analyzeSwitchConsequences: Analyze Consequences of Isoform Switches

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/analyze_switch_consequences.R

Description

This function extracts all isoforms with an absolute dIF change larger than dIFcutoff from genes with a significant isoform switch (as defined by alpha). For each gene these isoforms are then analyzed for differences in the functional annotation (defined by consequencesToAnalyze) by pairwise comparing the isoforms that are used more (switching up (dIF > 0)) with the isoforms that are used less (switching down (dIF < 0)). For each comparison a small report of the analyzed features is returned.

Usage

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analyzeSwitchConsequences(
    switchAnalyzeRlist,
    consequencesToAnalyze=c(
        'intron_retention',
        'coding_potential',
        'ORF_seq_similarity',
        'NMD_status',
        'domains_identified',
        'IDR_identified',
        'IDR_type',
        'signal_peptide_identified'
    ),
    alpha=0.05,
    dIFcutoff=0.1,
    onlySigIsoforms=FALSE,
    ntCutoff=50,
    ntFracCutoff=NULL,
    ntJCsimCutoff=0.8,
    AaCutoff=10,
    AaFracCutoff=0.5,
    AaJCsimCutoff=0.9,
    removeNonConseqSwitches=TRUE,
    showProgress=TRUE,
    quiet=FALSE
)

Arguments

switchAnalyzeRlist

A switchAnalyzeRlist object containing the result of an isoform switch analysis (such as the one provided by isoformSwitchTestDEXSeq) as well as additional annotation data for the isoforms.

consequencesToAnalyze

A vector of strings indicating what type of functional consequences to analyze. Do note that there is bound to be some differences between isoforms (else they would be identical and not annotated as separate isoforms). See details for full list of usable strings and their meaning. Default is c('intron_retention','coding_potential','ORF_seq_similarity','NMD_status','domains_identified','signal_peptide_identified') (corresponding to analyze: intron retention, CPAT result, ORF AA sequence similarity, NMD status, protein domains annotated and signal peptides annotated by Pfam).

alpha

The cutoff which the FDR correct p-values (q-values) must be smaller than for calling significant switches. Default is 0.05.

dIFcutoff

The cutoff which the changes in (absolute) isoform usage must be larger than before an isoform is considered switching. This cutoff can remove cases where isoforms with (very) low dIF values are deemed significant and thereby included in the downstream analysis. This cutoff is analogous to having a cutoff on log2 fold change in a normal differential expression analysis of genes to ensure the genes have a certain effect size. Default is 0.1 (10%).

onlySigIsoforms

A logic indicating whether to only consider significant isoforms, meaning only analyzing genes where at least two isoforms which both have significant usage changes in opposite direction (quite strict) Naturally this only works if the isoform switch test used have isoform resolution (which the build in isoformSwitchTestDEXSeq has). If FALSE all isoforms with an absolute dIF value larger than dIFcutoff in a gene with significant switches (defined by alpha and dIFcutoff) are included in the pairwise comparison. Default is FALSE (non significant isoforms are also considered based on the logic that if one isoform changes it contribution - there must be an equivalent opposite change in usage in the other isoforms from that gene).

ntCutoff

An integer indicating the length difference (in nt) a comparison must be larger than for reporting differences when evaluating 'isoform_length', 'ORF_length', '5_utr_length', '3_utr_length', 'isoform_seq_similarity', '5_utr_seq_similarity' and '3_utr_seq_similarity'. Default is 50 (nt).

ntFracCutoff

An numeric indicating the cutoff in length difference, measured as a fraction of the length of the downregulated isoform, a comparison must be larger than for reporting differences when evaluating 'isoform_length', 'ORF_length', '5_utr_length', '3_utr_length'. For example does 0.05 mean the upregulated isoform must be 5% longer/shorter before it is reported. NULL disables the filter. Default is NULL.

ntJCsimCutoff

An numeric (between 0 and 1) indicating the cutoff on Jaccard Similarity (JCsim) (see details) between the overlap of two nucloetide (nt) sequences. If the measured JCsim is smaller than this cutoff the sequences are considered different and reported as such. This cutoff affects the result of the 'isoform_seq_similarity', '5_utr_seq_similarity' and '3_utr_seq_similarity' analysis. Default is 0.8

AaCutoff

An integer indicating the length difference (in AA) a comparison must be larger than for reporting differences when evaluating 'ORF_seq_similarity', primarily implemented to avoid differences in very short AA sequences being classified as different. Default is 10 (AA).

AaFracCutoff

An numeric indicating the cutoff of length difference of the protein domain or IDR. The difference is measured as a fraction of the longest region, a comparison must be larger than before reporting it. Only used when analyzing 'domain_length' or 'IDR_length'. For example setting AaFracCutoff = 0.5 means the short protein domain (or IDR) must be <50% of the length of the long region, before it is reported. NULL disables the filter. Default is 0.5.

