TDbasedUFEadv

Workflows

Standard Workflow

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

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.

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.

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