Package 'tidybulk'

Description

adjust_abundance() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with an additional adjusted abundance column. This method uses scaled counts if present.

Usage

adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = NULL
)

## S4 method for signature 'spec_tbl_df'
adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = NULL
)

## S4 method for signature 'tbl_df'
adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = NULL
)

## S4 method for signature 'tidybulk'
adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = NULL
)

## S4 method for signature 'SummarizedExperiment'
adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
adjust_abundance(
  .data,
  .formula = NULL,
  .factor_unwanted = NULL,
  .factor_of_interest = NULL,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "combat_seq",
  action = "add",
  ...,
  log_transform = NULL,
  transform = NULL,
  inverse_transform = NULL
)

Arguments

Details

'r lifecycle::badge("maturing")'

This function adjusts the abundance for (known) unwanted variation. At the moment just an unwanted covariate is allowed at a time using Combat (DOI: 10.1093/bioinformatics/bts034)

Underlying method: sva::ComBat(data, batch = my_batch, mod = design, prior.plots = FALSE, ...)

Value

A consistent object (to the input) with additional columns for the adjusted counts as '<COUNT COLUMN>_adjusted'

A consistent object (to the input) with additional columns for the adjusted counts as '<COUNT COLUMN>_adjusted'

A consistent object (to the input) with additional columns for the adjusted counts as '<COUNT COLUMN>_adjusted'

A consistent object (to the input) with additional columns for the adjusted counts as '<COUNT COLUMN>_adjusted'

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

cm = tidybulk::se_mini
cm$batch = 0
cm$batch[colnames(cm) %in% c("SRR1740035", "SRR1740043")] = 1

cm |>
identify_abundant() |>
adjust_abundance(	.factor_unwanted = batch, .factor_of_interest =  condition, method="combat"	)

Description

aggregate_duplicates() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with aggregated transcripts that were duplicated.

Usage

aggregate_duplicates(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  aggregation_function = sum,
  keep_integer = TRUE
)

## S4 method for signature 'spec_tbl_df'
aggregate_duplicates(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  aggregation_function = sum,
  keep_integer = TRUE
)

## S4 method for signature 'tbl_df'
aggregate_duplicates(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  aggregation_function = sum,
  keep_integer = TRUE
)

## S4 method for signature 'tidybulk'
aggregate_duplicates(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  aggregation_function = sum,
  keep_integer = TRUE
)

## S4 method for signature 'SummarizedExperiment'
aggregate_duplicates(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  aggregation_function = sum,
  keep_integer = TRUE
)

## S4 method for signature 'RangedSummarizedExperiment'
aggregate_duplicates(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  aggregation_function = sum,
  keep_integer = TRUE
)

Arguments

Details

'r lifecycle::badge("maturing")'

This function aggregates duplicated transcripts (e.g., isoforms, ensembl). For example, we often have to convert ensembl symbols to gene/transcript symbol, but in doing so we have to deal with duplicates. 'aggregate_duplicates' takes a tibble and column names (as symbols; for 'sample', 'transcript' and 'count') as arguments and returns a tibble with aggregate transcript with the same name. All the rest of the column are appended, and factors and boolean are appended as characters.

Underlying custom method: data |> filter(n_aggr > 1) |> group_by(!!.sample,!!.transcript) |> dplyr::mutate(!!.abundance := !!.abundance |> aggregation_function())

Value

A consistent object (to the input) with aggregated transcript abundance and annotation

A consistent object (to the input) with aggregated transcript abundance and annotation

A consistent object (to the input) with aggregated transcript abundance and annotation

A consistent object (to the input) with aggregated transcript abundance and annotation

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

# Create a aggregation column
se_mini = tidybulk::se_mini
SummarizedExperiment::rowData(se_mini )$gene_name = rownames(se_mini )

   aggregate_duplicates(
     se_mini,
   .transcript = gene_name
   )

Description

'arrange()' order the rows of a data frame rows by the values of selected columns.

Unlike other dplyr verbs, 'arrange()' largely ignores grouping; you need to explicit mention grouping variables (or use 'by_group = TRUE') in order to group by them, and functions of variables are evaluated once per data frame, not once per group.

Arguments

Details

## Locales The sort order for character vectors will depend on the collating sequence of the locale in use: see [locales()].

## Missing values Unlike base sorting with 'sort()', 'NA' are: * always sorted to the end for local data, even when wrapped with 'desc()'. * treated differently for remote data, depending on the backend.

Value

An object of the same type as '.data'.

* All rows appear in the output, but (usually) in a different place. * Columns are not modified. * Groups are not modified. * Data frame attributes are preserved.

A tibble

Methods

This function is a **generic**, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages:

See Also

Other single table verbs: filter(), mutate(), rename(), summarise()

Examples

arrange(mtcars, cyl, disp)

Description

Get matrix from tibble

Usage

as_matrix(tbl, rownames = NULL, do_check = TRUE)

Arguments

Value

A matrix

Examples

tibble(.feature = "CD3G", count=1) |> as_matrix(rownames=.feature)

Description

as_SummarizedExperiment() creates a 'SummarizedExperiment' object from a 'tbl' or 'tidybulk' tbl formatted as | <SAMPLE> | <TRANSCRIPT> | <COUNT> | <...> |

Usage

as_SummarizedExperiment(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL
)

## S4 method for signature 'spec_tbl_df'
as_SummarizedExperiment(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL
)

## S4 method for signature 'tbl_df'
as_SummarizedExperiment(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL
)

## S4 method for signature 'tidybulk'
as_SummarizedExperiment(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL
)

Arguments

Value

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Description

Left join datasets

Arguments

Value

A tt object

Examples

annotation = tidybulk::se_mini |> tidybulk() |> as_tibble() |> distinct(.sample) |> mutate(source = "AU")
tidybulk::se_mini |> tidybulk() |> as_tibble() |> left_join(annotation)

Description

This is an efficient implementation of the common pattern of 'do.call(rbind, dfs)' or 'do.call(cbind, dfs)' for binding many data frames into one.

Arguments

Details

The output of 'bind_rows()' will contain a column if that column appears in any of the inputs.

Value

'bind_rows()' and 'bind_cols()' return the same type as the first input, either a data frame, 'tbl_df', or 'grouped_df'.

Examples

data(se_mini)

se_mini_tidybulk = se_mini |> tidybulk()
bind_rows(    se_mini_tidybulk, se_mini_tidybulk  )

tt_bind = se_mini_tidybulk |> select(time, condition)
se_mini_tidybulk |> bind_cols(tt_bind)

Description

Needed for vignette breast_tcga_mini_SE

Usage

breast_tcga_mini_SE

Format

An object of class SummarizedExperiment with 500 rows and 251 columns.

Description

cluster_elements() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and identify clusters in the data.

Usage

cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

## S4 method for signature 'spec_tbl_df'
cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

## S4 method for signature 'tbl_df'
cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

## S4 method for signature 'tidybulk'
cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

## S4 method for signature 'SummarizedExperiment'
cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

Arguments

Details

'r lifecycle::badge("maturing")'

identifies clusters in the data, normally of samples. This function returns a tibble with additional columns for the cluster annotation. At the moment only k-means (DOI: 10.2307/2346830) and SNN clustering (DOI:10.1016/j.cell.2019.05.031) is supported, the plan is to introduce more clustering methods.

Underlying method for kmeans do.call(kmeans(.data, iter.max = 1000, ...)

Underlying method for SNN .data Seurat::CreateSeuratObject() Seurat::ScaleData(display.progress = TRUE,num.cores = 4, do.par = TRUE) Seurat::FindVariableFeatures(selection.method = "vst") Seurat::RunPCA(npcs = 30) Seurat::FindNeighbors() Seurat::FindClusters(method = "igraph", ...)

