Biology subjects

Ranjan, B.

Publications and source records attributed to Ranjan, B..

4 recordsLinked to original sources

bioRxiv2024

Embryonic Signatures of Intergenerational Inheritance across Paternal Environments and Genetic Backgrounds

The paternal preconception environment has been implicated as a modulator of phenotypic traits and disease risk in F1 offspring. However, the prevalence and mechanisms of such intergenerational epigenetic inheritance (IEI) in mammals remain poorly defined. Moreover, the interplay between paternal exposure, genetics, and age on emergent offspring features is unexplored. Here, we measure the quantitative impact of three paternal environments on early embryogenesis across genetic backgrounds. Using in vitro fertilisation (IVF) at scale, we capture batch-robust transcriptomic signatures of IEI with single-blastocyst resolution. Amongst these, paternal gut microbiota dysbiosis is linked with aberrant expression of lineage regulators in blastocysts, particularly affecting extra-embryonic tissues. Conversely, paternal low-protein high-sugar diet associates with subtle preimplantation developmental delay. We further identify gene expression variability as a paternally-induced F1 phenotype, and highlight confounding issues for IEI such as batch-effects and under-sampling. Finally, paternal genetic background and age exert a dominant influence over the inherited environmental signature. This study systematically characterises how paternal conditioning programmes subtle but detectable molecular responses in early embryos, and suggests guiding principles to dissect intergenerational phenomenology.

genomics↗

bioRxiv2021

Robust clustering and interpretation ofscRNA-seq data using reference componentanalysis

MotivationThe transcriptomic diversity of the hundreds of cell types in the human body can be analysed in unprecedented detail using single cell (SC) technologies. Though clustering of cellular transcriptomes is the default technique for defining cell types and subtypes, single cell clustering can be strongly influenced by technical variation. In fact, the prevalent unsupervised clustering algorithms can cluster cells by technical, rather than biological, variation. ResultsCompared to de novo (unsupervised) clustering methods, we demonstrate using multiple benchmarks that supervised clustering, which uses reference transcriptomes as a guide, is robust to batch effects. To leverage the advantages of supervised clustering, we present RCA2, a new, scalable, and broadly applicable version of our RCA algorithm. RCA2 provides a user-friendly framework for supervised clustering and downstream analysis of large scRNA-seq data sets. RCA2 can be seamlessly incorporated into existing algorithmic pipelines. It incorporates various new reference panels for human and mouse, supports generation of custom panels and uses efficient graph-based clustering and sparse data structures to ensure scalability. We demonstrate the applicability of RCA2 on SC data from human bone marrow, healthy PBMCs and PBMCs from COVID-19 patients. Importantly, RCA2 facilitates cell-type-specific QC, which we show is essential for accurate clustering of SC data from heterogeneous tissues. In the era of cohort-scale SC analysis, supervised clustering methods such as RCA2 will facilitate unified analysis of diverse SC datasets. AvailabilityRCA2 is implemented in R and is available at github.com/prabhakarlab/RCAv2

bioinformatics↗

bioRxiv2020

DUBStepR: correlation-based feature selection for clustering single-cell RNA sequencing data

Feature selection (marker gene selection) is widely believed to improve clustering accuracy, and is thus a key component of single cell clustering pipelines. However, we found that the performance of existing feature selection methods was inconsistent across benchmark datasets, and occasionally even worse than without feature selection. Moreover, existing methods ignored information contained in gene-gene correlations. We therefore developed DUBStepR (Determining the Underlying Basis using Stepwise Regression), a feature selection algorithm that leverages gene-gene correlations with a novel measure of inhomogeneity in feature space, termed the Density Index (DI). Despite selecting a relatively small number of genes, DUBStepR substantially outperformed existing single-cell feature selection methods across diverse clustering benchmarks. In a published scRNA-seq dataset from sorted monocytes, DUBStepR sensitively detected a rare and previously invisible population of contaminating basophils. DUBStepR is scalable to over a million cells, and can be straightforwardly applied to other data types such as single-cell ATAC-seq. We propose DUBStepR as a general-purpose feature selection solution for accurately clustering single-cell data.

bioinformatics↗

bioRxiv2020

scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

Clustering is a crucial step in the analysis of single-cell data. Clusters identified using unsupervised clustering are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering strategies have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. We present O_SCPLOWSCC_SCPLOWCO_SCPLOWONSENSUSC_SCPLOW, an R framework for generating a consensus clustering by (i) integrating the results from both unsupervised and supervised approaches and (ii) refining the consensus clusters using differentially expressed (DE) genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. O_SCPLOWSCC_SCPLOWCO_SCPLOWONSENSUSC_SCPLOW is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

bioinformatics↗