Package 'proDA' reference manual

Title:	Differential Abundance Analysis of Label-Free Mass Spectrometry Data
Description:	Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins.
Authors:	Constantin Ahlmann-Eltze [aut, cre] , Simon Anders [ths]
Maintainer:	Constantin Ahlmann-Eltze <[email protected]>
License:	GPL-3
Version:	1.21.0
Built:	2024-12-17 03:14:02 UTC
Source:	https://github.com/bioc/proDA

Fluent use of accessor methods

Description

The 'proDAFit' object overwrites the dollar function to make it easy to call functions to access values inside the object. This has the advantage that it is very easy to discover the relevant methods but nonetheless have an isolated implementation. Unlike the `@` operator which directly accesses the underlying implementation, the `$` operator only exposes a limited set of functions

abundances
hyper_parameters
feature_parameters
coefficients
convergence
design
reference_level
result_names
coefficient_variance_matrices
colData
rowData

Usage

## S3 method for class 'proDAFit'
.DollarNames(x, pattern = "")

## S4 method for signature 'proDAFit'
x$name

## S4 replacement method for signature 'proDAFit'
x$name <- value
## S3 method for class 'proDAFit'
.DollarNames(x, pattern = "")

## S4 method for signature 'proDAFit'
x$name

## S4 replacement method for signature 'proDAFit'
x$name <- value

Arguments

`x`	an object of class 'proDAFit' produced by `proDA()`
`pattern`	the regex pattern that is provided by the IDE
`name`	one of the functions listed above
`value`	Warning: modifying the content of a 'proDAFit' object is not allowed

Value

whatever the function called name returns.

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)

  # The two styles are identical
  design(fit)
  fit$design

  # More functions
  fit$abundances



syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)

  # The two styles are identical
  design(fit)
  fit$design

  # More functions
  fit$abundances

Get the abundance matrix

Description

Get the abundance matrix

Usage

abundances(object, ...)
abundances(object, ...)

Arguments

`object`	the object to get from
`...`	additional arguments used by the concrete implementation

Value

the original matrix that was fitted

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  abundances(fit)

syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  abundances(fit)

Get different features and elements of the 'proDAFit' object

Description

The functions listed below can all be accessed using the fluent dollar notation (ie. fit$abundances[1:3,1:3]) without any additional parentheses.

Usage

## S4 method for signature 'proDAFit'
abundances(object)

## S4 method for signature 'proDAFit'
design(object, formula = FALSE)

## S4 method for signature 'proDAFit'
hyper_parameters(object)

## S4 method for signature 'proDAFit'
feature_parameters(object)

## S4 method for signature 'proDAFit'
coefficients(object)

## S4 method for signature 'proDAFit'
coefficient_variance_matrices(object)

## S4 method for signature 'proDAFit'
reference_level(object)

## S4 method for signature 'proDAFit'
convergence(object)
## S4 method for signature 'proDAFit'
abundances(object)

## S4 method for signature 'proDAFit'
design(object, formula = FALSE)

## S4 method for signature 'proDAFit'
hyper_parameters(object)

## S4 method for signature 'proDAFit'
feature_parameters(object)

## S4 method for signature 'proDAFit'
coefficients(object)

## S4 method for signature 'proDAFit'
coefficient_variance_matrices(object)

## S4 method for signature 'proDAFit'
reference_level(object)

## S4 method for signature 'proDAFit'
convergence(object)

Arguments

`object`	the 'proDAFit' object
`formula`	specific argument for the `design` function to get the formula that was used to create the linear model. If no formula was used `NULL` is returned.

Value

See the documentation of the generics to find out what each method returns

Get the coefficients

Description

Get the coefficients

Usage

coefficient_variance_matrices(object, ...)
coefficient_variance_matrices(object, ...)

Arguments

`object`	the object to get from
`...`	additional arguments used by the concrete implementation

Value

a list with as many entries as rows in the data. Each element is a p*p matrix

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  coefficient_variance_matrices(fit)

syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  coefficient_variance_matrices(fit)

Get the coefficients

Description

Get the coefficients

Usage

coefficients(object, ...)
coefficients(object, ...)

Arguments

`object`	the object to get from
`...`	additional arguments used by the concrete implementation

Value

a numeric matrix of size 'nrow(fit) * p'

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  coefficients(fit)

syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  coefficients(fit)

Get the convergence information

Description

Get the convergence information

Usage

convergence(object, ...)
convergence(object, ...)

Arguments

`object`	the object to get from
`...`	additional arguments used by the concrete implementation

Value

a list with information on the convergence

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  convergence(fit)

syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  convergence(fit)

Calculate an approximate distance for 'object'

Description

Calculate an approximate distance for 'object'

Usage

dist_approx(object, ...)
dist_approx(object, ...)

