%global _empty_manifest_terminate_build 0
Name: python-luciferase
Version: 2.4.5
Release: 1
Summary: Helper functions for luciferase data
License: MIT License
URL: https://gitlab.com/aaylward/luciferase
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/54/6d/d2564e403a1eedfa546319bc74c558fde7860b39f016617cb1fc83fb6516/luciferase-2.4.5.tar.gz
BuildArch: noarch
Requires: python3-estimateratio
Requires: python3-matplotlib
Requires: python3-pandas
Requires: python3-scipy
Requires: python3-seaborn
Requires: python3-xlrd
%description
# luciferase
Tool for plotting luciferase reporter data. Thanks due to Joshua Chiou and Mei-lin Okino for inspiration and contributions.
## Installation
To use `luciferase`, you must first have [python](https://www.python.org/downloads/) installed. Then, you can install `luciferase` using `pip`:
```sh
pip install luciferase
```
or
```sh
pip install --user luciferase
```
## Command-line interface for barplots
## Introduction
This introduction demonstrates a simple analysis of data in Excel format.
Here is our [example dataset](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel.xlsx), `example-excel.xlsx`:
The first row contains headers and subsequent rows contain firefly/renilla ratios normalized to the empty vector.
We can analyze these data by running:
```luciferase-swarmplot example-excel.xlsx example-excel-plot.pdf```
Here is the result:
We can also use input data with more columns to produce plots with more bars. For example:
```luciferase-swarmplot example-excel-expanded.xlsx example-excel-expanded-plot.pdf```
See the following sections for more details.
### Barplots of enhancer activity
A command-line tool called `luciferase-barplot` for creating bar plots is
included. After installing `luciferase`, you can use it like this:
```sh
luciferase-barplot example.json example.pdf
luciferase-barplot example.csv example.png
luciferase-barplot example.tsv example.svg
luciferase-barplot example.xls example.pdf
luciferase-barplot example.xlsx example.png
```
JSON, CSV, TSV, or Excel files may be used as inputs, and output
can be written in PDF, PNG, or SVG.
See also the help message:
```sh
luciferase-barplot -h
```
Examples of luciferase reporter data in JSON format:
```json
{
"Non-risk, Fwd": [8.354, 12.725, 8.506],
"Risk, Fwd": [5.078, 5.038, 5.661],
"Non-risk, Rev": [9.564, 9.692, 12.622],
"Risk, Rev": [10.777, 11.389, 10.598],
"Empty": [1.042, 0.92, 1.042]
}
```
```json
{
"Alt, MIN6": [5.47, 7.17, 6.15],
"Ref, MIN6": [3.16, 3.04, 4.34],
"Empty, MIN6": [1.07, 0.83, 0.76],
"Alt, ALPHA-TC6": [2.50, 3.47, 3.33],
"Ref, ALPHA-TC6": [2.01, 1.96, 2.31],
"Empty, ALPHA-TC6": [1.042, 0.92, 1.042]
}
```
Examples of luciferase reporter data in TSV format:
```
Non-risk, Fwd Risk, Fwd Non-risk, Rev Risk, Rev Empty
8.354 5.078 9.564 10.777 1.042
12.725 5.038 9.692 11.389 0.92
8.506 5.661 12.622 10.598 1.042
```
```
Ref, untreated Alt, untreated Empty, untreated Ref, dex Alt, dex Empty, dex
33.2 19.7 1.0 149.4 44.6 1.1
30.3 16.2 1.0 99.7 37.6 1.0
33.3 18.3 1.0 124.5 37.7 0.9
```
Significance indicators will be written above the bars: `***` if p<0.001,
`**` if p<0.01, `*` if p<0.05, `ns` otherwise.
