dittoSeq: universal user-friendly single-cell and bulk RNA sequencing visualization toolkit

Bunis, Daniel; Andrews, Jared; Fragiadakis, Gabriela K.; Burt, Trevor D.; Sirota, Marina

doi:10.1093/bioinformatics/btaa1011

Cited by 149 publications

(134 citation statements)

References 6 publications

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“…Interpretation of complex and rich data often relies on visualization strategies that use color, leading to difficulties in perceiving patterns for a substantial proportion of the population with color vision deficiencies and leading to different data interpretations between individuals. We recommend presenting accessible scientific information using colorblind-friendly visualizations and palettes [42][43][44][45] with a limit of 10 colors. Additional hatched areas or point shapes can also reduce the dependence on colors.…”

Section: Improving Interpretationmentioning

confidence: 99%

Community-wide hackathons to identify central themes in single-cell multi-omics

et al. 2021

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Section: Improving Interpretationmentioning

confidence: 99%

Community-wide hackathons to identify central themes in single-cell multi-omics

et al. 2021

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“…For subclustering of the osteoblast population, we subset the osteoblast2 cells from the combined dataset and again applied the standard Seurat data processing workflow (number of PCs = 15, resolution = 0.8). All plots were generated in R with either Seurat, base R graphics, or with the dittoSeq package [56].…”

Section: Tissue Collectionmentioning

confidence: 99%

Single-cell resolution of MET- and EMT-like programs in osteoblasts during zebrafish fin regeneration

Cj²,

Olmstead³

et al. 2021

Preprint

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While humans have limited potential for limb regeneration, some vertebrates can regenerate bony appendages following amputation. During zebrafish fin regeneration, mature osteoblasts at the amputation stump dedifferentiate and migrate to the blastema, where they re-enter the cell cycle and then re-differentiate to form new bone. Osteoblastic cells exhibit dual mesenchymal and epithelial characteristics during fin regeneration, however little is known about why or how this occurs. Using single-cell RNA-sequencing, we found osteoprogenitors are enriched with components associated with the epithelial-to-mesenchymal transition (EMT) and its reverse, mesenchymal-to-epithelial transition (MET). In trajectory analyses, osteoblastic cells solely expressed EMT components, or transiently expressed MET components prior to expressing those for EMT. We found that cdh11, a cancer EMT marker, is expressed during osteoblast dedifferentiation. We also found that esrp1, a regulator of alternative splicing in epithelial cells whose expression is important for MET, is expressed in a subset of osteoprogenitors during outgrowth. This study provides a valuable single cell resource for the study of osteoblast differentiation during zebrafish fin regeneration, and identifies MET- and EMT-associated components which may be important for successful appendage regeneration.

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“…Schematic representations were made using the Inkscape (https://inkscape.org/) software. Dim plots, bar plots and box plots were generated using the dittoSeq (v1.1.7) package with default parameters 99 . Violin plots were generated using the patchwork (v1.1.0) package and ggplot2 (v3.3.2) package in R with default parameters.…”

Section: Statistics and Reproducibilitymentioning

confidence: 99%

Redefining colorectal cancer classification and clinical stratification through a single-cell atlas

Khaliq

Kurt

et al. 2021

Preprint

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Colorectal cancer (CRC), a disease of high incidence and mortality, exhibits a large degree of inter- and intra-tumoral heterogeneity. The cellular etiology of this heterogeneity is poorly understood. Here, we generated and analyzed a single-cell transcriptome atlas of 49,859 CRC cells from 16 patients, validated with an additional 31,383 cells from an independent CRC patient cohort. We describe subclonal transcriptomic heterogeneity of CRC tumor epithelial cells, as well as discrete stromal populations of cancer-associated fibroblasts (CAFs). Within CRC CAFs, we identify the transcriptional signature of specific subtypes that significantly stratifies overall survival in more than 1,500 CRC patients with bulk transcriptomic data. We demonstrate that scRNA analysis of malignant, stromal, and immune cells exhibit a more complex picture than portrayed by bulk transcriptomic-based Consensus Molecular Subtypes (CMS) classification. By demonstrating an abundant degree of heterogeneity amongst these cell types, our work shows that CRC is best represented in a transcriptomic continuum crossing traditional classification systems boundaries. Overall, this CRC cell map provides a framework to re-evaluate CRC tumor biology with implications for clinical trial design and therapeutic development.

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dittoSeq: universal user-friendly single-cell and bulk RNA sequencing visualization toolkit

Cited by 149 publications

References 6 publications

Community-wide hackathons to identify central themes in single-cell multi-omics

Community-wide hackathons to identify central themes in single-cell multi-omics

Single-cell resolution of MET- and EMT-like programs in osteoblasts during zebrafish fin regeneration

Redefining colorectal cancer classification and clinical stratification through a single-cell atlas

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