bigPint 1.2.2
Researchers may wish to superimpose a subset of the full dataset onto the full dataset. If a researcher is using the package to visualize RNA-seq data, then this subset of data is often differentially expressed genes (DEGs) returned from a model. In this case, the user may wish to use the dataMetrics
input parameter, which contains at least one quantitative variable returned from a model such as FDR, p-value, and log fold change.
As was shown in the article Data object, the data
object called soybean_ir_sub
contained 5,604 genes and two treatment groups, N and P (Lauter and Graham 2016). We can examine the structure of its corresponding dataMetrics
object called soybean_ir_sub_metrics
as follows:
library(bigPint)
data("soybean_ir_sub_metrics")
str(soybean_ir_sub_metrics, strict.width = "wrap")
## List of 1
## $ N_P:'data.frame': 5604 obs. of 6 variables:
## ..$ ID : chr [1:5604] "Glyma.19G168700.Wm82.a2.v1" "Glyma.13G293500.Wm82.a2.v1"
## "Glyma.05G188700.Wm82.a2.v1" "Glyma.13G173100.Wm82.a2.v1" ...
## ..$ logFC : num [1:5604] -5.92 2.99 -3.51 -3.91 -3.51 ...
## ..$ logCPM: num [1:5604] 7.52 8.08 8.83 8.27 10.19 ...
## ..$ LR : num [1:5604] 266 171 167 157 154 ...
## ..$ PValue: num [1:5604] 9.18e-60 3.65e-39 2.73e-38 6.04e-36 2.58e-35 ...
## ..$ FDR : num [1:5604] 5.14e-56 1.02e-35 5.09e-35 8.46e-33 2.89e-32 ...
Similarly, as was shown in the data page, the data
object called soybean_cn_sub
contained 7,332 genes and three treatment groups, S1, S2, and S3 (Brown and Hudson 2015). We can examine the structure of its corresponding dataMetrics
object called soybean_cn_sub_metrics
as follows:
As demonstrated in the two examples above, the dataMetrics
object must meet the following conditions:
list
data
object. For example, the soybean_ir_sub_metrics
object contains one list element (“N_P”) and the soybean_cn_sub_metrics
object contains three list elements (“S1_S2”, “S1_S3”, “S2_S3”).data.frame
^[a-zA-Z0-9]+_[a-zA-Z0-9]+
, where
character
consisting of the unique names of the genesnumeric
or integer
consisting of a quantitative variable. This can be called anything. In the examples above, there are five of such columns called “logFC”, “logCPM”, “LR”, “PValue”, and “FDR”.You can quickly double-check the names of the list elements in your dataMetrics
object as follows:
names(soybean_ir_sub_metrics)
## [1] "N_P"
names(soybean_cn_sub_metrics)
## [1] "S1_S2" "S1_S3" "S2_S3"
If your dataMetrics
object does not fit this format, bigPint
will likely throw an informative error about why your format was not recognized.
Brown, Anne V., and Karen A. Hudson. 2015. “Developmental Profiling of Gene Expression in Soybean Trifoliate Leaves and Cotyledons.” BMC Plant Biology 15 (1). BioMed Central:169.
Lauter, AN Moran, and MA Graham. 2016. “NCBI Sra Bioproject Accession: PRJNA318409.”