Here are the resulting plots:
### Barplots of allelic ratio
A second tool called `luciferase-ratioplot` takes the same input data and
produces a comparative plot of allelic ratios:
```sh
luciferase-ratioplot --xlab control dexamethasone --ylab "Ref:Alt ratio" --title Default ratio.json ratio.png
luciferase-ratioplot --xlab control dexamethasone --ylab "Alt:Ref ratio" --title Inverted --invert ratio.json ratio.png
```
The resulting plot shows the estimated allelic ratio of enhancer activity
with confidence intervals (95% by default). Here is an example input dataset
and plot:
JSON:
```json
{
"Ref, untreated": [33.2, 30.3, 33.3],
"Alt, untreated": [19.7, 16.2, 18.3],
"Empty, untreated": [1.0, 1.0, 1.0],
"Ref, dex": [149.4, 99.7, 124.5],
"Alt, dex": [44.6, 37.6, 37.7],
"Empty, dex": [1.1, 1.0, 0.9]
}
```
TSV:
```
Alt, dex Ref, dex Empty, dex Alt, untreated Ref, untreated Empty, untreated
44.6 149.4 1.1 19.7 33.2 1.0
37.6 99.7 1.0 16.2 30.3 1.0
37.7 124.5 0.9 18.3 33.3 1.0
```
## Meta-analysis
For this section, we'll use another included command called
`luciferase-swarmplot`. It functions exactly like `luciferase-barplot` except
that individual data points will be plotted over the bars.
It may be that we have performed two or more experiments
(from separate minipreps) and wish to meta-analyze the results. As an example,
let's consider the results of two identical experiments on a regulatory
variant at the _SIX3_ locus: [SIX3-MP0](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp0.json) and [SIX3-MP1](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp1.json). First we'll plot both datasets separately:
```sh
luciferase-swarmplot six3-mp0.json six3-mp0.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP0'
luciferase-swarmplot six3-mp1.json six3-mp1.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP1'
```
We can see that the results are fairly consistent in character, but checking
the y-axis tells us that they are on different scales. Intuitively, we might
conclude from these results that there are allelic effects under all three
conditions. Ideally though, we would like to use all of the data at once for
one plot to get the most accurate conclusions about allelic effects.
We might simply combine the data into one dataset, (as [here](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta-nobatch.json)) and plot it:
```sh
luciferase-swarmplot six3-meta-nobatch.json six3-meta-nobatch.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C'
```
The bar heights look reasonable, and the allelic effects appear clear from
looking at them, but all of the hypothesis tests returned non-significant
results. What gives?
The answer is that combining data from experiments with different scales
breaks the assumptions of the significance test (a t-test). To meta-analyze
these data in a useful way, we first need to re-normalize the two experiments
to put both of them on the same scale. `luciferase-barplot` and
`luciferase-swarmplot` will re-normalize the data automatically if the dataset
includes an additional entry ("Batch") indicating the batch of each data point,
as in this example:
[SIX3-META](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta.json).
```json
{
"Alt, untreated": [19.7, 16.2, 18.3, 6.5, 8.0, 4.4],
"Ref, untreated": [33.2, 30.3, 33.3, 8.4, 13.6, 17.1],
"Empty, untreated": [1.0, 1.0, 1.0, 1.1, 1.0, 0.9],
"Alt, hi_cyt_noTNFA": [11.0, 8.8, 10.1, 3.2, 3.7, 3.3],
"Ref, hi_cyt_noTNFA": [17.1, 16.7, 18.8, 7.6, 6.7, 5.5],
"Empty, hi_cyt_noTNFA": [1.1, 0.9, 1.0, 1.1, 0.9, 1.0],
"Alt, hi_cyt": [10.8, 10.9, 9.1, 3.1, 2.7, 4.0],
"Ref, hi_cyt": [17.8, 16.1, 18.0, 7.7, 7.0, 7.1],
"Empty, hi_cyt": [1.0, 1.0, 1.0, 1.0, 1.0, 1.1],
"Batch": [0, 0, 0, 1, 1, 1]
}
```
Here is what the results look like when they're re-normalized to correct for
batch
```sh
luciferase-swarmplot six3-meta.json six3-meta.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-META'
```
See a more detailed explanation of the normalization procedure [here](https://github.com/anthony-aylward/luciferase/blob/master/example/meta-analysis.pdf)
%package -n python3-luciferase
Summary: Helper functions for luciferase data
Provides: python-luciferase
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-luciferase
# luciferase
Tool for plotting luciferase reporter data. Thanks due to Joshua Chiou and Mei-lin Okino for inspiration and contributions.
