![[Experimental]](figures/lifecycle-experimental.svg)
The correlate() method can calculate the correlation matrix between the sample vectors of
counts from a specified assay. This produces a HermesDataCor object, which is an extension
of a matrix with additional quality flags in the slot flag_data
(containing the tech_failure_flag and low_depth_flag columns describing the original
input samples).
An autoplot() method then afterwards can produce the corresponding heatmap.
# S4 method for AnyHermesData
correlate(object, assay_name = "counts", method = "pearson", ...)
# S4 method for HermesDataCor
autoplot(
object,
flag_colors = c(`FALSE` = "green", `TRUE` = "red"),
cor_colors = circlize::colorRamp2(c(0, 0.5, 1), c("red", "yellow", "green")),
...
)Arguments
- object
(
AnyHermesData)
object to calculate the correlation.- assay_name
(
string)
the name of the assay to use.- method
(
string)
the correlation method, seestats::cor()for details.- ...
other arguments to be passed to
ComplexHeatmap::Heatmap().- flag_colors
(named
character)
a vector that specifies the colors forTRUEandFALSEflag values.- cor_colors
(
function)
color scale function for the correlation values in the heatmap, produced bycirclize::colorRamp2().
Value
A HermesDataCor object.
Functions
autoplot,HermesDataCor-method: Thisautoplot()method uses theComplexHeatmap::Heatmap()function to plot the correlations between samples saved in aHermesDataCorobject.
Examples
object <- hermes_data
# Calculate the sample correlation matrix.
correlate(object)
#> An object of class "HermesDataCor"
#> 06520011B0023R 06520067C0018R 06520063C0043R 06520105C0017R
#> 06520011B0023R 1.0000000 0.9609738 0.6473003 0.3904017
#> 06520067C0018R 0.9609738 1.0000000 0.6758462 0.4167789
#> 06520063C0043R 0.6473003 0.6758462 1.0000000 0.7742640
#> 06520105C0017R 0.3904017 0.4167789 0.7742640 1.0000000
#> 06520092C0017R 0.6633748 0.6604377 0.6113716 0.4520703
#> 06520103C0017R 0.9079581 0.8775734 0.5978126 0.4172803
#> 06520001B0023R 0.8818454 0.8570700 0.6658466 0.5111085
#> 06520022C0017R 0.9117386 0.9142934 0.8104461 0.6131335
#> 06520062C0017R 0.6753364 0.7157011 0.8896542 0.7652040
#> 06520046C0018R 0.3208473 0.3148823 0.4431801 0.3814626
#> 06520101B0017R 0.9314543 0.9041533 0.6417565 0.4319133
#> 06520047C0017R 0.6815391 0.6707660 0.5759576 0.4652142
#> 06520024B0014R 0.5835149 0.5819907 0.6588440 0.5231136
#> 06520080B0023R 0.5168947 0.5524994 0.8949026 0.8217901
#> 06520093C0017R 0.4138013 0.4497814 0.7953623 0.7202175
#> 06520070C0018R 0.4697837 0.5046432 0.8295754 0.8553639
#> 06520023C0018R 0.4033409 0.4404680 0.8266231 0.8575140
#> 06520099B0017R 0.4224672 0.4766716 0.7936238 0.6681055
#> 06520015C0016R 0.8093514 0.8036729 0.7640655 0.6482306
#> 06520019C0023R 0.6139914 0.6389785 0.8583166 0.8280060
#> 06520092C0017R 06520103C0017R 06520001B0023R 06520022C0017R
