comparison_matrix {ISAnalytics} | R Documentation |
Takes a list of integration matrices referring to different qunatification
types and merges them in a single data frame that has multiple
value columns, each renamed according to their quantification type
of reference.
comparison_matrix( x, fragmentEstimate = "fragmentEstimate", seqCount = "seqCount", barcodeCount = "barcodeCount", cellCount = "cellCount", ShsCount = "ShsCount" )
x |
A named list of integration matrices, ideally obtained via import_parallel_Vispa2Matrices_interactive or import_parallel_Vispa2Matrices_auto. Names must be quantification types. |
fragmentEstimate |
The name of the output column for fragment estimate values |
seqCount |
The name of the output column for sequence count values |
barcodeCount |
The name of the output column for barcode count values |
cellCount |
The name of the output column for cell count values |
ShsCount |
The name of the output column for Shs count values |
A tibble
Other Analysis functions:
CIS_grubbs()
,
compute_abundance()
,
cumulative_count_union()
,
sample_statistics()
,
separate_quant_matrices()
,
threshold_filter()
,
top_integrations()
op <- options("ISAnalytics.widgets" = FALSE) path <- system.file("extdata", "ex_association_file.tsv", package = "ISAnalytics" ) root_pth <- system.file("extdata", "fs.zip", package = "ISAnalytics") root <- unzip_file_system(root_pth, "fs") matrices <- import_parallel_Vispa2Matrices_auto( association_file = path, root = root, quantification_type = c("fragmentEstimate", "seqCount"), matrix_type = "annotated", workers = 2, patterns = NULL, matching_opt = "ANY", dates_format = "dmy", multi_quant_matrix = FALSE ) total_matrix <- comparison_matrix(matrices) options(op)