Package 'qsvaR'

Degradation time t-statistics

Description

These t-statistics are derived from the degradation timepoints data built into qsvaR. They are the results from multiple models where we determined the association of transcripts with degradation time adjusting for brain region (so parallel degradation effects across brain regions). They are used for plotting in DEqual().

Format

A data.frame() with the t statistics for degradation time. The rownames() are the GENCODE transcript IDs.

See Also

DEqual

---

Differential expression quality (DEqual) plot

Description

A DEqual plot compares the effect of RNA degradation from an independent degradation experiment on the y axis to the effect of the outcome of interest. They were orignally described by Jaffe et al, PNAS, 2017 https://doi.org/10.1073/pnas.1617384114. Other DEqual versions are included in Collado-Torres et al, Neuron, 2019 https://doi.org/10.1016/j.neuron.2019.05.013. This function compares your t-statistics of interest computed on transcripts against the t-statistics from degradation time adjusting for the six brain regions from degradation experiment data used for determining rse_tx.

Usage

DEqual(
  DE,
  deg_tstats = qsvaR::degradation_tstats,
  show.legend = TRUE,
  show.cor = c("caption", "corner-top", "corner-bottom", "none"),
  font.size = 12,
  cor.size = font.size/2,
  cor.label = "cor: "
)

Arguments

DE	a data.frame() with a column "t" containing the t-statistics from Differential Expression, typically generated with limma::topTable(). rownames(DE) must have transcript Ensembl/Gencode IDs.
deg_tstats	an optional data.frame() with a column "t" containing t-statistics resulted from a degradation experiment. Default is the internal qsvaR::degradation_tstats from the package authors.
show.legend	logical (default TRUE) to show legend in the plot
show.cor	specify where to show the correlation value. Can be one of "caption", "corner-top", "corner-bottom", or "none".
font.size	numeric value to set the base font size of the plot
cor.size	numeric (default font.size/2) to set the font size for the correlation text
cor.label	character (default "cor: ") to set the text preceding the correlation value

Value

a ggplot object of the DE t-statistic vs the DE statistic from degradation

Examples

## Random differential expression t-statistics for the same transcripts
## we have degradation t-statistics for in `degradation_tstats`.
set.seed(101)
random_de <- data.frame(
    t = rt(nrow(degradation_tstats), 5),
    row.names = sample(
        rownames(degradation_tstats),
        nrow(degradation_tstats)
    )
)

## Create the DEqual plot
DEqual(random_de)

---

Generate matrix of qsvs

Description

Using the pcs and the k number of components be included, we generate the qsva matrix.

Usage

get_qsvs(qsvPCs, k)

Arguments

qsvPCs	prcomp object generated by taking the pcs of degraded transcripts
k	number of qsvs to be included.

Value

matrix with k principal components for each sample.

Examples

qsv <- getPCs(rse_tx, "tpm")
get_qsvs(qsv, 2)

---

Obtain expression matrix for degraded transcripts

Description

This function is used to obtain a RangedSummarizedExperiment-class of transcripts and their expression values #' These transcripts are selected based on a prior study of RNA degradation in postmortem brain tissues. This object can later be used to obtain the principle components necessary to remove the effect of degradation in differential expression.

Usage

getDegTx(
  rse_tx,
  sig_transcripts = select_transcripts(),
  assayname = "tpm",
  verbose = TRUE
)

Arguments

rse_tx	A RangedSummarizedExperiment-class object containing the transcript data desired to be studied.
sig_transcripts	A character() vector of transcripts that should be associated with degradation, expected to be present in rownames(rse_tx).
assayname	character string specifying the name of the assay desired in rse_tx
verbose	specify if the function should report how many model transcripts were matched

Value

A RangedSummarizedExperiment-class object.

Examples

degTx <- getDegTx(rse_tx)

---

PCs from transcripts

Description

This function returns the pcs from the obtained RangedSummarizedExperiment object of selected transcripts

Usage

getPCs(rse_tx, assayname = "tpm")

Arguments

rse_tx	Ranged Summarizeed Experiment with only trancsripts selected for qsva
assayname	character string specifying the name of the assay desired in rse_tx

Value

prcomp object generated by taking the pcs of degraded transcripts

Examples

getPCs(rse_tx, "tpm")

---

Apply num.sv algorithm to determine the number of pcs to be included

Description

Apply num.sv algorithm to determine the number of pcs to be included

Usage

k_qsvs(rse_tx, mod, assayname)

Arguments

rse_tx	A RangedSummarizedExperiment-class object containing the transcript data desired to be studied.
mod	Model Matrix with necessary variables the you would model for in differential expression
assayname	character string specifying the name of the assay desired in rse_tx

