Interactive analysis of single-cell data using flexible workflows with SCTK2

Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell Analysis

“…To broaden user accessibility, we developed Ursa, which is a computationally less intensive tool for single-cell multiomics analyses. We compared Ursa against a selection of tools known for their minimal programming complexity ( Gardeux et al 2017 ; Hwang et al 2018 ; Stuart and Satija 2019 ; Amezquita et al 2020 ; Franzén and Björkegren 2020 ; Kashima et al 2020 ; Lee et al 2020 ; Ma et al 2020 ; Taverna et al 2020 ; Yousif et al 2020 ; Moreno et al 2021 ; Pereira et al 2021 ; Zappia and Theis 2021 ; Hasanaj et al 2022 ; Jiang et al 2022 ; Prieto et al 2022 ; Wang et al 2023 ; Chen et al 2023 ) ( Table 1 ). It is evident that many of these tools possess a relatively constrained range in their analytic capabilities.…”

“…It is evident that many of these tools possess a relatively constrained range in their analytic capabilities. Tools such as Asc-Seurat ( Pereira et al 2021 ), Alona ( Franzén and Björkegren 2020 ), SCTK ( Wang et al 2023 ), NASQAR ( Yousif et al 2020 ), and ICARUS ( Jiang et al 2022 ) primarily focus on analytic workflows tailored for scRNA-Seq technologies ( Table 1 ). Conversely, platforms such as Partek Flow, Qiagen, ezSingleCell ( Chen et al 2023 ), and Cellar ( Hasanaj et al 2022 ) have expanded their range to encompass up to 3 single-cell omics, including scRNA-Seq, scATAC-Seq, and either spatial transcriptomics or scImmune profiling.…”

“…The influx of multidimensional data requires biology to be more and more computationally dependent, which has now transformed biology into computational biology that brings even more challenges to scientists, especially to clinicians or bench scientists who are computationally less competent. To date, big data complexity and data analysis methods have undergone revolutionary changes in data volume and their dimension and interpretation ( Hwang et al 2018 ; Stuart and Satija 2019 ; Amezquita et al 2020 ; Kashima et al 2020 ; Lee et al 2020 ; Ma et al 2020 ) and are accompanied by incremental growth in the formulation of new analysis methods and computational tools for the interpretation of big data ( Gardeux et al 2017 ; Hwang et al 2018 ; Stuart and Satija 2019 ; Amezquita et al 2020 ; Franzén and Björkegren 2020 ; Kashima et al 2020 ; Lee et al 2020 ; Ma et al 2020 ; Taverna et al 2020 ; Yousif et al 2020 ; Moreno et al 2021 ; Pereira et al 2021 ; Zappia and Theis 2021 ; Hasanaj et al 2022 ; Jiang et al 2022 ; Prieto et al 2022 ; Wang et al 2023 ; Chen et al 2023 ). Currently, the majority of the bioinformatics tools available are predominantly designed for single-cell RNA-sequencing (scRNA-Seq) data analysis ( Zappia and Theis 2021 ), and fewer tools are tailored to other omics types such as scATAC-Seq ( Stuart et al 2021 ) and spatial transcriptomics ( Kleino et al 2022 ).…”

A specialized population of monocyte-derived tracheal macrophages promote airway epithelial regeneration through a CCR2-dependent mechanism

“… 58 Macrophage gene lists ranked by relative expression in murine data were converted to human symbols using BioMart in R. 59 Metagene scores for the top 100 genes in preserved order from the murine gene lists were calculated with VAM (Variance Adjusted Mahalanobis) using the Single Cell Toolkit with default parameters on the log-normalized counts assay. 60 , 61 …”