## Installation
To use `luciferase`, you must first have [python](https://www.python.org/downloads/) installed. Then, you can install `luciferase` using `pip`:
```sh
pip install luciferase
```
or
```sh
pip install --user luciferase
```
## Command-line interface for barplots
## Introduction
This introduction demonstrates a simple analysis of data in Excel format.
Here is our [example dataset](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel.xlsx), `example-excel.xlsx`:
The first row contains headers and subsequent rows contain firefly/renilla ratios normalized to the empty vector.
We can analyze these data by running:
```luciferase-swarmplot example-excel.xlsx example-excel-plot.pdf```
Here is the result:
We can also use input data with more columns to produce plots with more bars. For example:
```luciferase-swarmplot example-excel-expanded.xlsx example-excel-expanded-plot.pdf```
See the following sections for more details.
### Barplots of enhancer activity
A command-line tool called `luciferase-barplot` for creating bar plots is
included. After installing `luciferase`, you can use it like this:
```sh
luciferase-barplot example.json example.pdf
luciferase-barplot example.csv example.png
luciferase-barplot example.tsv example.svg
luciferase-barplot example.xls example.pdf
luciferase-barplot example.xlsx example.png
```
JSON, CSV, TSV, or Excel files may be used as inputs, and output
can be written in PDF, PNG, or SVG.
See also the help message:
```sh
luciferase-barplot -h
```
Examples of luciferase reporter data in JSON format:
```json
{
"Non-risk, Fwd": [8.354, 12.725, 8.506],
"Risk, Fwd": [5.078, 5.038, 5.661],
"Non-risk, Rev": [9.564, 9.692, 12.622],
"Risk, Rev": [10.777, 11.389, 10.598],
"Empty": [1.042, 0.92, 1.042]
}
```
```json
{
"Alt, MIN6": [5.47, 7.17, 6.15],
"Ref, MIN6": [3.16, 3.04, 4.34],
"Empty, MIN6": [1.07, 0.83, 0.76],
"Alt, ALPHA-TC6": [2.50, 3.47, 3.33],
"Ref, ALPHA-TC6": [2.01, 1.96, 2.31],
"Empty, ALPHA-TC6": [1.042, 0.92, 1.042]
}
```
Examples of luciferase reporter data in TSV format:
```
Non-risk, Fwd Risk, Fwd Non-risk, Rev Risk, Rev Empty
8.354 5.078 9.564 10.777 1.042
12.725 5.038 9.692 11.389 0.92
8.506 5.661 12.622 10.598 1.042
```
```
Ref, untreated Alt, untreated Empty, untreated Ref, dex Alt, dex Empty, dex
33.2 19.7 1.0 149.4 44.6 1.1
30.3 16.2 1.0 99.7 37.6 1.0
33.3 18.3 1.0 124.5 37.7 0.9
```
Significance indicators will be written above the bars: `***` if p<0.001,
`**` if p<0.01, `*` if p<0.05, `ns` otherwise.
