Comparison of scSCOPE with other methods in terms of running time.

<p>(A) Runtime of scSCOPE and other DEG methods represented as a line graph with different number of cells. (B) Bar plot showing comparison of SCOPE and scSCOPE to run a single iteration of LASSO algorithm. (C) Comparison of SCOPE and scSCOPE to run a single iteration of co-expression analysis...

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Bibliographic Details
Main Author:	Sandesh Acharya (11261473) (author)
Other Authors:	Pathum Kossinna (17081585) (author), Qingrun Zhang (82414) (author), Jiami Guo (8755959) (author)
Published:	2025
Subjects:	Cell Biology Genetics Molecular Biology Cancer Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified utilizes stabilized lasso specific marker genes least absolute shrinkage identified marker genes highest consistency across different sequencing technologies depth molecular insights becoming increasingly important reproducible marker genes consistency across datasets scscope &# 8217 cell transcriptomics datasets differential gene expression identify reproducible gene co cell type scrnaseq datasets xlink "> selection operator selected markers scscope outperforms rapid emergence present scscope pathway enrichments functional investigations functional annotations functional annotation feature selection expression networks experimental validation conventional methods bootstrapped co based platform analysis lack
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Comparison of scSCOPE with other methods in terms of running time.

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