PMC141
Gorringe
lab Ovarian 3D
Screen QC
Last updated: 2023-05-02
Checks: 6 1
Knit directory: PMC141/
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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
Kylie Gorringe, Olivia Craig
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
[1] "/home/mli/ML_KylieLab/PMC141"Screen details
Screen starting date (yyyy-mm-dd)
2022-10-21,
2022-11-07
Cell lines
Org 60,
Org 49 and Org 60 repeat
Screen outline
Library: Custom collection
Controls
The normalising negative control is DMSO.
Plate layout
Known technical issues
Janus seeding per row was from 1 to 12 and 13 to 24
The Org
49 grew slower than Org 60.
So we have decided to delay drugging by
3 days (over the weekend) so they are of decent size.
Data cleaning
Filtering
The raw data was read into R Studio.
[1] "/home/mli/ML_KylieLab/PMC141/data/raw_data/CTG/Org49"
[1] "Org49"
Plate Well_ID Row Col SIDE CTG Cell_Line
1: 1 A1 A 1 left 55596 Org49
2: 1 A2 A 2 right 42916 Org49
3: 1 A3 A 3 right 52683 Org49
4: 1 A4 A 4 right 48314 Org49
5: 1 A5 A 5 right 46352 Org49
---
380: 1 P20 P 20 right 14771 Org49
381: 1 P21 P 21 right 15699 Org49
382: 1 P22 P 22 right 13947 Org49
383: 1 P23 P 23 right 17199 Org49
384: 1 P24 P 24 right 16289 Org49
[1] "/home/mli/ML_KylieLab/PMC141/data/raw_data/CTG/Org60"
[1] "Org60"
Plate Well_ID Row Col SIDE CTG Cell_Line
1: 1 A1 A 1 left 13144 Org60
2: 1 A2 A 2 right 15226 Org60
3: 1 A3 A 3 right 15602 Org60
4: 1 A4 A 4 right 16355 Org60
5: 1 A5 A 5 right 19284 Org60
---
764: 2 P20 P 20 right 3961 Org60
765: 2 P21 P 21 right 3129 Org60
766: 2 P22 P 22 right 3691 Org60
767: 2 P23 P 23 right 3433 Org60
768: 2 P24 P 24 right 2769 Org60Data processing
Annotation
Well annotations were added to the data.
Normalisation
The values were normalised to the median of the negative control DMSO wells on a per-plate basis. The left and right SIDE of the plates were normalised separate from each other.
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:
The Org 60 plate 1 Raw CTG data was consistent with its high
seeding cell density, contrasting to the lower seeding density in plate
2. However the Left end of the plate 1 Org 60 had visible batch effect
(left compared to right). This is odd, usually it is the other way
around. I suspect there might be many clumpy cells in the seeding. Need
to verify it with BF imaging.
Org 49 displayed left to right seeding difference.
Interpretation shall be carried to analyse the normalized data
(normalization was done taking consideration of seeding orientation)
CTG Raw values
CTG Normalised values
for (i in unique(data_norm$CELL_LINE)){
data_plot <-
data_norm %>%
filter(CELL_LINE == i)
# make plots
heapmap <-
data_plot %>%
dplyr::group_by(Barcode) %>%
dplyr::do(plots = platetools::raw_map(data = .$CTG_Norm,
well = .$WELL_ID,
plate = 384) +
ggtitle(.$Barcode[1]) +
scale_fill_gradient2(low = "blue",
mid = "white",
high = "red",
midpoint = median(data_plot$CTG_Norm),
limits = c(floor(0), ceiling(max(data_plot$CTG_Norm))),
name = "CTG range \nby CellLine") +
theme(plot.title = element_text(size = 22, hjust = 0.05, vjust = -0.1, margin = margin(b = -5)),
axis.text.x = element_text(size = 4),
axis.text.y = element_text(size = 4))
)
myplots <- list()
for (i in 1:length(unique(data_plot$Barcode))){
p1 <- eval(substitute(print(heapmap$plots[[i]]), list(i=i)))
myplots[[i]] <- p1
}
}
Analysed by Mark Li
Victorian Centre for Functional Genomics
Screen QC Metrics
Comments: You can download the data in excel or
csv format. You can drag columns and turn them on and off
Analysed by Mark Li
Victorian Centre for Functional Genomics
PLATE QC Metrics
Comments: