In annikabuerger/Cogito: Compare genomic intervals tool - Automated, complete, reproducible and clear report about genomic and epigenomic data sets
knitr::opts_chunk$set(echo = TRUE)
Introduction
Biological studies often consist of multiple conditions which are examined
with different laboratory set ups like RNA-sequencing or ChIP-sequencing. To get
an overview about the whole resulting data set, Cogito provides an automated
and complete report about all samples and basic comparisons between all
different samples. This overview can be used as documentation about the data
set or as starting point for further custom analysis.
Cogito is not meant to do detailed genetic or epigenetic analysis. But provides
a reproducible and clear report about data sets consisting of samples of
different conditions and mesuared with different laboratory base technologies.
Installation
The package can be installed via Bioconductor and loaded into R by typing
BiocManager::install("Cogito")
library("Cogito")
library("Cogito", quietly = TRUE, verbose = FALSE)
into the R console.
Workflow
The workflow of Cogito consists of two main functions:
aggregateRanges(ranges, configfile, organism,
referenceRanges, name, verbose)summarizeRanges(aggregated.ranges, outputFormat, verbose)
The process is demonstrated using a small murine example. The data set from
King et al.^[\@ref(References)] was downloaded from the NCBI GEO database under
accession number
GSE77004 and
preprocessed^[for details on preprocessing of the data have a look at the
manual pages of the single data sets] to GRanges/GRangesList objects and
restricted to chromosome 5 to save space and processing time.
This example data set is loaded with the package and consists of three data
objects.
The first GRanges object contains gene expression values from RNA-sequencing
with r length(MurEpi.RNA.small) ranges and r ncol(mcols(MurEpi.RNA.small))
columns with the corresponding expression values. Each column represents an
experimental condition.
head(MurEpi.RNA.small[, 1:3])
The second GRanges object contains information about the methylation status
from RRBS. The object has r length(MurEpi.RRBS.small) ranges and
r ncol(mcols(MurEpi.RRBS.small)) columns with the corresponding methylation
status. Where there is not information about the methylation status the value
is NA. Each column represents an experimental condition.
head(MurEpi.RRBS.small[, 1:3])
The third object is a GRangesList object containing
r length(MurEpi.ChIP.small) GRanges object. Each of this objects
represents an experimental condition and contains between
r min(unlist(lapply(MurEpi.ChIP.small, length))) and
r max(unlist(lapply(MurEpi.ChIP.small, length))) ranges with one column of
attached value each. This value is the peak score resulting from the peak
calling with homer findPeaks^[\@ref(References)].
head(MurEpi.ChIP.small[[1]])
The data set consists of the following samples:
| | RNA-seq | RRBS | ChIP-seq |
|:------ |:-------:|:----:|:--------:|
|J1 | 1 | 2 | 5 |
|TKO | 1 | 2 | 5 |
|TKO3a1 | 2 | 2 | 4 |
|TKO3a2 | 2 | 2 | 5 |
|TKO3b1 | 1 | 2 | 4 |
|DKO | 1 | 1 | 4 |
|DKO3a1 | 1 | 1 | 3 |
|DKO3a2 | 1 | 1 | 3 |
|DKO3b1 | 1 | 1 | 3 |
: (#tab:table) Overview of murine example data set. There are several samples
of different conditions (J1, TKO, ...) and base technologies (RNA-seq,
RRBS and ChIP-seq).
The function aggregateRanges is called with the example data set and the
corresponding genomic information.
mm9 <- TxDb.Mmusculus.UCSC.mm9.knownGene::TxDb.Mmusculus.UCSC.mm9.knownGene
example.dataset <- list(ChIP = MurEpi.ChIP.small,
RNA = MurEpi.RNA.small,
RRBS = MurEpi.RRBS.small)
aggregated.ranges <- aggregateRanges(ranges = example.dataset,
organism = mm9,
name = "murine.example")
The function aggregates all provided data to the genes of the genome, given in
the parameter organism: all single IRanges are assigned to the closest
gene within a predefined default maximal distance of 100.000bp. This
parameter can be changed in the configuration file if necessary. Consequently,
the columns of attached values of each provided IRanges object are assigned
to the corresponding gene. The result is one single GRanges object, each row
representing a gene and each column a sample of the provided input data.
The function aggregateRanges returns a list object containing this single
GRanges objects, i.e. a GRanges object with rows representing genes with
columns of attached values mcols where each column contains values from a
specific experimental condition (such as wildtype) and a specific underlying
base technology (such as RNA-seq expression).
head(aggregated.ranges$genes[, c(1, 2:3, 13:14, 27:28)])
The return value of the function aggregateRanges as well contains information
about the supposed underlying base technologies and conditions.
lapply(aggregated.ranges$config$technologies, head, 3)
head(lapply(aggregated.ranges$config$conditions, head, 3), 3)
To advance in the workflow, these two lists should be carefully checked and
corrected if necessary. Also it is possible to add user defined groups of
conditions to include them in the following analysis.
summarizeRanges(aggregated.ranges = aggregated.ranges)
The function summarizeRanges does not has a return value but produces three
output files:
The main result is the report about the given data in a pdf (or html). The report
is divided into four chapters. The introduction holds general information about
the underlying data set as the numbers of conditions (wildtype etc.) and used
base technologies (ChIP-seq, RNA-seq, etc.).
