Navigating the TapestriExperiment Class
Compiled on September 18, 2023
Source:vignettes/articles/TapExp-navigation.Rmd
TapExp-navigation.RmdIntroduction
The TapestriExperiment object class is the container
that holds all data and metadata related to a KaryoTap experiment. The
TapestriExperiment class is built on top of theSingleCellExperiment
and SummarizedExperiment
classes, and they inherit their basic functionality and interface. More
information can be found in their respective documentations. This
tutorial will cover the basics to the structure and navigation of the
TapestriExperiment class.
TapestriExperiment
We’ll use the cell mixture experiment from the KaryoTap publication as an example.
Calling the object will print a summary of the contained data.
cellmix
#> class: TapestriExperiment
#> dim: 317 2987
#> metadata(7): sample.name pipeline.panel.name ... date.h5.created mean.reads.per.cell.per.probe
#> assays(3): counts normcounts copyNumber
#> rownames(317): AMPL158802 AMPL146998 ... AMPL162086 AMPL161738
#> rowData names(9): probe.id chr ... arm norm.count.sd
#> colnames(2987): AACAACCTAATAGTGGTT-1 AACAACCTAGTCCTAGTT-1 ... TTGTAATGCTCGTACCTT-1
#> TTGTATCACACTTGATCT-1
#> colData names(3): cell.barcode total.reads cluster
#> reducedDimNames(0):
#> mainExpName: CNV
#> altExpNames(3): alleleFrequency smoothedCopyNumberByChr smoothedCopyNumberByArm
#> barcodeProbe: not specified
#> grnaProbe: not specified
#> gmmParams(2): chr armThe centerpiece of the object is the assay slot. This is
organized as a matrix, and calling dim() on the object will
accordingly return the dimensions. For TapestriExperiment,
the rows represent probes (i.e., features) and the columns represent
cells (i.e., samples). Calling colData() and
rowData() will return the metadata for the cells and probes
respectively.
dim(cellmix)
#> [1] 317 2987
colData(cellmix)
#> DataFrame with 2987 rows and 3 columns
#> cell.barcode total.reads cluster
#> <character> <numeric> <factor>
#> AACAACCTAATAGTGGTT-1 AACAACCTAATAGTGGTT-1 60264 LS513
#> AACAACCTAGTCCTAGTT-1 AACAACCTAGTCCTAGTT-1 22455 LS513
#> AACAACCTATGGACGAGA-1 AACAACCTATGGACGAGA-1 51206 LS513
#> AACAACTGGGACATAACG-1 AACAACTGGGACATAACG-1 17998 CL11
#> AACAACTGGGGAACCTAG-1 AACAACTGGGGAACCTAG-1 14202 LoVo
#> ... ... ... ...
#> TTGGAGAACTGGTAGCAG-1 TTGGAGAACTGGTAGCAG-1 26435 RPE1
#> TTGGTAACTCCATATCTT-1 TTGGTAACTCCATATCTT-1 44154 SW48
#> TTGTAATGCCTATAGCTC-1 TTGTAATGCCTATAGCTC-1 24469 RPE1
#> TTGTAATGCTCGTACCTT-1 TTGTAATGCTCGTACCTT-1 16216 SW48
#> TTGTATCACACTTGATCT-1 TTGTATCACACTTGATCT-1 18831 SW48
rowData(cellmix)
#> DataFrame with 317 rows and 9 columns
#> probe.id chr start.pos end.pos total.reads median.reads cytoband arm norm.count.sd
#> <character> <factor> <numeric> <numeric> <numeric> <integer> <character> <factor> <numeric>
#> AMPL158802 AMPL158802 1 1479191 1479385 202832 49 p36.33 chr1p 1.365966
#> AMPL146998 AMPL146998 1 6196653 6196900 112408 24 p36.31 chr1p 1.264583
#> AMPL158817 AMPL158817 1 11832076 11832330 99472 22 p36.22 chr1p 1.093818
#> AMPL158827 AMPL158827 1 17087135 17087388 1162986 304 p36.13 chr1p 0.665911
#> AMPL147006 AMPL147006 1 34285091 34285360 248023 53 p35.1 chr1p 1.181844
#> ... ... ... ... ... ... ... ... ... ...
