xtail analysis wrapper for SummarizedExperiment objects

The xtail function can be used directly with SummarizedExperiment objects. The mrna and rpf data must be stored as two separate assays.

runXtail(x, mrna_assay, rpf_assay, ...)

# S4 method for SummarizedExperiment
runXtail(x, mrna_assay, rpf_assay, ...)

addXtail(x, ...)

# S4 method for SummarizedExperiment
addXtail(x, ...)

Arguments

x	a RpfSummarizedExperiment object
mrna_assay	a scalar character. The name of the assay containing the mRNA data.
rpf_assay	a scalar character. The name of the assay containing the rpf data.
...	For runXtail: additional parameters passed on to xtail. For addXtail: additional parameters passed on to runXtail and xtail.

Value

A xtail object for runXtail or an object of class(x).

Details

See xtail for more details on the analysis function.

Examples

data(xtaildata)
test.mrna <- xtaildata$mrna[1:100,]
test.rpf <- xtaildata$rpf[1:100,]
condition <- c("control","control","treat","treat")

se <- SummarizedExperiment(assays = list(mrna = test.mrna, rpf = test.rpf),
                           colData = DataFrame(condition = condition))
xtail <- runXtail(se, "mrna", "rpf", condition = colData(se)$condition,
                  bins = 1000, threads = 2)
#> Calculating the library size factors
#> 1. Estimate the log2 fold change in mrna
#> 2. Estimate the log2 fold change in rpf
#> 3. Estimate the difference between two log2 fold changes
#> 4. Estimate the log2 ratio in first condition
#> 5. Estimate the log2 ratio in second condition
#> 6. Estimate the difference between two log2 ratios
#> Number of the log2FC and log2R used in determining the final p-value
#>  log2FC: 16
#>  log2R: 84
xtail
#> A xtail object:
#> Number of genes tested: 100 
#> Number of the log2FC and log2R used in determining the final p-value:
#>      log2FC: 16 
#>      log2R : 84 
#> 
#> Number of result with adjusted pvalue <  0.1 
#>      log2FC_TE > 0 (up)  : 0 
#>      log2FC_TE < 0 (down): 2 

se <- addXtail(se, "mrna", "rpf", condition = colData(se)$condition,
               bins = 1000, threads = 2)
#> Calculating the library size factors
#> 1. Estimate the log2 fold change in mrna
#> 2. Estimate the log2 fold change in rpf
#> 3. Estimate the difference between two log2 fold changes
#> 4. Estimate the log2 ratio in first condition
#> 5. Estimate the log2 ratio in second condition
#> 6. Estimate the difference between two log2 ratios
#> Number of the log2FC and log2R used in determining the final p-value
#>  log2FC: 16
#>  log2R: 84
rowData(se)
#> DataFrame with 100 rows and 8 columns
#>                 log2FC_TE_v1 pvalue_v1 treat_log2TE log2FC_TE_v2 pvalue_v2
#>                    <numeric> <numeric>    <numeric>    <numeric> <numeric>
#> ENSG00000000003    0.0546202 0.8086310     0.389025     0.057880  0.810210
#> ENSG00000000419    0.3673323 0.0845792     1.211761     0.367332  0.080353
#> ENSG00000000457   -0.2989464 0.6586512    -0.211788    -0.276236  0.702403
#> ENSG00000000460   -0.2944345 0.4683274     0.655145    -0.299734  0.485571
#> ENSG00000000971   -0.4746186 0.6119576     1.213196    -0.469285  0.636172
#> ...                      ...       ...          ...          ...       ...
#> ENSG00000006459   -0.0346566  0.970913   -0.4062572   -0.0338558  0.964283
#> ENSG00000006468   -0.0216823  0.934741    0.0284734   -0.0216823  0.942783
#> ENSG00000006530    0.1441922  0.583793    0.7738261    0.1507464  0.604515
#> ENSG00000006534   -0.3085919  0.649289   -0.8103674   -0.3013847  0.687958
#> ENSG00000006555   -1.0652561  0.394785   -1.0512473   -1.0456365  0.438515
#>                 log2FC_TE_final pvalue_final pvalue.adjust
#>                       <numeric>    <numeric>     <numeric>
#> ENSG00000000003        0.057880    0.8102096      0.964435
#> ENSG00000000419        0.367332    0.0845792      0.964435
#> ENSG00000000457       -0.276236    0.7024034      0.964435
#> ENSG00000000460       -0.299734    0.4855705      0.964435
#> ENSG00000000971       -0.469285    0.6361716      0.964435
#> ...                         ...          ...           ...
#> ENSG00000006459      -0.0346566     0.970913      0.975041
#> ENSG00000006468      -0.0216823     0.942783      0.975041
#> ENSG00000006530       0.1507464     0.604515      0.964435
#> ENSG00000006534      -0.3013847     0.687958      0.964435
#> ENSG00000006555      -1.0456365     0.438515      0.964435