Arguments

`object`	the object for which the distance is approximated
`...`	additional arguments used by the concrete implementation

Value

a list with two elements: 'mean' and 'sd' both are formally of class "dist"

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  dist_approx(fit)

syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  dist_approx(fit)

Distance method for 'proDAFit' object

Description

The method calculates either the euclidean distance between samples or proteins taking into account the missing values and the associated uncertainty. Because with missing value no single deterministic distance can be calculated two objects are returned: the mean and the associated standard deviation of the distance estimates.

Usage

## S4 method for signature 'proDAFit'
dist_approx(object, by_sample = TRUE, blind = TRUE)

## S4 method for signature 'SummarizedExperiment'
dist_approx(object, by_sample = TRUE, blind = TRUE, ...)

## S4 method for signature 'ANY'
dist_approx(object, by_sample = TRUE, blind = TRUE, ...)
## S4 method for signature 'proDAFit'
dist_approx(object, by_sample = TRUE, blind = TRUE)

## S4 method for signature 'SummarizedExperiment'
dist_approx(object, by_sample = TRUE, blind = TRUE, ...)

## S4 method for signature 'ANY'
dist_approx(object, by_sample = TRUE, blind = TRUE, ...)

Arguments

`object`	the 'proDAFit' object for which we calculate the distance or a matrix like object for which 'proDAFit' is created internally
`by_sample`	a boolean that indicates if the distances is calculated between the samples ('by_sample = TRUE') or between the proteins ('by_sample = FALSE'). Default: 'TRUE'
`blind`	fit an intercept model for the missing values to make sure that the results are not biased for the expected result. Default: 'TRUE'
`...`	additional arguments to `proDA()` in case object is a `SummarizedExperiment` or a `matrix`

Value

a list with two elements: 'mean' and 'sd' both are formally of class "dist"

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  dist_approx(fit)

syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  dist_approx(fit)

Get the feature parameters

Description

Get the feature parameters

Usage

feature_parameters(object, ...)
feature_parameters(object, ...)

Arguments

`object`	the object to get from
`...`	additional arguments used by the concrete implementation

Value

a data.frame with information on each fit

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  feature_parameters(fit)

syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  feature_parameters(fit)

Generate a dataset according to the probabilistic dropout model

Description

Generate a dataset according to the probabilistic dropout model

Usage

generate_synthetic_data(
  n_proteins,
  n_conditions = 2,
  n_replicates = 3,
  frac_changed = 0.1,
  dropout_curve_position = 18.5,
  dropout_curve_scale = -1.2,
  location_prior_mean = 20,
  location_prior_scale = 4,
  variance_prior_scale = 0.05,
  variance_prior_df = 2,
  effect_size = 2,
  return_summarized_experiment = FALSE
)
generate_synthetic_data(
  n_proteins,
  n_conditions = 2,
  n_replicates = 3,
  frac_changed = 0.1,
  dropout_curve_position = 18.5,
  dropout_curve_scale = -1.2,
  location_prior_mean = 20,
  location_prior_scale = 4,
  variance_prior_scale = 0.05,
  variance_prior_df = 2,
  effect_size = 2,
  return_summarized_experiment = FALSE
)

Arguments

`n_proteins`	the number of rows in the dataset
`n_conditions`	the number of conditions. Default: 2
`n_replicates`	the number of replicates per condition. Can either be a single number or a vector with `length(n_replicates) == n_conditions`. Default: 3
`frac_changed`	the fraction of proteins that actually differ between the conditions. Default: 0.1
`dropout_curve_position`	the point where the chance to observe a value is 50%. Can be a single number or a vector of `length(dropout_curve_position) == n_conditions * n_replicates`. Default: 18.5
`dropout_curve_scale`	The width of the dropout curve. Negative numbers mean that lower intensities are more likely to be missing. Can be a single number or a vector of `length(dropout_curve_position) == n_conditions * n_replicates`. Default: -1.2
`location_prior_mean`, `location_prior_scale`	the position and the variance around which the individual condition means (`t_mu`) scatter. Default: 20 and 4
`variance_prior_scale`, `variance_prior_df`	the scale and the degrees of freedom of the inverse Chi-squared distribution used as a prior for the variances. Default: 0.05 and 2
`effect_size`	the standard deviation that is used to draw different values for the `frac_changed` part of the proteins. Default: 2
`return_summarized_experiment`	a boolean indicator if the method should return a `SummarizedExperiment` object instead of a list. Default: `FALSE`

Value

a list with the following elements

Y: the intensity matrix including the missing values
Z: the intensity matrix before dropping out values
t_mu: a matrix with n_proteins rows and n_conditions columns that contains the underlying means for each protein
t_sigma2: a vector with the true variances for each protein
changed: a vector with boolean values if the protein is actually changed
group: the group structure mapping samples to conditions

if return_summarized_experiment is FALSE. Otherwise returns a SummarizedExperiment with the same information.