Value

integer representing number of pcs to be included

Examples

## First we need to define a statistical model. We'll use the example
## rse_tx data. Note that the model you'll use in your own data
## might look different from this model.
mod <- model.matrix(~ mitoRate + Region + rRNA_rate + totalAssignedGene + RIN,
    data = colData(rse_tx)
)

## To ensure that the results are reproducible, you will need to set a
## random seed with the set.seed() function. Internally, we are using
## sva::num.sv() which needs a random seed to ensure reproducibility of the
## results.
set.seed(20230621)
k_qsvs(rse_tx, mod, "tpm")

---

Remove version number from Gencode/Ensembl transcript names

Description

This function removes the Gencode/ENSEMBL version from the transcript ID, while protecting _PAR_Y suffixes if present

Usage

normalize_tx_names(txnames)

Arguments

txnames

A character() vector of GENCODE or ENSEMBL transcript IDs

Value

A character() vector of transcript names without versioning

Examples

ensIDs <- normalize_tx_names(rownames(rse_tx))

---

A wrapper function used to perform qSVA in one step.

Description

A wrapper function used to perform qSVA in one step.

Usage

qSVA(rse_tx, sig_transcripts = select_transcripts(), mod, assayname)

Arguments

rse_tx	A RangedSummarizedExperiment-class object containing the transcript data desired to be studied.
sig_transcripts	A character() vector of transcripts that should be associated with degradation, expected to be present in rownames(rse_tx).
mod	Model Matrix with necessary variables the you would model for in differential expression.
assayname	character string specifying the name of the assay desired in rse_tx

Value

matrix with k principal components for each sample

Examples

## First we need to define a statistical model. We'll use the example
## rse_tx data. Note that the model you'll use in your own data
## might look different from this model.
mod <- model.matrix(~ mitoRate + Region + rRNA_rate + totalAssignedGene + RIN,
    data = colData(rse_tx)
)

## To ensure that the results are reproducible, you will need to set a
## random seed with the set.seed() function. Internally, we are using
## sva::num.sv() which needs a random seed to ensure reproducibility of the
## results.
set.seed(20230621)
qSVA(rse_tx = rse_tx, mod = mod, assayname = "tpm")

---

Example of RSE object with RNA-seq transcript quantification data

Description

This data is a RangedSummarizedExperiment-class with transcript quantification data stored in an "tpm" assay. It is used to demonstrate the use of qsvaR in bulk RNA-seq data.

Format

A RangedSummarizedExperiment-class

See Also

getPCs k_qsvs getDegTx qSVA

---

Select transcripts associated with degradation

Description

Helper function to select which experimental model(s) will be used to generate the qSVs. Degradation-associated transcripts are derived in four different models (transcripts). The cell_component parameter controls whether the models with cell-type proportions are included. This function extract the top top_n transcripts found to be significant in each considered model, then returns the union of transcripts across all considered models. Up to 10,000 transcripts are available to select from each model prior to taking the union.

Usage

select_transcripts(top_n = 1000, cell_component = FALSE)

Arguments

top_n	An integer(1) specifying how many significant transcripts to extract from each model prior to taking a union across models.
cell_component	A logical(1). If FALSE, only include transcripts from the main and interaction models (see main_model and int_model here: transcripts). If TRUE, additionally include main and interaction models that include cell-type proportions (a total of 4 models).

Value

A character() with the transcript IDs.

Examples

## Default set of transcripts associated with degradation
sig_transcripts <- select_transcripts()
length(sig_transcripts)
head(sig_transcripts)

## Select more transcripts if desired
length(select_transcripts(top_n = 5000))

---

Transcripts for Degradation Models

Description

This object is a list of four tibbles where each element corresponds to the top 10,000 transcripts (by significance) and their adjusted p-values for a given degradation model. The main_model model is a linear model modelling expression against a sample's degradation time, with brain region as a covariate. The int_model model is similar but includes an interaction term with degradation time and brain region. The cell_main_model and cell_int_model models are like the respective main_model and int_model models, but including cell-type fractions from deconvolution as linear terms.

Usage

transcripts

Format

A list() of tibble()s containing the transcripts and adjusted p-values selected by each model. Each string is a GENCODE transcript IDs.

See Also

select_transcripts

---

Check validity of transcript vectors and return a vector matching indexes in tx1

Description

This function is used to check if tx1 and tx2 are GENCODE or ENSEMBL transcript IDs and return an integer vector of tx1 transcript indexes that are in tx2.

Usage

which_tx_names(txnames, sig_tx)

Arguments

txnames	A character() vector of GENCODE or ENSEMBL transcript IDs.
sig_tx	A character() vector of GENCODE or ENSEMBL signature transcript IDs.

Value

A integer() vector of txnames transcript indexes in sig_tx.

Examples

sig_tx <- select_transcripts(cell_component = TRUE)
whichTx <- which_tx_names(rownames(rse_tx), sig_tx)

---