Single Cell Cloud tools (scCloud)

scCloud is a tool for analyzing transcriptomes of millions of single cells. It is a command line tool, a python package and a base for Cloud-based analysis workflows.

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Version 0.10.0 January 31, 2019

Added ‘scCloud find_markers’ to find markers using LightGBM.

Improved file loading speed and enabled the parsing of channels from barcode strings for cellranger aggregated h5 files.

Version 0.9.0 January 17, 2019

In ‘scCloud cluster’, changed ‘–output-seurat-compatible’ to ‘–make-output-seurat-compatible’. scCloud will not generate output_name.seurat.h5ad. Instead, output_name.h5ad should be able to convert to a Seurat object directly. In the seurat object, slot refers to the filtered count data, data slot refers to the log-normalized expression data, and refers to the variable-gene-selected, scaled data.

In ‘scCloud cluster’, added ‘–min-umis’ and ‘–max-umis’ options to filter cells based on UMI counts.

In ‘scCloud cluster’, ‘–output-filtration-results’ option does not require a spreadsheet name anymore. In addition, added more statistics such as median number of genes per cell in the spreadsheet.

In ‘scCloud cluster’, added ‘–plot-filtration-results’ and ‘–plot-filtration-figsize’ to support plotting filtration results. Improved documentation on ‘scCloud cluster’ outputs.

Added ‘scCloud parquet’ command to transfer h5ad file into a parquet file for web-based interactive visualization.

Version 0.8.0 November 26, 2018

Added support for checking index collision for CITE-Seq/hashing experiments.

Version 0.7.0 October 26, 2018

Added support for CITE-Seq analysis.

Version 0.6.0 October 23, 2018

Renamed scrtools to scCloud.

Added demuxEM module for cell/nuclei-hashing.

Version 0.5.0 August 21, 2018

Fixed a problem related AnnData.

Added support for BigQuery.

Version 0.4.0 August 2, 2018

Added mouse brain markers.

Allow aggregate matrix to take ‘Sample’ as attribute.

Version 0.3.0 June 26, 2018

scrtools supports fast preprocessing, batch-correction, dimension reduction, graph-based clustering, diffusion maps, force-directed layouts, and differential expression analysis, annotate clusters, and plottings.