# Tdbasedufeadv

> TDbasedUFEadv

- Skill: `biomate-ai/tdbasedufeadv` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/tdbasedufeadv`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/tdbasedufeadv/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/tdbasedufeadv

---


# TDbasedUFEadv

## Workflows

### Standard Workflow

Integrate two omics datasets sharing the same features using memory-efficient SVD with partial summation.

```r
library(TDbasedUFEadv)
library(RTCGA.rnaseq)

# Prepare drug and disease expression datasets
Cancer_cell_lines <- list(ACC.rnaseq, BLCA.rnaseq, BRCA.rnaseq, CESC.rnaseq)
Drug_and_Disease <- prepareexpDrugandDisease(Cancer_cell_lines)
expDrug <- Drug_and_Disease$expDrug
expDisease <- Drug_and_Disease$expDisease

# Compute SVD on the two matrices
SVD <- computeSVD(exprs(expDrug), exprs(expDisease))

# Generate the matrix product and prepare the tensor
Z <- t(exprs(expDrug)) %*% exprs(expDisease)
sample <- outer(
  colnames(expDrug),
  colnames(expDisease),
  function(x, y) { paste(x, y) }
)
Z <- PrepareSummarizedExperimentTensor(
  sample = sample,
  feature = rownames(expDrug),
  value = Z
)
```
*Inputs are two expression matrices sharing features; output is a SummarizedExperiment-like tensor and SVD results.*

### Multi Omics Sharing Features

Integrate multiple omics datasets sharing the same features but having different samples using projection-based SVD and HOSVD.

```r
library(TDbasedUFEadv)
library(RTCGA.rnaseq)
library(RTCGA.clinical)

# Prepare a list of matrices sharing features
Multi <- list(
  BLCA.rnaseq[seq_len(100), 1 + seq_len(1000)],
  BRCA.rnaseq[seq_len(100), 1 + seq_len(1000)],
  CESC.rnaseq[seq_len(100), 1 + seq_len(1000)],
  COAD.rnaseq[seq_len(100), 1 + seq_len(1000)]
)

# Prepare a tensor from the list of matrices
Z <- prepareTensorfromList(Multi, 10L)

# Permute the tensor modes to align features
Z <- aperm(Z, c(2, 1, 3))

# Wrap the tensor using PrepareSummarizedExperimentTensor
Clinical <- list(BLCA.clinical, BRCA.clinical, CESC.clinical, COAD.clinical)
Multi_sample <- list(
  BLCA.rnaseq[seq_len(100), 1, drop = FALSE],
  BRCA.rnaseq[seq_len(100), 1, drop = FALSE],
  CESC.rnaseq[seq_len(100), 1, drop = FALSE],
  COAD.rnaseq[seq_len(100), 1, drop = FALSE]
)
ID_column_of_Multi_sample <- c(770, 1482, 773, 791)
ID_column_of_Clinical <- c(20, 20, 12, 14)

Z <- PrepareSummarizedExperimentTensor(
  feature = colnames(ACC.rnaseq)[1 + seq_len(1000)],
  sample = array("", 1),
  value = Z,
  sampleData = prepareCondTCGA(
    Multi_sample,
    Clinical,
    ID_column_of_Multi_sample,
    ID_column_of_Clinical
  )
)

# Compute HOSVD
HOSVD <- computeHosvd(Z)

# Select features using selectFeatureProj in batch mode
cond <- attr(Z, "sampleData")
index <- selectFeatureProj(HOSVD, Multi, cond, de = 1e-3, input_all = 3)

# Extract and display selected features
head(tableFeatures(Z, index))
```
*Inputs are a list of matrices sharing features and clinical metadata; output is a table of selected features with p-values.*

### Multi Omics Sharing Samples

Integrate multiple omics datasets sharing the same samples using projection-based SVD and HOSVD.

```r
library(TDbasedUFEadv)

# Prepare a tensor from the list of matrices and wrap in a SummarizedExperiment-like object
# Z <- PrepareSummarizedExperimentTensor(sample = sample, feature = feature, value = value)

# Compute HOSVD
# HOSVD <- computeHosvd(Z)

# Select features across individual profiles
# index <- selectFeature(HOSVD, input_all, de = 0.05)

# Extract, print, and save selected features
# head(tableFeatures(Z, index))
```
*Inputs are multi-omics matrices sharing samples; output is a table of selected features.*

## When to Use
- Unsupervised feature extraction (e.g., gene selection) from multi-omics datasets where either features (genes) or samples are shared.
- When dealing with a small number of samples associated with a large number of features (common in genomics).
- Evaluating selected genes via enrichment analysis using tools like `enrichR::enrichr`, `STRINGdb::STRINGdb`, or `DOSE::enrichDGN`.

## When NOT to Use
- For supervised differential expression analysis where group labels are strictly used to guide the mathematical decomposition; use `DESeq2` or `limma` instead.
- When you prefer a fully automated, non-interactive pipeline without manual/interactive selection of singular value vectors; use standard `SVD` or `HOSVD` packages directly.

## Data Requirements
- Input data should be matrix-like (e.g., gene expression matrices from `RTCGA.rnaseq` like `BLCA.rnaseq`).
- For feature-sharing multi-omics, a list of matrices (e.g., `Multi`) where rows represent features and columns represent samples.
- Metadata/clinical data (e.g., `BLCA.clinical`) to construct condition vectors for selecting singular value vectors.

## Key Parameters
- **de** (1e-3): Standard deviation threshold for feature selection in `selectFeatureProj` or `selectFeature`.
- **input_all**: Vector of selected singular value vectors in `selectSingularValueVectorLarge` or `selectFeatureProj`.

## Best Practices
- Master the basic `TDbasedUFE` package workflows before moving to the advanced features of `TDbasedUFEadv`.
- Use memory-efficient SVD with partial summation (`computeSVD`) when the number of features is too large to construct a full tensor.
- Verify the success of singular value vector selection interactively using histograms and standard deviation optimization via `selectSingularValueVectorLarge`.
- Perform downstream enrichment analysis on selected genes using `enrichR::enrichr` or `STRINGdb::STRINGdb` to biologically validate the unsupervised feature selection.

## Common Pitfalls
- **Out of memory errors**: Attempting to apply `computeHosvd` on a full tensor with too many features. *Fix*: Use `computeSVD` with partial summation to reduce memory requirements.
- **Incorrect tensor permutation**: Forgetting to align features across datasets. *Fix*: Use `aperm` to permute the tensor modes appropriately before wrapping with `PrepareSummarizedExperimentTensor`.

## Alternatives
- `DESeq2`: For supervised differential expression analysis.
- `limma`: For linear modeling of gene expression data.
- `TDbasedUFE`: For simpler, standard unsupervised feature extraction workflows.

## Citations
- Taguchi, Y-H. 2020. Unsupervised Feature Extraction Applied to Bioinformatics. Springer International Publishing. https://doi.org/10.1007/978-3-030-22456-1
- Taguchi, Y-H. 2023. TDbasedUFE: Tensor Decomposition Based Unsupervised Feature Extraction.

## References
- Homepage: bioconductor.org/packages/TDbasedUFEadv
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/TDbasedUFEadv/inst/doc/Enrichment.html