Value

A tbl object with additional columns with cluster labels

A tbl object with additional columns with cluster labels

A tbl object with additional columns with cluster labels

A tbl object with additional columns with cluster labels

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

cluster_elements(tidybulk::se_mini,	centers = 2, method="kmeans")

Description

Counts with ensembl annotation

Usage

counts_ensembl

Format

An object of class tbl_df (inherits from tbl, data.frame) with 119 rows and 6 columns.

Description

deconvolve_cellularity() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with the estimated cell type abundance for each sample

Usage

deconvolve_cellularity(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

## S4 method for signature 'spec_tbl_df'
deconvolve_cellularity(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

## S4 method for signature 'tbl_df'
deconvolve_cellularity(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

## S4 method for signature 'tidybulk'
deconvolve_cellularity(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

## S4 method for signature 'SummarizedExperiment'
deconvolve_cellularity(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

## S4 method for signature 'RangedSummarizedExperiment'
deconvolve_cellularity(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  reference = NULL,
  method = "cibersort",
  prefix = "",
  action = "add",
  ...
)

Arguments

Details

'r lifecycle::badge("maturing")'

This function infers the cell type composition of our samples (with the algorithm Cibersort; Newman et al., 10.1038/nmeth.3337).

Underlying method: CIBERSORT(Y = data, X = reference, ...)

Value

A consistent object (to the input) including additional columns for each cell type estimated

A consistent object (to the input) including additional columns for each cell type estimated

A consistent object (to the input) including additional columns for each cell type estimated

A consistent object (to the input) including additional columns for each cell type estimated

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

# Subsetting for time efficiency
tidybulk::se_mini |> deconvolve_cellularity(cores = 1)

Description

Get DESCRIPTION from gene SYMBOL for Human and Mouse

describe_transcript

describe_transcript

describe_transcript

describe_transcript

describe_transcript

describe_transcript

Usage

describe_transcript(.data, .transcript = NULL)

## S4 method for signature 'spec_tbl_df'
describe_transcript(.data, .transcript = NULL)

## S4 method for signature 'tbl_df'
describe_transcript(.data, .transcript = NULL)

## S4 method for signature 'tidybulk'
describe_transcript(.data, .transcript = NULL)

.describe_transcript_SE(.data, .transcript = NULL)

## S4 method for signature 'SummarizedExperiment'
describe_transcript(.data, .transcript = NULL)

## S4 method for signature 'RangedSummarizedExperiment'
describe_transcript(.data, .transcript = NULL)

Arguments

Value

A tbl

A consistent object (to the input) including additional columns for transcript symbol

A consistent object (to the input) including additional columns for transcript symbol

A consistent object (to the input) including additional columns for transcript symbol

A 'SummarizedExperiment' object

A consistent object (to the input) including additional columns for transcript symbol

A consistent object (to the input) including additional columns for transcript symbol

Examples

describe_transcript(tidybulk::se_mini)

Description

distinct

Arguments

Value

A tt object

Examples

tidybulk::se_mini |> tidybulk() |> distinct()

Description

Data set

Usage

ensembl_symbol_mapping

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 291249 rows and 3 columns.

Description

ensembl_to_symbol() takes as input a 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with the additional transcript symbol column

Usage

ensembl_to_symbol(.data, .ensembl, action = "add")

## S4 method for signature 'spec_tbl_df'
ensembl_to_symbol(.data, .ensembl, action = "add")

## S4 method for signature 'tbl_df'
ensembl_to_symbol(.data, .ensembl, action = "add")

## S4 method for signature 'tidybulk'
ensembl_to_symbol(.data, .ensembl, action = "add")

Arguments

Details

This is useful since different resources use ensembl IDs while others use gene symbol IDs. At the moment this work for human (genes and transcripts) and mouse (genes) data.

Value

A consistent object (to the input) including additional columns for transcript symbol

A consistent object (to the input) including additional columns for transcript symbol

A consistent object (to the input) including additional columns for transcript symbol

A consistent object (to the input) including additional columns for transcript symbol

Examples

# This function was designed for data.frame
# Convert from SummarizedExperiment for this example. It is NOT reccomended.

tidybulk::se_mini |> tidybulk() |> as_tibble() |> ensembl_to_symbol(.feature)

Description

fill_missing_abundance() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with new observations

Usage

fill_missing_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  fill_with
)

## S4 method for signature 'spec_tbl_df'
fill_missing_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  fill_with
)

## S4 method for signature 'tbl_df'
fill_missing_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  fill_with
)

## S4 method for signature 'tidybulk'
fill_missing_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  fill_with
)

Arguments

Details

This function fills the abundance of missing sample-transcript pair using the median of the sample group defined by the formula

Value

A consistent object (to the input) non-sparse abundance

A consistent object (to the input) with filled abundance

A consistent object (to the input) with filled abundance

A consistent object (to the input) with filled abundance

Examples

print("Not run for build time.")

# tidybulk::se_mini |>  fill_missing_abundance( fill_with = 0)

Description

'filter()' retains the rows where the conditions you provide a 'TRUE'. Note that, unlike base subsetting with '[', rows where the condition evaluates to 'NA' are dropped.

Arguments

Details

dplyr is not yet smart enough to optimise filtering optimisation on grouped datasets that don't need grouped calculations. For this reason, filtering is often considerably faster on [ungroup()]ed data.

Value

An object of the same type as '.data'.

* Rows are a subset of the input, but appear in the same order. * Columns are not modified. * The number of groups may be reduced (if '.preserve' is not 'TRUE'). * Data frame attributes are preserved.

Useful filter functions

* ['=='], ['>'], ['>='] etc * ['&'], ['|'], ['!'], [xor()] * [is.na()] * [between()], [near()]

Grouped tibbles

Because filtering expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped filtering:

The former keeps rows with 'mass' greater than the global average whereas the latter keeps rows with 'mass' greater than the gender

average.

Methods

This function is a **generic**, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages:

See Also

[filter_all()], [filter_if()] and [filter_at()].

Other single table verbs: arrange(), mutate(), rename(), summarise()

Examples

data(se)

se |> tidybulk() |> filter(dex=="untrt")

# Learn more in ?dplyr_tidy_eval

Description

flybaseIDs

Usage

flybaseIDs

Format

An object of class character of length 14599.

Description

get_bibliography() takes as input a 'tidybulk'

Usage

get_bibliography(.data)

## S4 method for signature 'tbl'
get_bibliography(.data)

## S4 method for signature 'tbl_df'
get_bibliography(.data)

## S4 method for signature 'spec_tbl_df'
get_bibliography(.data)

## S4 method for signature 'tidybulk'
get_bibliography(.data)

## S4 method for signature 'SummarizedExperiment'
get_bibliography(.data)

## S4 method for signature 'RangedSummarizedExperiment'
get_bibliography(.data)

Arguments

Details

'r lifecycle::badge("maturing")'

This methods returns the bibliography list of your workflow from the internals of a tidybulk object (attr(., "internals"))

Value

NULL. It prints a list of bibliography references for the software used through the workflow.

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

Examples

get_bibliography(tidybulk::se_mini)

Description

Most data operations are done on groups defined by variables. 'group_by()' takes an existing tbl and converts it into a grouped tbl where operations are performed "by group". 'ungroup()' removes grouping.

Arguments

Value

A [grouped data frame][grouped_df()], unless the combination of '...' and 'add' yields a non empty set of grouping columns, a regular (ungrouped) data frame otherwise.

Methods

These function are **generic**s, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages:

Examples

by_cyl <- mtcars |> group_by(cyl)

Description

identify_abundant() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with additional columns for the statistics from the hypothesis test.

