Package 'mxnorm'

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)

---