siscreenr: Analyze Genome-Wide siRNA Screening Campaigns

Description Usage Arguments Details Value Methods (by class) Grouping variables Grouped data frames (dplyr package)

Calculate normalized percent inhibition (NPI) or activation (NPA) for variable(s) in a data frame.

percentage(
  x,
  variables,
  positive,
  negative,
  mode = c("inhibition", "activation")
)

## S3 method for class 'data.frame'
percentage(
  x,
  variables,
  positive,
  negative,
  mode = c("inhibition", "activation")
)

## S3 method for class 'grouped_df'
percentage(
  x,
  variables,
  positive,
  negative,
  mode = c("inhibition", "activation")
)

`x`	a data frame
`variables`	column to process
`positive`	logical predicate to define positive control observations (max inhibition); bare or string
`negative`	logical predicate to define negative control observations (no inhibition); bare or string
`mode`	character that decides whether to calculate normalized percent inhibition or normalized percent activation

NPI is the extent to which the signal of interest is diminished compared to a positive control:

NPI = (Neg - x) / (Neg - Pos) * 100%

NPA is the extent to which the signal is activated compared to a positive control:

NPA = (x - Neg) / (Pos - Neg) * 100%

This is a method of data normalization in a high throughput screening campaign.

a modified data.frame

data.frame: uses positive and negative to calculate mean reference values, runs function for NPI or NPA over desired variables with lapply, then cbinds the result to x
grouped_df: see data.frame__to__grouped_df

Grouped data frames (class grouped_df) may cause problems if grouping variables are used to define positive and negative controls. It is best to create a separate variable to base the positive and negative predicates on.

Grouped data frames (`dplyr` package)

The method for class grouped_df is home made because the native behavior of grouped data frames does not readily support functions that return data frames.

olobiolo/siscreenr documentation built on Nov. 26, 2021, 3:08 p.m.