One of the main applications of metabolic RNA labeling is to identify differentially expressed genes upon some perturbation [1]. The main benefit of using metabolic RNA labeling is that short-term changes can be resolved much better as compared to standard RNA-seq, since any changes of transcription that indeed happened are masked to some extent by pre-existing RNA.

To perform differential analyses for data from [2]. These are SLAM-seq data from multiple time points (1h,2h,4h,6h and 20h) after acute depletion of BANP. BANP is a transcriptional activator that binds to unmethylated CGCG motifs in CpG island promoters.

We first load the grandR package and the read the GRAND-SLAM output table directly from zenodo:

suppressPackageStartupMessages({
library(grandR)
library(ggplot2)
library(patchwork)
})
banp <- ReadGRAND("https://zenodo.org/record/6976391/files/BANP.tsv.gz",design=c("Cell","Experimental.time","Genotype",Design$dur.4sU,Design$has.4sU,Design$Replicate)) Warning: Duplicate gene symbols (e.g. 4930556M19Rik,A230057D06Rik,Adat3,Aldoa,Ccl21a,Ccl21b) present, making unique! Refer to the Loading data and working with grandR objects vignette to learn more about how to load data. Note that sample metadata has been automatically extracted from the sample names via the design parameter given to ReadGRAND: Coldata(banp) Name Cell Genotype has.4sU Replicate duration.4sU duration.4sU.original Experimental.time Experimental.time.original no4sU mESC.0h.wt.0h.no4sU.A mESC.0h.wt.0h.no4sU.A mESC wt no4sU A 0.0 0h 0 0h TRUE mESC.0h.wt.0h.no4sU.B mESC.0h.wt.0h.no4sU.B mESC wt no4sU B 0.0 0h 0 0h TRUE mESC.0h.wt.0h.no4sU.C mESC.0h.wt.0h.no4sU.C mESC wt no4sU C 0.0 0h 0 0h TRUE mESC.0h.wt.2h.4sU.A mESC.0h.wt.2h.4sU.A mESC wt 4sU A 2.0 2h 0 0h FALSE mESC.0h.wt.2h.4sU.B mESC.0h.wt.2h.4sU.B mESC wt 4sU B 2.0 2h 0 0h FALSE mESC.0h.wt.2h.4sU.C mESC.0h.wt.2h.4sU.C mESC wt 4sU C 2.0 2h 0 0h FALSE mESC.0h.dTag.2h.4sU.A mESC.0h.dTag.2h.4sU.A mESC dTag 4sU A 2.0 2h 0 0h FALSE mESC.0h.dTag.2h.4sU.B mESC.0h.dTag.2h.4sU.B mESC dTag 4sU B 2.0 2h 0 0h FALSE mESC.1h.dTag.30min.4sU.A mESC.1h.dTag.30min.4sU.A mESC dTag 4sU A 0.5 30min 1 1h FALSE mESC.1h.dTag.30min.4sU.B mESC.1h.dTag.30min.4sU.B mESC dTag 4sU B 0.5 30min 1 1h FALSE mESC.1h.dTag.30min.4sU.C mESC.1h.dTag.30min.4sU.C mESC dTag 4sU C 0.5 30min 1 1h FALSE mESC.2h.dTag.90min.4sU.A mESC.2h.dTag.90min.4sU.A mESC dTag 4sU A 1.5 90min 2 2h FALSE mESC.2h.dTag.90min.4sU.B mESC.2h.dTag.90min.4sU.B mESC dTag 4sU B 1.5 90min 2 2h FALSE mESC.2h.dTag.90min.4sU.C mESC.2h.dTag.90min.4sU.C mESC dTag 4sU C 1.5 90min 2 2h FALSE mESC.4h.dTag.120min.4sU.A mESC.4h.dTag.120min.4sU.A mESC dTag 4sU A 2.0 120min 4 4h FALSE mESC.4h.dTag.120min.4sU.B mESC.4h.dTag.120min.4sU.B mESC dTag 4sU B 2.0 120min 4 4h FALSE mESC.4h.dTag.120min.4sU.C mESC.4h.dTag.120min.4sU.C mESC dTag 4sU C 2.0 120min 4 4h FALSE mESC.6h.dTag.120min.4sU.A mESC.6h.dTag.120min.4sU.A mESC dTag 4sU A 2.0 120min 6 6h FALSE mESC.6h.dTag.120min.4sU.B mESC.6h.dTag.120min.4sU.B mESC dTag 4sU B 2.0 120min 6 6h FALSE mESC.6h.dTag.120min.4sU.C mESC.6h.dTag.120min.4sU.C mESC dTag 4sU C 2.0 120min 6 6h FALSE mESC.20h.dTag.120min.4sU.A mESC.20h.dTag.120min.4sU.A mESC dTag 4sU A 2.0 120min 20 20h FALSE mESC.20h.dTag.120min.4sU.B mESC.20h.dTag.120min.4sU.B mESC dTag 4sU B 2.0 120min 20 20h FALSE mESC.20h.dTag.120min.4sU.C mESC.20h.dTag.120min.4sU.C mESC dTag 4sU C 2.0 120min 20 20h FALSE The experimental time column indicates the period of time the sample was treated with the dTAG13 compound that induces acute depletion of BANP within 30 minutes. By default GRAND-SLAM will report data on all genes (with at least one mapped read), and ReadGRAND will read all these genes from the output: banp grandR: Read from https://zenodo.org/record/6976391/files/BANP 24616 genes, 23 samples/cells Available data slots: count,ntr,alpha,beta Available analyses: Available plots: Default data slot: count Thus, we filter to only include genes that have at least 100 reads in at least 6 samples: banp <- FilterGenes(banp,minval = 100, mincol = 11) # as 100 reads and half of the sample is the default, this is identical to banp <- FilterGenes(banp) banp grandR: Read from https://zenodo.org/record/6976391/files/BANP 11147 genes, 23 samples/cells Available data slots: count,ntr,alpha,beta Available analyses: Available plots: Default data slot: count The actual data is available in so-called “data slots”. ReadGRAND adds the read counts, new to total RNA ratios (NTRs) and information on the NTR posterior distribution (alpha,beta). As a quick quality check, we can inspect a principal component analysis of all samples involved: PlotPCA(banp) By default, the samples are colored according to the Condition annotation. Condition has a special meaning in grandR, not only for PlotPCA, but also for other analyses (see below). We do not have a condition set (see the Coldata table above, there is no column named Condition). There are two ways how the condition can be set: 1. Use the keyword “Condition” in the design parameter to ReadGRAND. Use this, if you have a useful condition as part of your sample names. 2. Call the Condition function Here a useful condition is to combine the genotype, timepoint and 4sU status. We can set this like this (for more information, see the Loading data vignette): Condition(banp) <- c("Genotype","Experimental.time.original","has.4sU") Coldata(banp) Name Cell Genotype has.4sU Replicate duration.4sU duration.4sU.original Experimental.time Experimental.time.original no4sU Condition mESC.0h.wt.0h.no4sU.A mESC.0h.wt.0h.no4sU.A mESC wt no4sU A 0.0 0h 0 0h TRUE wt.0h.no4sU mESC.0h.wt.0h.no4sU.B mESC.0h.wt.0h.no4sU.B mESC wt no4sU B 0.0 0h 0 0h TRUE wt.0h.no4sU mESC.0h.wt.0h.no4sU.C mESC.0h.wt.0h.no4sU.C mESC wt no4sU C 0.0 0h 0 0h TRUE wt.0h.no4sU mESC.0h.wt.2h.4sU.A mESC.0h.wt.2h.4sU.A mESC wt 4sU A 2.0 2h 0 0h FALSE wt.0h.4sU mESC.0h.wt.2h.4sU.B mESC.0h.wt.2h.4sU.B mESC wt 4sU B 2.0 2h 0 0h FALSE wt.0h.4sU mESC.0h.wt.2h.4sU.C mESC.0h.wt.2h.4sU.C mESC wt 4sU C 2.0 2h 0 0h FALSE wt.0h.4sU mESC.0h.dTag.2h.4sU.A mESC.0h.dTag.2h.4sU.A mESC dTag 4sU A 2.0 2h 0 0h FALSE dTag.0h.4sU mESC.0h.dTag.2h.4sU.B mESC.0h.dTag.2h.4sU.B mESC dTag 4sU B 2.0 2h 0 0h FALSE dTag.0h.4sU mESC.1h.dTag.30min.4sU.A mESC.1h.dTag.30min.4sU.A mESC dTag 4sU A 0.5 30min 1 1h FALSE dTag.1h.4sU mESC.1h.dTag.30min.4sU.B mESC.1h.dTag.30min.4sU.B mESC dTag 4sU B 0.5 30min 1 1h FALSE dTag.1h.4sU mESC.1h.dTag.30min.4sU.C mESC.1h.dTag.30min.4sU.C mESC dTag 4sU C 0.5 30min 1 1h FALSE dTag.1h.4sU mESC.2h.dTag.90min.4sU.A mESC.2h.dTag.90min.4sU.A mESC dTag 4sU A 1.5 90min 2 2h FALSE dTag.2h.4sU mESC.2h.dTag.90min.4sU.B mESC.2h.dTag.90min.4sU.B mESC dTag 4sU B 1.5 90min 2 2h FALSE dTag.2h.4sU mESC.2h.dTag.90min.4sU.C mESC.2h.dTag.90min.4sU.C mESC dTag 4sU C 1.5 90min 2 2h