Dataset Information

ScAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data.

ABSTRACT: Single cell transcriptomics has recently seen a surge in popularity, leading to the need for data analysis pipelines that are reproducible, modular, and interoperable across different systems and institutions.To meet this demand, we introduce scAN1.0, a processing pipeline for analyzing 10X single cell RNA sequencing data. scAN1.0 is built using the Nextflow DSL2 and can be run on most computational systems. The modular design of Nextflow pipelines enables easy integration and evaluation of different blocks for specific analysis steps.We demonstrate the usefulness of scAN1.0 by showing its ability to examine the impact of the mapping step during the analysis of two datasets: (i) a 10X scRNAseq of a human pituitary gonadotroph tumor dataset and (ii) a murine 10X scRNAseq acquired on CD8 T cells during an immune response.

SUBMITTER: Lepetit M

PROVIDER: S-EPMC10741331 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

scAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data.

Lepetit Maxime M Ilie Mirela Diana MD Chanal Marie M Raverot Gerald G Bertolino Philippe P Arpin Christophe C Picard Franck F Gandrillon Olivier O

In silico biology 20230101 1-2

Single cell transcriptomics has recently seen a surge in popularity, leading to the need for data analysis pipelines that are reproducible, modular, and interoperable across different systems and institutions.To meet this demand, we introduce scAN1.0, a processing pipeline for analyzing 10X single cell RNA sequencing data. scAN1.0 is built using the Nextflow DSL2 and can be run on most computational systems. The modular design of Nextflow pipelines enables easy integration and evaluation of diff ...[more]

PMID: 37927254

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Project description:Electroenchephalography (EEG) recordings collected with developmental populations present particular challenges from a data processing perspective. These EEGs have a high degree of artifact contamination and often short recording lengths. As both sample sizes and EEG channel densities increase, traditional processing approaches like manual data rejection are becoming unsustainable. Moreover, such subjective approaches preclude standardized metrics of data quality, despite the heightened importance of such measures for EEGs with high rates of initial artifact contamination. There is presently a paucity of automated resources for processing these EEG data and no consistent reporting of data quality measures. To address these challenges, we propose the Harvard Automated Processing Pipeline for EEG (HAPPE) as a standardized, automated pipeline compatible with EEG recordings of variable lengths and artifact contamination levels, including high-artifact and short EEG recordings from young children or those with neurodevelopmental disorders. HAPPE processes event-related and resting-state EEG data from raw files through a series of filtering, artifact rejection, and re-referencing steps to processed EEG suitable for time-frequency-domain analyses. HAPPE also includes a post-processing report of data quality metrics to facilitate the evaluation and reporting of data quality in a standardized manner. Here, we describe each processing step in HAPPE, perform an example analysis with EEG files we have made freely available, and show that HAPPE outperforms seven alternative, widely-used processing approaches. HAPPE removes more artifact than all alternative approaches while simultaneously preserving greater or equivalent amounts of EEG signal in almost all instances. We also provide distributions of HAPPE's data quality metrics in an 867 file dataset as a reference distribution and in support of HAPPE's performance across EEG data with variable artifact contamination and recording lengths. HAPPE software is freely available under the terms of the GNU General Public License at https://github.com/lcnhappe/happe.

Project description:BackgroundSingle-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used.ResultsWe show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required.ConclusionOur comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.

Dataset Information

ScAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data.

Publications

scAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data.

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