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  names(syn_data)
  head(syn_data$Y)

  # Returning a SummarizedExperiment
  se <- generate_synthetic_data(n_proteins = 10, return_summarized_experiment = TRUE)
  se
  head(SummarizedExperiment::assay(se))

syn_data <- generate_synthetic_data(n_proteins = 10)
  names(syn_data)
  head(syn_data$Y)

  # Returning a SummarizedExperiment
  se <- generate_synthetic_data(n_proteins = 10, return_summarized_experiment = TRUE)
  se
  head(SummarizedExperiment::assay(se))

Get the hyper parameters

Description

Get the hyper parameters

Usage

hyper_parameters(object, ...)
hyper_parameters(object, ...)

Arguments

`object`	the object to get from
`...`	additional arguments used by the concrete implementation

Value

a list with the values for each fitted hyper-parameter

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  hyper_parameters(fit)

syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  hyper_parameters(fit)

Inverse probit function

Description

Calculate the values of the sigmoidal function that is defined by the cumulative normal distribution function (pnorm). This method provides a convenient wrapper for the pnorm that automatically handles negative zeta and is more consistent in its naming.

Usage

invprobit(x, rho, zeta, log = FALSE, oneminus = FALSE)
invprobit(x, rho, zeta, log = FALSE, oneminus = FALSE)

Arguments

`x`	numeric vector
`rho`	numeric vector of length 1 or the same length as x. Specifies the inflection point of the inverse probit curve.
`zeta`	numeric vector of length 1 or the same length as x. Specifies the scale of the curve at the inflection point of the inverse probit curve.
`log`	boolean if the log of the result is returned
`oneminus`	boolean if one minus the result is returned

Value

a numeric vector of length(x).

Examples

 xg <- seq(-5, 5, length.out=101)
 plot(xg, invprobit(xg, rho=-2, zeta=-0.3))
xg <- seq(-5, 5, length.out=101)
 plot(xg, invprobit(xg, rho=-2, zeta=-0.3))

Column wise median normalization of the data matrix

Description

The method calculates for each sample the median change (i.e. the difference between the observed value and the row average) and subtracts it from each row. Missing values are ignored in the procedure. The method is based on the assumption that a majority of the rows did not change.

Usage

median_normalization(X, spike_in_rows = NULL)
median_normalization(X, spike_in_rows = NULL)

Arguments

`X`	a matrix or SummarizedExperiment of proteins and samples
`spike_in_rows`	a numeric or boolean vector that is used to to normalize the intensities across samples. Default: `NULL` which means that all rows are used.

Value

the normalized matrix

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  normalized_data <- median_normalization(syn_data$Y)
  normalized_data
  
  # If we assume that the first 5 proteins are spike-ins
  normalized_data2 <- median_normalization(syn_data$Y, spike_in_rows = 1:5)

syn_data <- generate_synthetic_data(n_proteins = 10)
  normalized_data <- median_normalization(syn_data$Y)
  normalized_data
  
  # If we assume that the first 5 proteins are spike-ins
  normalized_data2 <- median_normalization(syn_data$Y, spike_in_rows = 1:5)

Fit a single linear probabilistic dropout model

Description

The function works similar to the classical lm but with special handling of NA's. Whereas lm usually just ignores response value that are missing, pd_lm applies a probabilistic dropout model, that assumes that missing values occur because of the dropout curve. The dropout curve describes for each position the chance that that a value is missed. A negative dropout_curve_scale means that the lower the intensity was, the more likely it is to miss the value.

Usage

pd_lm(
  formula,
  data = NULL,
  subset = NULL,
  dropout_curve_position,
  dropout_curve_scale,
  location_prior_mean = NULL,
  location_prior_scale = NULL,
  variance_prior_scale = NULL,
  variance_prior_df = NULL,
  location_prior_df = 3,
  method = c("analytic_hessian", "analytic_grad", "numeric"),
  verbose = FALSE
)
pd_lm(
  formula,
  data = NULL,
  subset = NULL,
  dropout_curve_position,
  dropout_curve_scale,
  location_prior_mean = NULL,
  location_prior_scale = NULL,
  variance_prior_scale = NULL,
  variance_prior_df = NULL,
  location_prior_df = 3,
  method = c("analytic_hessian", "analytic_grad", "numeric"),
  verbose = FALSE
)

