In some cases you have certain genes of interest and you would like
to find other genes that are close to the genes of interest. This can be
done using the genefinder function.
You need to specify either the index position of the genes you want
(which row of the expression array the gene is in) or the name
(consistent with the featureNames of the
ExpressionSet).
A vector of names can be specified and matches for all will be
computed. The number of matches and the distance measure used can all be
specified. The examples will be carried out using the artificial data
set, sample.ExpressionSet.
Two other options for genefinder are scale
and method. The scale option controls the
scaling of the rows (this is often desirable), while the
method option controls the distance measure used between
genes. The possible values and their meanings are listed at the end of
this document.
library("Biobase")
library("genefilter")
data(sample.ExpressionSet)
igenes <- c(300,333,355,419) ##the interesting genes
closeg <- genefinder(sample.ExpressionSet, igenes, 10, method="euc", scale="none")
names(closeg)## [1] "31539_r_at" "31572_at" "31594_at" "31658_at"The Affymetrix identifiers (since these were originally Affymetrix
data) are 31539_r_at, 31572_at,
31594_at and 31658_at. We can find the nearest
genes (by index) for any of these by simply accessing the relevant
component of closeg.
## $indices
## [1] 220 425 457 131 372 137 380 231 161 38
##
## $dists
## [1] 70.30643 70.94069 71.66043 71.66962 73.55186 73.66967 74.77823 77.42745
## [9] 77.86960 83.57073## [1] "31459_i_at" "31664_at" "31696_at" "31370_at"
## [5] "31611_s_at" "31376_at" "31619_at" "31470_at"
## [9] "31400_at" "AFFX-TrpnX-3_at"You could then take these names (from Nms1) and the
annotate
package and explore them further. See the various HOWTO’s in annotate to
see how to further explore your data. Examples include finding and
searching all PubMed abstracts associated with these data. Finding and
downloading associated sequence information. The data can also be
visualized using the geneplotter
package (again there are a number of HOWTO documents there).
The scale parameter can take the following values:
none No scaling is done.
range Scaling is done by (xi − x(1))/(x(n) − x(1)).
zscore Scaling is done by (xi − x̄)/sx. Where sx is the standard deviation.
The method parameter can take the following values:
euclidean Euclidean distance is used.
maximum Maximum distance between any two elements of x and y (supremum norm).
manhattan Absolute distance between the two vectors (1 norm).
canberra The ∑(|xi − yi|/|xi + yi|). Terms with zero numerator and denominator are omitted from the sum and treated as if the values were missing.
binary (aka asymmetric binary): The vectors are regarded as binary bits, so non-zero elements are on and zero elements are off. The distance is the proportion of bits in which only one is on amongst those in which at least one is on.
The version number of R and packages loaded for generating the vignette were:
## R version 4.4.2 (2024-10-31)
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## Running under: Ubuntu 24.04.1 LTS
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## [1] class_7.3-22 genefilter_1.89.0 Biobase_2.67.0
## [4] BiocGenerics_0.53.3 generics_0.1.3 BiocStyle_2.35.0
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## loaded via a namespace (and not attached):
## [1] sass_0.4.9 lattice_0.22-6 RSQLite_2.3.8
## [4] digest_0.6.37 grid_4.4.2 evaluate_1.0.1
## [7] fastmap_1.2.0 blob_1.2.4 Matrix_1.7-1
## [10] jsonlite_1.8.9 AnnotationDbi_1.69.0 GenomeInfoDb_1.43.1
## [13] DBI_1.2.3 survival_3.7-0 BiocManager_1.30.25
## [16] httr_1.4.7 UCSC.utils_1.3.0 XML_3.99-0.17
## [19] Biostrings_2.75.1 jquerylib_0.1.4 cli_3.6.3
## [22] rlang_1.1.4 crayon_1.5.3 XVector_0.47.0
## [25] splines_4.4.2 bit64_4.5.2 cachem_1.1.0
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## [37] matrixStats_1.4.1 stats4_4.4.2 lifecycle_1.0.4
## [40] zlibbioc_1.52.0 KEGGREST_1.47.0 S4Vectors_0.45.2
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## [49] knitr_1.49 xtable_1.8-4 htmltools_0.5.8.1
## [52] rmarkdown_2.29 maketools_1.3.1 compiler_4.4.2