Accessors for dimensionality reduction results

Source: R/classes.R

Read the contents of mesaDimRed, mesaPCA and mesaUMAP objects, as returned by getPCA() and getUMAP().

# S4 method for class 'mesaDimRed'
getSampleTable(object)

# S4 method for class 'mesaDimRed'
getSampleNames(object)

getResults(object, ...)

# S4 method for class 'mesaDimRed'
getResults(object)

# S4 method for class 'mesaDimRed'
getParameters(object)

getCoordinates(object, ...)

# S4 method for class 'mesaPCA'
getCoordinates(object)

# S4 method for class 'mesaUMAP'
getCoordinates(object)

# S4 method for class 'mesaPCA'
getSampleNames(object)

# S4 method for class 'mesaUMAP'
getSampleNames(object)

# S4 method for class 'mesaDimRed'
getCoordinates(object)

getPrcomp(object, ...)

# S4 method for class 'mesaPCA'
getPrcomp(object)

# S4 method for class 'mesaDimRed'
getPrcomp(object)

# S4 method for class 'mesaDimRed'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

object

A mesaDimRed object, or a mesaPCA or mesaUMAP object where stated.

...

Not used.

x

A mesaDimRed object.

row.names, optional

Not used.

Value

  • getResults(): a named list of mesaPCA or mesaUMAP objects, one per result (e.g. per topVarNum value); empty when there are no results.

  • getSampleTable(): the sample annotations, a data.frame with the sample IDs in its sample_name column.

  • getSampleNames(): the sample IDs the results cover, or character() when there are no results; also works on a mesaPCA or mesaUMAP, giving its sample IDs. With useGroupMeans = TRUE they are sample group names.

  • getParameters(): the list of parameters used to compute the results, including method ("PCA" or "UMAP").

  • getDataTable(): if returnDataTable = TRUE, the input data used to compute the results, as a data.frame with windows in rows and samples in columns; otherwise, an empty data.frame.

  • getCoordinates(): for a mesaPCA, its principal component scores (prcomp$x); for a mesaUMAP, its UMAP coordinates. Each is a data.frame with one row per sample and sample IDs as row names. For a mesaDimRed, a named list of these, one per result.

  • getPrcomp(): the stats::prcomp() fit of a mesaPCA. For a mesaDimRed of PCA results, a named list of these, one per result; an error for UMAP results.

  • getWindowNames(): the window IDs a result was computed over. For a mesaDimRed, a named list of these, one per result.

  • as.data.frame(): one data.frame with a row per result and sample: resName (the result's name, as in getResults()), sample_name, the coordinate columns (PC1, PC2, ... or UMAP1, UMAP2), then the sampleTable columns. With useGroupMeans = TRUE the ID column is group and no sample annotations are added.

Examples

data(exampleTumourNormal, package = "mesa")
pca <- getPCA(exampleTumourNormal, topVarNum = c(100, 500),
    verbose = FALSE)
#> Generating table of beta values for 819 regions across 10 samples.

names(getResults(pca))
#> [1] "pca1" "pca2"
head(getSampleTable(pca))
#>          sample_name    group patient        type tumour tissue age gender
#> Colon1_N    Colon1_N Colon1_N  Colon1 NormalColon Normal  Colon  80      F
#> Colon1_T    Colon1_T Colon1_T  Colon1         CRC Tumour  Colon  80      F
#> Colon2_N    Colon2_N Colon2_N  Colon2 NormalColon Normal  Colon  67      F
#> Colon2_T    Colon2_T Colon2_T  Colon2         CRC Tumour  Colon  67      F
#> Lung1_N      Lung1_N  Lung1_N   Lung1  NormalLung Normal   Lung  64      F
#> Lung1_T      Lung1_T  Lung1_T   Lung1        LUAD Tumour   Lung  64      F
#>          stage
#> Colon1_N  <NA>
#> Colon1_T   III
#> Colon2_N  <NA>
#> Colon2_T    IV
#> Lung1_N   <NA>
#> Lung1_T    IIA
getSampleNames(pca)
#>  [1] "Colon1_N" "Colon1_T" "Colon2_N" "Colon2_T" "Lung1_N"  "Lung1_T" 
#>  [7] "Lung2_N"  "Lung2_T"  "Lung3_N"  "Lung3_T" 
getParameters(pca)$method
#> [1] "PCA"

first <- getResults(pca)[[1]]
head(getCoordinates(first))
#>                 PC1         PC2         PC3         PC4         PC5
#> Colon1_N -1.5548660  1.44750438  0.94467418  0.97768551  0.15374220
#> Colon1_T -2.6914778  0.34334463 -1.58832065  0.06878159 -0.11682077
#> Colon2_N  0.3780406  0.43242959 -0.08074868 -0.06182247 -1.39952095
#> Colon2_T -0.9233616  0.44363244  1.51991730 -1.35115706  0.20034742
#> Lung1_N   1.5101990  0.01393992 -0.28749699  0.38421605 -0.09776136
#> Lung1_T   0.7346282 -0.22198374 -0.72104166 -1.41025272 -0.02523040
summary(getPrcomp(first))$importance[, 1:3]
#>                             PC1      PC2      PC3
#> Standard deviation     1.446439 1.168279 0.880966
#> Proportion of Variance 0.337400 0.220110 0.125160
#> Cumulative Proportion  0.337400 0.557510 0.682670

# Coordinates of every result at once
lapply(getCoordinates(pca), dim)
#> $pca1
#> [1] 10  5
#> 
#> $pca2
#> [1] 10  5
#> 

# Everything in one table, e.g. for a custom plot
head(as.data.frame(pca)[, 1:6])
#>   resName sample_name        PC1         PC2         PC3         PC4
#> 1    pca1    Colon1_N -1.5548660  1.44750438  0.94467418  0.97768551
#> 2    pca1    Colon1_T -2.6914778  0.34334463 -1.58832065  0.06878159
#> 3    pca1    Colon2_N  0.3780406  0.43242959 -0.08074868 -0.06182247
#> 4    pca1    Colon2_T -0.9233616  0.44363244  1.51991730 -1.35115706
#> 5    pca1     Lung1_N  1.5101990  0.01393992 -0.28749699  0.38421605
#> 6    pca1     Lung1_T  0.7346282 -0.22198374 -0.72104166 -1.41025272