mecan4cna 0.18

Minimum Error Calibration and Normalization for Copy Number Analysis

Due to sample impurity and measurement bias, a copy number profile usually needs to be calibrated for the position of baseline (normal copy numbers) and DNA levels. Because each profile usually has different signal scale, it’s also important to normalize profiles in comparitive analysis.

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

Key features:

• Calibration of single file or normalization of multiple files

• Only requires 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
segment_liftover --help

How to use

See the manual for details.

Quick start

mecan4can -i [SEGMENT_FILE] -o [OUTPUT_PATH]

Demo mode

mecan4can --demo

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

General Usage

Usage: mecan4cna [OPTIONS]

Options:
  -i, --input_file FILENAME       The input file.
  -o, --output_path TEXT          The path for output files.
  -p, --plot                      Whether to show the signal histogram.
  -b, --bins_per_interval INTEGER RANGE
                                  The number of bins in each copy number
                                  interval.
  -v, --intervals INTEGER RANGE   The number of copy number intervals.
  -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                Minimun Peak Distance coefficient in peak
                                  detection.
  --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 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.
  --help                          Show this message and exit.

Required options are:

• -i FILENAME

• -o TEXT

Input file format

The input should be a segmentation file:

• have at least 5 columns as 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 the mecan fails to detect anything (not enough aberrant segments or no valid models), only the histogram will be created:

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

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

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

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

Common problems

Error of matplotlib in conda environment

If you run into this prolbem, simply re-install matplotlib with:

conda install -n YOURENV matplotlib