Binned point class — giottoBinPoints-class • GiottoClass

S4 class allowing point detection-like access patterns for binned spatial values. Implemented more efficiently by only representing the spatial points once and mapping the sparse values information against the points.

Usage

createGiottoBinPoints(expr_values, spatial_locs, feat_type = "rna")

Arguments

expr_values: exprObj Bin counts/values
spatial_locs: spatLocsObj Spatial locations of bins
feat_type: character (default = "rna"). Feature type of the data

Functions

createGiottoBinPoints(): constructor function

Slots

spatial: ANY (currently SpatVector only). Row-indexed spatial points
counts: data.table with integer cols i and j mapping to @fid and @bid respectively. x is a numeric col standing for count or value of a feature for this bin point. Row indexing of this object is based on this slot.
bid: character. Bin IDs to map against
pmap: integer. For each spatial point, gives the index into @bid. Length equals length(spatial). Forms a bridge between spatial and counts: both pmap and counts$j are indices into @bid. Invariant: every bin ID in counts$j must appear in pmap (i.e., counts is always a subset of spatial). Allows subsetting counts without modifying the more expensive spatial representation until compaction.
fid: character. Feature IDs to map against
compact: logical. State of compaction. When TRUE, @bid, @pmap, and @spatial contain only bins that appear in @counts (bidirectional relationship). When FALSE, they may contain bins not present in @counts (unidirectional: every bin in counts has spatial, but not every spatial has counts).

Examples

ids <- sprintf("bin_%d", 1:50)
sl <- createSpatLocsObj(rnorm(100))
sl$cell_ID <- ids
m <- matrix(floor(runif(500) * 3),
    ncol = 50,
    dimnames = list(letters[1:10], ids)
)
ex <- createExprObj(m)
gbp <- createGiottoBinPoints(ex, sl)

# basics -------------------------------------------------------- #
force(gbp)
#> An object of class giottoBinPoints
#> feat_type : "rna"
#> dimensions : 325, 2
#> compact    : TRUE
#>    feat_ID count
#>     <char> <num>
#> 1:       b     2
#> 2:       c     1
#> 3:       d     2
#> 4:       e     2
#> 5:       f     1
#> 6:       h     2
#> 
nrow(gbp)
#> [1] 325
dim(gbp)
#> [1] 325   2
data.table::as.data.table(gbp)
#>      feat_ID count
#>       <char> <num>
#>   1:       b     2
#>   2:       c     1
#>   3:       d     2
#>   4:       e     2
#>   5:       f     1
#>  ---              
#> 321:       c     1
#> 322:       d     1
#> 323:       f     1
#> 324:       h     1
#> 325:       i     2
head(gbp)
#> An object of class giottoBinPoints
#> feat_type : "rna"
#> dimensions : 6, 2
#> compact    : TRUE
#>    feat_ID count
#>     <char> <num>
#> 1:       b     2
#> 2:       c     1
#> 3:       d     2
#> 4:       e     2
#> 5:       f     1
#> 6:       h     2
#> 
tail(gbp)
#> An object of class giottoBinPoints
#> feat_type : "rna"
#> dimensions : 6, 2
#> compact    : TRUE
#>    feat_ID count
#>     <char> <num>
#> 1:       j     1
#> 2:       c     1
#> 3:       d     1
#> 4:       f     1
#> 5:       h     1
#> 6:       i     2
#> 
objName(gbp)
#> [1] "rna"
featType(gbp)
#> [1] "rna"

# subsetting ---------------------------------------------------- #
gbp[50:100]
#> An object of class giottoBinPoints
#> feat_type : "rna"
#> dimensions : 51, 2
#> compact    : FALSE
#>    feat_ID count
#>     <char> <num>
#> 1:       c     2
#> 2:       d     1
#> 3:       g     1
#> 4:       i     2
#> 5:       j     1
#> 6:       a     1
#> 
gbp["a"] # get only points for feature "a"
#> An object of class giottoBinPoints
#> feat_type : "rna"
#> dimensions : 25, 2
#> compact    : FALSE
#>    feat_ID count
#>     <char> <num>
#> 1:       a     1
#> 2:       a     1
#> 3:       a     1
#> 4:       a     2
#> 5:       a     1
#> 6:       a     1
#> 
gbp[letters[1:4]] # get only points for features "a", "b", "c", "d"
#> An object of class giottoBinPoints
#> feat_type : "rna"
#> dimensions : 134, 2
#> compact    : FALSE
#>    feat_ID count
#>     <char> <num>
#> 1:       b     2
#> 2:       c     1
#> 3:       d     2
#> 4:       a     1
#> 5:       b     2
#> 6:       c     1
#> 

# plotting ------------------------------------------------------ #
plot(gbp, dens = TRUE) # will take a long time on large datasets

plot(gbp["a"]) # plot feature "a" only

plot(gbp[c("a", "d")]) # plot features "a" and "d" together


# spatial ------------------------------------------------------- #
ext(gbp) # spatial extent
#> SpatExtent : -2.30322375260073, 2.46431165767245, -1.30801840243713, 2.91912824385047 (xmin, xmax, ymin, ymax)

d <- Giotto::hexVertices(1)
#> Error in loadNamespace(x): there is no package called ‘Giotto’
d$poly_ID <- "a"
#> Error: object 'd' not found
hex <- createGiottoPolygon(d)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'createGiottoPolygon': object 'd' not found
plot(gbp, col = "blue")

plot(hex, add = TRUE, border = "red")
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'hex' not found
plot(crop(gbp, hex), add = TRUE, col = "green") # cropping
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': error in evaluating the argument 'y' in selecting a method for function 'crop': object 'hex' not found

hex2 <- tessellate(ext(gbp), shape_size = 1)
#> 16 polygons generated
res <- calculateOverlap(hex2, gbp) # overlapped feature calculation
m <- overlapToMatrix(res) # overlap info to expression matrix
force(m)
#> 10 x 16 sparse Matrix of class "dgCMatrix"
#>   [[ suppressing 16 column names ‘ID_1’, ‘ID_2’, ‘ID_3’ ... ]]
#>                                  
#> a . 3 2 1 . 3 4 . 2 3 2 1 . . 1 1
#> b 2 4 2 2 1 3 5 1 3 3 3 1 . 1 . .
#> c 2 3 4 2 3 5 5 1 2 3 4 1 . 1 . 1
#> d 2 4 2 . 3 5 5 1 2 3 2 1 . . 1 1
#> e . 5 3 . 2 3 4 1 2 3 2 . . 1 . 1
#> f 2 5 1 . 3 3 5 . 3 2 1 1 . 1 1 1
#> g 2 3 3 2 2 2 7 1 3 3 1 1 . 1 1 1
#> h 2 5 2 . 3 4 2 1 2 1 4 1 . . 1 .
#> i 1 5 2 2 2 5 5 1 1 2 3 . . . . 1
#> j 2 2 3 1 3 3 4 . 3 1 3 1 . . . 1