I want to use the satuRn package to test for differential transcript usage between two diets. My data is in the sumExp. I set up the contrast matrix L and try to run the satuRn::testDTU function as described in the vignette. I keep on running into the same error and dont unterstand fully where it comes from. Does someone have experience with that can can help?
Thank you so much
<code>1. Count data in SummarizedExperiment
>sumExp
class: SummarizedExperiment
dim: 1500 24959
metadata(1): formula
assays(1): counts
rownames(1500): Gsta3_pericentral Il1r1_pericentral ... Sat1_midzone Tmsb4x_midzone
rowData names(3): isoform_id gene_id fitDTUModels
colnames(24959): AACAGGTTATTGCACC-1 AACCGCCAGACTACTT-1 ... TGTCGTTCATACGATA-18 TGTTCACTGTCTTCCT-18
colData names(6): nCount_RNA nFeature_RNA ... zone sample_name
2.Set up contrast matrix
diet <- as.factor(meta$diet)
design <- model.matrix(~ 0 + diet) # construct design matrix
colnames(design) <- levels(diet)
# Initialize contrast matrix
L <- matrix(0, ncol = 1, nrow = ncol(design)) # One contrast
rownames(L) <- colnames(design)
colnames(L) <- "Chow_vs_HTF"
# Set the contrast for comparing `chow` vs `HTF`
L["HTF", "Chow_vs_HTF"] <- 1
L["chow", "Chow_vs_HTF"] <- -1
> L
chow_vs_HTF
chow -1
HTF 1
3. Perform test
sumExp <- satuRn::testDTU(
object = sumExp,
contrasts = L,
diagplot1 = TRUE,
diagplot2 = TRUE,
sort = FALSE
)
**
Error in locfdr(zz = zvalues_mid, main = paste0("diagplot 1: ", main)) :
ML estimation failed. Rerun with nulltype=2**
</code>
<code>1. Count data in SummarizedExperiment
>sumExp
class: SummarizedExperiment
dim: 1500 24959
metadata(1): formula
assays(1): counts
rownames(1500): Gsta3_pericentral Il1r1_pericentral ... Sat1_midzone Tmsb4x_midzone
rowData names(3): isoform_id gene_id fitDTUModels
colnames(24959): AACAGGTTATTGCACC-1 AACCGCCAGACTACTT-1 ... TGTCGTTCATACGATA-18 TGTTCACTGTCTTCCT-18
colData names(6): nCount_RNA nFeature_RNA ... zone sample_name
2.Set up contrast matrix
diet <- as.factor(meta$diet)
design <- model.matrix(~ 0 + diet) # construct design matrix
colnames(design) <- levels(diet)
# Initialize contrast matrix
L <- matrix(0, ncol = 1, nrow = ncol(design)) # One contrast
rownames(L) <- colnames(design)
colnames(L) <- "Chow_vs_HTF"
# Set the contrast for comparing `chow` vs `HTF`
L["HTF", "Chow_vs_HTF"] <- 1
L["chow", "Chow_vs_HTF"] <- -1
> L
chow_vs_HTF
chow -1
HTF 1
3. Perform test
sumExp <- satuRn::testDTU(
object = sumExp,
contrasts = L,
diagplot1 = TRUE,
diagplot2 = TRUE,
sort = FALSE
)
**
Error in locfdr(zz = zvalues_mid, main = paste0("diagplot 1: ", main)) :
ML estimation failed. Rerun with nulltype=2**
</code>
1. Count data in SummarizedExperiment
>sumExp
class: SummarizedExperiment
dim: 1500 24959
metadata(1): formula
assays(1): counts
rownames(1500): Gsta3_pericentral Il1r1_pericentral ... Sat1_midzone Tmsb4x_midzone
rowData names(3): isoform_id gene_id fitDTUModels
colnames(24959): AACAGGTTATTGCACC-1 AACCGCCAGACTACTT-1 ... TGTCGTTCATACGATA-18 TGTTCACTGTCTTCCT-18
colData names(6): nCount_RNA nFeature_RNA ... zone sample_name
2.Set up contrast matrix
diet <- as.factor(meta$diet)
design <- model.matrix(~ 0 + diet) # construct design matrix
colnames(design) <- levels(diet)
# Initialize contrast matrix
L <- matrix(0, ncol = 1, nrow = ncol(design)) # One contrast
rownames(L) <- colnames(design)
colnames(L) <- "Chow_vs_HTF"
# Set the contrast for comparing `chow` vs `HTF`
L["HTF", "Chow_vs_HTF"] <- 1
L["chow", "Chow_vs_HTF"] <- -1
> L
chow_vs_HTF
chow -1
HTF 1
3. Perform test
sumExp <- satuRn::testDTU(
object = sumExp,
contrasts = L,
diagplot1 = TRUE,
diagplot2 = TRUE,
sort = FALSE
)
**
Error in locfdr(zz = zvalues_mid, main = paste0("diagplot 1: ", main)) :
ML estimation failed. Rerun with nulltype=2**