ISS_ratiocor: Calculate and plot correlation and ratio of total reads...
In mashranga/MolDia: Single cell In-Situ sequencing (ISS) data analysis

Calculate and plot correlation and ratio of total reads between genes or group of genes.

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ISS_ratiocor(data, gene = marker_gene, select_gene = NULL,
  errorbar = TRUE, plty = "both", logratio = TRUE,
  sig.level = 0.05, main = "", stat = "sum")

`data`	Data in list of groups with group name. Individual data is in class MolDiaISS. Output of readISS
`gene`	Gene names to be consider. Object in vector or list class. In list formated input, every list element is a group of interested genes. Every list element should have a name.
`select_gene`	Select gene of interest, Default is NULL i.e. all genes in gene list.
`errorbar`	Show error bar or not. Default is TRUE
`plty`	Show plot type. Available balue is "corr", "ratio" and "both". Default is "both".
`logratio`	Taking log2 of the ratio of gene's total reads count.
`sig.level`	confidence level for the returned confidence interval. Currently only used for the Pearson product moment correlation coefficient if there are at least 4 complete pairs of observations.
`main`	Title of the plot
`stat`	Mode of operation. Possible value is "sum", "gene" and "present". Default is "sum".

## Read data: Left and right HC
hcleft  <- readISS(file = system.file("extdata", "Hypocampus_left.csv", package="MolDia"),
                  cellid = "CellId",centX = "centroid_x", centY = "centroid_y")
hcright <- readISS(file = system.file("extdata", "Hypocampus_right.csv", package="MolDia"),
                  cellid = "CellId",centX = "centroid_x", centY = "centroid_y")

## Arrange marker gene
data(marker_gene)
marker_gene <- marker_gene
mark_gene <- list(genr = marker_gene$genr, neuron = c(marker_gene$genr_neuro,
                                                      marker_gene$genr_neuro_pyra1,
                                                      marker_gene$genr_neuro_pyra2,
                                                      marker_gene$genr_neuro_inter1,
                                                      marker_gene$genr_neuro_inter2,
                                                      marker_gene$genr_neuro_inter3,
                                                      marker_gene$genr_neuro_inter4,
                                                      marker_gene$genr_neuro_inter5,
                                                      marker_gene$genr_neuro_inter6),
                                            nonneuron = marker_gene$genr_nonneuro) 

res <- ISS_ratiocor(data = list(Left_HC = c(hcleft),Right_HC = c(hcright)), gene = mark_gene, plty = "ratio")
res <- ISS_ratiocor(data = list(Left_HC = c(hcleft),Right_HC = c(hcright)), gene = mark_gene)