FALSE dTag.2h.4sU mESC.4h.dTag.120min.4sU.A mESC.4h.dTag.120min.4sU.A mESC dTag 4sU A 2.0 120min 4 4h FALSE dTag.4h.4sU mESC.4h.dTag.120min.4sU.B mESC.4h.dTag.120min.4sU.B mESC dTag 4sU B 2.0 120min 4 4h FALSE dTag.4h.4sU mESC.4h.dTag.120min.4sU.C mESC.4h.dTag.120min.4sU.C mESC dTag 4sU C 2.0 120min 4 4h FALSE dTag.4h.4sU mESC.6h.dTag.120min.4sU.A mESC.6h.dTag.120min.4sU.A mESC dTag 4sU A 2.0 120min 6 6h FALSE dTag.6h.4sU mESC.6h.dTag.120min.4sU.B mESC.6h.dTag.120min.4sU.B mESC dTag 4sU B 2.0 120min 6 6h FALSE dTag.6h.4sU mESC.6h.dTag.120min.4sU.C mESC.6h.dTag.120min.4sU.C mESC dTag 4sU C 2.0 120min 6 6h FALSE dTag.6h.4sU mESC.20h.dTag.120min.4sU.A mESC.20h.dTag.120min.4sU.A mESC dTag 4sU A 2.0 120min 20 20h FALSE dTag.20h.4sU mESC.20h.dTag.120min.4sU.B mESC.20h.dTag.120min.4sU.B mESC dTag 4sU B 2.0 120min 20 20h FALSE dTag.20h.4sU mESC.20h.dTag.120min.4sU.C mESC.20h.dTag.120min.4sU.C mESC dTag 4sU C 2.0 120min 20 20h FALSE dTag.20h.4sU PlotPCA(banp) We see that (i) 4sU treatment had an effect on total RNA levels (there is a clear difference between no4sU and 4sU for wt), and that (ii) inserting the degron tag also had an effect (there is a clear difference between wt and dTag.0h, the latter is a sample treated with 4sU for 2h, but was not treated with dTAG13). Before we can investigate these effects, we need to learn how to performing pairwise differential expression analysis in grandR. Such pairwise analyses are based on so-called “contrast matrices”, which can conveniently be constructed by using the GetContrasts function: GetContrasts(banp) wt.0h.4sU vs dTag.0h.4sU wt.0h.4sU vs dTag.1h.4sU wt.0h.4sU vs dTag.2h.4sU wt.0h.4sU vs dTag.4h.4sU wt.0h.4sU vs dTag.6h.4sU wt.0h.4sU vs dTag.20h.4sU dTag.0h.4sU vs dTag.1h.4sU dTag.0h.4sU vs dTag.2h.4sU dTag.0h.4sU vs dTag.4h.4sU dTag.0h.4sU vs dTag.6h.4sU dTag.0h.4sU vs dTag.20h.4sU dTag.1h.4sU vs dTag.2h.4sU dTag.1h.4sU vs dTag.4h.4sU dTag.1h.4sU vs dTag.6h.4sU dTag.1h.4sU vs dTag.20h.4sU dTag.2h.4sU vs dTag.4h.4sU dTag.2h.4sU vs dTag.6h.4sU dTag.2h.4sU vs dTag.20h.4sU dTag.4h.4sU vs dTag.6h.4sU dTag.4h.4sU vs dTag.20h.4sU dTag.6h.4sU vs dTag.20h.4sU mESC.0h.wt.0h.no4sU.A 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.wt.0h.no4sU.B 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.wt.0h.no4sU.C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.wt.2h.4sU.A 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.wt.2h.4sU.B 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.wt.2h.4sU.C 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.dTag.2h.4sU.A -1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 mESC.0h.dTag.2h.4sU.B -1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 mESC.1h.dTag.30min.4sU.A 0 -1 0 0 0 0 -1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 mESC.1h.dTag.30min.4sU.B 0 -1 0 0 0 0 -1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 mESC.1h.dTag.30min.4sU.C 0 -1 0 0 0 0 -1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 mESC.2h.dTag.90min.4sU.A 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 0 1 1 1 0 0 0 mESC.2h.dTag.90min.4sU.B 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 0 1 1 1 0 0 0 mESC.2h.dTag.90min.4sU.C 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 0 1 1 1 0 0 0 mESC.4h.dTag.120min.4sU.A 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 0 1 1 0 mESC.4h.dTag.120min.4sU.B 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 0 1 1 0 mESC.4h.dTag.120min.4sU.C 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 0 1 1 0 mESC.6h.dTag.120min.4sU.A 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 0 1 mESC.6h.dTag.120min.4sU.B 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 0 1 mESC.6h.dTag.120min.4sU.C 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 0 1 mESC.20h.dTag.120min.4sU.A 0 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 -1 mESC.20h.dTag.120min.4sU.B 0 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 -1 mESC.20h.dTag.120min.4sU.C 0 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 -1 Each column of the contrast matrix defines a single pairwise comparison: Samples with 0 are left out, and samples with 1 are compared vs samples with -1 (e.g. if log2 fold change are computed, comparing A vs B means log2(A/B)). Without specifying any parameters, GetContrasts will discard no4sU samples, and all pairwise comparisons of the remaining conditions (here there are 7 conditions having 4sU, i.e. 21 pairwise comparisons) are generated. This behavior can be changed by the contrasts parameter. This parameter is a vector of length 1, 2 or 3. The first element always is a name of a column in the Coldata table, the second and third (if given) need to be values of this column. If only the column is given, all pairwise comparisons are generated (i.e. this is the default behavior shown above, for columns “Condition”): GetContrasts(banp,contrast = c("Condition")) wt.0h.4sU vs dTag.0h.4sU wt.0h.4sU vs dTag.1h.4sU wt.0h.4sU vs dTag.2h.4sU wt.0h.4sU vs dTag.4h.4sU wt.0h.4sU vs dTag.6h.4sU wt.0h.4sU vs dTag.20h.4sU dTag.0h.4sU vs dTag.1h.4sU dTag.0h.4sU vs dTag.2h.4sU dTag.0h.4sU vs dTag.4h.4sU dTag.0h.4sU vs dTag.6h.4sU dTag.0h.4sU vs dTag.20h.4sU dTag.1h.4sU vs dTag.2h.4sU dTag.1h.4sU vs dTag.4h.4sU dTag.1h.4sU vs dTag.6h.4sU dTag.1h.4sU vs dTag.20h.4sU dTag.2h.4sU vs dTag.4h.4sU dTag.2h.4sU vs dTag.6h.4sU dTag.2h.4sU vs dTag.20h.4sU dTag.4h.4sU vs dTag.6h.4sU dTag.4h.4sU vs dTag.20h.4sU dTag.6h.4sU vs dTag.20h.4sU mESC.0h.wt.0h.no4sU.A 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.wt.0h.no4sU.B 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.wt.0h.no4sU.C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.wt.2h.4sU.A 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.wt.2h.4sU.B 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.wt.2h.4sU.C 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 mESC.0h.dTag.2h.4sU.A -1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 mESC.0h.dTag.2h.4sU.B -1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 mESC.1h.dTag.30min.4sU.A 0 -1 0 0 0 0 -1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 mESC.1h.dTag.30min.4sU.B 0 -1 0 0 0 0 -1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 mESC.1h.dTag.30min.4sU.C 0 -1 0 0 0 0 -1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 mESC.2h.dTag.90min.4sU.A 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 0 1 1 1 0 0 0 mESC.2h.dTag.90min.4sU.B 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 0 1 1 1 0 0 0 mESC.2h.dTag.90min.4sU.C 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 0 1 1 1 0 0 0 mESC.4h.dTag.120min.4sU.A 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 0 1 1 0 mESC.4h.dTag.120min.4sU.B 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 0 1 1 0 mESC.4h.dTag.120min.4sU.C 