AfterQC – Open Source Biology & Genetics Interest Group

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CRISPR Web Analyser

Removing Unwanted Variation Using Pseudoreplicates and Pseudosamples

Identifying Differentially Abundant Phosphoproteome Sites With ProteomeRiver

Notes: The audio for the first 6 minutes of this talk was not recorded due to an error with the hybrid meeting.

Extension of scPipe Bioconductor Package for scATAC-seq Data

Shanika Amarasinghe (ARMI/WEHI, Australia)

3:30 PM - 3:45 PM AEDT (Australian Eastern Daylight Time) on Friday, Dec. 2nd, 2022
LONG TALK

Single-cell Assay for Transposase Accessible Chromatin using sequencing (scATAC-seq) enables genome-wide profiling of chromatin accessibility that can be used to improve gene expression modelling at single-cell resolution. scATAC-seq data are intrinsically sparse, since the signals detected at a genomic position are limited by DNA copy number, thereby making it difficult to discriminate signals from noise. Therefore, thorough and clear pre-processing steps are needed prior to downstream analyses. Furthermore, these pre-processing steps need to be sufficiently adaptable to user requirements. Currently available scATAC-seq pre-processing tools are either not fully R-based or have limited flexibility. We have extended scPipe, our current pre-processing tool for single-cell RNA data, to be able to process scATAC-seq data from a variety of different protocols. We take the power and flexibility of the R environment and create a workflow that combines multiple Bioconductor packages to achieve this. We also evaluated the performance of scPipe’s scATAC-Seq module on an in-house benchmarking dataset to assess its speed and ability to recover known population structure.

Moderator: Peter Hickey (WEHI, Australia)

Extension of scPipe Bioconductor Package for scATAC-seq Data

Matilda for Single-cell Multi-omics Data Integration

cellXY for Exploring Gender-specific Genes in Single Cell RNA-seq Data

Stereopy as an Advanced Tool for Interpreting Spatial Transcriptomics Data

Spectre Toolkit for Rapid Analysis of Cytometry Data

A Bioconductor Framework for High-dimensional in situ Cytometry Analysis

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Is it normal for RCorrector to remove millions of reads?

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