Description Usage Arguments Details Value Examples
test_differential_abundance() takes as input a 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | and returns a 'tbl' with additional columns for the statistics from the hypothesis test.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | test_differential_abundance(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
.contrasts = NULL,
method = "edgeR_quasi_likelihood",
scaling_method = "TMM",
omit_contrast_in_colnames = FALSE,
prefix = "",
action = "add",
significance_threshold = NULL,
fill_missing_values = NULL
)
## S4 method for signature 'spec_tbl_df'
test_differential_abundance(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
.contrasts = NULL,
method = "edgeR_quasi_likelihood",
scaling_method = "TMM",
omit_contrast_in_colnames = FALSE,
prefix = "",
action = "add",
significance_threshold = NULL,
fill_missing_values = NULL
)
## S4 method for signature 'tbl_df'
test_differential_abundance(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
.contrasts = NULL,
method = "edgeR_quasi_likelihood",
scaling_method = "TMM",
omit_contrast_in_colnames = FALSE,
prefix = "",
action = "add",
significance_threshold = NULL,
fill_missing_values = NULL
)
## S4 method for signature 'tidybulk'
test_differential_abundance(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
.contrasts = NULL,
method = "edgeR_quasi_likelihood",
scaling_method = "TMM",
omit_contrast_in_colnames = FALSE,
prefix = "",
action = "add",
significance_threshold = NULL,
fill_missing_values = NULL
)
## S4 method for signature 'SummarizedExperiment'
test_differential_abundance(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
.contrasts = NULL,
method = "edgeR_quasi_likelihood",
scaling_method = "TMM",
omit_contrast_in_colnames = FALSE,
prefix = "",
action = "add",
significance_threshold = NULL,
fill_missing_values = NULL
)
## S4 method for signature 'RangedSummarizedExperiment'
test_differential_abundance(
.data,
.formula,
.sample = NULL,
.transcript = NULL,
.abundance = NULL,
.contrasts = NULL,
method = "edgeR_quasi_likelihood",
scaling_method = "TMM",
omit_contrast_in_colnames = FALSE,
prefix = "",
action = "add",
significance_threshold = NULL,
fill_missing_values = NULL
)
|
.data |
A 'tbl' formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> | |
.formula |
A formula with no response variable, representing the desired linear model |
.sample |
The name of the sample column |
.transcript |
The name of the transcript/gene column |
.abundance |
The name of the transcript/gene abundance column |
.contrasts |
This parameter takes the shape of the contrast parameter of the method of choice. For edgeR and limma-voom is a character vector. For DESeq2 is a list including a character vectors of length three. If contrasts are not present the first covariate is the one the model is tested against (e.g., ~ factor_of_interest) |
method |
A string character. Either "edgeR_quasi_likelihood" (i.e., QLF), "edgeR_likelihood_ratio" (i.e., LRT), "DESeq2", "limma_voom" |
scaling_method |
A character string. The scaling method passed to the back-end function (i.e., edgeR::calcNormFactors; "TMM","TMMwsp","RLE","upperquartile") |
omit_contrast_in_colnames |
If just one contrast is specified you can choose to omit the contrast label in the colnames. |
prefix |
A character string. The prefix you would like to add to the result columns. It is useful if you want to compare several methods. |
action |
A character string. Whether to join the new information to the input tbl (add), or just get the non-redundant tbl with the new information (get). |
significance_threshold |
A real between 0 and 1 (usually 0.05). |
fill_missing_values |
A boolean. Whether to fill missing sample/transcript values with the median of the transcript. This is rarely needed. |
maturing
This function provides the option to use edgeR https://doi.org/10.1093/bioinformatics/btp616, limma-voom https://doi.org/10.1186/gb-2014-15-2-r29, or DESeq2 https://doi.org/10.1186/s13059-014-0550-8 to perform the testing. All methods use raw counts, irrespective of if scale_abundance or adjust_abundance have been calculated, therefore it is essential to add covariates such as batch effects (if applicable) in the formula.
Underlying method for edgeR framework: .data
# Filter keep_abundant( factor_of_interest = !!(as.symbol(parse_formula(.formula)[1])), minimum_counts = minimum_counts, minimum_proportion = minimum_proportion )
# Format select(!!.transcript,!!.sample,!!.abundance) spread(!!.sample,!!.abundance) as_matrix(rownames = !!.transcript)
# edgeR edgeR::DGEList(counts = .) edgeR::calcNormFactors(method = scaling_method) edgeR::estimateDisp(design)
# Fit edgeR::glmQLFit(design) edgeR::glmQLFTest(coef = 2, contrast = my_contrasts) // or glmLRT according to choice
Underlying method for DESeq2 framework: keep_abundant( factor_of_interest = !!as.symbol(parse_formula(.formula)[[1]]), minimum_counts = minimum_counts, minimum_proportion = minimum_proportion )
# DESeq2 DESeq2::DESeqDataSet( design = .formula) DESeq2::DESeq() DESeq2::results()
A 'tbl' with additional columns for the statistics from the test (e.g., log fold change, p-value and false discovery rate).
A 'tbl' with additional columns for the statistics from the test (e.g., log fold change, p-value and false discovery rate).
A 'tbl' with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).
A 'tbl' with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).
A 'SummarizedExperiment' object
A 'SummarizedExperiment' object
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | tidybulk::counts_mini %>%
tidybulk(sample, transcript, count) %>%
identify_abundant() %>%
test_differential_abundance( ~ condition )
# The function `test_differential_abundance` operates with contrasts too
tidybulk::counts_mini %>%
tidybulk(sample, transcript, count) %>%
identify_abundant() %>%
test_differential_abundance(
~ 0 + condition,
.contrasts = c( "conditionTRUE - conditionFALSE")
)
|
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