promor (version 0.2.1)

filterbygroup_na: Filter proteins by group level missing data

Description

This function filters out proteins based on missing data at the group level.

Usage

filterbygroup_na(raw_df, set_na = 0.34, filter_condition = "either")

Value

A raw_df object.

Arguments

raw_df

A raw_df object (output of create_df)

set_na

The proportion of missing data allowed. Default is 0.34 (one third of the samples in the group).

filter_condition

If set to "each", proteins that exceed the missing value proportion threshold set by set_na in each group will be removed (lenient). If set to "either"(default), proteins that exceed the missing value proportion threshold set by set_na in at least one group will be removed (stringent).

Author

Chathurani Ranathunge

Details

• If filter_condition = "each", it then removes proteins (rows) from the data frame if the proportion of NAs in each group exceeds the threshold indicated by set_na (default is 0.34). This option is more lenient in comparison to filter_condition = "either", where proteins that exceeds the missing data threshold in either group gets removed from the data frame.

See Also

create_df

Examples

# \donttest{
# Generate a raw_df object with default settings. No technical replicates.
raw_df <- create_df(
prot_groups = "https://raw.githubusercontent.com/caranathunge/promor_example_data/main/pg1.txt",
exp_design = "https://raw.githubusercontent.com/caranathunge/promor_example_data/main/ed1.txt"
)

## Remove proteins that exceed 34% NAs in either group (default)
rawdf_filt1 <- filterbygroup_na(raw_df)

## Remove proteins that exceed 34% NAs in each group
rawdf_filt2 <- filterbygroup_na(raw_df, filter_condition = "each")

## Proportion of samples with NAs allowed in each group = 0.5
rawdf_filt3 <- filterbygroup_na(raw_df, set_na = 0.5, filter_condition = "each")

# }