Calibrate multiple probes by calculating detection efficiency and R squared

See calibration vignette for example of usage.

calculate_efficiency_bytargetid(
  cq_df,
  formula = cq ~ log2(dilution) + biol_rep,
  use_prep_types = "+RT"
)

Arguments

cq_df	a data frame with cq (quantification cycle) data, 1 row per well Must have columns prep_type, target_id, cq, dilution. Only prep_type=="+RT" columns are used.
formula	formula to use for log-log regression fit. Default value assumes multiple biological replicates, cq ~ log2(dilution) + biol_rep. If only a single Biological Replicate, change to cq ~ log2(dilution). If multiple sample_ids, change to cq ~ log2(dilution) + sample_id. See ?formula for background and help.
use_prep_types	prep_type column values to use, default "+RT" for RT-qPCR. By default, this includes only reverse-transcribed values in the efficiency estimation, so excludes negative controls such as no-template and no-RT. To skip this filtering step, set use_prep_types=NA.

cq_df

a data frame with cq (quantification cycle) data, 1 row per well

Must have columns prep_type, target_id, cq, dilution. Only prep_type=="+RT" columns are used.

formula

formula to use for log-log regression fit.

Default value assumes multiple biological replicates, cq ~ log2(dilution) + biol_rep.

If only a single Biological Replicate, change to cq ~ log2(dilution). If multiple sample_ids, change to cq ~ log2(dilution) + sample_id.

See ?formula for background and help.

use_prep_types

prep_type column values to use, default "+RT" for RT-qPCR.

By default, this includes only reverse-transcribed values in the efficiency estimation, so excludes negative controls such as no-template and no-RT.

To skip this filtering step, set use_prep_types=NA.

data frame with columns: target_id, efficiency, efficiency.sd, r.squared.

Note efficiency is given in ratio, not per cent; multiply by 100 for that.