Here are the resulting plots:
### Barplots of allelic ratio
A second tool called `luciferase-ratioplot` takes the same input data and
produces a comparative plot of allelic ratios:
```sh
luciferase-ratioplot --xlab control dexamethasone --ylab "Ref:Alt ratio" --title Default ratio.json ratio.png
luciferase-ratioplot --xlab control dexamethasone --ylab "Alt:Ref ratio" --title Inverted --invert ratio.json ratio.png
```
The resulting plot shows the estimated allelic ratio of enhancer activity
with confidence intervals (95% by default). Here is an example input dataset
and plot:
JSON:
```json
{
"Ref, untreated": [33.2, 30.3, 33.3],
"Alt, untreated": [19.7, 16.2, 18.3],
"Empty, untreated": [1.0, 1.0, 1.0],
"Ref, dex": [149.4, 99.7, 124.5],
"Alt, dex": [44.6, 37.6, 37.7],
"Empty, dex": [1.1, 1.0, 0.9]
}
```
TSV:
```
Alt, dex Ref, dex Empty, dex Alt, untreated Ref, untreated Empty, untreated
44.6 149.4 1.1 19.7 33.2 1.0
37.6 99.7 1.0 16.2 30.3 1.0
37.7 124.5 0.9 18.3 33.3 1.0
```
## Meta-analysis
For this section, we'll use another included command called
`luciferase-swarmplot`. It functions exactly like `luciferase-barplot` except
that individual data points will be plotted over the bars.
It may be that we have performed two or more experiments
(from separate minipreps) and wish to meta-analyze the results. As an example,
let's consider the results of two identical experiments on a regulatory
variant at the _SIX3_ locus: [SIX3-MP0](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp0.json) and [SIX3-MP1](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp1.json). First we'll plot both datasets separately:
```sh
luciferase-swarmplot six3-mp0.json six3-mp0.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP0'
luciferase-swarmplot six3-mp1.json six3-mp1.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP1'
```
We can see that the results are fairly consistent in character, but checking
the y-axis tells us that they are on different scales. Intuitively, we might
conclude from these results that there are allelic effects under all three
conditions. Ideally though, we would like to use all of the data at once for
one plot to get the most accurate conclusions about allelic effects.
We might simply combine the data into one dataset, (as [here](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta-nobatch.json)) and plot it:
```sh
luciferase-swarmplot six3-meta-nobatch.json six3-meta-nobatch.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C'
```
The bar heights look reasonable, and the allelic effects appear clear from
looking at them, but all of the hypothesis tests returned non-significant
results. What gives?
The answer is that combining data from experiments with different scales
breaks the assumptions of the significance test (a t-test). To meta-analyze
these data in a useful way, we first need to re-normalize the two experiments
to put both of them on the same scale. `luciferase-barplot` and
`luciferase-swarmplot` will re-normalize the data automatically if the dataset
includes an additional entry ("Batch") indicating the batch of each data point,
as in this example:
[SIX3-META](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta.json).
```json
{
"Alt, untreated": [19.7, 16.2, 18.3, 6.5, 8.0, 4.4],
"Ref, untreated": [33.2, 30.3, 33.3, 8.4, 13.6, 17.1],
"Empty, untreated": [1.0, 1.0, 1.0, 1.1, 1.0, 0.9],
"Alt, hi_cyt_noTNFA": [11.0, 8.8, 10.1, 3.2, 3.7, 3.3],
"Ref, hi_cyt_noTNFA": [17.1, 16.7, 18.8, 7.6, 6.7, 5.5],
"Empty, hi_cyt_noTNFA": [1.1, 0.9, 1.0, 1.1, 0.9, 1.0],
"Alt, hi_cyt": [10.8, 10.9, 9.1, 3.1, 2.7, 4.0],
"Ref, hi_cyt": [17.8, 16.1, 18.0, 7.7, 7.0, 7.1],
"Empty, hi_cyt": [1.0, 1.0, 1.0, 1.0, 1.0, 1.1],
"Batch": [0, 0, 0, 1, 1, 1]
}
```
Here is what the results look like when they're re-normalized to correct for
batch
```sh
luciferase-swarmplot six3-meta.json six3-meta.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-META'
```
See a more detailed explanation of the normalization procedure [here](https://github.com/anthony-aylward/luciferase/blob/master/example/meta-analysis.pdf)
%package help
Summary: Development documents and examples for luciferase
Provides: python3-luciferase-doc
%description help
# luciferase
Tool for plotting luciferase reporter data. Thanks due to Joshua Chiou and Mei-lin Okino for inspiration and contributions.
