A new normalization for Nanostring nCounter gene expression data

The Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different datasets. We also offer suggestions on the design and analysis of studies involving this technology

tidybulk: an R tidy framework for modular transcriptomic data analysis

Recently, efforts have been made toward the harmonization of transcriptomic data structures and workflows using the concept of data tidiness, to facilitate modularisation. We present tidybulk, a modular framework for bulk transcriptional analyses that introduces a tidy transcriptomic data structure paradigm and analysis grammar. Tidybulk covers a wide variety of analysis procedures and integrates a large ecosystem of publicly available analysis algorithms under a common framework. Tidybulk decreases coding burden, facilitates reproducibility, increases efficiency for expert users, lowers the learning curve for inexperienced users, and bridges transcriptional data analysis with the tidyverse. Tidybulk is available at R/Bioconductor bioconductor.org/packages/tidybulk

RUV-III-NB: normalization of single cell RNA-seq data

Normalization of single cell RNA-seq data remains a challenging task. The performance of different methods can vary greatly between datasets when unwanted factors and biology are associated. Most normalization methods also only remove the effects of unwanted variation for the cell embedding but not from gene-level data typically used for differential expression (DE) analysis to identify marker genes. We propose RUV-III-NB, a method that can be used to remove unwanted variation from both the cell embedding and gene-level counts. Using pseudo-replicates, RUV-III-NB explicitly takes into account potential association with biology when removing unwanted variation. The method can be used for both UMI or read counts and returns adjusted counts that can be used for downstream analyses such as clustering, DE and pseudotime analyses. Using published datasets with different technological platforms, kinds of biology and levels of association between biology and unwanted variation, we show that RUV-III-NB manages to remove library size and batch effects, strengthen biological signals, improve DE analyses, and lead to results exhibiting greater concordance with independent datasets of the same kind. The performance of RUV-III-NB is consistent and is not sensitive to the number of factors assumed to contribute to the unwanted variation

LRH-1 expression patterns in breast cancer tissues are associated with tumour aggressiveness

The significance and regulation of liver receptor homologue 1 (LRH-1, NR5A2), a tumour-promoting transcription factor in breast cancer cell lines, is unknown in clinical breast cancers. This study aims to determine LRH-1/NR5A2 expression in breast cancers and relationship with DNA methylation and tumour characteristics. In The Cancer Genome Atlas breast cancer cohort NR5A2 expression was positively associated with intragenic CpG island methylation (1.4-fold expression for fully methylated versus not fully methylated, p=0.01) and inversely associated with promoter CpG island methylation (0.6-fold expression for fully methylated versus not fully methylated, p=0.036). LRH-1 immunohistochemistry of 329 invasive carcinomas and ductal carcinoma in situ (DCIS) was performed. Densely punctate/coarsely granular nuclear reactivity was significantly associated with high tumour grade (p<0.005, p=0.033 in invasive carcinomas and DCIS respectively), negative estrogen receptor status (p=0.008, p=0.038 in overall cohort and invasive carcinomas, respectively), negative progesterone receptor status (p=0.003, p=0.013 in overall cohort and invasive carcinomas, respectively), HER2 amplification (overall cohort p=0.034) and non-luminal intrinsic subtype (p=0.018, p=0.038 in overall cohort and invasive carcinomas, respectively). These significant associations of LRH-1 protein expression with tumour phenotype suggest that LRH-1 is an important indicator of tumour biology in breast cancers and may be useful in risk stratification