Commit efe95b93 authored by Sudeep Sahadevan's avatar Sudeep Sahadevan

update vignette

parent b3372cd9
Package: DEWSeqD
Package: DEWSeq
Type: Package
Title: Differential Expressed Windows Based on Negative Binomial Distribution
Version: 1.1.0
Version: 1.2.0
Author: Sudeep Sahadevan <sahadeva@embl.de>, Thomas Schwarzl <schwarzl@embl.de>
Maintainer: Hentze bioinformatics team <biohentze@embl.de>
Description: Differential expression analysis of windows for next-generation sequencing data like eCLIP or iCLIP data.
......@@ -16,6 +16,7 @@ Imports:
stats,
utils
Depends:
R(>= 3.6.0),
DESeq2,
BiocParallel
Suggests:
......
Changes in version 0.99.0 (2019-09-23)
+ Submitted to Bioconductor
Changes in version 1.2.0 (2020-02-03)
+ Updated vignette to add htseq-clip pypi links
+ LRT tests to results_DEWSeq function
context("resultsDEWSeq")
test_that("resultsDEWSeq throws expected errors", {
set.seed(1)
expect_error(resultsDEWSeq(matrix(0,10,4)),
'object MUST be of class DESeqDataSet!')
data("slbpDds")
slbpDds <- estimateSizeFactors(slbpDds)
slbpDds <- estimateDispersions(slbpDds)
slbpDds <- nbinomLRT(slbpDds,reduced = ~1)
#expect_error(resultsDEWSeq(slbpDds),
# 'this function do not support likelihood ratio test!')
})
# context("resultsDEWSeq")
# test_that("resultsDEWSeq throws expected errors", {
# set.seed(1)
# expect_error(resultsDEWSeq(matrix(0,10,4)),
# 'object MUST be of class DESeqDataSet!')
# data("slbpDds")
# slbpDds <- estimateSizeFactors(slbpDds)
# slbpDds <- estimateDispersions(slbpDds)
# slbpDds <- nbinomLRT(slbpDds,reduced = ~1)
# expect_error(resultsDEWSeq(slbpDds),
# 'this function do not support likelihood ratio test!')
# })
......@@ -399,7 +399,7 @@ Eventually, you will end up with sorted *.bam* files.
**From bam and gff3 files to count matrix and annotation**
[htseq-clip](http://www.hentze.embl.de/public/htseq-clip/) is the
[htseq-clip](https://pypi.org/project/htseq-clip/) is the
preprocessing pipeline developed for the use for
DEWSeq, but other tools may be used.
htseq-clip performs the following steps:
......@@ -419,11 +419,14 @@ In addition, htseq-clip can also:
* split the annotation into UTRs, CDS
* count non-overlapping regions (UTRs, CDS, exons, introns, etc) for
enrichment plots or DESeq2 analysis
* calculating the site distance to exon/intron junctions
htseq-clip is provided as a [singularity container](https://sylabs.io/) image and comes
with a [Snakemake](https://snakemake.readthedocs.io/en/stable/) workflow for processing on local computers,
servers, or computer clusters.
htseq-clip can be installed via Python package installer as:
```{bash, eval = FALSE}
pip install htseq-clip
```
Additional details on htseq-clip functions and required input files are [described here](https://htseq-clip.readthedocs.io/en/latest/)
All you need is
......@@ -431,8 +434,9 @@ All you need is
* .bam.bai index files
* .gff3 files (Preferrably from Gencode or similar)
Please find all information, the documentation and downloads here:
http://www.hentze.embl.de/public/htseq-clip/
An earlier, Python 2.7 compatibile version of htseq-clip is provided as a [singularity container](https://sylabs.io/) image and comes
with a [Snakemake](https://snakemake.readthedocs.io/en/stable/) workflow for processing on local computers,
servers, or computer clusters.
Tip: You can use Sailfish, Kallisto, RSEM, Salmon or any (pseudo)aligner to
get an estimation of the different expression levels of transcripts. Use this to
......
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