## Warning: package 'dplyr' was built under R version 3.5.1
## bcbioRNASeq 0.2.7
## class: RangedSummarizedExperiment 
## dim: 47729 4 
## metadata(29): version level ... utilsSessionInfo
##   devtoolsSessionInfo
## assays(5): counts tpm length normalized vst
## rownames(47729): ENSMUSG00000000001 ENSMUSG00000000003 ...
##   ENSMUSG00000109577 ENSMUSG00000109578
## rowData names(7): broadClass description ... geneName
##   seqCoordSystem
## colnames(4): group1_1 group1_2 group2_1 group2_2
## colData names(21): sampleName description ... x5x3Bias xGC
## ================================================================================
## Upload Dir: "/Users/travis/build/hbc/bcbio_rnaseq_output_example"
## Upload Date: 2017-05-23
## R Load Date: 2018-08-24
## Level: "genes"
## Caller: "salmon"
## Organism: "Mus musculus"
## Interesting Groups: "group"
## AnnotationHub: "AH53222"
## Ensembl Release: "87"
## Genome Build: "GRCm38"

Overview

  • Principal Investigator:
  • Researcher:
  • Experiment:

bcbio run data was imported from /Users/travis/build/hbc/bcbio_rnaseq_output_example.

Count matrices

## Saving raw_counts.rda, normalized_counts.rda, tpm.rda to /Users/travis/build/hbc/bcbio_rnaseq_output_example/report/data
## Writing raw_counts, normalized_counts, tpm to /Users/travis/build/hbc/bcbio_rnaseq_output_example/report/results/2018-08-24/quality_control

The results are saved as gzip-compressed comma separated values (CSV). Gzip compression is natively supported on macOS and Linux-based operating systems. If you’re running Windows, we recommend installing 7-Zip. CSV files can be opened in Excel or RStudio.

  • normalized_counts.csv.gz: Use to evaluate individual genes and/or generate plots. These counts are normalized for the variation in sequencing depth across samples.
  • tpm.csv.gz: Transcripts per million, scaled by length and also suitable for plotting.
  • raw_counts.csv.gz: Only use to perform a new differential expression analysis. These counts will vary across samples due to differences in sequencing depth, and have not been normalized. Do not use this file for plotting genes.

Read metrics

Total reads

High quality RNA-seq samples ideally should have at least 10 million reads per sample.

Mapped reads

The number of mapped reads should correspond to the number of total reads.