keep_abundant | R Documentation |
keep_abundant() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with additional columns for the statistics from the hypothesis test.
keep_abundant(
.data,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
factor_of_interest = NULL,
minimum_counts = 10,
minimum_proportion = 0.7
)
## S4 method for signature 'spec_tbl_df'
keep_abundant(
.data,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
factor_of_interest = NULL,
minimum_counts = 10,
minimum_proportion = 0.7
)
## S4 method for signature 'tbl_df'
keep_abundant(
.data,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
factor_of_interest = NULL,
minimum_counts = 10,
minimum_proportion = 0.7
)
## S4 method for signature 'tidybulk'
keep_abundant(
.data,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
factor_of_interest = NULL,
minimum_counts = 10,
minimum_proportion = 0.7
)
## S4 method for signature 'SummarizedExperiment'
keep_abundant(
.data,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
factor_of_interest = NULL,
minimum_counts = 10,
minimum_proportion = 0.7
)
## S4 method for signature 'RangedSummarizedExperiment'
keep_abundant(
.data,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
factor_of_interest = NULL,
minimum_counts = 10,
minimum_proportion = 0.7
)
.data |
A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) |
.sample |
The name of the sample column |
.transcript |
The name of the transcript/gene column |
.abundance |
The name of the transcript/gene abundance column |
factor_of_interest |
The name of the column of the factor of interest. This is used for defining sample groups for the filtering process. It uses the filterByExpr function from edgeR. |
minimum_counts |
A real positive number. It is the threshold of count per million that is used to filter transcripts/genes out from the scaling procedure. |
minimum_proportion |
A real positive number between 0 and 1. It is the threshold of proportion of samples for each transcripts/genes that have to be characterised by a cmp bigger than the threshold to be included for scaling procedure. |
questioning
At the moment this function uses edgeR (DOI: 10.1093/bioinformatics/btp616)
Underlying method: edgeR::filterByExpr( data, min.count = minimum_counts, group = string_factor_of_interest, min.prop = minimum_proportion )
A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).
A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).
A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).
A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).
A 'SummarizedExperiment' object
A 'SummarizedExperiment' object
keep_abundant(
tidybulk::se_mini
)
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