The first chapter contains summaries about each single column of attached
values, consisting of a location and a dispersion parameter as well as a plot
for a visual impression.
There are r ncol(mcols(aggregated.ranges$genes))-1 samples in total included
in the presented murine example data set.
The second chapter summarizes groups of columns of attached values which have
the same condition and the same base technology, for example if there are two
RNA-sequencing replicates of the condition wildtpye present.
In the given murine example data set there are among others 2 samples of
methylation status examined with RRBS in the condition J1, 5 samples of
condition J1 of ChIP-seq experiments and 2 RNA-seq samples of condition
TKO3a1, see table \@ref(tab:table). Consequentely, this results in 16 plots
in total.
The third chapter summarizes groups of columns of attached values which are
examined by the same base technology but do not necessarily have the same
underlying condition. This is visualized in an appropriate plot.
The presented murine example has three involved base technologies: RNA-seq,
ChIP-seq and RRBS.
Finally the fourth chapter compares every column of attached value to every
other column of attached value. The comparison is visualized with an appropriate
plot and a correlation test is made. Because this section may be long, the
report concentrates on comparisons where a significant correlation is found
(corrected p-value < 0.01).
In the exemplary murine data set there are
r ncol(mcols(aggregated.ranges$genes))-1 columns of attached values, so this
results in (61*60)/2=1830 comparisons, but of not all of them show a
significant correlation.
Besides the pdf report the function summarizeRanges also produces a rmd file
with which the user may customize the report or take it as a starting point for
further analysis, as well as a RData file which holds all used processed data.
References {#References}
King AD, Huang K, Rubbi L, Liu S, Wang CY, Wang Y, Pellegrini M, Fan G.
Reversible Regulation of Promoter and Enhancer Histone Landscape by DNA
Methylation in Mouse Embryonic Stem Cells.
Cell Rep. 2016 Sep 27;17(1):289-302. doi: 10.1016/j.celrep.2016.08.083
Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C,
Singh H, Glass CK.
Simple combinations of lineage-determining transcription
factors prime cis-regulatory elements required for macrophage and B cell
identities.
Mol Cell. 2010 May 28;38(4):576-89. doi: 10.1016/j.molcel.2010.05.004
The color scheme used by Cogito was generated with help of:
iWantHue by Mathieu Jacomy at the at
the Sciences-Po Medialab
Session Information
sessionInfo()
annikabuerger/Cogito documentation built on July 7, 2022, 4:38 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set(echo = TRUE)
Introduction
Biological studies often consist of multiple conditions which are examined with different laboratory set ups like RNA-sequencing or ChIP-sequencing. To get an overview about the whole resulting data set, Cogito provides an automated and complete report about all samples and basic comparisons between all different samples. This overview can be used as documentation about the data set or as starting point for further custom analysis.
Cogito is not meant to do detailed genetic or epigenetic analysis. But provides a reproducible and clear report about data sets consisting of samples of different conditions and mesuared with different laboratory base technologies.
Installation
The package can be installed via Bioconductor and loaded into R by typing
BiocManager::install("Cogito") library("Cogito")
library("Cogito", quietly = TRUE, verbose = FALSE)
into the R console.
Workflow
The workflow of Cogito consists of two main functions:
aggregateRanges(ranges, configfile, organism,referenceRanges, name, verbose)summarizeRanges(aggregated.ranges, outputFormat, verbose)
The process is demonstrated using a small murine example. The data set from
King et al.^[\@ref(References)] was downloaded from the NCBI GEO database under
accession number
GSE77004 and
preprocessed^[for details on preprocessing of the data have a look at the
manual pages of the single data sets] to GRanges/GRangesList objects and
restricted to chromosome 5 to save space and processing time.
This example data set is loaded with the package and consists of three data objects.
The first GRanges object contains gene expression values from RNA-sequencing
with r length(MurEpi.RNA.small) ranges and r ncol(mcols(MurEpi.RNA.small))
columns with the corresponding expression values. Each column represents an
experimental condition.
head(MurEpi.RNA.small[, 1:3])
The second GRanges object contains information about the methylation status
from RRBS. The object has r length(MurEpi.RRBS.small) ranges and
r ncol(mcols(MurEpi.RRBS.small)) columns with the corresponding methylation
status. Where there is not information about the methylation status the value
is NA. Each column represents an experimental condition.
head(MurEpi.RRBS.small[, 1:3])
The third object is a GRangesList object containing
r length(MurEpi.ChIP.small) GRanges object. Each of this objects
represents an experimental condition and contains between
r min(unlist(lapply(MurEpi.ChIP.small, length))) and
r max(unlist(lapply(MurEpi.ChIP.small, length))) ranges with one column of
attached value each. This value is the peak score resulting from the peak
calling with homer findPeaks^[\@ref(References)].