AaJCsimCutoff

An numeric (between 0 and 1) indicating the cutoff on Jaccard Similarity (JCsim) (see details) between the overlap of two amino acid (AA) sequences. If the measured JCsim is smaller than this cutoff the sequences are considered different and reported as such. This cutoff affect the result of the 'ORF_seq_similarity' analysis. Default is 0.9

removeNonConseqSwitches

A logic indicating whether to, in the "switchConsequence" entry added to the switchAnalyzeRList, remove the comparison of isoforms where no consequences were found (if TRUE) or to keep then (if FALSE). Defaults is TRUE.

showProgress

A logic indicating whether to make a progress bar (if TRUE) or not (if FALSE). Default is TRUE.

quiet

A logic indicating whether to avoid printing progress messages (incl. progress bar). Default is FALSE

Details

Changes in isoform usage are measure as the difference in isoform fraction (dIF) values, where isoform fraction (IF) values are calculated as <isoform_exp> / <gene_exp>.

The idea is that once we know there is (at least) one isoform with a significant change in how much it is used (as defined by alpha and dIFcutoff) in a gene we take that/those isoform(s) and compare the functional annotation of this isoform to the isoform(s) with the compensatory change(s) in isoform usage (since if one isoform is use more another/others have to be used less). Here we only require that one of the isoforms in the comparison of annotation is significant (unless onlySigIsoforms=TRUE, then both must be), but all isoforms considered must have a change in isoform usage larger than dIFcutoff.

Note that sometimes we find complex switches meaning that many isoforms passes all the filters. In these cases we compare all pairwise combinations of the isoform(s) used more (positive dIF) vs the isoform(s) used less (negative dIF).

For sequences similarity analysis the two compared sequences are (globally) aligned to one another and the Jaccard similarity (JCsim) is calculated. Here JCsim is defined as the length of the aligned regions (omitting gaps) divided by the total combined unique sequence length: JCsim = (length of aligned region w.o gaps) / ( (length of sequence a) + (length of sequence b) - (length of aligned region w.o gaps) ). The pairwise alignment is done with pairwiseAlignment{Biostrings} as a Needleman-Wunsch global alignment which is guaranteed to find the optimal global alignment. The pairwise alignment is done with end gap penalties for the full sequences alignments ('isoform_seq_similarity' and 'ORF_seq_similarity') and without gap penalties for the alignment of sub-sequence ('5_utr_seq_similarity' and '3_utr_seq_similarity') by specifying type='global' and type='overlap' respectively.

If AA sequences were trimmed in the process of exporting the fasta files when using extractSequence the regions not analyzed in both isoforms will be ignored.

The arguments passed to consequencesToAnalyze must be a combination of:

Value

The supplied switchAnalyzeRlist is returned, but now annotated with the predicted functional consequences as follows. First a column called 'switchConsequencesGene' is added to isoformFeatures entry of switchAnalyzeRlist. This column containing a binary indication (TRUE/FALSE (and NA)) of whether the switching gene have predicted functional consequences or not.

Secondly the data.frame 'switchConsequence' is added to the switchAnalyzeRlist containing one row feature analyzed per comparison of isoforms pr comparison of condition. It contains 8 columns:

Author(s)

Kristoffer Vitting-Seerup

References

Vitting-Seerup et al. The Landscape of Isoform Switches in Human Cancers. Mol. Cancer Res. (2017).

See Also

analyzeORF
analyzeCPAT
analyzePFAM
analyzeSignalP
extractConsequenceSummary
extractConsequenceEnrichment
extractConsequenceEnrichmentComparison
extractConsequenceGenomeWide

Examples

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### Prepare example data
data("exampleSwitchListAnalyzed")

# subset for fast runtime
exampleSwitchListAnalyzed <- subsetSwitchAnalyzeRlist(
    exampleSwitchListAnalyzed,
    exampleSwitchListAnalyzed$isoformFeatures$gene_id %in% sample(exampleSwitchListAnalyzed$isoformFeatures$gene_id, 10)
)

### Analyze consequences
consequencesOfInterest <- c(
    'intron_retention',
    'coding_potential',
    'NMD_status',
    'domains_identified'
)

exampleSwitchListAnalyzed <- analyzeSwitchConsequences(
    exampleSwitchListAnalyzed,
    consequencesToAnalyze = consequencesOfInterest,

)

### simple overview
extractSwitchSummary(exampleSwitchListAnalyzed, filterForConsequences = FALSE)
extractSwitchSummary(exampleSwitchListAnalyzed, filterForConsequences = TRUE)


### Detailed switch overview
consequenceSummary <- extractConsequenceSummary(
    exampleSwitchListAnalyzed,
    includeCombined = TRUE,
    returnResult = TRUE,        # return data.frame with summary
    plotGenes = TRUE            # plot summary
)



### Now switches are analyzed we can also extract the the largest/most significant switches with the extractTopSwitches() function
# Extract top 2 switching genes (by q-value)
extractTopSwitches(
    exampleSwitchListAnalyzed,
    filterForConsequences = TRUE,
    n = 2,
    extractGenes = TRUE,
    sortByQvals = TRUE
)

# Extract top 2 switching isoforms (by q-value)
extractTopSwitches(
    exampleSwitchListAnalyzed,
    filterForConsequences = TRUE,
    n = 2,
    extractGenes = FALSE,
    sortByQvals = TRUE
)

# Extract top 2 switching isoforms (by dIF)
extractTopSwitches(
    exampleSwitchListAnalyzed,
    filterForConsequences = TRUE,
    n = 2,
    extractGenes = FALSE,
    sortByQvals = FALSE
)

### Note the function ?extractConsequenceSummary is specific made for the post analysis of switching consequences

kvittingseerup/IsoformSwitchAnalyzeR documentation built on May 12, 2021, 12:25 p.m.