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Title:

Apply Normalization Methods to Multiplexed Images

Version:

1.0.3

Description:

Implements methods to normalize multiplexed imaging data, including statistical metrics and visualizations to quantify technical variation in this data type. Reference for methods listed here: Harris, C., Wrobel, J., & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging Data. Journal of Open Source Software, 7(71), 4180, <doi:10.21105/joss.04180>.

License:

MIT + file LICENSE

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.1.2

Depends:

R (≥ 3.5.0)

Suggests:

knitr, janitor (≥ 2.1.0), testthat (≥ 3.0.0), rmarkdown, markdown, covr

Config/testthat/edition:

Config/reticulate:

list( packages = list( list(package = "scikit-image") ) )

Imports:

magrittr, dplyr, rlang, fda, stats, data.table, reticulate, caret, e1071, ggplot2, tidyr, uwot, lme4, stringr, KernSmooth, kSamples, psych, fossil, glue, methods

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2023-05-01 16:19:08 UTC; colemanharris

Author:

Coleman Harris

[aut, cre]

Maintainer:

Coleman Harris <coleman.r.harris@vanderbilt.edu>

Repository:

CRAN

Date/Publication:

2023-05-01 17:10:02 UTC

Generates mx_dataset

Description

Takes in data from data.frame of cell-level multiplexed data to create a mx_dataset S3 object.

Usage

mx_dataset(data, slide_id, image_id, marker_cols, metadata_cols = NULL)

Arguments

data

multiplexed data to normalize. Data assumed to be a data.frame with cell-level data.

slide_id

String slide identifier of input data. This must be a column in the data data.frame.

image_id

String image identifier of input data. This must be a column in the data data.frame.

marker_cols

vector of column name(s) in data corresponding to marker values.

metadata_cols

other identifiers of the input data (default=NULL). This must be a vector of column name(s) in the data data.frame.

Value

data.frame object in the mx_dataset format with attribute for input type

Examples

mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
  c("marker1_vals","marker2_vals","marker3_vals"),
  c("metadata1_vals"))

Normalizes multiplexed data

Description

Normalizes some given image input according to the method specified

Usage

mx_normalize(
  mx_data,
  transform = "None",
  method = "None",
  method_override = NULL,
  method_override_name = NULL,
  ...
)

Arguments

mx_data

mx_dataset object to normalize

transform

transformation to perform on the input data. Options include: c("None", "log10", "mean_divide","log10_mean_divide")

method

normalization method to perform on the input data. Options include: c("None", "ComBat","Registration")

method_override

optional user-defined function to perform own normalization method (default=NULL). If using a user-defined function, it must include a mx_data parameter.

method_override_name

optional name for method_override (default=NULL).

...

optional additional arguments for normalization functions

Value

Multiplexed data normalized according to the method specified, in the mx_dataset format. Normalized data will be included a new table with normalized values and attributes describing the transformation.

Examples

mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
  c("marker1_vals","marker2_vals","marker3_vals"),
  c("metadata1_vals"))
mx_data = mx_normalize(mx_data, transform="log10",method="None")

Sample multiplexed dataset for `mxnorm`.

Description

A simulated multiplexed dataset containing: 4 slides, with 3 images each, and 3 markers, and 1 metadata column.

Usage

mx_sample

Format

A data frame with 3000 rows and 6 variables:

slide_id: slide identifier
image_id: image identifier
marker1_vals: simulated marker 1 values
marker2_vals: simulated marker 2 values
marker3_vals: simulated marker 3 values
metadata1_vals: simulated metadata 1 values

...

Visualize marker density before/after normalization by marker and slide

Description

Visualize marker density before/after normalization by marker and slide

Usage

plot_mx_density(mx_data)

Arguments

mx_data

mx_dataset object that been used with run_otsu_discordance() to compute Otsu discordance scores (necessary for the density rug plot). Note that the table attribute must be set when running run_otsu_discordance().

Value

ggplot2 object with density plot

Examples

mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
  c("marker1_vals","marker2_vals","marker3_vals"),
  c("metadata1_vals"))
mx_data = mx_normalize(mx_data, transform="log10",method="None")
## using `threshold_override` here in case users haven't installed `scikit-image`
mx_data = run_otsu_discordance(mx_data, table="normalized",
threshold_override = function(thold_data){quantile(thold_data, 0.5)})
plot_mx_density(mx_data)

Visualize Otsu discordance scores by marker and slide

Description

Visualize Otsu discordance scores by marker and slide

Usage

plot_mx_discordance(mx_data)

Arguments

mx_data

mx_dataset object that been used with run_otsu_discordance() to compute Otsu discordance scores. Note that the table attribute must be set when running run_otsu_discordance().

