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.
The package can be installed via Bioconductor and loaded
into R by typing
into the R console.
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.1 was downloaded from the NCBI GEO database
under accession number GSE77004
and preprocessed2 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.
## GRanges object with 6 ranges and 3 metadata columns:
## seqnames ranges strand | J1.RPKM TKO.RPKM TKO3a1_1.RPKM
## <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 genomeThe 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.
## GRanges object with 6 ranges and 3 metadata columns:
## seqnames ranges strand | J1_1.Methylation status
## <Rle> <IRanges> <Rle> | <ordered>
## [1] chr5 3021138-3021139 * | high
## [2] chr5 3021182-3021183 * | high
## [3] chr5 3021357-3021358 * | high
## [4] chr5 3021446-3021447 * | high
## [5] chr5 3025756-3025757 * | high
## [6] chr5 3025763-3025764 * | high
## J1_2.Methylation status DKO.Methylation status
## <ordered> <ordered>
## [1] medium low
## [2] medium low
## [3] NA low
## [4] NA medium
## [5] high medium
## [6] high low
## -------
## seqinfo: 35 sequences (1 circular) from mm9 genomeThe 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.
## GRanges object with 6 ranges and 1 metadata column:
## seqnames ranges strand | Homer peak score
## <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 genomeThe 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 |
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).
## GRanges object with 6 ranges and 7 metadata columns:
## seqnames ranges strand | gene_id RNA.J1.RPKM RNA.TKO.RPKM
## <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.Methylation status RRBS.J1_2.Methylation status
## <ordered> <ordered>
## 12571 low low
## 68152 low low
## 77036 high high
## 71382 low low
## 75010 high medium
## 79264 low low
## ChIP.H3K4me3_J1.Homer peak score ChIP.H3K4me3_TKO.Homer peak score
## <numeric> <numeric>
## 12571 111 83
## 68152 199 164
## 77036 NA NA
## 71382 212 186
## 75010 NA NA
## 79264 199 163
## -------
## seqinfo: 35 sequences (1 circular) from mm9 genomeThe return value of the function aggregateRanges as well
contains information about the supposed underlying base technologies and
conditions.
## $ChIP
## [1] "ChIP.H3K4me3_J1.Homer peak score"
## [2] "ChIP.H3K4me3_TKO.Homer peak score"
## [3] "ChIP.H3K4me3_TKO3a2.Homer peak score"
##
## $RNA
## [1] "RNA.J1.RPKM" "RNA.TKO.RPKM" "RNA.TKO3a1_1.RPKM"
##
## $RRBS
## [1] "RRBS.J1_1.Methylation status" "RRBS.J1_2.Methylation status"
## [3] "RRBS.DKO.Methylation status"## $J1
## [1] "RNA.J1.RPKM" "RRBS.J1_1.Methylation status"
## [3] "RRBS.J1_2.Methylation status"
##
## $TKO
## [1] "RNA.TKO.RPKM" "RRBS.TKO_1.Methylation status"
## [3] "RRBS.TKO_2.Methylation status"
##
## $TKO3a1
## [1] "RNA.TKO3a1_1.RPKM" "RNA.TKO3a1_2.RPKM"
## [3] "RRBS.TKO3a1_1.Methylation status"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.
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 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 @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 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.
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
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] Cogito_1.13.0 entropy_1.3.1 GenomicFeatures_1.57.1
## [4] AnnotationDbi_1.69.0 Biobase_2.67.0 jsonlite_1.8.9
## [7] GenomicRanges_1.57.2 GenomeInfoDb_1.41.2 IRanges_2.39.2
## [10] S4Vectors_0.43.2 BiocGenerics_0.53.0 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.45.1
## [2] SummarizedExperiment_1.35.5
## [3] gtable_0.3.6
## [4] TxDb.Mmusculus.UCSC.mm9.knownGene_3.2.2
## [5] ggplot2_3.5.1
## [6] rjson_0.2.23
## [7] xfun_0.48
## [8] bslib_0.8.0
## [9] lattice_0.22-6
## [10] vctrs_0.6.5
## [11] tools_4.4.1
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## [13] curl_5.2.3
## [14] parallel_4.4.1
## [15] fansi_1.0.6
## [16] tibble_3.2.1
## [17] RSQLite_2.3.7
## [18] blob_1.2.4
## [19] pkgconfig_2.0.3
## [20] Matrix_1.7-1
## [21] lifecycle_1.0.4
## [22] GenomeInfoDbData_1.2.13
## [23] compiler_4.4.1
## [24] Rsamtools_2.21.2
## [25] Biostrings_2.75.0
## [26] munsell_0.5.1
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## [41] digest_0.6.37
## [42] restfulr_0.0.15
## [43] maketools_1.3.1
## [44] fastmap_1.2.0
## [45] grid_4.4.1
## [46] colorspace_2.1-1
## [47] SparseArray_1.5.45
## [48] cli_3.6.3
## [49] magrittr_2.0.3
## [50] S4Arrays_1.5.11
## [51] utf8_1.2.4
## [52] XML_3.99-0.17
## [53] scales_1.3.0
## [54] UCSC.utils_1.1.0
## [55] bit64_4.5.2
## [56] rmarkdown_2.28
## [57] XVector_0.45.0
## [58] httr_1.4.7
## [59] matrixStats_1.4.1
## [60] bit_4.5.0
## [61] png_0.1-8
## [62] memoise_2.0.1
## [63] evaluate_1.0.1
## [64] knitr_1.48
## [65] BiocIO_1.17.0
## [66] rtracklayer_1.65.0
## [67] rlang_1.1.4
## [68] glue_1.8.0
## [69] DBI_1.2.3
## [70] BiocManager_1.30.25
## [71] R6_2.5.1
## [72] MatrixGenerics_1.17.1
## [73] GenomicAlignments_1.41.0
## [74] zlibbioc_1.51.2