lfm: Show significant clusters of mutations

In ste-depo/LowMACA: LowMACA - Low frequency Mutation Analysis via Consensus Alignment

Show significant clusters of mutations

Description

The method lfm (low frequency mutations) retrieve the original mutations that created the significant clusters calculated with entropy on the consensus

Usage

lfm(object , metric='qvalue', threshold=.05, conservation=NULL)

Arguments

object	a LowMACA class object
metric	a character that defines whether to use 'pvalue' or 'qvalue' to select significant positions. Default: 'qvalue'
threshold	a numeric defining the threshold of significance for the defined metric. Default: 0.05
conservation	a numeric value in the range of 0-1 that defines the threshold of trident conservation score to include the specified position. The default value is inherited from the slot entropy, whose default is 0.1

Details

After the alignment, we lose every information about the original sequences used as input. The consensus sequence is in fact an alignment that could not represent the reality of human proteins. lfm allows to go back on the original dataset and retrieve the proteins and the real positions of the mutations that we consider 'conserved'.

Value

A data.frame with 13 columns corresponding to the mutations retrieved:

1. Gene_Symbol gene symbols of the mutations

2. Amino_Acid_Position amino acidic positions relative to original protein

3. Amino_Acid_Change amino acid changes in hgvs format

4. Sample Sample barcode where the mutation was found

5. Tumor_Type Tumor type of the Sample

6. Envelope_Start start of the pfam domain in the protein

7. Envelope_End end of the pfam domain in the protein

8. Multiple_Aln_pos positions in the consensus

9. Entrez entrez ids of the mutations

10. Entry Uniprot entry of the protein

11. UNIPROT other protein names for Uniprot

12. Chromosome cytobands of the genes

13. Protein.name extended protein names

Author(s)

Stefano de Pretis , Giorgio Melloni

See Also

entropy

Examples

#Load homeobox example and launch entropy method
data(lmObj)
lmObj <- entropy(lmObj)
significant_muts <- lfm(lmObj)
#Display original mutations that formed significant clusters (column Multiple_Aln_pos)
head(significant_muts)
#Position 4 has a qvalue<0.05
#What are the genes mutated in position 4 in the consensus?
cluster_4_genes <- significant_muts[ significant_muts[['Multiple_Aln_pos']]==4 , 'Gene_Symbol']
#Display the genes and their number of mutation in consensus position 4
sort(table(cluster_4_genes))

ste-depo/LowMACA documentation built on Oct. 15, 2022, 11:53 p.m.