ANOVA_DDCt() now supports complex experimental designs.
Users can perform ddCt analysis across interactions of multiple factors
(e.g., specs = "FactorA | FactorB"). As analysis across
interactions of multiple factors output was not adopted with the
plotSingleGene() function, this function was removed.mainFactor.column (Integer) is now deprecated in favor
of specs.specs now accepts one or more column names as a
character (e.g. “A” or “A | B”, or “A | B* C”).mainFactor.level.order whic already accepted a vector,
is now deprecated in favor of calibratorLevel which accepts
one of the levels of the main factor (e.g. A), Default
(NULL) is the first level.B, or B*C).In ANOVA_DDCt() function the se.type
argument was added to control how standard errors (SE) are calculated
for relative expression (RE = fold change) estimates. If set to
"paired.sample" the se is computed from paired differences
between factor levels, matching samples by id. This is
automatically used when an id random effect is detected in
a user-provided mixed model. "two.sample" computes SE using
an unpaired samples against the reference level, and
"single.sample" computes SE within each factor level. When
a random id effect is detected in the model, paired
standard errors are used automatically (with a warning if another
se.type is requested).
The default behavior in ANOVA_DDCt() function for
standard error calculation has been updated. Standard errors are now
calculated from model-based residuals
(modelBased_se = TRUE) by default. Setting
modelBased_se = FALSE restores the previous behavior
i.e. direct computing from observed wDCt values. For single factor data,
both methods are the same. It is recommended let it use
modelBased_se = TRUE (default).
A function called plotSingleGene() was added that
creates a bar plot of relative gene expression (fold change) values from
single gene analysis showing all pairwise significances.
In version 2.1.3, optional custom model formula can be supplied
by user to ANOVA_DDCt() and ANOVA_DCt()
functions via model argument. If provided, this overrides
the default formula (single- or multi-factorial CRD or RCBD design based
on the availability of block and the number of factors). The formula
uses wDCt as the response variable. For mixed models,
random effects can be supplied (e.g.,
wDCt ~ Treatment * (1| id)).
Handling missing Ct values for target genes using the
set_missing_target_Ct_to_40 function. If TRUE,
missing target gene Ct values become 40; if FALSE
(default), they become NA. missing Ct values of reference genes are
always converted to NA.
The ANOVA_DDCt() and ANOVA_DCt()
functions detect singular fits when a user-defined mixed model (lmer) is
used, and genes that have singular fits after the analysis will be
reported.
Because, currently a model (including repeated measure and ancova
models) can be supplied to ANOVA_DDCt() by user,
REPEATED_DDCt() and ANCOVA_DDCt()functions
were removed. Refer to the vignette for a sample of most common models
and description
For CRD, RCBD, and factorial experiments under CRD or RCBD designs no model is required as one of these models is appropriately selected based on the input arguments.
The non-parametric WILCOX_DDCt() function was added
to analyze the gene expression (ddCt method) using wilcox.test.
ANCOVA analysis which already was performed via the
analysisType = "ancova" argument of the
ANOVA_DDCt() function, now is performed using the
ANCOVA_DDCt() new function. The analysisType
argument was removed.
A function named compute_wDCt() was added which
cleans the data and computes weighted delta Ct (wDCt) values. Handling
missing data by this function has been described in the function
details.
Two arguments of the REPEATED_DDCt() function were
updated in order to be similar to those of the ANOVA_DDCt()
function: The repeatedFactor and
calibratorLevel arguments were changed to
mainFactor.column and mainFactor.level.order,
respectively.
The font size and legend position controls were added to the
efficiency() function.
It is now possible to return the lm formula that is used for
variance analysis in ANOVA_DDCt(), and
REPEATED_DDCt() functions using
object$perGene$gene_name$lm_formula command.
The rtpcr package now accepts any number of references and any number of target genes in a single analysis. The package computes the geometric mean of reference genes considering efficiency values.
Missing data can be denoted by NA in the input data frame. Values such as “0” and “undetermined” (for any E or Ct) are automatically converted to NA before being passed to downstream analyses. For target genes, NA values for E or Ct measurements result in NA for the corresponding ΔCt for that replicate, which is then propagated through downstream statistical analyses. When more than one reference gene is used, NA values in either the E or Ct field for any reference gene cause that reference gene to be skipped, and the remaining reference genes are geometrically averaged.
The long_to_wide() function was added to convert a
4-column qPCR data with long format to wide.