Troubleshooting NaN Values In WNN Matrix With Trex Embedding And PCA In Seurat

by StackCamp Team 79 views

Hey guys! So, you're running into a bit of a snag with NaN values popping up in your Weighted Nearest Neighbor (WNN) matrix when using Seurat, especially after integrating Trex embeddings and PCA. It's a tricky situation, but let's break it down and see if we can get things sorted out. This article will guide you through understanding the issue, troubleshooting steps, and potential solutions.

Understanding the Problem

So, you've been working with Seurat, a powerful tool for single-cell data analysis, and you've incorporated Trex for T-cell receptor (TCR) analysis alongside PCA for dimensionality reduction. Everything seems to be going smoothly until you hit the RunUMAP step, and bam! You're faced with an error related to NaN (Not a Number) distances in your WNN matrix. That's super frustrating, but let's discuss what's happening under the hood.

What are NaN Values?

First off, let's talk about NaN values. In the world of computing, NaN is a special value representing something that is undefined or unrepresentable. Think of it like trying to divide by zero – the result isn't a real number. In the context of your Seurat analysis, NaN values in the distance matrix mean that the algorithm couldn't calculate a meaningful distance between certain cells based on the input embeddings.

Why NaN Values Appear in WNN Matrix?

Now, why are these NaN values showing up in your WNN matrix? Well, the WNN matrix is constructed by combining multiple data modalities (in your case, PCA and Trex embeddings) to create a more comprehensive representation of cell similarity. When you have NaN values, it usually points to a problem in one of the underlying distance calculations. This can happen due to several reasons:

  • Zero Variance: If a feature (e.g., a gene or a principal component) has zero variance, it means all cells have the same value for that feature. This can lead to division by zero or other undefined operations when calculating distances.
  • Infinite or Undefined Values: Sometimes, intermediate calculations can result in infinite values or other undefined states, which then propagate as NaN.
  • Data Scaling Issues: If the data isn't properly scaled or normalized, it can lead to numerical instability during distance calculations.

In your specific case, you've already checked for null values in your PCA and Trex embeddings, which is a great first step. However, the issue might be more subtle, arising during the distance calculation itself.

Diving into the Code

Let's take a closer look at the code you're using. This will help us pinpoint where the NaN values might be creeping in. Here's the snippet you provided:

seur <- runTrex(
    seur,
    chains = "TRA",
    method = "encoder",
    encoder.model = "VAE",
    encoder.input = "AF",
    reduction.name = "Trex_tra"
)

seur <- NormalizeData(seur) %>%
    FindVariableFeatures() %>%
    quietTCRgenes() %>%
    ScaleData() %>%
    RunPCA(verbose = FALSE)



seur <- FindMultiModalNeighbors(
    seur,
    reduction.list = list("pca", "Trex_trb"),
    dims.list = list(1:50, 1:30),
    modality.weight.name = c("RNA.weight", "TRb_weight")
)







seur <- RunUMAP(
    seur,
    nn.name = "weighted.nn",
    reduction.name = "wnn.umap",
    reduction.key = "wnnUMAP_" #error
)

You're using runTrex to generate Trex embeddings, followed by standard Seurat steps like normalization, scaling, PCA, and then FindMultiModalNeighbors to create the WNN graph. The error occurs during the RunUMAP step, which relies on this WNN graph. This suggests that the issue likely originates in the FindMultiModalNeighbors function or its inputs.

Breaking Down the Steps

  1. Trex Embedding: You're using Trex to embed your TCR data. This is a crucial step for integrating TCR information into your single-cell analysis.
  2. Normalization, Scaling, and PCA: These are standard steps in Seurat for preparing your RNA sequencing data. Normalization accounts for differences in sequencing depth, scaling ensures that highly expressed genes don't dominate the analysis, and PCA reduces the dimensionality of your data while preserving the most important information.
  3. FindMultiModalNeighbors: This is where things get interesting. This function combines the PCA and Trex embeddings to create a WNN graph, which represents the relationships between cells based on both RNA expression and TCR similarity. You're using the first 50 PCA dimensions and the first 30 Trex dimensions.
  4. RunUMAP: Finally, you're using UMAP to visualize your data in a lower-dimensional space, based on the WNN graph. This is where the error occurs, indicating that the WNN graph contains NaN values.

Troubleshooting Steps

Okay, so we've got a good grasp of the problem and the code. Now, let's dive into some troubleshooting steps to nail down the root cause and, more importantly, fix it!

1. Inspecting Intermediate Results

First things first, let's inspect the intermediate results to see where the NaN values might be introduced. You've already checked the PCA and Trex embeddings for null values, which is excellent. However, let's dig a bit deeper.

