Discover and read the best of Twitter Threads about #spatialtranscriptomics
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Do you need to analyze Spatial Transcriptomics data, but are lost in the endless sea of methods?
Here's an explainer of the new @NatureComms paper benchmarking 18 spatial cellular deconvolution methods🧵🧵
nature.com/articles/s4146…
Here's an explainer of the new @NatureComms paper benchmarking 18 spatial cellular deconvolution methods🧵🧵
nature.com/articles/s4146…
This thread is organized as follows:
1️⃣ Intro to Spatial Transcriptomics
2️⃣ Intro to Cellular Deconvolution
3️⃣ Methods benchmarked
4️⃣ Datasets used (real & simulated)
5️⃣ Performance assessment
6️⃣ Benchmarking results
7️⃣ Accuracy
8️⃣ Robustness
9️⃣ Usability
🔟 Guidelines
1️⃣ Intro to Spatial Transcriptomics
2️⃣ Intro to Cellular Deconvolution
3️⃣ Methods benchmarked
4️⃣ Datasets used (real & simulated)
5️⃣ Performance assessment
6️⃣ Benchmarking results
7️⃣ Accuracy
8️⃣ Robustness
9️⃣ Usability
🔟 Guidelines
1️⃣ What is Spatial Transcriptomics & why is it important?
Spatial Transcriptomics (Method of the Year 2020) is a fast evolving field.
It holds great potential to further our understanding of development & disease, by placing cells in their spatial native tissue context.
Spatial Transcriptomics (Method of the Year 2020) is a fast evolving field.
It holds great potential to further our understanding of development & disease, by placing cells in their spatial native tissue context.
Excited to announce our new method #CytoSPACE for mapping single cells from a reference atlas to #SpatialTranscriptomics (ST) data at high resolution, out now in @NatureBiotech! 1/10
nature.com/articles/s4158…
nature.com/articles/s4158…
Available assays for ST lack single-cell resolution (e.g. #Visium) or have limited gene recovery (e.g. #MERSCOPE).
CytoSPACE computationally reconstructs tissue specimens to obtain high gene coverage and spatially-resolved scRNA-seq data suitable for downstream analysis. 2/10
CytoSPACE computationally reconstructs tissue specimens to obtain high gene coverage and spatially-resolved scRNA-seq data suitable for downstream analysis. 2/10
CytoSPACE works by constrained global optimization to yield robust mappings, outperforming existing methods across benchmarks. 3/10
🚨New #SpatialTranscriptomics #Bioinformatics data resource out in @naturemethods.
SODB, a platform with >2,400 manually curated spatial experiments from >25 spatial omics technologies & interactive analytical modules.
This🧵will walk you through all the features of SODB [1/33]
SODB, a platform with >2,400 manually curated spatial experiments from >25 spatial omics technologies & interactive analytical modules.
This🧵will walk you through all the features of SODB [1/33]
First, some background.
Spatial technologies complement classical genomics by also providing information about spatial context & tissue organization in:
- embriogenesis
- disease development
- normal tissue homeostasis
The field has exploded 🔥 in the past 2 years. [2/33]
Spatial technologies complement classical genomics by also providing information about spatial context & tissue organization in:
- embriogenesis
- disease development
- normal tissue homeostasis
The field has exploded 🔥 in the past 2 years. [2/33]
But, data from different studies is stored in different configurations/repositories, such as:
- GEO
- zenodo
- fig share
- SingleCellPortal
- IONPath for MIBI
- 10XGenomics website
This makes data sharing & re-analysis challenging.
Databases exist, but have limitations. [3/33]
- GEO
- zenodo
- fig share
- SingleCellPortal
- IONPath for MIBI
- 10XGenomics website
This makes data sharing & re-analysis challenging.
Databases exist, but have limitations. [3/33]
Amazing week for #DeepLearning in #spatial #singlecell biology, with 2🔥new Graph Neural Networks methods!
1.STELLAR🇺🇸 @jure: a cell type annotation & discovery atlas-type framework
2.NCEM🇪🇺 @fabian_theis: an approach to infer cellular communication patterns
Deep dive below🧵
1.STELLAR🇺🇸 @jure: a cell type annotation & discovery atlas-type framework
2.NCEM🇪🇺 @fabian_theis: an approach to infer cellular communication patterns
Deep dive below🧵
But first, some background.
Spatial molecular biology has actually been around since the 70s. @lpachter's wonderful book-like article "Museum of Spatial Transcriptomics" comprehensively discusses history, tech & methodology advances in the past 50 years.
nature.com/articles/s4159…
Spatial molecular biology has actually been around since the 70s. @lpachter's wonderful book-like article "Museum of Spatial Transcriptomics" comprehensively discusses history, tech & methodology advances in the past 50 years.
nature.com/articles/s4159…
Nevertheless, recent advances in single cell molecular technologies (brought by e.g. @10xGenomics & @AkoyaBio) have facilitated the high-throughout profiling of (groups of) single cells in their tissue context across embryogenesis, normal tissue development & disease progression.
Excited to share that our @NatureBiotech paper with Aviv Regev on Multicellular Programs (MCPs) is now out with a fully automated data-driven method to identify MCPs from #singlecell or spatial data #behindthepaper: go.nature.com/3iQ8fgW nature.com/articles/s4158… (1/19)
Gene programs are often studied within a cell, with each cell considered an “independent entity”. However, cells from the same niche/tissue/organism are not independent, but share external signals, genetic background/lineage, and can directly impact one another. (2/19)
Here we define “Multicellular Programs (MCPs)” as different sets of genes working in concert across different cell types. (3/19)
My highlights of the @10xGenomics #Xperience2021 event. The list of products keeps growing, I would highlight Chromium Connect as an underappreciated tool to bring the products up another level of throughput. Important as with #NGS, it won't take long to go from n=1 to n=96+.
#CellPlex species-agnostic multiplexing up to 12 samples: not dissimilar to products such as TotalSeq, but baked-in so that it's been tested to work with the rest of the workflow.
Going close to 1M cells, the #ChromiumX brings about 100x fold throughput increase, all marked with 'HT' in the Kits. I'd be interesting to know how the different #BodyAtlas projects embrace this and for what.