Usage

identify_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'spec_tbl_df'
identify_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'tbl_df'
identify_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'tidybulk'
identify_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'SummarizedExperiment'
identify_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'RangedSummarizedExperiment'
identify_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

Arguments

Details

'r lifecycle::badge("maturing")'

At the moment this function uses edgeR (DOI: 10.1093/bioinformatics/btp616)

Underlying method: edgeR::filterByExpr( data, min.count = minimum_counts, group = string_factor_of_interest, min.prop = minimum_proportion )

Value

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) 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

Examples

identify_abundant(
	tidybulk::se_mini
	)

Description

impute_missing_abundance() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with additional sample-transcript pairs with imputed transcript abundance.

Usage

impute_missing_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  suffix = "",
  force_scaling = FALSE
)

## S4 method for signature 'spec_tbl_df'
impute_missing_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  suffix = "",
  force_scaling = FALSE
)

## S4 method for signature 'tbl_df'
impute_missing_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  suffix = "",
  force_scaling = FALSE
)

## S4 method for signature 'tidybulk'
impute_missing_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  suffix = "",
  force_scaling = FALSE
)

## S4 method for signature 'SummarizedExperiment'
impute_missing_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  suffix = "",
  force_scaling = FALSE
)

## S4 method for signature 'RangedSummarizedExperiment'
impute_missing_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  suffix = "",
  force_scaling = FALSE
)

Arguments

Details

'r lifecycle::badge("maturing")'

This function imputes the abundance of missing sample-transcript pair using the median of the sample group defined by the formula

Value

A consistent object (to the input) non-sparse abundance

A consistent object (to the input) with imputed abundance

A consistent object (to the input) with imputed abundance

A consistent object (to the input) with imputed abundance

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

res =
	impute_missing_abundance(
		tidybulk::se_mini,
	~ condition
)

Description

Inner join datasets

Right join datasets

Full join datasets

Arguments

Value

A tt object

A tt object

A tt object

Examples

annotation = tidybulk::se_mini |> tidybulk() |> as_tibble() |> distinct(.sample) |> mutate(source = "AU")
tidybulk::se_mini |> tidybulk() |> as_tibble() |> inner_join(annotation)


annotation = tidybulk::se_mini |> tidybulk() |> as_tibble() |> distinct(.sample) |> mutate(source = "AU")
tidybulk::se_mini |> tidybulk() |> as_tibble() |> right_join(annotation)


annotation = tidybulk::se_mini |> tidybulk() |> as_tibble() |> distinct(.sample) |> mutate(source = "AU")
tidybulk::se_mini |> tidybulk() |> as_tibble() |> full_join(annotation)

Description

keep_abundant() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with additional columns for the statistics from the hypothesis test.

Usage

keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'spec_tbl_df'
keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'tbl_df'
keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'tidybulk'
keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'SummarizedExperiment'
keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

## S4 method for signature 'RangedSummarizedExperiment'
keep_abundant(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  factor_of_interest = NULL,
  minimum_counts = 10,
  minimum_proportion = 0.7
)

Arguments

Details

At the moment this function uses edgeR (DOI: 10.1093/bioinformatics/btp616)

Underlying method: edgeR::filterByExpr( data, min.count = minimum_counts, group = string_factor_of_interest, min.prop = minimum_proportion )

Value

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) 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

Examples

keep_abundant(
	tidybulk::se_mini
	)

Description

keep_variable() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with additional columns for the statistics from the hypothesis test.

Usage

keep_variable(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  top = 500,
  transform = log1p,
  log_transform = TRUE
)

## S4 method for signature 'spec_tbl_df'
keep_variable(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  top = 500,
  transform = log1p,
  log_transform = NULL
)

## S4 method for signature 'tbl_df'
keep_variable(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  top = 500,
  transform = log1p,
  log_transform = NULL
)

## S4 method for signature 'tidybulk'
keep_variable(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  top = 500,
  transform = log1p,
  log_transform = NULL
)

## S4 method for signature 'SummarizedExperiment'
keep_variable(.data, top = 500, transform = log1p)

## S4 method for signature 'RangedSummarizedExperiment'
keep_variable(.data, top = 500, transform = log1p)

Arguments

Details

'r lifecycle::badge("maturing")'

At the moment this function uses edgeR https://doi.org/10.1093/bioinformatics/btp616

Value

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

Underlying method: s <- rowMeans((x - rowMeans(x)) ^ 2) o <- order(s, decreasing = TRUE) x <- x[o[1L:top], , drop = FALSE] variable_trancripts = rownames(x)

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) 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

Examples

keep_variable(tidybulk::se_mini, top = 500)

Description

it perform log scaling and reverse the axis. Useful to plot negative log probabilities. To not be used directly but with ggplot (e.g. scale_y_continuous(trans = "log10_reverse") )

Usage

log10_reverse_trans()

Details

'r lifecycle::badge("maturing")'

Value

A scales object

Examples

library(ggplot2)
library(tibble)

tibble(pvalue = c(0.001, 0.05, 0.1), fold_change = 1:3) %>%
 ggplot(aes(fold_change , pvalue)) +
 geom_point() +
 scale_y_continuous(trans = "log10_reverse")

Description

it perform logit scaling with right axis formatting. To not be used directly but with ggplot (e.g. scale_y_continuous(trans = "log10_reverse") )

Usage

logit_trans()

Details

'r lifecycle::badge("maturing")'

Value

A scales object

Examples

library(ggplot2)
library(tibble)

tibble(pvalue = c(0.001, 0.05, 0.1), fold_change = 1:3) %>%
 ggplot(aes(fold_change , pvalue)) +
 geom_point() +
 scale_y_continuous(trans = "log10_reverse")

Description

'mutate()' adds new variables and preserves existing ones; 'transmute()' adds new variables and drops existing ones. New variables overwrite existing variables of the same name. Variables can be removed by setting their value to 'NULL'.

Arguments

Value

An object of the same type as '.data'.

For 'mutate()':

* Rows are not affected. * Existing columns will be preserved unless explicitly modified. * New columns will be added to the right of existing columns. * Columns given value 'NULL' will be removed * Groups will be recomputed if a grouping variable is mutated. * Data frame attributes are preserved.

For 'transmute()':

* Rows are not affected. * Apart from grouping variables, existing columns will be remove unless explicitly kept. * Column order matches order of expressions. * Groups will be recomputed if a grouping variable is mutated. * Data frame attributes are preserved.

Useful mutate functions

* ['+'], ['-'], [log()], etc., for their usual mathematical meanings

* [lead()], [lag()]

* [dense_rank()], [min_rank()], [percent_rank()], [row_number()], [cume_dist()], [ntile()]

* [cumsum()], [cummean()], [cummin()], [cummax()], [cumany()], [cumall()]

* [na_if()], [coalesce()]

* [if_else()], [recode()], [case_when()]

Grouped tibbles

Because mutating expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped mutate:

With the grouped equivalent:

The former normalises 'mass' by the global average whereas the latter normalises by the averages within gender levels.

Methods

These function are **generic**s, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

Methods available in currently loaded packages:

See Also

Other single table verbs: arrange(), filter(), rename(), summarise()

Examples

# Newly created variables are available immediately
mtcars |> as_tibble() |> mutate(
  cyl2 = cyl * 2,
  cyl4 = cyl2 * 2
)

Description

pivot_sample() takes as input a 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a 'tbl' with only sample-related columns

Usage

pivot_sample(.data, .sample = NULL)

## S4 method for signature 'spec_tbl_df'
pivot_sample(.data, .sample = NULL)

## S4 method for signature 'tbl_df'
pivot_sample(.data, .sample = NULL)

## S4 method for signature 'tidybulk'
pivot_sample(.data, .sample = NULL)

## S4 method for signature 'SummarizedExperiment'
pivot_sample(.data, .sample = NULL)

## S4 method for signature 'RangedSummarizedExperiment'
pivot_sample(.data, .sample = NULL)

Arguments

Details

'r lifecycle::badge("maturing")'

This functon extracts only sample-related information for downstream analysis (e.g., visualisation). It is disruptive in the sense that it cannot be passed anymore to tidybulk function.