#> 06520011B0023R 0.6633748 0.9079581 0.8818454 0.9117386
#> 06520067C0018R 0.6604377 0.8775734 0.8570700 0.9142934
#> 06520063C0043R 0.6113716 0.5978126 0.6658466 0.8104461
#> 06520105C0017R 0.4520703 0.4172803 0.5111085 0.6131335
#> 06520092C0017R 1.0000000 0.7033489 0.6871754 0.7101840
#> 06520103C0017R 0.7033489 1.0000000 0.9863276 0.9000953
#> 06520001B0023R 0.6871754 0.9863276 1.0000000 0.9227436
#> 06520022C0017R 0.7101840 0.9000953 0.9227436 1.0000000
#> 06520062C0017R 0.5895186 0.5725651 0.6362937 0.8010688
#> 06520046C0018R 0.2638231 0.1279450 0.1317451 0.3080865
#> 06520101B0017R 0.7254041 0.9743803 0.9576231 0.9137366
#> 06520047C0017R 0.5043505 0.5349226 0.5188468 0.6281087
#> 06520024B0014R 0.4709462 0.4768380 0.5041802 0.6294704
#> 06520080B0023R 0.5609389 0.4894940 0.5836644 0.7307929
#> 06520093C0017R 0.6241234 0.4080609 0.4799501 0.6398497
#> 06520070C0018R 0.5547028 0.5587841 0.6561649 0.7291685
#> 06520023C0018R 0.5100178 0.4636074 0.5662647 0.6677198
#> 06520099B0017R 0.5000248 0.3018044 0.3613744 0.5875554
#> 06520015C0016R 0.6949487 0.9262604 0.9682275 0.9302252
#> 06520019C0023R 0.6235283 0.6475062 0.7162892 0.8016153
#> 06520062C0017R 06520046C0018R 06520101B0017R 06520047C0017R
#> 06520011B0023R 0.6753364 0.3208473 0.9314543 0.6815391
#> 06520067C0018R 0.7157011 0.3148823 0.9041533 0.6707660
#> 06520063C0043R 0.8896542 0.4431801 0.6417565 0.5759576
#> 06520105C0017R 0.7652040 0.3814626 0.4319133 0.4652142
#> 06520092C0017R 0.5895186 0.2638231 0.7254041 0.5043505
#> 06520103C0017R 0.5725651 0.1279450 0.9743803 0.5349226
#> 06520001B0023R 0.6362937 0.1317451 0.9576231 0.5188468
#> 06520022C0017R 0.8010688 0.3080865 0.9137366 0.6281087
#> 06520062C0017R 1.0000000 0.4747608 0.6236545 0.6631752
#> 06520046C0018R 0.4747608 1.0000000 0.2381617 0.7655772
#> 06520101B0017R 0.6236545 0.2381617 1.0000000 0.6128255
#> 06520047C0017R 0.6631752 0.7655772 0.6128255 1.0000000
#> 06520024B0014R 0.6813321 0.4423075 0.5302252 0.5880107
#> 06520080B0023R 0.8997510 0.4212484 0.5343559 0.5282159
#> 06520093C0017R 0.7530101 0.3954200 0.5019182 0.4276193
#> 06520070C0018R 0.8248334 0.3424986 0.5564180 0.4850496
#> 06520023C0018R 0.8045479 0.4105629 0.4729066 0.5008448
#> 06520099B0017R 0.7839665 0.5424584 0.4246231 0.5237444
#> 06520015C0016R 0.7306067 0.1605127 0.8992993 0.5037995
#> 06520019C0023R 0.8610609 0.4903751 0.6754787 0.6867600
#> 06520024B0014R 06520080B0023R 06520093C0017R 06520070C0018R
#> 06520011B0023R 0.5835149 0.5168947 0.4138013 0.4697837
#> 06520067C0018R 0.5819907 0.5524994 0.4497814 0.5046432
#> 06520063C0043R 0.6588440 0.8949026 0.7953623 0.8295754
#> 06520105C0017R 0.5231136 0.8217901 0.7202175 0.8553639
#> 06520092C0017R 0.4709462 0.5609389 0.6241234 0.5547028
#> 06520103C0017R 0.4768380 0.4894940 0.4080609 0.5587841
#> 06520001B0023R 0.5041802 0.5836644 0.4799501 0.6561649