head(MurEpi.ChIP.small[[1]])
The data set consists of the following samples:
| | RNA-seq | RRBS | ChIP-seq | |:------ |:-------:|:----:|:--------:| |J1 | 1 | 2 | 5 | |TKO | 1 | 2 | 5 | |TKO3a1 | 2 | 2 | 4 | |TKO3a2 | 2 | 2 | 5 | |TKO3b1 | 1 | 2 | 4 | |DKO | 1 | 1 | 4 | |DKO3a1 | 1 | 1 | 3 | |DKO3a2 | 1 | 1 | 3 | |DKO3b1 | 1 | 1 | 3 |
: (#tab:table) Overview of murine example data set. There are several samples of different conditions (J1, TKO, ...) and base technologies (RNA-seq, RRBS and ChIP-seq).
The function aggregateRanges is called with the example data set and the
corresponding genomic information.
mm9 <- TxDb.Mmusculus.UCSC.mm9.knownGene::TxDb.Mmusculus.UCSC.mm9.knownGene example.dataset <- list(ChIP = MurEpi.ChIP.small, RNA = MurEpi.RNA.small, RRBS = MurEpi.RRBS.small) aggregated.ranges <- aggregateRanges(ranges = example.dataset, organism = mm9, name = "murine.example")
The function aggregates all provided data to the genes of the genome, given in
the parameter organism: all single IRanges are assigned to the closest
gene within a predefined default maximal distance of 100.000bp. This
parameter can be changed in the configuration file if necessary. Consequently,
the columns of attached values of each provided IRanges object are assigned
to the corresponding gene. The result is one single GRanges object, each row
representing a gene and each column a sample of the provided input data.
The function aggregateRanges returns a list object containing this single
GRanges objects, i.e. a GRanges object with rows representing genes with
columns of attached values mcols where each column contains values from a
specific experimental condition (such as wildtype) and a specific underlying
base technology (such as RNA-seq expression).
head(aggregated.ranges$genes[, c(1, 2:3, 13:14, 27:28)])
The return value of the function aggregateRanges as well contains information
about the supposed underlying base technologies and conditions.
lapply(aggregated.ranges$config$technologies, head, 3) head(lapply(aggregated.ranges$config$conditions, head, 3), 3)
To advance in the workflow, these two lists should be carefully checked and
corrected if necessary. Also it is possible to add user defined groups of
conditions to include them in the following analysis.
summarizeRanges(aggregated.ranges = aggregated.ranges)
The function summarizeRanges does not has a return value but produces three
output files:
The main result is the report about the given data in a pdf (or html). The report is divided into four chapters. The introduction holds general information about the underlying data set as the numbers of conditions (wildtype etc.) and used base technologies (ChIP-seq, RNA-seq, etc.).
The first chapter contains summaries about each single column of attached
values, consisting of a location and a dispersion parameter as well as a plot
for a visual impression.
There are r ncol(mcols(aggregated.ranges$genes))-1 samples in total included
in the presented murine example data set.
The second chapter summarizes groups of columns of attached values which have the same condition and the same base technology, for example if there are two RNA-sequencing replicates of the condition wildtpye present. In the given murine example data set there are among others 2 samples of methylation status examined with RRBS in the condition J1, 5 samples of condition J1 of ChIP-seq experiments and 2 RNA-seq samples of condition TKO3a1, see table \@ref(tab:table). Consequentely, this results in 16 plots in total.
The third chapter summarizes groups of columns of attached values which are examined by the same base technology but do not necessarily have the same underlying condition. This is visualized in an appropriate plot. The presented murine example has three involved base technologies: RNA-seq, ChIP-seq and RRBS.
Finally the fourth chapter compares every column of attached value to every
other column of attached value. The comparison is visualized with an appropriate
plot and a correlation test is made. Because this section may be long, the
report concentrates on comparisons where a significant correlation is found
(corrected p-value < 0.01).
In the exemplary murine data set there are
r ncol(mcols(aggregated.ranges$genes))-1 columns of attached values, so this
results in (61*60)/2=1830 comparisons, but of not all of them show a
significant correlation.
Besides the pdf report the function summarizeRanges also produces a rmd file
with which the user may customize the report or take it as a starting point for
further analysis, as well as a RData file which holds all used processed data.
References {#References}
King AD, Huang K, Rubbi L, Liu S, Wang CY, Wang Y, Pellegrini M, Fan G. Reversible Regulation of Promoter and Enhancer Histone Landscape by DNA Methylation in Mouse Embryonic Stem Cells. Cell Rep. 2016 Sep 27;17(1):289-302. doi: 10.1016/j.celrep.2016.08.083
Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010 May 28;38(4):576-89. doi: 10.1016/j.molcel.2010.05.004
The color scheme used by Cogito was generated with help of: iWantHue by Mathieu Jacomy at the at the Sciences-Po Medialab
Session Information
sessionInfo()
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.