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 0 1 1 0 mESC.6h.dTag.120min.4sU.A 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 0 1 mESC.6h.dTag.120min.4sU.B 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 0 1 mESC.6h.dTag.120min.4sU.C 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 0 1 mESC.20h.dTag.120min.4sU.A 0 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 -1 mESC.20h.dTag.120min.4sU.B 0 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 -1 mESC.20h.dTag.120min.4sU.C 0 0 0 0 0 -1 0 0 0 0 -1 0 0 0 -1 0 0 -1 0 -1 -1 If one value is given, all pairwise comparisons with this as reference are created (i.e., if A is given, all X vs A for all other values X): GetContrasts(banp,contrast = c("Condition","dTag.0h.4sU")) wt.0h.4sU vs dTag.0h.4sU dTag.1h.4sU vs dTag.0h.4sU dTag.2h.4sU vs dTag.0h.4sU dTag.4h.4sU vs dTag.0h.4sU dTag.6h.4sU vs dTag.0h.4sU dTag.20h.4sU vs dTag.0h.4sU mESC.0h.wt.0h.no4sU.A 0 0 0 0 0 0 mESC.0h.wt.0h.no4sU.B 0 0 0 0 0 0 mESC.0h.wt.0h.no4sU.C 0 0 0 0 0 0 mESC.0h.wt.2h.4sU.A 1 0 0 0 0 0 mESC.0h.wt.2h.4sU.B 1 0 0 0 0 0 mESC.0h.wt.2h.4sU.C 1 0 0 0 0 0 mESC.0h.dTag.2h.4sU.A -1 -1 -1 -1 -1 -1 mESC.0h.dTag.2h.4sU.B -1 -1 -1 -1 -1 -1 mESC.1h.dTag.30min.4sU.A 0 1 0 0 0 0 mESC.1h.dTag.30min.4sU.B 0 1 0 0 0 0 mESC.1h.dTag.30min.4sU.C 0 1 0 0 0 0 mESC.2h.dTag.90min.4sU.A 0 0 1 0 0 0 mESC.2h.dTag.90min.4sU.B 0 0 1 0 0 0 mESC.2h.dTag.90min.4sU.C 0 0 1 0 0 0 mESC.4h.dTag.120min.4sU.A 0 0 0 1 0 0 mESC.4h.dTag.120min.4sU.B 0 0 0 1 0 0 mESC.4h.dTag.120min.4sU.C 0 0 0 1 0 0 mESC.6h.dTag.120min.4sU.A 0 0 0 0 1 0 mESC.6h.dTag.120min.4sU.B 0 0 0 0 1 0 mESC.6h.dTag.120min.4sU.C 0 0 0 0 1 0 mESC.20h.dTag.120min.4sU.A 0 0 0 0 0 1 mESC.20h.dTag.120min.4sU.B 0 0 0 0 0 1 mESC.20h.dTag.120min.4sU.C 0 0 0 0 0 1 If two values are given, a single pairwise comparison is created: GetContrasts(banp,contrast = c("Condition","dTag.2h.4sU","dTag.0h.4sU")) dTag.2h.4sU vs dTag.0h.4sU mESC.0h.wt.0h.no4sU.A 0 mESC.0h.wt.0h.no4sU.B 0 mESC.0h.wt.0h.no4sU.C 0 mESC.0h.wt.2h.4sU.A 0 mESC.0h.wt.2h.4sU.B 0 mESC.0h.wt.2h.4sU.C 0 mESC.0h.dTag.2h.4sU.A -1 mESC.0h.dTag.2h.4sU.B -1 mESC.1h.dTag.30min.4sU.A 0 mESC.1h.dTag.30min.4sU.B 0 mESC.1h.dTag.30min.4sU.C 0 mESC.2h.dTag.90min.4sU.A 1 mESC.2h.dTag.90min.4sU.B 1 mESC.2h.dTag.90min.4sU.C 1 mESC.4h.dTag.120min.4sU.A 0 mESC.4h.dTag.120min.4sU.B 0 mESC.4h.dTag.120min.4sU.C 0 mESC.6h.dTag.120min.4sU.A 0 mESC.6h.dTag.120min.4sU.B 0 mESC.6h.dTag.120min.4sU.C 0 mESC.20h.dTag.120min.4sU.A 0 mESC.20h.dTag.120min.4sU.B 0 mESC.20h.dTag.120min.4sU.C 0 It is possible to restrict the samples to be used by defining the columns parameter (samples for bulk experiments or cells for single cell experiments are jointly called “columns” in grandR): GetContrasts(banp,contrast = c("Condition","dTag.0h.4sU"),columns = Genotype=="dTag") dTag.1h.4sU vs dTag.0h.4sU dTag.2h.4sU vs dTag.0h.4sU dTag.4h.4sU vs dTag.0h.4sU dTag.6h.4sU vs dTag.0h.4sU dTag.20h.4sU vs dTag.0h.4sU mESC.0h.wt.0h.no4sU.A 0 0 0 0 0 mESC.0h.wt.0h.no4sU.B 0 0 0 0 0 mESC.0h.wt.0h.no4sU.C 0 0 0 0 0 mESC.0h.wt.2h.4sU.A 0 0 0 0 0 mESC.0h.wt.2h.4sU.B 0 0 0 0 0 mESC.0h.wt.2h.4sU.C 0 0 0 0 0 mESC.0h.dTag.2h.4sU.A -1 -1 -1 -1 -1 mESC.0h.dTag.2h.4sU.B -1 -1 -1 -1 -1 mESC.1h.dTag.30min.4sU.A 1 0 0 0 0 mESC.1h.dTag.30min.4sU.B 1 0 0 0 0 mESC.1h.dTag.30min.4sU.C 1 0 0 0 0 mESC.2h.dTag.90min.4sU.A 0 1 0 0 0 mESC.2h.dTag.90min.4sU.B 0 1 0 0 0 mESC.2h.dTag.90min.4sU.C 0 1 0 0 0 mESC.4h.dTag.120min.4sU.A 0 0 1 0 0 mESC.4h.dTag.120min.4sU.B 0 0 1 0 0 mESC.4h.dTag.120min.4sU.C 0 0 1 0 0 mESC.6h.dTag.120min.4sU.A 0 0 0 1 0 mESC.6h.dTag.120min.4sU.B 0 0 0 1 0 mESC.6h.dTag.120min.4sU.C 0 0 0 1 0 mESC.20h.dTag.120min.4sU.A 0 0 0 0 1 mESC.20h.dTag.120min.4sU.B 0 0 0 0 1 mESC.20h.dTag.120min.4sU.C 0 0 0 0 1 # note that you can use names from the Coldata table here to build an expression If you want to perform comparisons involving no4sU samples (which usually are control samples only), you can do this by setting the no4sU parameter to TRUE: GetContrasts(banp,contrast=c("has.4sU","4sU","no4sU"),columns=Genotype=="wt",no4sU = TRUE) 4sU vs no4sU mESC.0h.wt.0h.no4sU.A -1 mESC.0h.wt.0h.no4sU.B -1 mESC.0h.wt.0h.no4sU.C -1 mESC.0h.wt.2h.4sU.A 1 mESC.0h.wt.2h.4sU.B 1 mESC.0h.wt.2h.4sU.C 1 mESC.0h.dTag.2h.4sU.A 0 mESC.0h.dTag.2h.4sU.B 0 mESC.1h.dTag.30min.4sU.A 0 mESC.1h.dTag.30min.4sU.B 0 mESC.1h.dTag.30min.4sU.C 0 mESC.2h.dTag.90min.4sU.A 0 mESC.2h.dTag.90min.4sU.B 0 mESC.2h.dTag.90min.4sU.C 0 mESC.4h.dTag.120min.4sU.A 0 mESC.4h.dTag.120min.4sU.B 0 mESC.4h.dTag.120min.4sU.C 0 mESC.6h.dTag.120min.4sU.A 0 mESC.6h.dTag.120min.4sU.B 0 mESC.6h.dTag.120min.4sU.C 0 mESC.20h.dTag.120min.4sU.A 0 mESC.20h.dTag.120min.4sU.B 0 mESC.20h.dTag.120min.4sU.C 0 Sometimes, you want to perform comparisons only among samples from specific groups of samples. This is useful e.g. if you want to compare treatment vs control under different conditions (e.g. cell types), or perform paired pairwise tests (if replicates A, B and C were paired, like in the following example): GetContrasts(banp,contrast=c("has.4sU","4sU","no4sU"),columns=Genotype=="wt",no4sU = TRUE, group="Replicate") 4sU vs no4sU.A 4sU vs no4sU.B 4sU vs no4sU.C mESC.0h.wt.0h.no4sU.A -1 0 0 mESC.0h.wt.0h.no4sU.B 0 -1 0 mESC.0h.wt.0h.no4sU.C 0 0 -1 mESC.0h.wt.2h.4sU.A 1 0 0 mESC.0h.wt.2h.4sU.B 0 1 0 mESC.0h.wt.2h.4sU.C 0 0 1 mESC.0h.dTag.2h.4sU.A 0 0 0 mESC.0h.dTag.2h.4sU.B 0 0 0 mESC.1h.dTag.30min.4sU.A 0 0 0 mESC.1h.dTag.30min.4sU.B 0 0 0 mESC.1h.dTag.30min.4sU.C 0 0 0 mESC.2h.dTag.90min.4sU.A 0 0 0 mESC.2h.dTag.90min.4sU.B 0 0 0 mESC.2h.dTag.90min.4sU.C 0 0 0 mESC.4h.dTag.120min.4sU.A 0 0 0 mESC.4h.dTag.120min.4sU.B 0 0 0 mESC.4h.dTag.120min.4sU.C 0 0 0 mESC.6h.dTag.120min.4sU.A 0 0 0 mESC.6h.dTag.120min.4sU.B 0 0 0 mESC.6h.dTag.120min.4sU.C 0 0 0 mESC.20h.dTag.120min.4sU.A 0 0 0 mESC.20h.dTag.120min.4sU.B 0 0 0 mESC.20h.dTag.120min.4sU.C 0 0 0 Finally, if you are not happy with the column names (which end up as the names for analysis tables), you can change this as well: GetContrasts(banp,contrast=c("has.4sU","4sU","no4sU"),columns=Genotype=="wt",no4sU = TRUE, group="Replicate",name.format = "$GRP")
A B C
mESC.0h.wt.0h.no4sU.A -1 0 0
mESC.0h.wt.0h.no4sU.B 0 -1 0
mESC.0h.wt.0h.no4sU.C 0 0 -1
mESC.0h.wt.2h.4sU.A 1 0 0
mESC.0h.wt.2h.4sU.B 0 1 0
mESC.0h.wt.2h.4sU.C 0 0 1
mESC.0h.dTag.2h.4sU.A 0 0 0
mESC.0h.dTag.2h.4sU.B 0 0 0
mESC.1h.dTag.30min.4sU.A 0 0 0
mESC.1h.dTag.30min.4sU.B 0 0 0
mESC.1h.dTag.30min.4sU.C 0 0 0
mESC.2h.dTag.90min.4sU.A 0 0 0
mESC.2h.dTag.90min.4sU.B 0 0 0
mESC.2h.dTag.90min.4sU.C 0 0 0
mESC.4h.dTag.120min.4sU.A 0 0 0
mESC.4h.dTag.120min.4sU.B 0 0 0
mESC.4h.dTag.120min.4sU.C 0 0 0
mESC.6h.dTag.120min.4sU.A 0 0 0
mESC.6h.dTag.120min.4sU.B 0 0 0
mESC.6h.dTag.120min.4sU.C 0 0 0
mESC.20h.dTag.120min.4sU.A 0 0 0
mESC.20h.dTag.120min.4sU.B 0 0 0
mESC.20h.dTag.120min.4sU.C 0 0 0

