Read a nested list of expression data inputs in order to generate a list of giotto-native exprObj
Usage
readExprData(
data_list,
sparse = TRUE,
cores = determine_cores(),
default_feat_type = NULL,
verbose = TRUE,
provenance = NULL,
expression_matrix_class = c("dgCMatrix", "DelayedArray")
)Arguments
- data_list
(nested) list of expression input data
- sparse
(boolean, default = TRUE) read matrix data in a sparse manner
- cores
number of cores to use
- default_feat_type
(optional) default feat_type to use
- verbose
be verbose
- provenance
(optional) provenance information
- expression_matrix_class
class of expression matrix to use (e.g. 'dgCMatrix', 'DelayedArray')
Details
mylistA = list('a' = matrix(seq(5)), 'b' = matrix(seq(5))) depth(mylistA)
mylistB = list(A = list('a' = matrix(seq(5)), 'b' = matrix(seq(5))), B = list('c' = matrix(seq(5)),'d' = matrix(seq(5)))) depth(mylistB)
mylistC = list('RNA' = list('RAW' = list('cell' = matrix(seq(5)), 'nucleus' = matrix(seq(6,10))), 'NORM' = list('cell' = matrix(seq(11,15)), 'nucleus' = matrix(seq(20,25)))), 'PROT' = list('RAW' = list('cell' = matrix(seq(16,20))))) depth(mylistC)
mymatD = matrix(data = seq(4))
Examples
x <- matrix(seq_len(100), nrow = 10)
temporal_dir <- tempdir()
write.csv(x, paste0(temporal_dir, "/mymatrix.csv"))
readExprData(paste0(temporal_dir, "/mymatrix.csv"))
#> list depth of 1
#>
#> List item [1]:
#> spat_unit: cell
#> feat_type: rna
#> name: raw
#> [[1]]
#> An object of class exprObj : "raw"
#> spat_unit : "cell"
#> feat_type : "rna"
#> provenance: cell
#>
#> contains:
#> 10 x 10 sparse Matrix of class "dgCMatrix"
#>
#> 1 1 11 21 31 41 51 61 71 81 91
#> 2 2 12 22 32 42 52 62 72 82 92
#> 3 3 13 23 33 43 53 63 73 83 93
#> 4 4 14 24 34 44 54 64 74 84 94
#>
#> ........suppressing 2 rows in show(); maybe adjust options(max.print=, width=)
#>
#> 7 7 17 27 37 47 57 67 77 87 97
#> 8 8 18 28 38 48 58 68 78 88 98
#> 9 9 19 29 39 49 59 69 79 89 99
#> 10 10 20 30 40 50 60 70 80 90 100
#>
#> First four colnames:
#> V1 V2 V3 V4
#>