Apply Normalization Methods to Multiplexed Images.

mxnorm: An R package to normalize multiplexed imaging data.

A package designed to handle multiplexed imaging data in R, implementing normalization methods and quality metrics detailed in our paper here. Further information about the package, usage, the vignettes, and more can be found on CRAN.

Installation

To install from CRAN, use:

install.packages("mxnorm")

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("ColemanRHarris/mxnorm")

Dependencies

This package imports lme4 (and its dependency nloptr) which use CMake to build the packages. To install CMake, please see here or select from the following:

- yum install cmake          (Fedora/CentOS; inside a terminal)
- apt install cmake          (Debian/Ubuntu; inside a terminal).
- pacman -S cmake            (Arch Linux; inside a terminal).
- brew install cmake         (MacOS; inside a terminal with Homebrew)
- port install cmake         (MacOS; inside a terminal with MacPorts)

This package also uses the reticulate package to interface with the scikit-learn Python package. Depending on the user’s environment, sometimes Python/conda/Miniconda is not detected, producing an option like the following:

No non-system installation of Python could be found.
Would you like to download and install Miniconda?
Miniconda is an open source environment management system for Python.
See https://docs.conda.io/en/latest/miniconda.html for more details.

Would you like to install Miniconda? [Y/n]: 

In this case, installing Miniconda within the R environment will ensure that both Python and the scikit-image package are properly installed. However, if you want to use a separate Python installation, please respond N to this prompt and use reticulate::py_config() to setup your Python environment. Please also ensure that scikit-image is installed in your desired Python environment via pip install scikit-image.

Community Guidelines

Please report any issues, bugs, or problems with the software here: https://github.com/ColemanRHarris/mxnorm/issues. For any contributions, feel free to fork the package repository on GitHub or submit pull requests. Any other contribution questions and requests for support can be directed to the package maintainer Coleman Harris ([email protected]).

Analysis Example

This is a basic example using the mx_sample dataset, which is simulated data to demonstrate the package’s functionality with slide effects.

library(mxnorm)
head(mx_sample)
#>   slide_id image_id marker1_vals marker2_vals marker3_vals metadata1_vals
#> 1   slide1   image1           15           17           28            yes
#> 2   slide1   image1           11           22           31             no
#> 3   slide1   image1           12           16           22            yes
#> 4   slide1   image1           11           19           33            yes
#> 5   slide1   image1           12           21           24            yes
#> 6   slide1   image1           11           17           19            yes

mx_dataset objects

How to build the mx_dataset object with mx_sample data in the mxnorm package:

mx_dataset = mx_dataset(data=mx_sample,
                        slide_id="slide_id",
                        image_id="image_id",
                        marker_cols=c("marker1_vals","marker2_vals","marker3_vals"),
                        metadata_cols=c("metadata1_vals"))

We can use the built-in summary() function to observe mx_dataset object:

summary(mx_dataset)
#> Call:
#> `mx_dataset` object with 4 slide(s), 3 marker column(s), and 1 metadata column(s)

Normalization with mx_normalize()

And now we can normalize this data using the mx_normalize() function:

mx_norm = mx_normalize(mx_data = mx_dataset,
                       transform = "log10_mean_divide",
                       method="None")

And we again use summary() to capture the following attributes for the mx_dataset object:

summary(mx_norm)
#> Call:
#> `mx_dataset` object with 4 slide(s), 3 marker column(s), and 1 metadata column(s)
#> 
#> Normalization:
#> Data normalized with transformation=`log10_mean_divide` and method=`None`
#> 
#> Anderson-Darling tests:
#>       table mean_test_statistic mean_std_test_statistic mean_p_value
#>  normalized              34.565                  24.111            0
#>         raw              32.490                  22.525            0

Otsu discordance scores with run_otsu_discordance()

Using the above normalized data, we can run an Otsu discordance score analysis to determine how well our normalization method performs (here, we look for lower discordance scores to distinguish better performing methods):

mx_otsu = run_otsu_discordance(mx_norm,
                        table="both",
                        threshold_override = NULL,
                        plot_out = FALSE)

We can also begin to visualize these results using some of mxnorm’s plotting features built using ggplot2.

First, we can visualize the densities of the marker values as follows:

plot_mx_density(mx_otsu)

We can also visualize the results of the Otsu misclassification analysis stratified by slide and marker:

plot_mx_discordance(mx_otsu)

UMAP dimension reduction with run_reduce_umap()

We can also use the UMAP algorithm to reduce the dimensions of our markers in the dataset as follows, using the metadata_col parameter for later (e.g., similar to a tissue type in practice with multiplexed data):

mx_umap = run_reduce_umap(mx_otsu,
                        table="both",
                        marker_list = c("marker1_vals","marker2_vals","marker3_vals"),
                        downsample_pct = 0.8,
                        metadata_col = "metadata1_vals")

We can further visualize the results of the UMAP dimension reduction as follows:

plot_mx_umap(mx_umap,metadata_col = "metadata1_vals")

Note that since the sample data is simulated, we don’t see separation of the groups like we would expect with biological samples that have some underlying correlation. What we can observe, however, is the separation of slides in the raw data and subsequent mixing of these slides in the normalized data:

plot_mx_umap(mx_umap,metadata_col = "slide_id")

Variance components analysis with run_var_proportions()

We can also leverage lmer() from the lme4 package to perform random effect modeling on the data to determine how much variance is present at the slide level, as follows:

mx_var = run_var_proportions(mx_umap,
                             table="both",
                             metadata_cols = "metadata1_vals")

And we can use summary() to capture the following attributes for the mx_dataset object:

summary(mx_var)
#> Call:
#> `mx_dataset` object with 4 slide(s), 3 marker column(s), and 1 metadata column(s)
#> 
#> Normalization:
#> Data normalized with transformation=`log10_mean_divide` and method=`None`
#> 
#> Anderson-Darling tests:
#>       table mean_test_statistic mean_std_test_statistic mean_p_value
#>  normalized              34.565                  24.111            0
#>         raw              32.490                  22.525            0
#> 
#> Otsu discordance scores:
#>       table mean_discordance sd_discordance
#>  normalized            0.054          0.071
#>         raw            0.373          0.141
#> 
#> Clustering consistency (UMAP):
#>       table adj_rand_index cohens_kappa
#>  normalized          0.048       -0.083
#>         raw          0.587        0.214
#> 
#> Variance proportions (slide-level):
#>       table  mean    sd
#>  normalized 0.001 0.001
#>         raw 0.940 0.055

And we can also visualize the results of the variance proportions after normalization:

plot_mx_proportions(mx_var)