Value

A 'tbl' with transcript-related information

A consistent object (to the input)

A consistent object (to the input)

Examples

pivot_sample(tidybulk::se_mini )

Description

pivot_transcript() takes as input a 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a 'tbl' with only transcript-related columns

Usage

pivot_transcript(.data, .transcript = NULL)

## S4 method for signature 'spec_tbl_df'
pivot_transcript(.data, .transcript = NULL)

## S4 method for signature 'tbl_df'
pivot_transcript(.data, .transcript = NULL)

## S4 method for signature 'tidybulk'
pivot_transcript(.data, .transcript = NULL)

## S4 method for signature 'SummarizedExperiment'
pivot_transcript(.data, .transcript = NULL)

## S4 method for signature 'RangedSummarizedExperiment'
pivot_transcript(.data, .transcript = NULL)

Arguments

Details

'r lifecycle::badge("maturing")'

This functon extracts only transcript-related information for downstream analysis (e.g., visualisation). It is disruptive in the sense that it cannot be passed anymore to tidybulk function.

Value

A 'tbl' with transcript-related information

A consistent object (to the input)

A consistent object (to the input)

Examples

pivot_transcript(tidybulk::se_mini 	)

Description

quantile_normalise_abundance() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and Scales transcript abundance compansating for sequencing depth (e.g., with TMM algorithm, Robinson and Oshlack doi.org/10.1186/gb-2010-11-3-r25).

Usage

quantile_normalise_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  action = "add"
)

## S4 method for signature 'spec_tbl_df'
quantile_normalise_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  action = "add"
)

## S4 method for signature 'tbl_df'
quantile_normalise_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  action = "add"
)

## S4 method for signature 'tidybulk'
quantile_normalise_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  action = "add"
)

## S4 method for signature 'SummarizedExperiment'
quantile_normalise_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  action = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
quantile_normalise_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "limma_normalize_quantiles",
  action = NULL
)

Arguments

Details

'r lifecycle::badge("maturing")'

Scales transcript abundance compensating for sequencing depth (e.g., with TMM algorithm, Robinson and Oshlack doi.org/10.1186/gb-2010-11-3-r25). Lowly transcribed transcripts/genes (defined with minimum_counts and minimum_proportion parameters) are filtered out from the scaling procedure. The scaling inference is then applied back to all unfiltered data.

Underlying method edgeR::calcNormFactors(.data, method = c("TMM","TMMwsp","RLE","upperquartile"))

Value

A tbl object with additional columns with scaled data as '<NAME OF COUNT COLUMN>_scaled'

A tbl object with additional columns with scaled data as '<NAME OF COUNT COLUMN>_scaled'

A tbl object with additional columns with scaled data as '<NAME OF COUNT COLUMN>_scaled'

A tbl object with additional columns with scaled data as '<NAME OF COUNT COLUMN>_scaled'

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

tidybulk::se_mini |>
   quantile_normalise_abundance()

Description

reduce_dimensions() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and calculates the reduced dimensional space of the transcript abundance.

Usage

reduce_dimensions(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  .dims = 2,
  top = 500,
  of_samples = TRUE,
  transform = log1p,
  scale = TRUE,
  action = "add",
  ...,
  log_transform = NULL
)

## S4 method for signature 'spec_tbl_df'
reduce_dimensions(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  .dims = 2,
  top = 500,
  of_samples = TRUE,
  transform = log1p,
  scale = TRUE,
  action = "add",
  ...,
  log_transform = NULL
)

## S4 method for signature 'tbl_df'
reduce_dimensions(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  .dims = 2,
  top = 500,
  of_samples = TRUE,
  transform = log1p,
  scale = TRUE,
  action = "add",
  ...,
  log_transform = NULL
)

## S4 method for signature 'tidybulk'
reduce_dimensions(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  .dims = 2,
  top = 500,
  of_samples = TRUE,
  transform = log1p,
  scale = TRUE,
  action = "add",
  ...,
  log_transform = NULL
)

## S4 method for signature 'SummarizedExperiment'
reduce_dimensions(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  .dims = 2,
  top = 500,
  of_samples = TRUE,
  transform = log1p,
  scale = TRUE,
  action = "add",
  ...,
  log_transform = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
reduce_dimensions(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  .dims = 2,
  top = 500,
  of_samples = TRUE,
  transform = log1p,
  scale = TRUE,
  action = "add",
  ...,
  log_transform = NULL
)

Arguments

Details

'r lifecycle::badge("maturing")'

This function reduces the dimensions of the transcript abundances. It can use multi-dimensional scaling (MDS; DOI.org/10.1186/gb-2010-11-3-r25), principal component analysis (PCA), or tSNE (Jesse Krijthe et al. 2018)

Underlying method for PCA: prcomp(scale = scale, ...)

Underlying method for MDS: limma::plotMDS(ndim = .dims, plot = FALSE, top = top)

Underlying method for tSNE: Rtsne::Rtsne(data, ...)

Underlying method for UMAP:

df_source = .data |>

# Filter NA symbol filter(!!.feature |> is.na() |> not()) |>

# Prepare data frame distinct(!!.feature,!!.element,!!.abundance) |>

# Filter most variable genes keep_variable_transcripts(top) |> reduce_dimensions(method="PCA", .dims = calculate_for_pca_dimensions, action="get" ) |> as_matrix(rownames = quo_name(.element)) |> uwot::tumap(...)

Value

A tbl object with additional columns for the reduced dimensions

A tbl object with additional columns for the reduced dimensions

A tbl object with additional columns for the reduced dimensions

A tbl object with additional columns for the reduced dimensions

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

counts.MDS =
 tidybulk::se_mini |>
 identify_abundant() |>
 reduce_dimensions( method="MDS", .dims = 3)


counts.PCA =
 tidybulk::se_mini |>
 identify_abundant() |>
 reduce_dimensions(method="PCA", .dims = 3)

Description

remove_redundancy() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) for correlation method or | <DIMENSION 1> | <DIMENSION 2> | <...> | for reduced_dimensions method, and returns a consistent object (to the input) with dropped elements (e.g., samples).

Usage

remove_redundancy(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  correlation_threshold = 0.9,
  top = Inf,
  transform = identity,
  Dim_a_column,
  Dim_b_column,
  log_transform = NULL
)

## S4 method for signature 'spec_tbl_df'
remove_redundancy(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  correlation_threshold = 0.9,
  top = Inf,
  transform = identity,
  Dim_a_column = NULL,
  Dim_b_column = NULL,
  log_transform = NULL
)

## S4 method for signature 'tbl_df'
remove_redundancy(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  correlation_threshold = 0.9,
  top = Inf,
  transform = identity,
  Dim_a_column = NULL,
  Dim_b_column = NULL,
  log_transform = NULL
)

## S4 method for signature 'tidybulk'
remove_redundancy(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  correlation_threshold = 0.9,
  top = Inf,
  transform = identity,
  Dim_a_column = NULL,
  Dim_b_column = NULL,
  log_transform = NULL
)

## S4 method for signature 'SummarizedExperiment'
remove_redundancy(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  correlation_threshold = 0.9,
  top = Inf,
  transform = identity,
  Dim_a_column = NULL,
  Dim_b_column = NULL,
  log_transform = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
remove_redundancy(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  correlation_threshold = 0.9,
  top = Inf,
  transform = identity,
  Dim_a_column = NULL,
  Dim_b_column = NULL,
  log_transform = NULL
)

Arguments

Details

'r lifecycle::badge("maturing")'

This function removes redundant elements from the original data set (e.g., samples or transcripts). For example, if we want to define cell-type specific signatures with low sample redundancy. This function returns a tibble with dropped redundant elements (e.g., samples). Two redundancy estimation approaches are supported: (i) removal of highly correlated clusters of elements (keeping a representative) with method="correlation"; (ii) removal of most proximal element pairs in a reduced dimensional space.

