Looper uses object oriented programming (OOP) under the hood. This means that concepts like a sample to be processed or a project are modeled as objects in Python. These project objects are actually useful outside of looper. At some point, we will likely separate the project objects into their own Python package, but for now, you can use them independently of looper, even though they are embedded within the looper package. They are functionally independent.
If you define your project using looper’s standardized project definition format , you can use the project models to instantiate an in memory representation of your project and all of its samples, without using looper. Here is a brief description of how you would do this.
from looper import models my_project = models.Project("path/to/project_config.yaml") my_samples = my_project.samples
Once you have your project and samples in your Python session, the possibilities are endless. This is the way looper reads your project; looper uses these objects to loop through each sample and submit pipelines for each. You could just as easily use these objects for other purposes; for example, one way we use these objects is for post-pipeline processing. After we use looper to run each sample through its pipeline, we can load the project and it sample objects into an analysis session, where we do comparisons across samples. We are also working on on R package that will similarly read this standardized project definition format, giving you access to the same information within R.
This is a work in progress, but you can find more information and examples in the API.
To interact with the various
models and become acquainted with their
features and behavior, there is a lightweight module that provides small
working versions of a couple of the core objects. Specifically, from
tests directory, the Python code in the
module can be copied and pasted into an interpreter. This provides a
Project instance called
proj and a
pi. Additionally, this provides logging information in great detail,
affording visibility into some what’s happening as the
models are created
Extending sample objects¶
By default we use generic models (see the API for more) to handle samples in Looper, but these can also be reused in other contexts by importing
models or by means of object serialization through YAML files.
Since these models provide useful methods to store, update, and read attributes in the objects created from them (most notably a sample -
Sample object), a useful use case is during the run of a pipeline: a pipeline can create a more tailored
Sample model, adding attributes or providing altered or additional methods.
You have several samples, of different experiment types,
each yielding different varieties of data and files. For each sample of a given
experiment type that uses a particular pipeline, the set of file path types
that are relevant for the initial pipeline processing or for downstream
analysis is known. For instance, a peak file with a certain genomic location
will likely be relevant for a ChIP-seq sample, while a transcript
abundance/quantification file will probably be used when working with a RNA-seq
sample. This common situation, in which one or more file types are specific
to a pipeline and analysis both benefits from and is amenable to a bespoke
Sample type. Rather than working with a base
Sample instance and
repeatedly specifying paths to relevant files, those locations can be provided
just once, stored in an instance of the custom
Sample type, and later
used or modified as needed by referencing a named attribute on the object.
This approach can dramatically reduce the number of times that a full filepath
must be accurately keyed and thus saves some typing time. More significant,
it’s likely to save time lost to diagnostics of typo-induced errors. The most
rewarding aspect of employing the
Sample extension strategy, though, is
a drastic readability boost. As the visual clutter of raw filepaths clears,
code readers can more clearly focus on questions of what a filepath points
to and how it’s being used, rather than on the path itself.
It’s the specification of both an experiment or data type (“library” or
“protocol”) and a pipeline with which to process that input type that
Looper uses to determine which type of
Sample object(s) to create for
pipeline processing and analysis (i.e., which
Sample extension to use).
There’s a pair of symmetric reasons for this–the relationship between input
type and pipeline can be one-to-many, in both directions. That is, it’s
possible for a single pipeline to process more than one input type, and a
single input type may be processed by more than one pipeline.
There are a few different
Sample extension scenarios. Most basic is the
one in which an extension, or subtype, is neither defined nor needed–the
pipeline author does not provide one, and users do not request one. Almost
equally effortless on the user side is the case in which a pipeline author
intends for a single subtype to be used with her pipeline. In this situation,
the pipeline author simply implements the subtype within the pipeline module,
and nothing further is required–of the pipeline author or of a user! The
Sample subtype will be found within the pipeline module, and the inference
will be made that it’s intended to be used as the fundamental representation
of a sample within that pipeline. If a pipeline author extends the base
Sample type in the pipeline module, it’s likely that the pipeline’s proper
functionality depends on the use of that subtype. In a rare case, though, it
may be desirable to use the base
Sample type even if the pipeline author
has provided a more customized version with her pipeline. To favor the base
Sample over the tailored one created by a pipeline author, the user may
null in his own version of the pipeline
interface, either for all types of inpute to that pipeline, or for just a
subset of them. Read on for further information.
# atacseq.py from models import Sample class ATACseqSample(Sample): """ Class to model ATAC-seq samples based on the generic Sample class. :param series: Pandas `Series` object. :type series: pandas.Series """ def __init__(self, series): if not isinstance(series, pd.Series): raise TypeError("Provided object is not a pandas Series.") super(ATACseqSample, self).__init__(series) self.make_sample_dirs() def set_file_paths(self, project=None): """Sets the paths of all files for this sample.""" # Inherit paths from Sample by running Sample's set_file_paths() super(ATACseqSample, self).set_file_paths(project) self.fastqc = os.path.join(self.paths.sample_root, self.name + ".fastqc.zip") self.trimlog = os.path.join(self.paths.sample_root, self.name + ".trimlog.txt") self.fastq = os.path.join(self.paths.sample_root, self.name + ".fastq") self.trimmed = os.path.join(self.paths.sample_root, self.name + ".trimmed.fastq") self.mapped = os.path.join(self.paths.sample_root, self.name + ".bowtie2.bam") self.peaks = os.path.join(self.paths.sample_root, self.name + "_peaks.bed")
To leverage the power of a
Sample subtype, the relevant model is the
PipelineInterface. For each pipeline defined in the
pipeline_interface.yaml, there’s accommodation for a
subsection to communicate this information. The value for each such key may be
either a single string or a collection of key-value pairs. If it’s a single
string, the value is the name of the class that’s to be used as the template
Sample object created for processing by that pipeline. If instead
it’s a collection of key-value pairs, the keys should be names of input data
types (as in the
protocol_mapping), and each value is the name of the class
that should be used for each sample object of the corresponding key*for that
pipeline*. This underscores that it’s the combination of a pipeline and input
type that determines the subtype.
# Content of pipeline_interface.yaml protocol_mapping: ATAC: atacseq.py pipelines: atacseq.py: ... ... sample_subtypes: ATACseqSample ... ... ... ...
If a pipeline author provides more than one subtype, the
section is needed to select from among them once it’s time to create
Sample objects. If multiple options are available, and the
sample_subtypes section fails to clarify the decision, the base/generic
type will be used. The responsibility for supplying the
section, as is true for the rest of the pipeline interface, therefore rests
primarily with the pipeline developer. It is possible for an end user to
modify these settings, though.
Since the mechanism for subtype detection is
inspect-ion of each of the
pipeline module’s classes and retention of those which satisfy a subclass
status check against
Sample, it’s possible for pipeline authors to
implement a class hierarchy with multi-hop inheritance relationships. For
example, consider the addition of the following class to the previous example
of a pipeline module
class DNaseSample(ATACseqSample): ...
In this case there are now two
Sample subtypes available, and more
generally, there will necessarily be multiple subtypes available in any
pipeline module that uses a subtype scheme with multiple, serial inheritance
steps. In such cases, the pipeline interface should include an unambiguous
# Content of pipeline_interface.yaml protocol_mapping: ATAC: atacseq.py DNase: atacseq.py pipelines: atacseq.py: ... ... sample_subtypes: ATAC: ATACseqSample DNase: DNaseSample ... ... ... ...