chooseClusters           package:beadarray           R Documentation

_C_h_o_o_s_e _C_l_u_s_t_e_r_s

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

     Find large clusters of beads.

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

     chooseClusters(IDs, neighbours, cutoff = 8)

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

     IDs: IDs of beads to be clustered.

neighbours: A Neighbours matrix - obtained from 'generateNeighbours'.

  cutoff: Integer - threshold for the minimum size a cluster must be.

_D_e_t_a_i_l_s:

     This function will find which beads are in large clusters. Using a
     flood fill algorithm, it finds clusters of beads, determines the
     size of each, and then returns only the beads in clusters of size
     greater than 'cutoff'. It is primarily used in 'BASHCompact' and
     'BASHDiffuse'.

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

     Vector of bead IDs. (This will be a subset of the argument 'IDs')

_A_u_t_h_o_r(_s):

     Jonathan Cairns

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

     'BASHCompact', 'BASHDiffuse', 'closeImage'

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

     data(BLData)
     neighbours <- generateNeighbours(BLData,1)
     o <- findAllOutliers(BLData,1,log = TRUE)
     ##clusters8 <- chooseClusters(o, neighbours)
     ##clusters12 <- chooseClusters(o, neighbours, cutoff = 12) ## only ##larger clusters

     ##x11()
     ##plotBeadLocations(BLData,array=1,BeadIDs = clusters8, pch = ".")

