GEO series

Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER [scRNA-seq]

GSE272457 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2024/10/08 Platform GPL25526Platform GPL24247Platform GPL24676

At a glance

Single-cell RNA-seq · human · 16 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 16 samples: 16 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper: Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSERbioRxiv : the preprint server for biology · 2024

Summary (written by the submitters)

Recent technological developments in single-cell RNA-seq CRISPR screens enable high-throughput investigation of the genome. Through transduction of a gRNA library to a cell population followed by transcriptomic profiling by scRNA-seq, it is possible to characterize the effects of thousands of genomic perturbations on global gene expression.

Read the full summary ↓

A major source of noise in scRNA-seq CRISPR screens are ambient gRNAs, which are contaminating gRNAs that likely originate from other cells. If not properly filtered, ambient gRNAs can result in an excess of false positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNA noise in single-cell CRISPR screens. We use these datasets to develop and train CLEANSER, a mixture model that identifies and filters ambient gRNA noise. This model takes advantage of the bimodal distribution between native and ambient gRNAs and includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy.

Published in

Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER

Liu S, Hamilton MC, Cowart T et al. · bioRxiv : the preprint server for biology 2024 · PMID 39282389 · doi:10.1101/2024.09.04.611293

This dataset

Open GSE272457 in finder → Find more RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE272457_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1137951 and SRA study SRP521065. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39282389) ↗

Study design

16 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 16 sample titles: 16 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

GSM8403014 cropseq_hek293T_library1_gene_exp
GSM8403015 cropseq_nih3t3_library2_gene_exp
GSM8403016 cropseq_hek293T_library1_nih3t3_library2_0hr_gene_exp
GSM8403017 cropseq_hek293T_library1_nih3t3_library2_72hr_gene_exp
GSM8403018 direct_capture_hek293T_library1_gene_exp
GSM8403019 direct_capture_nih3t3_library2_gene_exp
GSM8403020 direct_capture_hek293T_library1_nih3t3_library2_0hr_gene_exp
GSM8403021 direct_capture_hek293T_library1_nih3t3_library2_72hr_gene_exp
GSM8403022 cropseq_hek293T_library1_grna
GSM8403023 cropseq_nih3t3_library2_grna
GSM8403024 cropseq_hek293T_library1_nih3t3_library2_0hr_grna
GSM8403025 cropseq_hek293T_library1_nih3t3_library2_72hr_grna
GSM8403026 direct_capture_hek293T_library1_grna
GSM8403027 direct_capture_nih3t3_library2_grna
GSM8403028 direct_capture_hek293T_library1_nih3t3_library2_0hr_grna
GSM8403029 direct_capture_hek293T_library1_nih3t3_library2_72hr_grna

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