BumpyMatrix 1.2.0
The BumpyMatrix class is a two-dimensional object where each entry contains a non-scalar object of constant type/class but variable length.
This can be considered to be raggedness in the third dimension, i.e., “bumpiness”.
The BumpyMatrix is intended to represent complex data that has zero-to-many mappings between individual data points and each feature/sample,
allowing us to store it in Bioconductor’s standard 2-dimensional containers such as the SummarizedExperiment.
One example could be to store transcript coordinates for highly multiplexed FISH data;
the dimensions of the BumpyMatrix can represent genes and cells while each entry is a data frame with the relevant x/y coordinates.
A variety of BumpyMatrix subclasses are implemented but the most interesting is probably the BumpyDataFrameMatrix.
This is an S4 matrix class where each entry is a DataFrame object, i.e., Bioconductor’s wrapper around the data.frame.
To demonstrate, let’s mock up some data for our hypothetical FISH experiment:
library(S4Vectors)
df <- DataFrame(
x=rnorm(10000), y=rnorm(10000),
gene=paste0("GENE_", sample(100, 10000, replace=TRUE)),
cell=paste0("CELL_", sample(20, 10000, replace=TRUE))
)
df ## DataFrame with 10000 rows and 4 columns
## x y gene cell
## <numeric> <numeric> <character> <character>
## 1 1.262954 -1.2195513 GENE_81 CELL_9
## 2 -0.326233 -1.2014602 GENE_10 CELL_14
## 3 1.329799 -0.4960425 GENE_66 CELL_7
## 4 1.272429 0.0669311 GENE_4 CELL_12
## 5 0.414641 -0.0569491 GENE_74 CELL_5
## ... ... ... ... ...
## 9996 0.2136543 -0.771187 GENE_82 CELL_20
## 9997 0.7330922 -2.094271 GENE_36 CELL_5
## 9998 0.7570839 -0.328441 GENE_58 CELL_6
## 9999 0.7986270 0.849142 GENE_85 CELL_3
## 10000 -0.0556033 0.279099 GENE_52 CELL_5We then use the splitAsBumpyMatrix() utility to easily create our BumpyDataFrameMatrix based on the variables on the x- and y-axes.
Here, each row is a gene, each column is a cell, and each entry holds all coordinates for that gene/cell combination.
library(BumpyMatrix)
mat <- splitAsBumpyMatrix(df[,c("x", "y")], row=df$gene, column=df$cell)
mat## 100 x 20 BumpyDataFrameMatrix
## rownames: GENE_1 GENE_10 ... GENE_98 GENE_99
## colnames: CELL_1 CELL_10 ... CELL_8 CELL_9
## preview [1,1]:
## DataFrame with 5 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 -0.456681 1.1802570
## 2 2.260953 0.0664656
## 3 1.589902 -0.5645328
## 4 -0.932046 -0.9511974
## 5 1.073516 0.8238178mat[1,1][[1]]## DataFrame with 5 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 -0.456681 1.1802570
## 2 2.260953 0.0664656
## 3 1.589902 -0.5645328
## 4 -0.932046 -0.9511974
## 5 1.073516 0.8238178We can also set sparse=TRUE to use a more efficient sparse representation, which avoids explicit storage of empty DataFrames.
This may be necessary for larger datasets as there is a limit of 2147483647 (non-empty) entries in each BumpyMatrix.
chosen <- df[1:100,]
smat <- splitAsBumpyMatrix(chosen[,c("x", "y")], row=chosen$gene,
column=chosen$cell, sparse=TRUE)
smat## 67 x 20 BumpyDataFrameMatrix
## rownames: GENE_1 GENE_10 ... GENE_97 GENE_99
## colnames: CELL_1 CELL_10 ... CELL_8 CELL_9
## preview [1,1]:
## DataFrame with 0 rows and 2 columnsThe BumpyMatrix implements many of the standard matrix operations, e.g., nrow(), dimnames(), the combining methods and transposition.
