micom.stats
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Data warngling and statistics for MICOM.
Module Contents#
Functions#
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Get FDR cutoffs for p-values with Benjamini-Hochberg. |
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Compare fluxes form different sample groups. |
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Compare fluxes form different sample groups. |
- micom.stats.fdr_adjust(p)[source]#
Get FDR cutoffs for p-values with Benjamini-Hochberg.
- Parameters:
p (list[float]) – The original p-values. Can not contain naNs.
- Returns:
A numpy array of FDR cutoffs (q-values). This is commonly known as “adjusted”
p-values.
- micom.stats.compare_groups(fluxes, metadata_column, groups=None, threads=1, progress=True)[source]#
Compare fluxes form different sample groups.
Note
This uses a non-parametric test by default. By default it will use a Mann-Whitney test for two groups and a Kruskal-Wallis test for >2 groups.
- Parameters:
fluxes (pandas.DataFrame) – A frame with net fluxes as returned by production_rates or consumption_rates.
metatdata_column (str) – The column of the DataFrame denoting the groups.
groups (list[str] or None) – Specify a subset of groups you want to compare or define the order (1st will be the reference group). If None will use the groups as they appear in the DataFrame.
threads (int) – How many threads to use to run tests in parallel.
progress (bool) – Whether to show a progress bar.
- Return type:
Returns the metabolite with their respective test statistics.
- micom.stats.correlate_fluxes(fluxes, metadata_column, groups=None, threads=1, progress=True)[source]#
Compare fluxes form different sample groups.
Note
This uses a non-parametric test by default (Spearman rank correlation).
- Parameters:
fluxes (pandas.DataFrame) – A frame with net fluxes as returned by production_rates or consumption_rates.
metatdata_column (str) – The column of the DataFrame denoting the covariate.
threads (int) – How many threads to use to run tests in parallel.
progress (bool) – Whether to show a progress bar.
- Return type:
Returns the metabolite with their respective test statistics.