Perturbation analysis of spatial single cell RNA-seq with ‘augur’

Spatial single cell RNA-seq data are essentially regular single-cell RNA-seq data that have spatial coordinates associated through localization on a special capture slide. I had previously used so-called “perturbation” analysis successfully with 10X single-cell data and I wanted to apply the technique to spatial single cell to understand how a treatment affects the spatially-resolved clusters.

Here, I want to briefly describe the steps I went through to perform ‘augur’ perturbation analysis of 10X Visium Spatial single cell RNA-seq data. augur works as follows:

Augur is an R package to prioritize cell types involved in the response to an experimental perturbation within high-dimensional single-cell data. The intuition underlying Augur is that cells undergoing a profound response to a given experimental stimulus become more separable, in the space of molecular measurements, than cells that remain unaffected by the stimulus. Augur quantifies this separability by asking how readily the experimental sample labels associated with each cell (e.g., treatment vs. control) can be predicted from molecular measurements alone. This is achieved by training a machine-learning model specific to each cell type, to predict the experimental condition from which each individual cell originated. The accuracy of each cell type-specific classifier is evaluated in cross-validation, providing a quantitative basis for cell type prioritization.

I followed both the Seurat 10X Visium vignette as well as a dataset integration protocol to combine two treatment (a gene knockout, in this case) and control samples (S1 and S2). Normalization was performed by “SCTransform” as recommended for spatial RNA-seq data prior to integration. PCA, K-nearest neighbors, clustering, and uMAP were calculated as described in the Seurat vignette using default values. Cell types were assigned in collaboration with the experimentalists.

With the integrated, clustered and, assigned dataset in hand, I was ready to enter the “augur” workflow as described in the paper, with some minor tweaks. First, because this is spatial and not regular scRNA-seq, there is no “RNA” default assay to set after integration. I chose to set “SCT” as the assay instead, because this represents the normalized and scaled dataset which is what you want for input to an ML model.

```{r, celltype_priority}

library(Augur)
DefaultAssay(s1s2.int) <- "SCT"
augur <- Augur::calculate_auc(s1s2.int, label_col = "orig.ident", cell_type_col = "cell_type", 
                              n_threads = 6, 
                              rf_params = list(trees = 15, mtry = 2, min_n = NULL, importance = "accuracy"),
                              n_subsamples = 25,
                              )
```

Above, you can see the actual call to augur “calculate_auc” method. I found that by specifying ‘rf_params’ and reducing the number of trees, I got better separation between cell types in the AUC readout. The calculation takes about 20 minutes to run on a 2018 MacBook Pro 13 inch laptop.

When the algorithm completes, you can visualize your results. Using the vignette for regular scRNA-seq you can do this:

library(patchwork)
p1 <- plot_umap(augur, s1s2.int, mode = "default", palette = "Spectral")
p1 <- p1 + geom_point(size=0.1) + ggtitle("Augur Perturbation by Type (Red = Most)")
p2 <- DimPlot(s1s2.int, reduction = "umap", group.by = "cell_type") + ggtitle("S1/S2 Integrated Cell Types")
p1 + p2 

The resulting plot looks like this:

Augur perturbation analysis by AUC (red is more perturbed; left) and UMAP plot of cell types (right).

This is great and helpful, but it doesn’t take advantage of the spatially resolved nature of the data. To do that, you have to modify the integrated seurat object with the augur results:

### Make a dataframe of AUC results 
auc_tab <- augur$AUC
auc_tab$rank <- c(1:9)

### Grab the cells by type and barcode 
tib <- s1s2.int$cell_type %>% as_tibble(rownames = "Barcode") %>% rename(cell_type=value)

### Join the AUC information to the barcode on cell_type 
tib <- tib %>% left_join(., auc_tab)

### Sanity check 
assertthat::are_equal(colnames(s1s2.int), tib$Barcode)

### Update the seurat object with new augur metadata 
s1s2.int$AUC <- round(tib$auc, 3) 
s1s2.int$RANK <- tib$rank

Here, I am simply pulling out the AUC results into a table by cell type. Then I get the cell type information from the seurat object and merge the AUC information into it. I just set new metadata on the seurat object to transfer information about AUC and Rank for each barcode (i.e., cell). I do a sanity check to make sure the barcodes match (they do, as expected).

Now you can plot the spatially resolved AUC information:

SpatialDimPlot(s1s2.int, group.by = "AUC", cols = rev(c("#D73027", "#F46D43", "#FDAE61", "#FEE090", "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4")))

This takes advantage of the “group.by” flag in the Spatial Dim Plots command to use the AUC metadata. I’m also using a custom color scheme from ColorBrewer that shades the cell types from low to high AUC along a rainbow for ease of viewing. The plot looks like this:

Spatially-resolved perturbation (AUC) of cell clusters in the WT (left) and knockout (right) samples.

