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Remove low-count genes

Source: R/filter.R
remove_low_count_genes.Rd

TODO this function also transforms raw counts to CPM, but that should be a separate function before this step, before filter_counts function()

Usage

remove_low_count_genes(
  counts_dat,
  sample_metadata,
  sample_id_colname = NULL,
  feature_id_colname,
  group_colname,
  use_cpm_counts_to_filter = TRUE,
  use_group_based_filtering = FALSE,
  minimum_count_value_to_be_considered_nonzero = 8,
  minimum_number_of_samples_with_nonzero_counts_in_total = 7,
  minimum_number_of_samples_with_nonzero_counts_in_a_group = 3
)

Arguments

sample_id_colname

The column from the sample metadata containing the sample names. The names in this column must exactly match the names used as the sample column names of your input Counts Matrix. (Default: NULL - first column in the sample metadata will be used.)

feature_id_colname

The column from the counts data containing the Feature IDs (Usually Gene or Protein ID). This is usually the first column of your input Counts Matrix. Only columns of Text type from your input Counts Matrix will be available to select for this parameter. (Default: NULL - first column in the counts matrix will be used.)

group_colname

The column from the sample metadata containing the sample group information. This is usually a column showing to which experimental treatments each sample belongs (e.g. WildType, Knockout, Tumor, Normal, Before, After, etc.).

use_cpm_counts_to_filter

If no transformation has been been performed on counts matrix (eg Raw Counts) set to TRUE. If TRUE counts will be transformed to CPM and filtered based on given criteria. If gene counts matrix has been transformed (eg log2, CPM, FPKM or some form of Normalization) set to FALSE. If FALSE no further transformation will be applied and features will be filtered as is. For RNAseq data RAW counts should be transformed to CPM in order to properly filter.

use_group_based_filtering

If TRUE, only keeps features (e.g. genes) that have at least a certain number of samples passing the threshold in at least one group

minimum_count_value_to_be_considered_nonzero

Minimum value in the selected filtering table required for a sample to be considered nonzero. If use_cpm_counts_to_filter is TRUE, this threshold is applied to CPM values. If use_cpm_counts_to_filter is FALSE, this threshold is applied directly to the selected count_type table.

minimum_number_of_samples_with_nonzero_counts_in_total

Minimum number of samples in total that must meet the minimum_count_value_to_be_considered_nonzero threshold for a feature to be kept.

minimum_number_of_samples_with_nonzero_counts_in_a_group

Only keeps genes that have at least this number of samples meeting the threshold in at least one group

Value

counts matrix with low-count genes removed