#> 06520022C0017R 0.6294704 0.7307929 0.6398497 0.7291685
#> 06520062C0017R 0.6813321 0.8997510 0.7530101 0.8248334
#> 06520046C0018R 0.4423075 0.4212484 0.3954200 0.3424986
#> 06520101B0017R 0.5302252 0.5343559 0.5019182 0.5564180
#> 06520047C0017R 0.5880107 0.5282159 0.4276193 0.4850496
#> 06520024B0014R 1.0000000 0.6059874 0.5317071 0.5407754
#> 06520080B0023R 0.6059874 1.0000000 0.8384252 0.8961184
#> 06520093C0017R 0.5317071 0.8384252 1.0000000 0.7933036
#> 06520070C0018R 0.5407754 0.8961184 0.7933036 1.0000000
#> 06520023C0018R 0.5403914 0.8941102 0.8082247 0.9658703
#> 06520099B0017R 0.5677268 0.8092670 0.8884675 0.6897805
#> 06520015C0016R 0.5410291 0.7177895 0.6165727 0.8088963
#> 06520019C0023R 0.6251164 0.8780061 0.7894097 0.9381035
#> 06520023C0018R 06520099B0017R 06520015C0016R 06520019C0023R
#> 06520011B0023R 0.4033409 0.4224672 0.8093514 0.6139914
#> 06520067C0018R 0.4404680 0.4766716 0.8036729 0.6389785
#> 06520063C0043R 0.8266231 0.7936238 0.7640655 0.8583166
#> 06520105C0017R 0.8575140 0.6681055 0.6482306 0.8280060
#> 06520092C0017R 0.5100178 0.5000248 0.6949487 0.6235283
#> 06520103C0017R 0.4636074 0.3018044 0.9262604 0.6475062
#> 06520001B0023R 0.5662647 0.3613744 0.9682275 0.7162892
#> 06520022C0017R 0.6677198 0.5875554 0.9302252 0.8016153
#> 06520062C0017R 0.8045479 0.7839665 0.7306067 0.8610609
#> 06520046C0018R 0.4105629 0.5424584 0.1605127 0.4903751
#> 06520101B0017R 0.4729066 0.4246231 0.8992993 0.6754787
#> 06520047C0017R 0.5008448 0.5237444 0.5037995 0.6867600
#> 06520024B0014R 0.5403914 0.5677268 0.5410291 0.6251164
#> 06520080B0023R 0.8941102 0.8092670 0.7177895 0.8780061
#> 06520093C0017R 0.8082247 0.8884675 0.6165727 0.7894097
#> 06520070C0018R 0.9658703 0.6897805 0.8088963 0.9381035
#> 06520023C0018R 1.0000000 0.7296291 0.7286448 0.9323267
#> 06520099B0017R 0.7296291 1.0000000 0.4846711 0.7380609
#> 06520015C0016R 0.7286448 0.4846711 1.0000000 0.8240416
#> 06520019C0023R 0.9323267 0.7380609 0.8240416 1.0000000
#> Slot "flag_data":
#> DataFrame with 20 rows and 2 columns
#> tech_failure_flag low_depth_flag
#> <logical> <logical>
#> 06520011B0023R FALSE FALSE
#> 06520067C0018R FALSE FALSE
#> 06520063C0043R FALSE TRUE
#> 06520105C0017R FALSE FALSE
#> 06520092C0017R FALSE FALSE
#> ... ... ...
#> 06520070C0018R FALSE FALSE
#> 06520023C0018R FALSE FALSE
#> 06520099B0017R FALSE FALSE
#> 06520015C0016R FALSE FALSE
#> 06520019C0023R FALSE FALSE
#>
# We can specify another correlation coefficient to be calculated.
result <- correlate(object, method = "spearman")
# Plot the correlation matrix.
autoplot(result)
# We can customize the heatmap.
autoplot(result, show_column_names = FALSE, show_row_names = FALSE)
# Including changing the axis label text size.
autoplot(
result,
row_names_gp = grid::gpar(fontsize = 8),
column_names_gp = grid::gpar(fontsize = 8)
)
