Data Summary and Filtering Tab

Single Cell Toolkit

David Jenkins

The Section of Computational Biomedicine, Boston University School of Medicine, Boston, MAProgram in Bioinformatics, Boston University, Boston, MA

Tyler Faits

The Section of Computational Biomedicine, Boston University School of Medicine, Boston, MAProgram in Bioinformatics, Boston University, Boston, MA

W. Evan Johnson

The Section of Computational Biomedicine, Boston University School of Medicine, Boston, MAProgram in Bioinformatics, Boston University, Boston, MA
Source: vignettes/v04-tab02_Data-Summary-and-Filtering.Rmd

Introduction

The data summary and filtering tab provides several summary statistics and ways to filter your data. The summary statistics and filtering are performed on the selected assay, which can be changed using the “Select Assay” drop-down.

Data summary table

The summary table provides several summary statistics about your data including:

  • The number of samples
  • The number of genes
  • The average number of reads per cell
  • The average number of genes per cell
  • Samples with fewer than the selected cutoff expressed genes
  • Genes with no expression across all samples

Filtering

Several filtering options are available

Cutoffs

Data can be filtered by user selected cutoff values including:

  • Remove genes with no expression across all samples. These genes are uninformative
  • Minimum detected genes per sample. Samples with few expressed genes could indicate an empty well.
  • Percent low gene expression. This filter will remove the genes with the least expression in the dataset

When you have set the cutoff filters to the desired level, you can filter data with the “Filter Data” button.

Delete Outliers

If there are specific cells that should be excluded from the dataset, you can select them and they will be removed when “Filter Data” is clicked.

Reset

The SCTK saves a copy of the originally uploaded data, which can be restored by clicking the “Reset” button.

Filter samples by annotation

Data stored in the annotation data frame can be used for sample filtering. Choose a column from the annotation data frame, the values from this column to keep, and click “Filter.”

Filter genes by feature annotation

Similarly, gene annotations can be used to filter genes. Select a column from the row data frame and the values to keep and click “Filter.”

Convert gene annotations

The SCTK can use annotation data from Bioconductor annotation packages such as org.Hs.eg.db to convert gene annotations between standard annotation types such as Entrez gene ids, Ensembl gene ids, or gene symbols. By default, these packages are not installed, so you will need to manually install the package for your appropriate organism. Instructions for installation can be found on the Bioconductor website

Delete an annotation column

You can remove an unnecessary or unwanted annotation column by selecting it and clicking the “Delete Column” button.

Randomly Subset

If you are working with a large dataset, you may want to randomly subset the data for exploratory analysis. Specify the number of samples that you wish to keep in the subset data frame and click the “Subset Data” button.

Download SCtkExperiment

To export the SCtkExperiment object from the SCTK, you can download the object in RDS format by clicking the “Download SCtkExperiment” button. This file can be loaded into the R console for downstream analysis.

Assay Details

The assay details tab describes the available assays and reduced dimensionality data and allows the user to modify the SCTK experiment object

Available Assays and reducedDim Data

The assays and reduced dimension data listed on this tab are stored in the underlying SCTK experiment object and are available on the tabs in the app.

Add and Delete Assays and reducedDim Data

If a count matrix is available, users can add a log(counts), CPM (counts per million), and log(CPM) matrix directly through the app. Users can also delete unwanted assays or reducedDim objects on this tab

Annotation Data

The annotation tab displays the annotation matrix.

Convert Annotation Data Columns

For plotting, annotations that contain character values are interpreted as factors and will be displayed with discrete colors. Numeric annotations will be plotted with a continuous color bar. To change a numeric value to a factor, select the annotation column and change the “Field Type” from numeric to factor.

Replace Annotation Data

To modify the annotation data frame, download the annotation data using the “Download Annotation Data” button, modify the data using a text editor or Excel, save the data as a .csv file, and re-upload the data in the “Upload and replace the annotation data” field.

Session info 

## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] BiocStyle_2.22.0
## 
## loaded via a namespace (and not attached):
##  [1] rstudioapi_0.13     knitr_1.37          magrittr_2.0.2     
##  [4] R6_2.5.1            ragg_1.2.2          rlang_1.0.2        
##  [7] fastmap_1.1.0       stringr_1.4.0       tools_4.1.2        
## [10] xfun_0.30           cli_3.2.0           jquerylib_0.1.4    
## [13] systemfonts_1.0.4   htmltools_0.5.2     yaml_2.3.5         
## [16] digest_0.6.29       rprojroot_2.0.2     pkgdown_2.0.2      
## [19] bookdown_0.24       textshaping_0.3.6   BiocManager_1.30.16
## [22] purrr_0.3.4         sass_0.4.0          fs_1.5.2           
## [25] memoise_2.0.1       cachem_1.0.6        evaluate_0.15      
## [28] rmarkdown_2.13      stringi_1.7.6       compiler_4.1.2     
## [31] bslib_0.3.1         desc_1.4.1          jsonlite_1.8.0