Normalization Of RNA-seq Data Research Articles

Clinicopathologic and molecular landscape of invasive mucinous adenocarcinoma of the lung.

8037 Background: Invasive mucinous adenocarcinoma (IMA), accounting for 2–10% of lung adenocarcinomas, often mimics pneumonia with multicentric opacities in multiple lobes and both lungs. Notable for its microscopic skip lesions leading to tumor spread through air spaces (STAS), IMA is still under-researched in terms of its comprehensive clinical and genetic characteristics in large, multicenter studies. Methods: Primary lung cancer cases were obtained from the Tempus database, categorized into either IMA or non-IMA, and compared by patient, clinical, biopsy, and molecular characteristics. The normalization of RNA-seq data involved computing transcripts per million (TPM), performing log2 transformations, and adjusting for assay and batch effects. Significantly up- and down-regulated genes were defined as false discovery rate q-values < 0.05 and |log2(fold change)| > 0.5. Results: The study analyzed 18,857 cases, with 655 (3.5%) in the IMA group and 18,202 (96.5%) in the non-IMA group. The IMA group predominantly consisted of older individuals (median age 70 vs. 68; p<0.001), had fewer smokers (63% vs. 73%; p<0.001), and included more individuals of Hispanic or Latino ethnicity (4.3% vs. 2.7%; p=0.007). This group was more likely to be diagnosed with early-stage cancer and less likely to have stage 4 cancer at diagnosis (stage 1: 23% vs. 14%, stage 2: 18% vs. 7.3%, stage 3: 23% vs. 17%, stage 4: 36% vs. 62%; p<0.001). They exhibited significantly lower tumor mutational and neoantigen burdens (median 3.1 vs. 4.6 mutations/Mb and 6 vs. 9 neoantigens/Mb, respectively; both p<0.001), fewer positive PD-L1 statuses (27% vs. 58%; p<0.001), and different immune cell infiltration patterns (Table). Gene expression analysis revealed 416 upregulated and 258 downregulated genes in the IMA group compared to the non-IMA group. Conclusions: This analysis underscores the unique clinicopathologic and molecular characteristics of IMAs. An improved understanding of IMA’s biological behavior could lead to the development of more effective, personalized treatment strategies. [Table: see text]

Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference.

The normalization of RNA sequencing data is a primary step for downstream analysis. The most popular method used for the normalization is the trimmed mean of M values (TMM) and DESeq. The TMM tries to trim away extreme log fold changes of the data to normalize the raw read counts based on the remaining non-deferentially expressed genes. However, the major problem with the TMM is that the values of trimming factor M are heuristic. This paper tries to estimate the adaptive value of M in TMM based on Jaeckel's Estimator, and each sample acts as a reference to find the scale factor of each sample. The presented approach is validated on SEQC, MAQC2, MAQC3, PICKRELL and two simulated datasets with two-group and three-group conditions by varying the percentage of differential expression and the number of replicates. The performance of the present approach is compared with various state-of-the-art methods, and it is better in terms of area under the receiver operating characteristic curve and differential expression.

The gene expression profile and cell of origin of canine peripheral T-cell lymphoma

BackgroundPeripheral T-cell lymphoma (PTCL) refers to a heterogenous group of T-cell neoplasms with poor treatment responses and survival times. Canine PTCL clinically and immunophenotypically resembles the most common human subtype, PTCL-not otherwise specified (PTCL-NOS), leading to interest in this canine disease as a naturally occurring model for human PTCL. Gene expression profiling in human PTCL-NOS has helped characterize this ambiguous diagnosis into distinct subtypes, but similar gene expression profiling in canine PTCL is lacking.MethodsBulk RNA-sequencing was performed on tumor samples from 33 dogs with either CD4+ (26/33), CD8+ (4/33), or CD4-CD8- (3/33) PTCL as diagnosed by flow cytometry, and sorted CD4+ and CD8+ lymphocytes from healthy control dogs. Following normalization of RNA-seq data, we performed differential gene expression and unsupervised clustering methods. Gene set enrichment analysis was performed to determine the enrichment of canine CD4+ PTCL for human PTCL-NOS, oncogenic pathways, and various stages of T-cell development gene signatures. We utilized gene set variation analysis to evaluate individual canine CD4+ PTCLs for various human and murine T-cell and thymocyte gene signatures. Cultured canine PTCL cells were treated with a pan-PI3K inhibitor, and cell survival and proliferation were compared to DMSO-treated controls. Expression of GATA3 and phosphorylated AKT was validated by immunohistochemistry.ResultsWhile the canine CD4+ PTCL phenotype exhibited a consistent gene expression profile, the expression profiles of CD8+ and CD4-CD8- canine PTCLs were more heterogeneous. Canine CD4+ PTCL had increased expression of GATA3, upregulation of its target genes, enrichment for PI3K/AKT/mTOR signaling, and downregulation of PTEN, features consistent with the more aggressive GATA3-PTCL subtype of human PTCL-NOS. In vitro assays validated the reliance of canine CD4+ PTCL cells on PI3K/AKT/mTOR signaling for survival and proliferation. Canine CD4+ PTCL was enriched for thymic precursor gene signatures, exhibited increased expression of markers of immaturity (CD34, KIT, DNTT, and CCR9), and downregulated genes associated with the T-cell receptor, MHC class II associated genes (DLA-DQA1, DLA-DRA, HLA-DQB1, and HLA-DQB2), and CD25.ConclusionsCanine CD4+ PTCL most closely resembled the GATA3-PTCL subtype of PTCL-NOS and may originate from an earlier stage of T-cell development than the more conventionally posited mature T-helper cell origin.

