents.

Using longitudes to locate country

To do this arrangement, we will use longitude coordinate countries which can be found in the dataset column Country longitude.

# Find the average of longitude of samples in each country
mean_population_longitude = sample_metadata.groupby('Population')['Country longitude'].mean()

# Add a new column that conveys mean population values for each sample
sample_metadata['Population_long'] = sample_metadata['Population'].map(mean_population_longitude)

Splitting countries with multi-populations

We identified three countries (Kenya, India, and Thailand) where the sampling locations are associated with more than one major sub-population”. For example, Kenya has sampling locations from AF-NE and AF-E, and this causes problems with ordering on country longitude because AF-NE and AF-E become mixed up in the table.

To accurately represent this diversity, we created a new column called Country_or_admin1 and Country_or_admin1_long in our sample metadata.

These columns categorizes these countries based on their first-level administrative divisions.

# Create a duplicate column with country names
sample_metadata['Country_or_admin1'] = sample_metadata['Country']
sample_metadata['Country_or_admin1_long'] = sample_metadata['Country longitude']

# Rename each 'Admin level 1' of split-countries
sample_metadata.loc[(sample_metadata['Country'] == 'Kenya') & (sample_metadata['Admin level 1'] == 'Kilifi'), 'Country_or_admin1'] = 'Kenya, Kilifi'
sample_metadata.loc[(sample_metadata['Country'] == 'Kenya') & (sample_metadata['Admin level 1'] == 'Kisumu'), 'Country_or_admin1'] = 'Kenya, Kisumu'
sample_metadata.loc[(sample_metadata['Country'] == 'India') & (sample_metadata['Admin level 1'] == 'Tripura'), 'Country_or_admin1'] = 'India, Tripura'
sample_metadata.loc[(sample_metadata['Country'] == 'India') & (sample_metadata['Admin level 1'] == 'Odisha'), 'Country_or_admin1'] = 'India, Odisha or West Bengal'
sample_metadata.loc[(sample_metadata['Country'] == 'India') & (sample_metadata['Admin level 1'] == 'West Bengal'), 'Country_or_admin1'] = 'India, Odisha or West Bengal'
sample_metadata.loc[(sample_metadata['Country'] == 'Thailand') & (sample_metadata['Admin level 1'] == 'Sisakhet'), 'Country_or_admin1'] = 'Thailand, Sisakhet'
sample_metadata.loc[(sample_metadata['Country'] == 'Thailand') & (sample_metadata['Admin level 1'] == 'Tak'), 'Country_or_admin1'] = 'Thailand, Tak or Ranong'
sample_metadata.loc[(sample_metadata['Country'] == 'Thailand') & (sample_metadata['Admin level 1'] == 'Ranong'), 'Country_or_admin1'] = 'Thailand, Tak or Ranong'

# Set longitude to that of admin1 for split countries
sample_metadata.loc[
    sample_metadata['Country_or_admin1'] != sample_metadata['Country'],
    'Country_or_admin1_long'
] = sample_metadata.loc[
    sample_metadata['Country_or_admin1'] != sample_metadata['Country'],
    'Admin level 1 longitude'
]

# Set longitude to that of admin1 with most samples for countries with more than one admin1 in population
sample_metadata.loc[
    sample_metadata['Country_or_admin1'] == 'India, Odisha or West Bengal',
    'Country_or_admin1_long'
] = sample_metadata.loc[
    ( sample_metadata['Country'] == 'India' )
     & ( sample_metadata['Admin level 1'] == 'Odisha' ),
    'Country_or_admin1_long'
].values[0]
sample_metadata.loc[
    sample_metadata['Country_or_admin1'] == 'Thailand, Tak or Ranong',
    'Country_or_admin1_long'
] = sample_metadata.loc[
    ( sample_metadata['Country'] == 'Thailand' )
     & ( sample_metadata['Admin level 1'] == 'Tak' ),
    'Country_or_admin1_long'
].values[0]

Next, we want to arrange the divisions from the same countries adjacent to each other in order to facilitate meaningful comparisons when we look at the figure.

In order to do that we simply adjust their longitude values.

# Adjust the longitude values to appear first or last
sample_metadata.loc[sample_metadata['Country_or_admin1'] == 'Kenya, Kisumu', 'Country_or_admin1_long'] = 40 # Want it to appear last in AF-NE
sample_metadata.loc[sample_metadata['Country_or_admin1'] == 'Kenya, Kilifi', 'Country_or_admin1_long'] = 34 # Want it to appear first in AF-E
sample_metadata.loc[sample_metadata['Country_or_admin1'] == 'India, Tripura', 'Country_or_admin1_long'] = 90 # Want it to appear first in AS-S-FE
sample_metadata.loc[sample_metadata['Country_or_admin1'] == 'Thailand, Sisakhet', 'Country_or_admin1_long'] = 103 # Want it to appear first in AS-SE-E