## Installation
To use `luciferase`, you must first have [python](https://www.python.org/downloads/) installed. Then, you can install `luciferase` using `pip`:
```sh
pip install luciferase
```
or
```sh
pip install --user luciferase
```
## Command-line interface for barplots
## Introduction
This introduction demonstrates a simple analysis of data in Excel format.
Here is our [example dataset](https://github.com/anthony-aylward/luciferase/raw/master/example/example-excel.xlsx), `example-excel.xlsx`:
The first row contains headers and subsequent rows contain firefly/renilla ratios normalized to the empty vector.
We can analyze these data by running:
```luciferase-swarmplot example-excel.xlsx example-excel-plot.pdf```
Here is the result:
We can also use input data with more columns to produce plots with more bars. For example:
```luciferase-swarmplot example-excel-expanded.xlsx example-excel-expanded-plot.pdf```
See the following sections for more details.
### Barplots of enhancer activity
A command-line tool called `luciferase-barplot` for creating bar plots is
included. After installing `luciferase`, you can use it like this:
```sh
luciferase-barplot example.json example.pdf
luciferase-barplot example.csv example.png
luciferase-barplot example.tsv example.svg
luciferase-barplot example.xls example.pdf
luciferase-barplot example.xlsx example.png
```
JSON, CSV, TSV, or Excel files may be used as inputs, and output
can be written in PDF, PNG, or SVG.
See also the help message:
```sh
luciferase-barplot -h
```
Examples of luciferase reporter data in JSON format:
```json
{
"Non-risk, Fwd": [8.354, 12.725, 8.506],
"Risk, Fwd": [5.078, 5.038, 5.661],
"Non-risk, Rev": [9.564, 9.692, 12.622],
"Risk, Rev": [10.777, 11.389, 10.598],
"Empty": [1.042, 0.92, 1.042]
}
```
```json
{
"Alt, MIN6": [5.47, 7.17, 6.15],
"Ref, MIN6": [3.16, 3.04, 4.34],
"Empty, MIN6": [1.07, 0.83, 0.76],
"Alt, ALPHA-TC6": [2.50, 3.47, 3.33],
"Ref, ALPHA-TC6": [2.01, 1.96, 2.31],
"Empty, ALPHA-TC6": [1.042, 0.92, 1.042]
}
```
Examples of luciferase reporter data in TSV format:
```
Non-risk, Fwd Risk, Fwd Non-risk, Rev Risk, Rev Empty
8.354 5.078 9.564 10.777 1.042
12.725 5.038 9.692 11.389 0.92
8.506 5.661 12.622 10.598 1.042
```
```
Ref, untreated Alt, untreated Empty, untreated Ref, dex Alt, dex Empty, dex
33.2 19.7 1.0 149.4 44.6 1.1
30.3 16.2 1.0 99.7 37.6 1.0
33.3 18.3 1.0 124.5 37.7 0.9
```
Significance indicators will be written above the bars: `***` if p<0.001,
`**` if p<0.01, `*` if p<0.05, `ns` otherwise.
Here are the resulting plots:
### Barplots of allelic ratio
A second tool called `luciferase-ratioplot` takes the same input data and
produces a comparative plot of allelic ratios:
```sh
luciferase-ratioplot --xlab control dexamethasone --ylab "Ref:Alt ratio" --title Default ratio.json ratio.png
luciferase-ratioplot --xlab control dexamethasone --ylab "Alt:Ref ratio" --title Inverted --invert ratio.json ratio.png
```
The resulting plot shows the estimated allelic ratio of enhancer activity
with confidence intervals (95% by default). Here is an example input dataset
and plot:
JSON:
```json
{
"Ref, untreated": [33.2, 30.3, 33.3],
"Alt, untreated": [19.7, 16.2, 18.3],
"Empty, untreated": [1.0, 1.0, 1.0],
"Ref, dex": [149.4, 99.7, 124.5],
"Alt, dex": [44.6, 37.6, 37.7],
"Empty, dex": [1.1, 1.0, 0.9]
}
```
TSV:
```
Alt, dex Ref, dex Empty, dex Alt, untreated Ref, untreated Empty, untreated
44.6 149.4 1.1 19.7 33.2 1.0
37.6 99.7 1.0 16.2 30.3 1.0
37.7 124.5 0.9 18.3 33.3 1.0
```
## Meta-analysis
For this section, we'll use another included command called
`luciferase-swarmplot`. It functions exactly like `luciferase-barplot` except
that individual data points will be plotted over the bars.
