runEISA {eisaR} | R Documentation

## Run Exon-Intron Split Analysis.

### Description

Starting from count tables with exonic and intronic counts for two conditions, perform all the steps in EISA (normalize, identify quantifyable genes, calculate contrasts and their significance).

### Usage

    runEISA(
      cntEx,
      cntIn,
      cond,
      method = NULL,
      modelSamples = TRUE,
      geneSelection = c("filterByExpr", "none", "Gaidatzis2015"),
      statFramework = c("QLF", "LRT"),
      effects = c("predFC", "Gaidatzis2015"),
      pscnt = 2,
      sizeFactor = c("exon", "intron", "individual"),
      recalcNormFactAfterFilt = TRUE,
      recalcLibSizeAfterFilt = FALSE,
      ...
    )

### Arguments

`cntEx` | Gene by sample `matrix` with exonic counts, OR a `SummarizedExperiment` with two assays named `exon` and `intron`, containing exonic and intronic counts, respectively. If `cntEx` is a `SummarizedExperiment`, `cntIn` will be disregarded.
`cntIn` | Gene by sample `matrix` with intronic counts. Must have the same structure as `cntEx` (same number and order of rows and columns) if `cntEx` is a matrix. Will be disregarded if `cntEx` is a `SummarizedExperiment`.
`cond` | `numeric`, `character` or `factor` with two levels that groups the samples (columns of `cntEx` and `cntIn`) into two conditions. The contrast will be defined as secondLevel - firstLevel.
`method` | One of `NULL` (the default) or `"Gaidatzis2015"`. If `"Gaidatzis2015"`, gene filtering, statistical analysis and calculation of contrasts is performed as described in Gaidatzis et al. 2015, and the statistical analysis is based on `glmFit` and `glmLRT`. This is done by setting the arguments `modelSamples`, `geneSelection`, `effects`, `pscnt`, `statFramework`, `sizeFactor`, `recalcNormFactAfterFilt` and `recalcLibSizeAfterFilt` to appropriate values (see details), overriding the defaults or any value passed to these arguments. If `NULL`, the default values of the arguments will be used instead (recommended).
`modelSamples` | Whether to include a sample identifier in the design matrix of the statistical model. If `TRUE`, potential sample effects that affect both exonic and intronic counts of that sample will be taken into account, which could result in higher sensitivity (default: `TRUE`).
`geneSelection` | Controls how to select quantifyable genes. One of the following: `"filterByExpr"`: (default) First, counts are normalized using `calcNormFactors`, treating intronic and exonic counts as individual samples. Then, `filterByExpr` is used with default parameters to select quantifyable genes. `"none"`: This will use all the genes provided in the count tables, assuming that an appropriate selection of quantifyable genes has already been done. `"Gaidatzis2015"`: First, intronic and exonic counts are linearly scaled to the mean library size (estimated as the sum of all intronic or exonic counts, respectively). Then, quantifyable genes are selected as the genes with counts `x` that fulfill `log2(x + 8) > 5` in both exons and introns.
`statFramework` | Selects the framework within `edgeR` that is used for the statistical analysis. One of: `"QLF"`: (default) Quasi-likelihood F-test using `glmQLFit` and `glmQLFTest`. This framework is highly recommended as it gives stricter error rate control by accounting for the uncertainty in dispersion estimation. `"LRT"`: Likelihood ratio test using `glmFit` and `glmLRT` .
`effects` | How the effects (contrasts or log2 fold-changes) are calculated. One of: `"predFC"`: (default) Fold-changes are calculated using the fitted model with `predFC` with `prior.count = pscnt`. Please note that if a sample factor is included in the model (`modelSamples=TRUE`), effects cannot be obtained from that model. In that case, effects are obtained from a simpler model without sample effects. `"Gaidatzis2015"`: Fold-changes are calculated using the formula `log2((x + pscnt)/(y + pscnt))`. If `pscnt` is not set to 8, `runEISA` will warn that this deviates from the method used in Gaidatzis et al., 2015.
`pscnt` | `numeric(1)` with pseudocount to add to read counts (default: 2). For `method = "Gaidatzis2015"`, it is set to 8. It is added to scaled read counts used in `geneSelection = "Gaidatzis2015"` and `effects = "Gaidatzis2015"`, or else used in `cpm(..., prior.count = pscnt)` and `predFC(..., prior.count = pscnt)`.
`sizeFactor` | How the size factors are calculated in the analysis. If 'exon' (default), the exon-derived size factors are used also for the columns corresponding to intronic counts. If 'intron', the intron-derived size factors are used also for the columns corresponding to exonic counts. If 'individual', column-wise size factors are calculated.
`recalcNormFactAfterFilt` | Logical, indicating whether normalization factors should be recalculated after filtering out lowly expressed genes.
`recalcLibSizeAfterFilt` | Logical, indicating whether library sizes should be recalculated after filtering out lowly expressed genes.
`...` | additional arguments passed to the `DGEList` constructor, such as `lib.size` or `genes`.

### Details

Setting `method = "Gaidatzis2015"` has precedence over other argument values and corresponds to setting: `modelSamples = FALSE, geneSelection = "Gaidatzis2015", statFramework = "LRT", effects = "Gaidatzis2015", pscnt = 8, sizeFactor = "individual", recalcNormFactAfterFilt = TRUE, recalcLibSizeAfterFilt = FALSE`.

### Value

a `list` with elements

fracIn

  fraction intronic counts in each sample

contrastName

  contrast name

contrasts

  contrast matrix for quantifyable genes, with average log2 fold-changes in exons (`Dex`), in introns (`Din`), and average difference between log2 fold-changes in exons and introns (`Dex.Din`)

DGEList

  `DGEList` object used in model fitting

tab.ExIn

  statisical results for differential changes between exonic and intronic contrast, an indication for post-transcriptional regulation.

contr.ExIn

  contrast vector used for testing the difference between exonic and intronic contrast (results in `tab.ExIn`)

designMatrix

  design matrix used for testing the difference between exonic and intronic contrast (results in `tab.ExIn`)

params

  a `list` with parameter values used to run EISA

### Author(s)

Michael Stadler

### References

Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation. Dimos Gaidatzis, Lukas Burger, Maria Florescu and Michael B. Stadler Nature Biotechnology, 2015 Jul;33(7):722-9. doi: 10.1038/nbt.3269.

### See Also

`DGEList` for `DGEList` object construction, `calcNormFactors` for normalization, `filterByExpr` for gene selection, `glmFit` and `glmQLFit` for statistical analysis.

### Examples

    cntEx <- readRDS(system.file("extdata", "Fig3abc_GSE33252_rawcounts_exonic.rds",
                                 package = "eisaR"))[,-1]
    cntIn <- readRDS(system.file("extdata", "Fig3abc_GSE33252_rawcounts_intronic.rds",
                                 package = "eisaR"))[,-1]
    cond <- factor(c("ES","ES","TN","TN"))
    res <- runEISA(cntEx, cntIn, cond)
    plotEISA(res)

[Package eisaR version 1.2.0 Index]