runTest.RdPerform pairwise comparisons. If colData(sce) contains a column
named 'batch', this will be included as a covariate if testType is
'limma' or 'proDA'. If it contains a column named 'sampleweights', these
will be used as sample weights if testType is 'limma'.
runTest(
sce,
comparisons,
groupComposition = NULL,
testType,
assayForTests,
assayImputation = NULL,
minNbrValidValues = 2,
minlFC = 0,
featureCollections = list(),
complexFDRThr = 0.1,
volcanoAdjPvalThr = 0.05,
volcanoLog2FCThr = 1,
baseFileName = NULL,
seed = 123,
samSignificance = TRUE,
nperm = 250,
volcanoS0 = 0.1,
addAbundanceValues = FALSE,
aName = NULL,
singleFit = TRUE,
subtractBaseline = FALSE,
baselineGroup = "",
extraColumns = NULL
)A SummarizedExperiment object (or a derivative).
A list of character vectors of length 2, each giving the two groups to be compared.
A list providing the composition of each group
used in any of the comparisons. If NULL, assumes that each
group used in comparisons consists of a single group in the
group column of colData(sce).
Character scalar, either "limma", "ttest" or "proDA".
Character scalar, the name of an assay of the
SummarizedExperiment object with values that will be used to
perform the test.
Character scalar, the name of an assay of
sce with logical values indicating whether an entry was imputed
or not.
Numeric scalar, the minimum number of valid (non-imputed) values that must be present for a features to include it in the result table.
Non-negative numeric scalar, the logFC threshold to use for
limma-treat. If minlFC = 0, limma::eBayes is used instead.
List of CharacterLists with feature collections.
Numeric scalar giving the significance (FDR) threshold below which a complex will be considered significant.
Numeric scalar giving the FDR threshold for significance (for later use in volcano plots).
Numeric scalar giving the logFC threshold for significance (for later use in volcano plots).
Character scalar or NULL, the base file name of
the output text files. If NULL, no result files are generated.
Numeric scalar, the random seed to use for permutation (only
used if testType is "ttest").
Logical scalar, indicating whether the SAM statistic
should be used to determine significance (similar to the approach used by
Perseus). Only used if testType = "ttest". If FALSE, the
p-values are adjusted using the Benjamini-Hochberg approach and used
to determine significance.
Numeric scalar, the number of permutations (only
used if testType is "ttest").
Numeric scalar, the S0 value to use for creating
significance curves (only used if testType is "ttest").
Logical scalar, whether to extract abundance and add to the result table.
Character vector, the names of the assays in the
SummarizedExperiment object to get abundance values from (only
required if addAbundanceValues is TRUE).
Logical scalar, whether to fit a single model to the full
data set and extract relevant results using contrasts. If FALSE,
the data set will be subset for each comparison to only the relevant
samples. Setting singleFit to TRUE is only supported
for testType = "limma" or "proDA".
Logical scalar, whether to subtract the background/
reference value for each feature in each batch before fitting the
model. If TRUE, requires that a 'batch' column is available.
Character scalar representing the reference group.
Only used if subtractBaseline is TRUE, in which case the
abundance values for a given sample will be adjusted by subtracting the
average value across all samples in the baselineGroup from the
same batch as the original sample.
Character vector (or NULL) indicating columns
of rowData(sce) to include in the result table.
A list with the following components:
tests - a list with test results
plotnotes - the prior df used by limma
plottitles - indicating the type of test
plotsubtitles - indicating the significance thresholds
messages - any messages for the user
design - information about the experimental design
featureCollections - list of feature sets, expanded with
results from camera
topsets - a list with the significant feature sets
curveparams - information required to create Perseus-like
significance curves
In addition, if
baseFileName is not NULL, text files with test results
(including only features and feature sets passing the imposed significance
thresholds) are saved.
