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Minimum Error Calibration and Normalization for Copy Number Analysis

Project description

A copy number profile usually needs to be calibrated for the position of baseline (normal copy numbers) due to sample impurity and measurement bias. It’s crucial to normalize CN profiles when comparing them in analysis, because usually each profile has a different signal scale.

Mecan4CNA (Minimum Error Calibration and Normalization for Copy Number Analysis) uses an algebraic method to estimate the baseline and the distance between DNA levels (referred to as level distance). It can be used for both single file analysis and multi-file normalization.

Key features:

  • Calibration of a segmentation file, so that the signal of normal is aligned to 2 (0 in log2)

  • Estimation of the distance between DNA levels

  • Normalizing multiple files to a uniformed signal scale, so that 3 (0.585 in log2) and 1 (-1 in log2) actually correspond to one copy gain and one copy lost

  • Needs only a segmentation file (from any platform)

  • Detailed results and plots for in-depth analysis

  • Fast

How to install

The easiest way is to install through pip:

pip install mecan4cna
mecan4cna --help

How to use

See the manual for details.

Quick start

mecan4cna -i [SEGMENT_FILE] -o [OUTPUT_PATH]

Demo mode

mecan4cna --demo

This will copy 5 example files to the current directory and run with default settings. It invokes the script, which will also be copied over and can be used as a template for customized analysis.

General Usage

Usage: mecan4cna [OPTIONS]

  -i, --input_file FILENAME       The input file.
  -o, --output_path TEXT          The path for output files.
  -n, --normalize                 Calibrate and normalize the input file.
  -p, --plot                      Whether to save the signal histogram.
  -b, --bins_per_interval INTEGER RANGE
                                  The number of bins in each copy number
  -v, --intervals INTEGER RANGE   The number of copy number intervals.
  --demo                          Copy example files and run a demo script in
                                  the current directory.
  -pt, --peak_thresh INTEGER RANGE
                                  The minimum probes of a peak.
  -st, --segment_thresh INTEGER RANGE
                                  The minimum probes of a segment.
  --model_steps INTEGER RANGE     The incremental step size in modeling.
  --mpd_coef FLOAT                Minimum Peak Distance coefficient in peak
  --max_level_distance FLOAT      The maximum value of level distance.
  --min_level_distance FLOAT      The minimum value of level distance.
  --min_model_score INTEGER RANGE
                                  The minimum value of the model score.
  --info_lost_ratio_thresh FLOAT  The threshold of information lost ratio.
  --info_lost_range_low FLOAT     The low end of information lost range.
  --info_lost_range_high FLOAT    The high end of information lost range.
  --ld_scaler FLOAT               The scaler of level distance in
  --help                          Show this message and exit.

Required options are:



Input file format

The input should be a segmentation file:

  • have at least 5 columns:id, chromosome, start, end, probes and value (in exact order, names do not matter). Any additional columns will be ignored.

  • the first line of the file is assumed to be column names, and will be ignored. Do not put empty lines at the beginning of the file.

  • be tab separated, without quoted values

An example:

id  chro    start   end num_probes  seg_mean
GSM378022   1   775852  143752373   9992    0.025
GSM378022   1   143782024   214220966   6381    0.1607
GSM378022   2   88585000    144628991   4256    0.0131
GSM378022   2   144635510   146290468   146 0.1432
GSM378022   3   48603   8994748 1469    0.0544

Output files

4 files will be created in the output path. If mecan fails to detect anything (not enough aberrant segments or no valid models), only the histogram will be created:

  • base_level.txt : contains the estimated baseline and level distance.

  • histogram.pdf : a visual illustration of signal distributions.

  • models.tsv : a tab separated table that details all information of all models.

  • peaks.tsv : a tab separated table shows the determined signal peaks and their relative DNA levels compared to the baseline.

Calibration and normalization

With the -n flag, the input file will be normalized and saved as normalized.tsv.

Import as a python library

import mecan.mecan4cna.algorithms as alg
import mecan.mecan4cna.common as comm

with open('examples\segment_example_1.tsv', 'r') as fin:
    segments = comm.file2list(fin)
m = alg.mecan()
r =

Common problems

Error of matplotlib

It seems there is a bug in the latest version (3.0.3) of matplotlib, which may cause problems in OSX. Mecan uses an older verison of matplotlib (2.0.2) to avoid this problem. If you need to use the latest version and run into runtime problems, please check the following links.

Project details

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Source Distribution

mecan4cna-0.28.tar.gz (65.2 kB view hashes)

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