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network select nodeList="YDL194W,YLR345W"

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# .bashrc

# User specific aliases and functions

alias rm='rm -i'
alias cp='cp -i'
alias mv='mv -i'

export CPATH=/root/hdf5-1.8.14-linux-centos5-x86_64-gcc482-shared/include:/root/boost_1_59_0
export LD_LIBRARY_PATH=/root/hdf5-1.8.14-linux-centos5-x86_64-gcc482-shared/lib:$LD_LIBRARY_PATH

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"""
# Basic Data Visualization

**Cytoscape is an open source software platform for integrating, visualizing, and analyzing measurement data in the context of networks.**

This tutorial presents a scenario of how expression and network data can be combined to tell a biological story and includes these concepts:

 - Visualizing networks using expression data.
 - Filtering networks based on expression data.
 - Assessing expression data in the context of a biological network.

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###markdown
# Charty Usage

## You can write Markdown!

- item1
- item2
###

require 'charty'

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Checking with: BiocCheck( MSEAp_0.99.0.tar.gz ) 
12.62s$ Rscript -e "BiocCheck::BiocCheck(\"${PKG_TARBALL}\", 'quit-with-status'=TRUE)"
This is BiocCheck version 1.20.0. BiocCheck is a work in progress.
Output and severity of issues may change. Installing package...
* Checking Package Dependencies...
* Checking if other packages can import this one...
* Checking to see if we understand object initialization...
* Checking for deprecated package usage...
* Checking for remote package usage...

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name: example-environment
channels:
  - plotly
  - defaults
  - conda-forge  
dependencies:
  - jupyterlab-dash
  - dash

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Rewriting Cytoscape tutorials with Jupyter Notebook and R Markdown

Rewriting Getting Started

• Rewriting "Tour of Cytoscape"
• Rewriting "Basic Data Visualization"
• Rewriting "Differentially Expressed Genes"

Rewriting Protocols

Rewriting Cytoscape Apps

• Rewriting stringApp
• Rewriting WikiPathways App

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import dash
import dash_core_components as dcc
import dash_html_components as html

import dash_bio
import pandas as pd
import kegg2cyjs
import plotly.graph_objs as go

# df = pd.read_csv("./ecoliLeucineRMA.csv")

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import pandas as pd

df = pd.read_csv("./eLeuRMA.txt", sep='\t')
gene_df = df[df['Unnamed: 0'].str.contains(pat='b[0-9]{4}')]
foo = gene_df['Unnamed: 0'].str.extract(r'(b[0-9]{4})')
foo = foo[0].str.replace("b", "eco:b")
gene_df.update(foo.rename('Unnamed: 0'))
gene_df.columns = gene_df.columns.str.replace("/home/laurent/Affymetrix_data/ecoli_sample//", "")
gene_df.columns = gene_df.columns.str.replace("Unnamed: 0", "keggids")
gene_df.to_csv('ecoliLeucineRMA.csv', index=False)

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library(ecoliLeucine)
data("ecoliLeucine")
library(affy)
eset <- rma(ecoliLeucine)
write.exprs(eset, "eLeuRMA.txt")