AmajLime analysis
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Dataframe construction¶
The data analysed in this notebook was generated from an endpoint fluorescent measurement. The data was parsed from the original .txt file to create the following dataframe.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
data_structure = {
'Number': [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11],
'Sample': ['Negative', 'Negative', 'Negative', 'T7-1', 'T7-1', 'T7-1', 'T7-2', 'T7-2', 'T7-2', 'T7-3', 'T7-3', 'T7-3', 'T7-4', 'T7-4', 'T7-4', 'T7-5', 'T7-5', 'T7-5', 'T7-6', 'T7-6', 'T7-6', 'T7-7', 'T7-7', 'T7-7', 'T7-8', 'T7-8', 'T7-8', 'T7-9', 'T7-9', 'T7-9', 'T7-10', 'T7-10', 'T7-10'],
'Fluorescence Units': [6070.0, 6233.0, 6261.0, 4380.0, 6351.0, 2616.0, 6449.0, 6562.0, 6224.0, 19444.0, 20506.0, 18755.0, 7198.0, 6906.0, 6796.0, 45736.0, 43061.0, 47363.0, 27326.0, 31660.0, 30357.0, 13390.0, 12505.0, 11574.0, 442462.0, 442018.0, 427332.0, 1098370.0, 1047753.0, 1026269.0, 1026679.0, 992618.0, 1026745.0]
}
data = pd.DataFrame(data_structure)
#data.headBackground subtraction¶
A new column was added to the dataframe,“blanked”, in which the mean fluorescence value of the “Negative” replicates was substracted from the fluorescence values of all samples.
background = data.loc[data["Sample"] == "Negative", "Fluorescence Units"].mean()
data["Blanked"] = data["Fluorescence Units"] - background
data.head<bound method NDFrame.head of Number Sample Fluorescence Units Blanked
0 1 Negative 6070.0 -118.0
1 1 Negative 6233.0 45.0
2 1 Negative 6261.0 73.0
3 2 T7-1 4380.0 -1808.0
4 2 T7-1 6351.0 163.0
5 2 T7-1 2616.0 -3572.0
6 3 T7-2 6449.0 261.0
7 3 T7-2 6562.0 374.0
8 3 T7-2 6224.0 36.0
9 4 T7-3 19444.0 13256.0
10 4 T7-3 20506.0 14318.0
11 4 T7-3 18755.0 12567.0
12 5 T7-4 7198.0 1010.0
13 5 T7-4 6906.0 718.0
14 5 T7-4 6796.0 608.0
15 6 T7-5 45736.0 39548.0
16 6 T7-5 43061.0 36873.0
17 6 T7-5 47363.0 41175.0
18 7 T7-6 27326.0 21138.0
19 7 T7-6 31660.0 25472.0
20 7 T7-6 30357.0 24169.0
21 8 T7-7 13390.0 7202.0
22 8 T7-7 12505.0 6317.0
23 8 T7-7 11574.0 5386.0
24 9 T7-8 442462.0 436274.0
25 9 T7-8 442018.0 435830.0
26 9 T7-8 427332.0 421144.0
27 10 T7-9 1098370.0 1092182.0
28 10 T7-9 1047753.0 1041565.0
29 10 T7-9 1026269.0 1020081.0
30 11 T7-10 1026679.0 1020491.0
31 11 T7-10 992618.0 986430.0
32 11 T7-10 1026745.0 1020557.0>Plotting: AmajLime summary plot¶
The plot order and color map is first defined to categorise the reporters into color coded low, medium, and high expression level groups. This enables easier visualisation of the dataset. The endpoint background substracted fluorescence units are then plotted for each promoter. A scatter plot is used to illustrate the overall spread of the data.
plot_order = [
'Negative',
'T7-1',
'T7-4',
'T7-3',
'T7-5',
'T7-6',
'T7-7',
'T7-8',
'T7-9',
'T7-10'
]
color_map = {
'Negative': 'grey',
'T7-1': 'green',
'T7-4': 'green',
'T7-3': 'orange',
'T7-5': 'orange',
'T7-6': 'orange',
'T7-7': 'orange',
'T7-8': 'red',
'T7-9': 'red',
'T7-10': 'red'
}
amajLime_plot=sns.catplot(
data=data,
x='Sample',
y='Blanked',
order=plot_order,
palette=color_map,
kind='strip',
height=5,
aspect=1.5
)
amajLime_plot.fig.suptitle('amajLime', x=0.54)
amajLime_plot.set_axis_labels('Promoter', 'Fluorescence units')
amajLime_plot.set_xticklabels(rotation=45, ha='right')
amajLime_plot.set(yscale="log", ylim=(1, 10000000))
plt.tight_layout()
amajLime_plot.savefig('Summary_data_amajLime.png', dpi=600)