Underlying method for correlation: widyr::pairwise_cor(sample, transcript,count, sort = TRUE, diag = FALSE, upper = FALSE)

Underlying custom method for reduced dimensions: select_closest_pairs = function(df) couples <- df |> head(n = 0)

while (df |> nrow() > 0) pair <- df |> arrange(dist) |> head(n = 1) couples <- couples |> bind_rows(pair) df <- df |> filter( !'sample 1' !'sample 2' )

couples

Value

A tbl object with with dropped redundant elements (e.g., samples).

A tbl object with with dropped redundant elements (e.g., samples).

A tbl object with with dropped redundant elements (e.g., samples).

A tbl object with with dropped redundant elements (e.g., samples).

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

tidybulk::se_mini |>
 identify_abundant() |>
   remove_redundancy(
	   .element = sample,
	   .feature = transcript,
	   	.abundance =  count,
	   	method = "correlation"
	   	)

counts.MDS =
 tidybulk::se_mini |>
 identify_abundant() |>
  reduce_dimensions( method="MDS", .dims = 3)

remove_redundancy(
	counts.MDS,
	Dim_a_column = `Dim1`,
	Dim_b_column = `Dim2`,
	.element = sample,
  method = "reduced_dimensions"
)

Description

Rename individual variables using 'new_name = old_name' syntax.

Arguments

Value

An object of the same type as '.data'. * Rows are not affected. * Column names are changed; column order is preserved * Data frame attributes are preserved. * Groups are updated to reflect new names.

Scoped selection and renaming

Use the three scoped variants ([rename_all()], [rename_if()], [rename_at()]) to renaming a set of variables with a function.

Methods

This function is a **generic**, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages:

See Also

Other single table verbs: arrange(), filter(), mutate(), summarise()

Examples

iris <- as_tibble(iris) # so it prints a little nicer
rename(iris, petal_length = Petal.Length)

Description

rotate_dimensions() takes as input a 'tbl' formatted as | <DIMENSION 1> | <DIMENSION 2> | <...> | and calculates the rotated dimensional space of the transcript abundance.

Usage

rotate_dimensions(
  .data,
  dimension_1_column,
  dimension_2_column,
  rotation_degrees,
  .element = NULL,
  of_samples = TRUE,
  dimension_1_column_rotated = NULL,
  dimension_2_column_rotated = NULL,
  action = "add"
)

## S4 method for signature 'spec_tbl_df'
rotate_dimensions(
  .data,
  dimension_1_column,
  dimension_2_column,
  rotation_degrees,
  .element = NULL,
  of_samples = TRUE,
  dimension_1_column_rotated = NULL,
  dimension_2_column_rotated = NULL,
  action = "add"
)

## S4 method for signature 'tbl_df'
rotate_dimensions(
  .data,
  dimension_1_column,
  dimension_2_column,
  rotation_degrees,
  .element = NULL,
  of_samples = TRUE,
  dimension_1_column_rotated = NULL,
  dimension_2_column_rotated = NULL,
  action = "add"
)

## S4 method for signature 'tidybulk'
rotate_dimensions(
  .data,
  dimension_1_column,
  dimension_2_column,
  rotation_degrees,
  .element = NULL,
  of_samples = TRUE,
  dimension_1_column_rotated = NULL,
  dimension_2_column_rotated = NULL,
  action = "add"
)

## S4 method for signature 'SummarizedExperiment'
rotate_dimensions(
  .data,
  dimension_1_column,
  dimension_2_column,
  rotation_degrees,
  .element = NULL,
  of_samples = TRUE,
  dimension_1_column_rotated = NULL,
  dimension_2_column_rotated = NULL,
  action = "add"
)

## S4 method for signature 'RangedSummarizedExperiment'
rotate_dimensions(
  .data,
  dimension_1_column,
  dimension_2_column,
  rotation_degrees,
  .element = NULL,
  of_samples = TRUE,
  dimension_1_column_rotated = NULL,
  dimension_2_column_rotated = NULL,
  action = "add"
)

Arguments

Details

'r lifecycle::badge("maturing")'

This function to rotate two dimensions such as the reduced dimensions.

Underlying custom method: rotation = function(m, d) // r = the angle // m data matrix r = d * pi / 180 ((bind_rows( c('1' = cos(r), '2' = -sin(r)), c('1' = sin(r), '2' = cos(r)) ) |> as_matrix())

Value

A tbl object with additional columns for the reduced dimensions. additional columns for the rotated dimensions. The rotated dimensions will be added to the original data set as '<NAME OF DIMENSION> rotated <ANGLE>' by default, or as specified in the input arguments.

A tbl object with additional columns for the reduced dimensions. additional columns for the rotated dimensions. The rotated dimensions will be added to the original data set as '<NAME OF DIMENSION> rotated <ANGLE>' by default, or as specified in the input arguments.

A tbl object with additional columns for the reduced dimensions. additional columns for the rotated dimensions. The rotated dimensions will be added to the original data set as '<NAME OF DIMENSION> rotated <ANGLE>' by default, or as specified in the input arguments.

A tbl object with additional columns for the reduced dimensions. additional columns for the rotated dimensions. The rotated dimensions will be added to the original data set as '<NAME OF DIMENSION> rotated <ANGLE>' by default, or as specified in the input arguments.

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

counts.MDS =
 tidybulk::se_mini |>
 identify_abundant() |>
 reduce_dimensions( method="MDS", .dims = 3)

counts.MDS.rotated =  rotate_dimensions(counts.MDS, `Dim1`, `Dim2`, rotation_degrees = 45, .element = sample)

Description

See [this repository](https://github.com/jennybc/row-oriented-workflows) for alternative ways to perform row-wise operations.

Arguments

Details

'rowwise()' is used for the results of [do()] when you create list-variables. It is also useful to support arbitrary complex operations that need to be applied to each row.

Currently, rowwise grouping only works with data frames. Its main impact is to allow you to work with list-variables in [summarise()] and [mutate()] without having to use [[1]]. This makes 'summarise()' on a rowwise tbl effectively equivalent to [plyr::ldply()].

Value

A consistent object (to the input)

A 'tbl'

Examples

df <- expand.grid(x = 1:3, y = 3:1)
df_done <- df |> rowwise()

Description

scale_abundance() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and Scales transcript abundance compansating for sequencing depth (e.g., with TMM algorithm, Robinson and Oshlack doi.org/10.1186/gb-2010-11-3-r25).

