Journal: MedComm

Article Title: Single‐Cell and Spatial Multiomics: Applications for Diseases

doi: 10.1002/mco2.70553

Figure Lengend Snippet: Timeline of single‐cell and spatial multiomics approach (by FigDraw). The development of single‐cell and spatially multiomics technologies between 2009 and 2022. Researchers can analyze molecular expression profiles at single‐cell resolution and also determine their spatial coordinates within tissues. Abbreviations : STRT‐seq: single‐cell‐tagged reverse transcription sequencing; CEL‐seq: cell expression by linear amplification and sequencing; DP‐seq: differential privacy sequence; MARS‐seq: massively parallel single‐cell RNA sequencing; SCRB‐seq: single‐cell RNA barcoding sequencing; SC3‐seq: single‐cell consensus clustering sequencing; DR‐seq: direct RNA sequencing; G&T‐seq: genome and transcriptome sequencing; ScM&T‐seq: simultaneous single‐cell methylome and transcriptome sequencing; Div‐seq: single‐nucleus RNA‐sequencing; ScNOME‐seq: single‐cell multiomics sequencing; SIDR: simultaneous isolation of genomic DNA and total RNA; SPLiT‐seq: split‐pool barcoding sequencing; mcSCRB‐seq: single cell RNA barcoding sequencing; snDrop‐seq: single nucleus RNA sequencing; MAPS‐seq: metagenomic plot sampling by sequencing; DBiT‐seq: deterministic barcoding in tissue for spatial omics sequencing; ASTAR‐seq: automated single‐cell transcriptome and chromatin accessibility sequencing; DNTR‐seq: direct nuclear tagging and RNA sequencing; CoTECH: combinatorial barcoding and targeted chromatin release; FlsnRNA‐seq: protoplasting‐free full‐length single‐nucleus RNA profiling in plants; VASA‐seq: vast transcriptome analysis of single cells by dA‐tailing.

Article Snippet: Particularly in the single‐cell community, deep learning approaches have drawn a lot of interest because of their adaptability to a variety of applications and their capacity to hand deep learning diverse, sparse, noisy, and high‐dimensional single‐cell omics data [ ].

Techniques: Expressing, Reverse Transcription, Sequencing, Amplification, RNA Sequencing, Isolation, Sampling

Journal: MedComm

Article Title: Single‐Cell and Spatial Multiomics: Applications for Diseases

doi: 10.1002/mco2.70553

Figure Lengend Snippet: Brief schematic diagram of the single‐cell and spatial omics approaches process (by FigDraw). These techniques have played a significant role in enhancing our understanding of the composition of complex cell types and their corresponding cellular functions in living organisms.

Article Snippet: Particularly in the single‐cell community, deep learning approaches have drawn a lot of interest because of their adaptability to a variety of applications and their capacity to hand deep learning diverse, sparse, noisy, and high‐dimensional single‐cell omics data [ ].

Techniques:

Journal: MedComm

Article Title: Single‐Cell and Spatial Multiomics: Applications for Diseases

doi: 10.1002/mco2.70553

Figure Lengend Snippet: Workflow for single‐cell and spatial multiomics ensemble approaches to deep learning (by FigDraw). The research introduces deep learning‐based data integration studies using different omics data, where most related research belongs: (1) feature selection/reduction, (2) clinical outcome prediction, (3) survival analysis and (4) clustering for subtype discovery. Abbreviations : CNV: copy number variation; SNP: single nucleotide polymorphism.

Article Snippet: Particularly in the single‐cell community, deep learning approaches have drawn a lot of interest because of their adaptability to a variety of applications and their capacity to hand deep learning diverse, sparse, noisy, and high‐dimensional single‐cell omics data [ ].

Techniques: Selection