# Getting started

Here we will show a very basic example how to use grandR to perform kinetic modeling. For much more vignettes, see the grandR website.

We will use data from [1]. These are SLAM-seq data from multiple time points (1h,2h,3h,4h) after infecting Calu-3 cells with SARS-CoV-2 (or mock as control).

We first load the grandR package and the read the GRAND-SLAM that is part of the grandR package:

suppressPackageStartupMessages({
library(grandR)
library(ggplot2)
library(patchwork)
})

sars <- ReadGRAND(system.file("extdata", "sars.tsv.gz", package = "grandR"),
design=c(Design$Condition,Design$dur.4sU,Design\$Replicate))
sars <- Normalize(sars)
sars
grandR:
1045 genes, 12 samples/cells
Available data slots: count,ntr,alpha,beta,norm
Available analyses:
Available plots:
Default data slot: norm

The GRAND-SLAM output normally contains any gene with at least 1 read, i.e. >30k genes. The data set that is part of grandR has been prefiltered and only consists of 1045 genes. For a complete workflow including filter see the full vignette. Note that we also normalized the read counts (by using size factors), which added an additional data “slot”.

We start by creating a plot showing the kinetics for a gene:

PlotGeneProgressiveTimecourse(sars,"SRSF6",steady.state=list(Mock=TRUE,SARS=FALSE))

Note that this automatically fit the kinetic model for this gene, separately for the two conditions. Modeling used the default data slot, which are the size-factor normalized values, as indicated above. By using the steady.state parameter, we defined the mock infected control samples to be in steady state, whereas the virus infected samples should not be assumed to be in steady state.

We now fit the kinetic model for all genes:

SetParallel(cores = 2)  # increase on your system, or omit the cores = 2 for automatic detection
sars<-FitKinetics(sars,"kinetics",steady.state=list(Mock=TRUE,SARS=FALSE))

Modeling results are stored in two “analysis tables”:

Analyses(sars)
[1] "kinetics.Mock" "kinetics.SARS"

We can retrieve this table (for more information, see the data handling vignette:

head(GetAnalysisTable(sars))
Gene Symbol Length Type kinetics.Mock.Synthesis kinetics.Mock.Half-life kinetics.SARS.Synthesis kinetics.SARS.Half-life
UHMK1 ENSG00000152332 UHMK1 8478 Cellular 175.36782 7.506985 309.9754 3.0485254
ATF3 ENSG00000162772 ATF3 2103 Cellular 33.97185 0.944292 992.6315 0.2736086
PABPC4 ENSG00000090621 PABPC4 3592 Cellular 213.42534 6.532308 516.2158 2.7149065
ROR1 ENSG00000185483 ROR1 5832 Cellular 193.67200 3.556425 471.7370 1.2872501
ZC3H11A ENSG00000058673 ZC3H11A 11825 Cellular 251.54548 2.376063 781.5054 1.0152370
ZBED6 ENSG00000257315 ZBED6 12481 Cellular 251.27907 2.354266 779.3275 1.0252531

We can also easily plot the RNA half-lives of mock infected cells against virus infected cells:

PlotScatter(sars,kinetics.Mock.Half-life,kinetics.SARS.Half-life,
lim=c(0,24),correlation = FormatCorrelation())+
geom_abline()