Arguments

`formula`	a formula that specifies a linear model
`data`	an optional data.frame whose columns can be used to specify the `formula`
`subset`	an optional selection vector for data to subset it
`dropout_curve_position`	the value where the chance to observe a value is 50%. Can either be a single value that is repeated for each row or a vector with one element for each row. Not optional.
`dropout_curve_scale`	the width of the dropout curve. Smaller values mean that the sigmoidal curve is steeper. Can either be a single value that is repeated for each row or a vector with one element for each row. Not optional.
`location_prior_mean`, `location_prior_scale`	the optional mean and variance of the prior around which the predictions are supposed to scatter. If no value is provided no location regularization is applied.
`variance_prior_scale`, `variance_prior_df`	the optional scale and degrees of freedom of the variance prior. If no value is provided no variance regularization is applied.
`location_prior_df`	The degrees of freedom for the t-distribution of the location prior. If it is large (> 30) the prior is approximately Normal. Default: 3
`method`	one of 'analytic_hessian', 'analytic_gradient', or 'numeric'. If 'analytic_hessian' the `nlminb` optimization routine is used, with the hand derived first and second derivative. Otherwise, `optim` either with or without the first derivative is used.
`verbose`	boolean that signals if the method prints informative messages. Default: `FALSE`.

Value

a list with the following entries

coefficients: a named vector with the fitted values
coef_variance_matrix: a p*p matrix with the variance associated with each coefficient estimate
n_approx: the estimated "size" of the data set (n_hat - variance_prior_df)
df: the estimated degrees of freedom (n_hat - p)
s2: the estimated unbiased variance
n_obs: the number of response values that were not 'NA'

Examples

  # Without missing values
  y <- rnorm(5, mean=20)
  lm(y ~ 1)
  pd_lm(y ~ 1,
        dropout_curve_position = NA,
        dropout_curve_scale = NA)

  # With some missing values
  y <- c(23, 21.4, NA)
  lm(y ~ 1)
  pd_lm(y ~ 1,
        dropout_curve_position = 19,
        dropout_curve_scale = -1)


  # With only missing values
  y <- c(NA, NA, NA)
  # lm(y ~ 1)  # Fails
  pd_lm(y ~ 1,
        dropout_curve_position = 19,
        dropout_curve_scale = -1,
        location_prior_mean = 21,
        location_prior_scale = 3,
        variance_prior_scale = 0.1,
        variance_prior_df = 2)


# Without missing values
  y <- rnorm(5, mean=20)
  lm(y ~ 1)
  pd_lm(y ~ 1,
        dropout_curve_position = NA,
        dropout_curve_scale = NA)

  # With some missing values
  y <- c(23, 21.4, NA)
  lm(y ~ 1)
  pd_lm(y ~ 1,
        dropout_curve_position = 19,
        dropout_curve_scale = -1)


  # With only missing values
  y <- c(NA, NA, NA)
  # lm(y ~ 1)  # Fails
  pd_lm(y ~ 1,
        dropout_curve_position = 19,
        dropout_curve_scale = -1,
        location_prior_mean = 21,
        location_prior_scale = 3,
        variance_prior_scale = 0.1,
        variance_prior_df = 2)

Row-wise tests of difference using the probabilistic dropout model

Description

This is a helper function that combines the call of proDA() and test_diff(). If you need more flexibility use those functions.

Usage

pd_row_t_test(
  X,
  Y,
  moderate_location = TRUE,
  moderate_variance = TRUE,
  alternative = c("two.sided", "greater", "less"),
  pval_adjust_method = "BH",
  location_prior_df = 3,
  max_iter = 20,
  epsilon = 0.001,
  return_fit = FALSE,
  verbose = FALSE
)

pd_row_f_test(
  X,
  ...,
  groups = NULL,
  moderate_location = TRUE,
  moderate_variance = TRUE,
  pval_adjust_method = "BH",
  location_prior_df = 3,
  max_iter = 20,
  epsilon = 0.001,
  return_fit = FALSE,
  verbose = FALSE
)
pd_row_t_test(
  X,
  Y,
  moderate_location = TRUE,
  moderate_variance = TRUE,
  alternative = c("two.sided", "greater", "less"),
  pval_adjust_method = "BH",
  location_prior_df = 3,
  max_iter = 20,
  epsilon = 0.001,
  return_fit = FALSE,
  verbose = FALSE
)

pd_row_f_test(
  X,
  ...,
  groups = NULL,
  moderate_location = TRUE,
  moderate_variance = TRUE,
  pval_adjust_method = "BH",
  location_prior_df = 3,
  max_iter = 20,
  epsilon = 0.001,
  return_fit = FALSE,
  verbose = FALSE
)

