From c051b515a05f4172c4421df5e519ee6cec8a0c63 Mon Sep 17 00:00:00 2001
From: aduvermy <arnaud.duvermy@ens-lyon.fr>
Date: Wed, 17 Jan 2024 11:34:24 +0100
Subject: [PATCH] try relaunch CI
---
dev/flat_full.Rmd | 12 ++++++------
vignettes/htrfit.Rmd | 23 +++++++++++++----------
2 files changed, 19 insertions(+), 16 deletions(-)
diff --git a/dev/flat_full.Rmd b/dev/flat_full.Rmd
index 738abec..94bb04c 100644
--- a/dev/flat_full.Rmd
+++ b/dev/flat_full.Rmd
@@ -9560,7 +9560,7 @@ l_tmb <- fitModelParallel(formula = kij ~ varA,
As the model family can be customized, HTRfit is not exclusively tailored for RNA-seq data.
-```{r example-fitModelParallel_nonRNA, warning=FALSE, message=FALSE, eval=FALSE}
+```{r example-fitModelParallel_nonRNA, warning=FALSE, message=FALSE}
data("iris")
l_tmb_iris <- fitModelParallel(formula = Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width ,
data = iris,
@@ -9571,7 +9571,7 @@ l_tmb_iris <- fitModelParallel(formula = Sepal.Length ~ Sepal.Width + Petal.Len
## Extracts a tidy result table from a list tmb object
-The tidy_results function extracts a dataframe containing estimates of ln(fold changes), standard errors, test statistics, p-values, and adjusted p-values for fixed effects. Additionally, it provides access to correlation terms and standard deviations for random effects, offering a detailed view of HTRfit modeling results.
+The tidy_results function extracts a data frame containing estimates of ln(fold changes), standard errors, test statistics, p-values, and adjusted p-values for fixed effects. Additionally, it provides access to correlation terms and standard deviations for random effects, offering a detailed view of HTRfit modeling results.
```{r example-tidyRes, warning=FALSE, message=FALSE}
## -- get tidy results
@@ -9648,7 +9648,7 @@ l_anova <- anovaParallel(list_tmb = l_tmb, type = "III" )
In this section, we delve into the evaluation of your simulation results. The `evaluation_report()` function provide valuable insights into the performance of your simulated data and models.
-```{r example-evaluation_report, warning = FALSE, message = FALSE, results='hide', fig.keep='none'}
+```{r example-evaluation_report, warning = FALSE, message = FALSE}
## -- get simulation/fit evaluation
resSimu <- evaluation_report(list_tmb = l_tmb,
dds = NULL,
@@ -9761,7 +9761,7 @@ resSimu$performances
In this section, we showcase the assessment of model performance on a subset of genes. Specifically, we focus on evaluating genes with low expression levels, identified by their basal expression ($bexpr_i$) initialized below 0 during the simulation.
-```{r example-subsetGenes, warning = FALSE, message = FALSE, results='hide', fig.keep='none'}
+```{r example-subsetGenes, warning = FALSE, message = FALSE}
## -- Focus on low expressed genes
low_expressed_df <- mock_data$groundTruth$effects[ mock_data$groundTruth$effects$basalExpr < 0, ]
l_genes <- unique(low_expressed_df$geneID)
@@ -9828,9 +9828,9 @@ resSimu$performances
For certain experimental scenarios, such as those involving a high number of levels or longitudinal data, the utilization of mixed effects within your design formula can be beneficial. The **HTRfit** simulation framework also offers the capability to assess this type of design formula.
-```{r example-evalMixed, warning = FALSE, message = FALSE, results='hide', fig.keep='none'}
+```{r example-evalMixed, warning = FALSE, message = FALSE}
## -- init a design with a high number of levels
-input_var_list <- init_variable( name = "varA", mu = 0, sd = 0.29, level = 60) %>%
+input_var_list <- init_variable( name = "varA", mu = 0.2, sd = 0.74, level = 60) %>%
init_variable( name = "varB", mu = 0.27, sd = 0.6, level = 2) %>%
add_interaction( between_var = c("varA", "varB"), mu = 0.44, sd = 0.89)
## -- simulate RNAseq data
diff --git a/vignettes/htrfit.Rmd b/vignettes/htrfit.Rmd
index 12444de..cc567c0 100644
--- a/vignettes/htrfit.Rmd
+++ b/vignettes/htrfit.Rmd
@@ -15,7 +15,6 @@ knitr::opts_chunk$set(
```
```{r setup}
-devtools::load_all()
library(HTRfit)
```
@@ -130,13 +129,13 @@ MIN_REPLICATES = 2
MAX_REPLICATES = 10
########################
-## -- simulate RNAseq data based on input_var_list, minimum input required
+## -- simulate RNAseq data based on list_var, minimum input required
## -- number of replicate randomly defined between MIN_REP and MAX_REP
mock_data <- mock_rnaseq(list_var, N_GENES,
min_replicates = MIN_REPLICATES,
max_replicates = MAX_REPLICATES)
-## -- simulate RNAseq data based on input_var_list, minimum input required
+## -- simulate RNAseq data based on list_var, minimum input required
## -- Same number of replicates between conditions
mock_data <- mock_rnaseq(list_var, N_GENES,
min_replicates = MAX_REPLICATES,
@@ -321,7 +320,7 @@ l_tmb <- fitModelParallel(formula = kij ~ varA,
As the model family can be customized, HTRfit is not exclusively tailored for RNA-seq data.