Usage

scale_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "TMM",
  reference_sample = NULL,
  .subset_for_scaling = NULL,
  action = "add",
  reference_selection_function = NULL
)

## S4 method for signature 'spec_tbl_df'
scale_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "TMM",
  reference_sample = NULL,
  .subset_for_scaling = NULL,
  action = "add",
  reference_selection_function = NULL
)

## S4 method for signature 'tbl_df'
scale_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "TMM",
  reference_sample = NULL,
  .subset_for_scaling = NULL,
  action = "add",
  reference_selection_function = NULL
)

## S4 method for signature 'tidybulk'
scale_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "TMM",
  reference_sample = NULL,
  .subset_for_scaling = NULL,
  action = "add",
  reference_selection_function = NULL
)

## S4 method for signature 'SummarizedExperiment'
scale_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "TMM",
  reference_sample = NULL,
  .subset_for_scaling = NULL,
  action = NULL,
  reference_selection_function = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
scale_abundance(
  .data,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "TMM",
  reference_sample = NULL,
  .subset_for_scaling = NULL,
  action = NULL,
  reference_selection_function = NULL
)

Arguments

Details

'r lifecycle::badge("maturing")'

Scales transcript abundance compensating for sequencing depth (e.g., with TMM algorithm, Robinson and Oshlack doi.org/10.1186/gb-2010-11-3-r25). Lowly transcribed transcripts/genes (defined with minimum_counts and minimum_proportion parameters) are filtered out from the scaling procedure. The scaling inference is then applied back to all unfiltered data.

Underlying method edgeR::calcNormFactors(.data, method = c("TMM","TMMwsp","RLE","upperquartile"))

Value

A tbl object with additional columns with scaled data as '<NAME OF COUNT COLUMN>_scaled'

A tbl object with additional columns with scaled data as '<NAME OF COUNT COLUMN>_scaled'

A tbl object with additional columns with scaled data as '<NAME OF COUNT COLUMN>_scaled'

A tbl object with additional columns with scaled data as '<NAME OF COUNT COLUMN>_scaled'

A 'SummarizedExperiment' object

A 'SummarizedExperiment' object

Examples

tidybulk::se_mini |>
   identify_abundant() |>
   scale_abundance()

Description

SummarizedExperiment

Usage

se

Format

An object of class RangedSummarizedExperiment with 100 rows and 8 columns.

Description

SummarizedExperiment mini for vignette

Usage

se_mini

Format

An object of class SummarizedExperiment with 527 rows and 5 columns.

Description

'summarise()' creates a new data frame. It will have one (or more) rows for each combination of grouping variables; if there are no grouping variables, the output will have a single row summarising all observations in the input. It will contain one column for each grouping variable and one column for each of the summary statistics that you have specified.

'summarise()' and 'summarize()' are synonyms.

Arguments

Value

An object _usually_ of the same type as '.data'.

* The rows come from the underlying 'group_keys()'. * The columns are a combination of the grouping keys and the summary expressions that you provide. * If 'x' is grouped by more than one variable, the output will be another [grouped_df] with the right-most group removed. * If 'x' is grouped by one variable, or is not grouped, the output will be a [tibble]. * Data frame attributes are **not** preserved, because 'summarise()' fundamentally creates a new data frame.

Useful functions

* Center: [mean()], [median()] * Spread: [sd()], [IQR()], [mad()] * Range: [min()], [max()], [quantile()] * Position: [first()], [last()], [nth()], * Count: [n()], [n_distinct()] * Logical: [any()], [all()]

Backend variations

The data frame backend supports creating a variable and using it in the same summary. This means that previously created summary variables can be further transformed or combined within the summary, as in [mutate()]. However, it also means that summary variables with the same names as previous variables overwrite them, making those variables unavailable to later summary variables.

This behaviour may not be supported in other backends. To avoid unexpected results, consider using new names for your summary variables, especially when creating multiple summaries.

Methods

This function is a **generic**, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

The following methods are currently available in loaded packages:

See Also

Other single table verbs: arrange(), filter(), mutate(), rename()

Examples

# A summary applied to ungrouped tbl returns a single row

mtcars |>
  summarise(mean = mean(disp))

Description

Get ENTREZ id from gene SYMBOL

Usage

symbol_to_entrez(.data, .transcript = NULL, .sample = NULL)

Arguments

Value

A tbl

Examples

# This function was designed for data.frame
# Convert from SummarizedExperiment for this example. It is NOT reccomended.

tidybulk::se_mini |> tidybulk() |> as_tibble() |> symbol_to_entrez(.transcript = .feature, .sample = .sample)

Description

test_differential_abundance() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with additional columns for the statistics from the hypothesis test.

Usage

test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = 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",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = NULL
)

## S4 method for signature 'tbl_df'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = NULL
)

## S4 method for signature 'tidybulk'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = NULL
)

## S4 method for signature 'SummarizedExperiment'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
test_differential_abundance(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  contrasts = NULL,
  method = "edgeR_quasi_likelihood",
  test_above_log2_fold_change = NULL,
  scaling_method = "TMM",
  omit_contrast_in_colnames = FALSE,
  prefix = "",
  action = "add",
  ...,
  significance_threshold = NULL,
  fill_missing_values = NULL,
  .contrasts = NULL
)

Arguments

Details

'r lifecycle::badge("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, limma_voom_sample_weights https://doi.org/10.1093/nar/gkv412 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) |> // or glmFit according to choice 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()

Underlying method for glmmSeq framework:

counts = .data assay(my_assay)

# Create design matrix for dispersion, removing random effects design = model.matrix( object = .formula |> lme4::nobars(), data = metadata )

dispersion = counts |> edgeR::estimateDisp(design = design)

glmmSeq( .formula, countdata = counts , metadata = metadata |> as.data.frame(), dispersion = dispersion, progress = TRUE, method = method |> str_remove("(?i)^glmmSeq_" ), )

Value

A consistent object (to the input) with additional columns for the statistics from the test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) with additional columns for the statistics from the test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) 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

Examples

# edgeR

 tidybulk::se_mini |>
 identify_abundant() |>
	test_differential_abundance( ~ condition )

	# The function `test_differential_abundance` operates with contrasts too

 tidybulk::se_mini |>
 identify_abundant(factor_of_interest = condition) |>
 test_differential_abundance(
	    ~ 0 + condition,
	    contrasts = c( "conditionTRUE - conditionFALSE")
 )

 # DESeq2 - equivalent for limma-voom

my_se_mini = tidybulk::se_mini
my_se_mini$condition  = factor(my_se_mini$condition)

# demontrating with `fitType` that you can access any arguments to DESeq()
my_se_mini  |>
   identify_abundant(factor_of_interest = condition) |>
       test_differential_abundance( ~ condition, method="deseq2", fitType="local")

# testing above a log2 threshold, passes along value to lfcThreshold of results()
res <- my_se_mini  |>
   identify_abundant(factor_of_interest = condition) |>
        test_differential_abundance( ~ condition, method="deseq2",
            fitType="local",
            test_above_log2_fold_change=4 )

# Use random intercept and random effect models

 se_mini[1:50,] |>
  identify_abundant(factor_of_interest = condition) |>
  test_differential_abundance(
    ~ condition + (1 + condition | time),
    method = "glmmseq_lme4", cores = 1
  )

# confirm that lfcThreshold was used
## Not run: 
    res |>
        mcols() |>
        DESeq2::DESeqResults() |>
        DESeq2::plotMA()

## End(Not run)

# The function `test_differential_abundance` operates with contrasts too

 my_se_mini |>
 identify_abundant() |>
 test_differential_abundance(
	    ~ 0 + condition,
	    contrasts = list(c("condition", "TRUE", "FALSE")),
	    method="deseq2",
         fitType="local"
 )

Description

test_differential_cellularity() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with additional columns for the statistics from the hypothesis test.

