Convert a transcript (molecule) or boundary dataframe to the ME list format

The goal of this function is to standardise transcript and boundary files for input to a MoleculeExperiment object.

Usage

dataframeToMEList(
  df,
  dfType = NULL,
  assayName = NULL,
  sampleCol = "sample_id",
  factorCol = NULL,
  xCol = "x_location",
  yCol = "y_location",
  keepCols = "essential",
  scaleFactor = 1
)

Arguments

df: A data.frame containing the transcript information or the boundary information. NOTE: this dataframe should, at a minimum, have the following 4 columns: sample_id, factorCol (e.g., feature_id in transcripts, or cell_id in boundaries), x_location and y_location.
dfType: Character string specifying contents of the dataframe. Can be either "molecules" or "boundaries".
assayName: Character string specifying the name with which to identify the information later on in an ME object.
sampleCol: Character string specifying the name of the column with the sample id.
factorCol: Character string specifying the name of the column with the factors with which to group the data in the lists. When working with molecules, this column would be e.g., "feature_id" in xenium. When working with boundaries, this column would be e.g., "cell_id" in xenium.
xCol: Character string specifying the name of the column with global x coordinates.
yCol: Character string specifying the name of the column with global y coordinates.
keepCols: Character string which can be either "essential" or "all". If "essential", the function will only work with the x and y location information.
scaleFactor: Integer specifying the scale factor by which to change the scale of the x and y locations (e.g., to change from pixel to micron). The default value is 1.

Value

A list with the format required to input it into slots of a MoleculeExperiment object.

Examples

moleculesDf <- data.frame(
    sample_id = rep(c("sample1", "sample2"), times = c(30, 20)),
    features = rep(c("gene1", "gene2"), times = c(20, 30)),
    x_coords = runif(50),
    y_coords = runif(50)
)

moleculesMEList <- dataframeToMEList(moleculesDf,
                                  dfType = "molecules",
                                  assayName = "detected",
                                  sampleCol = "sample_id",
                                  factorCol = "features",
                                  xCol = "x_coords",
                                  yCol = "y_coords")

moleculesMEList
#> $detected
#> $detected$sample1
#> $detected$sample1$gene1
#> # A tibble: 20 × 2
#>    x_location y_location
#>         <dbl>      <dbl>
#>  1     0.568      0.453 
#>  2     0.273      0.426 
#>  3     0.973      0.766 
#>  4     0.694      0.0527
#>  5     0.391      0.325 
#>  6     0.0696     0.176 
#>  7     0.102      0.843 
#>  8     0.132      0.862 
#>  9     0.283      0.569 
#> 10     0.373      0.641 
#> 11     0.985      0.548 
#> 12     0.617      0.198 
#> 13     0.0465     0.367 
#> 14     0.830      0.158 
#> 15     0.452      0.825 
#> 16     0.804      0.836 
#> 17     0.668      0.416 
#> 18     0.116      0.917 
#> 19     0.651      0.193 
#> 20     0.114      0.510 
#> 
#> $detected$sample1$gene2
#> # A tibble: 10 × 2
#>    x_location y_location
#>         <dbl>      <dbl>
#>  1     0.957       0.816
#>  2     0.109       0.822
#>  3     0.741       0.582
#>  4     0.979       0.218
#>  5     0.0887      0.116
#>  6     0.441       0.643
#>  7     0.473       0.546
#>  8     0.675       0.777
#>  9     0.406       0.315
#> 10     0.206       0.367
#> 
#> 
#> $detected$sample2
#> $detected$sample2$gene2
#> # A tibble: 20 × 2
#>    x_location y_location
#>         <dbl>      <dbl>
#>  1     0.386      0.145 
#>  2     0.728      0.258 
#>  3     0.104      0.768 
#>  4     0.0229     0.421 
#>  5     0.701      0.351 
#>  6     0.574      0.725 
#>  7     0.498      0.627 
#>  8     0.749      0.744 
#>  9     0.331      0.739 
#> 10     0.936      0.461 
#> 11     0.554      0.893 
#> 12     0.873      0.611 
#> 13     0.942      0.286 
#> 14     0.659      0.702 
#> 15     0.956      0.297 
#> 16     0.694      0.0900
#> 17     0.262      0.322 
#> 18     0.0142     0.0419
#> 19     0.992      0.814 
#> 20     0.494      0.476 
#> 
#> 
#>