Show significant clusters of mutations
lfm (low frequency mutations) retrieve
the original mutations that created the significant clusters
entropy on the consensus
a LowMACA class object
a character that defines whether to use 'pvalue' or 'qvalue' to select significant positions. Default: 'qvalue'
a numeric defining the threshold of significance for the defined metric. Default: 0.05
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
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'.
A data.frame with 13 columns corresponding to the mutations retrieved:
Gene_Symbol gene symbols of the mutations
Amino_Acid_Position amino acidic positions relative to original protein
Amino_Acid_Change amino acid changes in hgvs format
Sample Sample barcode where the mutation was found
Tumor_Type Tumor type of the Sample
Envelope_Start start of the pfam domain in the protein
Envelope_End end of the pfam domain in the protein
Multiple_Aln_pos positions in the consensus
Entrez entrez ids of the mutations
Entry Uniprot entry of the protein
UNIPROT other protein names for Uniprot
Chromosome cytobands of the genes
Protein.name extended protein names
Stefano de Pretis , Giorgio Melloni
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#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))
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