Emma
optimisation Stewart_Lab
Screen Test QC
Last updated: 2023-05-15
Checks: 7 0
Knit directory: CTG_analysis/
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Aim
The aim of this analysis is to assess the quality of the screen in terms of viability and performance of negative and positive controls.
Researcher
Xiaodan Zhang
Readout
CTG, high content imaging - daily brightfield, end-point Hoechst/PI (1 field @ 2.5X, Cytation5)
Image analysis software
CellProfiler 4.1.3
Required R packages
data.table, DT, platetools, reshape2, tidyverse, patchwork
# load the required packages
library(data.table)
library(DT)
library(platetools)
library(reshape2)
library(readxl)
library(tidyverse)
library(patchwork)
library(here)
library(htmltools)
# check if here() correctly identified the directory
here::here()[1] "/home/mli/ML_StewartLab/CTG_analysis"# set the file prefix
prefix <- "Stewart"Plate layout
Known technical issues
Some rows had visible Matrigel lose (Paclitaxel wells)
Data cleaning
Filtering
The raw data was read into R Studio.
Data processing
Annotation
Well annotations were added to the data.
Normalisation
The values were normalised to the median of the negative control media only wells on a per-media testing group basis.
Screen quality
See the Screen quality section of the
Methods page for a more information regarding what’s expected in terms
of heat maps and screen and PLATE QC metrics, including %CVs and Z’
Factor values.
Heat maps
Comments:
CTG Raw values
Screen QC Metrics
Comments: Media and DMSO %CV are in the
acceptable range for negative controls all media testing groups (MM+BSA
is a little high).
The positive control (Staurosporin, STS) at low doses and moderate
toxicity are also showing good %CV . However, the high dose positive
controls with strong cell killing show %CV outSIDE of the range, which
is to be expected (e.g. Staurosporin 1 and 10uM). The Media group MM+BSA
seems to have overall elevated CV%, in contrast the MM+FCS looked
tighter.
Analysed by Mark Li
Victorian Centre for Functional Genomics
Analysed by Mark Li
Victorian Centre for Functional Genomics
Notched box plots
Comments: The box plots show that the majority
of the negative and positive controls are very tight and reproducible in
all media conditions. However, a few compounds had a large variation,
note the inverted notched boxplot (this has to do with median confidence
intervals that go beyond Q1 (and/or Q3) and beyond the lower (and/or
upper) fence value)
In the case for Paclitaxel, Emma did mention those wells might be
compromised by BioTek aspiration error.
Analysed by Mark Li
Victorian Centre for Functional Genomics
Dot plots
The plots are interactive: x axis arranged by treatments, the
idea is to see which well has behaved differently
Raw
Analysed by Mark Li
Victorian Centre for Functional Genomics
Analysed by Mark Li
Victorian Centre for Functional Genomics
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /usr/lib64/libblas.so.3.4.2
LAPACK: /usr/lib64/liblapack.so.3.4.2
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] htmltools_0.5.4 here_1.0.1 patchwork_1.1.2 forcats_1.0.0
[5] stringr_1.5.0 dplyr_1.1.0 purrr_1.0.1 readr_2.1.4
[9] tidyr_1.3.0 tibble_3.1.8 ggplot2_3.4.1 tidyverse_1.3.2
[13] readxl_1.4.2 reshape2_1.4.4 platetools_0.1.5 DT_0.27
[17] data.table_1.14.8 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.6.1 lubridate_1.9.2 bit64_4.0.5
[4] RColorBrewer_1.1-3 httr_1.4.4 rprojroot_2.0.3
[7] tools_4.2.0 backports_1.4.1 bslib_0.4.2
[10] utf8_1.2.3 R6_2.5.1 lazyeval_0.2.2
[13] DBI_1.1.3 colorspace_2.1-0 withr_2.5.0
[16] tidyselect_1.2.0 processx_3.8.0 bit_4.0.5
[19] compiler_4.2.0 git2r_0.31.0 cli_3.6.0
[22] rvest_1.0.3 formatR_1.14 xml2_1.3.3
[25] plotly_4.10.1 labeling_0.4.2 sass_0.4.5
[28] scales_1.2.1 callr_3.7.3 digest_0.6.31
[31] rmarkdown_2.20 pkgconfig_2.0.3 highr_0.10
[34] dbplyr_2.3.0 fastmap_1.1.0 htmlwidgets_1.6.1
[37] rlang_1.0.6 rstudioapi_0.14 farver_2.1.1
[40] jquerylib_0.1.4 generics_0.1.3 jsonlite_1.8.4
[43] crosstalk_1.2.0 vroom_1.6.1 googlesheets4_1.0.1
[46] magrittr_2.0.3 Rcpp_1.0.10 munsell_0.5.0
[49] fansi_1.0.4 lifecycle_1.0.3 stringi_1.7.12
[52] whisker_0.4.1 yaml_2.3.7 plyr_1.8.8
[55] grid_4.2.0 parallel_4.2.0 promises_1.2.0.1
[58] crayon_1.5.2 haven_2.5.1 hms_1.1.2
[61] knitr_1.42 ps_1.7.2 pillar_1.8.1
[64] reprex_2.0.2 glue_1.6.2 evaluate_0.20
[67] getPass_0.2-2 modelr_0.1.10 vctrs_0.5.2
[70] tzdb_0.3.0 httpuv_1.6.9 cellranger_1.1.0
[73] gtable_0.3.1 assertthat_0.2.1 cachem_1.0.6
[76] xfun_0.37 mime_0.12 broom_1.0.3
[79] later_1.3.0 viridisLite_0.4.1 googledrive_2.0.0
[82] gargle_1.3.0 timechange_0.2.0 ellipsis_0.3.2