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28 changes: 13 additions & 15 deletions README.Rmd
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Expand Up @@ -74,18 +74,21 @@ The next table summarizes the analyses done at each level.
</figcaption>
</figure>

All `R` scripts created to perform such analyses can be found in [code on GitHub](https://github.com/LieberInstitute/smoking-nicotine-mouse/).



## smoking Mouse datasets

The mouse datasets contain the following data in a single object for each feature (genes, exons, transcripts and exon-exon junctions):
The mouse datasets contain the following data in a single `R` `RangedSummarizedExperiment`* object for each feature (genes, exons, transcripts and exon-exon junctions):

* **Raw data**: original read counts of the features, also including the original metadata of features and samples.
* **Processed data**: normalized and log-scaled counts of the same features. In addition to the feature and sample information, the datasets contain information of which ones were used in downstream analyses (the ones that passed filtering steps) and which features were DE in the different experiments.

Moreover, you can find human data generated in Semick et al. (2018) in Mol Psychiatry (DOI: https://doi.org/10.1038/s41380-018-0223-1) that contain the results of a DEA in adult and prenatal human brain samples exposed to cigarette smoke.

*For more details, check the documentation for `RangedSummarizedExperiment` objects.


## Data specifics

Expand All @@ -101,13 +104,6 @@ All the above datasets contain the sample and feature metadata and additional da



## R/Bioconductor package

The `smokingMouse` package contains functions for:

* Accessing the expression data from the LIBD smoking-nicotine-mouse project ([code on GitHub](https://github.com/LieberInstitute/smokingMouse_Indirects)). The datasets are retrieved from [Bioconductor](http://bioconductor.org/)'s `ExperimentHub`.


## Installation instructions

Get the latest stable `R` release from [CRAN](http://cran.r-project.org/). Then install `smokingMouse` from [Bioconductor](http://bioconductor.org/) using the following code:
Expand All @@ -129,9 +125,7 @@ BiocManager::install("LieberInstitute/smokingMouse")