Value

ggplot2 object with Otsu discordance scores plot

Examples

mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
  c("marker1_vals","marker2_vals","marker3_vals"),
  c("metadata1_vals"))
mx_data = mx_normalize(mx_data, transform="log10",method="None")
## using `threshold_override` here in case users haven't installed `scikit-image`
mx_data = run_otsu_discordance(mx_data, table="normalized",
threshold_override = function(thold_data){quantile(thold_data, 0.5)})
plot_mx_discordance(mx_data)

Visualize variance proportions by marker and table

Description

Visualize variance proportions by marker and table

Usage

plot_mx_proportions(mx_data)

Arguments

mx_data

mx_dataset object that been used with run_var_proportions() to run random effects modeling. Note that the table attribute must be set when running run_var_proportions().

Value

ggplot2 object with proportions plot

Examples

mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
  c("marker1_vals","marker2_vals","marker3_vals"),
  c("metadata1_vals"))
mx_data = mx_normalize(mx_data, transform="log10",method="None")
mx_data = run_var_proportions(mx_data, table="both")
plot_mx_proportions(mx_data)

Visualize UMAP dimension reduction algorithm

Description

Visualize UMAP dimension reduction algorithm

Usage

plot_mx_umap(mx_data, metadata_col = NULL)

Arguments

mx_data

mx_dataset object that been used with run_reduce_umap() to compute the UMAP dimensions for the dataset. Note that the table attribute must be set when running run_reduce_umap().

metadata_col

column denoted in the run_reduce_umap() to change the scale_color attribute of the ggplot (default=NULL)

Value

ggplot2 object with density plot

Examples

mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
  c("marker1_vals","marker2_vals","marker3_vals"),
  c("metadata1_vals"))
mx_data = mx_normalize(mx_data, transform="log10",method="None")
mx_data = run_reduce_umap(mx_data, table="normalized",
c("marker1_vals","marker2_vals","marker3_vals"))
plot_mx_umap(mx_data)

Extension of `print` S3 method to print `summary.mx_dataset` objects

Description

Extension of print S3 method to print summary.mx_dataset objects

Usage

## S3 method for class 'summary.mx_dataset'
print(x, ...)

Arguments

x

summary.mx_dataset object to summarize

...

option for additional params given S3 logic

Examples

mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
  c("marker1_vals","marker2_vals","marker3_vals"),
  c("metadata1_vals"))
print(summary(mx_data))

Calculate Otsu discordance scores using specified threshold for an `mx_dataset` object.

Description

The Otsu discordance analysis quantifies slide-to-slide agreement by summarizing the distance between slide-level Otsu thresholds and the global Otsu threshold for a given marker in a single metric.

Usage

run_otsu_discordance(
  mx_data,
  table,
  threshold_override = NULL,
  plot_out = FALSE,
  ...
)

Arguments

mx_data

mx_dataset object used to compute Otsu discordance scores

table

dataset in mx_data used to compute metrics. Options include: c("raw","normalized","both"), e.g. a y-axis parameter.

threshold_override

optional user-defined function or alternate thresholding algorithm adaptable from Python skimage module filters (Note: not all algorithms in filters adapted). Options include supplying a function or any of the following: c("isodata", "li", "mean", "otsu", "triangle","yen"). More detail available here: https://scikit-image.org/docs/dev/api/skimage.filters.html. If using a user-defined function, it must include a thold_data parameter.

plot_out

boolean to generate Otsu discordance plots (default=FALSE)

...

optional additional arguments for Otsu discordance functions

Value

mx_dataset object with analysis results of Otsu discordance in otsu_data table

References

Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.

Examples

mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
  c("marker1_vals","marker2_vals","marker3_vals"),
  c("metadata1_vals"))
mx_data = mx_normalize(mx_data, transform="log10",method="None")
## using `threshold_override` here in case users haven't installed `scikit-image`
mx_data = run_otsu_discordance(mx_data, table="normalized",
threshold_override = function(thold_data){quantile(thold_data, 0.5)})

Run UMAP dimension reduction algorithm on an `mx_dataset` object.

Description

Run UMAP dimension reduction algorithm on an mx_dataset object.

