Replicate averaging

Every Profile subclass (rnav.data.Profile, SHAPEMaP, DanceMaP, RNPMaP) optionally accepts a list of file names or rnav.Data objects as input_data.

1. the active metric column is averaged across replicates

2. the standard error of the mean is stored as <metric_column>_sem and registered as the error_column, so error bars appear automatically on profile and skyline plots

This applies to the shapemap, dancemap, rnpmap, profile and other related standard data keywords.

1. Averaging replicate profiles

2. Loading replicates from files

3. Normalizing before averaging

import numpy as np
import rnavigate as rnav

Averaging replicate profiles

Below, 3 synthetic replicate measurements are built around a shared “true” signal using rnav.data.Profile.from_array. Each behaves exactly like a Profile loaded from a file. Passing a list of them to rnav.data.Profile averages them the same way a list of shapemap profile.txt paths would.

rng = np.random.default_rng(0)
sequence = "AUGC" * 40  # 160 nt shared sequence
true_signal = rng.random(160) * 2

replicates = [
    rnav.data.Profile.from_array(
        input_data=np.clip(true_signal + rng.normal(scale=0.15, size=160), 0, None),
        sequence=sequence,
        metric="Profile",
    )
    for _ in range(3)
]

combined = rnav.data.Profile(input_data=replicates, metric="Profile")
combined.data[["Nucleotide", "Profile", "Profile_sem"]].head()

	Nucleotide	Profile	Profile_sem
0	1	1.241486	0.008419
1	2	0.254430	0.127734
2	3	0.111551	0.056524
3	4	0.103458	0.009227
4	5	1.672009	0.110121

combined.error_column is now "Profile_sem", so error bars are drawn automatically:

my_sample = rnav.Sample(sample="combined replicates", my_profile=combined)

plot = rnav.plot_profile(
    samples=[my_sample],
    profile="my_profile",
)

Loading replicates from files

In practice, replicates usually come from files. Any profile-creating data keyword accepts a list of paths in place of a single file. RNAvigate loads each one, checks that they share a sequence, and averages them:

my_sample = rnav.Sample(
    sample="example",
    shapemap=[
        "replicate_1_profile.txt",
        "replicate_2_profile.txt",
        "replicate_3_profile.txt",
    ],
)

This is equivalent to constructing each SHAPEMaP replicate individually and combining them with SHAPEMaP.from_replicates([...]). The same list syntax works for dancemap and rnpmap.

Normalizing before averaging

Replicates are sometimes on different scales, e.g. differing sequencing depth or overall reactivity. Pass normalize_kwargs to renormalize each replicate with Profile.normalize(**normalize_kwargs) before averaging, rather than only normalizing the final combined profile.

scaled_replicates = [
    rnav.data.Profile.from_array(
        input_data=np.clip((true_signal + rng.normal(scale=0.15, size=160)) * scale, 0, None),
        sequence=sequence,
        metric="Profile",
    )
    for scale in [0.5, 1.0, 1.5]  # 3 replicates on very different scales
]

raw_mean = rnav.data.Profile(
    input_data=scaled_replicates, metric="Profile"
).data["Profile"].mean()

rescaled_mean = rnav.data.Profile(
    input_data=scaled_replicates,
    metric="Profile",
    normalize_kwargs={"norm_method": "boxplot", "new_profile": "Profile"},
).data["Profile"].mean()

print(f"averaged without renormalizing: {raw_mean:.3f}")
print(f"averaged after per-replicate renormalizing: {rescaled_mean:.3f}")

Warning: seq-object missing expected error column
Warning: seq-object missing expected error column
Warning: seq-object missing expected error column
averaged without renormalizing: 1.055
averaged after per-replicate renormalizing: 0.532

Note: If the metric has a color_column distinct from its metric column, RNAvigate warns that coloring falls back to the first replicate. Averaging does not currently combine colors.

See also: Custom profiles for other ways to construct Profile objects, and standard data keywords for the shapemap, dancemap, rnpmap, and profile keywords.