View source: R/quality_control.R
tof_assess_channels | R Documentation |
Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data
tof_assess_channels(
tof_tibble,
channel_cols = where(tof_is_numeric),
negative_threshold = asinh(10/5),
negative_proportion_flag = 0.95
)
tof_tibble |
A 'tof_tbl' or 'tibble'. |
channel_cols |
A vector of unquoted column names representing columns that contain single-cell protein measurements. Supports tidyselect helpers. If nothing is specified, the default is to analyze all numeric columns. |
negative_threshold |
A scalar indicating the threshold below which a measurement should be considered negative. Defaults to the hyperbolic arcsine transformation of 10 counts. |
negative_proportion_flag |
A scalar between 0 and 1 indicating the proportion of cells in tof_tibble that need to be below 'negative_threshold' for a given marker in order for that marker to be flagged. Defaults to 0.95. |
A tibble 3 columns and a number of rows equal to the number of columns in 'tof_tibble' chosen by 'channel_cols'. The three columns are "channel", a character vector of channel names, "negative_proportion", a numeric vector with values between 0 and 1 indicating how many cells in 'tof_tibble' below 'negative_threshold' for each channel, and 'flagged_channel', a boolean vector indicating whether or not a channel has been flagged as potentially failed (TRUE means that the channel had a large number of cells below 'negative_threshold').
# simulate some data
sim_data <-
data.frame(
cd4 = rnorm(n = 100, mean = 5, sd = 0.5),
cd8 = rnorm(n = 100, mean = 0, sd = 0.1),
cd33 = rnorm(n = 100, mean = 10, sd = 0.1)
)
tof_assess_channels(tof_tibble = sim_data)
tof_assess_channels(tof_tibble = sim_data, channel_cols = c(cd4, cd8))
tof_assess_channels(tof_tibble = sim_data, negative_threshold = 2)
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