Usage

test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

## S4 method for signature 'spec_tbl_df'
test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

## S4 method for signature 'tbl_df'
test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

## S4 method for signature 'tidybulk'
test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

## S4 method for signature 'SummarizedExperiment'
test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

## S4 method for signature 'RangedSummarizedExperiment'
test_differential_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  significance_threshold = 0.05,
  ...
)

Arguments

Details

'r lifecycle::badge("maturing")'

This routine applies a deconvolution method (e.g., Cibersort; DOI: 10.1038/nmeth.3337) and passes the proportions inferred into a generalised linear model (DOI:dx.doi.org/10.1007/s11749-010-0189-z) or a cox regression model (ISBN: 978-1-4757-3294-8)

Underlying method for the generalised linear model: data |> deconvolve_cellularity( !!.sample, !!.transcript, !!.abundance, method=method, reference = reference, action="get", ... ) [..] betareg::betareg(.my_formula, .)

Underlying method for the cox regression: data |> deconvolve_cellularity( !!.sample, !!.transcript, !!.abundance, method=method, reference = reference, action="get", ... ) [..] mutate(.proportion_0_corrected = .proportion_0_corrected |> boot::logit()) survival::coxph(.my_formula, .)

Value

A consistent object (to the input) 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

Examples

# Regular regression
	test_differential_cellularity(
	 tidybulk::se_mini ,
	    . ~ condition,
	    cores = 1
	)

	# Cox regression - multiple

tidybulk::se_mini |>

	# Test
	test_differential_cellularity(
	    survival::Surv(days, dead) ~ .,
	    cores = 1
	)

Description

test_gene_enrichment() takes as input a 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a 'tbl' of gene set information

Usage

test_gene_enrichment(
  .data,
  .formula,
  .sample = NULL,
  .entrez,
  .abundance = NULL,
  contrasts = NULL,
  methods = c("camera", "roast", "safe", "gage", "padog", "globaltest", "ora"),
  gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6", "c7", "kegg_disease",
    "kegg_metabolism", "kegg_signaling"),
  species,
  cores = 10,
  method = NULL,
  .contrasts = NULL
)

## S4 method for signature 'spec_tbl_df'
test_gene_enrichment(
  .data,
  .formula,
  .sample = NULL,
  .entrez,
  .abundance = NULL,
  contrasts = NULL,
  methods = c("camera", "roast", "safe", "gage", "padog", "globaltest", "ora"),
  gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6", "c7", "kegg_disease",
    "kegg_metabolism", "kegg_signaling"),
  species,
  cores = 10,
  method = NULL,
  .contrasts = NULL
)

## S4 method for signature 'tbl_df'
test_gene_enrichment(
  .data,
  .formula,
  .sample = NULL,
  .entrez,
  .abundance = NULL,
  contrasts = NULL,
  methods = c("camera", "roast", "safe", "gage", "padog", "globaltest", "ora"),
  gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6", "c7", "kegg_disease",
    "kegg_metabolism", "kegg_signaling"),
  species,
  cores = 10,
  method = NULL,
  .contrasts = NULL
)

## S4 method for signature 'tidybulk'
test_gene_enrichment(
  .data,
  .formula,
  .sample = NULL,
  .entrez,
  .abundance = NULL,
  contrasts = NULL,
  methods = c("camera", "roast", "safe", "gage", "padog", "globaltest", "ora"),
  gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6", "c7", "kegg_disease",
    "kegg_metabolism", "kegg_signaling"),
  species,
  cores = 10,
  method = NULL,
  .contrasts = NULL
)

## S4 method for signature 'SummarizedExperiment'
test_gene_enrichment(
  .data,
  .formula,
  .sample = NULL,
  .entrez,
  .abundance = NULL,
  contrasts = NULL,
  methods = c("camera", "roast", "safe", "gage", "padog", "globaltest", "ora"),
  gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6", "c7", "kegg_disease",
    "kegg_metabolism", "kegg_signaling"),
  species,
  cores = 10,
  method = NULL,
  .contrasts = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
test_gene_enrichment(
  .data,
  .formula,
  .sample = NULL,
  .entrez,
  .abundance = NULL,
  contrasts = NULL,
  methods = c("camera", "roast", "safe", "gage", "padog", "globaltest", "ora"),
  gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6", "c7", "kegg_disease",
    "kegg_metabolism", "kegg_signaling"),
  species,
  cores = 10,
  method = NULL,
  .contrasts = NULL
)

Arguments

Details

'r lifecycle::badge("maturing")'

This wrapper executes ensemble gene enrichment analyses of the dataset using EGSEA (DOI:0.12688/f1000research.12544.1)

dge = data |> keep_abundant( factor_of_interest = !!as.symbol(parse_formula(.formula)[[1]]), !!.sample, !!.entrez, !!.abundance )

# Make sure transcript names are adjacent [...] as_matrix(rownames = !!.entrez) edgeR::DGEList(counts = .)

idx = buildIdx(entrezIDs = rownames(dge), species = species, msigdb.gsets = msigdb.gsets, kegg.exclude = kegg.exclude)

dge |>

# Calculate weights limma::voom(design, plot = FALSE) |>

# Execute EGSEA egsea( contrasts = my_contrasts, baseGSEAs = methods, gs.annots = idx, sort.by = "med.rank", num.threads = cores, report = FALSE )

Value

A consistent object (to the input)

A consistent object (to the input)

A consistent object (to the input)

A consistent object (to the input)

A consistent object (to the input)

A consistent object (to the input)

Examples

## Not run: 

library(SummarizedExperiment)
se = tidybulk::se_mini
rowData( se)$entrez = rownames(se )
df_entrez = aggregate_duplicates(se,.transcript = entrez )

library("EGSEA")

	test_gene_enrichment(
		df_entrez,
		~ condition,
		.sample = sample,
		.entrez = entrez,
		.abundance = count,
         methods = c("roast" , "safe", "gage"  ,  "padog" , "globaltest", "ora" ),
         gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6", "c7", "kegg_disease", "kegg_metabolism", "kegg_signaling"),
		species="human",
		cores = 2
	)


## End(Not run)

Description

test_gene_overrepresentation() takes as input a 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a 'tbl' with the GSEA statistics

Usage

test_gene_overrepresentation(
  .data,
  .entrez,
  .do_test,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

## S4 method for signature 'spec_tbl_df'
test_gene_overrepresentation(
  .data,
  .entrez,
  .do_test,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

## S4 method for signature 'tbl_df'
test_gene_overrepresentation(
  .data,
  .entrez,
  .do_test,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

## S4 method for signature 'tidybulk'
test_gene_overrepresentation(
  .data,
  .entrez,
  .do_test,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

## S4 method for signature 'SummarizedExperiment'
test_gene_overrepresentation(
  .data,
  .entrez,
  .do_test,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
test_gene_overrepresentation(
  .data,
  .entrez,
  .do_test,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

Arguments

Details

'r lifecycle::badge("maturing")'

This wrapper execute gene enrichment analyses of the dataset using a list of transcripts and GSEA. This wrapper uses clusterProfiler (DOI: doi.org/10.1089/omi.2011.0118) on the back-end.

Undelying method: msigdbr::msigdbr(species = species) |> nest(data = -gs_cat) |> mutate(test = map( data, ~ clusterProfiler::enricher( my_entrez_rank, TERM2GENE=.x |> select(gs_name, entrez_gene), pvalueCutoff = 1 ) |> as_tibble() ))

Value

A consistent object (to the input)

A 'spec_tbl_df' object

A 'tbl_df' object

A 'tidybulk' object

A 'SummarizedExperiment' object

A 'RangedSummarizedExperiment' object

Examples

print("Not run for build time.")