dim(mat)## [1] 100 20dimnames(mat)## [[1]]
## [1] "GENE_1" "GENE_10" "GENE_100" "GENE_11" "GENE_12" "GENE_13"
## [7] "GENE_14" "GENE_15" "GENE_16" "GENE_17" "GENE_18" "GENE_19"
## [13] "GENE_2" "GENE_20" "GENE_21" "GENE_22" "GENE_23" "GENE_24"
## [19] "GENE_25" "GENE_26" "GENE_27" "GENE_28" "GENE_29" "GENE_3"
## [25] "GENE_30" "GENE_31" "GENE_32" "GENE_33" "GENE_34" "GENE_35"
## [31] "GENE_36" "GENE_37" "GENE_38" "GENE_39" "GENE_4" "GENE_40"
## [37] "GENE_41" "GENE_42" "GENE_43" "GENE_44" "GENE_45" "GENE_46"
## [43] "GENE_47" "GENE_48" "GENE_49" "GENE_5" "GENE_50" "GENE_51"
## [49] "GENE_52" "GENE_53" "GENE_54" "GENE_55" "GENE_56" "GENE_57"
## [55] "GENE_58" "GENE_59" "GENE_6" "GENE_60" "GENE_61" "GENE_62"
## [61] "GENE_63" "GENE_64" "GENE_65" "GENE_66" "GENE_67" "GENE_68"
## [67] "GENE_69" "GENE_7" "GENE_70" "GENE_71" "GENE_72" "GENE_73"
## [73] "GENE_74" "GENE_75" "GENE_76" "GENE_77" "GENE_78" "GENE_79"
## [79] "GENE_8" "GENE_80" "GENE_81" "GENE_82" "GENE_83" "GENE_84"
## [85] "GENE_85" "GENE_86" "GENE_87" "GENE_88" "GENE_89" "GENE_9"
## [91] "GENE_90" "GENE_91" "GENE_92" "GENE_93" "GENE_94" "GENE_95"
## [97] "GENE_96" "GENE_97" "GENE_98" "GENE_99"
##
## [[2]]
## [1] "CELL_1" "CELL_10" "CELL_11" "CELL_12" "CELL_13" "CELL_14" "CELL_15"
## [8] "CELL_16" "CELL_17" "CELL_18" "CELL_19" "CELL_2" "CELL_20" "CELL_3"
## [15] "CELL_4" "CELL_5" "CELL_6" "CELL_7" "CELL_8" "CELL_9"rbind(mat, mat)## 200 x 20 BumpyDataFrameMatrix
## rownames: GENE_1 GENE_10 ... GENE_98 GENE_99
## colnames: CELL_1 CELL_10 ... CELL_8 CELL_9
## preview [1,1]:
## DataFrame with 5 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 -0.456681 1.1802570
## 2 2.260953 0.0664656
## 3 1.589902 -0.5645328
## 4 -0.932046 -0.9511974
## 5 1.073516 0.8238178cbind(mat, mat)## 100 x 40 BumpyDataFrameMatrix
## rownames: GENE_1 GENE_10 ... GENE_98 GENE_99
## colnames: CELL_1 CELL_10 ... CELL_8 CELL_9
## preview [1,1]:
## DataFrame with 5 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 -0.456681 1.1802570
## 2 2.260953 0.0664656
## 3 1.589902 -0.5645328
## 4 -0.932046 -0.9511974
## 5 1.073516 0.8238178t(mat)## 20 x 100 BumpyDataFrameMatrix
## rownames: CELL_1 CELL_10 ... CELL_8 CELL_9
## colnames: GENE_1 GENE_10 ... GENE_98 GENE_99
## preview [1,1]:
## DataFrame with 5 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 -0.456681 1.1802570
## 2 2.260953 0.0664656
## 3 1.589902 -0.5645328
## 4 -0.932046 -0.9511974
## 5 1.073516 0.8238178Subsetting will yield a new BumpyMatrix object corresponding to the specified submatrix.
If the returned submatrix has a dimension of length 1 and drop=TRUE, the underlying CompressedList of values (in this case, the list of DataFrames) is returned.
