ANOVA_DDCt() function which already accepted only character as the experimental factor levels, now accepts numeric as well.meanTech() function.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.