## Differential analysis of 4sU treatment and dTag

Now we can investigate the two unwanted effects mentioned above using differential analysis using the lfc package [3] and DESeq2:

contrasts <- cbind(
GetContrasts(banp,contrast=c("has.4sU","4sU","no4sU"),
columns=Genotype=="wt",no4sU = TRUE,name.format = "4sU effect"),
# set up the contrast matrix for compare wt.2h.4sU vs wt.2h.no4sU, as above
GetContrasts(banp,contrast=c("Genotype","dTag","wt"),
columns=Experimental.time==0,name.format="dTag effect")
# set up the contrast matrix for compare dTag vs wt without dTAG13 treatment
)
banp <- LFC(banp,name.prefix = "QC",contrasts=contrasts)
banp
grandR:
11147 genes, 23 samples/cells
Available data slots: count,ntr,alpha,beta
Available analyses: QC.4sU effect,QC.dTag effect
Available plots:
Default data slot: count

As you see, now we have two analyses added to our grandR object. We can use PlotScatter to directly plot them:

PlotScatter(banp,xlim=c(-2,2),ylim=c(-2,2))

This works since there are only two analysis values overall that are stored in the object:

Analyses(banp,description = TRUE)
$QC.4sU effect [1] "LFC"$QC.dTag effect
[1] "LFC"

We see that there are quite some effects for both pairwise comparison.

