This data is used for the Seurat version of the batch correction and integration tutorial on the Galaxy Training Network. The input data was provided by Seurat in the 'Integrative Analysis in Seurat v5' tutorial. The input dataset provided here has been filtered to include only cells for which nFeature_RNA > 1000. The other datasets were produced on Galaxy. The original dataset was published as: Ding, J., Adiconis, X., Simmons, S.K. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38, 737–746 (2020). https://doi.org/10.1038/s41587-020-0465-8.

Test Data for Galaxy Tutorial "Clustering 3k PBMCs with Seurat"

This is the Seurat object used for running SOP of InterCellDB. SOP is the tutorial 'Running InterCellDB step-by-step' in website https://github.com/ZJUDBlab/InterCellDB.

Remark 0: See https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev For the cell cycle analysis

Remark 1:

Full dataset in https://www.kaggle.com/datasets/alexandervc/scrnaseq-crisprperturbseq-normanweissman But it is huge, and loading crashes memory, so here are cropped pieces to start with.

Remark 2:

dataset used in: "GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations" Yusuf Roohani, Kexin Huang, Jure Leskovec https://www.biorxiv.org/content/10.1101/2022.07.12.499735v1 https://twitter.com/yusufroohani/status/1547965695744360448 Accepted in ICLR

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data - Perturb-seq: Single-cell, pooled CRISPR screening experiment comparing the transcriptional effects of overexpressing genes alone or in combination

Paper: Norman TM, Horlbeck MA, Replogle JM, Ge AY et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 2019 Aug 23;365(6455):786-793. PMID: 31395745 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746554/

Data: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133344

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

Necessary datasets to run the Immcantation 10x Tutorial. Below is the description of the files in the data set.

BCR_data_sample1.tsv: data corresponding to the first sample (sample 1) of the two samples analyzed in the 10x tutorial. This is the sample used to show the Change-O steps.

filtered_contig_annotations.csv: filtered contig annotations file for sample 1, output of cellranger vdj.

filtered_contig.fasta: sequence fasta file for sample 1, output of cellranger vdj.

BCR_data.tsv: AIRR rearrangement file containing the data for both samples 1 and 2 used in the 10x tutorial.

BCR.data_08112023.rds: R dataframe object containing the single-cell BCR sequencing data for both samples 1 and 2 used in the 10x tutorial.

GEX.data_08112023.rds: Seurat object containing the single-cell gene expression data used in the 10x tutorial.

The demo datasets for scMetabolismThe demo data is the dataset of Peripheral Blood Mononuclear Cells (PBMC) from 10X Genomics open access dataset (~2,700 single cells, also used by Seurat tutorial).

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.
To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.

Data:

1. Immgen_cell_types.cls : Microarray Phase 1 expression

2. Immgen_norm_exp_data.gct : Microarray Phase 1 class

Remark: for cell cycle analysis - see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data: https://pubmed.ncbi.nlm.nih.gov/30104629/ Nat Immunol. 2018 Sep;19(9):1013-1024. doi: 10.1038/s41590-018-0181-4. Epub 2018 Aug 13. Human germinal center transcriptional programs are de-synchronized in B cell lymphoma Pierre Milpied 1, Iñaki Cervera-Marzal 2, Marie-Laure Mollichella 2, Bruno Tesson 3, Gabriel Brisou 2, Alexandra Traverse-Glehen 4, Gilles Salles 4, Lionel Spinelli 2, Bertrand Nadel 5

Data in two variants: 1) Downloaded from GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3190075 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3190076 2) Downloaded from PanglaoDB: https://panglaodb.se/view_data.php?sra=SRA721637&srs=SRS3416994