#se_mini = aggregate_duplicates(tidybulk::se_mini, .transcript = entrez)
#df_entrez = mutate(df_entrez, do_test = feature %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))

## Not run: 
	test_gene_overrepresentation(
		df_entrez,
		.sample = sample,
		.entrez = entrez,
		.do_test = do_test,
		species="Homo sapiens",
   gene_sets =c("C2")
	)

## End(Not run)

Description

test_gene_rank() takes as input a 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a 'tbl' with the GSEA statistics

Usage

test_gene_rank(
  .data,
  .entrez,
  .arrange_desc,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

## S4 method for signature 'spec_tbl_df'
test_gene_rank(
  .data,
  .entrez,
  .arrange_desc,
  species,
  .sample = NULL,
  gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6", "c7"),
  gene_set = NULL
)

## S4 method for signature 'tbl_df'
test_gene_rank(
  .data,
  .entrez,
  .arrange_desc,
  species,
  .sample = NULL,
  gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6", "c7"),
  gene_set = NULL
)

## S4 method for signature 'tidybulk'
test_gene_rank(
  .data,
  .entrez,
  .arrange_desc,
  species,
  .sample = NULL,
  gene_sets = c("h", "c1", "c2", "c3", "c4", "c5", "c6", "c7"),
  gene_set = NULL
)

## S4 method for signature 'SummarizedExperiment'
test_gene_rank(
  .data,
  .entrez,
  .arrange_desc,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

## S4 method for signature 'RangedSummarizedExperiment'
test_gene_rank(
  .data,
  .entrez,
  .arrange_desc,
  species,
  .sample = NULL,
  gene_sets = NULL,
  gene_set = NULL
)

Arguments

Details

This wrapper execute gene enrichment analyses of the dataset using a list of transcripts and GSEA. This wrapper uses clusterProfiler (DOI: doi.org/10.1089/omi.2011.0118) on the back-end.

Undelying method: # Get gene sets signatures msigdbr::msigdbr(species = species)

# Filter specific gene_sets if specified. This was introduced to speed up examples executionS when( !is.null(gene_sets ) ~ filter(., gs_cat ~ (.) ) |>

# Execute calculation nest(data = -gs_cat) |> mutate(fit = map( data, ~ clusterProfiler::GSEA( my_entrez_rank, TERM2GENE=.x |> select(gs_name, entrez_gene), pvalueCutoff = 1 )

))

Value

A consistent object (to the input)

A 'spec_tbl_df' object

A 'tbl_df' object

A 'tidybulk' object

A 'SummarizedExperiment' object

A 'RangedSummarizedExperiment' object

Examples

print("Not run for build time.")

## Not run: 

df_entrez = tidybulk::se_mini
df_entrez = mutate(df_entrez, do_test = .feature %in% c("TNFRSF4", "PLCH2", "PADI4", "PAX7"))
df_entrez  = df_entrez |> test_differential_abundance(~ condition)


test_gene_rank(
	df_entrez,
		.sample = .sample,
	.entrez = entrez,
		species="Homo sapiens",
   gene_sets =c("C2"),
 .arrange_desc = logFC
	)

## End(Not run)

Description

test_stratification_cellularity() takes as input A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and returns a consistent object (to the input) with additional columns for the statistics from the hypothesis test.

Usage

test_stratification_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  ...
)

## S4 method for signature 'spec_tbl_df'
test_stratification_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  ...
)

## S4 method for signature 'tbl_df'
test_stratification_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  ...
)

## S4 method for signature 'tidybulk'
test_stratification_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  ...
)

## S4 method for signature 'SummarizedExperiment'
test_stratification_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  ...
)

## S4 method for signature 'RangedSummarizedExperiment'
test_stratification_cellularity(
  .data,
  .formula,
  .sample = NULL,
  .transcript = NULL,
  .abundance = NULL,
  method = "cibersort",
  reference = X_cibersort,
  ...
)

Arguments

Details

'r lifecycle::badge("maturing")'

This routine applies a deconvolution method (e.g., Cibersort; DOI: 10.1038/nmeth.3337) and passes the proportions inferred into a generalised linear model (DOI:dx.doi.org/10.1007/s11749-010-0189-z) or a cox regression model (ISBN: 978-1-4757-3294-8)

Underlying method for the test: data |> deconvolve_cellularity( !!.sample, !!.transcript, !!.abundance, method=method, reference = reference, action="get", ... ) [..] |> mutate(.high_cellularity = .proportion > median(.proportion)) |> survival::survdiff(data = data, .my_formula)

Value

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

A consistent object (to the input) with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

Examples

tidybulk::se_mini |>
test_stratification_cellularity(
	survival::Surv(days, dead) ~ .,
	cores = 1
)

Description

tidybulk() creates an annotated 'tidybulk' tibble from a 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment))

Usage

tidybulk(.data, .sample, .transcript, .abundance, .abundance_scaled = NULL)

## S4 method for signature 'spec_tbl_df'
tidybulk(.data, .sample, .transcript, .abundance, .abundance_scaled = NULL)

## S4 method for signature 'tbl_df'
tidybulk(.data, .sample, .transcript, .abundance, .abundance_scaled = NULL)

## S4 method for signature 'SummarizedExperiment'
tidybulk(.data, .sample, .transcript, .abundance, .abundance_scaled = NULL)

## S4 method for signature 'RangedSummarizedExperiment'
tidybulk(.data, .sample, .transcript, .abundance, .abundance_scaled = NULL)

Arguments

Details

'r lifecycle::badge("maturing")'

This function creates a tidybulk object and is useful if you want to avoid to specify .sample, .transcript and .abundance arguments all the times. The tidybulk object have an attribute called internals where these three arguments are stored as metadata. They can be extracted as attr(<object>, "internals").

Value

A 'tidybulk' object

A 'tidybulk' object

A 'tidybulk' object

A 'tidybulk' object

A 'tidybulk' object

Examples

tidybulk(tidybulk::se_mini)

Description

tidybulk_SAM_BAM() creates a 'tt' object from A 'tbl' (with at least three columns for sample, feature and transcript abundance) or 'SummarizedExperiment' (more convenient if abstracted to tibble with library(tidySummarizedExperiment))

Usage

tidybulk_SAM_BAM(file_names, genome = "hg38", ...)

## S4 method for signature 'character,character'
tidybulk_SAM_BAM(file_names, genome = "hg38", ...)

Arguments

Details

'r lifecycle::badge("maturing")'

This function is based on FeatureCounts package (DOI: 10.1093/bioinformatics/btt656). This function creates a tidybulk object and is useful if you want to avoid to specify .sample, .transcript and .abundance arguments all the times. The tidybulk object have an attribute called internals where these three arguments are stored as metadata. They can be extracted as attr(<object>, "internals").

Underlying core function Rsubread::featureCounts(annot.inbuilt = genome,nthreads = n_cores, ...)

Value

A 'tidybulk' object

A 'tidybulk' object

Description

Needed for tests tximeta_summarizeToGene_object, It is SummarizedExperiment from tximeta

Usage

tximeta_summarizeToGene_object

Format

An object of class RangedSummarizedExperiment with 10 rows and 1 columns.

Description

unnest

nest

Arguments

Value

A tidySummarizedExperiment objector a tibble depending on input

A tt object

Examples

tidybulk::se_mini |> tidybulk() |> nest( data = -.feature) |> unnest(data)


tidybulk::se_mini %>% tidybulk() %>% nest( data = -.feature)

Description

Needed for vignette vignette_manuscript_signature_boxplot

Usage

vignette_manuscript_signature_boxplot

Format

An object of class tbl_df (inherits from tbl, data.frame) with 899 rows and 12 columns.

Description

Needed for vignette vignette_manuscript_signature_tsne

Usage

vignette_manuscript_signature_tsne

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 283 rows and 10 columns.

Description

Needed for vignette vignette_manuscript_signature_tsne2

Usage

vignette_manuscript_signature_tsne2

Format

An object of class tbl_df (inherits from tbl, data.frame) with 283 rows and 9 columns.

Description

Cibersort reference

Usage

X_cibersort

Format

An object of class data.frame with 547 rows and 22 columns.