NOISeq is available as a package in Bioconductor. This package includes both the former NOISeq and the new NOISeqBIO. As any Bioconductor package, it can be installed from R prompt using the following commands:

  1. biocLite(“NOISeq”)
  2. library(NOISeq)
  3. The Users' Guide and the Reference Manual may be downloaded from Bioconductor website. Please, read them to learn more about the use of NOISeq.

Data used to test the R NOISeq package


RNA-seq data from human B-cells (CD20+ cell line) and monocytes (CD14+ cell line) were obtained by Cold Spring Harbor Laboratory for the ENCODE project. Two different RNA extracting protocols were applied: the PolyA+ extraction method (Pap) and PolyAselection procedure (Pam). Sequencing was performed with an Illumina GAIIx platform. The read files were downloaded from ENCODE website and mapped to the reference genome downloaded from UCSC (hg19 GRCh37) (42) using TopHat v2.0.8. Gene expression was quantified using the HTSeq Python package version 0.5.3p3 and an in-house script to take multihits into account by equitably dividing each read mapping to different genes among all of them.

ENCODE count matrix

Fusarium oxysporum data

The samples were sequenced using the Applied Biosystems SOLiD 4 system with SOLiD MM50 chemistry. The length of the sequencing reads is 50 bases. Two biological replicates were obtained for each condition. One of the conditions corresponds to the fungus being cultured in human blood (wt_B_30_37) and the other in minimum medium (wt_M_30_37). The reads were mapped to the reference genome downloaded from the Ensembl Fungi database (release 14) using Lifescope software. CLC Bio tools were used to quantify the gene expression.

F.oxysporum count matrix (Please note that for confidentiality reasons the gene IDs have been removed)

Prostate cancer data

This RNA-seq data set was downloaded from the SRA repository (ERP000550). In this study Ren et al. (2012) sequenced samples of tumoral and healthy prostate which came from Chinese patients. There were 11 biological replicates for tumoral prostate (T) and 12 replicates for healthy prostate (N). The sequencing was done with an Illumina HiSeqTM 2000 and the reads were mapped to the reference human genome downloaded from Ensembl (release 68) using TopHat 1.4.1. Gene expression was quantified using the HTSeq Python package, version 0.5.3p3.

Prostate Cancer count matrix

R code used to test the NOISeqBIO method

Simulation algorithm

R scripts for simulating RNA-seq count data for two experimental conditions:

Simulation of HIGH variability scenarios

Simulation of LOW variability scenarios

Differential expression on simulated data

These scripts show how the differential expression methods were applied on simulated data sets:

DE methods on simulations

Analysis of DE results

Analysis on experimental data

R script used for generating exploratory figures, differential expression and functional enrichment analysis on experimental datasets (F.oxysporum and Prostate Cancer):

Experimental Data Analysis