## Example of how to access the data

Through the `smokingMouse` package you can access the mouse datasets of the project that include the raw and processed data.
Below there's code you can use to access the gene data but can do the same for any of the datasets. For more details, check the documentation for `RangedSummarizedExperiment` objects.
You can also find code to access human data.
Below there's example code on how to access the gene mouse and human data but can do the same for any of the datasets previously described. The datasets are retrieved from [Bioconductor](http://bioconductor.org/) `ExperimentHub`.

```{r 'experiment_hub'}
## Connect to ExperimentHub
Expand All @@ -158,7 +152,7 @@ colData(rse_gene)[1:5, 1:5]
rowData(rse_gene)[1:5, 1:5]
## Access the original counts
original_counts <- assays(rse_gene)$counts
## Access the log normalized counts
## Access the log-normalized counts
logcounts <- assays(rse_gene)$logcounts
Expand All @@ -182,6 +176,12 @@ Below is the citation output from using `citation('smokingMouse')` in R. Please
```{r 'citation', eval = requireNamespace('smokingMouse')}
print(citation('smokingMouse'), bibtex = TRUE)
#>
#>
#> To cite the original smoking-nicotine mouse work please use:
#>
#> (TODO)
#>
#>
#> To cite the original work from which human data come please use the following citation:
#>
#> Semick, S. A., Collado-Torres, L., Markunas, C. A., Shin, J. H., Deep-Soboslay, A., Tao, R., ...
Expand All @@ -190,9 +190,7 @@ print(citation('smokingMouse'), bibtex = TRUE)
#>
```



Please note that the `smokingMouse` and the [smoking mouse](https://github.com/LieberInstitute/smokingMouse_Indirects) project were only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.
Please note that the `smokingMouse` package and the [study analyses](https://github.com/LieberInstitute/smoking-nicotine-mouse/) were only made possible thanks to many other `R` and bioinformatics software authors, which are cited either in the vignette and/or the paper describing this study.

## Code of Conduct

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49 changes: 25 additions & 24 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -136,10 +136,14 @@ use disorder; DGE: differential gene expression. </font>
</figcaption>
</figure>

All `R` scripts created to perform such analyses can be found in [code
on GitHub](https://github.com/LieberInstitute/smoking-nicotine-mouse/).

## smoking Mouse datasets

The mouse datasets contain the following data in a single object for
each feature (genes, exons, transcripts and exon-exon junctions):
The mouse datasets contain the following data in a single `R`
`RangedSummarizedExperiment`\* object for each feature (genes, exons,
transcripts and exon-exon junctions):

- **Raw data**: original read counts of the features, also including the
original metadata of features and samples.
Expand All @@ -154,6 +158,9 @@ Mol Psychiatry (DOI: <https://doi.org/10.1038/s41380-018-0223-1>) that
contain the results of a DEA in adult and prenatal human brain samples
exposed to cigarette smoke.

\*For more details, check the documentation for
`RangedSummarizedExperiment` objects.

## Data specifics

- *‘rse_gene_mouse_RNAseq_nic-smo.Rdata’*: (`rse_gene` object) the gene
Expand Down Expand Up @@ -186,16 +193,6 @@ DEA.
statistics of 18,067 human genes for cigarette smoke exposure in adult
human cortical tissue.

## R/Bioconductor package

The `smokingMouse` package contains functions for:

- Accessing the expression data from the LIBD smoking-nicotine-mouse
project ([code on
GitHub](https://github.com/LieberInstitute/smokingMouse_Indirects)).
The datasets are retrieved from
[Bioconductor](http://bioconductor.org/)’s `ExperimentHub`.

## Installation instructions

Get the latest stable `R` release from
Expand All @@ -219,12 +216,10 @@ BiocManager::install("LieberInstitute/smokingMouse")

## Example of how to access the data

Through the `smokingMouse` package you can access the mouse datasets of
the project that include the raw and processed data. Below there’s code
you can use to access the gene data but can do the same for any of the
datasets. For more details, check the documentation for
`RangedSummarizedExperiment` objects. You can also find code to access
human data.
Below there’s example code on how to access the gene mouse and human
data but can do the same for any of the datasets previously described.
The datasets are retrieved from [Bioconductor](http://bioconductor.org/)
`ExperimentHub`.

``` r
## Connect to ExperimentHub
Expand Down Expand Up @@ -310,7 +305,7 @@ rowData(rse_gene)[1:5, 1:5]
#> ENSMUSG00000103377.1 TEC NA
## Access the original counts
original_counts <- assays(rse_gene)$counts
## Access the log normalized counts
## Access the log-normalized counts
logcounts <- assays(rse_gene)$logcounts


Expand Down Expand Up @@ -393,6 +388,12 @@ print(citation('smokingMouse'), bibtex = TRUE)
#> url = {https://www.biorxiv.org/content/10.1101/TODO},
#> }
#>
#>
#> To cite the original smoking-nicotine mouse work please use:
#>
#> (TODO)
#>
#>
#> To cite the original work from which human data come please use the following citation:
#>
#> Semick, S. A., Collado-Torres, L., Markunas, C. A., Shin, J. H., Deep-Soboslay, A., Tao, R., ...
Expand All @@ -401,11 +402,11 @@ print(citation('smokingMouse'), bibtex = TRUE)
#>
```

Please note that the `smokingMouse` and the [smoking
mouse](https://github.com/LieberInstitute/smokingMouse_Indirects)
project were only made possible thanks to many other R and
bioinformatics software authors, which are cited either in the vignettes
and/or the paper(s) describing this package.
Please note that the `smokingMouse` package and the [study
analyses](https://github.com/LieberInstitute/smoking-nicotine-mouse/)
were only made possible thanks to many other `R` and bioinformatics
software authors, which are cited either in the vignette and/or the
paper describing this study.

## Code of Conduct

Expand Down

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