mat[c("GENE_2", "GENE_20"),]## 2 x 20 BumpyDataFrameMatrix
## rownames: GENE_2 GENE_20
## colnames: CELL_1 CELL_10 ... CELL_8 CELL_9
## preview [1,1]:
## DataFrame with 9 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 0.6006726 2.2264549
## 2 0.5633180 -0.5785644
## 3 -0.9858359 0.5030357
## 4 1.9680003 1.8404334
## 5 0.2817334 0.0957103
## 6 -0.0787825 -1.1799429
## 7 -1.5499071 -0.1132942
## 8 0.3269564 0.2557983
## 9 -0.5103373 -0.3180972mat[,1:5]## 100 x 5 BumpyDataFrameMatrix
## rownames: GENE_1 GENE_10 ... GENE_98 GENE_99
## colnames: CELL_1 CELL_10 CELL_11 CELL_12 CELL_13
## preview [1,1]:
## DataFrame with 5 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 -0.456681 1.1802570
## 2 2.260953 0.0664656
## 3 1.589902 -0.5645328
## 4 -0.932046 -0.9511974
## 5 1.073516 0.8238178mat["GENE_10",]## SplitDataFrameList of length 20
## $CELL_1
## DataFrame with 6 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 -0.4437397 -1.1806866
## 2 2.6989263 0.9616407
## 3 -0.7392878 0.5467073
## 4 0.0485278 0.3189288
## 5 -1.6825538 0.7924868
## 6 0.1882398 0.0267774
##
## $CELL_10
## DataFrame with 7 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 -0.294196 0.531934
## 2 0.216737 0.690170
## 3 -0.603301 -1.024468
## 4 -1.198632 0.866599
## 5 -1.160206 1.029362
## 6 -0.996214 -0.731540
## 7 -1.497422 -0.635969
##
## $CELL_11
## DataFrame with 3 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 1.3397324 -0.8455987
## 2 0.0355647 0.9836901
## 3 -0.9233099 0.0480218
##
## ...
## <17 more elements>For BumpyDataFrameMatrix objects, we have an additional third index that allows us to easily extract an individual column of each DataFrame into a new BumpyMatrix.
In the example below, we extract the x-coordinate into a new BumpyNumericMatrix:
out.x <- mat[,,"x"]
out.x## 100 x 20 BumpyNumericMatrix
## rownames: GENE_1 GENE_10 ... GENE_98 GENE_99
## colnames: CELL_1 CELL_10 ... CELL_8 CELL_9
## preview [1,1]:
## num [1:5] -0.457 2.261 1.59 -0.932 1.074out.x[,1]## NumericList of length 100
## [["GENE_1"]] -0.456680962301832 2.26095325993182 ... 1.07351568637374
## [["GENE_10"]] -0.443739703296155 2.69892631656385 ... 0.188239840894589
## [["GENE_100"]] -0.0521083287975451 -0.959848960471883 -1.69849738722303 1.20187171507774
## [["GENE_11"]] 0.170720585504045 -0.632693519163165 0.0474731889735527 0.686758871296805
## [["GENE_12"]] -0.995342637599223 -0.145945860989099 ... -0.75308908554855
## [["GENE_13"]] -0.449876006420393 -0.491325515925785 -0.0800044195125232 1.47031802805851
## [["GENE_14"]] 0.837394397366885 -1.09885654076545 ... 0.0788476240211941
## [["GENE_15"]] -1.30967954169979 -1.07803449338206 ... 1.10399748359464
## [["GENE_16"]] -0.299215117897316 -0.655378696153722 ... 1.04146605380096
## [["GENE_17"]] 0.387769028629511 1.27455066651572 0.0634504926146246 -0.654969069758301
## ...
## <90 more elements>Common arithmetic and logical operations are already implemented for BumpyNumericMatrix subclasses.
Almost all of these operations will act on each entry of the input object (or corresponding entries, for multiple inputs)
and produce a new BumpyMatrix of the appropriate type.