-```{r example-fitModelParallel_nonRNA, warning = FALSE, message = FALSE, eval = TRUE}
+```{r example-fitModelParallel_nonRNA, warning = FALSE, message = FALSE}
data("iris")
l_tmb_iris <- fitModelParallel(formula = Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width ,
data = iris,
@@ -332,7 +331,7 @@ l_tmb_iris <- fitModelParallel(formula = Sepal.Length ~ Sepal.Width + Petal.Len
## Extracts a tidy result table from a list tmb object
-The tidy_results function extracts a dataframe containing estimates of ln(fold changes), standard errors, test statistics, p-values, and adjusted p-values for fixed effects. Additionally, it provides access to correlation terms and standard deviations for random effects, offering a detailed view of HTRfit modeling results.
+The tidy_results function extracts a data frame containing estimates of ln(fold changes), standard errors, test statistics, p-values, and adjusted p-values for fixed effects. Additionally, it provides access to correlation terms and standard deviations for random effects, offering a detailed view of HTRfit modeling results.
```{r example-tidyRes, warning = FALSE, message = FALSE}
@@ -408,7 +407,7 @@ l_anova <- anovaParallel(list_tmb = l_tmb, type = "III" )
In this section, we delve into the evaluation of your simulation results. The `evaluation_report()` function provide valuable insights into the performance of your simulated data and models.
-```{r example-evaluation_report, warning = FALSE, message = FALSE, results = 'hide', fig.keep = 'none'}
+```{r example-evaluation_report, warning = FALSE, message = FALSE}
## -- get simulation/fit evaluation
resSimu <- evaluation_report(list_tmb = l_tmb,
dds = NULL,
@@ -523,7 +522,7 @@ resSimu$performances
In this section, we showcase the assessment of model performance on a subset of genes. Specifically, we focus on evaluating genes with low expression levels, identified by their basal expression ($bexpr_i$) initialized below 0 during the simulation.
-```{r example-subsetGenes, warning = FALSE, message = FALSE, results = 'hide', fig.keep = 'none'}
+```{r example-subsetGenes, warning = FALSE, message = FALSE}
## -- Focus on low expressed genes
low_expressed_df <- mock_data$groundTruth$effects[ mock_data$groundTruth$effects$basalExpr < 0, ]
l_genes <- unique(low_expressed_df$geneID)
@@ -589,9 +588,9 @@ resSimu$performances
For certain experimental scenarios, such as those involving a high number of levels or longitudinal data, the utilization of mixed effects within your design formula can be beneficial. The **HTRfit** simulation framework also offers the capability to assess this type of design formula.
-```{r example-evalMixed, warning = FALSE, message = FALSE, results = 'hide', fig.keep = 'none'}
+```{r example-evalMixed, warning = FALSE, message = FALSE}
## -- init a design with a high number of levels
-input_var_list <- init_variable( name = "varA", mu = 0, sd = 0.29, level = 60) %>%
+input_var_list <- init_variable( name = "varA", mu = 0.2, sd = 0.74, level = 60) %>%
init_variable( name = "varB", mu = 0.27, sd = 0.6, level = 2) %>%
add_interaction( between_var = c("varA", "varB"), mu = 0.44, sd = 0.89)
## -- simulate RNAseq data
@@ -643,7 +642,11 @@ str(resSimu, max.level = 1)
## About mixed model evaluation
-**HTRfit** offers a versatile simulation framework capable of fitting various types of models involving mixed effects, thanks to its implementation of **glmmTMB**. By combining the functionalities of `init_variable()` and `add_interaction()`, **HTRfit** enables the simulation of even the most complex experimental designs. However, it's important to note that as of now, HTRfit supports the evaluation of only *Type I* mixed models. In this context, *Type I* models are defined as those that follow the structure: `~ varA + (1 | varB)` or `~ varA + (varA | varB)`. Models not conforming to this specific form cannot be evaluated using **HTRfit's** current implementation. Nonetheless, you are welcome to extend its capabilities by implementing support for additional model types.
+**HTRfit** offers a versatile simulation framework capable of fitting various types of models involving mixed effects, thanks to its implementation of **glmmTMB**. By combining the functionalities of `init_variable()` and `add_interaction()`, **HTRfit** enables the simulation of complex experimental designs. As of now, HTRfit supports the evaluation of *Type I* mixed models. In this context, *Type I* models are defined as those that follow the structure:
+- `~ varA + (1 | varB)` : where `varA` is defined as fixed effect and `varB` as random effect
+- `~ varA + (varA | varB)`: where `varA` is defined as mixed effect (fixed + random) and `varB` as random effect.
+
+Models not conforming to this specific form cannot be evaluated using **HTRfit's** current implementation. Nonetheless, you are welcome to extend its capabilities by implementing support for additional model types.
--
GitLab