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@NoahHenrikKleinschmidt
NoahHenrikKleinschmidt committed Feb 3, 2022
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9 changes: 5 additions & 4 deletions Examples/2_pipeline_tutorial.ipynb
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50 changes: 25 additions & 25 deletions Examples/Example Results/rel_28S+actin_df.csv
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,Sample,group,group_name,assay,HNRNPL_nmd_rel_28S+actin,HNRNPL_prot_rel_28S+actin,SRSF11_nmd_rel_28S+actin,SRSF11_prot_rel_28S+actin
0,NK1,0,WT-,HNRNPL_nmd,1.0,1.0,1.0,1.0
1,NK1,0,WT-,HNRNPL_nmd,1.0792282365044292,1.0352649238413778,0.7791645796604999,1.0497166836230671
2,NK2,0,WT-,HNRNPL_nmd,1.0880488996396767,1.0012091607244138,0.7747181241755291,0.9942933038782275
3,NK2,0,WT-,HNRNPL_nmd,1.0572390014040527,1.109801418350823,0.8707325446613162,1.05723900140405
4,NK3,0,WT-,HNRNPL_nmd,1.043294056387618,0.9938815612482366,1.0432940563876154,0.9801985000959309
5,NK3,0,WT-,HNRNPL_nmd,1.03252823502543,1.0112791112155102,0.84450696894633,0.9768312322058901
6,NK4,1,WT+,HNRNPL_nmd,6.310374054599821,0.8337724023003084,3.7262527246788455,1.2292049869381958
7,NK4,1,WT+,HNRNPL_nmd,6.887363726323204,0.8790102348414844,4.1524185466769605,1.3139877890952123
8,NK5,1,WT+,HNRNPL_nmd,6.422127916233298,0.9157663131880778,3.5628985407835714,1.1511305036139579
9,NK5,1,WT+,HNRNPL_nmd,7.012171955907117,0.9999039170911177,3.7577299085412252,1.2656349763947943
10,NK6,1,WT+,HNRNPL_nmd,4.782559988997035,0.9187747187638251,2.7278854522059484,1.1233309772858573
11,NK6,1,WT+,HNRNPL_nmd,4.9025631764648745,0.9353227744488053,2.319053690824958,1.1046093326919457
12,NK7,2,KO-,HNRNPL_nmd,8.845115047525042,0.8321300409852326,4.9071348168308395,0.8918548953072112
13,NK7,2,KO-,HNRNPL_nmd,9.094833933263882,0.904409025950457,5.716163946583826,0.9693215932919985
14,NK8,2,KO-,HNRNPL_nmd,9.35052451792937,0.8615744038943108,5.0108220195558255,0.8094687525992064
15,NK8,2,KO-,HNRNPL_nmd,9.897270926024007,0.8687607147403164,4.9144527409707965,0.8105824086038116
16,NK9,2,KO-,HNRNPL_nmd,10.331935135552149,0.8639612945073741,5.614037054581197,0.9454274027124937
17,NK9,2,KO-,HNRNPL_nmd,9.879322512626029,0.8091124409459096,5.557407807903533,0.8977664639438797
18,NK10,3,KO+,HNRNPL_nmd,16.75106867287843,0.9177551648001887,7.978852262813689,1.0689402645976718
19,NK10,3,KO+,HNRNPL_nmd,17.132845704563888,0.8758118606697259,7.561607119201992,1.1240396233762797
20,NK11,3,KO+,HNRNPL_nmd,14.748511510906145,0.8365347942851852,6.554553257284912,0.947696787934922
21,NK11,3,KO+,HNRNPL_nmd,16.796966017661763,0.9461420871172547,7.210647229891951,1.0498103761038602
22,NK12,3,KO+,HNRNPL_nmd,18.100463901623364,0.9645679424819025,8.100184493689827,1.2639634694015187
23,NK12,3,KO+,HNRNPL_nmd,18.112133462077868,1.0131758460345714,8.218554030248292,1.3649845190470775
,Sample,group,group_name,HNRNPL_nmd_rel_28S+actin,HNRNPL_prot_rel_28S+actin,SRSF11_nmd_rel_28S+actin,SRSF11_prot_rel_28S+actin
0,NK1,0,WT-,1.0,1.0,1.0,1.0
1,NK1,0,WT-,1.0792282365044292,1.0352649238413778,0.7791645796604999,1.0497166836230671
2,NK2,0,WT-,1.0880488996396767,1.0012091607244138,0.7747181241755291,0.9942933038782275
3,NK2,0,WT-,1.0572390014040527,1.109801418350823,0.8707325446613162,1.05723900140405
4,NK3,0,WT-,1.043294056387618,0.9938815612482366,1.0432940563876154,0.9801985000959309
5,NK3,0,WT-,1.03252823502543,1.0112791112155102,0.84450696894633,0.9768312322058901
6,NK4,1,WT+,6.310374054599821,0.8337724023003084,3.7262527246788455,1.2292049869381958
7,NK4,1,WT+,6.887363726323204,0.8790102348414844,4.1524185466769605,1.3139877890952123
8,NK5,1,WT+,6.422127916233298,0.9157663131880778,3.5628985407835714,1.1511305036139579
9,NK5,1,WT+,7.012171955907117,0.9999039170911177,3.7577299085412252,1.2656349763947943
10,NK6,1,WT+,4.782559988997035,0.9187747187638251,2.7278854522059484,1.1233309772858573
11,NK6,1,WT+,4.9025631764648745,0.9353227744488053,2.319053690824958,1.1046093326919457
12,NK7,2,KO-,8.845115047525042,0.8321300409852326,4.9071348168308395,0.8918548953072112
13,NK7,2,KO-,9.094833933263882,0.904409025950457,5.716163946583826,0.9693215932919985
14,NK8,2,KO-,9.35052451792937,0.8615744038943108,5.0108220195558255,0.8094687525992064
15,NK8,2,KO-,9.897270926024007,0.8687607147403164,4.9144527409707965,0.8105824086038116
16,NK9,2,KO-,10.331935135552149,0.8639612945073741,5.614037054581197,0.9454274027124937
17,NK9,2,KO-,9.879322512626029,0.8091124409459096,5.557407807903533,0.8977664639438797
18,NK10,3,KO+,16.75106867287843,0.9177551648001887,7.978852262813689,1.0689402645976718
19,NK10,3,KO+,17.132845704563888,0.8758118606697259,7.561607119201992,1.1240396233762797
20,NK11,3,KO+,14.748511510906145,0.8365347942851852,6.554553257284912,0.947696787934922
21,NK11,3,KO+,16.796966017661763,0.9461420871172547,7.210647229891951,1.0498103761038602
22,NK12,3,KO+,18.100463901623364,0.9645679424819025,8.100184493689827,1.2639634694015187
23,NK12,3,KO+,18.112133462077868,1.0131758460345714,8.218554030248292,1.3649845190470775
62 changes: 57 additions & 5 deletions README.md
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This project represents a python package that includes a number of functions useful for the analysis of qPCR data generated generated by Qiagen RotorGene®
and is taylored to work with the Excel Spreadhseet exported from this device (or, more precisely, a `csv`-copy of the same). However, any `csv`-formatted data should be valid input.

