Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq

Li, Bo; Gould, Joshua; Yang, Yiming; Sarkizova, Siranush; Tabaka, Marcin; Ashenberg, Orr; Rosen, Yanay; Slyper, Michal; Kowalczyk, Monika S.; Villani, Alexandra‐Chloé; Tickle, Timothy L.; Hacohen, Nir; Rozenblatt-Rosen, Orit; Regev, Aviv

doi:10.1038/s41592-020-0905-x

Cited by 148 publications

(121 citation statements)

References 58 publications

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“…The total run times per dataset of the Salmon-Alevin-fry programs were consistently much higher than that of kallisto-bustools, and the memory requirements were much higher as well (Figure 3). Salmon-Alevin-fry was on average 3 times slower than kallisto-bustools, a result consistent with the benchmarks in (Li et al 2020). Importantly, while kallisto-bustools ran in under 4Gb of RAM for all datasets except the human-mouse mixed samples, Salmon-Alevin-fry required up to 18.8 Gb of RAM for some of those datasets.…”

Section: Resultssupporting

confidence: 82%

Benchmarking of lightweight-mapping based single-cell RNA-seq pre-processing

Booeshaghi

Pachter

2021

Preprint

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We compare and benchmark the two lightweight-mapping tools that have been developed for pre-processing single-cell RNA-seq data, namely the kallisto-bustools and Salmon-Alevin-fry programs. We find that they output similar results, and to the extent that there are differences, they are irrelevant for downstream analysis. However, the Salmon-Alevin-fry program is significantly slower and requires much more memory to run, making it much more expensive to process large datasets limiting its use to larger servers.

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Section: Resultssupporting

confidence: 82%

Benchmarking of lightweight-mapping based single-cell RNA-seq pre-processing

Booeshaghi

Pachter

2021

Preprint

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“…Cell Ranger mkfastq (10x Genomics) was used to demultiplex the raw sequencing reads, and Cell Ranger count on Terra using the cellranger_workflow in Cumulus 33 was used to align sequencing reads and generate a counts matrix. Reads were aligned to a custom-built Human GRCh38 and SARS-CoV-2 RNA reference.…”

Section: Methodsmentioning

confidence: 99%

“…Resolution 1.3 was sufficient for coarse annotations of clusters. Clustering was performed using Pegasus 33 with default parameters (except the resolution as mentioned above).…”

Section: Methodsmentioning

confidence: 99%

“…After identifying the main cell lineages, we studied each lineage by sub-clustering it separately from the rest of the cells, to enable identification of subtle cell states. For each of the three immune cell types and the endothelial cells we re-computed highly variable genes, performed PCA, batch corrected with Harmony, generated K neighbor neighbors (k=100), and performed Leiden clustering, all with Pegasus 33 . We tested different options for the number of highly variable genes and PCs, followed by manual inspection of the resulting UMAP, and choosing the parameters where cell clusters were evenly distributed and devoid of finger-like structure.…”

Section: Methodsmentioning

confidence: 99%

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A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2

Delorey¹,

Ziegler²,

Heimberg³

et al. 2021

Preprint

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The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63+ intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles in situ and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies.

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“…We generated tSNE plots per compartment from NMF loading matrices, with a perplexity value of 30 and the Barnes-Hut approximation method 81 . A global tSNE of all cells was generated using Pegasus with the default parameters and using SVD for the preliminary embedding (v0.17.0, 82 ).…”

Section: Experimental Methodsmentioning

confidence: 99%

Multicellular immune hubs and their organization in MMRd and MMRp colorectal cancer

Pelka

Hofree

Chen

et al. 2021

Preprint

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Immune responses to cancer are highly variable, with mismatch repair-deficient (MMRd) tumors exhibiting more anti-tumor immunity than mismatch repair-proficient (MMRp) tumors. To understand the rules governing these varied responses, we transcriptionally profiled 371,223 cells from colorectal tumors and adjacent normal tissues of 28 MMRp and 34 MMRd patients. Analysis of 88 cell subsets and their 204 associated gene expression programs revealed extensive transcriptional and spatial remodeling across tumors. To discover hubs of interacting malignant and immune cells, we identified expression programs in different cell types that co-varied across patient tumors and used spatial profiling to localize coordinated programs. We discovered a myeloid cell-attracting hub at the tumor-luminal interface associated with tissue damage, and an MMRd-enriched immune hub within the tumor, with activated T cells together with malignant and myeloid cells expressing T-cell-attracting chemokines. By identifying interacting cellular programs, we thus reveal the logic underlying spatially organized immune-malignant cell networks.

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Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq

Cited by 148 publications

References 58 publications

Benchmarking of lightweight-mapping based single-cell RNA-seq pre-processing

Benchmarking of lightweight-mapping based single-cell RNA-seq pre-processing

A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2

Multicellular immune hubs and their organization in MMRd and MMRp colorectal cancer

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