A novel method for predicting cell abundance based on single-cell RNA-seq data
Abstract Background It is important to understand the composition of cell type and its proportion in intact tissues, as changes in certain cell types are the underlying cause of disease in humans. Although compositions of cell type and ratios can be obtained by single-cell sequencing, single-cell se...
Main Authors: | Jiajie Peng, Lu Han, Xuequn Shang |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2021-08-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12859-021-04187-4 |
Similar Items
-
Comparison of Different Estimation Methods for Categorical and Ordinal Data in Confirmatory Factor Analysis
by: Hakan Koğar, et al.
Published: (2015-11-01) -
Fast Measurements with MOX Sensors: A Least-Squares Approach to Blind Deconvolution
by: Dominique Martinez, et al.
Published: (2019-09-01) -
Comparison of straight line curve fit approaches for determining parameter variances and covariances
by: Ramnath Vishal
Published: (2020-01-01) -
An improved bus signal priority system for networks with nearside bus stops
by: Kim, Wonho
Published: (2005) -
An Efficient and Flexible Method for Deconvoluting Bulk RNA-Seq Data with Single-Cell RNA-Seq Data
by: Xifang Sun, et al.
Published: (2019-09-01)