Activities

Data processing in Perseus generally takes the form of loading a matrix of data and then applying a series of transformations to that matrix. These transformations are called “activities”. They are organized into a series of “Types”, and possibly a number of “Headings” under the Type.

You can search this page for the name of the activity of interest, or you can browse the Type or Heading of interest using the Table of Contents on the right.

1 Matrix Upload

• Binary upload
• Create gene list
• Create random matrix
• Generic matrix upload
• Next generation sequencing data upload
• Raw upload

2 Matrix Export

• Generic matrix export

3 Matrix Analysis

3.1 Clustering/PCA

• Hierarchical clustering
• Principal component analysis
• Co-expression clustering

3.2 Misc. (Analysis)

• Numeric venn diagram
• Select rows manually
• Sequence logos
• Volcano plot
• Hawaii plot

3.3 Visualization

• 3D plot
• Histogram
• Multi scatter plot
• Profile plot
• Scatter plot

4 Matrix MultiProcessing

4.1 Basic (MultiProcessing)

• Matching columns by name
• Matching rows by name
• Replace strings
• Row correlation
• Change column names

4.2 CI

• Assert matrix equals

5 Matrix Processing

5.1 Annot. columns

• 1D annotation enrichment
• 2D annotation enrichment
• Add annotation
• Annotation matrix
• Average categories
• Category counting
• Fisher exact test
• To base identifiers

5.2 Annot. rows

• Average groups
• Categorical annotation rows
• Join terms in categorical row
• Numerical annotation rows

5.3 Basic (Processing)

• Add noise
• Clone
• Column correlation
• Combine main columns
• Combine rows by identifiers
• Density estimation
• Performance curves
• Quantiles
• Row correlation
• Summary statistics (columns)
• Summary statistics (rows)
• Transform

5.4 Clustering

• Generic clustering

5.5 Filter columns

• Filter columns based on categorical row
• Filter columns based on valid values

5.6 Filter rows

• Filter rows based on categorical column
• Filter rows based on numerical/main column
• Filter rows based on random sampling
• Filter rows based on text column
• Filter rows based on valid values

5.7 Imputation

• Replace missing values by constant
• Replace missing
• Replace imputed values by NaN

5.8 Learning

• Classification (cross-validation and prediction
• Classification feature optimization
• Classification parameter optimization

5.9 Modifications

• Add known sites
• Add linear motifs
• Add modification counts
• Add regulatory sites
• Add sequence features
• Expand site table
• Kinase-substrate relations
• Shorten motif length

5.10 Normalization

• Cluster normalization
• Divide
• Modify by column
• Rank
• Scale to interval
• Subtract
• Subtract row cluster
• Un-Z-score
• Unit vectors
• Width adjustment
• Z-score

5.11 Outliers

• Significance A
• Significance B

5.12 Quality

• Convert to NaN
• Create quality matrix
• Filter quality

5.13 Rearrange

• Change column type
• Combine annotations
• Combine categorical columns
• Convert multi-numeric column
• De-hyphenate ids
• Duplicate columns
• Expand multi-numeric and text columns
• Fill categorical columns
• Process text column
• Remove empty columns
• Rename columns
• Rename columns [reg. ex.]
• Reorder columns by numerical annotation row
• Reorder/remove annotation rows
• remove columns
• Search text column
• Sort by column
• Transpose
• Unique rows
• Unique values

5.14 Tests

• Multiple-sample tests
• One-sample tests
• Post hoc tests
• Three-way ANOVA
• Two-sample tests
• Two-way ANOVA

5.15 Time series

• Cyclic annotation enrichment
• Periodicity analysis
• Periodogram
• Time series ordering