Contents

1 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.

2 Installation

The package can be installed via Bioconductor and loaded into R by typing

BiocManager::install("Cogito")
library("Cogito")

into the R console.

3 Workflow

The workflow of Cogito consists of two main functions:

  1. aggregateRanges(ranges, configfile, organism, name, verbose)
  2. summarizeRanges(aggregated.ranges, verbose)

The process is demonstrated using a small murine example. The data set from King et al.1 4 was downloaded from the NCBI GEO database under accession number GSE77004 and preprocessed2 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 1195 ranges and 11 columns with the corresponding expression values. Each column represents an experimental condition.

head(MurEpi.RNA.small[, 1:3])
## GRanges object with 6 ranges and 3 metadata columns:
##       seqnames              ranges strand |        J1       TKO  TKO3a1_1
##          <Rle>           <IRanges>  <Rle> | <numeric> <numeric> <numeric>
##   [1]     chr5 101234211-101249984      - |     7.904     7.142     8.420
##   [2]     chr5   73039035-73062317      + |     0.101     0.344     0.431
##   [3]     chr5     6769030-6876523      - |     0.000     0.000     0.011
##   [4]     chr5 115303673-115333668      + |     0.150     0.000     0.022
##   [5]     chr5     3657004-3680325      + |     0.000     0.000     0.000
##   [6]     chr5     3583978-3596585      - |     8.752     6.195     8.677
##   -------
##   seqinfo: 35 sequences (1 circular) from mm9 genome

The second GRanges object contains information about the methylation status from RRBS. The object has 147644 ranges and 14 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])
## GRanges object with 6 ranges and 3 metadata columns:
##       seqnames          ranges strand | J1_1.meth J1_2.meth  DKO.meth
##          <Rle>       <IRanges>  <Rle> | <ordered> <ordered> <ordered>
##   [1]     chr5 3021138-3021139      * |      high    medium    low   
##   [2]     chr5 3021182-3021183      * |      high    medium    low   
##   [3]     chr5 3021357-3021358      * |      high    NA        low   
##   [4]     chr5 3021446-3021447      * |      high    NA        medium
##   [5]     chr5 3025756-3025757      * |      high    high      medium
##   [6]     chr5 3025763-3025764      * |      high    high      low   
##   -------
##   seqinfo: 35 sequences (1 circular) from mm9 genome

The third object is a GRangesList object containing 36 GRanges object. Each of this objects represents an experimental condition and contains between 10 and 2468 ranges with one column of attached value each. This value is the peak score resulting from the peak calling with homer findPeaks3 4.

head(MurEpi.ChIP.small[[1]])
## GRanges object with 6 ranges and 1 metadata column:
##       seqnames              ranges strand | findPeaksScore
##          <Rle>           <IRanges>  <Rle> |      <numeric>
##   [1]     chr5 125869822-125870015      * |            282
##   [2]     chr5 114221367-114221560      * |            273
##   [3]     chr5 115729732-115729925      * |            269
##   [4]     chr5 122734457-122734650      * |            265
##   [5]     chr5 124875261-124875454      * |            275
##   [6]     chr5 117565637-117565830      * |            263
##   -------
##   seqinfo: 35 sequences (1 circular) from mm9 genome

The data set consists of the following samples:

Table 1: Overview of murine example data set
There are several samples of different conditions (J1, TKO, …) and base technologies (RNA-seq, RRBS and ChIP-seq).
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

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")
##   84 genes were dropped because they have exons located on both strands
##   of the same reference sequence or on more than one reference sequence,
##   so cannot be represented by a single genomic range.
##   Use 'single.strand.genes.only=FALSE' to get all the genes in a
##   GRangesList object, or use suppressMessages() to suppress this message.

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)])
## GRanges object with 6 ranges and 7 metadata columns:
##         seqnames          ranges strand |     gene_id    RNA.J1   RNA.TKO
##            <Rle>       <IRanges>  <Rle> | <character> <numeric> <numeric>
##   12571     chr5 3343893-3522225      + |       12571     5.647     3.996
##   68152     chr5 3543833-3570546      + |       68152        NA        NA
##   77036     chr5 3571716-3584341      + |       77036     3.176     1.396
##   71382     chr5 3596066-3637101      + |       71382     5.631     5.225
##   75010     chr5 3657004-3680325      + |       75010     0.000     0.000
##   79264     chr5 3803165-3844515      + |       79264        NA        NA
##         RRBS.J1_1.meth RRBS.J1_2.meth ChIP.H3K4me3_J1.findPeaksScore
##              <ordered>      <ordered>                      <numeric>
##   12571           low          low                               111
##   68152           low          low                               199
##   77036           high         high                               NA
##   71382           low          low                               212
##   75010           high         medium                             NA
##   79264           low          low                               199
##         ChIP.H3K4me3_TKO.findPeaksScore
##                               <numeric>
##   12571                              83
##   68152                             164
##   77036                              NA
##   71382                             186
##   75010                              NA
##   79264                             163
##   -------
##   seqinfo: 35 sequences (1 circular) from mm9 genome

