ML_PMC141_OC_Org_level
analysis
Last updated: 2023-05-02
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Knit directory: PMC141_Obj_level_analysis/
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Aim
The aim of this analysis sequence is to prototyping the workflow for
MACseq.
In this particular cases, 3D MCF7 cells treated with MOA
plate for 24hr recovered by Macseq workflow (Cell Recovery Solution),
and a parallel plate analysed via CTG assay.
To select for treatment wells for investigation, CTG data is used to
help me selecting a few candidates.
I would like to perform RNAseq QC, and differential expression
analysis of treatment wells compared to negative control DMSO.
Researcher
ML
library(data.table)
library(DT)
library(here)
library(dplyr)
library(ggplot2)
library(tidyverse)
# check if here() correctly identified the directory
here::here()[1] "/home/mli/ML_KylieLab/PMC141_compressed images and masks/PMC141_Obj_level_analysis"Analysis aims
This is the effort in mining the data from Bright Field images collected from OC’s PMC141.
The idea is to use similar DL base models to perform decent cell segmentation on BF images, then use CellProfiler to extract organoid level measurements.
In this instance we will be focusing on the object size (inferred from the area measurement in pixels)
Data Preprocessing
Spheroid count
The drop in spheroid count could be the small spheroids merging into
large ones. The main idea is to make sure we have sufficient number of
objects identified in each treatment well. Therefore the spheroid level
size analysis has some power. ### DMSO {.tabset .tabset-fade
.tabset-pills}
| Version | Author | Date |
|---|---|---|
| 3df8d97 | Mark Li | 2023-05-02 |
Spheroid size
Certain timepoint might not have yield trusworthy segementation masks, therefore the data point is omitted (lacking the line plot). Here we should not be fixing our eyes on a single time point, rather the trend displayed across time is more trusworthy. We want to see the trend difference between treatment and between organoids.
The general idea is to see what drug at what dose do we see flattening of the line plot.
So far we can see Docetaxel and Doxorubicin (to a lesser extend Irinotecan)seem to have that effect in Org 60.
Org60 DMSO sample well Day 4 and Day7
Org60 DMSO sample well Day 4 and Day7
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] forcats_1.0.0 stringr_1.5.0 purrr_1.0.1 readr_2.1.4
[5] tidyr_1.3.0 tibble_3.1.8 tidyverse_1.3.2 ggplot2_3.4.1
[9] dplyr_1.1.0 here_1.0.1 DT_0.27 data.table_1.14.8
[13] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.4 sass_0.4.5 bit64_4.0.5
[4] vroom_1.6.1 jsonlite_1.8.4 modelr_0.1.10
[7] bslib_0.4.2 assertthat_0.2.1 getPass_0.2-2
[10] highr_0.10 googlesheets4_1.0.1 cellranger_1.1.0
[13] yaml_2.3.7 pillar_1.8.1 backports_1.4.1
[16] glue_1.6.2 digest_0.6.31 promises_1.2.0.1
[19] rvest_1.0.3 colorspace_2.1-0 htmltools_0.5.4
[22] httpuv_1.6.9 pkgconfig_2.0.3 broom_1.0.3
[25] haven_2.5.1 scales_1.2.1 processx_3.8.0
[28] whisker_0.4.1 later_1.3.0 tzdb_0.3.0
[31] timechange_0.2.0 git2r_0.31.0 googledrive_2.0.0
[34] farver_2.1.1 generics_0.1.3 ellipsis_0.3.2
[37] cachem_1.0.6 withr_2.5.0 cli_3.6.0
[40] magrittr_2.0.3 crayon_1.5.2 readxl_1.4.2
[43] mime_0.12 evaluate_0.20 ps_1.7.2
[46] fs_1.6.1 fansi_1.0.4 xml2_1.3.3
[49] tools_4.2.0 hms_1.1.2 gargle_1.3.0
[52] formatR_1.14 lifecycle_1.0.3 munsell_0.5.0
[55] reprex_2.0.2 callr_3.7.3 compiler_4.2.0
[58] jquerylib_0.1.4 rlang_1.0.6 grid_4.2.0
[61] rstudioapi_0.14 htmlwidgets_1.6.1 labeling_0.4.2
[64] rmarkdown_2.20 gtable_0.3.1 DBI_1.1.3
[67] R6_2.5.1 lubridate_1.9.2 knitr_1.42
[70] bit_4.0.5 fastmap_1.1.0 utf8_1.2.3
[73] rprojroot_2.0.3 stringi_1.7.12 parallel_4.2.0
[76] Rcpp_1.0.10 vctrs_0.5.2 dbplyr_2.3.0
[79] tidyselect_1.2.0 xfun_0.37