Finally, we don’t want Kenya returning travellers shown on plot, as we don’t know which admin division(s) they could have visited.

sample_metadata.loc[sample_metadata['Country_or_admin1'] == 'Kenya', 'Population'] = None

Sorting countries

Now the countries are ready to sort geographically.

df_country_or_admin1 = (
    pd.DataFrame(
        sample_metadata
        .groupby(['Continent', 'Population', 'population_colour',
                  'Country_or_admin1', 'Population_long',
                  'Country_or_admin1_long'])
        .size()
    )
    .reset_index()
    .set_index('Country_or_admin1')
    .sort_values(['Population_long', 'Country_or_admin1_long'])
    .rename(columns={0: 'Frequency (number of samples)'})
)

print(df_country_or_admin1.shape)
df_country_or_admin1

(36, 6)
Continent Population population_colour Population_long Country_or_admin1_long Frequency (number of samples)
Country_or_admin1
Peru South America SA #4daf4a -73.157010 -74.356842 21
Colombia South America SA #4daf4a -73.157010 -73.086731 159
Venezuela South America SA #4daf4a -73.157010 -66.145936 2
Gambia West Africa AF-W #e31a1c -3.519507 -15.372910 1247
Senegal West Africa AF-W #e31a1c -3.519507 -14.470363 155
Guinea West Africa AF-W #e31a1c -3.519507 -10.936960 199
Mauritania West Africa AF-W #e31a1c -3.519507 -10.337093 104
Côte d'Ivoire West Africa AF-W #e31a1c -3.519507 -5.554446 71
Mali West Africa AF-W #e31a1c -3.519507 -3.522152 1804
Burkina Faso West Africa AF-W #e31a1c -3.519507 -1.745660 58
Ghana West Africa AF-W #e31a1c -3.519507 -1.210711 4090
Benin West Africa AF-W #e31a1c -3.519507 2.339713 334
Nigeria West Africa AF-W #e31a1c -3.519507 8.097575 140
Gabon West Africa AF-W #e31a1c -3.519507 11.784989 59
Cameroon West Africa AF-W #e31a1c -3.519507 12.741504 294
Democratic Republic of the Congo Central Africa AF-C #fd8d3c 23.660758 23.660758 573
Sudan Northeast Africa AF-NE #bb8129 32.741894 30.005646 203
Uganda Northeast Africa AF-NE #bb8129 32.741894 32.391932 15
Ethiopia Northeast Africa AF-NE #bb8129 32.741894 39.626195 34
Kenya, Kisumu Northeast Africa AF-NE #bb8129 32.741894 40.000000 64
Kenya, Kilifi East Africa AF-E #fecc5c 35.999707 34.000000 662
Malawi East Africa AF-E #fecc5c 35.999707 34.300482 371
Tanzania East Africa AF-E #fecc5c 35.999707 34.825685 697
Mozambique East Africa AF-E #fecc5c 35.999707 35.551437 91
Madagascar East Africa AF-E #fecc5c 35.999707 46.698618 25
India, Odisha or West Bengal Eastern South Asia AS-S-E #dfc0eb 79.622525 84.418059 244
India, Tripura Far-eastern South Asia AS-S-FE #984ea3 89.833945 90.000000 72
Bangladesh Far-eastern South Asia AS-S-FE #984ea3 89.833945 90.277384 1658
Myanmar Western Southeast Asia AS-SE-W #9ecae1 98.496702 96.510201 1260
Thailand, Tak or Ranong Western Southeast Asia AS-SE-W #9ecae1 98.496702 98.791050 994
Thailand, Sisakhet Eastern Southeast Asia AS-SE-E #3182bd 105.173981 103.000000 112
Laos Eastern Southeast Asia AS-SE-E #3182bd 105.173981 103.768157 1052
Cambodia Eastern Southeast Asia AS-SE-E #3182bd 105.173981 104.916873 1723
Vietnam Eastern Southeast Asia AS-SE-E #3182bd 105.173981 106.551796 1733
Indonesia Oceania OC-NG #f781bf 135.577208 117.314980 133
Papua New Guinea Oceania OC-NG #f781bf 135.577208 145.254007 251

We want to seperate and sort QC pass samples which will help to distinguish QC-fail samples in the figure.

df_country_or_admin1_pass = (
    pd.DataFrame(
        sample_metadata
        .loc[sample_metadata['QC pass']]
        .groupby(['Continent', 'Population',
                  'population_colour', 'Country_or_admin1',
                  'Population_long', 'Country_or_admin1_long'])
        .size()
    )
    .reset_index()
    .set_index('Country_or_admin1')
    .sort_values(['Population_long', 'Country_or_admin1_long'])
    .rename(columns={0: 'Frequency (number of samples)'})
)
print(df_country_or_admin1_pass.shape)
df_country_or_admin1_pass

(36, 6)
Continent Population population_colour Population_long Country_or_admin1_long Frequency (number of samples)
Country_or_admin1
Peru South America SA #4daf4a -73.157010 -74.356842 21
Colombia South America SA #4daf4a -73.157010 -73.086731 135
Venezuela South America SA #4daf4a -73.157010 -66.145936 2
Gambia West Africa AF-W #e31a1c -3.519507 -15.372910 863
Senegal West Africa AF-W #e31a1c -3.519507 -14.470363 150
Guinea West Africa AF-W #e31a1c -3.519507 -10.936960 151
Mauritania West Africa AF-W #e31a1c -3.519507 -10.337093 92
Côte d'Ivoire West Africa AF-W #e31a1c -3.519507 -5.554446 71
Mali West Africa AF-W #e31a1c -3.519507 -3.522152 1167
Burkina Faso West Africa AF-W #e31a1c -3.519507 -1.745660 57
Ghana West Africa AF-W #e31a1c -3.519507 -1.210711 3131
Benin West Africa AF-W #e31a1c -3.519507 2.339713 150
Nigeria West Africa AF-W #e31a1c -3.519507 8.097575 110
Gabon West Africa AF-W #e31a1c -3.519507 11.784989 55
Cameroon West Africa AF-W #e31a1c -3.519507 12.741504 264
Democratic Republic of the Congo Central Africa AF-C #fd8d3c 23.660758 23.660758 520
Sudan Northeast Africa AF-NE #bb8129 32.741894 30.005646 76
Uganda Northeast Africa AF-NE #bb8129 32.741894 32.391932 12
Ethiopia Northeast Africa AF-NE #bb8129 32.741894 39.626195 21
Kenya, Kisumu Northeast Africa AF-NE #bb8129 32.741894 40.000000 63
Kenya, Kilifi East Africa AF-E #fecc5c 35.999707 34.000000 627
Malawi East Africa AF-E #fecc5c 35.999707 34.300482 265
Tanzania East Africa AF-E #fecc5c 35.999707 34.825685 589
Mozambique East Africa AF-E #fecc5c 35.999707 35.551437 34
Madagascar East Africa AF-E #fecc5c 35.999707 46.698618 24
India, Odisha or West Bengal Eastern South Asia AS-S-E #dfc0eb 79.622525 84.418059 233
India, Tripura Far-eastern South Asia AS-S-FE #984ea3 89.833945 90.000000 67
Bangladesh Far-eastern South Asia AS-S-FE #984ea3 89.833945 90.277384 1310
Myanmar Western Southeast Asia AS-SE-W #9ecae1 98.496702 96.510201 985
Thailand, Tak or Ranong Western Southeast Asia AS-SE-W #9ecae1 98.496702 98.791050 895
Thailand, Sisakhet Eastern Southeast Asia AS-SE-E #3182bd 105.173981 103.000000 59
Laos Eastern Southeast Asia AS-SE-E #3182bd 105.173981 103.768157 991
Cambodia Eastern Southeast Asia AS-SE-E #3182bd 105.173981 104.916873 1267
Vietnam Eastern Southeast Asia AS-SE-E #3182bd 105.173981 106.551796 1404
Indonesia Oceania OC-NG #f781bf 135.577208 117.314980 121
Papua New Guinea Oceania OC-NG #f781bf 135.577208 145.254007 221

Finally, we rename some countries with long names to shorter names to prevent the restriction of figure size.

# rename the long-name countries in total samples df
df_country_or_admin1.rename(index={'Democratic Republic of the Congo': 'DRC'},inplace=True)
df_country_or_admin1.rename(index={'India, Odisha or West Bengal': 'India, Odisha\nor West Bengal'},inplace=True)
df_country_or_admin1.rename(index={'Thailand, Tak or Ranong': 'Thailand, Tak\nor Ranong'},inplace=True)

# rename the long-name countries in QC-pass samples df
df_country_or_admin1_pass.rename(index={'Democratic Republic of the Congo': 'DRC'},inplace=True)
df_country_or_admin1_pass.rename(index={'India, Odisha or West Bengal': 'India, Odisha\nor West Bengal'},inplace=True)
df_country_or_admin1_pass.rename(index={'Thailand, Tak or Ranong': 'Thailand, Tak\nor Ranong'},inplace=True)

Make the figure

We have the following considerations when making of this figure:

  1. While QC failed samples shown as outline only, others should have a solid-background to distinguish from each other

  2. Lines and annotations at bottom for both continent and population.

  3. The y-axis is truncated at 2,000 samples for visual clarity. With over 3,000 samples, Ghana is affected by this truncation. Therefore, specific annotations for QC pass and fail are positioned above Ghana’s bar to highlight its significance.

