Last updated: 2020-11-18

Checks: 7 0

Knit directory: cytomapper_publication/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

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.

Seed: set.seed(20200602)

The command set.seed(20200602) 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.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 6ccdd51

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 6ccdd51. 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:    .Rproj.user/
    Ignored:    data/PancreasData/pancreas_images.rds
    Ignored:    data/PancreasData/pancreas_masks.rds
    Ignored:    data/PancreasData/pancreas_sce.rds

Unstaged changes:
    Modified:   analysis/04-Figure_1.Rmd
    Modified:   analysis/04-Figure_S3.Rmd

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.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/about.Rmd) and HTML (docs/about.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html 2684af4 Nils Eling 2020-09-22 Added htmls after commiting .Rmds
html 3c9f49b Nils Eling 2020-09-22 Recompiled from new docker
html 05d2b29 Nils Eling 2020-09-14 Recompiled htmls
html 8e6633e Nils Eling 2020-08-21 Recompiled scripts
Rmd 765d0e9 nilseling 2020-08-21 Moved image
html 765d0e9 nilseling 2020-08-21 Moved image
html 9ddf2b3 Nils Eling 2020-08-20 Recompiled from with docker container
Rmd 5ff8ee7 nilseling 2020-07-22 Added info to website
html 5ff8ee7 nilseling 2020-07-22 Added info to website
Rmd 5476a61 nilseling 2020-07-08 Recompiled files
html 5476a61 nilseling 2020-07-08 Recompiled files
html 2fbf302 nilseling 2020-06-02 Re-compiled htmls
html 7960caa nilseling 2020-06-02 Compiled htmls of preprocessing steps
Rmd 12bdedf nilseling 2020-06-02 Start workflowr project.

Highly-multiplexed imaging acquires the spatial expression of multiple biological molecules such as RNA and proteins (here referred to as “markers”). Such data is usually represented as multi-channel images where each channel contains the pixel-intensities of individual molecules. A common step in multiplexed imaging analysis is image segmentation to obtain the outlines of individual cells and quantify cell-specific features such as marker expression, spatial location and morphology. Multiple GUI-based tools, such as histoCAT, giotto, cytokit, ImaCytE and CytoMAP, have been developed to quide users through multiplexed imaging analyses. Those tools however lack full reproducibility and are limited by a small set of selected algortihms. Here, we present cytomapper, a Bioconductor package for visualizing highly-multiplexed imaging data. The cytomapper package builds up on the EBImage functionality to visualize imaging data in R and the SingleCellExperiment container to store cell-specific expression and metadata.

Fig1

Getting help

For a full documentation, please refer to the vignette (vignette("cytomapper")) or the individual help functions (e.g. ?plotCells).

Basic functionailty

The cytomapper package offers three main functions: plotCells, plotPixels and cytomapperShiny

The plotCells function

The plotCells function takes at least a CytoImageList object to visualize segmentation masks. When additionally providing a SingleCellExperiment object, masks can be coloured based on the cells’ expression or their metadata (e.g. cell-type). The cytomapper package supports subsetting of the SingleCellExperiment object to only visualize a selected subset of cells.

The plotPixels function

The plotPixels function requires at least a multi-channel image CytoImageList object. By default, the first marker is visualized. Displayed markers can be selected by by setting colour_by to the entries in channelNames(images). Furthermore, a SingleCellExperiment object and a CytoImageList segmentation mask object can be supplied to outline cells on images.

The cytomapperShiny function

The cytomapper package provides a Shiny app, which can be used to hierarchically gate cells based on their expression and to visualize selected cells on images. This approach can be useful to robustly label cells based on their cell-type. A classifier can be trained using these labels and classify the remaining cells.


sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.1 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5      rstudioapi_0.13 whisker_0.4     knitr_1.30     
 [5] magrittr_2.0.1  R6_2.5.0        rlang_0.4.8     stringr_1.4.0  
 [9] tools_4.0.3     xfun_0.19       git2r_0.27.1    htmltools_0.5.0
[13] ellipsis_0.3.1  rprojroot_2.0.2 yaml_2.2.1      digest_0.6.27  
[17] tibble_3.0.4    lifecycle_0.2.0 crayon_1.3.4    later_1.1.0.1  
[21] vctrs_0.3.5     promises_1.1.1  fs_1.5.0        glue_1.4.2     
[25] evaluate_0.14   rmarkdown_2.5   stringi_1.5.3   compiler_4.0.3 
[29] pillar_1.4.6    httpuv_1.5.4    pkgconfig_2.0.3