A toolkit for navigating and analyzing gene expression datasets

Find documentation here

---

Development Notes:

XPRESSplot supports Python 2.7 and >=3.5

Citation:

Berg JA, et. al. (2020). XPRESSyourself: Enhancing, standardizing, and
automating ribosome profiling computational analyses yields improved insight
into data. PLoS Comp Biol. doi: https://doi.org/10.1371/journal.pcbi.1007625

Installation:

pip install xpressplot

Other Requirements:

• Tested on 64-bit Linux, compatible with Mac OS X
• Python and R are required
• If PyPi and Conda are not already installed, these should be installed
• If using the interactive notebook provided, Jupyter needs to be installed if not already

QuickStart:

• Download the repository and modify the interactive Jupyter notebook to get started fast!
• Read the instructions as you navigate through the code blocks for a guide on how to use the example code
• Code blocks are run by selecting the block and pressing Shift + Enter
• See documentation for more detailed instructions

Important Notes:

• If working with XPRESSplot within an interactive notebook (i.e. Jupyter Notebook, Atom Hydrogen, etc), you must include the following line of code after importing XPRESSplot

import XPRESSplot as xp
%matplotlib inline

• Assumes all dataframes are i * j (or genes * samples, except in certain cases, see documentation for help)

>>> geo.head()
name       GSM523242  GSM523243  GSM523244  GSM523245  GSM523246  GSM523247    ...     
GeneA    8.98104    8.59941    8.25395    8.72981    8.70794    8.10693    ...       
GeneB      5.84313    6.59168    8.27881    6.64005    4.65107    7.19090    ...       
GeneC       6.17189    5.73603    5.55673    5.69374    6.77618    5.84524    ...       
GeneD      6.97009    6.80003    5.56620    7.43816    7.36375    5.85687    ...       
GeneE   10.24611   10.13807    8.84743    9.72365   10.42940    9.17510    ...   

[5 rows x 145 columns]   

Updates

Information on updates to the software can be found here.