## Differential analysis of 4sU treatment

To focus on the 4sU effect, we first create toxicity plots. 4sU labeling can have impact on transcription before cell viability suffers. Such an effect would be observable in these plots by a substantial negative correlation, i.e. genes with high NTR (which are to the right), should tend to be downregulated in the 4sU labeled sample vs an equivalent 4sU naive sample. It is important that these analyses are only done by comparing biologically equivalent samples (and not on the SARS infected samples here, as the no4sU sample is from 3h post infection, and all others are later).

Condition(banp)="Genotype"  # PlotToxicityTestRankAll will use all no4sU samples within the same Condition as reference
PlotToxicityTestRankAll(banp)
$mESC.0h.wt.2h.4sU.A $mESC.0h.wt.2h.4sU.B


$mESC.0h.wt.2h.4sU.C Condition(banp) <- c("Genotype","Experimental.time.original","has.4sU") # reset Here, there is no negative correlation, so it is not a shutdown of transcription that results in the observed differences between the 4sU treated and 4sU naive samples. So, let’s see whether observed changes are reproducible among replicates. For that we will compute the Wald test implemented in DESeq2 for all comparisons defined above (as we will need this also for the dTag effect). banp <- PairwiseDESeq2(banp,name.prefix = "QC",contrasts=contrasts) banp grandR: Read from https://zenodo.org/record/6976391/files/BANP 11147 genes, 23 samples/cells Available data slots: count,ntr,alpha,beta Available analyses: QC.4sU effect,QC.dTag effect Available plots: Default data slot: count This obviously did not add new analysis tables, but since the names matched, it added the new statistics computed by DESeq2 to the existing tables (if there were statistics with the same name as already existing, i.e. “LFC”, we would get a warning). Analyses(banp,description = TRUE) $QC.4sU effect
[1] "LFC" "M"   "S"   "P"   "Q"