Arguments

`X`, `Y`, `...`	the matrices for condition 1, 2 and so on. They must have the same number of rows.
`moderate_location`	boolean values to indicate if the location and the variances are moderated. Default: `TRUE`
`moderate_variance`	boolean values to indicate if the location and the variances are moderated. Default: `TRUE`
`alternative`	a string that decides how the hypothesis test is done. This parameter is only relevant for the Wald-test specified using the 'contrast' argument. Default: `"two.sided"`
`pval_adjust_method`	a string the indicates the method that is used to adjust the p-value for the multiple testing. It must match the options in `p.adjust`. Default: `"BH"`
`location_prior_df`	the number of degrees of freedom used for the location prior. A large number (> 30) means that the prior is approximately Normal. Default: `3`
`max_iter`	the maximum of iterations `proDA()` tries to converge to the hyper-parameter estimates. Default: `20`
`epsilon`	if the remaining error is smaller than `epsilon` the model has converged. Default: `1e-3`
`return_fit`	boolean that signals that in addition to the data.frame with the hypothesis test results, the fit from `proDA()` is returned. Default: `FALSE`
`verbose`	boolean that signals if the method prints messages during the fitting. Default: `FALSE`
`groups`	a factor or character vector with that assignes the columns of `X` to different conditions. This parameter is only applicable for the F-test and must be specified if only a single matrix is provided.

Details

The pd_row_t_test is not actually doing a t-test, but rather a Wald test. But, as the two are closely related and term t-test is more widely understood, we choose to use that name.

Value

If return_fit == FALSE a data.frame is returned with the content that is described in test_diff.

If return_fit == TRUE a list is returned with two elements: fit with a reference to the object returned from proDA() and a test_result() with the data.frame returned from test_diff().

Examples

  data1 <- matrix(rnorm(10 * 3), nrow=10)
  data2 <- matrix(rnorm(10 * 4), nrow=10)
  data3 <- matrix(rnorm(10 * 2), nrow=10)

  # Comparing two datasets
  pd_row_t_test(data1, data2)

  # Comparing multiple datasets
  pd_row_f_test(data1, data2, data3)

  # Alternative
  data_comb <- cbind(data1, data2, data3)
  pd_row_f_test(data_comb,
     groups = c(rep("A",3), rep("B", 4), rep("C", 2)))

  # t.test, lm, pd_row_t_test, and pd_row_f_test are
  # approximately equivalent on fully observed data
  set.seed(1)
  x <- rnorm(5)
  y <- rnorm(5, mean=0.3)

  t.test(x, y)
  summary(lm(c(x, y) ~ cond,
             data = data.frame(cond = c(rep("x", 5),
                                        rep("y", 5)))))$coefficients[2,]
  pd_row_t_test(matrix(x, nrow=1), matrix(y, nrow=1),
                moderate_location = FALSE,
                moderate_variance = FALSE)
  pd_row_f_test(matrix(x, nrow=1), matrix(y, nrow=1),
                moderate_location = FALSE,
                moderate_variance = FALSE)


data1 <- matrix(rnorm(10 * 3), nrow=10)
  data2 <- matrix(rnorm(10 * 4), nrow=10)
  data3 <- matrix(rnorm(10 * 2), nrow=10)

  # Comparing two datasets
  pd_row_t_test(data1, data2)

  # Comparing multiple datasets
  pd_row_f_test(data1, data2, data3)

  # Alternative
  data_comb <- cbind(data1, data2, data3)
  pd_row_f_test(data_comb,
     groups = c(rep("A",3), rep("B", 4), rep("C", 2)))

  # t.test, lm, pd_row_t_test, and pd_row_f_test are
  # approximately equivalent on fully observed data
  set.seed(1)
  x <- rnorm(5)
  y <- rnorm(5, mean=0.3)

  t.test(x, y)
  summary(lm(c(x, y) ~ cond,
             data = data.frame(cond = c(rep("x", 5),
                                        rep("y", 5)))))$coefficients[2,]
  pd_row_t_test(matrix(x, nrow=1), matrix(y, nrow=1),
                moderate_location = FALSE,
                moderate_variance = FALSE)
  pd_row_f_test(matrix(x, nrow=1), matrix(y, nrow=1),
                moderate_location = FALSE,
                moderate_variance = FALSE)

Predict the parameters or values of additional proteins

Description

This function can either predict the abundance matrix for proteins (type = "response") without missing values according to the linear probabilistic dropout model, fitted with proDA(). Or, it can predict the feature parameters for additional proteins given their abundances including missing values after estimating the hyper-parameters on a dataset with the same sample structure (type = "feature_parameters").

Usage

## S4 method for signature 'proDAFit'
predict(
  object,
  newdata,
  newdesign,
  type = c("response", "feature_parameters"),
  ...
)
## S4 method for signature 'proDAFit'
predict(
  object,
  newdata,
  newdesign,
  type = c("response", "feature_parameters"),
  ...
)

Arguments

`object`	an 'proDAFit' object that is produced by `proDA()`.
`newdata`	a matrix or a SummarizedExperiment which contains the new abundances for which values are predicted.
`newdesign`	a formula or design matrix that specifies the new structure that will be fitted
`type`	either "response" or "feature_parameters". Default: `"response"`
`...`	additional parameters for the construction of the 'proDAFit' object.

