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Table 1 Comparison of Asc-Seurat's capabilities with the most relevant web applications currently available

From: Asc-Seurat: analytical single-cell Seurat-based web application

 

Asc-Seurat

NASQR [11]

SCiAp [13]

PIVOT [8]

SC1 [14]

Stream [9]

alona [12]

ASAP [7]

BingleSeq [10]

Usability

Is it a web application?

Yes

Yes

Yes (2)

Yes

Yes

Yes

Yes

Yes

Yes

Easy to install (Docker)?

Yes

Yes

No

Yes

No

Yes

No

No

No

Easy to use by wet-lab biologists?

Yes

Yes

Yes (3)

Yes

Yes

Yes

Yes

Yes

Yes

Clustering

Does the clustering?

Yes

Yes

Yes

Yes

Yes

No

Yes

Yes

Yes

Does the clustering using Seurat?

Yes

Yes

Yes

No

No

No

Yes

Yes

Yes

Is it capable of integrating multiple samples?

Yes

Yes (1)

NA

Yes

Yes (7)

No

No

NA

No

Offers the SCtransform normalization?

Yes

Yes

No

No

No

No

No

No

No

Allows the filtering of clusters of interest?

Yes

No

Yes (4)

No

NA

No

No

No

No

High-quality plots of the clustering and expression?

Yes

Yes

Yes

Yes (6)

Yes

No

Yes

Yes

Yes

Trajectory inference

Performs trajectory inference (TI)?

Yes

No

Yes

Yes

Yes

Yes

No

No

No

Offers multiple models for TI?

Yes

No

Yes

No

No

No

No

No

No

Differential Expression analysis within the trajectory?

Yes

No

Yes

Yes

No

Yes

No

No

No

Expression visualization within the trajectory?

Yes

No

Yes

Yes

No

Yes

No

No

No

Annotation

Gene annotation?

Yes

Yes

Yes (5)

Yes

Yes

No

No

Yes

Yes (8)

GO terms enrichment

Yes

Yes

Yes (5)

Yes

Yes

No

No

Yes

No

  1. NA (not available): the authors could not address this question with the information available in the manual, tutorials, or publications related to the web application. 1—It is not clear how the integration is performed, but it appears not to use Seurat's integration approach. 2—It is based on the Galaxy framework. 3—Requires training in the Galaxy framework. 4—Users need to provide the cell IDs manually for exclusion. 5—It is possible when using other Galaxy modules. 6—Does not provide UMAP, which has become the most used visualization method for scRNA-seq clustered data. 7—It is capable of analyzing multiple samples. However, it seems not to apply Seurat's integration approach. 8—Available only for a limited set of model organisms