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Single Cell Transcriptomics

Overview of attention for book
Cover of 'Single Cell Transcriptomics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Guidance on Processing the 10x Genomics Single Cell Gene Expression Assay
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    Chapter 2 BD Rhapsody™ Single-Cell Analysis System Workflow: From Sample to Multimodal Single-Cell Sequencing Data
  4. Altmetric Badge
    Chapter 3 Profiling Transcriptional Heterogeneity with Seq-Well S3: A Low-Cost, Portable, High-Fidelity Platform for Massively Parallel Single-Cell RNA-Seq
  5. Altmetric Badge
    Chapter 4 A MATQ-seq-Based Protocol for Single-Cell RNA-seq in Bacteria
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    Chapter 5 Full-Length Single-Cell RNA-Sequencing with FLASH-seq
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    Chapter 6 Plant Single-Cell/Nucleus RNA-seq Workflow
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    Chapter 7 Ensuring Quality Cell Input for Single Cell Sequencing Experiments by Viability and Singlet Enrichment Using Cell Sorting
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    Chapter 8 Tissue RNA Integrity in Visium Spatial Protocol (Fresh Frozen Samples)
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    Chapter 9 Single-Cell RNAseq Data QC and Preprocessing
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    Chapter 10 Single-Cell RNAseq Complexity Reduction
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    Chapter 11 Functional-Feature-Based Data Reduction Using Sparsely Connected Autoencoders
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    Chapter 12 Single-Cell RNAseq Clustering
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    Chapter 13 Identifying Gene Markers Associated with Cell Subpopulations
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    Chapter 14 A Guide to Trajectory Inference and RNA Velocity
  16. Altmetric Badge
    Chapter 15 Integration of scATAC-Seq with scRNA-Seq Data
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    Chapter 16 Using “Galaxy-rCASC”: A Public Galaxy Instance for Single-Cell RNA-Seq Data Analysis
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    Chapter 17 Bringing Cell Subpopulation Discovery on a Cloud-HPC Using rCASC and StreamFlow
  19. Altmetric Badge
    Chapter 18 Profiling RNA Editing in Single Cells
  20. Altmetric Badge
    Chapter 19 Practical Considerations for Complex Tissue Dissociation for Single-Cell Transcriptomics
Attention for Chapter 11: Functional-Feature-Based Data Reduction Using Sparsely Connected Autoencoders
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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Chapter title
Functional-Feature-Based Data Reduction Using Sparsely Connected Autoencoders
Chapter number 11
Book title
Single Cell Transcriptomics
Published in
Methods in molecular biology, January 2023
DOI 10.1007/978-1-0716-2756-3_11
Pubmed ID
36495453
Book ISBNs
978-1-07-162755-6, 978-1-07-162756-3
Authors

Alessandri, Luca, Calogero, Raffaele A.

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 Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 December 2022.
All research outputs
#14,910,731
of 24,981,585 outputs
Outputs from Methods in molecular biology
#4,035
of 14,070 outputs
Outputs of similar age
#197,694
of 470,796 outputs
Outputs of similar age from Methods in molecular biology
#122
of 720 outputs
Altmetric has tracked 24,981,585 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,070 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 69% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 470,796 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 720 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.

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