#> AMPL161732 AMPL161732 X 128880366 128880635 568729 133 q26.1 chrXq 1.14576
#> AMPL161734 AMPL161734 X 130415574 130415843 231144 49 q26.2 chrXq 1.34813
#> AMPL161735 AMPL161735 X 135429800 135430069 156462 33 q26.3 chrXq 1.80859
#> AMPL162086 AMPL162086 X 140993717 140993974 195494 47 q27.2 chrXq 1.24238
#> AMPL161738 AMPL161738 X 142794979 142795248 400235 93 q27.3 chrXq 1.33365Metadata columns can be added to either rowData or
colData by assignment
rowData(cellmix)$example.name <- "example.value"The entire object can be subset using bracket notation, either by index or with a vector of probe and/or cell names.
cellmix[1:5, 1:5]
#> class: TapestriExperiment
#> dim: 5 5
#> metadata(7): sample.name pipeline.panel.name ... date.h5.created mean.reads.per.cell.per.probe
#> assays(3): counts normcounts copyNumber
#> rownames(5): AMPL158802 AMPL146998 AMPL158817 AMPL158827 AMPL147006
#> rowData names(10): probe.id chr ... norm.count.sd example.name
#> colnames(5): AACAACCTAATAGTGGTT-1 AACAACCTAGTCCTAGTT-1 AACAACCTATGGACGAGA-1 AACAACTGGGACATAACG-1
#> AACAACTGGGGAACCTAG-1
#> colData names(3): cell.barcode total.reads cluster
#> reducedDimNames(0):
#> mainExpName: CNV
#> altExpNames(3): alleleFrequency smoothedCopyNumberByChr smoothedCopyNumberByArm
#> barcodeProbe: not specified
#> grnaProbe: not specified
#> gmmParams(2): chr armAdditional metadata generated automatically when the object is
created can be retrieved using metadata().
metadata(cellmix)
#> $sample.name
#> [1] "Teresa_s_cell_line_mix"
#>
#> $pipeline.panel.name
#> [1] "CO261_NYU_Davoli_03102021_hg19"
#>
#> $pipeline.version
#> [1] "2.0.2"
#>
#> $number.of.cells
#> [1] "3555"
#>
#> $number.of.probes
#> [1] "330"
#>
#> $date.h5.created
#> [1] "2021-09-15"
#>
#> $mean.reads.per.cell.per.probe
#> [1] "89.22"Assays
The basic unit of the TapestriExperiment is the “assay”
which can be found in the assays slot. Each
assay holds values of the probe x cell matrix. There can be
multiple assays in the slot, each representing some value corresponding
to the same probes and cells. Here we use assays to store raw count
values from sequencing, the values of those counts after normalization,
and the copy number score for each cell and probe unit. Calling
assay() will return the matrix of the first-indexed
assay by default; others can be called by specifying their
name.
# list assays
assays(cellmix)
#> List of length 3
#> names(3): counts normcounts copyNumber
# get counts assay
corner(assay(cellmix))
#> AACAACCTAATAGTGGTT-1 AACAACCTAGTCCTAGTT-1 AACAACCTATGGACGAGA-1 AACAACTGGGACATAACG-1
#> AMPL158802 97 129 63 66
#> AMPL146998 36 18 94 10
#> AMPL158817 71 17 62 1
#> AMPL158827 495 330 489 192
#> AMPL147006 142 26 95 37
#> AACAACTGGGGAACCTAG-1
#> AMPL158802 15
#> AMPL146998 21
#> AMPL158817 5
#> AMPL158827 149
#> AMPL147006 15
# get copyNumber assay
corner(assay(cellmix, "copyNumber"))
#> AACAACCTAATAGTGGTT-1 AACAACCTAGTCCTAGTT-1 AACAACCTATGGACGAGA-1 AACAACTGGGACATAACG-1
#> AMPL158802 1.706083 6.0371189 1.302930 3.8389024
#> AMPL146998 1.163343 1.5477091 3.571785 1.0686610
#> AMPL158817 2.765227 1.7617041 2.839333 0.1287974
#> AMPL158827 1.239042 2.1978910 1.439268 1.5893391
#> AMPL147006 1.944232 0.9472066 1.529451 1.6753140
#> AACAACTGGGGAACCTAG-1
#> AMPL158802 1.1006006
#> AMPL146998 2.8309654
#> AMPL158817 0.8123674
#> AMPL158827 1.5558830
#> AMPL147006 0.8567637Like any matrix, assays can also be subset using bracket notation.