$QC.dTag effect [1] "LFC" "M" "S" "P" "Q"  Now we can have a look at a Vulcano plot for the 4sU effect: VulcanoPlot(banp,analysis = "QC.4sU effect",lfc.cutoff = 0.5,ylim=c(-9,50)) We can also create an MA plot: MAPlot(banp,analysis = "QC.4sU effect",lfc.cutoff = 1) We see that there are indeed quite some genes with statistically significant changes. We can also retrieve the genes: GetSignificantGenes(banp,analysis = "QC.4sU effect",criteria = LFC< -1 & Q<0.05)  [1] "Lefty1" "Lef1" "Ccng2" "Plac8" "Usp29" "Bhlhe40" "Bnip3" [8] "Kcnk1" "Ap1m2" "P4ha1" "Gstt1" "Crispld2" "Stc2" "Slc16a3" [15] "Egln3" "Hist1h2aa" "Hist1h1t" "Cldn6" "Egr1"  GetSignificantGenes(banp,analysis = "QC.4sU effect",criteria = LFC>1 & Q<0.05)  [1] "Dst" "Sned1" "Dock10" "Gm20342" "Gm10801" [6] "Gm26901" "Gm20045" "Gm10800" "Cdh4" "Map1a" [11] "Ttn" "Fam212b" "Hist2h2ab" "Gm37584" "Cavin4" [16] "Gm13067" "Zc3h12a" "2700016F22Rik" "Klhdc7a" "Hspg2" [21] "Macf1" "Rprl1" "Plaur" "Dyrk1b" "Iqck" [26] "Cox6b2" "Gm26890" "Sox1" "Mt2" "Mt1" [31] "Gm26532" "Gpr83" "Gm45250" "Exoc3l" "Hyal1" [36] "Slc25a42" "4833445I07Rik" "Izumo4" "Soga3" "AU041133" [41] "Eid3" "Vamp2" "D930048N14Rik" "Gm21781" "B130006D01Rik" [46] "Zfp672" "Baiap2" "Gm5784" "Ahnak2" "Hist1h2ah" [51] "Hist1h2af" "Mir124a-1hg" "Zfhx2" "Gm29666" "Rapgef3" [56] "Pdzd2" "Ankrd33b" "Gm9918" "2610037D02Rik" "Spaca6" [61] "Yipf3" "Mcc" "Gm26917" "Gm42418" "Ahnak" [66] "Gm26992" "Rnf128" "Gm21887" "Trpc5os" "Neat1"  The criteria parameter can be used to define which genes you want to have. The criteria must be an expression that either evaluates into a numeric or logical vector. In the first case, you will get all genes ordered according to the given criteria, in the latter case (as done above), you will get the genes meeting the criteria. The columns of the given analysis table (see above) can be used to build this expression. There are no obvious (at least to us) biological functions for these genes. We therefore perform gene set enrichment analysis (GSEA) on MSigDB hallmark gene sets: gsea <- AnalyzeGeneSets(banp,analysis = "QC.4sU effect",category="H") Found 50 gene sets! Performing GSEA (using fgsea)... preparing geneSet collections... GSEA analysis... Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.14% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results. leading edge analysis... done... clusterProfiler::ridgeplot(gsea) Picking joint bandwidth of 0.0667 Thus, there are quite some pathways that are affected. Technically, AnalyzeGeneSets provides a convenient wrapper to perform either GSEA and ORA (overrepresentation analyses) using the clusterProfiler package on a lot of predefined gene sets from the msigdbr package. You can learn about the gene sets using the ListGeneSets function: ListGeneSets() category subcategory n category description C1 299 Positional gene sets for each human chromosome and cytogenetic band. C2 CGP 3384 Curated gene sets from online pathway databases, publications in PubMed, and knowledge of domain experts. C2 CP 29 Curated gene sets from online pathway databases, publications in PubMed, and knowledge of domain experts. C2 CP:BIOCARTA 292 Curated gene sets from online pathway databases, publications in PubMed, and knowledge of domain experts. C2 CP:KEGG 186 Curated gene sets from online pathway databases, publications in PubMed, and knowledge of domain experts. C2 CP:PID 196 Curated gene sets from online pathway databases, publications in PubMed, and knowledge of domain experts. C2 CP:REACTOME 1615 Curated gene sets from online pathway databases, publications in PubMed, and knowledge of domain experts. C2 CP:WIKIPATHWAYS 664 Curated gene sets from online pathway databases, publications in PubMed, and knowledge of domain experts. C3 MIR:MIR_Legacy 221 Regulatory target gene sets based on gene target predictions for microRNA seed sequences and predicted transcription factor binding sites. C3 MIR:MIRDB 2377 Regulatory target gene sets based on gene target predictions for microRNA seed sequences and predicted transcription factor binding sites. C3 TFT:GTRD 518 Regulatory target gene sets based on gene target predictions for microRNA seed sequences and predicted transcription factor binding sites. C3 TFT:TFT_Legacy 610 Regulatory target gene sets based on gene target predictions for microRNA seed sequences and predicted transcription factor binding sites. C4 CGN 427 Computational gene sets defined by mining large collections of cancer-oriented microarray data. C4 CM 431 Computational gene sets defined by mining large collections of cancer-oriented microarray data. C5 GO:BP 7658 Ontology gene sets consist of genes annotated by the same ontology term. C5 GO:CC 1006 Ontology gene sets consist of genes annotated by the same ontology term. C5 GO:MF 1738 Ontology gene sets consist of genes annotated by the same ontology term. C5 HPO 5071 Ontology gene sets consist of genes annotated by the same ontology term. C6 189 Oncogenic signature gene sets defined directly from microarray gene expression data from cancer gene perturbations. C7 IMMUNESIGDB 4872 Immunologic signature gene sets represent cell states and perturbations within the immune system. C7 VAX 347 Immunologic signature gene sets represent cell states and perturbations within the immune system. C8 700 Cell type signature gene sets curated from cluster markers identified in single-cell sequencing studies of human tissue. H 50 Hallmark gene sets are coherently expressed signatures derived by aggregating many MSigDB gene sets to represent well-defined biological states or processes. The values from the category and subcategory columns can be used to the corresponding parameters of AnalyzeGeneSets. The criteria parameter can be used to define how analyses are performed. The criteria must be an expression that either evaluates into a numeric or logical vector. In the first case, GSEA is performed, in the latter it is ORA. The columns of the given analysis table (see above) can be used to build this expression. Thus, to perform ORA for down-regulated genes: AnalyzeGeneSets(banp,analysis = "QC.4sU effect",category="H",criteria = LFC<0.5 & Q<0.05) Found 50 gene sets! Performing ORA (for n=5382/11147 genes)... # # over-representation test # #...@organism UNKNOWN #...@ontology UNKNOWN #...@gene chr [1:11147] "ENSMUSG00000025776" "ENSMUSG00000025931" "ENSMUSG00000025940" ... #...pvalues adjusted by 'BH' with cutoff <0.05 #...0 enriched terms found #...Citation T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141  ## Differential analysis of dTag vs wt We next investigate whether there is anything peculiar about the expression differences between the dTag and wild-type cells: VulcanoPlot(banp,analysis = "QC.dTag effect",ylim=c(-40,300)) There are a lot of genes that are substantially and statistically significantly downregulated. gsea <- AnalyzeGeneSets(banp,analysis = "QC.dTag effect",category="C5",subcategory = "GO:BP") Querying msigdb for C5 (GO:BP) in Mus musculus ... Found 7656 gene sets! Performing GSEA (using fgsea)... preparing geneSet collections... GSEA analysis... Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.16% of the list). The order of those tied genes will be arbitrary, which may produce unexpected results. leading edge analysis... done... clusterProfiler::ridgeplot(gsea) Picking joint bandwidth of 0.0992 Apparently, predominantly genes that have to do with transmembrane transport are downregulated. ## Differential analysis of BANP depletion Before we continue, we add the information which genes are have BANP-ChIP-seq peaks in their promoters: tar <- readLines("https://zenodo.org/record/6976391/files/targets.genes") GeneInfo(banp,"BANP-Target")<-factor(ifelse(Genes(banp) %in% tar,"yes","no"),levels=c("yes","no")) table(GeneInfo(banp,"BANP-Target"))  yes no 457 10690  We remove the “wt” samples and define contrasts: banp <- subset(banp,columns = Genotype=="dTag") contrasts <- GetContrasts(banp,contrast=c("Experimental.time.original","0h")) contrasts 1h vs 0h 2h vs 0h 4h vs 0h 6h vs 0h 20h vs 0h mESC.0h.dTag.2h.4sU.A -1 -1 -1 -1 -1 mESC.0h.dTag.2h.4sU.B -1 -1 -1 -1 -1 mESC.1h.dTag.30min.4sU.A 1 0 0 0 0 mESC.1h.dTag.30min.4sU.B 1 0 0 0 0 mESC.1h.dTag.30min.4sU.C 1 0 0 0 0 mESC.2h.dTag.90min.4sU.A 0 1 0 0 0 mESC.2h.dTag.90min.4sU.B 0 1 0 0 0 mESC.2h.dTag.90min.4sU.C 0 1 0 0 0 mESC.4h.dTag.120min.4sU.A 0 0 1 0 0 mESC.4h.dTag.120min.4sU.B 0 0 1 0 0 mESC.4h.dTag.120min.4sU.C 0 0 1 0 0 mESC.6h.dTag.120min.4sU.A 0 0 0 1 0 mESC.6h.dTag.120min.4sU.B 0 0 0 1 0 mESC.6h.dTag.120min.4sU.C 0 0 0 1 0 mESC.20h.dTag.120min.4sU.A 0 0 0 0 1 mESC.20h.dTag.120min.4sU.B 0 0 0 0 1 mESC.20h.dTag.120min.4sU.C 0 0 0 0 1 Now we perform LFC and DESeq2 analyses. We will perform this on the level of both total an new RNA. Also for new RNA fold changes, we normalize with respect to total RNA (i.e. size factors are computed based on total RNA before performing differential analysis with new RNA). banp <- LFC(banp,name.prefix = "total",contrasts = contrasts) banp <- PairwiseDESeq2(banp,name.prefix = "total",contrasts = contrasts) banp <- LFC(banp,name.prefix = "new",contrasts = contrasts,mode="new", normalization = "total") banp <- PairwiseDESeq2(banp,name.prefix = "new",contrasts = contrasts,mode="new", normalization = "total") Note that actually new RNA comparisons do not make sense at all for 1h and (maybe) 2h, since the labeling times for these samples were 30min and 90 min, respectively, whereas the labeling time for all other samples was 2h: MAPlot(banp,analysis = "new.1h vs 0h")| MAPlot(banp,analysis = "new.2h vs 0h") The MA plots indicate that changes in new RNA are much more pronounced than changes in total RNA, even after 4h: MAPlot(banp,analysis = "total.4h vs 0h",highlight=GeneInfo(banp,"BANP-Target")=="yes")| MAPlot(banp,analysis = "new.4h vs 0h",highlight=GeneInfo(banp,"BANP-Target")=="yes") We compare this using boxplots: df <- GetAnalysisTable(banp,analyses = "4h",by.rows = TRUE) ggplot(df,aes(Analysis,LFC,fill=BANP-Target))+ geom_hline(yintercept = 0)+ geom_boxplot() Let’s investigate this further. We first roughly estimate RNA half-lives from steady state samples using the method described in [1], and add half-life classes as additional gene annotations: banp <- ComputeSteadyStateHalfLives(banp,name="HL",columns = Experimental.time==0) GeneInfo(banp,"HL simple") <- rowMeans(GetAnalysisTable(banp,analyses = "HL",gene.info = FALSE)) GeneInfo(banp,"HL category") <- cut(GeneInfo(banp,"HL simple"), breaks=c(0,2,4,8,Inf), labels=c("0-2h","2-4h","4-8h",">8h")) table(GeneInfo(banp,"HL category"))  0-2h 2-4h 4-8h >8h 1763 3433 3730 2207  Now we can do the same comparison as above but differentiate between half-life classes: df <- GetAnalysisTable(banp,analyses = "4h vs 0h",by.rows = TRUE) df <- df[!is.na(df$HL category),]
ggplot(df,aes(HL category,LFC,fill=BANP-Target))+
geom_hline(yintercept = 0)+
geom_boxplot()+
RotatateAxisLabels()+
facet_wrap(~Analysis)

For genes with long RNA half-lives, down-regulation of BANP targets is masked by pre-existing RNA, when total RNA is analyzed. Analysis of new RNA does not suffer from this. This indicates that it indeed was important to use metabolic RNA labeling, because many changes that actually happened in terms of gene regulation are simply masked on the level of total RNA by all the pre-existing RNA that has not been degraded yet.

