# rtpcr 2.1.8

## Correction
- Minor correction to the `meanTech()` function.

# rtpcr 2.1.7

## New Features
- Minor correction to the vignette’s method file so that it builds on Debian as well.


# rtpcr 2.1.6

## New Features
- Possibility of using shiny version of the rtpcr package as described in the manual.


# rtpcr 2.1.5

## New Features
* `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.

## Deprecated
* `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.
* This allows for ddCt calculations to be performed separately within each level of a secondary factor or across factor combinations (e.g., `B`, or `B*C`).

# rtpcr 2.1.4

## New features

- 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.


# rtpcr 2.1.3

## New features

- 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.



# rtpcr 2.1.2

## New features

- 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. 



# rtpcr 2.1.1

## New features

- 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. 