assay(cellmix)[1:5, 1:5]
#> AACAACCTAATAGTGGTT-1 AACAACCTAGTCCTAGTT-1 AACAACCTATGGACGAGA-1 AACAACTGGGACATAACG-1
#> AMPL158802 97 129 63 66
#> AMPL146998 36 18 94 10
#> AMPL158817 71 17 62 1
#> AMPL158827 495 330 489 192
#> AMPL147006 142 26 95 37
#> AACAACTGGGGAACCTAG-1
#> AMPL158802 15
#> AMPL146998 21
#> AMPL158817 5
#> AMPL158827 149
#> AMPL147006 15Alternate Experiments
Alternate Experiments (altExps) allow the user to store
values from the same samples with different features. In other words,
cells in the data have associated read counts for probes and allele
frequencies for variants. The altExp framework allows us to
store both datasets for the same columns/cells, one where the rows are
probes and one where the rows are variants. We use this to store the
smoothed chromosome scores and copy number calls as well, where the
features/rows are chromosomes or chromosome arms. Each
altExp can have its own set of features and several
assays. The assays contained within an
altExp must have the same set of features.
# list alternate experiments
altExps(cellmix)
#> List of length 3
#> names(3): alleleFrequency smoothedCopyNumberByChr smoothedCopyNumberByArm
# retrieve alternate experiment
altExp(cellmix, "smoothedCopyNumberByChr")
#> class: TapestriExperiment
#> dim: 23 2987
#> metadata(0):
#> assays(3): smoothedCopyNumber discreteCopyNumber gmmCopyNumber
#> rownames(23): 1 2 ... 22 X
#> rowData names(0):
#> colnames(2987): AACAACCTAATAGTGGTT-1 AACAACCTAGTCCTAGTT-1 ... TTGTAATGCTCGTACCTT-1
#> TTGTATCACACTTGATCT-1
#> colData names(0):
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> barcodeProbe:
#> grnaProbe:
#> gmmParams(0):
# list assays in altExp
assays(altExp(cellmix, "smoothedCopyNumberByChr"))
#> List of length 3
#> names(3): smoothedCopyNumber discreteCopyNumber gmmCopyNumberOther Slots
The barcodeProbe and grnaProbe slots are
set automatically by the panel.id parameter of
createTapestriExperiment(). They are used as shortcuts for
the barcoded
read parsing functions of the package. The values for these slots
can be retrieved and set manually as well.
barcodeProbe(cellmix)
#> [1] "not specified"
grnaProbe(cellmix)
#> [1] "not specified"
# setting manually
grnaProbe(cellmix) <- "probe_id_123"The gmmParams slot stores information for the GMM
models generated for copy number calling in the form of two nested
matrices one for whole chromosome calls and one for chromosome arm
calls. The data include the Gaussian model parameters fit for each
chromosome or arm, the probability density functions for those models,
the probabilities of each cell belonging to a copy number class, and the
classification results.
gmmParams(cellmix)
#> $chr
#> # A tibble: 23 × 7
#> feature.id smoothed.cn model pdf model.evidence cn.probability cn.class
#> <fct> <list> <list> <list> <list> <list> <list>
#> 1 1 <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 2 2 <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 3 3 <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 4 4 <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 5 5 <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 6 6 <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 7 7 <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 8 8 <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 9 9 <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 10 10 <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> # ℹ 13 more rows
#>
#> $arm
#> # A tibble: 41 × 7
#> feature.id smoothed.cn model pdf model.evidence cn.probability cn.class
#> <fct> <list> <list> <list> <list> <list> <list>
#> 1 chr1p <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 2 chr1q <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 3 chr2p <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 4 chr2q <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 5 chr3p <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 6 chr3q <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 7 chr4p <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 8 chr4q <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 9 chr5p <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> 10 chr5q <tibble [2,987 × 2]> <tibble [6 × 3]> <tibble [2,987 × 6]> <dbl [2,987]> <tibble> <tibble>
#> # ℹ 31 more rows
sessioninfo::session_info()
#> ─ Session info ────────────────────────────────────────────────────────────────────────────────────────────────
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