deriveSampleScaling: Function deriveSampleScaling create init_scaling and...
In CshlSiepelLab/ACE: Analysis of CRISPR Essentiality in R

Function deriveSampleScaling create init_scaling and dep_scaling. Misspecification will cause our 'neutral' model to actually be depleted, reducing all essentiality scores. The expected scaling factor is the ratio of read counts to their corresponding prior, with the median taken across sgrna in a sample. This method assumes the median sgrna is nonessential. This is also approximated by the ratio of the total sample read counts to the total expected read counts. If indicated, the scaling factor will be calculated only using negative control sgrna (recommended). If the master library is used as the prior, the expected read count is the product of the master library sgrna frequency and the total number of infected cells. If the master library sgrna frequency is not used, the expected frequency for each guide is determined by the average percent abundance for that guide across all (mean-normalized) initial samples.

deriveSampleScaling(
  user_DataObj,
  master_freq_dt,
  use_master_library,
  use_neg_ctrl,
  neg_ctrls,
  write_log
)

`user_DataObj`	all data; a DataObj.
`master_freq_dt`	normalized masterlibrary frequencies (log)
`use_master_library`	Boolean
`use_neg_ctrl`	Boolean
`neg_ctrls`	Vector of gene name strings.
`write_log`	Function to write messages to log.