pos <- out.x > 0
pos[,1]## LogicalList of length 100
## [["GENE_1"]] FALSE TRUE TRUE FALSE TRUE
## [["GENE_10"]] FALSE TRUE FALSE TRUE FALSE TRUE
## [["GENE_100"]] FALSE FALSE FALSE TRUE
## [["GENE_11"]] TRUE FALSE TRUE TRUE
## [["GENE_12"]] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE
## [["GENE_13"]] FALSE FALSE FALSE TRUE
## [["GENE_14"]] TRUE FALSE FALSE TRUE FALSE FALSE TRUE
## [["GENE_15"]] FALSE FALSE TRUE TRUE FALSE TRUE
## [["GENE_16"]] FALSE FALSE TRUE FALSE FALSE TRUE
## [["GENE_17"]] TRUE TRUE TRUE FALSE
## ...
## <90 more elements>shift <- 10 * out.x + 1
shift[,1]## NumericList of length 100
## [["GENE_1"]] -3.56680962301832 23.6095325993182 ... 11.7351568637374
## [["GENE_10"]] -3.43739703296155 27.9892631656385 ... 2.88239840894589
## [["GENE_100"]] 0.478916712024549 -8.59848960471883 -15.9849738722303 13.0187171507774
## [["GENE_11"]] 2.70720585504045 -5.32693519163165 1.47473188973553 7.86758871296805
## [["GENE_12"]] -8.95342637599223 -0.459458609890994 ... -6.5308908554855
## [["GENE_13"]] -3.49876006420393 -3.91325515925785 0.199955804874768 15.7031802805851
## [["GENE_14"]] 9.37394397366885 -9.98856540765453 ... 1.78847624021194
## [["GENE_15"]] -12.0967954169979 -9.78034493382059 ... 12.0399748359464
## [["GENE_16"]] -1.99215117897316 -5.55378696153722 ... 11.4146605380095
## [["GENE_17"]] 4.87769028629511 13.7455066651572 1.63450492614625 -5.54969069758301
## ...
## <90 more elements>out.y <- mat[,,"y"]
greater <- out.x < out.y
greater[,1]## LogicalList of length 100
## [["GENE_1"]] TRUE FALSE FALSE FALSE FALSE
## [["GENE_10"]] FALSE FALSE TRUE TRUE TRUE FALSE
## [["GENE_100"]] TRUE TRUE TRUE FALSE
## [["GENE_11"]] TRUE TRUE FALSE FALSE
## [["GENE_12"]] TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE
## [["GENE_13"]] TRUE TRUE TRUE FALSE
## [["GENE_14"]] TRUE FALSE TRUE FALSE TRUE TRUE TRUE
## [["GENE_15"]] TRUE TRUE FALSE FALSE FALSE TRUE
## [["GENE_16"]] TRUE TRUE FALSE TRUE FALSE FALSE
## [["GENE_17"]] TRUE FALSE TRUE TRUE
## ...
## <90 more elements>diff <- out.y - out.x
diff[,1]## NumericList of length 100
## [["GENE_1"]] 1.63693791572228 -2.19448769611817 ... -0.249697865694356
## [["GENE_10"]] -0.736946857599669 -1.73728562932688 ... -0.161462478389356
## [["GENE_100"]] 0.385070842100422 0.830947088787695 1.28842673980641 -0.930637428293555
## [["GENE_11"]] 2.19924827461933 1.2939182751824 -0.73459027884472 -0.974124539123142
## [["GENE_12"]] 0.0112704909161504 -2.10310060588711 ... -0.902168554270107
## [["GENE_13"]] 1.16478634799064 0.186402515479371 0.996944355639376 -0.116586294264194
## [["GENE_14"]] 0.157749191982352 -0.397552739170927 ... 1.36241042737296
## [["GENE_15"]] 2.15036798366318 0.253710634937292 ... 0.39941302358738
## [["GENE_16"]] 0.96817595171046 0.114756919804117 ... -2.01727368917211
## [["GENE_17"]] 1.28069410635393 -2.78487321775219 0.152233191585686 1.9392696521926
## ...
## <90 more elements>When subsetting a BumpyMatrix, we can use another BumpyMatrix containing indexing information for each entry.
