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These arguments can be specified as a list or vector.

If supplied using the formula method, the arguments can be specified as part of the formula interface; i.e. By default, swarms from different groups are not prevented from overlapping.

It is a generic function used to produce one dimensional scatter plots (or dot plots) of the given data, along with text indicating sample size and estimates of location (mean or median) and scale (standard deviation or interquartile range), as well as confidence intervals for the population location parameter.

One dimensional scatterplots are a good alternative to "strip Chart"(x, method = ifelse(paired && paired.lines, "overplot", "stack"), seed = 47, jitter = 0.1 * cex, offset = 1/2, vertical = TRUE, group.names, group.= cex, drop.unused.levels = TRUE, add = FALSE, at = NULL, xlim = NULL, ylim = NULL, ylab = NULL, xlab = NULL, dlab = "", glab = "", log = "", pch = 1, col = par("fg"), cex = par("cex"), = cex, axes = TRUE, = axes, = TRUE, = 16, = cex, conf.level = 0.95, min.ci = 2, ci.offset = 3/ifelse(n 2, (n-1)^(1/3), 1), lwd = cex, ends = TRUE, = 0.5 * cex, gap = FALSE, = "bottom", line = ifelse(== "bottom", 2, 0), cex = cex, location.= "top", location.scale.digits = 1, nsmall = location.scale.digits, location.line = ifelse(location.== "top", 0, 3.5), location.cex = cex * 0.8 * ifelse(n 6, max(0.4, 1 - (n-6) * 0.06), 1), p.value = FALSE, p.value.digits = 3, p.= 2, p.= cex, group.= p.value, group.level = 0.95, group.difference.digits = location.scale.digits, test = "parametric", list = NULL, list = NULL, alternative = "two.sided", = FALSE, = col[1], diff.method = "stack", = pch[1], paired = FALSE, paired.lines = paired, = 1:6, = 1, = , = NULL, = NULL, cex = group.names.cex, = TRUE, = 2, = cex, = "gray", = NULL, = NULL, label = NULL, mar = c(5, 4, 4, 4) 0.1, ...) the data from which the plots are to be produced.

"beeswarm"(x, method = c("swarm", "center", "hex", "square"), vertical = TRUE, horizontal = !

vertical, cex = 1, spacing = 1, breaks = NULL, labels, at = NULL, corral = c("none", "gutter", "wrap", "random", "omit"), corral Width, side = 0L, priority = c("ascending", "descending", "density", "random", "none"), pch = par("pch"), col = par("col"), bg = NA, pwpch = NULL, pwcol = NULL, pwbg = NULL, = TRUE, add = FALSE, axes = TRUE, log = FALSE, xlim = NULL, ylim = NULL, dlim = NULL, glim = NULL, xlab = NULL, ylab = NULL, dlab = "", glab = "", ...) uses a square grid to produce a symmetric swarm.

# if you have a vector v, you can update it in realtime with # v set $list # init the vectors to a fixed size.

Robust and thorough analyses are rapidly obtained via a broad range of algorithms for clustering, classification and statistical testing.

GMine is suitable for the analysis of genomics, metagenomics, transcriptomics and proteomics datasets with several hundred to a few thousand features (e.g. The software does not support the analysis of datasets with ten thousands of features, such as genome-wide expression arrays.

GMine is a powerful, yet easy to use, tool for the higher-level analysis of biomolecular data.

The software has been developed with a focus on protein microarrays, but can be used for any n x m data matrix (with n x m The data of the demo project was generated in a prospective study investigating host immune response to Plasmodium falciparum (Crompton et al. Compton et al used a protein microarray consisting of 2,320 probes (representing ~23% of the P. USA.2010;1586963 Columns represent samples and rows malaria parasite proteins (antigens or features).

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