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

Single-cell RNA-seq datasets from turtle and lizard samples, provided as Seurat objects in both .Robj and .h5seurat formats. Each dataset includes the raw gene–cell count matrix, cell and gene metadata, normalized and batch-corrected data, PCA and t-SNE embeddings, and cluster annotations. For more details on Seurat usage and data structures, refer to the Seurat tutorials at http://satijalab.org/seurat/. The original objects were created using Seurat version 1.4 (October 2016) and have since been updated for compatibility with Seurat version 2.3.4 (.Robj files) and Seurat version 5.3.0 (.h5seurat files). ANNOTATION FILES Genome annotation: RefFlat files with the extended annotations (extension based on MACE results). - annChrPicBel_19Apr2016.refFlat (turtle) - lizard_annotation.refFlat (lizard) Functional annotation EGGNOG - chrysemys_eggnog_pruned.txt (turtle) - pogona_eggnog_pruned.txt (lizard) - mus_eggnog_pruned.txt (mouse) - homo_eggnog_pruned.txt (human) These files were generated from the functional annotations of turtle, lizard, mouse and human genes produced by EggNOG Mapper (see http://eggnogdb.embl.de/). The original EggNOG Mapper annotations were pruned to remove ambiguous terms (e.g. one-to-many assignments). Matching functional annotations were used to identify one-to-one orthologs across species. Transcription factors list - 170712_TFs_list_ensembl.txt List of human transcription factors, annotated in ENSEMBL under the GO terms GO:0003700 (transcription factor activity), GO:0003702 (RNA polymerase II transcription factor activity), GO:0003709 (RNA polymerase III transcription factor activity), GO:0016563 (transcriptional activator activity) and GO:0016564 (transcriptional repressor activity).

This is a single cell transcriptomics dataset containing roughly 3,000 PBMCs. The original data was downloaded from the Seurat 3k PBMC tutorial: https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html. We reprocessed the dataset using the Besca package (https://github.com/bedapub/besca).

Context

Dataset from NeurIPS2021 challenge similar to Kaggle 2022 competition: https://www.kaggle.com/competitions/open-problems-multimodal "Open Problems - Multimodal Single-Cell Integration Predict how DNA, RNA & protein measurements co-vary in single cells"

It is https://en.wikipedia.org/wiki/ATAC-seq#Single-cell_ATAC-seq single cell ATAC-seq data. And single cell RNA-seq data: https://en.wikipedia.org/wiki/Single-cell_transcriptomics#Single-cell_RNA-seq

Single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

(For companion dataset on CITE-seq = scRNA-seq + Proteomics, see: https://www.kaggle.com/datasets/alexandervc/citeseqscrnaseqproteins-challenge-neurips2021)

Particular data

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122

Expression profiling by high throughput sequencing Genome binding/occupancy profiling by high throughput sequencing Summary Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors. Half the samples were measured using the 10X Multiome Gene Expression and Chromatin Accessability kit and half were measured using the 10X 3' Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site. In the competition, participants were tasked with challenges including modality prediction, matching profiles from different modalities, and learning a joint embedding from multiple modalities.

Overall design Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors.

Contributor(s) Burkhardt DB, Lücken MD, Lance C, Cannoodt R, Pisco AO, Krishnaswamy S, Theis FJ, Bloom JM Citation https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/158f3069a435b314a80bdcb024f8e422-Abstract-round2.html

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Context

Dataset from NeurIPS2021 challenge similar to Kaggle 2022 competition: https://www.kaggle.com/competitions/open-problems-multimodal "Open Problems - Multimodal Single-Cell Integration Predict how DNA, RNA & protein measurements co-vary in single cells"

CITE-seq - joint single cell RNA sequencing + single cell measurements of CD** proteins. (https://en.wikipedia.org/wiki/CITE-Seq) (For companion dataset on scRNA-seq + scATAC-seq, see: https://www.kaggle.com/datasets/alexandervc/scrnaseq-scatacseq-challenge-at-neurips-2021 )

Single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194122

Expression profiling by high throughput sequencing Genome binding/occupancy profiling by high throughput sequencing Summary Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors. Half the samples were measured using the 10X Multiome Gene Expression and Chromatin Accessability kit and half were measured using the 10X 3' Single-Cell Gene Expression kit with Feature Barcoding in combination with the BioLegend TotalSeq B Universal Human Panel v1.0. The dataset was generated to support Multimodal Single-Cell Data Integration Challenge at NeurIPS 2021. Samples were prepared using a standard protocol at four sites. The resulting data was then annotated to identify cell types and remove doublets. The dataset was designed with a nested batch layout such that some donor samples were measured at multiple sites with some donors measured at a single site. In the competition, participants were tasked with challenges including modality prediction, matching profiles from different modalities, and learning a joint embedding from multiple modalities.

Overall design Single-cell multiomics data collected from bone marrow mononuclear cells of 12 healthy human donors.