  • Check Scaled Data: Before PCA, inspect the scaled data for any unusual values. You can do this by looking at the range and distribution of the scaled data matrix.
    range(Seurat::GetAssayData(seur, slot = "scale.data"))
hist(Seurat::GetAssayData(seur, slot = "scale.data"))
    This will give you an idea if there are any extremely large or small values that might be causing issues later on.
  • Inspect PCA Loadings: Check the PCA loadings to see if any genes have unusually high loadings. This might indicate that these genes are driving the PCA and potentially causing issues.
    head(sort(apply(seur@reductions$pca@feature.loadings, 1, var), decreasing = TRUE))
    If you find any genes with very high variance in their loadings, consider removing them from the analysis or further investigating their behavior.
  • Examine Trex Embeddings: While you've checked for null values, it's worth examining the distribution of the Trex embeddings. Are there any clusters of cells with very similar embeddings? This could lead to zero variance issues during distance calculations.
    plot(density(seur@reductions$Trex_trb@cell.embeddings))

2. Diving into FindMultiModalNeighbors

Since the error occurs after FindMultiModalNeighbors, let's focus our attention there. This function is crucial for integrating the different modalities, so any issues here can propagate to the UMAP step.

  • Check Input Dimensions: You're using the first 50 PCA dimensions and the first 30 Trex dimensions. Experiment with different numbers of dimensions. Sometimes, using too many dimensions can introduce noise and lead to instability.
    seur <- FindMultiModalNeighbors(
    seur,
    reduction.list = list("pca", "Trex_trb"),
    dims.list = list(1:30, 1:20),
    modality.weight.name = c("RNA.weight", "TRb_weight")
)
    Try reducing the number of dimensions and see if the issue persists. If it resolves the problem, it might indicate that some of the higher dimensions are introducing noise.
  • Inspect Distance Matrices: The FindMultiModalNeighbors function calculates distance matrices for each modality. Let's inspect these matrices directly to see if any NaN values are present before they're combined.
    pca_distances <- dist(seur@reductions$pca@cell.embeddings[, 1:50])
trex_distances <- dist(seur@reductions$Trex_trb@cell.embeddings[, 1:30])
table(pca_distances %in% NaN)
table(trex_distances %in% NaN)
    This will tell you if the NaN values are originating from the distance calculation for either PCA or Trex embeddings.
  • Modality Weights: You're using modality.weight.name to specify weights for the RNA and Trex modalities. Experiment with different weights. Sometimes, one modality might be dominating the analysis, leading to issues.
    seur <- FindMultiModalNeighbors(
    seur,
    reduction.list = list("pca", "Trex_trb"),
    dims.list = list(1:50, 1:30),
    modality.weight.name = c("RNA.weight", "TRb_weight"),
    weights = c(0.5, 0.5) # Example: Equal weights
)
    Try using equal weights or adjusting them to see if it makes a difference.

3. Handling Zero Variance

As mentioned earlier, zero variance can be a major culprit for NaN values. Let's look for features with zero variance and address them.

  • Identify Zero Variance Features: Check for features (genes or principal components) with zero variance. You can calculate the variance of each feature and identify those with zero variance.
    pca_var <- apply(seur@reductions$pca@cell.embeddings, 2, var)
zero_var_pca <- which(pca_var == 0)
print(paste("PCA dimensions with zero variance:", paste(zero_var_pca, collapse = ", ")))

trex_var <- apply(seur@reductions$Trex_trb@cell.embeddings, 2, var)
zero_var_trex <- which(trex_var == 0)
print(paste("Trex dimensions with zero variance:", paste(zero_var_trex, collapse = ", ")))
  • Remove or Regularize: If you find features with zero variance, you have a couple of options. You can remove them from the analysis, or you can add a small amount of noise (regularization) to their values to avoid division by zero errors.

4. Numerical Stability

Sometimes, NaN values can arise due to numerical instability in the distance calculations. This can happen if the values are very large or very small.

  • Scaling and Normalization: Double-check your scaling and normalization steps. Ensure that your data is properly scaled to a reasonable range.
  • Add a Small Constant: In some cases, adding a small constant to the distance calculations can help avoid division by zero errors.

Potential Solutions

Okay, so we've gone through a bunch of troubleshooting steps. Based on what we've discussed, here are some potential solutions you can try:

  1. Reduce the Number of Dimensions: Try using fewer PCA and Trex dimensions in FindMultiModalNeighbors. This can help reduce noise and improve the stability of the distance calculations.
  2. Adjust Modality Weights: Experiment with different weights for the RNA and Trex modalities. This can help balance the influence of each modality on the WNN graph.
  3. Remove Zero Variance Features: Identify and remove features with zero variance. This can prevent division by zero errors and improve the stability of the analysis.
  4. Regularize Data: Add a small amount of noise to the data to avoid zero variance issues. This can be done by adding a small constant to the feature values.
  5. Inspect and Filter Cells: Look for cells that might be causing issues due to poor data quality or other factors. Filtering out these cells can sometimes resolve the problem.

Final Thoughts

Troubleshooting NaN values can be a bit of a detective game, but hopefully, these steps will help you track down the culprit in your Seurat analysis. Remember, the key is to systematically inspect intermediate results, understand the potential causes, and try different solutions. Don't get discouraged if the first thing you try doesn't work – keep experimenting, and you'll eventually crack the case!

I hope this comprehensive guide helps you guys out. Let me know if you have any other questions or run into more snags along the way. Happy analyzing!