sample_statistics {ISAnalytics} | R Documentation |
The function operates on a data frame by grouping the content by
the sample key and computing every function specified on every
column in the
value_columns
parameter. After that the metadata
data frame is updated by including the computed results as columns
for the corresponding key.
For this reason it's required that both x
and metadata
have the
same sample key, and it's particularly important if the user is
working with previously aggregated data.
For example:
### Importing association file and matrices path_AF <- system.file("extdata", "ex_association_file.tsv", package = "ISAnalytics") root_correct <- system.file("extdata", "fs.zip", package = "ISAnalytics") root_correct <- unzip_file_system(root_correct, "fs") association_file <- import_association_file(path_AF, root_correct) matrices <- import_parallel_Vispa2Matrices_auto( association_file = association_file , root = NULL, quantification_type = c("seqCount","fragmentEstimate"), matrix_type = "annotated", workers = 2, patterns = NULL, matching_opt = "ANY", dates_format = "dmy") ### Aggregating data (both by same key) aggreggated_x <- aggregate_values_by_key(matrices$seqCount, association_file) aggregated_meta <- aggregate_metadata(association_file) ### Sample statistics sample_stats <- sample_statistics(x = aggregated_x, metadata = aggregated_meta, sample_key = c("SubjectID", "CellMarker","Tissue", "TimePoint"))
sample_statistics( x, metadata, sample_key = "CompleteAmplificationID", value_columns = "Value", functions = default_stats() )
x |
A data frame |
metadata |
The metadata data frame |
sample_key |
Character vector representing the key for identifying a sample |
value_columns |
THe name of the columns to be computed, must be numeric or integer |
functions |
A named list of function or purrr-style lambdas |
A list with modified x and metadata data frames
Other Analysis functions:
CIS_grubbs()
,
comparison_matrix()
,
compute_abundance()
,
cumulative_count_union()
,
separate_quant_matrices()
,
threshold_filter()
,
top_integrations()
op <- options(ISAnalytics.widgets = FALSE) path_AF <- system.file("extdata", "ex_association_file.tsv", package = "ISAnalytics" ) root_correct <- system.file("extdata", "fs.zip", package = "ISAnalytics" ) root_correct <- unzip_file_system(root_correct, "fs") association_file <- import_association_file(path_AF, root_correct, dates_format = "dmy" ) matrices <- import_parallel_Vispa2Matrices_auto( association_file = association_file, root = NULL, quantification_type = c("seqCount", "fragmentEstimate"), matrix_type = "annotated", workers = 2, patterns = NULL, matching_opt = "ANY", multi_quant_matrix = FALSE ) stats <- sample_statistics(matrices$seqCount, association_file) options(op)