Plot a gene's values in two samples at the shared pixels

Scatter plot of a gene's values in two SpatialExperiment objects on their shared pixels, with the correlation coefficient and the permutation p-value of the gene in the title.

Usage

plotCorrelationGeneExp(speList, spatialCorrelation, geneName, assayName = NULL)

Arguments

speList

list List of two SpatialExperiment objects with matched spatial locations. The first element corresponds to the first SpatialExperiment (X), and the second to the second SpatialExperiment (Y). The names of the list label the axes.

spatialCorrelation

dataframe: the output of spatialCorrelationGeneExp() or spatialCorrelationGeneExpIterPermutations() for speList, or of compareSpatial() for the same objects.

geneName

character: The name of the gene (row) in both SpatialExperiment objects of speList and in spatialCorrelation.

assayName

character or integer A character string or numeric specifying the assay in the SpatialExperiment to use. Default is NULL. If no value is supplied for assayName, then the first assay is used as a default

Value

A ggplot object: a scatterplot with the values of the gene in the first SpatialExperiment, i.e SummarizedExperiment::assay(speList[[1]], assayName)[geneName, ], on the x-axis and values of the gene in the second SpatialExperiment on the y-axis. The title includes the gene name, the correlation coefficient (rounded to 3 decimals), and a permutation p-value (3 significant digits): p_adj, the adjusted permutation p-value pValuePermuteAdj, for a compareSpatial() result (recognised by that column); p_adj, the greater of the two adjusted p-values pValuePermuteXAdj and pValuePermuteYAdj (NA if either is NA), for the output of spatialCorrelationGeneExp() or spatialCorrelationGeneExpIterPermutations(); or p_E, the greater of the unadjusted pValuePermuteX and pValuePermuteY, for a table without adjusted columns. A table saved by spatialCorrelationGeneExpIterPermutations() before version 2.0.0 has no adjusted columns, although its pValuePermuteX and pValuePermuteY were adjusted, so it is shown with the p_E label; recompute it to have the p-value labelled p_adj.

Details

The pixels are matched by name, as in spatialCorrelationGeneExp(). Both axes start at 0, or at the smallest value if a value is negative, and end at the largest value, so every pixel with values in both objects is drawn.

See also

plotGene(), the standardized figure of a gene in a compareSpatial() result, whose scatter plot is labelled with every p-value of the gene.

Examples

data(speKidney)

##### Rasterize to get pixels at matched spatial locations #####
rastKidney <- SEraster::rasterizeGeneExpression(speKidney,
               assay_name = 'counts', resolution = 0.2, fun = "mean",
               square = FALSE)

##### Use STcompare to calculate Pearson's correlation coefficient #####
rastGexpListAB <- list(A = rastKidney$A, B = rastKidney$B)
rastGexpListAC <- list(A = rastKidney$A, C = rastKidney$C)

negCorrelation <- spatialCorrelationGeneExp(rastGexpListAB, nThreads = 2)
#> spatialCorrelationGeneExp: 1 gene(s) x 2 directions on 273 shared pixels, 100 permutations, 2 thread(s)
#> spatialCorrelationGeneExp: done in 0.0 s (0 of 1 gene(s) with NA permutation p-values)
posCorrelation <- spatialCorrelationGeneExp(rastGexpListAC, nThreads = 2)
#> spatialCorrelationGeneExp: 1 gene(s) x 2 directions on 277 shared pixels, 100 permutations, 2 thread(s)
#> spatialCorrelationGeneExp: done in 0.0 s (0 of 1 gene(s) with NA permutation p-values)

negCorrelation
#>      correlationCoef   pValueNaive pValuePermuteX pValuePermuteY
#> Gene      -0.9472813 5.652003e-136     0.00990099     0.00990099
#>      pValuePermuteXAdj pValuePermuteYAdj deltaStarMedianX deltaStarMedianY
#> Gene        0.00990099        0.00990099              0.2              0.2
#>        deltaStarX   deltaStarY nullCorrelationsX nullCorrelationsY
#> Gene 0.2, 0.2.... 0.2, 0.2....      -0.16074....      -0.08458....
posCorrelation
#>      correlationCoef   pValueNaive pValuePermuteX pValuePermuteY
#> Gene       0.9431531 1.409195e-133     0.00990099     0.00990099
#>      pValuePermuteXAdj pValuePermuteYAdj deltaStarMedianX deltaStarMedianY
#> Gene        0.00990099        0.00990099              0.2              0.2
#>        deltaStarX   deltaStarY nullCorrelationsX nullCorrelationsY
#> Gene 0.2, 0.4.... 0.2, 0.2....      -0.00632....      0.052321....

expAB <- plotCorrelationGeneExp(rastGexpListAB, negCorrelation, "Gene")
expAC <- plotCorrelationGeneExp(rastGexpListAC, posCorrelation, "Gene")

expAB

expAC