Remove Low-quality Spots
Last updated on 2024-06-20 | Edit this page
Overview
Questions
- How do I remove low-quality spots?
- What kinds of problems produce low-quality spots?
- What happens if I skip quality control and proceed with analysis?
Objectives
- Understand how to look for low quality spots.
- Decide whether to retain or remove low quality spots.
Introduction
Spatial transcriptomics involves a complex process that may involve some technical failures. If the processing of the entire slide fails, it should be obvious due to a large number of gene appearing in spots outside of the tissue or low UMIs across the whole tissue.
However, there may also be variation in spot quality in a slide that has largely high-quality spots. These artifacts are much rarer than in single-cell transcriptomics because the process of tissue sectioning is less disruptive than tissue dissociation. Because of this, we recommend light spot filtering.
There are three metrics that we will use to identify and remove low-quality spots:
- Mitochondrial gene expression,
- Total UMI counts,
- Number of detected genes.
During tissue processing, it is possible that some cells will be lysed, spilling out the transcripts, but retaining the mitochondria. These spots will appear with much higher mitochondrial gene expression. We will also examine the total UMI counts and number of genes detected in each spot because high counts may indicate spots with lysed cells whose contents bled into other spots.
However, these metrics may be tissue-dependent. In some tissues, there may be biological reasons for differential expression across the tissue. For example, in a cancer sample, mitochondrial or total gene expression may vary between stromal and tumor regions. It will be important for you to familiarize yourself with the structure of the tissue that you are analyzing in order to make rational judgments about filtering.
Filtering by Mitochondrial Gene Count
In single-cell RNA sequencing experiments, the tissue is digested and the cells are dissociated. This mechanical disruption is stressful to the cells and some of them are damaged in the process. Elevated levels of mitochondrial genes often indicate cell death or damage because, when a cell’s membrane is compromised, it loses most cytoplasmic content while retaining mitochondrial RNA. Therefore, spots with high mitochondrial RNA may represent damaged or dying cells, and their exclusion helps focus the analysis on healthy, intact cells.
More details on this relationship can be found in the literature on mitochondrial DNA and cell death.
However, in spatial transcriptomics, the tissue is either frozen or formalin-fixed and there is much less mechanical disruption of the tissue. Because of this, we are skeptical of the value of filtering spots based on mitochondrial gene counts.
For completeness, we show how to obtain the mitochondrial genes, calculate the percentage of counts produced by these genes in each spot, and add this to the Seurat object metadata.
We will search the gene symbols in the feature metadata to identify mitochondrial genes. We do not need to find all genes in these categories, so we will search for genes with symbols that start with “MT”.
The Seurat object is designed to be flexible and may contains several data types. For example, it may contain both gene counts and open chromatin peaks. In this analysis, the Seurat object only contains gene counts. The different types of data are called “Layers” in Seurat and may be accessed using the Layers function.
R
Layers(filter_st)
OUTPUT
[1] "counts"This tells us that the “filter_st” object only contains one data Layer called “counts”. We can access this using the LayerData function using “counts” as an argument.
R
counts <- LayerData(filter_st, 'counts')
head(counts)[,1:5]
OUTPUT
6 x 5 sparse Matrix of class "dgCMatrix"
AAACAAGTATCTCCCA-1 AAACAATCTACTAGCA-1 AAACACCAATAACTGC-1
MIR1302-2HG . . .
AL627309.1 . . .
AL669831.5 . . .
FAM87B . . .
LINC00115 . . .
FAM41C . . .
AAACAGAGCGACTCCT-1 AAACAGCTTTCAGAAG-1
MIR1302-2HG . .
AL627309.1 . .
AL669831.5 . .
FAM87B . .
LINC00115 . .
FAM41C . .The output above may look odd to you since there are no numbers. Notice that the text above the table says “sparse Matrix”. Many of the counts in the file are likely to be zero. Due to the manner in which numbers are stored in computer memory, a zero takes up as much space as a number. If we had to store all of these zeros, it would consume a lot of computer memory. A sparse matrix is a special data structure which only stores the non-zero values. In the table above, each dot (.) represents a position with zero counts.
If we look at another part of the “counts” matrix, we can see numbers.
R
counts[20000:20005,1:5]
OUTPUT
6 x 5 sparse Matrix of class "dgCMatrix"
AAACAAGTATCTCCCA-1 AAACAATCTACTAGCA-1 AAACACCAATAACTGC-1
DNAJB1 . . .
TECR 3 1 .
NDUFB7 3 1 1
ZNF333 . . .
ADGRE2 . . .
OR7C1 . . .
AAACAGAGCGACTCCT-1 AAACAGCTTTCAGAAG-1
DNAJB1 1 .
TECR 2 1
NDUFB7 2 4
ZNF333 . .
ADGRE2 . .
OR7C1 . .As you can see in the table above, the gene symbols are stored in the rownames of “counts”. We will find find mitochondrial genes by searching for gene symbols which start with “MT”.
