high throughput screening analysis package
My very own package for analyzing our siRNA based high throughput screens. All functions were designed and written (and sometimes re-written) by myself.
The package is designed to be used in siRNA based screening campaigns with microscopy readout by the ScanR imaging system (Olympus). It is assumed the screen is done in replicates. I attempted to build general tools but my own needs are reflected in the core design philosophy.
Run the following to install the package:
if (!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github("olobiolo/siscreenr")
data.table
.lubridate
.ggplot2
and lattice
.reutils
.metamethods
.Hadley Wickham's Advanced R.
Patrick Burns's The R inferno.
Version 2 was built using packages dplyr
and tidyr
, later incorporated into tidyverse.
Version 3 abandons the tidyr
and dplyr
in favor of data.table. dplyr
is only used in unit tests.
This is a work in progress. There may well be bugs I missed. All feedback is welcome.
There is extensive documentation in the form of help pages.
Long form documentation (vignettes) is pending. This has to suffice for now.
The package is meant for interactive use and thus requiers the User to have a handle on R.
Besides functions immediately involved in data analysis, there are some utilities, e.g. for updating the siRNA library annotation and building layout files from parts, in case the plate layout changes during the campaign.
This workflow was developed for screens in which a phenotype is quantified and silencing target genes can cause the phenotype to be enhanced or diminished.
zi = (xi - mean(x)) / sd(x)
Example: In a screen with three replicates the z-score treshold is 2.4 and the stringency criterion is 2.
Finally, a well with z-scores of 2.23, 2.1 and 2.5 has hit scores of 0, 0 and 1: also not a hit.
Once hits are determined, well annotation is attached.
A slightly altered workflow is implemented for screens in which a phenotype occurs in a known range, from a minimum in a negative control to a maximum in a positive control. Silencing of target genes is expressed within that range. This is commonly called Normalized Percent Inhibition/Activation, depending on whether the positive control inhibits or activates the phenotype, and is commonly used in chemical screenings.
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