Cell group analysis reveals changes in upper-layer neurons associated with schizophrenia

Dai, Rujia; Chen, Lulu; Liu, Sihan; Chen, Yu; Jiang, Yi; Dai, Jiacheng; Wang, Qihang; Kuney, Liz; Kopp, Richard F.; Yu, Guoqiang; Wang, Yue; Chen, Chao; Liu, Chunyu

doi:10.1101/2020.10.22.351213

Cited by 2 publications

(1 citation statement)

References 76 publications

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“…To further demonstrate biomedical utility of the swCAM method, we applied the swCAM software tool to enable detecting STS co-expression networks and differential expressed genes from RNA-seq data of human brains (Dai et al, 2021). The analytic pipeline is given in Figure 8a.…”

Section: Real-life Biomedical Case Studiesmentioning

confidence: 99%

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

et al. 2021

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Motivation Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and subtype-specific expressions. Existing deconvolution methods can only estimate averaged subtype-specific expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations. Results We report a sample-wise Convex Analysis of Mixtures (swCAM) method that can estimate subtype proportions and subtype-specific expressions in individual samples from bulk tissue transcriptomes. We extend our previous CAM framework to include a new term accounting for between-sample variations and formulate swCAM as a nuclear-norm and ℓ2,1-norm regularized matrix factorization problem. We determine hyperparameter values using cross-validation with random entry exclusion and obtain a swCAM solution using an efficient alternating direction method of multipliers. Experimental results on realistic simulation data show that swCAM can accurately estimate subtype-specific expressions in individual samples and successfully extract co-expression networks in particular subtypes that are otherwise unobtainable using bulk data. In two real-world applications, swCAM analysis of bulk RNASeq data from brain tissue of cases and controls with bipolar disorder or Alzheimer’s disease identified significant changes in cell proportion, expression pattern and co-expression module in patient neurons. Comparative evaluation of swCAM versus peer methods is also provided. Availability The R Scripts of swCAM are freely available at https://github.com/Lululuella/swCAM. A user’s guide and a vignette are provided. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Real-life Biomedical Case Studiesmentioning

confidence: 99%

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

et al. 2021

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Comprehensive and integrative analyses identify TYW5 as a schizophrenia risk gene

Zhang

Zhao

et al. 2022

BMC Med

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Background Identifying the causal genes at the risk loci and elucidating their roles in schizophrenia (SCZ) pathogenesis remain significant challenges. To explore risk variants associated with gene expression in the human brain and to identify genes whose expression change may contribute to the susceptibility of SCZ, here we report a comprehensive integrative study on SCZ. Methods We systematically integrated the genetic associations from a large-scale SCZ GWAS (N = 56,418) and brain expression quantitative trait loci (eQTL) data (N = 175) using a Bayesian statistical framework (Sherlock) and Summary data-based Mendelian Randomization (SMR). We also measured brain structure of 86 first-episode antipsychotic-naive schizophrenia patients and 152 healthy controls with the structural MRI. Results Both Sherlock (P = 3. 38 × 10−6) and SMR (P = 1. 90 × 10−8) analyses showed that TYW5 mRNA expression was significantly associated with risk of SCZ. Brain-based studies also identified a significant association between TYW5 protein abundance and SCZ. The single-nucleotide polymorphism rs203772 showed significant association with SCZ and the risk allele is associated with higher transcriptional level of TYW5 in the prefrontal cortex. We further found that TYW5 was significantly upregulated in the brain tissues of SCZ cases compared with controls. In addition, TYW5 expression was also significantly higher in neurons induced from pluripotent stem cells of schizophrenia cases compared with controls. Finally, combining analysis of genotyping and MRI data showed that rs203772 was significantly associated with gray matter volume of the right middle frontal gyrus and left precuneus. Conclusions We confirmed that TYW5 is a risk gene for SCZ. Our results provide useful information toward a better understanding of the genetic mechanism of TYW5 in risk of SCZ.

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Cell group analysis reveals changes in upper-layer neurons associated with schizophrenia

Cited by 2 publications

References 76 publications

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution

Comprehensive and integrative analyses identify TYW5 as a schizophrenia risk gene

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