## A short report about normalization of spatially resolved transcriptomics data in the literature
One of the key step of the analysis of single-cell RNA-Seq data is the normalization of the counts, where the biological heterogeneity among cells can be confounded by technical differences.
When analyzing spatially resolved transcriptomics data, besides the cell-to-cell variation, also the tissue heterogeneity plays an important role.
Then, it is crucial to think about how to normalize the raw data coming from spatial transcriptomics. In other words, removing the noise and normalize the data should be two independent procedures.
Clearly, this in practice is very hard to accomplish.
What we will do here is to navigate into a bit of literature about spatial transcriptomics data normalization, in order to provide some points for users to ponder before they normalize their data.
## Getting started
#### Normalization by the total number of reads per cell/spot
In [this publication](https://www-nature-com.insb.bib.cnrs.fr/articles/s41421-021-00266-1) on MERFISH data, the authors normalize by the total number of reads per cell when they have to perform differential gene expression analyses or to investigate co-expression of genes. In particular, the copy number of each RNA species in the cell was divided by the copy number of all labeled RNA species in the cell, and then multiplied by a factor of 1000.
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
Another option is to normalize by correcting the mean total RNA counts per cell, setting the same mean value for all the cells. For example, [here](https://pubmed.ncbi.nlm.nih.gov/36945367/) the authors normalize for the volume of the cells and then corrected the RNA counts per cell to 250.
Similarly, [here](https://www.life-science-alliance.org/content/6/1/e202201701#sec-9) data were normalized to a total count of 10000 transcripts per cell.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Add your files
###### Is it always the right thing to do?
Clearly, the normalizations discussed above provide simple and effective ways to make data comparable and limit technical variations. On the other hand, we might have as unwanted effect the lost of biological heterogeneity, assuming that all the cells behave the same way, independently on their biological state and on their position on the tissue we are analyzing.
-[ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
For example, [here](https://academic.oup.com/jmcb/article/12/11/906/5861536?login=false) it has been show that normalizing by the total number of counts per cell/spot is not always a good idea, and the number of counts per cell/spot can be very informative. In this work, a papillary thyroid cancer was profiled using spatial transcriptomics as described in Salmén et al. (Nature Protocol, 2018). The authors quantified the cellular content of individual spots from image analysis and compared it with read counts. They assessed that for at least some genes (i.e. Vimentin), normalization affected the spatial expression pattern, and they show that genes tend to show a similar expression pattern that reflects total transcriptional output when raw counts are considered, while normalized expression highlights contrasts between genes. In conclusion, total counts per spot are biologically informative and do not necessarily need to be normalized out.
Of note, in 2019 C. Hafemeister and R. Satija proposed a [new method](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1874-1) for modeling, normalization and variance stabilization of UMI-based scRNA-seq datasets. This approach is also implemented in the R package [sctransform](https://github.com/satijalab/sctransform), and is part of the single-cell tooklit [Seurat](https://satijalab.org/seurat/).
Moreover and interestingly for us, in their vignettes, the authors provided guidelines to use sctransform both when dealing with [single-cell RNA-Seq data](https://satijalab.org/seurat/articles/sctransform_v2_vignette.html) and with [spatial transcriptomics data](https://satijalab.org/seurat/articles/spatial_vignette.html) from Visium technology.
## Integrate with your tools
In brief, the paper is based on the principle that a proper normalized expression level of a gene should correlate with the total sequencing depth of a cell, and that the variance of a normalized gene expression across cells should reflect biological variability, being independent on gene abundance and sequencing depth.
To address these points, the paper proposes a generalized linear model (GLM) for each gene, where UMI counts are the response variable and sequencing depth is the explanatory variable. Then, the authors explore the different possible error models of the GLM, and find that unconstrained negative binomial (NB) and zero-inflation negative binomial (ZINB) models cause overfitting of the data, while pooling information across genes with similar abundance can regularize parameter estimates.
We refer to the original paper for further mathematical details.
One computational limitation of sctransform is only available in R. If you are a python user and you are analyzing your data in the context of [scanpy](https://scanpy.readthedocs.io/en/stable/) or [squidpy](https://squidpy.readthedocs.io/en/stable/), you can have a look at this [wrapper](https://github.com/normjam/benchmark/blob/master/normbench/methods/ad2seurat.py), where sctransform is usable in python via [rpy2](https://rpy2.github.io/) and [anndata2ri](https://pypi.org/project/anndata2ri/).
-[ ] [Set up project integrations](http://gitbio.ens-lyon.fr/spatial-cell-id/normalization-of-spatially-resolved-data/-/settings/integrations)
#### 3D case: what to do when having several slices
During the last years, one of the goals of the spatially resolved transcriptomics has been to provide a three-dimensional resolution of the gene expression of tissues of interest. To do that, generally different slices of the same tissues are analyzed, in order to reconstruct the gene expression spatial features across all the dimensions. We wondered how to pre-process and normalize data in such case.
First thing to highlight is that data from different slices need to be normalized together, in order to proceed with a comprehensive, three-dimensional downstream analysis. At the end, it will be like having a count matrix where rows are cells/spot, regardless of the slice they come from.
About the mathematical way for normalizing 3D data, different methods have been published in literature.
For example, [here](https://www.biorxiv.org/content/10.1101/2023.03.06.531121v1.full.pdf) overall counts of genes were normalized by cell volume and then log2 transformed. [Here](https://www.biorxiv.org/content/10.1101/2023.03.06.531348v1.full.pdf), instead, the authors normalized the total count of each cell to 1000, then log1p transformed the counts and finally scaled the transformed counts to Z-scores.
A bit different is what the authors did in [this publication](https://www-ncbi-nlm-nih-gov.insb.bib.cnrs.fr/pmc/articles/PMC6482113/), where they analyzed hypothalamic preoptic region by using MERFISH-based imaging. First, they normalized RNA counts per cell by the imaged volume of each cell. As they observed batch effect between different MERFISH runs (mean total number of RNAs identified per cell varied by ~20% from run-to-run), they removed this batch effect by normalizing the mean total RNA density per cell for each MERFISH dataset so then this mean value was the same across all datasets.
## Collaborate with your team
-[ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
Use the built-in continuous integration in GitLab.
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-[ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
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#### Conclusions
Normalization of single-cell RNA-Seq and spatially resolved data is far from being a standard operation. Before proceeding, the biological context and the experimental technique should be carefully considered. A good balance between normalizing by the total number of reads per cell and preserving information on heterogeneity of tissue and cells/spot should be aimed. Also, in some cases, one could consider to use some normalizations methods like SCT transform, which provide a more complex mathematical model for pre-processing the data a getting normalized gene expression.
