##❗ Conclusion

If you get the following error when creating a Seurat object:

Error in validObject(object = value):
  invalid class "Assay" object: 'counts' must have the same cells as 'data'

This occurs because the column names (cell names) of counts and data don't match.

And if you don't align this information, you'll create an h5seurat that can be written but not read back.

##

When saving Seurat objects, you can save them in various formats. Sometimes you need to migrate data to Scanpy, and the h5seurat format has minimal data loss and is an excellent format.

seu = Read10X("/shared/oodake/sc-public-data/Autsornworawat2020/GSE151117/GSM4567001_ES1txp/")
seu = CreateSeuratObject(counts = seu, project = "ES1txp")
SaveH5Seurat(seu, "test1.h5seurat",overwrite = TRUE)

However, when you try to read this back, you get an error.

##✅ Solution (Just Do This)

# Key point
colnames(expr_mat) <- colnames(seurat_obj)
 
# Create new Assay object and set data same as counts
new_assay <- CreateAssayObject(counts = expr_mat)
new_assay@data <- new_assay@counts
 
# Replace the "RNA" assay in the existing Seurat object
seu[["RNA"]] <- new_assay
 
# Write out in h5seurat format (assuming the directory exists in the following code)
SaveH5Seurat(seu, filename = "Augsorworawat2020/ES1txp.h5seurat", overwrite = TRUE)
 

##🧠 Why Does This Happen? Seurat Object Structure and Cause Explanation

Seurat objects are implemented with S4 classes and have the following structure.

###Main Elements (Slots)

• @assays$RNA@counts: Raw count data (unprocessed)
• @assays$RNA@data: Normalized data (e.g., log1p-transformed, etc.)

###Common Causes

When loading h5seurat, it seems to reference data.

• counts is in order cells A, B, C, but data is in order A, C, B
• There are cells only in data, or vice versa
• Column names don't match (e.g., with/without prefix)

###Why Might Names Be Different?

• Differences between Seurat versions
• Data formatted by other tools
• Different naming depending on usage: counts, data, scale.data, raw.data, etc.

###How to Check?

• Check object slot names:

  slotNames(seurat_obj)

• Check structure:

  str(seurat_obj)

• View specific slots:

  colnames(seurat_obj@assays$RNA@counts)
  colnames(seurat_obj@assays$RNA@data)

##🔎 Others Who Had the Same Problem

This error seems to be common among Seurat users:

• Stack Overflow https://stackoverflow.com/questions/77449917/error-in-validobjectobject-value-invalid-class-assay-object-counts-must-hav

##📌 Details of This Problem - About Seurat Objects

Seurat objects are implemented with S4 classes, so each internal element (slot) can be accessed with the @ operator.

Example:

Raw counts:

object_name@assays$RNA@counts

Normalized data:

object_name@assays$RNA@data

###🧭 About Element Naming

In Seurat, element names are named according to their situation:

Standard Case (e.g., RNA single-cell analysis)

• "counts" → Storage location for raw data (count values)
• "data" → Data processed for analysis, such as normalization or log transformation

Other Cases

• In previous versions (like Seurat v2) or objects after specific processing
  • Different names like "raw.data" were sometimes used
• However, from v3 onwards, it's basically unified to "counts" and "data"

###❓ Why Might Names Be Different?

• With Seurat version upgrades, internal structure and naming conventions have been improved and unified
• Legacy data or objects that went through conversion tools may retain previous naming conventions

##🧪 How to Check Internal Elements

If element names or contents are unknown, you can check and verify with the following methods:

###slotNames() function

slotNames(object_name)

→ This gives you a list of all slot names the Seurat object has

###str() function

str(object_name)

→ You can check the entire object structure in detail

Like this, each element of a Seurat object (e.g., counts or data) is named according to its role, and depending on the version or processing flow, different names may be used.

When the internal contents are unknown, using slotNames() or str() to understand the current object's structure and contents is the key to solving problems.

# Simple check function
check_alignment <- function(counts, data) {
  if (!identical(colnames(counts), colnames(data))) {
    warning("Column names don't match")
  } else {
    message("OK: Column names match")
  }
}

##✍ Conclusion

Seurat is convenient, but because its internal structure is complex, you can stumble on "silent landmines" like this. Understanding the structure indicated by error messages like this will help you respond calmly next time.