wellcompare 1.0.5.dev1

A package for doing a well to well analysis of 96 well plates run in an Epoch2 microplate reader

GarciaLab

Info-

Code developed for research on modeling prion growth dynamics in budding yeast. This package is used to compare two 96 well plates well by well, for example comparing A1 of one plate to A1 or another plate, and so on. This code generates graphs, heatmaps, and sumarries of your data, while also performing some basic statistics.

Usage -

Begin by creating a directory for your screens called 'Screens'. Next, make a directory inside of that for every screen that you run (Ex. "Screen1"). Inside of your individual screen directory, create another folder called 'Data' and put the excel file produce from the Epoch2 plate reader inside. Then, from the outer directory, run the package ($ python -m wellcompare) on the terminal. The program will create several directories for you. Some notable ones are 'Graphs', which contains graphs and data summaries, and 'Heatmaps' (if -hm flag added).

Example Directory Structure:

|- Outer_folder    <- run python -m wellcompare inside this outer directory
|      |-Screen
|            |- Screen1
|                  |- Data  
|                       |- screen1.xslx
|            |- Screen2
|                  |- Data
|                       |- screen2.xslx

Requirements -

Also outlined in requirements.txt:

1. Python3
2. Pip3
3. Pandas
4. Numpy
5. Maplotlib
6. Scipy
7. Seaborn
8. ArgParse
9. xlrd

Setup & Installation (MacOS)-

Download Package (recommended):

• Type the following commands into terminal

1. $ pip install wellcombine
2. $ python -m wellcompare [options]

-- OR --

Clone Repo:

• Type the following commands into terminal

1. $ git clone https://github.com/jacobian0208/well_compare.git
2. $ pip install -r requirements.txt
3. $ python -m wellcompare [options]

Files & Directories -

1. graph.py : module to graph cleaned data
2. extract.py : module to extract and clean data from Epoch2 microplate reader output
3. combine.py : module to combine and further clean extracted data
4. main.py : main file to run package as script
5. requirements.txt : a list of required packages to install to run this program correctly
6. LICENSE : an MIT license to allow use of this program