User friendliness and quick and easy workflows were of primary concern during development. The exported results are formatted to be readily imported into graphing software. However, this module also includes methods to readily generate static or interactive figures of analysed results.
User friendliness and quick and easy workflows were of primary concern during development. The exported results are formatted to be readily imported into graphing software. However, this module also includes methods to readily generate static or interactive figures of analysed results. This is the second version of this module. Architecture and functionality have been fundamentally re-developed since the first version (which can still be accessed via the `legacy_V1` branch of the project repository). The current Version 2 implements a series of data-processing classes that interact to form an analysis-pipeline from start to finish.

### Version 2 is here
This is the second version of this module. Architecture and functionality have been fundamentally re-developed since the first version (which can still be accessed via the `legacy_V1` branch of this repository). The current Version 2 implements a series of data-processing classes that interact to form an analysis-pipeline from start to finish.
### 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:

### Getting started...
A set of basic introductory tutorials is available as `jupyter notebooks` in the `Examples` directory in this repository.
```python

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

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

sample_files = [
"../Example Data/HNRNPL_nmd.csv",
"../Example Data/HNRNPL_prot.csv",
"../Example Data/SRSF11_nmd.csv",
"../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")
preview.params( # setting some custom style
color = "xkcd:sandy yellow",
edgecolor = "black",
edgewidth = 1,
figsize = (8,4)
)
pipeline.add_plotters(preview)

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

# run the pipeline
pipeline.run()

# and at this point the results are already saved...
# plus we get the following preview figure:
```

![](https://github.com/NoahHenrikKleinschmidt/qpcr/blob/main/Examples/Example%20Results/PreviewResults_1.jpg)

### Getting started
A set of basic introductory tutorials is available as `jupyter notebooks` in the `Examples` directory in this repository.


#### Citation
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5 changes: 4 additions & 1 deletion _config.yml
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theme: jekyll-theme-cayman
theme: jekyll-theme-cayman
title: qpcr
description: A python module to analyse qPCR data easily and efficiently.

6 changes: 3 additions & 3 deletions setup.py
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import setuptools


long_description = "This project represents a python package that includes a number of functions useful for the analysis of qPCR data generated generated by Qiagen RotorGene® and is taylored to work with the Excel Spreadhseet exported from this device (or, more precisely, a csv copy of the same). However, any csv formatted Ct data should be valid input data."

with open("README.md", "r") as f:
long_description = f.read()
setuptools.setup(
name="qpcr",
version="0.2.1",
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