R
mito_pattern <- '^[Mm][Tt]-'
mito_genes <- rownames(counts)[grep(mito_pattern, rownames(counts))]
mito_genes
OUTPUT
[1] "MT-ND1" "MT-ND2" "MT-CO1" "MT-CO2" "MT-ATP8" "MT-ATP6" "MT-CO3"
[8] "MT-ND3" "MT-ND4L" "MT-ND4" "MT-ND5" "MT-ND6" "MT-CYB" We now have a set of mitochondrial genes. We will use these genes to estimate the percentage of gene counts expressed by mitochondrial genes in each cell and add this to the Seurat object. We will pass the mitochondrial gene symbols into PercentageFeatureSet, which will perform the calculation for us.
R
filter_st[["percent.mt"]] <- PercentageFeatureSet(filter_st, pattern = mito_pattern)
This syntax adds a new column called “percent.mt” to the spot metadata.
R
colnames(filter_st@meta.data)
OUTPUT
[1] "orig.ident" "nCount_Spatial" "nFeature_Spatial"
[4] "in_tissue" "array_row" "array_col"
[7] "pxl_row_in_fullres" "pxl_col_in_fullres" "percent.mt" Let’s look at histograms of the ribosomal and mitochondrial gene percentages.
R
hist(FetchData(filter_st, "percent.mt")[,1], main = "% Mitochondrial Genes",
xlab = "%")
In these plots, we are looking for spots which are outside of a normal distribution. It is difficult to generalize how to select a filtering threshold. Some tissue or cell types may have higher mitochondrial gene expression. Further, heterogeneous tissues may have subsets of cells with differing levels of mitochondrial gene expression.
Let’s visually check whether the mitochondrial gene expression is normally distributed.
R
mito_expr <- FetchData(filter_st, "percent.mt")[,1]
qqnorm(mito_expr, las = 1)
qqline(mito_expr)
In this case, there may be a reason to filter out spots with greater than 35% mitochondrial counts.
Filter by UMI Count and Number of Detected Genes
In the previous lesson, we plotted number of UMIs and genes detected spatially across the tissue. Let’s plot these values again, but this time as a histogram.
R
layout(matrix(1:2, ncol = 1))
hist(FetchData(filter_st, "nCount_Spatial")[,1],
main = 'UMIs per Spot', xlab = 'Counts', las = 1)
hist(FetchData(filter_st, "nFeature_Spatial")[,1],
main = 'Genes per Spot', xlab = 'Genes', las = 1)
Again, most of the spots fall within a reasonable distribution. The right tail of the distribution is not very thick. We might filter spots with over 14,000 UMIs or 3,000 genes. We will use these thresholds to add a “keep” column to the Seurat object metadata.
First, we will create variables for each threshold. While we could type the numbers directly into the logical comparison statements, creating variables makes it clear what each number represents.
R
mito_thr <- 32
counts_thr <- 14000
features_thr <- 5000
Next, we will create a “keep” variable which will be TRUE for spots that we want to keep.
R
keep <- FetchData(filter_st, "percent.mt")[,1] < mito_thr
keep <- FetchData(filter_st, "nCount_Spatial")[,1] < counts_thr & keep
keep <- FetchData(filter_st, "nFeature_Spatial")[,1] < features_thr & keep
filter_st$keep <- keep
Now let’s plot the spots on the tissue and color them based on whether we will keep them.
R
SpatialDimPlot(filter_st, group.by = "keep")
When you examine the spots that have been flagged, it is important to look for patterns. If a contiguous section of tissue contains spots that will be removed, it is worth looking at the histology slide to see if there are structures that correlate with the removed spots. If it is a section of necrotic tissue, then, depending on your experimental question, you may want to remove those spots. But you should always look for patterns in the removed spots and convince yourself that they are not biasing your results.
Challenge 1: Change the spot filtering thresholds.
- Change the threshold for the number of UMIs per spot to keep spots with more than 2000 counts. Note that we are filtering on the lower side of the distribution.
- Add new variable called “keep_counts” to the Seurat object.
- Plot the spot overlaid on the tissue section, colored by whether you are keeping them.
Is there a pattern to the removed spots that seems to correlate with the tissue structure?
R
new_counts_thr <- 2000
keep_counts <- FetchData(filter_st, "nCount_Spatial")[,1] > new_counts_thr
filter_st$keep_counts <- keep_counts
SpatialDimPlot(filter_st, group.by = "keep_counts")
Note that the spots that we have flagged seem to correspond to stripes in the tissue section. These may be regions of the brain which have lower levels of gene expression, so we may want to revise or remove this threshold. Overall, this exercise shows that it is important to use judgement when filtering spots.
Note that we will only remove a few spots in this filtering step.
R
table(FetchData(filter_st, "keep")[,1])
OUTPUT
FALSE TRUE
6 3633 We can remove the spots directly using the following syntax. In this case, the “columns” of the Seurat object correspond to the spots.
R
filter_st <- filter_st[,keep]