ML_Suad
and Niv Prelimenary test
cell measurements
Last updated: 2023-01-25
Checks: 6 1
Knit directory: 230112_Suad_test/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown is untracked by Git. To know which version of the R
Markdown file created these results, you’ll want to first commit it to
the Git repo. If you’re still working on the analysis, you can ignore
this warning. When you’re finished, you can run
wflow_publish to commit the R Markdown file and build the
HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20230117) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 6166f77. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: analysis/figure/
Ignored: output/Initial analysis/
Untracked files:
Untracked: analysis/ML_ImageProcessing_dev.Rmd
Untracked: analysis/ML_Test_data_analysis.Rmd
Untracked: data/Endpoint/
Untracked: data/Tracking/
Untracked: data/annot/
Untracked: data/details/
Untracked: from Suad.txt
Untracked: output/Initial analysisChange of normalised spheroid count under each treatment conditionsn over time.png
Untracked: output/Initial analysisChange of spheroid count under each treatment conditionsn over time.png
Untracked: output/Initial analysisSpheroid area under each treatment conditions.png
Untracked: output/Initial analysisTotal spheroid count under each treatment conditions.png
Untracked: output/ilastik/
Untracked: output/manipulated/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
There are no past versions. Publish this analysis with
wflow_publish() to start tracking its development.
Aim of the test data
Exploratory analysis to see if any effect is present by treatment
using BF images collected.
Researcher primary question: is there a
difference from Day 6 to 7 between 43C and 37C plates?
Researcher
ML/Suad/Niv
Readout
Spheroid counts and estimated area
Required R packages
here, ggplot2, ggpubr, tidyverse,
[1] "/home/mli/ML_CampbellLab/230112_Suad_test"Screen details
Screen date (yyyy-mm-dd)
2022-12-27 to 29
Known technical issues
Most likely the pair seeded the cells by hand And there seems to have some seeding differences between control wells (Day 0 to Day 5 images were not collected so we would never know)
Experiment details
Cell seeding conditions:
80% Matrigel base
layer
50% Matrigel embedding layer
1500 cells seeded per well
Cell culture conditions:
Cells were grown til
Day 6 before drugs treatments were introduced.
Then the 43C plate
were treated with 43C for once off 120 mins before returning to 37C for
the rest of the experiment (Ending Day 9).
Image Formatting and Preprocessing
The raw image data was read into R Studio.
Segmentation would be
handled by ilastik and feature measurements would be handled by Cell
Profiler.
Example of ilastik generated probability mask (colored) overlayed to BF images
Read in image feature data
Image level data (object count)
## Image level data (Normalised object count)
This helps me to see if the culturing temperature had an impact
on spheroid number through out time.
We can eyeball the trend
between solid and dashed lines. Each line is a sample well from the
plate.
## object level data wrangling
Admittedly the object number might not be accurate due to empty
wells, fielf of capture and sub optimal object identification.
But we can look at the the size comparison among identified
objects
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] ggpubr_0.5.0 Hmisc_4.7-2 Formula_1.2-4 survival_3.3-1
[5] lattice_0.20-45 htmltools_0.5.3 here_1.0.1 forcats_0.5.2
[9] stringr_1.4.1 dplyr_1.0.10 purrr_0.3.4 readr_2.1.2
[13] tidyr_1.2.0 tibble_3.1.8 ggplot2_3.4.0 tidyverse_1.3.2
[17] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] googledrive_2.0.0 colorspace_2.0-3 ggsignif_0.6.3
[4] deldir_1.0-6 ellipsis_0.3.2 rprojroot_2.0.3
[7] htmlTable_2.4.1 base64enc_0.1-3 fs_1.5.2
[10] rstudioapi_0.14 farver_2.1.1 bit64_4.0.5
[13] fansi_1.0.3 lubridate_1.8.0 xml2_1.3.3
[16] splines_4.2.0 cachem_1.0.6 knitr_1.40
[19] jsonlite_1.8.0 broom_1.0.1 cluster_2.1.4
[22] dbplyr_2.2.1 png_0.1-7 compiler_4.2.0
[25] httr_1.4.4 backports_1.4.1 assertthat_0.2.1
[28] Matrix_1.5-3 fastmap_1.1.0 gargle_1.2.0
[31] cli_3.4.1 formatR_1.12 later_1.3.0
[34] tools_4.2.0 gtable_0.3.1 glue_1.6.2
[37] Rcpp_1.0.9 carData_3.0-5 cellranger_1.1.0
[40] jquerylib_0.1.4 vctrs_0.5.1 xfun_0.32
[43] ps_1.7.2 rvest_1.0.3 mime_0.12
[46] lifecycle_1.0.3 rstatix_0.7.1 googlesheets4_1.0.1
[49] getPass_0.2-2 scales_1.2.1 vroom_1.5.7
[52] ragg_1.2.4 hms_1.1.2 promises_1.2.0.1
[55] parallel_4.2.0 RColorBrewer_1.1-3 yaml_2.3.5
[58] gridExtra_2.3 sass_0.4.2 rpart_4.1.16
[61] latticeExtra_0.6-30 stringi_1.7.8 highr_0.9
[64] checkmate_2.1.0 systemfonts_1.0.4 rlang_1.0.6
[67] pkgconfig_2.0.3 evaluate_0.16 htmlwidgets_1.5.4
[70] labeling_0.4.2 bit_4.0.4 processx_3.8.0
[73] tidyselect_1.1.2 ggsci_2.9 magrittr_2.0.3
[76] R6_2.5.1 generics_0.1.3 DBI_1.1.3
[79] pillar_1.8.1 haven_2.5.1 whisker_0.4
[82] foreign_0.8-82 withr_2.5.0 abind_1.4-5
[85] nnet_7.3-17 modelr_0.1.9 crayon_1.5.1
[88] car_3.1-1 interp_1.1-3 utf8_1.2.2
[91] tzdb_0.3.0 rmarkdown_2.16 jpeg_0.1-9
[94] grid_4.2.0 readxl_1.4.1 data.table_1.14.2
[97] callr_3.7.3 git2r_0.30.1 reprex_2.0.2
[100] digest_0.6.29 httpuv_1.6.5 textshaping_0.3.6
[103] munsell_0.5.0 bslib_0.4.0