## The real deal: Bayesian analysis of snapshot experiments

As indicated above, the 1h and 2h samples are not directly comparable to the other samples, due to different labeling times. Thus, we will use grandR to model changes of RNA half-lives and synthesis rates from the given snapshot experiments. We first check whether labeling times need to be recalibrated. Exact effective labeling times for important for modeling but are actually also important when you compare new RNA among samples using LFC and DESeq2 (see above).

For checking, we fit our model per experimental time. We use N=0 because we do not want to draw samples from the posterior, but we are happy with transforming the MAP estimates for this check:

# Restrict to 1000 genes to execute this vignette much faster
set.seed(42)
banp<-FilterGenes(banp,use=sample.int(nrow(banp),1000))

# use the previous time point as reference
reference.columns=FindReferences(banp,reference.function=
function(s) Experimental.time==max(c(0,Experimental.time[Experimental.time<s$Experimental.time]))) SetParallel(cores = 2) # increase 2 on your system, or omit the cores = 2 for automatic detection Condition(banp) <- "Experimental.time.original" banp <- FitKineticsSnapshot(banp,name.prefix = "MAP",reference.columns=reference.columns, verbose=T,time.experiment = "Experimental.time",N=0) Computing snapshot kinetics for 0h... Sampling from steady state for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B... Beta prior for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B: a=4.737, b=33.299 Computing snapshot kinetics for 1h... Sampling from non-steady state for mESC.1h.dTag.30min.4sU.A,mESC.1h.dTag.30min.4sU.B,mESC.1h.dTag.30min.4sU.C (reference: mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B)... Beta prior for mESC.1h.dTag.30min.4sU.A,mESC.1h.dTag.30min.4sU.B,mESC.1h.dTag.30min.4sU.C: a=4.665, b=30.348 Computing snapshot kinetics for 2h... Sampling from non-steady state for mESC.2h.dTag.90min.4sU.A,mESC.2h.dTag.90min.4sU.B,mESC.2h.dTag.90min.4sU.C (reference: mESC.1h.dTag.30min.4sU.A,mESC.1h.dTag.30min.4sU.B,mESC.1h.dTag.30min.4sU.C)... Beta prior for mESC.2h.dTag.90min.4sU.A,mESC.2h.dTag.90min.4sU.B,mESC.2h.dTag.90min.4sU.C: a=4.367, b=28.560 Computing snapshot kinetics for 4h... Sampling from non-steady state for mESC.4h.dTag.120min.4sU.A,mESC.4h.dTag.120min.4sU.B,mESC.4h.dTag.120min.4sU.C (reference: mESC.2h.dTag.90min.4sU.A,mESC.2h.dTag.90min.4sU.B,mESC.2h.dTag.90min.4sU.C)... Beta prior for mESC.4h.dTag.120min.4sU.A,mESC.4h.dTag.120min.4sU.B,mESC.4h.dTag.120min.4sU.C: a=4.140, b=27.149 Computing snapshot kinetics for 6h... Sampling from non-steady state for mESC.6h.dTag.120min.4sU.A,mESC.6h.dTag.120min.4sU.B,mESC.6h.dTag.120min.4sU.C (reference: mESC.4h.dTag.120min.4sU.A,mESC.4h.dTag.120min.4sU.B,mESC.4h.dTag.120min.4sU.C)... Beta prior for mESC.6h.dTag.120min.4sU.A,mESC.6h.dTag.120min.4sU.B,mESC.6h.dTag.120min.4sU.C: a=3.909, b=25.843 Computing snapshot kinetics for 20h... Sampling from non-steady state for mESC.20h.dTag.120min.4sU.A,mESC.20h.dTag.120min.4sU.B,mESC.20h.dTag.120min.4sU.C (reference: mESC.6h.dTag.120min.4sU.A,mESC.6h.dTag.120min.4sU.B,mESC.6h.dTag.120min.4sU.C)... Beta prior for mESC.20h.dTag.120min.4sU.A,mESC.20h.dTag.120min.4sU.B,mESC.20h.dTag.120min.4sU.C: a=2.830, b=18.939 Now we can obtain the analysis table and plot the distributions of half-live estimated from each timepoint: df <- GetAnalysisTable(banp,analyses = "^MAP",by.rows = TRUE) df$HL=pmin(df\$HL,48)   # for 4h, there are extreme outliers, which mess up the computation of violins
ggplot(df,aes(Analysis,HL))+
geom_boxplot()+
scale_y_log10()+
coord_cartesian(ylim=c(0.3,24))

Especially for 1h, the half-lives are strongly biased, indicating that the effective labeling time was different from the nominal labeling time. We recalibrate by finding effective labeling times such that the estimated half-lives for each sample match to our reference half-lives computed above. Note that CalibrateEffectiveLabelingTimeMatchHalflives will only do this on 1,000 genes selected by strong expression from each half-life class.