Consider the following code chunk:
i <- mat[,,'x'] > 0 & mat[,,'y'] > 0
i## 100 x 20 BumpyLogicalMatrix
## rownames: GENE_1 GENE_10 ... GENE_98 GENE_99
## colnames: CELL_1 CELL_10 ... CELL_8 CELL_9
## preview [1,1]:
## logi [1:5] FALSE TRUE FALSE FALSE TRUEi[,1]## LogicalList of length 100
## [["GENE_1"]] FALSE TRUE FALSE FALSE TRUE
## [["GENE_10"]] FALSE TRUE FALSE TRUE FALSE TRUE
## [["GENE_100"]] FALSE FALSE FALSE TRUE
## [["GENE_11"]] TRUE FALSE FALSE FALSE
## [["GENE_12"]] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE FALSE
## [["GENE_13"]] FALSE FALSE FALSE TRUE
## [["GENE_14"]] TRUE FALSE FALSE FALSE FALSE FALSE TRUE
## [["GENE_15"]] FALSE FALSE TRUE FALSE FALSE TRUE
## [["GENE_16"]] FALSE FALSE FALSE FALSE FALSE FALSE
## [["GENE_17"]] TRUE FALSE TRUE FALSE
## ...
## <90 more elements>sub <- mat[i]
sub## 100 x 20 BumpyDataFrameMatrix
## rownames: GENE_1 GENE_10 ... GENE_98 GENE_99
## colnames: CELL_1 CELL_10 ... CELL_8 CELL_9
## preview [1,1]:
## DataFrame with 2 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 2.26095 0.0664656
## 2 1.07352 0.8238178sub[,1]## SplitDataFrameList of length 100
## $GENE_1
## DataFrame with 2 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 2.26095 0.0664656
## 2 1.07352 0.8238178
##
## $GENE_10
## DataFrame with 3 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 2.6989263 0.9616407
## 2 0.0485278 0.3189288
## 3 0.1882398 0.0267774
##
## $GENE_100
## DataFrame with 1 row and 2 columns
## x y
## <numeric> <numeric>
## 1 1.20187 0.271234
##
## ...
## <97 more elements>Here, i is a BumpyLogicalMatrix where each entry is a logical vector.
When we do x[i], we effectively loop over the corresponding entries of x and i, using the latter to subset the DataFrame in the former.
This produces a new BumpyDataFrameMatrix containing, in this case, only the observations with positive x- and y-coordinates.
For BumpyDataFrameMatrix objects, subsetting to a single field in the third dimension will automatically drop to the type of the underlying column of the DataFrame.
This can be stopped with drop=FALSE to preserve the BumpyDataFrameMatrix output:
mat[,,'x']## 100 x 20 BumpyNumericMatrix
## rownames: GENE_1 GENE_10 ... GENE_98 GENE_99
## colnames: CELL_1 CELL_10 ... CELL_8 CELL_9
## preview [1,1]:
## num [1:5] -0.457 2.261 1.59 -0.932 1.074mat[,,'x',drop=FALSE]## 100 x 20 BumpyDataFrameMatrix
## rownames: GENE_1 GENE_10 ... GENE_98 GENE_99
## colnames: CELL_1 CELL_10 ... CELL_8 CELL_9
## preview [1,1]:
## DataFrame with 5 rows and 1 column
## x
## <numeric>
## 1 -0.456681
## 2 2.260953
## 3 1.589902
## 4 -0.932046
## 5 1.073516In situations where we want to drop the third dimension but not the first two dimensions (or vice versa), we use the .dropk argument.