Details

Note: this method behaves a little different from what one might expect from the classical predict.lm() function, because object is not just a single set of coefficients for one fit, but many fits (ie. one for each protein) with some more hyper-parameters. The classical predict function predicts the response for new samples. This function does not support this, instead it is useful for getting a matrix without missing values for additional proteins.

Value

If type = "response" a matrix with the same dimensions as object. Or, if type = "feature_parameters" a 'proDAFit' object with the same hyper-parameters and column data as object, but new fitted rowData().

Main function to fit the probabilistic dropout model

Description

The function fits a linear probabilistic dropout model and infers the hyper-parameters for the location prior, the variance prior, and the dropout curves. In addition it infers for each protein the coefficients that best explain the observed data and the associated uncertainty.

Usage

proDA(
  data,
  design = ~1,
  col_data = NULL,
  reference_level = NULL,
  data_is_log_transformed = TRUE,
  moderate_location = TRUE,
  moderate_variance = TRUE,
  location_prior_df = 3,
  n_subsample = nrow(data),
  max_iter = 20,
  epsilon = 0.001,
  verbose = FALSE,
  ...
)
proDA(
  data,
  design = ~1,
  col_data = NULL,
  reference_level = NULL,
  data_is_log_transformed = TRUE,
  moderate_location = TRUE,
  moderate_variance = TRUE,
  location_prior_df = 3,
  n_subsample = nrow(data),
  max_iter = 20,
  epsilon = 0.001,
  verbose = FALSE,
  ...
)

Arguments

`data`	a matrix like object (`matrix()`, `SummarizedExperiment()`, or anything that can be cast to `SummarizedExperiment()` (eg. 'MSnSet', 'eSet', ...)) with one column per sample and one row per protein. Missing values should be coded as `NA`.
`design`	a specification of the experimental design that is used to fit the linear model. It can be a `model.matrix()` with one row for each sample and one column for each coefficient. It can also be a formula with the entries referring to global objects, columns in the `col_data` argument or columns in the `colData(data)` if data is a `SummarizedExperiment`. Thirdly, it can be a vector that for each sample specifies the condition of that sample. Default: `~ 1`, which means that all samples are treated as if they are in the same condition.
`col_data`	a data.frame with one row for each sample in `data`. Default: `NULL`
`reference_level`	a string that specifies which level in a factor coefficient is used for the intercept. Default: `NULL`
`data_is_log_transformed`	the raw intensities from mass spectrometry experiments have a linear mean-variance relation. This is undesirable and can be removed by working on the log scale. The easiest way to find out if the data is already log- transformed is to see if the intensities are in the range of '0' to '100' in which case they are transformed or if they rather are between '1e5' to '1e12', in which case they are not. Default: `TRUE`
`moderate_location`, `moderate_variance`	boolean values to indicate if the location and the variances are moderated. Default: `TRUE`
`location_prior_df`	the number of degrees of freedom used for the location prior. A large number (> 30) means that the prior is approximately Normal. Default: `3`
`n_subsample`	the number of proteins that are used to estimate the hyper-parameter. Reducing this number can speed up the fitting, but also mean that the final estimate is less precise. By default all proteins are used. Default: `nrow(data)`
`max_iter`	the maximum of iterations `proDA()` tries to converge to the hyper-parameter estimates. Default: `20`
`epsilon`	if the remaining error is smaller than `epsilon` the model has converged. Default: `1e-3`
`verbose`	boolean that signals if the method prints messages during the fitting. Default: `FALSE`
`...`	additional parameters for the construction of the 'proDAFit' object

Details

By default, the method is moderating the locations and the variance of each protein estimate. The variance moderation is fairly standard in high-throughput experiments and can boost the power to detect differentially abundant proteins. The location moderation is important to handle the edge case where in one condition a protein is not observed in any sample. In addition it can help to get more precise estimates of the difference between conditions. Unlike 'DESeq2', which moderates the coefficient estimates (ie. the "betas") to be centered around zero, 'proDA' penalizes predicted intensities that strain far from the other observed intensities.

Value

An object of class 'proDAFit'. The object contains information on the hyper-parameters and feature parameters, the convergence, the experimental design etc. Internally, it is a sub-class of SummarizedExperiment which means the object is subsettable. The '$'-operator is overloaded for this object to make it easy to discover applicable functions.