sce <- readRDS(system.file("extdata", "mq_example", "1356_sce.rds",
package = "einprot"))
tres <- runTest(sce, comparisons = list(c("RBC_ctrl", "Adnp")),
testType = "limma", assayForTests = "log2_LFQ.intensity",
assayImputation = "imputed_LFQ.intensity")
head(tres$tests$Adnp_vs_RBC_ctrl)
#> pid logFC CI.L CI.R AveExpr t P.Value
#> 1 Dhx9 9.022469 7.3178397 10.727099 21.20317 12.095374 1.271544e-06
#> 2 Zmynd8 2.109792 -0.7438610 4.963444 18.97072 1.689516 1.276159e-01
#> 3 Zmym4 9.839518 5.5090078 14.170027 22.76805 5.192287 6.998028e-04
#> 4 Rlf 7.174602 2.3601311 11.989073 20.78440 3.405439 8.574818e-03
#> 5 Zfp600 1.057193 -0.6296855 2.744072 23.16597 1.432169 1.880666e-01
#> 6 Rpl32 3.435481 0.1777595 6.693203 20.46518 2.409892 4.097579e-02
#> adj.P.Val B s2.prior sigma se.logFC df.total mlog10p
#> 1 6.103411e-05 5.33424636 1.9903569 0.6037027 0.7459438 8.441054 5.8956686
#> 2 2.187701e-01 -4.99296345 3.3021534 1.3954445 1.2487549 8.441054 0.8940952
#> 3 4.633177e-03 -0.00816105 0.9482483 2.6818702 1.8950257 8.441054 3.1550243
#> 4 2.993391e-02 -2.43895360 2.2768805 2.9052289 2.1068064 8.441054 2.0667751
#> 5 2.911999e-01 -5.32702279 0.7532645 0.9183849 0.7381760 8.441054 0.7256883
#> 6 9.148082e-02 -3.94846663 2.4844680 1.8104766 1.4255749 8.441054 1.3874727
#> einprotGene einprotProtein einprotLabel
#> 1 Dhx9 A0A087WPL5;E9QNN1;O70133;O70133-2;O70133-3;Q3UR42 Dhx9
#> 2 Zmynd8 Q3UH28;Q3U1M7;A2A483;E9Q8D1;A2A482;A2A484;A2A485 Zmynd8
#> 3 Zmym4 A2A791;A2A791-2;F6VYE2 Zmym4
#> 4 Rlf A2A7F4;E9Q532 Rlf
#> 5 Zfp600 A2A7V0 Zfp600
#> 6 Rpl32 A2AD25;P62911 Rpl32
#> showInVolcano IDsForSTRING
#> 1 TRUE <NA>
#> 2 FALSE <NA>
#> 3 TRUE <NA>
#> 4 TRUE <NA>
#> 5 FALSE <NA>
#> 6 FALSE <NA>
tres$design
#> $design
#> (Intercept) fcChd4BF fcRBC_ctrl
#> Adnp_IP04 1 0 0
#> Adnp_IP05 1 0 0
#> Adnp_IP06 1 0 0
#> Chd4BF_IP07 1 1 0
#> Chd4BF_IP08 1 1 0
#> Chd4BF_IP09 1 1 0
#> RBC_ctrl_IP01 1 0 1
#> RBC_ctrl_IP02 1 0 1
#> RBC_ctrl_IP03 1 0 1
#> attr(,"assign")
#> [1] 0 1 1
#> attr(,"contrasts")
#> attr(,"contrasts")$fc
#> [1] "contr.treatment"
#>
#>
#> $sampleData
#> fc
#> Adnp_IP04 Adnp
#> Adnp_IP05 Adnp
#> Adnp_IP06 Adnp
#> Chd4BF_IP07 Chd4BF
#> Chd4BF_IP08 Chd4BF
#> Chd4BF_IP09 Chd4BF
#> RBC_ctrl_IP01 RBC_ctrl
#> RBC_ctrl_IP02 RBC_ctrl
#> RBC_ctrl_IP03 RBC_ctrl
#>
#> $contrasts
#> $contrasts$Adnp_vs_RBC_ctrl
#> [1] 0 0 -1
#>
#>
#> $sampleWeights
#> NULL
#>