Setting .dropk=FALSE will ensure that the third dimension is not dropped, as shown below:
mat[1,1,'x']## NumericList of length 1
## [[1]] -0.456680962301832 2.26095325993182 ... 1.07351568637374mat[1,1,'x',.dropk=FALSE]## SplitDataFrameList of length 1
## [[1]]
## DataFrame with 5 rows and 1 column
## x
## <numeric>
## 1 -0.456681
## 2 2.260953
## 3 1.589902
## 4 -0.932046
## 5 1.073516mat[1,1,'x',drop=FALSE]## 1 x 1 BumpyDataFrameMatrix
## rownames: GENE_1
## colnames: CELL_1
## preview [1,1]:
## DataFrame with 5 rows and 1 column
## x
## <numeric>
## 1 -0.456681
## 2 2.260953
## 3 1.589902
## 4 -0.932046
## 5 1.073516mat[1,1,'x',.dropk=TRUE,drop=FALSE]## 1 x 1 BumpyNumericMatrix
## rownames: GENE_1
## colnames: CELL_1
## preview [1,1]:
## num [1:5] -0.457 2.261 1.59 -0.932 1.074Subset replacement is also supported, which is most useful for operations to modify specific fields:
copy <- mat
copy[,,'x'] <- copy[,,'x'] * 20
copy[,1]## SplitDataFrameList of length 100
## $GENE_1
## DataFrame with 5 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 -9.13362 1.1802570
## 2 45.21907 0.0664656
## 3 31.79805 -0.5645328
## 4 -18.64092 -0.9511974
## 5 21.47031 0.8238178
##
## $GENE_10
## DataFrame with 6 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 -8.874794 -1.1806866
## 2 53.978526 0.9616407
## 3 -14.785756 0.5467073
## 4 0.970557 0.3189288
## 5 -33.651075 0.7924868
## 6 3.764797 0.0267774
##
## $GENE_100
## DataFrame with 4 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 -1.04217 0.332963
## 2 -19.19698 -0.128902
## 3 -33.96995 -0.410071
## 4 24.03743 0.271234
##
## ...
## <97 more elements>Some additional statistical operations are also implemented that will usually produce an ordinary matrix.
Here, each entry corresponds to the statistic computed from the corresponding entry of the BumpyMatrix.
mean(out.x)[1:5,1:5] # matrix## CELL_1 CELL_10 CELL_11 CELL_12 CELL_13
## GENE_1 0.70712887 0.2128110 -0.40556554 -0.3274332 0.11342557
## GENE_10 0.01168545 -0.7904620 0.15066241 -0.8528919 0.43556752
## GENE_100 -0.37714574 -0.6514196 0.28494400 0.5962598 -0.03492945
## GENE_11 0.06806478 0.3087175 -0.49577063 0.1712767 -0.08399315
## GENE_12 -0.37907379 0.5438429 0.03386324 -0.7309794 0.26734736var(out.x)[1:5,1:5] # matrix## CELL_1 CELL_10 CELL_11 CELL_12 CELL_13
## GENE_1 1.8423115 0.2797058 0.5265598 1.4911920 2.25707591
## GENE_10 2.1791223 0.3568664 1.2902758 0.1812921 0.07158465
## GENE_100 1.5614862 0.6738425 2.1982677 0.1351770 0.58199316
## GENE_11 0.2949356 0.2208074 0.5296845 1.3845811 1.79176470
## GENE_12 0.9008503 0.8346523 0.7860442 1.2395604 1.48033310The exception is with operations that naturally produce a vector, in which case a matching 3-dimensional array is returned:
quantile(out.x)[1:5,1:5,]## , , 0%
