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sleuth paper analysis

This repo contains all of the code to reproduce the results in the sleuth paper.

The repository at https://github.com/pachterlab/sleuth_paper_analysis should always have an updated version that is hopefully bug free.

Preliminaries

  • Install snakemake
  • Download and install R along with dependencies listed below (R dependencies section)
  • Updated the BASE variable in config.py to represent the base path on your system

Organization

The code is organized into a few different directories, each with a theme:

  • annotation: pulls down the different annotations used and creates indices
  • bottomly: analysis related to the Bottomly et al. data, particular the 'self-consistency FDR' experiments
  • cuffdiff2_analysis: analysis of the Trapnell et al. dataset to extract effect sizes from that dataset
  • geuvadis: the bulk of the simulations, based on results from the GEUVADIS data
  • simulation_core: dependencies for the simulations in the geuvadis directory
  • software: the bulk of the software used, not including the R dependencies

R dependencies

Install using install.packages()

from CRAN

  • cowplot
  • devtools
  • dplyr
  • data.table
  • ggplot2
  • jsonlite
  • reshape2
  • scales

from Bioconductor

First, install Bioconductor:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")

Then, you should be able to install packages using the biocLite() function.

  • biomaRt
  • BitSeq
  • DESeq
  • DESeq2
  • EBSeq
  • edgeR
  • limma

from GitHub

  • sleuth v0.28.1 fork with some modifications: devtools::install_github('pachterlab/sleuth', ref = 'nm')
  • mamabear v0.3: devtools::install_github('pimentel/mamabear', ref = 'v0.3')

Bug reports

Please make them in GitHub issues.

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Code to reproduce analyses from the Compositional Normalization paper

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