WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition
ABSTRACT The low capture rate of expressed RNAs from single-cell sequencing technology is one of the major obstacles to downstream functional genomics analyses. Recently, a number of imputation methods have emerged for single-cell transcriptome data, however, recovering missing values in very sparse expression matrices remains a substantial challenge. Here, we propose a new algorithm, WEDGE (WEighted Decomposition of Gene Expression), to impute gene expression matrices by using a biased low-rank matrix decomposition method (bLRMD). WEDGE successfully recovered expression matrices, reproduced the cell-wise and gene-wise correlations, and improved the clustering of cells, performing impressively for applications with multiple cell type datasets with high dropout rates. Overall, this study demonstrates a potent approach for imputing sparse expression matrix data, and our WEDGE algorithm should help many researchers to more profitably explore the biological meanings embedded in their scRNA-seq datasets..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
bioRxiv.org - (2021) vom: 15. Dez. Zur Gesamtaufnahme - year:2021 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Hu, Yinlei [VerfasserIn] |
|---|
| Links: |
|---|
| doi: |
10.1101/864488 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
XBI000781878 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | XBI000781878 | ||
| 003 | DE-627 | ||
| 005 | 20230429083026.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 200313s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1101/864488 |2 doi | |
| 035 | |a (DE-627)XBI000781878 | ||
| 035 | |a (biorXiv)10.1101/864488 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 570 |q DE-84 | |
| 100 | 1 | |a Hu, Yinlei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a ABSTRACT The low capture rate of expressed RNAs from single-cell sequencing technology is one of the major obstacles to downstream functional genomics analyses. Recently, a number of imputation methods have emerged for single-cell transcriptome data, however, recovering missing values in very sparse expression matrices remains a substantial challenge. Here, we propose a new algorithm, WEDGE (WEighted Decomposition of Gene Expression), to impute gene expression matrices by using a biased low-rank matrix decomposition method (bLRMD). WEDGE successfully recovered expression matrices, reproduced the cell-wise and gene-wise correlations, and improved the clustering of cells, performing impressively for applications with multiple cell type datasets with high dropout rates. Overall, this study demonstrates a potent approach for imputing sparse expression matrix data, and our WEDGE algorithm should help many researchers to more profitably explore the biological meanings embedded in their scRNA-seq datasets. | ||
| 700 | 1 | |a Li, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Wen |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Nianping |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Pengfei |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Falai |e verfasserin |4 aut | |
| 700 | 1 | |a Qu, Kun |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2021) vom: 15. Dez. |
| 773 | 1 | 8 | |g year:2021 |g day:15 |g month:12 |
| 856 | 4 | 0 | |u https://doi.org/10.1093/bib/bbab085 |z lizenzpflichtig |3 Volltext |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1101/864488 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XBI | ||
| 912 | |a SSG-OLC-PHA | ||
| 951 | |a AR | ||
| 952 | |j 2021 |b 15 |c 12 | ||
| 953 | |2 045F |a 570 | ||