Cdev: a ground-truth based measure to evaluate RNA-seq normalization performance.

Normalization of RNA-seq data has been an active area of research since the problem was first recognized a decade ago. Despite the active development of new normalizers, their performance measures have been given little attention. To evaluate normalizers, researchers have been relying on ad hoc measures, most of which are either qualitative, potentially biased, or easily confounded by parametric choices of downstream analysis. We propose a metric called condition-number based deviation, or cdev, to quantify normalization success. cdev measures how much an expression matrix differs from another. If a ground truth normalization is given, cdev can then be used to evaluate the performance of normalizers. To establish experimental ground truth, we compiled an extensive set of public RNA-seq assays with external spike-ins. This data collection, together with cdev, provides a valuable toolset for benchmarking new and existing normalization methods.

MtExpress, a Comprehensive and Curated RNAseq-based Gene Expression Atlas for the Model Legume Medicago truncatula.

Although RNA sequencing (RNAseq) has been becoming the main transcriptomic approach in the model legume Medicago truncatula, there is currently no genome-wide gene expression atlas covering the whole set of RNAseq data published for this species. Nowadays, such a tool is highly valuable to provide a global view of gene expression in a wide range of conditions and tissues/organs. Here, we present MtExpress, a gene expression atlas that compiles an exhaustive set of published M. truncatula RNAseq data (https://medicago.toulouse.inrae.fr/MtExpress). MtExpress makes use of recent releases of M. truncatula genome sequence and annotation, as well as up-to-date tools to perform mapping, quality control, statistical analysis and normalization of RNAseq data. MtExpress combines semi-automated pipelines with manual re-labeling and organization of samples to produce an attractive and user-friendly interface, fully integrated with other available Medicago genomic resources. Importantly, MtExpress is highly flexible, in terms of both queries, e.g. allowing searches with gene names and orthologous gene IDs from Arabidopsis and other legume species, and outputs, to customize visualization and redirect gene study to relevant Medicago webservers. Thanks to its semi-automated pipeline, MtExpress will be frequently updated to follow the rapid pace of M. truncatula RNAseq data publications, as well as the constant improvement of genome annotation. MtExpress also hosts legacy GeneChip expression data originally stored in the Medicago Gene Expression Atlas, as a very valuable and complementary resource.

SCnorm: robust normalization of single-cell RNA-seq data.

SummaryNormalization of RNA-sequencing data is essential for accurate downstream inference, but the assumptions upon which most methods are based do not hold in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of scRNA-seq data.

A normalization strategy for comparing tag count data

BackgroundHigh-throughput sequencing, such as ribonucleic acid sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses, enables various features of organisms to be compared through tag counts. Recent studies have demonstrated that the normalization step for RNA-seq data is critical for a more accurate subsequent analysis of differential gene expression. Development of a more robust normalization method is desirable for identifying the true difference in tag count data.ResultsWe describe a strategy for normalizing tag count data, focusing on RNA-seq. The key concept is to remove data assigned as potential differentially expressed genes (DEGs) before calculating the normalization factor. Several R packages for identifying DEGs are currently available, and each package uses its own normalization method and gene ranking algorithm. We compared a total of eight package combinations: four R packages (edgeR, DESeq, baySeq, and NBPSeq) with their default normalization settings and with our normalization strategy. Many synthetic datasets under various scenarios were evaluated on the basis of the area under the curve (AUC) as a measure for both sensitivity and specificity. We found that packages using our strategy in the data normalization step overall performed well. This result was also observed for a real experimental dataset.ConclusionOur results showed that the elimination of potential DEGs is essential for more accurate normalization of RNA-seq data. The concept of this normalization strategy can widely be applied to other types of tag count data and to microarray data.