Question: Using Kallisto and Sleuth for RNA-seq analysis - do I have to normalise before differential analysis?
0
gravatar for a.rex
2.8 years ago by
a.rex200
a.rex200 wrote:

I am relatively new to RNA-seq analysis, and I have been using Kallisto and Sleuth for this.

My code looks like this - I run an LRT test first on the data, and then a Wald's test on those that have passed this filter. I end up with a list of loci that are differentially expressed between my two condition ('kd' versus 'control').

library("sleuth")
base_dir <- "/path/to/data"
samples <- paste0("sample",1:4)
kal_dirs <- sapply(samples, function(id) file.path(base_dir, id))
s2c <- read.table(file.path(base_dir, "kd.txt"), header = TRUE, stringsAsFactors=FALSE)
s2c <- dplyr::select(s2c, sample = sample, condition)
s2c <- dplyr::mutate(s2c, path = kal_dirs)
so <- sleuth_prep(s2c, ~condition)
so <- sleuth_fit(so)
so <- sleuth_fit(so, ~1, "reduced")
so <- sleuth_lrt(so, 'reduced', 'full')
so <- sleuth_wt(so, ‘conditionkd’)
results_wt <- sleuth_results(so, 'conditionkd')
results_lrt <- sleuth_results(so, 'reduced:full', test_type = 'lrt')
kd.lrt.sig_ids <- results_lrt$target_id[which(results_lrt$qval < 0.05)]
kd.wt.sig_ids <- results_wt$target_id[which(results_wt$qval < 0.05)]
kd_ids <- kd.wt.sig_ids[kd.wt.sig_ids %in% kd.lrt.sig_ids]
shared_results <- results_table_wt[results_table_wt$target_id %in% shared_ids,]
write.csv(shared_results, file=“/path/to/kallisto_wald_test_lrt_passed_kdd3.csv")

However, do I have to first normalise the TPM/count column on the abundance.tsv file produced Kallisto for each of the samples? Or is this incorporated in the tests? No online tutorials have done this, so how is it normalising libraries?

rna-seq • 2.3k views
ADD COMMENTlink written 2.8 years ago by a.rex200
1

Hope this could be helpful: https://rawgit.com/pachterlab/sleuth/master/inst/doc/intro.html

so <- sleuth_prep(s2c, ~ condition)
## reading in kallisto results
## ......
## 
## normalizing est_counts
## 50844 targets passed the filter
## normalizing tpm
## merging in metadata
## normalizing bootstrap samples
## summarizing bootstraps
ADD REPLYlink modified 2.8 years ago • written 2.8 years ago by bioinfo8120
0
gravatar for a.rex
2.8 years ago by
a.rex200
a.rex200 wrote:

Sorry I realised that this is a silly question - so <- sleuth_prep(s2c, ~condition) does the tpm normalisation between samples

ADD COMMENTlink written 2.8 years ago by a.rex200
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