Contributor(s) Burkhardt DB, Lücken MD, Lance C, Cannoodt R, Pisco AO, Krishnaswamy S, Theis FJ, Bloom JM Citation https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/158f3069a435b314a80bdcb024f8e422-Abstract-round2.html

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

These are processed AnnData objects (converted from Seurat objects) for GeneTrajectory tutorials (https://github.com/KlugerLab/GeneTrajectory-python/):Human myeloid dataset analysisMyeloid cells were extracted from a publicly available 10x scRNA-seq dataset (https:// support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc 10k v3). QC was performed using the same workflow in (https://github.com/satijalab/ Integration2019/blob/master/preprocessing scripts/pbmc 10k v3.R). After standard normalization, highly-variable gene selection and scaling using the Seurat R package, we applied PCA and retained the top 30 principal components. Four sub-clusters of myeloid cells were identified based on Louvian clustering with a resolution of 0.3. Wilcoxon rank-sum test was employed to find cluster-specific gene markers for cell type annotation.For gene trajectory inference, we first applied Diffusion Map on the cell PC embedding (using a local-adaptive kernel, each bandwidth is determined by the distance to its k-nearest neighbor, k = 10) to generate a spectral embedding of cells. We constructed a cell-cell kNN (k = 10) graph based on their coordinates of the top 5 non-trivial Diffusion Map eigenvectors. Among the top 2,000 variable genes, genes expressed by 0.5% − 75% of cells were retained for pairwise gene-gene Wasserstein distance computation. The original cell graph was coarse-grained into a graph of size 1,000. We then built a gene-gene graph where the affinity between genes is transformed from the Wasserstein distance using a Gaussian kernel (local-adaptive, k = 5). Diffusion Map was employed to visualize the embedding of gene graph. For trajectory identification, we used a series of time steps (11,21,8) to extract three gene trajectories.Mouse embryo skin data analysisWe separated out dermal cell populations from the newly collected mouse embryo skin samples. Cells from the wildtype and the Wls mutant were pooled for analyses. After standard normalization, highly-variable gene selection and scaling using Seurat, we applied PCA and retained the top 30 principal components. Three dermal celltypes were stratified based on the expression of canonical dermal markers, including Sox2, Dkk1, and Dkk2. For gene trajectory inference, we first applied Diffusion Map on the cell PC embedding (using a local-adaptive kernel bandwidth, k = 10) to generate a spectral embedding of cells. We constructed a cell-cell kNN (k = 10) graph based on their coordinates of the top 10 non-trivial Diffusion Map eigenvectors. Among the top 2,000 variable genes, genes expressed by 1% − 50% of cells were retained for pairwise gene-gene Wasserstein distance computation. The original cell graph was coarse-grained into a graph of size 1,000. We then built a gene-gene graph where the affinity between genes is transformed from the Wasserstein distance using a Gaussian kernel (local-adaptive, k = 5). Diffusion Map was employed to visualize the embedding of gene graph. For trajectory identification, we used a series of time steps (9,16,5) to sequentially extract three gene trajectories. To compare the differences between the wiltype and the Wls mutant, we stratified Wnt-active UD cells into seven stages according to their expression profiles of the genes binned along the DC gene trajectory.

Remark: for cell cycle analysis see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data: Paper: Morarach K, Mikhailova A, Knoflach V, Memic F et al. Diversification of molecularly defined myenteric neuron classes revealed by single-cell RNA sequencing. Nat Neurosci 2021 Jan;24(1):34-46. PMID: 33288908 https://pubmed.ncbi.nlm.nih.gov/33288908/

Data: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149524

See package and tutorial based on that dataset: https://scfates.readthedocs.io/en/latest/Advanced_bifurcation_analysis.html

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

Remark:

dataset used in: "GEARS: Predicting transcriptional outcomes of novel multi-gene perturbations" Yusuf Roohani, Kexin Huang, Jure Leskovec https://www.biorxiv.org/content/10.1101/2022.07.12.499735v1 https://twitter.com/yusufroohani/status/1547965695744360448 Accepted in ICLR

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data - Perturb-seq: Single-cell, pooled CRISPR screening experiment comparing the transcriptional effects of overexpressing genes alone or in combination

Paper: Norman TM, Horlbeck MA, Replogle JM, Ge AY et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 2019 Aug 23;365(6455):786-793. PMID: 31395745 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746554/