It may be that we have performed two or more experiments
(from separate minipreps) and wish to meta-analyze the results. As an example,
let's consider the results of two identical experiments on a regulatory
variant at the _SIX3_ locus: [SIX3-MP0](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp0.json) and [SIX3-MP1](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-mp1.json). First we'll plot both datasets separately:
```sh
luciferase-swarmplot six3-mp0.json six3-mp0.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP0'
luciferase-swarmplot six3-mp1.json six3-mp1.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-MP1'
```
We can see that the results are fairly consistent in character, but checking
the y-axis tells us that they are on different scales. Intuitively, we might
conclude from these results that there are allelic effects under all three
conditions. Ideally though, we would like to use all of the data at once for
one plot to get the most accurate conclusions about allelic effects.
We might simply combine the data into one dataset, (as [here](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta-nobatch.json)) and plot it:
```sh
luciferase-swarmplot six3-meta-nobatch.json six3-meta-nobatch.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C'
```
The bar heights look reasonable, and the allelic effects appear clear from
looking at them, but all of the hypothesis tests returned non-significant
results. What gives?
The answer is that combining data from experiments with different scales
breaks the assumptions of the significance test (a t-test). To meta-analyze
these data in a useful way, we first need to re-normalize the two experiments
to put both of them on the same scale. `luciferase-barplot` and
`luciferase-swarmplot` will re-normalize the data automatically if the dataset
includes an additional entry ("Batch") indicating the batch of each data point,
as in this example:
[SIX3-META](https://github.com/anthony-aylward/luciferase/raw/master/example/six3-meta.json).
```json
{
"Alt, untreated": [19.7, 16.2, 18.3, 6.5, 8.0, 4.4],
"Ref, untreated": [33.2, 30.3, 33.3, 8.4, 13.6, 17.1],
"Empty, untreated": [1.0, 1.0, 1.0, 1.1, 1.0, 0.9],
"Alt, hi_cyt_noTNFA": [11.0, 8.8, 10.1, 3.2, 3.7, 3.3],
"Ref, hi_cyt_noTNFA": [17.1, 16.7, 18.8, 7.6, 6.7, 5.5],
"Empty, hi_cyt_noTNFA": [1.1, 0.9, 1.0, 1.1, 0.9, 1.0],
"Alt, hi_cyt": [10.8, 10.9, 9.1, 3.1, 2.7, 4.0],
"Ref, hi_cyt": [17.8, 16.1, 18.0, 7.7, 7.0, 7.1],
"Empty, hi_cyt": [1.0, 1.0, 1.0, 1.0, 1.0, 1.1],
"Batch": [0, 0, 0, 1, 1, 1]
}
```
Here is what the results look like when they're re-normalized to correct for
batch
```sh
luciferase-swarmplot six3-meta.json six3-meta.png --light-colors '#DECBE4' '#FED9A6' '#FBB4AE' --dark-colors '#984EA3' '#FF7F00' '#E41A1C' --title 'SIX3-META'
```
See a more detailed explanation of the normalization procedure [here](https://github.com/anthony-aylward/luciferase/blob/master/example/meta-analysis.pdf)
%prep
%autosetup -n luciferase-2.4.5
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-luciferase -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 17 2023 Python_Bot - 2.4.5-1
- Package Spec generated