banp <- Normalize(banp)
banp <- CalibrateEffectiveLabelingTimeMatchHalflives(banp,
reference.halflives = GeneInfo(banp,"HL simple"),
reference.columns = reference.columns,verbose=TRUE)
banp <- FitKineticsSnapshot(banp,name.prefix = "cMAP",reference.columns=reference.columns,
time.experiment = "Experimental.time",verbose=TRUE,
time.labeling = "calibrated_time",N=0,correct.labeling = TRUE)
Computing snapshot kinetics for 0h...
Sampling from steady state for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B...
Beta prior for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B: a=4.742, b=31.936
Computing snapshot kinetics for 1h...
Sampling from non-steady state for mESC.1h.dTag.30min.4sU.A,mESC.1h.dTag.30min.4sU.B,mESC.1h.dTag.30min.4sU.C (reference: mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B)...
Beta prior for mESC.1h.dTag.30min.4sU.A,mESC.1h.dTag.30min.4sU.B,mESC.1h.dTag.30min.4sU.C: a=4.656, b=31.514
Computing snapshot kinetics for 2h...
Sampling from non-steady state for mESC.2h.dTag.90min.4sU.A,mESC.2h.dTag.90min.4sU.B,mESC.2h.dTag.90min.4sU.C (reference: mESC.1h.dTag.30min.4sU.A,mESC.1h.dTag.30min.4sU.B,mESC.1h.dTag.30min.4sU.C)...
Beta prior for mESC.2h.dTag.90min.4sU.A,mESC.2h.dTag.90min.4sU.B,mESC.2h.dTag.90min.4sU.C: a=4.362, b=29.008
Computing snapshot kinetics for 4h...
Sampling from non-steady state for mESC.4h.dTag.120min.4sU.A,mESC.4h.dTag.120min.4sU.B,mESC.4h.dTag.120min.4sU.C (reference: mESC.2h.dTag.90min.4sU.A,mESC.2h.dTag.90min.4sU.B,mESC.2h.dTag.90min.4sU.C)...
Beta prior for mESC.4h.dTag.120min.4sU.A,mESC.4h.dTag.120min.4sU.B,mESC.4h.dTag.120min.4sU.C: a=4.141, b=27.111
Computing snapshot kinetics for 6h...
Sampling from non-steady state for mESC.6h.dTag.120min.4sU.A,mESC.6h.dTag.120min.4sU.B,mESC.6h.dTag.120min.4sU.C (reference: mESC.4h.dTag.120min.4sU.A,mESC.4h.dTag.120min.4sU.B,mESC.4h.dTag.120min.4sU.C)...
Beta prior for mESC.6h.dTag.120min.4sU.A,mESC.6h.dTag.120min.4sU.B,mESC.6h.dTag.120min.4sU.C: a=3.914, b=25.523
Computing snapshot kinetics for 20h...
Sampling from non-steady state for mESC.20h.dTag.120min.4sU.A,mESC.20h.dTag.120min.4sU.B,mESC.20h.dTag.120min.4sU.C (reference: mESC.6h.dTag.120min.4sU.A,mESC.6h.dTag.120min.4sU.B,mESC.6h.dTag.120min.4sU.C)...
Beta prior for mESC.20h.dTag.120min.4sU.A,mESC.20h.dTag.120min.4sU.B,mESC.20h.dTag.120min.4sU.C: a=2.839, b=18.888

Now we can obtain the analysis table after recalibration and plot the distributions of half-lives estimated from each timepoint:

df <- GetAnalysisTable(banp,analyses = "cMAP",by.rows = TRUE)
ggplot(df,aes(Analysis,HL))+
geom_boxplot()+
scale_y_log10()+
coord_cartesian(ylim=c(0.3,24))
Warning: Removed 180 rows containing non-finite values (stat_boxplot).

Now the distributions match much better. We can now go on and estimate log2 fold changes of synthesis and RNA half-lives. We again do not perform sampling from the posterior (N=0) which would take a long time:

banp <- EstimateRegulation(banp,"Regulation",
contrasts=contrasts,
reference.columns=reference.columns,
time.labeling = "calibrated_time",verbose=T,
time.experiment = "Experimental.time",correct.labeling=TRUE,
N=0)
Computing Regulation for 1h vs 0h...
Sampling from non-steady state for mESC.1h.dTag.30min.4sU.A,mESC.1h.dTag.30min.4sU.B,mESC.1h.dTag.30min.4sU.C (reference: mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B)...
Sampling from steady state for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B...
Beta prior for mESC.1h.dTag.30min.4sU.A,mESC.1h.dTag.30min.4sU.B,mESC.1h.dTag.30min.4sU.C: a=4.656, b=31.514
Beta prior for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B: a=4.742, b=31.936
Computing Regulation for 2h vs 0h...
Sampling from non-steady state for mESC.2h.dTag.90min.4sU.A,mESC.2h.dTag.90min.4sU.B,mESC.2h.dTag.90min.4sU.C (reference: mESC.1h.dTag.30min.4sU.A,mESC.1h.dTag.30min.4sU.B,mESC.1h.dTag.30min.4sU.C)...
Sampling from steady state for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B...
Beta prior for mESC.2h.dTag.90min.4sU.A,mESC.2h.dTag.90min.4sU.B,mESC.2h.dTag.90min.4sU.C: a=7.277, b=20.915
Beta prior for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B: a=4.742, b=31.936
Computing Regulation for 4h vs 0h...
Sampling from non-steady state for mESC.4h.dTag.120min.4sU.A,mESC.4h.dTag.120min.4sU.B,mESC.4h.dTag.120min.4sU.C (reference: mESC.2h.dTag.90min.4sU.A,mESC.2h.dTag.90min.4sU.B,mESC.2h.dTag.90min.4sU.C)...
Sampling from steady state for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B...
Beta prior for mESC.4h.dTag.120min.4sU.A,mESC.4h.dTag.120min.4sU.B,mESC.4h.dTag.120min.4sU.C: a=12.671, b=32.000
Beta prior for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B: a=4.742, b=31.936
Computing Regulation for 6h vs 0h...
Sampling from non-steady state for mESC.6h.dTag.120min.4sU.A,mESC.6h.dTag.120min.4sU.B,mESC.6h.dTag.120min.4sU.C (reference: mESC.4h.dTag.120min.4sU.A,mESC.4h.dTag.120min.4sU.B,mESC.4h.dTag.120min.4sU.C)...
Sampling from steady state for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B...
Beta prior for mESC.6h.dTag.120min.4sU.A,mESC.6h.dTag.120min.4sU.B,mESC.6h.dTag.120min.4sU.C: a=2.417, b=15.200
Beta prior for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B: a=4.742, b=31.936
Computing Regulation for 20h vs 0h...
Sampling from non-steady state for mESC.20h.dTag.120min.4sU.A,mESC.20h.dTag.120min.4sU.B,mESC.20h.dTag.120min.4sU.C (reference: mESC.6h.dTag.120min.4sU.A,mESC.6h.dTag.120min.4sU.B,mESC.6h.dTag.120min.4sU.C)...
Sampling from steady state for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B...
Beta prior for mESC.20h.dTag.120min.4sU.A,mESC.20h.dTag.120min.4sU.B,mESC.20h.dTag.120min.4sU.C: a=4.470, b=14.068
Beta prior for mESC.0h.dTag.2h.4sU.A,mESC.0h.dTag.2h.4sU.B: a=4.742, b=31.936

Now we can compare the estimated synthesis fold changes between the time points:

a<-PlotScatter(banp,x=Regulation.1h vs 0h.s.log2FC,y=Regulation.2h vs 0h.s.log2FC,
xlim=c(-3,3),ylim=c(-3,3),highlight = GeneInfo(banp,"BANP-Target")=="yes")+
geom_abline()
b<-PlotScatter(banp,x=Regulation.2h vs 0h.s.log2FC,y=Regulation.4h vs 0h.s.log2FC,
xlim=c(-3,3),ylim=c(-3,3),highlight = GeneInfo(banp,"BANP-Target")=="yes")+
geom_abline()
c<-PlotScatter(banp,x=Regulation.4h vs 0h.s.log2FC,y=Regulation.6h vs 0h.s.log2FC,
xlim=c(-3,3),ylim=c(-3,3),highlight = GeneInfo(banp,"BANP-Target")=="yes")+
geom_abline()
d<-PlotScatter(banp,x=Regulation.6h vs 0h.s.log2FC,y=Regulation.20h vs 0h.s.log2FC,
xlim=c(-3,3),ylim=c(-3,3),highlight = GeneInfo(banp,"BANP-Target")=="yes")+
geom_abline()

(a|b)/(c|d)

Note that for the first comparison (1h vs 0h against 2h vs 0h) most genes do not scatter around the main diagonal, but the actual BANP targets do. This suggests that there is a lot of noise w.r.t. the x axis (which is not unexpected considering the short labeling time), but the the estimation of synthesis rate fold changes is unbiased for genes that actually change their synthesis rate.