rots-py 1.4.0

ROTS gene ranking implementation in Python

rots-py

Python implementation of the Reproducibility-Optimized Test Statistic (ROTS) for gene ranking from the Bioconductor ROTS package.

ROTS belongs to a familly of gene ranking statistics that aim to rank genes based on evidence for differential expression in two-group comparisons. ROTS is a non-parametric method that uses a permutation test to assess the significance of the observed differential expression. ROTS is designed to be robust to outliers and to be reproducible across different studies.

NOTE: Data should have at least two non-null values per row for both groups.

Installation

pip install rots-py

Usage

from rotspy import rots, plot_rots, get_summary

# Load data
data = ...
group = ...

# Run ROTS
result = rots(data, group, B=500, log=True, verbose=True, progress=True)

# Get the ranking
ranking_statistic = result["d"]
fdr = result["fdr"]
logFC = result["logfc"]
pvalue = result["p"]

# Get the summary of result with FDR threshold of 0.05
summary = get_summary(result, fdr_c=0.05)

# Plot volcano plot from the results
plot_rots(result, fdr=0.05, type="volcano")

Methods

rots(...)

Runs the ROTS analysis on the given data. Returns a Python dictionary.

Parameters

• data: A pandas dataframe with genes/proteins as rows and samples as columns. (required)
• group: A pandas series with the group labels for each sample. (required)
• B: Number of permutations to perform. Default is 500. (optional)
• K: Top-list size. (optional)
• paried: Whether the samples are paired. Default is False. (optional)
• seed: Seed for the random number generator. Default is None. (optional)
• a1: Parameter for the ROTS statistic. If both a1 and a2 are specified optimization step is skipped. (optional)
• a2: Parameter for the ROTS statistic. If both a1 and a2 are specified optimization step is skipped. (optional)
• log: Whether data is log-transformed. Default is False. (optional)
• progress: Whether to show a progress bar. Default is False. (optional)
• verbose: Whether to print the progress of the analysis. Default is False. (optional)

Returns

Python dict object with the following keys:

• data: The original dataframe used for the input
• B: Number of permutations
• d: ROTS test statistic for each gene/protein
• logfc: Log2 fold change
• p: P-value
• FDR: False Detection Rate
• a1: Optimized parameter a1
• a2: Optimized parameter a2
• k: Top list size (None if optimization skipped)
• R: Reproducibility score (None if optimization skipped)
• Z: Z-score (None if optimization skipped)
• ztable: Z-score table
• cl: Group labels for each sample

get_summary(...)

Returns a summary of the ROTS results.

Parameters

• rots_res: The result of the rots function. (required)
• fdr_c: The FDR threshold for the summary. Default is None (required if n_features is not specified)
• n_features: The number of top rows to show in the summary. Default is None (required if fdr is not specified)
• verbose: Whether to print the summary. Default is True (optional)

Returns

A pandas dataframe with the following columns:

• Row: The row names of the input dataframe
• ROTS Statistic: The ROTS statistic for each row
• pvalue: The p-value for each row
• FDR: The FDR for each row

plot(...)

Plots the ROTS results.

Parameters

• rots_res: The result of the rots function. (required)
• fdr: The FDR threshold for the plot. Default is 0.05 (required)
• type: The type of plot to generate. (required)
  • "volcano"
  • "heatmap"
  • "ma"
  • "reproducibility"
  • "pvalue"
  • "pca"

Acknowledgements

This package was developed as part of the EDISS program in collaboration with Coffey Lab at the Turku Bioscience center.

Changelog

1.4.0

• Added support for Python 3.9 on Linux

1.3.0

• Added support for Python 3.8 on Linux

1.2.0

• Added get_summary function
• Added plot_rots function
• Modified the import statement to from rotspy import ...
• More optimizations
• Bug fixes

1.1.0

• Ported parts of code to Cython for better performance
• Fixed bugs

1.0.3

• Bug fixes

1.0.2

• Bug fixes
• Added numba for better performance

1.0.0

• Initial release