mixalime 2.2.2

Identification of allele-specific events in sequencing experiments.

MixALime: Mixture models for Allelic Imbalance Estimation

Python 3.10+ as of now is not supported due to the fact that datatable package does not support it either. This will change as soon as datatable is released to 1.1.0.

MixALime is a tool for the identification of allele-specific events in high-throughput sequencing data. It works by modelling counts data as a mixture of two Negative Binomial or Beta Negative Binomial distributions (where the latter is more applicable in case of noisy data at a cost of loss of sensitivity).

The package is almost easy to use and we advise everyone to just jump straight to installing MixALime and invoking the help command in a command line:

> pip3 install mixalime
> mixalime --help

We believe that the help section of MixALime covers its functionality well enough. Furthermore, the package arrives with a small demo dataset included and an easy-to-follow instruction in the abovementioned help section. So do not waste your time looking for how-to-clues or tutorials here, just use --help.

Actually this README.md will probably be more complete and detailed one day than it is now, the README-writing person was just too tired at the moment.

For the sake of following the social norms that impose a requirement of README files to be useful, in the next section you'll find the excerpt from --help command:

Demo

A typical MixALime session consists of sequential runs of create, fit, test, combine and, finally, export all, plot commands. For instance, we provide a demo dataset that consists of a bunch of BED-like files with allele counts at SNVs (just for the record, MixALime can work with most vcf and BED-like file formats):

> mixalime export demo

A scorefiles folder should appear now in a working directory with a plenty of BED-like files. First, we'd like to parse those files into a MixALime-friendly and efficient data structures for further usage, as well as perform some
basic filtering if necessary:

> mixalime create myprojectname scorefiles

Then we fit model parameters to the data with Negative Binomial distribution:

> mixalime fit myprojectname NB

Next we obtain raw p-values:

> mixalime test myprojectname

Usually we'd want to combine p-values across samples and apply a FDR correction:

> mixalime combine myprojectname

Finally, we obtain fancy plots fir diagnostic purposes and easy-to-work with tabular data:

> mixalime export all myprojectname results_folder
> mixalime plot myprojectname results_folder

You'll find everything of interest in results_folder.

Combination of p-values across groups

Note: a popular synonym for "combination" in this context is aggregation.

One important feature that is not covered by the glorified --help in a very obvious fashion is a combination of p-values across separate groups (e.g. one group can be a treatment and the other is a control). The combine command with default options combines all the p-values. This can be changed by supplying the --group option followed by either a list of filenames that make up that group or a file that contains a list (newline-separated) of those files (the most convenient approach, probably), e.g.:

> mixalime combine --subname treatment -g vcfs/file1.vcf.gz -g vfcfs/file2.vfc.gz -g vcfs/file3.vcf.gz myproject
> mixalime combine --subname control -g vcfs/file4.vcf.gz -g vfcfs/file5.vfc.gz -g vcfs/file6.vcf.gz myproject

or

> mixalime combine --subname treatment -g group_treatment.tsv combine myproject
> mixalime combine --subname control -g group_control.tsv combine myproject

The --subname option is necessary if you wish to avoid different combine invocations overriding each other.