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Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data

View Article: PubMed Central - PubMed

ABSTRACT

Single-cell RNA-Sequencing (scRNA-Seq) is a revolutionary technique for discovering and describing cell types in heterogeneous tissues, yet its measurement of expression often suffers from large systematic bias. A major source of this bias is the cell cycle, which introduces large within-cell-type heterogeneity that can obscure the differences in expression between cell types. The current method for removing the cell-cycle effect is unable to effectively identify this effect and has a high risk of removing other biological components of interest, compromising downstream analysis. We present ccRemover, a new method that reliably identifies the cell-cycle effect and removes it. ccRemover preserves other biological signals of interest in the data and thus can serve as an important pre-processing step for many scRNA-Seq data analyses. The effectiveness of ccRemover is demonstrated using simulation data and three real scRNA-Seq datasets, where it boosts the performance of existing clustering algorithms in distinguishing between cell types.

No MeSH data available.


Density plots of selected genes from the T-cell data.The densities are displayed for the original (red), scLVM corrected (green) and ccRemover corrected (blue) data. The genes were selected from among the top ranked genes on Cyclebase. The original data displays bimodal densities which are common in scRNA-Seq data indicating genes whose expression switches on and off. When the cell-cycle effect is removed using ccRemover or scLVM these bimodal densities disappear.
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f2: Density plots of selected genes from the T-cell data.The densities are displayed for the original (red), scLVM corrected (green) and ccRemover corrected (blue) data. The genes were selected from among the top ranked genes on Cyclebase. The original data displays bimodal densities which are common in scRNA-Seq data indicating genes whose expression switches on and off. When the cell-cycle effect is removed using ccRemover or scLVM these bimodal densities disappear.

Mentions: Both scLVM and ccRemover remove the cell-cycle effect efficiently on this data. To check this, in Fig. 2, we plot the density of the expression level of cell-cycle genes selected from the top ranked genes on Cyclebase43. On the original data (red lines), many genes display a bimodal density commonly seen in scRNA-Seq data indicating the on-off action of genes, in this case, controlled by the cell cycle18444546. On the scLVM (green lines) or ccRemover (blue lines) corrected data, the bimodality of the densities largely disappears and most genes display a unimodal distribution indicating that the cell-cycle effect has been reduced or removed completely for these genes.


Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data
Density plots of selected genes from the T-cell data.The densities are displayed for the original (red), scLVM corrected (green) and ccRemover corrected (blue) data. The genes were selected from among the top ranked genes on Cyclebase. The original data displays bimodal densities which are common in scRNA-Seq data indicating genes whose expression switches on and off. When the cell-cycle effect is removed using ccRemover or scLVM these bimodal densities disappear.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037372&req=5

f2: Density plots of selected genes from the T-cell data.The densities are displayed for the original (red), scLVM corrected (green) and ccRemover corrected (blue) data. The genes were selected from among the top ranked genes on Cyclebase. The original data displays bimodal densities which are common in scRNA-Seq data indicating genes whose expression switches on and off. When the cell-cycle effect is removed using ccRemover or scLVM these bimodal densities disappear.
Mentions: Both scLVM and ccRemover remove the cell-cycle effect efficiently on this data. To check this, in Fig. 2, we plot the density of the expression level of cell-cycle genes selected from the top ranked genes on Cyclebase43. On the original data (red lines), many genes display a bimodal density commonly seen in scRNA-Seq data indicating the on-off action of genes, in this case, controlled by the cell cycle18444546. On the scLVM (green lines) or ccRemover (blue lines) corrected data, the bimodality of the densities largely disappears and most genes display a unimodal distribution indicating that the cell-cycle effect has been reduced or removed completely for these genes.

View Article: PubMed Central - PubMed

ABSTRACT

Single-cell RNA-Sequencing (scRNA-Seq) is a revolutionary technique for discovering and describing cell types in heterogeneous tissues, yet its measurement of expression often suffers from large systematic bias. A major source of this bias is the cell cycle, which introduces large within-cell-type heterogeneity that can obscure the differences in expression between cell types. The current method for removing the cell-cycle effect is unable to effectively identify this effect and has a high risk of removing other biological components of interest, compromising downstream analysis. We present ccRemover, a new method that reliably identifies the cell-cycle effect and removes it. ccRemover preserves other biological signals of interest in the data and thus can serve as an important pre-processing step for many scRNA-Seq data analyses. The effectiveness of ccRemover is demonstrated using simulation data and three real scRNA-Seq datasets, where it boosts the performance of existing clustering algorithms in distinguishing between cell types.

No MeSH data available.