# Adjust the figure size
fig, ax = plt.subplots(1, 1, figsize=(23.3, 10))

# Create the bars for all samples with a white backgound
ax.bar(
    np.arange(len(df_country_or_admin1)),
    df_country_or_admin1['Frequency (number of samples)'],
    facecolor='white',
    edgecolor = df_country_or_admin1['population_colour'],
)
# Create the bars for QC pass with solid-colour background
ax.bar(
    np.arange(len(df_country_or_admin1_pass)),
    df_country_or_admin1_pass['Frequency (number of samples)'],
    color = df_country_or_admin1['population_colour'],
    edgecolor = df_country_or_admin1['population_colour'],
)
# Set x-axis labels and rotate them for readability
ax.set_xticks(np.arange(len(df_country_or_admin1)))
ax.set_xticklabels(df_country_or_admin1.index, rotation=90)
ax.grid(True, axis='y')
# Set the y-axis limit to truncate bars at a maximum of 2000
ax.set_ylim(0, 2000)
# Set axis labels
ax.set_xlabel('Country or region',fontsize=15)
ax.set_ylabel('Frequency (number of samples)',fontsize=15)
trans = ax.get_xaxis_transform()
# Add specific annotation to Ghana
total_samples = collections.OrderedDict()
pass_samples = collections.OrderedDict()
x_pos = collections.OrderedDict()
# Set the index number for Ghana
x_pos['Ghana'] = 10
for country in x_pos:
    total_samples[country] = df_country_or_admin1.loc[country, 'Frequency (number of samples)']
    pass_samples[country] = df_country_or_admin1_pass.loc[country, 'Frequency (number of samples)']
    ax.annotate(f"{pass_samples[country]:,}/", xy=(x_pos[country], 1.1), xycoords=trans, ha="center", va="top")
    ax.annotate(f"{total_samples[country] - pass_samples[country]:,}", xy=(x_pos[country], 1.05), xycoords=trans, ha="center", va="top")
y_offset = -0.6
text_offset = 0.05
x_offset = 0.3

# Add annotations for Continents
ax.annotate('Continent', xy=(-3, y_offset-text_offset), xycoords=trans, ha="left", va="top",fontsize=14)
ax.annotate('South\nAmerica', xy=(1, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([0-x_offset, 2+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('Africa', xy=(13.5, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([3-x_offset, 24+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('Asia', xy=(29, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([25-x_offset, 33+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('Oceania', xy=(34.5, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([34-x_offset, 35+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
y_offset = -0.45
text_offset = 0.05
x_offset = 0.3

# Add annotations for Populations
ax.annotate('Population', xy=(-3, y_offset-text_offset), xycoords=trans, ha="left", va="top",fontsize=14)
ax.annotate('SA', xy=(1, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([0-x_offset, 2+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('AF-W', xy=(8.5, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([3-x_offset, 14+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('AF-C', xy=(15, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([15-x_offset, 15+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('AF-NE', xy=(17.5, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([16-x_offset, 19+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('AF-E', xy=(22, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([20-x_offset, 24+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('AS-S-E', xy=(25, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([25-x_offset, 25+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('AS-S-FE', xy=(26.5, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([26-x_offset, 27+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('AS-SE-W', xy=(28.5, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([28-x_offset, 29+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('AS-SE-E', xy=(31.5, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
ax.plot([30-x_offset, 33+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)
ax.annotate('OC-NG', xy=(34.5, y_offset-text_offset), xycoords=trans, ha="center", va="top",fontsize=12)
_ = ax.plot([34-x_offset, 35+x_offset],[y_offset, y_offset], color="k", transform=trans, clip_on=False)

# Customize tick label fonts and spacing
for tick in ax.xaxis.get_major_ticks():
    tick.label1.set_fontsize(9)
ax.tick_params(axis='x', pad=-2)

fig.tight_layout()
../../_images/barplot-samples-by-country_40_0.png

Figure Legend. Breakdown of samples by country. Solid bars indicate samples which passed QC. Unfilled bars represent samples that failed QC. The y-axis is truncated at 2,000 samples, with the numbers of QC pass/QC fail samples in Ghana shown above the bar. Bars are coloured according to the major sub-population to which the location is assigned.

Save the figure

We can output this to a location in Google Drive

First we need to connect Google Drive by running the following:

# You will need to authorise Google Colab access to Google Drive
drive.mount('/content/drive')

Mounted at /content/drive

# This will send the file to your Google Drive, where you can download it from if needed
# Change the file path if you wish to send the file to a specific location
# Change the file name if you wish to call it something else

fig.savefig('/content/drive/My Drive/SamplesByCountry_Barplot_20210720.pdf')
fig.savefig('/content/drive/My Drive/SamplesByCountry_Barplot_20210720.png', dpi=480) # increase the dpi for higher resolution

previous

Visualising sample collections over time

next

Summarise Genetic Variants