Data: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133344

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

Remark: for cell cycle analysis - see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data: Paper: Vladoiu MC, El-Hamamy I, Donovan LK, Farooq H et al. Childhood cerebellar tumours mirror conserved fetal transcriptional programs. Nature 2019 Aug;572(7767):67-73. PMID: 31043743 https://pubmed.ncbi.nlm.nih.gov/31043743/

Data: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE118068

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

Seurat and ArchR test files for running SCARlink tutorial notebook. Related manuscript: https://www.nature.com/articles/s41588-024-01689-8

Remark: for cell cycle analysis - see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (csv file is vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data:

Paper: Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning https://www.nature.com/articles/s41467-022-30545-8 https://www.biorxiv.org/content/10.1101/2021.03.17.435887v1.full Andrea Riba, Attila Oravecz, Matej Durik, Sara Jiménez, Violaine Alunni, Marie Cerciat, Matthieu Jung, Céline Keime, William M. Keyes, View ORCID ProfileNacho Molina doi: https://doi.org/10.1101/2021.03.17.435887

datasets of processed single-cell RNA-seq for mESC and human fibroblasts related to GEO dataset GSE167609 and the preprint https://doi.org/10.1101/2021.03.17.435887

Data: ZENODO: https://zenodo.org/record/4719436#.YlartShBy38

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

Remark: for cell cycle analysis - see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data: (Remark, here is part 1, for part 2 see: https://www.kaggle.com/datasets/alexandervc/scrnaseq-developing-human-immune-system-part-2 )

Paper: Suo C, Dann E, Goh I, et al. Mapping the developing human immune system across organs. Science (New York, N.Y.). 2022 May:eabo0510. DOI: 10.1126/science.abo0510. PMID: 35549310. https://europepmc.org/article/MED/35549310

Data: Part (NOT the biggest datasets) of: https://developmental.cellatlas.io/fetal-immune Part of: https://developmental.cellatlas.io/ The datasets are rather huge from 10 to 100 thousands cells (!)

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell

Remark: See paper: https://arxiv.org/abs/2208.05229 results on cell cycle analysis discussed there. "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (or vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

See tutorials: https://scanpy.readthedocs.io/en/stable/tutorials.html ("Scanpy" - main Python package to work with scRNA-seq data). Or https://satijalab.org/seurat/ "Seurat" - "R" package

Particular data: Paper: CENP-A overexpression promotes distinct fates in human cells, depending on p53 status Daniel Jeffery, Alberto Gatto, Katrina Podsypanina, Charlène Renaud-Pageot, Rebeca Ponce Landete, Lorraine Bonneville, Marie Dumont, Daniele Fachinetti & Geneviève Almouzni https://www.nature.com/articles/s42003-021-01941-5

Data: https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-9861/

Related datasets:

Other single cell RNA seq datasets can be found on kaggle: Look here: https://www.kaggle.com/alexandervc/datasets Or search kaggle for "scRNA-seq"

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Also see review : Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Search scholar.google "challenges in single cell rna sequencing" https://scholar.google.fr/scholar?q=challenges+in+single+cell+rna+sequencing&hl=en&as_sdt=0&as_vis=1&oi=scholart gives many interesting and highly cited articles

(Cited 968) Computational and analytical challenges in single-cell transcriptomics Oliver Stegle, Sarah A. Teichmann, John C. Marioni Nat. Rev. Genet., 16 (3) (2015), pp. 133-145 https://www.nature.com/articles/nrg3833

Challenges in unsupervised clustering of single-cell RNA-seq data https://www.nature.com/articles/s41576-018-0088-9 Review Article 07 January 2019 Vladimir Yu Kiselev, Tallulah S. Andrews & Martin Hemberg Nature Reviews Genetics volume 20, pages273–282 (2019)

Challenges and emerging directions in single-cell analysis https://link.springer.com/article/10.1186/s13059-017-1218-y Published: 08 May 2017 Guo-Cheng Yuan, Long Cai, Michael Elowitz, Tariq Enver, Guoping Fan, Guoji Guo, Rafael Irizarry, Peter Kharchenko, Junhyong Kim, Stuart Orkin, John Quackenbush, Assieh Saadatpour, Timm Schroeder, Ramesh Shivdasani & Itay Tirosh Genome Biology volume 18, Article number: 84 (2017)

Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges https://www.sciencedirect.com/science/article/pii/S1097276519303569 Molecular Cell Volume 75, Issue 1, 11 July 2019, Pages 7-12 Journal home page for Molecular Cell