Last updated: 2020-04-24
Checks: 2 0
Knit directory: BgeeCall_practical/
This reproducible R Markdown analysis was created with workflowr (version 1.6.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 6da0a8d. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Untracked files:
Untracked: PCA_dim_1vs2.png
Untracked: PCA_prop_explained_variance.png
Untracked: analyis.R
Untracked: dif_expressed_genes.tsv
Untracked: inputFile.tsv
Untracked: merge.R
Untracked: release.tsv
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/classes_description.Rmd) and HTML (docs/classes_description.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6da0a8d | Julien | 2020-04-24 | wflow_publish(files = c(“analysis/analysis.Rmd”, “analysis/classes_description.Rmd”, |
| html | 15196bf | Julien | 2020-04-22 | Build site. |
| html | b5a9d73 | Julien | 2020-04-22 | Build site. |
| Rmd | 0779975 | Julien | 2020-04-22 | wrote general part |
| html | 0779975 | Julien | 2020-04-22 | wrote general part |
| html | d93bad7 | Julien | 2020-04-22 | update all html files |
| Rmd | 454071e | Julien | 2020-04-22 | create backbone of the website |
| html | 454071e | Julien | 2020-04-22 | create backbone of the website |
Present/absent expression calls will be generated using objects of 3 R classes specific to the BgeeCall package. These classes contain an important number of slots (attributes) in order to tune as much as possible how present/absent expression calls are generated. In this section we will describe these classes and their most important slots. slots written in bold type will be used during the exercices.
This class allows to tune how kallisto will be run but also how presetn/absent calls will be generated Most important slots are :
This class allows to tune which intergenic sequences to use Most important slot is :
Both KallistoMetadata and BgeeMetadata can be used with default values. The object of class UserMetadata is the only one it is necessary to modify. It contains information specific to the analysis the user want to run. Most important slots are :
The slot transcriptome_object has to be setup with one of the methods setTranscriptomeFromFile() or setTranscriptomeFromObject()
The slot annotation_object has to be setup with one of the methods setAnnotationFromFile() or setAnnotationFromObject()