FDR control with adaptive procedures and FDR monotonicity

The steep rise in availability and usage of high-throughput technologies in biology brought with it a clear need for methods to control the False Discovery Rate (FDR) in multiple tests. Benjamini and Hochberg (BH) introduced in 1995 a simple procedure and proved that it provided a bound on the expected value, $\mathit{FDR}\leq q$. Since then, many authors tried to improve the BH bound, with one approach being designing adaptive procedures, which aim at estimating the number of true null hypothesis in order to get a better FDR bound. Our two main rigorous results are the following: (i) a theorem that provides a bound on the FDR for adaptive procedures that use any estimator for the number of true hypotheses ($m_0$), (ii) a theorem that proves a monotonicity property of general BH-like procedures, both for the case where the hypotheses are independent. We also propose two improved procedures for which we prove FDR control for the independent case, and demonstrate their advantages over several available bounds, on simulated data and on a large number of gene expression data sets. Both applications are simple and involve a similar amount of computation as the original BH procedure. We compare the performance of our proposed procedures with BH and other procedures and find that in most cases we get more power for the same level of statistical significance.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS399 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Coupled pre-mRNA and mRNA dynamics unveil operational strategies underlying transcriptional responses to stimuli

Genome-wide simultaneous measurements of pre-mRNA and mRNA expression reveal unexpected time-dependent transcript production and degradation profiles in response to external stimulus, as well as a striking lack of concordance between mRNA abundance and transcript production profiles

Identification of novel DNA-damage tolerance genes reveals regulation of translesion DNA synthesis by nucleophosmin

Cells cope with replication-blocking lesions via translesion DNA synthesis (TLS). TLS is carried out by low-fidelity DNA polymerases that replicate across lesions, thereby preventing genome instability at the cost of increased point mutations. Here we perform a twostage siRNA-based functional screen for mammalian TLS genes and identify 17 validated TLS genes. One of the genes, NPM1, is frequently mutated in acute myeloid leukaemia (AML). We show that NPM1 (nucleophosmin) regulates TLS via interaction with the catalytic core of DNA polymerase-eta (pol eta), and that NPM1 deficiency causes a TLS defect due to proteasomal degradation of pol eta. Moreover, the prevalent NPM1c+ mutation that causes NPM1 mislocalization in similar to 30% of AML patients results in excessive degradation of pol eta. These results establish the role of NPM1 as a key TLS regulator, and suggest a mechanism for the better prognosis of AML patients carrying mutations in NPM1

Identification of novel DNA-damage tolerance genes reveals regulation of translesion DNA synthesis by nucleophosmin

Cells cope with replication-blocking lesions via translesion DNA synthesis (TLS). TLS is carried out by low-fidelity DNA polymerases that replicate across lesions, thereby preventing genome instability at the cost of increased point mutations. Here we perform a twostage siRNA-based functional screen for mammalian TLS genes and identify 17 validated TLS genes. One of the genes, NPM1, is frequently mutated in acute myeloid leukaemia (AML). We show that NPM1 (nucleophosmin) regulates TLS via interaction with the catalytic core of DNA polymerase-eta (pol eta), and that NPM1 deficiency causes a TLS defect due to proteasomal degradation of pol eta. Moreover, the prevalent NPM1c+ mutation that causes NPM1 mislocalization in similar to 30% of AML patients results in excessive degradation of pol eta. These results establish the role of NPM1 as a key TLS regulator, and suggest a mechanism for the better prognosis of AML patients carrying mutations in NPM1

Transcriptional Dynamics Reveal Critical Roles for Non-coding RNAs in the Immediate-Early Response

<div><p>The immediate-early response mediates cell fate in response to a variety of extracellular stimuli and is dysregulated in many cancers. However, the specificity of the response across stimuli and cell types, and the roles of non-coding RNAs are not well understood. Using a large collection of densely-sampled time series expression data we have examined the induction of the immediate-early response in unparalleled detail, across cell types and stimuli. We exploit cap analysis of gene expression (CAGE) time series datasets to directly measure promoter activities over time. Using a novel analysis method for time series data we identify transcripts with expression patterns that closely resemble the dynamics of known immediate-early genes (IEGs) and this enables a comprehensive comparative study of these genes and their chromatin state. Surprisingly, these data suggest that the earliest transcriptional responses often involve promoters generating non-coding RNAs, many of which are produced in advance of canonical protein-coding IEGs. IEGs are known to be capable of induction without de novo protein synthesis. Consistent with this, we find that the response of both protein-coding and non-coding RNA IEGs can be explained by their transcriptionally poised, permissive chromatin state prior to stimulation. We also explore the function of non-coding RNAs in the attenuation of the immediate early response in a small RNA sequencing dataset matched to the CAGE data: We identify a novel set of microRNAs responsible for the attenuation of the IEG response in an estrogen receptor positive cancer cell line. Our computational statistical method is well suited to meta-analyses as there is no requirement for transcripts to pass thresholds for significant differential expression between time points, and it is agnostic to the number of time points per dataset.</p></div

Visium dataset from Hochgerner et al 2023,

Data from visium spatial transcriptomics included in Hochgerner et al 2023 https://doi.org/10.1038/s41593-023-01469-3.</p

Placenta PE, single cell RNAseq cellranger files (raw data). Admati, Skarbianskis et al.

Raw data from scRNAseq experiments on human placenta from preeclampsia and matched control. The experiment were done using 10Xgenomics V3 (nextGem) technology. Each sample have three files as in normal outpt from cellranger pipeline, samplename_matrix.mtx, samplename_barcodes.tsv, samplename_features.tsv.</p

Goldfish telencephalon single cell RNAseq

Dataset of scRNAseq from goldfish telencephalon as describe in Tibi, Biton et. al. https://doi.org/10.1101/2023.06.19.545605. Data files are compressed tab delimiter tables of genes (rows) by cells (coloumns) and values corresponde to UMI counts. Metadata file contain the information available for each cell including the sample ID and cell type.</p