Examples


# Quick start

# Import the proDA package if you haven't already done so
# library(proDA)
set.seed(1)
syn_data <- generate_synthetic_data(n_proteins = 10)
fit <- proDA(syn_data$Y, design = syn_data$groups)
fit
result_names(fit)
test_diff(fit, Condition_1 - Condition_2)

# SummarizedExperiment
se <- generate_synthetic_data(n_proteins = 10,
                     return_summarized_experiment = TRUE)
se
proDA(se, design = ~ group)

# Design using model.matrix()
data_mat <- matrix(rnorm(5 * 10), nrow=10)
colnames(data_mat) <- paste0("sample", 1:5)
annotation_df <- data.frame(names = paste0("sample", 1:5),
                     condition = c("A", "A", "A", "B", "B"),
                     age = rnorm(5, mean=40, sd=10))

design_mat <- model.matrix(~ condition + age,
                           data=annotation_df)
design_mat
proDA(data_mat, design_mat, col_data = annotation_df)


# Quick start

# Import the proDA package if you haven't already done so
# library(proDA)
set.seed(1)
syn_data <- generate_synthetic_data(n_proteins = 10)
fit <- proDA(syn_data$Y, design = syn_data$groups)
fit
result_names(fit)
test_diff(fit, Condition_1 - Condition_2)

# SummarizedExperiment
se <- generate_synthetic_data(n_proteins = 10,
                     return_summarized_experiment = TRUE)
se
proDA(se, design = ~ group)

# Design using model.matrix()
data_mat <- matrix(rnorm(5 * 10), nrow=10)
colnames(data_mat) <- paste0("sample", 1:5)
annotation_df <- data.frame(names = paste0("sample", 1:5),
                     condition = c("A", "A", "A", "B", "B"),
                     age = rnorm(5, mean=40, sd=10))

design_mat <- model.matrix(~ condition + age,
                           data=annotation_df)
design_mat
proDA(data_mat, design_mat, col_data = annotation_df)

proDA: Identify differentially abundant proteins in label-free mass spectrometry

Description

Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins.

proDA Class Definition

Description

proDA Class Definition

Get the reference level

Description

Get the reference level

Usage

reference_level(object, ...)
reference_level(object, ...)

Arguments

`object`	the object to get from
`...`	additional arguments used by the concrete implementation

Value

a string

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups, reference_level = "Condition_1")
  reference_level(fit)

syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups, reference_level = "Condition_1")
  reference_level(fit)

Get the result_names

Description

Get the result_names

Usage

result_names(fit, ...)
result_names(fit, ...)

Arguments

`fit`	the fit to get the result_names from
`...`	additional arguments used by the concrete implementation

Value

a character vector

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  result_names(fit)

syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  result_names(fit)

Identify differentially abundant proteins

Description

The ‘test_diff()' function is used to test coefficients of a ’proDAFit' object. It provides a Wald test to test individual coefficients and a likelihood ratio F-test to compare the original model with a reduced model. The result_names method provides a quick overview which coefficients are available for testing.

Usage

test_diff(
  fit,
  contrast,
  reduced_model = ~1,
  alternative = c("two.sided", "greater", "less"),
  pval_adjust_method = "BH",
  sort_by = NULL,
  decreasing = FALSE,
  n_max = Inf,
  verbose = FALSE
)

## S4 method for signature 'proDAFit'
result_names(fit)
test_diff(
  fit,
  contrast,
  reduced_model = ~1,
  alternative = c("two.sided", "greater", "less"),
  pval_adjust_method = "BH",
  sort_by = NULL,
  decreasing = FALSE,
  n_max = Inf,
  verbose = FALSE
)

## S4 method for signature 'proDAFit'
result_names(fit)

Arguments

`fit`	an object of class 'proDAFit'. Usually, this is produced by calling `proDA()`
`contrast`	an expression or a string specifying which contrast is tested. It can be a single coefficient (to see the available options use `result_names(fit)`) or any linear combination of them. The contrast is always compared against zero. Thus, to find out if two coefficients differ use `coef1 - coef2`. Remember if the coefficient is not a valid identifier in R, to escape it using back ticks. For example if you test the interaction of A and B use `A:B`.
`reduced_model`	If you don't want to test an individual coefficient, you can can specify a reduced model and compare it with the original model using an F-test. This is useful to find out how a set of parameters affect the goodness of the fit. If neither a `contrast`, nor a `reduced_model` is specified, by default a comparison with an intercept model (ie. just the average across conditions) is done. Default: `~ 1`.
`alternative`	a string that decides how the hypothesis test is done. This parameter is only relevant for the Wald-test specified using the 'contrast' argument. Default: `"two.sided"`
`pval_adjust_method`	a string the indicates the method that is used to adjust the p-value for the multiple testing. It must match the options in `p.adjust`. Default: `"BH"`
`sort_by`	a string that specifies the column that is used to sort the resulting data.frame. Default: `NULL` which means the result is sorted by the order of the input matrix.
`decreasing`	a boolean to indicate if the order is reversed. Default: `FALSE`
`n_max`	the maximum number of rows returned by the method. Default: `Inf`
`verbose`	boolean that signals if the method prints informative messages. Default: `FALSE`.