A brief look at machine-learning powered literature search

Machine-learning (ML) and neural networks are transforming data science and life sciences. They are being applied to deal with the challenges of making sense of piles of ‘big data’ that are growing bigger all the time.

Now, these same tools are now being applied to searching the gigantic scientific literature databases (PubMed contains > 30M citations) in order to bring more relevant results to researchers.

A simple PubMed search proceeds by matching terms like the following:

…if you enter child rearing in the search box, PubMed will translate this search to: “child rearing”[MeSH Terms] OR (“child”[All Fields] AND “rearing”[All Fields]) OR “child rearing”[All Fields]

https://www.ncbi.nlm.nih.gov/books/NBK3827/

If you want to get potentially more sophisticated than simply searching on matching terms, like PubMed, take a look at the methods below. Without having used each one extensively, it’s difficult for me to tell if the results are an improvement on PubMed or Google, but let’s just jump in an explore each one briefly:

Semantic Scholar

First up is Semantic Scholar. According to the “about me” page, SS is aimed at helping researchers find relevant publications faster. It analyzes whole documents and extracts meaningful features using various types of ML. The authors claim that this method results in finding influential citations, key images and phrases, and allows the researcher to focus on impactful publications first. They claim to index 176M articles, and have filters for high-quality publications. Detail about this are scarce however.

A search results page from Semantic Scholar search for “single-cell RNA-seq”

The search results appear to have some nice features. Above is a screencap of the results for a “single-cell RNA-seq” search. In the image below, you can see that beneath each paper title and abstract are a couple of numbers in orange. The number on the left is the number of “highly influential citations.” This is the number of papers where this paper played an important role in the citing paper. The second number on the right is the “citation velocity” which represents the average number of citations per year for that work. Then there are several more useful buttons, including a link out, a button that brings up the citation in a variety of formats, a “save” button, and a button to add the paper to my Paperpile library.

Clicking through on one paper yields a page that looks like this:

A results page from Semantic Scholar. Key figures are pulled out and highlighted for quick viewing. Key topics covered in the work are shown on the right.

This nice, clean interface makes it easy to absorb the content of the paper, including browsing the abstract and key figures. You also have a metrics box in the upper right that shows how many times the paper is cited, how many are “highly influenced”, and where in the citing papers this paper is referenced. The headings across the middle of the results page break down the sections that are below. These include “Figures and Topics”, “Media Mentions” where SS finds blog posts and online reports that mention this topic, “Citations” which is a list of the citing papers, “References” which is the papers referenced by this paper, and “Similar Papers” which are papers that cover related topics.

Iris.ai

Iris.ai is machine-learning tool that uses neural networks to build knowledge graphs about publications. The “about me” section includes a cutesy intro in the first person, as if the algorithm were just a really smart person reading a lot of papers and not a research project. Anyway, Iris claims to have “read” at least 77M papers in the core database. There is a good article here detailing the evolution of Iris since her founding in 2016. And the Iris.AI blog is a good place to learn of updates to the method.

When you perform a search with Iris.ai the interface looks like this:

Above. The search interface for Iris.ai.

This looks like a standard search bar, but instead of searching keywords you either input a URL of a paper you are interested in, or you write a title and 300 word paragraph describing a research problem. So there is some work on the front end to get to useful results, but possibly worth it if you need to deep dive into the literature. Let’s take a look at those results below.

Above: Search results for the paper “CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing.”

OK, this is wild. I’ve never seen a search result like this “map” of the knowledge that results from searching a paper. In this case, I searched the “CNVkit” paper. Each “cell” in this map can be zoomed in on, revealing sub-categories that further break down the knowledge and context of the papers. Below that are the actual papers themselves.

Here I’ve zoomed in on “Target” cell and then “Re-sequencing” cell. Now I’m down to individual papers that make up this “cell.”

I hope you’ve enjoyed this brief tour through some advanced ML-powered literature searching tools. I am going to make an effort to incorporate these into my own work with literature searching and see what difference it makes (maybe a subject for a future post).

New job: Director of IIHG Bioinformatics

I’m thrilled to report that I’ve been promoted to the position of Director of our bioinformatics group here at the University of Iowa. We are within the Iowa Institute of Human Genetics (IIHG) and we support clinical activities in the institute, but also a wide array of research collaborations across the University.

I have a lot of goals and ideas for the group and look forward to working to implement those going forward. I may not be able to write posts here as often, but I’ll try to keep up with it. We also have a new twitter account: @iowabioinfo. Please follow us there.