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TapExp-navigation.Rmd
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TapExp-navigation.Rmd
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---
title: "Navigating the TapestriExperiment Class"
date: 'Compiled on September 18, 2023'
---
# Introduction
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 the[`SingleCellExperiment`](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) and [`SummarizedExperiment`](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html) 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.
```r
library(karyotapR)
```
Calling the object will print a summary of the contained data.
```r
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 arm
```
The 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.
```r
dim(cellmix)
#> [1] 317 2987
```
```r
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
```
```r
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 1.33365
```
Metadata columns can be added to either `rowData` or `colData` by assignment
```r
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.
```r
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 arm
```
Additional metadata generated automatically when the object is created can be
retrieved using `metadata()`.
```r
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.
```r
# list assays
assays(cellmix)
#> List of length 3
#> names(3): counts normcounts copyNumber
```
```r
# 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
```
```r
# 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 0.8567637
```
Like any matrix, assays can also be subset using bracket notation.
```r
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 15
```
# Alternate 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 `assay`s. The `assay`s contained
within an `altExp` must have the same set of features.
```r
# list alternate experiments
altExps(cellmix)
#> List of length 3
#> names(3): alleleFrequency smoothedCopyNumberByChr smoothedCopyNumberByArm
```
```r
# 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):
```
```r
# list assays in altExp
assays(altExp(cellmix, "smoothedCopyNumberByChr"))
#> List of length 3
#> names(3): smoothedCopyNumber discreteCopyNumber gmmCopyNumber
```
```r
# retrieve assay in altExp
corner(assay(altExp(cellmix, "smoothedCopyNumberByChr"), "gmmCopyNumber"))
#> AACAACCTAATAGTGGTT-1 AACAACCTAGTCCTAGTT-1 AACAACCTATGGACGAGA-1 AACAACTGGGACATAACG-1 AACAACTGGGGAACCTAG-1
#> 1 2 2 2 3 2
#> 2 2 2 2 2 3
#> 3 2 2 2 3 2
#> 4 2 2 3 2 2
#> 5 3 3 2 2 2
```
# Other 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](https://joeymays.xyz/karyotapR/articles/barcoding.html) functions of the package.
The values for these slots can be retrieved and set manually as well.
```r
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](https://joeymays.xyz/karyotapR/articles/GMMs-for-Copy-Number-Calling.html) 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.
```r
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
```
```r
sessioninfo::session_info()
#> ─ Session info ────────────────────────────────────────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.3.1 (2023-06-16)
#> os macOS Ventura 13.5.2
#> system x86_64, darwin20
#> ui RStudio
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2023-09-18
#> rstudio 2023.06.0+421 Mountain Hydrangea (desktop)
#> pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
#>
#> ─ Packages ────────────────────────────────────────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
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```