Figure 2d (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
from numpy import nan

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_2d.csv.

Show code cell source

Hide code cell source

# fname = "figure_2d.csv"
# data = (
#     (pd.read_csv(fname, header=[0, 1], index_col=0))
#     .rename_axis(index=[("time (h)", "")], columns=["condition", "replicate"])
#     .reset_index()
# )
# data.to_dict(orient="list")
data = {
    ("time (h)", ""): [0.5, 2.0, 3.0, 5.0, 7.0, 9.0, 11.25, 13.0, 15.0, 24.0, 27.5],
    ("IBA strain - OD600", "R1"): [
        0.103,
        0.116,
        0.23,
        0.75,
        2.1,
        3.97,
        5.63,
        7.43,
        8.2,
        7.72,
        7.36,
    ],
    ("IBA strain - OD600", "R2"): [
        0.1,
        0.111,
        0.22,
        0.74,
        2.02,
        3.84,
        4.37,
        7.14,
        8.14,
        7.63,
        7.78,
    ],
    ("IBA strain - OD600", "R3"): [
        0.097,
        0.117,
        0.216,
        0.74,
        1.89,
        3.67,
        5.35,
        7.03,
        7.72,
        7.65,
        7.72,
    ],
    ("Glucose (mM)", "R1"): [
        117.6111111,
        nan,
        nan,
        117.3888889,
        nan,
        nan,
        nan,
        79.05555556,
        nan,
        44.66666667,
        nan,
    ],
    ("Glucose (mM)", "R2"): [
        118.1111111,
        nan,
        nan,
        117.5,
        nan,
        nan,
        nan,
        81.27777778,
        nan,
        44.66666667,
        nan,
    ],
    ("Glucose (mM)", "R3"): [
        118.2777778,
        nan,
        nan,
        117.1666667,
        nan,
        nan,
        nan,
        81.55555556,
        nan,
        44.94444444,
        nan,
    ],
    ("Acetate (mM)", "R1"): [
        18.33333333,
        nan,
        nan,
        16.16666667,
        nan,
        nan,
        nan,
        3.5,
        nan,
        4.5,
        nan,
    ],
    ("Acetate (mM)", "R2"): [
        20.83333333,
        nan,
        nan,
        16.66666667,
        nan,
        nan,
        nan,
        5.833333333,
        nan,
        2.0,
        nan,
    ],
    ("Acetate (mM)", "R3"): [
        18.83333333,
        nan,
        nan,
        16.5,
        nan,
        nan,
        nan,
        4.166666667,
        nan,
        3.0,
        nan,
    ],
    ("IBA (mM)", "R1"): [
        0.0,
        nan,
        nan,
        0.0,
        nan,
        nan,
        nan,
        23.26167386,
        nan,
        53.36772159,
        nan,
    ],
    ("IBA (mM)", "R2"): [
        0.0,
        nan,
        nan,
        0.0,
        nan,
        nan,
        nan,
        22.78901023,
        nan,
        51.95629545,
        nan,
    ],
    ("IBA (mM)", "R3"): [
        0.0,
        nan,
        nan,
        0.0,
        nan,
        nan,
        nan,
        22.506725,
        nan,
        52.08759091,
        nan,
    ],
}
data = (
    pd.DataFrame(data)
    .set_index(("time (h)", ""))
    .rename_axis(index=["time (h)"], columns=["condition", "replicate"])
)
data

condition	IBA strain - OD600			Glucose (mM)			Acetate (mM)			IBA (mM)
replicate	R1	R2	R3	R1	R2	R3	R1	R2	R3	R1	R2	R3
time (h)
0.50	0.103	0.100	0.097	117.611111	118.111111	118.277778	18.333333	20.833333	18.833333	0.000000	0.000000	0.000000
2.00	0.116	0.111	0.117	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3.00	0.230	0.220	0.216	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5.00	0.750	0.740	0.740	117.388889	117.500000	117.166667	16.166667	16.666667	16.500000	0.000000	0.000000	0.000000
7.00	2.100	2.020	1.890	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9.00	3.970	3.840	3.670	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
11.25	5.630	4.370	5.350	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
13.00	7.430	7.140	7.030	79.055556	81.277778	81.555556	3.500000	5.833333	4.166667	23.261674	22.789010	22.506725
15.00	8.200	8.140	7.720	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
24.00	7.720	7.630	7.650	44.666667	44.666667	44.944444	4.500000	2.000000	3.000000	53.367722	51.956295	52.087591
27.50	7.360	7.780	7.720	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

You will need to operate on a multi-index DataFrame.

#