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https://github.com/mohanbolisetty/CellView

Simple App to visualize single cell datasets
https://github.com/mohanbolisetty/CellView

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Simple App to visualize single cell datasets

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# CellView



### A ShinyApp to visualize and explore single cell datasets



A live version of this app is hosted here: 

https://mbolisetty.shinyapps.io/CellView/



A preprint describing this software is available on 

[bioRxiv](https://www.biorxiv.org/content/early/2017/04/04/123810).



### Introduction



CellView reads expression, dimesionality reduction/clustering, and feature 

annotation data objects from an `.Rds` file, and provides functionality to 

quickly explore and interactively analyze single cell transcriptomic data.



- View 3D representation of your dataset.

- Analyze (co-)expression patterns within and among specific clusters.

- Compute differential gene expression analysis on the-fly using interactive 

  sample selection.



### Usage



The primary structure of the `.Rds` file comprises three dataframes, with the 

following object names and column names.  These structures will be updated in

the future to be more flexible but currently the following naming conventions

are *required*.



- `log2cpm` - Your (N x M) genes vs. cells expression matrix.  Gene names need 

  to be in Ensembl gene id format (e.g. ENSG,  ENSM).

- `tsne.data` – Your dimensionality reduction and sample clustering information.

  This dataframe contains `M` rows and 4 columns: `V1, V2, V3, dbCluster`.

  - `V1, V2, V3` store the 3 dimensional representation of your data, e.g. from

    t-SNE, PCA, etc.

  - `dbCluster` contains numerical cluster assignments.

  The row names of this data frame must correspond to the column names of your 

  expression matrix.

- `featuredata` - A dataframe representing gene annotations with row names in 

  ensembl gene id format.  The following 2 columns are required:

  - `Chromosome.Name` - integers representing chromosome numbers.

  - `Associated.Gene.Name` - Gene symbol

  Other columns are allowed but not utilized.  The number of rows can be larger 

  than the number of genes (N) in your   expression matrix, but beware of 

  duplicate gene names with unique ENSGIDs.  You can use one of the provided 

  files under 

  [CellView/FeatureData](https://github.com/mohanbolisetty/CellView/tree/master/Featuredata) 

  or create your own.



#### Sample code to generate an .Rds file for upload to CellView



```R

options(stringsAsFactors = FALSE, row.names = 1, as.is = T)

log2cpm <- read.csv('Data/Expression.csv', check.names = F)

featuredata <- read.csv('Databases/HG19_v74_FeatureData.csv', sep = ',')

tsne.data <- read.csv('Data/TNSE_dbscan.csv')



save(log2cpm, featuredata, tsne.data, file = 'Filename.Rds')

```