目录
- .githubupdate workflows2年前
- RUpdate tidy method for SingleCellExperiment6个月前
- dataRemove data/* files changes from commit7个月前
- instUpdate NEWS.rd11个月前
- manUpdate tidy method for SingleCellExperiment6个月前
- testsChange default shape parameter in join_features() and join_transcripts() from "long" to "wide", resulting in a return type of SingleCellExperiment by default. Update documentation and tests accordingly.11个月前
- vignettesModify import statements for dplyr and tidyr, enhance documentation links, and refine example code in the introduction vignette for clarity and uniqueness.7个月前
- .Rbuildignorefirst commit5年前
- .coveralls.ymlfirst commit5年前
- .gitignoreupdate README and Vignette2年前
- .travis.ymlfirst commit5年前
- DESCRIPTIONMerge branch 'RELEASE_3_22' of git.bioconductor.org:packages/tidySingleCellExperiment6个月前
- NAMESPACEdo not reexport7个月前
- README.RmdUpdate version to 1.19.2, introduce 'append_samples()' method for combining SingleCellExperiment objects, and deprecate 'bind_rows()' in favor of the new method. Update documentation and examples accordingly.11个月前
- README.mdUpdate version to 1.19.2, introduce 'append_samples()' method for combining SingleCellExperiment objects, and deprecate 'bind_rows()' in favor of the new method. Update documentation and examples accordingly.11个月前
- _pkgdown.ymlfirst commit5年前
- codecov.ymlfirst commit5年前
邀请码
tidySingleCellExperiment - part of tidytranscriptomics
Brings SingleCellExperiment to the tidyverse!
Website: tidySingleCellExperiment
Please also have a look at
Introduction
tidySingleCellExperiment provides a bridge between Bioconductor single-cell packages [@amezquita2019orchestrating] and the tidyverse [@wickham2019welcome]. It enables viewing the Bioconductor SingleCellExperiment object as a tidyverse tibble, and provides SingleCellExperiment-compatible dplyr, tidyr, ggplot and plotly functions. This allows users to get the best of both Bioconductor and tidyverse worlds.
Functions/utilities available
alltidySingleCellExperimentis a SingleCellExperiment object, just betterdplyrdplyrtibble functions (e.g.select)tidyrtidyrtibble functions (e.g.pivot_longer)ggplot2ggplot(ggplot)plotlyplot_ly(plot_ly)as_tibbletbl_dfjoin_featurestidySingleCellExperimentobjectaggregate_cellsInstallation
Load libraries used in this vignette.
Data representation of
tidySingleCellExperimentThis is a SingleCellExperiment object but it is evaluated as a tibble. So it is compatible both with SingleCellExperiment and tidyverse.
It looks like a tibble
But it is a SingleCellExperiment object after all
The
SingleCellExperimentobject’s tibble visualisation can be turned off, or back on at any time.Annotation polishing
We may have a column that contains the directory each run was taken from, such as the “file” column in
pbmc_small.We may want to extract the run/sample name out of it into a separate column. Tidyverse
extractcan be used to convert a character column into multiple columns using regular expression groups.Preliminary plots
Set colours and theme for plots.
We can treat
pbmc_small_polishedas a tibble for plotting.Here we plot number of features per cell.
Here we plot total features per cell.
Here we plot abundance of two features for each group.
Preprocess the dataset
We can also treat
pbmc_small_polishedas a SingleCellExperiment object and proceed with data processing with Bioconductor packages, such as scran [@lun2016pooling] and scater [@mccarthy2017scater].If a tidyverse-compatible package is not included in the tidySingleCellExperiment collection, we can use
as_tibbleto permanently converttidySingleCellExperimentinto a tibble.Identify clusters
We can proceed with cluster identification with scran.
And interrogate the output as if it was a regular tibble.
We can identify and visualise cluster markers combining SingleCellExperiment, tidyverse functions and tidyHeatmap [@mangiola2020tidyheatmap]
Reduce dimensions
We can calculate the first 3 UMAP dimensions using the SingleCellExperiment framework and scater.
And we can plot the result in 3D using plotly.
Cell type prediction
We can infer cell type identities using SingleR [@aran2019reference] and manipulate the output using tidyverse.
We can easily summarise the results. For example, we can see how cell type classification overlaps with cluster classification.
We can easily reshape the data for building information-rich faceted plots.
We can easily plot gene correlation per cell category, adding multi-layer annotations.
Nested analyses
A powerful tool we can use with tidySingleCellExperiment is tidyverse
nest. We can easily perform independent analyses on subsets of the dataset. First we classify cell types into lymphoid and myeloid, and then nest based on the new classification.Now we can independently for the lymphoid and myeloid subsets (i) find variable features, (ii) reduce dimensions, and (iii) cluster using both tidyverse and SingleCellExperiment seamlessly.
We can then unnest and plot the new classification.
We can perform a large number of functional analyses on data subsets. For example, we can identify intra-sample cell-cell interactions using SingleCellSignalR [@cabello2020singlecellsignalr], and then compare whether interactions are stronger or weaker across conditions. The code below demonstrates how this analysis could be performed. It won’t work with this small example dataset as we have just two samples (one for each condition). But some example output is shown below and you can imagine how you can use tidyverse on the output to perform t-tests and visualisation.
If the dataset was not so small, and interactions could be identified, you would see something like below.
Aggregating cells
Sometimes, it is necessary to aggregate the gene-transcript abundance from a group of cells into a single value. For example, when comparing groups of cells across different samples with fixed-effect models.
In tidySingleCellExperiment, cell aggregation can be achieved using the
aggregate_cellsfunction.