clusterSim             package:GOSemSim             R Documentation

_S_e_m_a_n_t_i_c _S_i_m_i_l_a_r_i_t_y _B_e_t_w_e_e_n _T_w_o _G_e_n_e _C_l_u_s_t_e_r_s

_D_e_s_c_r_i_p_t_i_o_n:

     Given two gene clusters, this function calculates semantic
     similarity between them.

_U_s_a_g_e:

     clusterSim(cluster1, cluster2, ont = "MF",  organism="human", measure="Wang", drop= "IEA")

_A_r_g_u_m_e_n_t_s:

cluster1: A set of gene IDs. 

cluster2: Another set of gene IDs. 

     ont: One of "MF", "BP", and "CC" subontologies.

 measure: One of "Resnik", "Lin", "Rel", "Jiang" and "Wang" methods.

organism: One of "human", "rat", "mouse", "fly" and "yeast".

    drop: A set of evidence codes based on which certain annotations
          are dropped. Use NULL to keep all GO annotations.

_V_a_l_u_e:

     sim: Semantic Similarity.

_R_e_f_e_r_e_n_c_e_s:

     Wang et al.(2007) A new method to measure the semantic similarity
     of go terms _Bioinformatics_ (Oxford, England), 23:0 1274-81, May 
     2007. ISSN 1460-2059 <URL:
     http://www.ncbi.nlm.nih.gov/pubmed/17344234> PMID: 17344234

     P W Lord et al.(2003) Semantic similarity measures as tools for
     exploring the gene ontology <URL: Pacific Symposium on
     Biocomputing> Pacific Symposium on Biocomputing, 2003:601-12, ISSN
     1793-5091 <URL: http://www.ncbi.nlm.nih.gov/pubmed/12603061> PMID:
     12603061

_S_e_e _A_l_s_o:

     'goSim' 'mgoSim' 'geneSim' 'mgeneSim' '\linke{mclusterSim}'

_E_x_a_m_p_l_e_s:

             cluster1 <- c("snR67","snR40","snR48", "snR17a","snR8")
             cluster2 <- c("YOR251C", "YPR137C-B", "YPR010C", "YPR072W")
             clusterSim(cluster1, cluster2, ont="MF", organism="yeast", measure="Wang")

