test_diff: Identify differentially abundant proteins
In const-ae/proDA: Differential Abundance Analysis of Label-Free Mass Spectrometry Data
test_diff R Documentation
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)
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
See Also
The contrast argument is inspired by
limma::makeContrasts.
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)
const-ae/proDA documentation built on Oct. 31, 2023, 9:39 p.m.
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| test_diff | R Documentation |
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)
Arguments
fit |
an object of class 'proDAFit'. Usually, this is
produced by calling |
contrast |
an expression or a string specifying which
contrast is tested. It can be a single coefficient (to see
the available options use |
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 |
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: |
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 |
sort_by |
a string that specifies the column that is used
to sort the resulting data.frame. Default: |
decreasing |
a boolean to indicate if the order is reversed.
Default: |
n_max |
the maximum number of rows returned by the method.
Default: |
verbose |
boolean that signals if the method prints informative
messages. Default: |
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
diffdivided by the standard errorse- 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
See Also
The contrast argument is inspired by
limma::makeContrasts.
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)
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