##
## CELL_1 CELL_10 CELL_11 CELL_12 CELL_13
## GENE_1 -0.9320460 -0.4345906 -1.3222878 -1.97005386 -2.9048991
## GENE_10 -1.6825538 -1.4974221 -0.9233099 -1.28504028 0.2463789
## GENE_100 -1.6984974 -1.6887599 -0.8260650 0.09508173 -1.2458351
## GENE_11 -0.6326935 -0.3023776 -1.0103989 -0.95832412 -1.2367482
## GENE_12 -2.5432442 -1.0981897 -0.7845837 -2.77582663 -0.7187626
##
## , , 25%
##
## CELL_1 CELL_10 CELL_11 CELL_12 CELL_13
## GENE_1 -0.4566810 -0.09455929 -0.9413205 -1.3130549 -0.2176967
## GENE_10 -0.6654008 -1.17941890 -0.4438726 -1.1460665 0.3409732
## GENE_100 -1.1445111 -1.20769109 -0.5568366 0.4201514 -0.3004851
## GENE_11 -0.1225685 0.10848147 -0.7530848 -0.6162069 -1.0592760
## GENE_12 -0.8848881 0.26482573 -0.3850977 -1.5437361 -0.6038440
##
## , , 50%
##
## CELL_1 CELL_10 CELL_11 CELL_12 CELL_13
## GENE_1 1.0735157 0.2666342 -0.34471488 0.03546206 0.18547368
## GENE_10 -0.1976059 -0.9962139 0.03556469 -0.89716938 0.43556752
## GENE_100 -0.5059786 -0.8892505 -0.28760824 0.56051620 -0.04182030
## GENE_11 0.1090969 0.2021316 -0.49577063 -0.39927829 -0.82739620
## GENE_12 -0.3351033 0.8535404 -0.17140432 -0.46267392 -0.06859857
##
## , , 75%
##
## CELL_1 CELL_10 CELL_11 CELL_12 CELL_13
## GENE_1 1.5899023 0.57400451 0.01671434 0.66592781 0.4748549
## GENE_10 0.1533118 -0.44874893 0.68764857 -0.60399476 0.5301618
## GENE_100 0.2613867 0.04925645 0.84044849 0.91384869 0.3956748
## GENE_11 0.2997302 0.44607885 -0.23845649 0.94710292 1.0263022
## GENE_12 0.3423434 1.21512396 0.01259719 0.06282157 0.8025928
##
## , , 100%
##
## CELL_1 CELL_10 CELL_11 CELL_12 CELL_13
## GENE_1 2.2609533 0.7525662 0.58132266 0.8210081 2.6843208
## GENE_10 2.6989263 0.2167373 1.33973245 -0.3321887 0.6247561
## GENE_100 1.2018717 0.5358361 1.96850521 0.9917012 0.9650991
## GENE_11 0.6867589 1.3118250 0.01885764 1.8947475 1.6771525
## GENE_12 0.8858188 1.2499268 1.93803165 0.7024325 1.9253492range(out.x)[1:5,1:5,]## , , 1
##
## CELL_1 CELL_10 CELL_11 CELL_12 CELL_13
## GENE_1 -0.9320460 -0.4345906 -1.3222878 -1.97005386 -2.9048991
## GENE_10 -1.6825538 -1.4974221 -0.9233099 -1.28504028 0.2463789
## GENE_100 -1.6984974 -1.6887599 -0.8260650 0.09508173 -1.2458351
## GENE_11 -0.6326935 -0.3023776 -1.0103989 -0.95832412 -1.2367482
## GENE_12 -2.5432442 -1.0981897 -0.7845837 -2.77582663 -0.7187626
##
## , , 2
##
## CELL_1 CELL_10 CELL_11 CELL_12 CELL_13
## GENE_1 2.2609533 0.7525662 0.58132266 0.8210081 2.6843208
## GENE_10 2.6989263 0.2167373 1.33973245 -0.3321887 0.6247561
## GENE_100 1.2018717 0.5358361 1.96850521 0.9917012 0.9650991
## GENE_11 0.6867589 1.3118250 0.01885764 1.8947475 1.6771525
## GENE_12 0.8858188 1.2499268 1.93803165 0.7024325 1.9253492Other operations may return another BumpyMatrix if the output length is variable:
pmax(out.x, out.y) ## 100 x 20 BumpyNumericMatrix
## rownames: GENE_1 GENE_10 ... GENE_98 GENE_99
## colnames: CELL_1 CELL_10 ... CELL_8 CELL_9
## preview [1,1]:
## num [1:5] 1.18 2.261 1.59 -0.932 1.074BumpyCharacterMatrix objects also have their own methods for grep(), tolower(), etc. to manipulate the strings in a convenient manner.
sessionInfo()## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BumpyMatrix_1.2.0 S4Vectors_0.32.0 BiocGenerics_0.40.0
## [4] BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] knitr_1.36 magrittr_2.0.1 IRanges_2.28.0
## [4] lattice_0.20-45 R6_2.5.1 rlang_0.4.12
## [7] fastmap_1.1.0 stringr_1.4.0 tools_4.1.1
## [10] grid_4.1.1 xfun_0.27 jquerylib_0.1.4
## [13] htmltools_0.5.2 yaml_2.2.1 digest_0.6.28
## [16] bookdown_0.24 Matrix_1.3-4 BiocManager_1.30.16
## [19] sass_0.4.0 evaluate_0.14 rmarkdown_2.11
## [22] stringi_1.7.5 compiler_4.1.1 bslib_0.3.1
## [25] jsonlite_1.7.2