In onertipaday/pyCOMPASSR: API to 'Python' Library 'pyCOMPASS'

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Introduction

Install

1. Install python and pip

1.1. Install python 3 (>= 3.3) (download page)

1.2. Install pip for python

1.3. Install reticulate for R

install.packages("reticulate")

1.4. Check that reticulate can now find python. You may have to restart RStudio.

reticulate::py_config()

You will see information about your python configuration, including one or more system paths where python is installed.

1.5. Check that pip can be found

system("pip --version")

You will see something like pip 19.1.1 from /usr/lib/python3.7/site-packages/pip (python 3.7)

2. Install pyCOMPASS

2.1. Find the default python path that reticulate is using

reticulate::py_config()

Take note of the path in the first line (e.g., "/usr/bin/python").

2.2. Find the path that the system pip command is using

system("pip --version")

For example, in "pip 19.1.1 from /usr/lib/python3.7/site-packages/pip (python 3.7)" the python path is "/usr/lib/python3.7". If the path here does not match the py_config() path, then you may need to manually set the path using use_python().

reticulate::use_python("/usr/lib/python3.7", required = TRUE)

You may have to restart RStudio before setting the path if you have run other reticulate operations.

2.3. Install pycompass from R

# create a new environment 
virtualenv_create("pycompassr")
# install pycompass
virtualenv_install("pycompassr", "pycompass")

2.3. Install pyCOMPASS using pip

system("pip install pycompass")

Or alternatively, run this command in the system terminal (sudo pip install pycompass for Mac users).

2.4. Check if you can now import pyCOMPASS. You may have to restart RStudio.

pycompass <- reticulate::import("pycompass")

3. Install pyCOMPASSR

3.1. Finally, you can install pyCOMPASSR.

if(!requireNamespace("devtools")) install.packages("devtools")
devtools::install_github("onertipaday/pycompassr")
# remotes::install_github("onertipaday/pyCOMPASSR")

Getting help

To get help open an issue on the pyCOMPASSR github page.

The resource

COMPASS GraphQL Endpoint COMPASS documentation pyCOMPASS

library(pyCOMPASSR)

Case Study

Here we illustrate one of the most common usages of pyCOMPASSR: how to create a module from a set of known genes and automatically selecting condition contrasts. To demonstrate all this we will focus on a set of genes regulated by the phytohormone abscisic acid (ABA) in pre-véraison berries in Vitis vinifera.

Step 1 - create the initial module

We start by creating a module with these 55 ABA modulated genes and refining it by selecting the most co-expressed genes. The module will be manually split to distinguish different condition contrasts, corresponding to different biological contexts, in which ABA affects gene expression. Finally, we will extend these modules to include other relevant genes by coexpression (which could hint at similar forms of regulation).

Get the access to Vitis vinifera compendium:

vv_compendium <- get_compendium(species = 'vitis_vinifera')

aba_genes <- c("VIT_05s0077g00430", "VIT_14s0060g00790", "VIT_14s0066g00810", "VIT_16s0050g00390", "VIT_18s0001g10450", "VIT_11s0016g03180", "VIT_08s0058g00470", "VIT_17s0000g01080", "VIT_06s0004g04860", "VIT_00s0322g00030", "VIT_06s0061g00730", "VIT_07s0005g00140", "VIT_00s0934g00010", "VIT_18s0001g04800", "VIT_01s0011g04820", "VIT_00s0216g00060", "VIT_18s0072g01220", "VIT_15s0048g02870", "VIT_00s0203g00070", "VIT_19s0014g03290", "VIT_01s0026g02710", "VIT_17s0000g08080", "VIT_00s0429g00040", "VIT_00s1206g00010", "VIT_09s0002g03690", "VIT_13s0067g00240", "VIT_07s0104g00350", "VIT_04s0023g02480", "VIT_18s0001g08490", "VIT_13s0019g02110", "VIT_04s0008g01120", "VIT_10s0003g00390", "VIT_03s0017g01410", "VIT_08s0007g08030", "VIT_08s0040g01820", "VIT_08s0058g01260", "VIT_09s0002g00630", "VIT_14s0068g01620", "VIT_18s0001g01030", "VIT_16s0050g01880", "VIT_08s0007g08280", "VIT_09s0002g06790", "VIT_09s0002g00640", "VIT_03s0063g01790", "VIT_04s0044g01230", "VIT_08s0040g02610", "VIT_01s0010g03680", "VIT_01s0137g00780", "VIT_02s0025g00500", "VIT_06s0004g05650", "VIT_07s0005g01080", "VIT_13s0156g00100", "VIT_17s0000g01120", "VIT_18s0001g07840", "VIT_18s0001g09290")

We can query the compendium with the list of gene names and get a list of BiologicalFeature objects that represents our genes of interest.

genes <- get_bf(compendium = vv_compendium, gene_names = as.list(aba_genes))

We are now ready to create our first module: the compendium will need the list of genes (BiologicalFeature objects) and the name of the normalization we want to use in order to automatically select the "best" conditions.¶

QuickSearch_ABAcoreset <- create_module(compendium = vv_compendium, biofeatures = genes)

# my_plot_dist <- plot_distribution(QuickSearch_ABAcoreset)
# tempDir <- tempfile()
# dir.create(tempDir)
# htmlFile <- file.path(tempDir, "test.html")
# xml2::write_html(my_plot_dist,file=htmlFile)
# rstudioapi::viewer(htmlFile)

# my_plot_html <- plot_heatmap(QuickSearch_ABAcoreset)
# tempDir <- tempfile()
# dir.create(tempDir)
# htmlFile <- file.path(tempDir, "plot_heatmap.html")
# xml2::write_html(my_plot_html,file=htmlFile)
# rstudioapi::viewer(htmlFile)

use heatmaply directly from R:

# # plotly::plot_ly(z=mod1$values,type="heatmap")
# heatmaply::heatmaply_cor(QuickSearch_ABAcoreset$values,
#               showticklabels = FALSE,
#               #scale = "row",
#               xlab = "Sample Sets",
#               ylab = "Biological Features",
#               k_col = 5,
#               k_row = 3#, show_dendrogram=F
#           )

Session information

print(sessionInfo(), locale = FALSE)

onertipaday/pyCOMPASSR documentation built on Feb. 27, 2020, 9 a.m.