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Papers: 10.1038/s41598-020-67753-5

https://doi.org/10.1038/s41598-020-67753-5

Quiescent stem cell marker genes in glioma gene networks are sufficient to distinguish between normal and glioblastoma (GBM) samples

Cited by: 13
Author(s): Shradha Mukherjee
Published: about 5 years ago

Software Mentions 14

bioconductor: edgeR
Empirical Analysis of Digital Gene Expression Data in R
Papers that mentioned: 6,568
Very Likely Science (100)
bioconductor: GeneOverlap
Test and visualize gene overlaps
Papers that mentioned: 40
Very Likely Science (95)
bioconductor: limma
Linear Models for Microarray Data
Papers that mentioned: 7,776
Very Likely Science (78)
bioconductor: sva
Surrogate Variable Analysis
Papers that mentioned: 511
Very Likely Science (100)
cran: corrplot
Visualization of a Correlation Matrix
Papers that mentioned: 1,077
Very Likely Science (100)
cran: ggplot2
Create Elegant Data Visualisations Using the Grammar of Graphics
Papers that mentioned: 11,441
Very Likely Science (100)
cran: MEGENA
Multiscale Clustering of Geometrical Network
Papers that mentioned: 23
Very Likely Science (100)
cran: sra
Selection Response Analysis
Papers that mentioned: 14
Very Likely Science (85)
cran: SWIM
Scenario Weights for Importance Measurement
Papers that mentioned: 48
Very Likely Science (85)
cran: VennDiagram
Generate High-Resolution Venn and Euler Plots
Papers that mentioned: 626
Very Likely Science (83)
cran: WGCNA
Weighted Correlation Network Analysis
Papers that mentioned: 3,479
Very Likely Science (85)
pypi: HTSeq
A framework to process and analyze data from high-throughput sequencing (HTS) assays
Papers that mentioned: 3,071
Very Likely Science (65)
pypi: package
package is a package to package your package
Papers that mentioned: 1,300
Very Likely Science (65)
pypi: sra
A simple deploy tool
Papers that mentioned: 14
Very Likely Science (65)