sig_pca_tool

Perform Principal Components Analysis on a dataset

Synopsis

sig_pca_tool [--ds DS] [--ds_meta DS_META] [--sample_dim SAMPLE_DIM] [--cid CID] [--rid RID] [--row_space ROW_SPACE] [--disable_table DISABLE_TABLE]

Arguments

--ds DS : Input dataset

--ds_meta DS_META : Optional annotations as a TSV table for the input dataset for the dimension being operated on. The first column must match the corresponding id field in ds

--sample_dim SAMPLE_DIM : Dimension of the dataset corresponding to samples or observations. Default is column. Options are {1|2|column|row}

--cid CID : List of column ids to use specified as a GRP file or cell array. If empty all columns are used.

--rid RID : List of row ids to to use specified as a GRP file or cell array. If empty all rows are used

--row_space ROW_SPACE : Common row-id space definitions to use as an alternative to the rid parameter. Default is all. Options are {all|lm|bing|aig|lm_probeset|bing_probeset|full_probeset|custom}

--disable_table DISABLE_TABLE : Disable generating annotated text table for first two components. The table can be generated post-hoc from the saved pc_score matrix if needed.. Default is 0

Description

This tool applies Principal Components Analysis (PCA) on raw data.

Examples

• Apply PCA on columns of a matrix

sig_pca_tool --ds 'raw_data.gctx'

• Merge datasets from a list of folders

sig_pca_tool --folders 'folders.grp' --cid 'columns.grp' --row_space 'lm'