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.

Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation—the assignment of cell type or cell state to each sequenced cell—is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts. **InferCNV** is a Bayesian method, which agglomerates the expression signal of genomically adjointed genes to ascertain whether there is a gain or loss of a certain larger genomic segment. We have used **inferCNV** to call copy number variations in all samples used in the manuscript.

Power analyses are considered important factors in designing high-quality experiments. However, such analyses remain a challenge in single-cell RNA-seq studies due to the presence of hierarchical structure within the data (Zimmerman et al., 2021). As cells sampled from the same individual share genetic and environmental backgrounds, these cells are more correlated than cells sampled from different individuals. Currently, most power analyses and hypothesis tests (e.g., differential expression) in scRNA-seq data treat cells as if they were independent, thus ignoring the intra-sample correlation, which could lead to incorrect inferences. Hierarchicell (Zimmerman, K.D. and Langefeld, C.D., 2021) is an R package proposed to estimate power for testing hypotheses of differential expression in scRNA-seq data while considering the hierarchical correlation structure that exists in the data. The method offers four important categories of functions: data loading and cleaning, empirical estimation of distributions, simulating expression data, and computing type 1 error or power. In this notebook, we will illustrate an example workflow of Hierarchicell. The notebook is inspired by Hierarchicell's vignette and modified to demonstrate how the tool works on BioTuring's platform.