Figure 2a (Rong, Frey et. al. 2024)

Caption: “Growth curve of the xylitol strain with (dark blue) or without (light blue) the CRISPRi switch induced. (…) Error bars and shaded areas indicate mean ± s.d. (n = 4 biological replicates (…) OD values (…) were measured using a Jenway 6705 UV/Vis spectrophotometer (…)”

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

For protability the data is directly included as a dictionary. See the commented out code how the data was obtained from the CSV file figure_2a.csv.

# fname = "figure_2a.csv"
# data = pd.read_csv(fname)
# data.to_dict(orient="list")
data = {
    "time (h)": [0, 2, 4, 6, 8, 12, 16, 24, 30],
    "rep1_uninduced": [0.046, 0.194, 0.74, 1.64, 2.62, 3.68, 4.08, 4.17, 3.88],
    "rep2_uninduced": [0.048, 0.213, 0.82, 1.8, 2.72, 3.73, 4.32, 4.13, 3.87],
    "rep3_uninduced": [0.06, 0.234, 0.85, 1.91, 2.81, 3.92, 4.36, 4.23, 3.95],
    "rep4_uninduced": [0.054, 0.205, 0.84, 1.72, 2.7, 3.88, 4.24, 4.18, 4.01],
    "rep1_induced": [0.046, 0.185, 0.5, 0.76, 0.97, 1.01, 0.93, 0.97, 0.9],
    "rep2_induced": [0.055, 0.184, 0.493, 0.766, 0.99, 0.94, 0.97, 1.03, 0.94],
    "rep3_induced": [0.05, 0.204, 0.534, 0.815, 1.06, 1.15, 1.06, 1.07, 1.01],
    "rep4_induced": [0.046, 0.192, 0.537, 0.825, 1.1, 1.17, 1.16, 1.13, 0.97],
}
data = pd.DataFrame(data).set_index("time (h)").rename_axis(columns="replicate")
# data.head()

data_long = (
    data.stack()
    .to_frame("OD600")
    .reset_index()
    .assign(
        condition=lambda x: x["replicate"].str.split("_").str[-1],
    )
)
data_long

	time (h)	replicate	OD600	condition
0	0	rep1_uninduced	0.046	uninduced
1	0	rep2_uninduced	0.048	uninduced
2	0	rep3_uninduced	0.060	uninduced
3	0	rep4_uninduced	0.054	uninduced
4	0	rep1_induced	0.046	induced
...	...	...	...	...
67	30	rep4_uninduced	4.010	uninduced
68	30	rep1_induced	0.900	induced
69	30	rep2_induced	0.940	induced
70	30	rep3_induced	1.010	induced
71	30	rep4_induced	0.970	induced

72 rows × 4 columns

Create a line plot with error bars for the measurement across the timepoints. Either use matplotlib or seaborn for a static plot.

• seaborn lineplot with error bars indicating standard deviation: seaborn.lineplot

• matplotlib error bars used matplotlib.axes.Axes.errorbar

fig, ax = plt.subplots(figsize=(7, 5))

ax = sns.lineplot(
    data=data_long,
    x="time (h)",
    y="OD600",
    hue="condition",
    ax=ax,
)
ax.set(
    xlabel="Time (h)",
    ylabel="$\\text{OD}_{600}$",
    title="Growth curve of the xylitol strain with/without CRISPRi switch induced",
)
ax.legend(title="Condition")
fig.tight_layout()

Show code of one possible solution

display(sns.color_palette("Paired"))
fig, ax = plt.subplots(figsize=(7, 5))

ax = sns.lineplot(
    data=data_long,
    x="time (h)",
    y="OD600",
    hue="condition",
    palette="Paired",
    ci="sd",
    marker="o",
    err_style="bars",
    err_kws={"capsize": 5},
    ax=ax,
)
ax.set(
    xlabel="Time (h)",
    ylabel="$\\text{OD}_{600}$",
    title="Growth curve of the xylitol strain with/without CRISPRi switch induced",
)
ax.legend(title="Condition")
fig.tight_layout()