Usage

run_reduce_umap(
  mx_data,
  table,
  marker_list,
  downsample_pct = 1,
  metadata_cols = NULL
)

Arguments

mx_data

mx_dataset object used to compute UMAP dimensions

table

dataset in mx_data used to compute metrics. Options include: c("raw","normalized","both"), e.g. a y-axis parameter.

marker_list

list of markers in the mx_dataset object to use for UMAP algorithm

downsample_pct

double, optional percentage (0, 1] of sample rows to include when running UMAP algorithm. (default=1)

metadata_cols

other identifiers of the input data (default=NULL). This must be a vector of column name(s) in the mx_dataset object

Value

mx_dataset object with analysis results of UMAP dimension results in umap_data table

Examples

mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
  c("marker1_vals","marker2_vals","marker3_vals"),
  c("metadata1_vals"))
mx_data = mx_normalize(mx_data, transform="log10",method="None")
mx_data = run_reduce_umap(mx_data, table="normalized",
c("marker1_vals","marker2_vals","marker3_vals"))

Run random effects modeling on `mx_dataset` object to determine proportions of variance at the slide level

Description

Run random effects modeling on mx_dataset object to determine proportions of variance at the slide level

Usage

run_var_proportions(
  mx_data,
  table,
  metadata_cols = NULL,
  formula_override = NULL,
  save_models = FALSE,
  ...
)

Arguments

mx_data

mx_dataset object used to compute UMAP dimensions

table

dataset in mx_data used to compute metrics. Options include: c("raw","normalized","both"), e.g. a y-axis parameter.

metadata_cols

other identifiers of the input data to use in the modeling process (default=NULL). This must be a vector of column name(s) in the mx_dataset object

formula_override

String with user-defined formula to use for variance proportions modeling analysis (default=NULL). This will be the RHS of a formula with ⁠marker~⁠ as the LHS.

save_models

Boolean flag to save lme4::lmer() models in a list to the mx_dataset object

...

optional additional arguments for lme4::lmer() modeling

Value

mx_dataset object with modeling results in var_data table

Examples

mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
  c("marker1_vals","marker2_vals","marker3_vals"),
  c("metadata1_vals"))
mx_data = mx_normalize(mx_data, transform="log10",method="None")
mx_data = run_var_proportions(mx_data, table="both")

Extension of `summary` S3 method to summarize `mx_dataset` objects

Description

Extension of summary S3 method to summarize mx_dataset objects

Usage

## S3 method for class 'mx_dataset'
summary(object, ...)

Arguments

object

mx_dataset object to summarize

...

option for additional params given S3 logic

Value

summary.mx_dataset object

Examples

mx_data = mx_dataset(mxnorm::mx_sample, "slide_id", "image_id",
  c("marker1_vals","marker2_vals","marker3_vals"),
  c("metadata1_vals"))
summary(mx_data)

Generates mx_dataset

Description

Usage

Arguments

Value

Examples

Normalizes multiplexed data

Description

Usage

Arguments

Value

Examples

Sample multiplexed dataset for mxnorm.

Description

Usage

Format

Visualize marker density before/after normalization by marker and slide

Description

Usage

Arguments

Value

Examples

Visualize Otsu discordance scores by marker and slide

Description

Usage

Arguments

Value

Examples

Visualize variance proportions by marker and table

Description

Usage

Arguments

Value

Examples

Visualize UMAP dimension reduction algorithm

Description

Usage

Arguments

Value

Examples

Extension of print S3 method to print summary.mx_dataset objects

Description

Usage

Arguments

Examples

Calculate Otsu discordance scores using specified threshold for an mx_dataset object.

Description

Usage

Arguments

Value

References

Examples

Run UMAP dimension reduction algorithm on an mx_dataset object.

Description

Usage

Arguments

Value

Examples

Run random effects modeling on mx_dataset object to determine proportions of variance at the slide level

Description

Usage

Arguments

Value

Examples

Extension of summary S3 method to summarize mx_dataset objects

Description

Usage

Arguments

Value

Examples

Sample multiplexed dataset for `mxnorm`.

Extension of `print` S3 method to print `summary.mx_dataset` objects

Calculate Otsu discordance scores using specified threshold for an `mx_dataset` object.

Run UMAP dimension reduction algorithm on an `mx_dataset` object.

Run random effects modeling on `mx_dataset` object to determine proportions of variance at the slide level

Extension of `summary` S3 method to summarize `mx_dataset` objects