nndist-methods.RdMethods computing the nearest neighbour indices and distances for
matrix and MSnSet instances.
signature(object = "matrix", k = "numeric", dist =
"character", ...)Calculates indices and distances to the
k (default is 3) nearest neighbours of each feature (row)
in the input matrix object. The distance dist can
be either of "euclidean" or
"mahalanobis". Additional parameters can be passed to the
internal function FNN::get.knn. Output is a matrix with
2 * k columns and nrow(object) rows.
signature(object = "MSnSet", k = "numeric", dist =
"character", ...)As above, but for an MSnSet
input. The indices and distances to the k nearest
neighbours are added to the object's feature metadata.
signature(object = "matrix", query = "matrix", k =
"numeric", ...)If two matrix instances are provided as
input, the k (default is 3) indices and distances of the
nearest neighbours of query in object are returned
as a matrix of dimensions 2 * k by
nrow(query). Additional parameters are passed to
FNN::get.knnx. Only euclidean distance is available.
library("pRolocdata")
data(dunkley2006)
## Using a matrix as input
m <- exprs(dunkley2006)
m[1:4, 1:3]
#> M1F1A M1F4A M1F7A
#> AT1G09210 0.323250 0.275500 0.216000
#> AT1G21750 0.332000 0.279667 0.222000
#> AT1G51760 0.397250 0.246500 0.168250
#> AT1G56340 0.336733 0.303267 0.201133
head(nndist(m, k = 5))
#> index1euc dist1euc index2euc dist2euc index3euc dist3euc
#> AT1G09210 4 0.07704563 151 0.07958198 2 0.08142734
#> AT1G21750 1 0.08142734 4 0.08262245 151 0.08645587
#> AT1G51760 150 0.09921614 156 0.10449514 151 0.10922063
#> AT1G56340 8 0.07268495 1 0.07704563 9 0.07958917
#> AT2G32920 9 0.08767137 4 0.10248944 155 0.10307756
#> AT2G47470 151 0.05067260 153 0.07809223 4 0.08231921
#> index4euc dist4euc index5euc dist5euc
#> AT1G09210 6 0.08738381 150 0.11859295
#> AT1G21750 150 0.09657314 6 0.10902502
#> AT1G51760 155 0.11456182 2 0.11749637
#> AT1G56340 6 0.08231921 2 0.08262245
#> AT2G32920 8 0.10670010 6 0.10806363
#> AT2G47470 1 0.08738381 378 0.09215796
tail(nndist(m[1:100, ], k = 2, dist = "mahalanobis"))
#> index1mah dist1mah index2mah dist2mah
#> AT3G26830 37 26.26210 33 25.95250
#> AT3G28580 60 25.95250 35 21.88428
#> AT3G28720 70 31.50309 57 28.63844
#> AT3G44330 72 26.74359 64 26.74359
#> AT3G48890 17 33.69198 36 32.85008
#> AT3G51430 56 26.83278 75 22.23005
## Same as above for MSnSet
d <- nndist(dunkley2006, k = 5)
head(fData(d))
#> assigned evidence method new pd.2013 pd.markers markers.orig
#> AT1G09210 ER predicted PLSDA known ER ER lumen ER
#> AT1G21750 ER predicted PLSDA known ER ER lumen ER
#> AT1G51760 ER unknown PLSDA new ER ER lumen unknown
#> AT1G56340 ER predicted PLSDA known ER ER lumen ER
#> AT2G32920 ER predicted PLSDA known ER ER lumen ER
#> AT2G47470 ER predicted PLSDA known ER ER lumen ER
#> markers index1euc dist1euc index2euc dist2euc index3euc
#> AT1G09210 ER lumen 4 0.07704563 151 0.07958198 2
#> AT1G21750 ER lumen 1 0.08142734 4 0.08262245 151
#> AT1G51760 ER lumen 150 0.09921614 156 0.10449514 151
#> AT1G56340 ER lumen 8 0.07268495 1 0.07704563 9
#> AT2G32920 ER lumen 9 0.08767137 4 0.10248944 155
#> AT2G47470 ER lumen 151 0.05067260 153 0.07809223 4
#> dist3euc index4euc dist4euc index5euc dist5euc
#> AT1G09210 0.08142734 6 0.08738381 150 0.11859295
#> AT1G21750 0.08645587 150 0.09657314 6 0.10902502
#> AT1G51760 0.10922063 155 0.11456182 2 0.11749637
#> AT1G56340 0.07958917 6 0.08231921 2 0.08262245
#> AT2G32920 0.10307756 8 0.10670010 6 0.10806363
#> AT2G47470 0.08231921 1 0.08738381 378 0.09215796
d <- nndist(dunkley2006[1:100, ], k = 2, dist = "mahalanobis")
tail(fData(d))
#> assigned evidence method new pd.2013 pd.markers markers.orig
#> AT3G26830 ER unknown PLSDA new ER unknown unknown
#> AT3G28580 ER unknown PLSDA new ER unknown unknown
#> AT3G28720 unknown unknown PLSDA unknown ER unknown unknown
#> AT3G44330 ER unknown PLSDA new ER unknown unknown
#> AT3G48890 unknown unknown PLSDA unknown ER unknown unknown
#> AT3G51430 ER unknown PLSDA new ER unknown unknown
#> markers index1mah dist1mah index2mah dist2mah
#> AT3G26830 unknown 37 26.26210 33 25.95250
#> AT3G28580 unknown 60 25.95250 35 21.88428
#> AT3G28720 unknown 70 31.50309 57 28.63844
#> AT3G44330 unknown 72 26.74359 64 26.74359
#> AT3G48890 unknown 17 33.69198 36 32.85008
#> AT3G51430 unknown 56 26.83278 75 22.23005
## Using a query
nndist(m[1:100, ], m[101:110, ], k = 2)
#> index1euc dist1euc index2euc dist2euc
#> AT3G51440 40 0.08584173 78 0.08761483
#> AT3G51460 59 0.07788915 86 0.07970032
#> AT3G57010 86 0.05580589 61 0.05615363
#> AT3G57030 34 0.07262401 14 0.07373216
#> AT3G57880 34 0.05350903 46 0.05471768
#> AT3G59500 91 0.10986265 24 0.11019115
#> AT3G60600 64 0.07059527 20 0.07176627
#> AT3G62360 48 0.04383448 17 0.05428251
#> AT3G66658 73 0.06079091 34 0.06115031
#> AT4G00175 75 0.07679760 92 0.07719277