Details

To test if coefficient is different from zero with a Wald test use the contrast function argument. To test if two models differ with an F-test use the reduced_model argument. Depending on the test that is conducted, the functions returns slightly different data.frames.

The function is designed to follow the principles of the base R test functions (ie. t.test and wilcox.test) and the functions designed for collecting the results of high-throughput testing (ie. limma::topTable and DESeq2::results).

Value

The 'result_names()' function returns a character vector.

The 'test_diff()' function returns a data.frame with one row per protein with the key parameters of the statistical test. Depending what kind of test (Wald or F test) the content of the 'data.frame' differs.

The Wald test, which can considered equivalent to a t-test, returns a 'data.frame' with the following columns:

name: the name of the protein, extracted from the rowname of the input matrix
pval: the p-value of the statistical test
adj_pval: the multiple testing adjusted p-value
diff: the difference that particular coefficient makes. In differential expression analysis this value is also called log fold change, which is equivalent to the difference on the log scale.
t_statistic: the diff divided by the standard error se
se: the standard error associated with the diff
df: the degrees of freedom, which describe the amount of available information for estimating the se. They are the sum of the number of samples the protein was observed in, the amount of information contained in the missing values, and the degrees of freedom of the variance prior.
avg_abundance: the estimate of the average abundance of the protein across all samples.
n_approx: the approximated information available for estimating the protein features, expressed as multiple of the information contained in one observed value.
n_obs: the number of samples a protein was observed in

The F-test returns a 'data.frame' with the following columns

name: the name of the protein, extracted from the rowname of the input matrix
pval: the p-value of the statistical test
adj_pval: the multiple testing adjusted p-value
f_statistic: the ratio of difference of normalized deviances from original model and the reduced model, divided by the standard deviation.
df1: the difference of the number of coefficients in the original model and the number of coefficients in the reduced model
df2: the degrees of freedom, which describe the amount of available information for estimating the se. They are the sum of the number of samples the protein was observed in, the amount of information contained in the missing values, and the degrees of freedom of the variance prior.
avg_abundance: the estimate of the average abundance of the protein across all samples.
n_approx: the information available for estimating the protein features, expressed as multiple of the information contained in one observed value.
n_obs: the number of samples a protein was observed in

Examples

  # "t-test"
  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  result_names(fit)
  test_diff(fit, Condition_1 - Condition_2)


  suppressPackageStartupMessages(library(SummarizedExperiment))
  se <- generate_synthetic_data(n_proteins = 10,
                                n_conditions = 3,
                                return_summarized_experiment = TRUE)
  colData(se)$age <- rnorm(9, mean=45, sd=5)
  colData(se)
  fit <- proDA(se, design = ~ group + age)
  result_names(fit)
  test_diff(fit, "groupCondition_2",
            n_max = 3, sort_by = "pval")

  # F-test
  test_diff(fit, reduced_model = ~ group)


# "t-test"
  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  result_names(fit)
  test_diff(fit, Condition_1 - Condition_2)


  suppressPackageStartupMessages(library(SummarizedExperiment))
  se <- generate_synthetic_data(n_proteins = 10,
                                n_conditions = 3,
                                return_summarized_experiment = TRUE)
  colData(se)$age <- rnorm(9, mean=45, sd=5)
  colData(se)
  fit <- proDA(se, design = ~ group + age)
  result_names(fit)
  test_diff(fit, "groupCondition_2",
            n_max = 3, sort_by = "pval")

  # F-test
  test_diff(fit, reduced_model = ~ group)

Package 'proDA'

Help Index

Fluent use of accessor methods

Description

Usage

Arguments

Value

See Also

Examples

Get the abundance matrix

Description

Usage

Arguments

Value

See Also

Examples

Get different features and elements of the 'proDAFit' object

Description

Usage

Arguments

Value

Get the coefficients

Description

Usage

Arguments

Value

See Also

Examples

Get the coefficients

Description

Usage

Arguments

Value

See Also

Examples

Get the convergence information

Description

Usage

Arguments

Value

See Also

Examples

Calculate an approximate distance for 'object'

Description

Usage

Arguments

Value

See Also

Examples

Distance method for 'proDAFit' object

Description

Usage

Arguments

Value

Examples

Get the feature parameters

Description

Usage

Arguments

Value

See Also

Examples

Generate a dataset according to the probabilistic dropout model

Description

Usage

Arguments

Value

Examples

Get the hyper parameters

Description

Usage

Arguments

Value

See Also

Examples

Inverse probit function

Description

Usage

Arguments

Value