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)
## $ChIP
## [1] "ChIP.H3K4me3_J1.findPeaksScore"     "ChIP.H3K4me3_TKO.findPeaksScore"   
## [3] "ChIP.H3K4me3_TKO3a2.findPeaksScore"
## 
## $RNA
## [1] "RNA.J1"       "RNA.TKO"      "RNA.TKO3a1_1"
## 
## $RRBS
## [1] "RRBS.J1_1.meth" "RRBS.J1_2.meth" "RRBS.DKO.meth"
head(lapply(aggregated.ranges$config$conditions, head, 3), 3)
## $J1
## [1] "RNA.J1"         "RRBS.J1_1.meth" "RRBS.J1_2.meth"
## 
## $TKO
## [1] "RNA.TKO"         "RRBS.TKO_1.meth" "RRBS.TKO_2.meth"
## 
## $TKO3a1
## [1] "RNA.TKO3a1_1"       "RNA.TKO3a1_2"       "RRBS.TKO3a1_1.meth"

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. 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 61 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 1. 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 61 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.

4 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

5 Session Information

## R version 4.2.0 RC (2022-04-21 r82226)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] Cogito_1.3.0           entropy_1.3.1          GenomicFeatures_1.49.1
##  [4] AnnotationDbi_1.59.0   Biobase_2.57.0         jsonlite_1.8.0        
##  [7] GenomicRanges_1.49.0   GenomeInfoDb_1.33.1    IRanges_2.31.0        
## [10] S4Vectors_0.35.0       BiocGenerics_0.43.0    BiocStyle_2.25.0      
## 
## loaded via a namespace (and not attached):
##  [1] MatrixGenerics_1.9.0                   
##  [2] httr_1.4.2                             
##  [3] sass_0.4.1                             
##  [4] bit64_4.0.5                            
##  [5] bslib_0.3.1                            
##  [6] assertthat_0.2.1                       
##  [7] BiocManager_1.30.17                    
##  [8] BiocFileCache_2.5.0                    
##  [9] blob_1.2.3                             
## [10] GenomeInfoDbData_1.2.8                 
## [11] Rsamtools_2.13.0                       
## [12] yaml_2.3.5                             
## [13] progress_1.2.2                         
## [14] lattice_0.20-45                        
## [15] pillar_1.7.0                           
## [16] RSQLite_2.2.13                         
## [17] glue_1.6.2                             
## [18] digest_0.6.29                          
## [19] XVector_0.37.0                         
## [20] colorspace_2.0-3                       
## [21] Matrix_1.4-1                           
## [22] htmltools_0.5.2                        
## [23] XML_3.99-0.9                           
## [24] pkgconfig_2.0.3                        
## [25] biomaRt_2.53.0                         
## [26] bookdown_0.26                          
## [27] zlibbioc_1.43.0                        
## [28] purrr_0.3.4                            
## [29] scales_1.2.0                           
## [30] BiocParallel_1.31.0                    
## [31] tibble_3.1.6                           
## [32] KEGGREST_1.37.0                        
## [33] ggplot2_3.3.5                          
## [34] generics_0.1.2                         
## [35] ellipsis_0.3.2                         
## [36] cachem_1.0.6                           
## [37] SummarizedExperiment_1.27.1            
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## [49] BiocIO_1.7.0                           
## [50] lifecycle_1.0.1                        
## [51] stringr_1.4.0                          
## [52] munsell_0.5.0                          
## [53] DelayedArray_0.23.0                    
## [54] Biostrings_2.65.0                      
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## [76] filelock_1.0.2                         
## [77] stringi_1.7.6                          
## [78] parallel_4.2.0                         
## [79] Rcpp_1.0.8.3                           
## [80] TxDb.Mmusculus.UCSC.mm9.knownGene_3.2.2
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