Description Usage Arguments Value See Also Examples
View source: R/analysis-functions.R
experimental
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$seqCount, association_file) aggregated_meta <- aggregate_metadata(association_file) ### Sample statistics sample_stats <- sample_statistics(x = aggregated_x, metadata = aggregated_meta, sample_key = c("SubjectID", "CellMarker","Tissue", "TimePoint"))
1 2 3 4 5 6 7 | sample_statistics(
x,
metadata,
sample_key = "CompleteAmplificationID",
value_columns = "Value",
functions = default_stats()
)
|
x |
A data frame |
metadata |
The metadata data frame |
sample_key |
Character vector representing the key for identifying a sample |
value_columns |
THe name of the columns to be computed, must be numeric or integer |
functions |
A named list of function or purrr-style lambdas |
A list with modified x and metadata data frames
Other Analysis functions:
CIS_grubbs()
,
comparison_matrix()
,
compute_abundance()
,
cumulative_count_union()
,
separate_quant_matrices()
,
threshold_filter()
,
top_integrations()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | op <- options(ISAnalytics.widgets = FALSE)
path_AF <- system.file("extdata", "ex_association_file.tsv",
package = "ISAnalytics"
)
root_correct <- system.file("extdata", "fs.zip",
package = "ISAnalytics"
)
root_correct <- unzip_file_system(root_correct, "fs")
association_file <- import_association_file(path_AF, root_correct,
dates_format = "dmy"
)
matrices <- import_parallel_Vispa2Matrices_auto(
association_file = association_file, root = NULL,
quantification_type = c("seqCount", "fragmentEstimate"),
matrix_type = "annotated", workers = 2, patterns = NULL,
matching_opt = "ANY", multi_quant_matrix = FALSE
)
stats <- sample_statistics(matrices$seqCount, association_file)
options(op)
|
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