@@ -1,6 +1,6 @@

 Package: fobitools

 Title: Tools For Manipulating FOBI Ontology

-Version: 1.3.0

+Version: 1.3.2

 Authors@R: 

     c(person(given = "Pol",

              family = "Castellano-Escuder",

@@ -1,4 +1,4 @@

-## fobitools 1.3.1

+## fobitools 1.3.2

 * Fix bugs in vignettes

@@ -2,7 +2,7 @@

 title: "Dietary text annotation"

 author: 

 - name: Pol Castellano-Escuder

-  affiliation: University of Barcelona, Spain.

+  affiliation: Duke University

   email: polcaes@gmail.com

 date: "`r BiocStyle::doc_date()`"

 output: 

@@ -19,7 +19,7 @@ link-citations: true

 **Compiled date**: `r Sys.Date()`

-**Last edited**: 2021-14-05

+**Last edited**: 2022-01-12

 **License**: `r packageDescription("fobitools")[["License"]]`

@@ -2,7 +2,7 @@

 title: "Use case: LC-MS Based Approaches to Investigate Metabolomic Differences in the Urine of Young Women after Drinking Cranberry Juice or Apple Juice"

 author: 

 - name: Pol Castellano-Escuder

-  affiliation: University of Barcelona, Spain.

+  affiliation: Duke University

   email: polcaes@gmail.com

 date: "`r BiocStyle::doc_date()`"

 output: 

@@ -19,7 +19,7 @@ link-citations: true

 **Compiled date**: `r Sys.Date()`

-**Last edited**: 2021-27-05

+**Last edited**: 2022-01-12

 **License**: `r packageDescription("fobitools")[["License"]]`

@@ -158,12 +158,12 @@ pdata <- colData(data_negative_mode) %>% # or "data_positive_mode". They are equ

 `POMA` provides a structured, reproducible and easy-to-use workflow for the visualization, preprocessing, exploration, and statistical analysis of metabolomics and proteomics data. The main aim of this package is to enable a flexible data cleaning and statistical analysis processes in one comprehensible and user-friendly R package. `POMA` uses the standardized `MSnbase` data structures, to achieve the maximum flexibility and reproducibility and makes `POMA` compatible with other Bioconductor packages [@POMA].

-## Create a `MSnbase::MSnSet` object

+## Create a `SummarizedExperiment` object

-First, we create a `MSnSet` object that integrates both metadata and features in the same data structure.

+First, we create a `SummarizedExperiment` object that integrates both metadata and features in the same data structure.

 ```{r, warning = FALSE, message = FALSE, comment = FALSE}

-data_msnset <- PomaMSnSetClass(target = pdata, features = t(features))

+data_sumexp <- PomaSummarizedExperiment(target = pdata, features = t(features))

 ```

 ## Preprocessing

@@ -171,7 +171,7 @@ data_msnset <- PomaMSnSetClass(target = pdata, features = t(features))

 Second, we perform the preprocessing step. This step includes the missing value imputation unsing the $k$-NN algorithm, log Pareto normalization (transformation and scaling) and outlier detection and cleaning. Once these steps are completed, we can proceed to the statistical analysis of these data.

 ```{r, warning = FALSE, message = FALSE, comment = FALSE}

-data_preprocessed <- data_msnset %>%

+data_preprocessed <- data_sumexp %>%

   PomaImpute(ZerosAsNA = TRUE, cutoff = 20, method = "knn") %>%

   PomaNorm(method = "log_pareto") %>%

   PomaOutliers(coef = 3)

@@ -184,7 +184,8 @@ We use a limma model [@limma] to identify those most significant metabolites bet

 ```{r, warning = FALSE, message = FALSE, comment = FALSE}

 limma_res <- data_preprocessed %>%

   PomaLimma(contrast = "Baseline-Cranberry", adjust = "fdr") %>%

-  tibble::rownames_to_column("PubChemCID")

+  dplyr::rename(PubChemCID = feature) %>% 

+  dplyr::mutate(PubChemCID = gsub("X", "", PubChemCID))

 # show the first 10 features

 limma_res %>%

@@ -2,7 +2,7 @@

 title: "Use case: Amino Acid Metabolites of Dietary Salt Effects on Blood Pressure in Human Urine"

 author: 

 - name: Pol Castellano-Escuder

-  affiliation: University of Barcelona, Spain.

+  affiliation: Duke University

   email: polcaes@gmail.com

 date: "`r BiocStyle::doc_date()`"

 output: 

@@ -19,7 +19,7 @@ link-citations: true

 **Compiled date**: `r Sys.Date()`

-**Last edited**: 2021-27-05

+**Last edited**: 2022-01-12

 **License**: `r packageDescription("fobitools")[["License"]]`

@@ -105,20 +105,20 @@ pdata <- colData(data) %>%

 `POMA` provides a structured, reproducible and easy-to-use workflow for the visualization, preprocessing, exploration, and statistical analysis of metabolomics and proteomics data. The main aim of this package is to enable a flexible data cleaning and statistical analysis processes in one comprehensible and user-friendly R package. `POMA` uses the standardized `MSnbase` data structures, to achieve the maximum flexibility and reproducibility and makes `POMA` compatible with other Bioconductor packages [@POMA].

-## Create a `MSnbase::MSnSet` object

+## Create a `SummarizedExperiment` object

-First, we create a `MSnSet` object that integrates both metadata and features in the same data structure.

+First, we create a `SummarizedExperiment` object that integrates both metadata and features in the same data structure.

 ```{r, warning = FALSE, message = FALSE, comment = FALSE}

-data_msnset <- PomaMSnSetClass(target = pdata, features = t(features))

+data_sumexp <- PomaSummarizedExperiment(target = pdata, features = t(features))

 ```

 ## Preprocessing

-Second, we perform the preprocessing step. This step includes the missing value imputation unsing the $k$-NN algorithm, log Pareto normalization (transformation and scaling) and outlier detection and cleaning. Once these steps are completed, we can proceed to the statistical analysis of these data.

+Second, we perform the preprocessing step. This step includes the missing value imputation using the $k$-NN algorithm, log Pareto normalization (transformation and scaling) and outlier detection and cleaning. Once these steps are completed, we can proceed to the statistical analysis of these data.

 ```{r, warning = FALSE, message = FALSE, comment = FALSE}

-data_preprocessed <- data_msnset %>%

+data_preprocessed <- data_sumexp %>%

   PomaImpute(ZerosAsNA = TRUE, cutoff = 20, method = "knn") %>%

   PomaNorm(method = "log_pareto") %>%

   PomaOutliers(coef = 3)

@@ -131,7 +131,7 @@ We use a limma model [@limma] to identify those most significant metabolites bet

 ```{r, warning = FALSE, message = FALSE, comment = FALSE}

 limma_res <- data_preprocessed %>%

   PomaLimma(contrast = "A-B", adjust = "fdr") %>%

-  tibble::rownames_to_column("ID")

+  dplyr::rename(ID = feature)

 # show the first 10 features

 limma_res %>%

@@ -2,7 +2,7 @@

 title: "Simple food over representation analysis (ORA)"

 author: 

 - name: Pol Castellano-Escuder

-  affiliation: University of Barcelona, Spain.

+  affiliation: Duke University

   email: polcaes@gmail.com

 date: "`r BiocStyle::doc_date()`"

 output: 

@@ -18,7 +18,7 @@ link-citations: true

 **Compiled date**: `r Sys.Date()`

-**Last edited**: 2021-20-07

+**Last edited**: 2022-01-12

 **License**: `r packageDescription("fobitools")[["License"]]`