Further reading

TODO update as we go and simplify formatting.

Further Reading · Emerging Omics Technologies

** Spatial Transcriptomics**

Entry-level overview, platform comparison, experimental design guidance - Williams CG et. al Genome Medicine 2022

spatial + single-cell integration, computational strategies- Vandereyken K et. al Nature Reviews Genetics 2023

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** Long-Read Sequencing & Isoform Biology**

Isoform resolution at single-cell level, library prep + bioinformatics - Kumari P et.al Human Genetics 2024.

Comprehensive guide: tools, applications, challenges - Parker MT et. al Nature Reviews Genetics 2025

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Further reading, Zero inflation and sparsity in omics

**Conceptual framework, applies across all platforms**

Jiang R, Sun T, Song D, Li JJ. Statistics or biology: the
zero-inflation controversy about scRNA seq data. *Genome Biology*
2022; 23: 31.
[doi:10.1186/s13059-022-02601-5](https://doi.org/10.1186/s13059-022-02601-5){target="_blank"}

Silverman JD, Roche K, Mukherjee S, David LA. Naught all zeros
in sequence count data are the same. *Computational and Structural
Biotechnology Journal* 2020; 18: 2789–2798.
[doi:10.1016/j.csbj.2020.09.014](https://doi.org/10.1016/j.csbj.2020.09.014){target="_blank"}


**Single cell RNAseq**

Svensson V. Droplet scRNA-seq is not zero-inflated. *Nature
Biotechnology* 2020; 38(2): 147–150.
[doi:10.1038/s41587-019-0379-5](https://doi.org/10.1038/s41587-019-0379-5){target="_blank"}

Wang X, He Y, Zhang Q, Ren X, Zhang Z. Direct comparative analyses
of 10X Genomics Chromium and Smart-seq2. *Genomics Proteomics
Bioinformatics* 2021; 19(2): 253–266.
[doi:10.1016/j.gpb.2020.02.005](https://doi.org/10.1016/j.gpb.2020.02.005){target="_blank"}

Ding J et al. Systematic comparison of single-cell and
single-nucleus RNA-sequencing methods. *Nature Biotechnology*
2020; 38: 737–746.
[doi:10.1038/s41587-020-0465-8](https://doi.org/10.1038/s41587-020-0465-8){target="_blank"}

Hou W et al. A systematic evaluation of single-cell RNA-sequencing
imputation methods. *Genome Biology* 2020; 21: 218.
[doi:10.1186/s13059-020-02132-x](https://doi.org/10.1186/s13059-020-02132-x){target="_blank"}


**16S amplicon and metagenomics**

Kaul A, Mandal S, Davidov O, Peddada SD. Analysis of microbiome
data in the presence of excess zeros. *Frontiers in Microbiology*
2017; 8: 2114.
[doi:10.3389/fmicb.2017.02114](https://doi.org/10.3389/fmicb.2017.02114){target="_blank"}


**Proteomics**

Lazar C, Gatto L, Ferro M, Bruley C, Burger T. Accounting for the
multiple natures of missing values in label-free quantitative
proteomics data sets to compare imputation strategies.
*Journal of Proteome Research* 2016; 15(4): 1116–1125.
[doi:10.1021/acs.jproteome.5b00981](https://doi.org/10.1021/acs.jproteome.5b00981){target="_blank"}


Kong W, Hui HWH, Peng H, Goh WWB. Dealing with missing values in
proteomics data. *Proteomics* 2022; 22(23–24): e2200092.
[doi:10.1002/pmic.202200092](https://doi.org/10.1002/pmic.202200092){target="_blank"}


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**Metabolomics**

Do KT, Wahl S, Raffler J et al. Characterization of missing values
in untargeted MS-based metabolomics data and evaluation of missing
data handling strategies. *Metabolomics* 2018; 14: 128.
[doi:10.1007/s11306-018-1420-2](https://doi.org/10.1007/s11306-018-1420-2){target="_blank"}

Foundational framewor of Compositionality in Omics Data

Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ.
Microbiome datasets are compositional: and this is not optional.
*Frontiers in Microbiology* 2017; 8: 2224.
[doi:10.3389/fmicb.2017.02224](https://doi.org/10.3389/fmicb.2017.02224){target="_blank"}


Quinn TP, Erb I, Richardson MF, Crowley TM. Understanding
sequencing data as compositions: an outlook and review.
*Bioinformatics* 2018; 34(16): 2870–2878.
[doi:10.1093/bioinformatics/bty175](https://academic.oup.com/bioinformatics/article/34/16/2870/4956011){target="_blank"}


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**The microbiome case, most extensively studied**

Weiss S et al. Normalization and microbial differential abundance
strategies depend upon data characteristics.
*Microbiome* 2017; 5: 27.
[doi:10.1186/s40168-017-0237-y](https://doi.org/10.1186/s40168-017-0237-y){target="_blank"}

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Statistical models for omics count data

Love MI, Huber W, Anders S. Moderated estimation of fold change
and dispersion for RNA seq data with DESeq2. *Genome Biology*
2014; 15: 550.
[doi:10.1186/s13059-014-0550-8](https://doi.org/10.1186/s13059-014-0550-8){target="_blank"}

Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package
for differential expression analysis of digital gene expression data.
*Bioinformatics* 2010; 26(1): 139–140.
[doi:10.1093/bioinformatics/btp616](https://doi.org/10.1093/bioinformatics/btp616){target="_blank"}

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK.
limma powers differential expression analyses for RNA sequencing
and microarray studies. *Nucleic Acids Research* 2015; 43(7): e47.
[doi:10.1093/nar/gkv007](https://doi.org/10.1093/nar/gkv007){target="_blank"}

Schurch NJ et al. How many biological replicates are needed in an
RNA seq experiment and which differential expression tool should
you use? *RNA* 2016; 22(6): 839–851.
[doi:10.1261/rna.053959.115](https://doi.org/10.1261/rna.053959.115){target="_blank"}

Gierliński M et al. Statistical models for RNA-seq data derived
from a two-condition 48-replicate experiment. *Bioinformatics*
2015; 31(22): 3625–3630.
[doi:10.1093/bioinformatics/btv425](https://doi.org/10.1093/bioinformatics/btv425){target="_blank"}

Squair JW et al. Confronting false discoveries in single-cell
differential expression. *Nature Communications* 2021; 12: 5692.
[doi:10.1038/s41467-021-25960-2](https://doi.org/10.1038/s41467-021-25960-2){target="_blank"}
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