qpcr 3.1.1

A module to perform analysis of qPCR data

qpcr

A python module to analyse qPCR data on single-datasets or high-throughput

This project presents a python module designed to facilitate the analysis of qPCR data through established Delta-Delta-Ct analysis. To that end, this module provides a set of processing classes that may be assembled into a fully-fledged analysis pipeline, starting from raw Ct values stored in csv or excel files all the way to finished visualisations of the results.

User-friendliness and quick and easy workflows were of primary concern during development, so that this module is suitable for both non-experienced users as well as veteran coders. Virtually all steps within the analysis workflow are customizible to allow a streamlined analysis of any given dataset.

The exported results are formatted to be readily imported into other analysis or graphing software.

Installation

This module can directly be installed via pip.

pip install qpcr

What does qpcr do?

The "core business" that qpcr was designed for is Delta-Delta-Ct analysis starting from raw Ct values. It offers automated processes to read datafiles, filter out outlier Ct values, compute Delta-Ct, normalise assays against one another, and visualise the results. Hence, qpcr offers a full suite for automated Delta-Delta-Ct analyses. However, even if your analysis is not going to be Delta-Delta-Ct, you may wish to check out how the qpcr.Readers might help you faciliate your workflow, as they are streamlined to read diversely structured datafiles contianing both a single and multiple qPCR datasets.

Customisability

A technical note at this point. At it's core qpcr offers very versatile data manipulation through two processing classes called the qpcr.Analyser and the qpcr.Normaliser. The qpcr.Analyser performs actions on a single qPCR datasets / assay stored in a qpcr.Assay object (the central data storage unit of the qpcr module). It is used to perform Delta-Ct computation. However, the precise function that it applies to the single Assay is costumisable, so there is no restriction as such to what the Analyser will do to an Assay. On the other hand the qpcr.Normaliser will perform actions on a single Assay using data from a second Assay. It is used to perform normalisation of assays-of-interst against normaliser-assays. However, here too the precise function that is applied to the Assay is costumisable.

Example usage

To facilitate data analysis, common workflows have been implemented in pre-defined pipelines that allow for quick data analysis with minimal user effort. An example analysis using the pre-defined BasicPlus pipeline:

from qpcr.Pipes import BasicPlus
from qpcr.Plotters import PreviewResults

# get our datafiles
normaliser_files = [
                    "./Examples/Example Data/28S.csv",
                    "./Examples/Example Data/actin.csv"
                ]

assay_files = [
                "./Examples/Example Data/HNRNPL_nmd.csv",
                "./Examples/Example Data/HNRNPL_prot.csv",
                "./Examples/Example Data/SRSF11_nmd.csv",
                "./Examples/Example Data/SRSF11_prot.csv",
            ]

# define our experimental parameters
reps = 6
group_names = ["WT-", "WT+", "KO-", "KO+"] 

# setting up the pipeline
pipeline = BasicPlus()
pipeline.save_to("./Example Results")

pipeline.replicates(reps)
pipeline.names(group_names)

# set up a preview of results
preview = PreviewResults(mode = "static")
colors = ["xkcd:pastel blue", "xkcd:sapphire", "xkcd:rose pink", "xkcd:raspberry"]
preview.params( # setting some custom style
                color = colors, 
                edgecolor = "black", 
                edgewidth = 1, 
                figsize = (8,4)
            )
pipeline.add_plotters(preview)

# feed in our data
pipeline.add_assays(assay_files)
pipeline.add_normalisers(normaliser_files)

# run the pipeline
pipeline.run()

# and at this point the results are already saved...

Getting started

A set of basic introductory tutorials is available as jupyter notebooks in the Examples directory in this repository. For more information about the API, checkout the documentation at the github-pages.

Citation

Kleinschmidt, N. (2022). qpcr - a python module for easy and versatile qPCR data analysis for small-scale datasets and high-throughput (Version 3.1.1) [Computer software]. https://github.com/NoahHenrikKleinschmidt/qpcr.git