Add cell metadata — addCellMetadata • GiottoClass

Adds cell metadata to the giotto object

Usage

addCellMetadata(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  new_metadata,
  vector_name = NULL,
  by_column = FALSE,
  column_cell_ID = NULL
)

Arguments

gobject: giotto object
spat_unit: spatial unit
feat_type: feature type
new_metadata: new cell metadata to use (data.table, data.frame, vector, factor, ...)
vector_name: (optional) custom name for new metadata column if single vector or factor is provided
by_column: merge metadata based on cell_ID column in pDataDT (default = FALSE)
column_cell_ID: column name of new metadata to use if by_column = TRUE

Value

giotto object

Details

You can add additional cell metadata in several manners:

1. Provide a data.frame-like object, vector, or factor with cell annotations in the same order as the cell_ID column in pDataDT(gobject). This is a bit risky and not the most recommended.
2. Provide a data.frame-like object with cell annotations and specify which column contains the cell IDs, these cell IDs need to match with the cell_ID column in pDataDT(gobject)
3. Provide a vector or factor that is named with the cell IDs they correspond to. These names will be matched against the cell_ID column in pDataDT(gobject).

Examples

# dummy matrix
m <- readRDS(system.file("extdata/toy_matrix.RDS", package = "GiottoClass"))
g <- createGiottoObject(m)
#> 
#> no external python path or giotto environment was specified, will check if a
#>  default python path is available
#> 
#> A default python path was found: /usr/bin/python3 and will be used
#>  If this is not the correct python path, either
#> 
#> 1. use installGiottoEnvironment() to install a local miniconda python
#>  environment along with required modules
#> 
#> 2. provide an existing python path to python_path to use your own python path
#>  which has all modules installed
#> Set options("giotto.use_conda" = FALSE) if python functionalities are not
#>  needed
#> Warning: module: pandas was not found with python path: /usr/bin/python3
#> Warning: module: igraph was not found with python path: /usr/bin/python3
#> Warning: module: leidenalg was not found with python path: /usr/bin/python3
#> Warning: module: community was not found with python path: /usr/bin/python3
#> Warning: module: networkx was not found with python path: /usr/bin/python3
#> Warning: module: sklearn was not found with python path: /usr/bin/python3
#> Consider to install these (optional) packages to run all possible Giotto
#>  commands for spatial analyses: scran MAST tiff biomaRt trendsceek multinet
#>  FactoMineR
#> Giotto does not automatically install all these packages as they are not
#>  absolutely required and this reduces the number of dependencies
#> There are non numeric or integer columns for the spatial location input at
#>  column position(s): 1
#>  The first non-numeric column will be considered as a cell ID to test for
#>  consistency with the expression matrix
#>  Other non numeric columns will be removed
#> Warning: module: pandas was not found with python path: /usr/bin/python3
#> Warning: module: igraph was not found with python path: /usr/bin/python3
#> Warning: module: leidenalg was not found with python path: /usr/bin/python3
#> Warning: module: community was not found with python path: /usr/bin/python3
#> Warning: module: networkx was not found with python path: /usr/bin/python3
#> Warning: module: sklearn was not found with python path: /usr/bin/python3

pDataDT(g)
#>     cell_ID
#>      <char>
#>  1:       A
#>  2:       B
#>  3:       C
#>  4:       D
#>  5:       E
#>  6:       F
#>  7:       G
#>  8:       H
#>  9:       I
#> 10:       J

# appending a character vector, merge on vector names
v <- seq(10)
names(v) <- sample(LETTERS[seq(10)])
force(v)
#>  G  E  F  J  D  A  I  H  C  B 
#>  1  2  3  4  5  6  7  8  9 10 

g <- addCellMetadata(g, new_metadata = v, by_column = TRUE)
pDataDT(g)
#> Key: <cell_ID>
#>     cell_ID     v
#>      <char> <int>
#>  1:       A     6
#>  2:       B    10
#>  3:       C     9
#>  4:       D     5
#>  5:       E     2
#>  6:       F     3
#>  7:       G     1
#>  8:       H     8
#>  9:       I     7
#> 10:       J     4

# appending a data.frame, merge on specified column "ID"
df <- data.frame(
    IDS = rev(LETTERS[seq(10)]), # reversed
    l = letters[seq(10)],
    x = c(rep(TRUE, 5), rep(FALSE, 5))
)
force(df)
#>    IDS l     x
#> 1    J a  TRUE
#> 2    I b  TRUE
#> 3    H c  TRUE
#> 4    G d  TRUE
#> 5    F e  TRUE
#> 6    E f FALSE
#> 7    D g FALSE
#> 8    C h FALSE
#> 9    B i FALSE
#> 10   A j FALSE

g <- addCellMetadata(
    g,
    new_metadata = df,
    by_column = TRUE,
    column_cell_ID = "IDS"
)
pDataDT(g)
#> Key: <cell_ID>
#>     cell_ID     v      l      x
#>      <char> <int> <char> <lgcl>
#>  1:       A     6      j  FALSE
#>  2:       B    10      i  FALSE
#>  3:       C     9      h  FALSE
#>  4:       D     5      g  FALSE
#>  5:       E     2      f  FALSE
#>  6:       F     3      e   TRUE
#>  7:       G     1      d   TRUE
#>  8:       H     8      c   TRUE
#>  9:       I     7      b   TRUE
#> 10:       J     4      a   TRUE