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Cell2location is a principled Bayesian model that can resolve fine-grained cell types in spatial transcriptomic data and create comprehensive cellular maps of diverse tissues. Cell2location accounts for technical sources of variation and borrows statistical strength across locations, thereby enabling the integration of single cell and spatial transcriptomics with higher sensitivity and resolution than existing tools. This is achieved by estimating which combination of cell types in which cell abundance could have given the mRNA counts in the spatial data, while modelling technical effects (platform/technology effect, contaminating RNA, unexplained variance). This tutorial shows how to use cell2location method for spatially resolving fine-grained cell types by integrating 10X Visium data with scRNA-seq reference of cell types. Cell2location is a principled Bayesian model that estimates which combination of cell types in which cell abundance could have given the mRNA counts in the spatial data, while modelling technical effects (platform/technology effect, contaminating RNA, unexplained variance).

Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. **Tangram** can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.

Single-cell RNA sequencing (scRNA-seq) protocols often face challenges in measuring the expression of all genes within a cell due to various factors, such as technical noise, the sensitivity of scRNA-seq techniques, or sample quality. This limitation gives rise to a need for the prediction of unmeasured gene expression values (also known as dropout imputation) from scRNA-seq data. ADImpute (Leote A, 2023) is an R package combining several dropout imputation methods, including two existing methods (DrImpute, SAVER), two novel implementations: Network, a gene regulatory network-based approach using gene-gene relationships learned from external data, and Baseline, a method corresponding to a sample-wide average.. This notebook is to illustrate an example workflow of ADImpute on sample datasets loaded from the package. The notebook content is inspired from ADImpute's vignette and modified to demonstrate how the tool works on BioTuring's platform.

Single-cell RNA sequencing (scRNA-seq) data often encountered technical artifacts called "doublets" which are two cells that are sequenced under the same cellular barcode. Doublets formed from different cell types or states are called heterotypic and homotypic otherwise. These factors constrain cell throughput and may result in misleading biological interpretations. DoubletFinder (McGinnis, Murrow, and Gartner 2019) is one of the methods proposed for doublet detection. In this notebook, we will illustrate an example workflow of DoubletFinder. We use a 10x Genomics dataset which captures peripheral blood mononuclear cells (PBMCs) from a healthy donor stained with a panel of 31 TotalSeq™-B antibodies (BioLegend).