InstaPrism: an R package for fast implementation of BayesPrism

Hu, Mengying; Chikina, Maria

doi:10.1101/2023.03.07.531579

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…The NNLS deconvolution was run with SciPy's NNLS's algorithm [25] with default settings. BayesPrism was run through InstaPrism [31], and CIBERSORTx was run through the Docker container with a token from the CIBERSORTx team.…”

Section: Deconvolution (Nnls Cibersortx Bayesprism)mentioning

confidence: 99%

Missing cell types in single-cell references impact deconvolution of bulk data but are detectable

Ivich,

Davidson,

Grieshober

et al. 2024

Preprint

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Advancements in RNA-sequencing have dramatically expanded our ability to study gene expression profiles of biological samples in bulk tissue and single cells. Deconvolution of bulk data with single-cell references provides the ability to study relative cell-type proportions, but most methods assume a reference is present for every cell type in bulk data. This is not true in all circumstances--cell types can be missing in single-cell profiles for many reasons. In this study, we examine the impact of missing cell types on deconvolution methods. Our experimental designs are simulation-based, using paired single-cell and single-nucleus data, since single-nucleus RNA-sequencing is able to preserve the nucleus of cell types that would otherwise be missing in a single-cell counterpart. These datasets allow us to examine the missing-cell-type phenomenon in deconvolution with realistic proportions. We apply three deconvolution methods that vary from straightforward to state-of-the-art: non-negative least squares, BayesPrism, and CIBERSORTx. We find that the performance of deconvolution methods is influenced by both the number and the similarity of missing cell types, consistent with prior results. Additionally, we find that missing cell-type profiles can be recovered from residuals using a simple non-negative matrix factorization strategy. We expect our simulation strategies and results to provide a starting point for those developing new deconvolution methods and help improve their to better account for the presence of missing cell types. Building off of our findings on simulated data, we then analyzed data from high-grade serous ovarian cancer; a tumor that has regions of highly variable levels of adipocytes dependent on the region from which it is sampled. We observe results consistent with simulation, namely that expression patterns from cell types likely to be missing appear present in residuals. Our results suggests that deconvolution methods should consider the possibility of missing cell types and provide a starting point to address this. Our source code for data simulation and analysis is freely available at https://github.com/greenelab/pred_missing_celltypes.

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Section: Deconvolution (Nnls Cibersortx Bayesprism)mentioning

confidence: 99%

Missing cell types in single-cell references impact deconvolution of bulk data but are detectable

Ivich,

Davidson,

Grieshober

et al. 2024

Preprint

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“…In order to utilize this data, bulk deconvolution was run using the single cell data as a reference. Instaprism ( 91 ) performs fast Bayesian based cell type deconvolution, and was used with default parameters to generate estimated cell type from the bulk RNA-seq data.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Cellular and transcriptional profiles of peripheral blood mononuclear cells pre-vaccination predict immune response to preventative MUC1 vaccine

Yuan,

McKeague,

Raghu

et al. 2024

Preprint

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A single arm trial (NCT007773097) and a double-blind, placebo controlled randomized trial (NCT02134925) were conducted in individuals with a history of advanced colonic adenoma to test the safety and immunogenicity of the MUC1 tumor antigen vaccine and its potential to prevent new adenomas. These were the first two trials of a non-viral cancer vaccine administered in the absence of cancer. The vaccine was safe and strongly immunogenic in 43% (NCT007773097) and 25% (NCT02134925) of participants. The lack of response in a significant number of participants suggested, for the first time, that even in a premalignant setting, the immune system may have already been exposed to some level of suppression previously reported only in cancer. Single-cell RNA-sequencing (scRNA-seq) on banked pre-vaccination peripheral blood mononuclear cells (PBMCs) from 16 immune responders and 16 non-responders identified specific cell types, genes, and pathways of a productive vaccine response. Responders had a significantly higher percentage of CD4+ naive T cells pre-vaccination, but a significantly lower percentage of CD8+ T effector memory (TEM) cells and CD16+ monocytes. Differential gene expression (DGE) and transcription factor inference analysis showed a higher level of expression of T cell activation genes, such as Fos and Jun, in CD4+ naive T cells, and pathway analysis showed enriched signaling activity in responders. Furthermore, Bayesian network analysis suggested that these genes were mechanistically connected to response. Our analyses identified several immune mechanisms and candidate biomarkers to be further validated as predictors of immune responses to a preventative cancer vaccine that could facilitate selection of individuals likely to benefit from a vaccine or be used to improve vaccine responses.

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“…Batch effects of the merged data were removed via the method ComBat from the R package sva 18 and data was normalized with quantile normalization 19 . Deconvolution was performed via the R package InstaPrism 5 which is an efficient…”

Section: Cluster Analysis Of Kidney and Brain-biopsy Scrna-seq Datamentioning

confidence: 99%

“…Several "deconvolution" tools have been introduced including CIBERSORTx 7 , MuSiC 2 , BisqueRNA 3 , BayesPrism 4 and InstaPrism 5 . While in terms of accuracy BayesPrism 4 out-performs the other approaches, InstaPrism 5 re-implements BayesPrism in a more memory-and runtime-efficient fashion.…”

Section: Introductionmentioning

confidence: 99%

Single cell data enables dissecting cell types present in bulk transcriptome data

Wruck,

Adjaye

2024

Preprint

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SummaryThe quality of organoid models can be assessed by single-cell-RNA-sequencing (scRNA-seq) but often only bulk transcriptome data is available. Here we present a pipeline for the analysis of scRNA-seq data and subsequent “deconvolution”, which is a method for estimating cell type fractions in bulk transcriptome data based on expression profiles and cell types found in scRNA-seq data derived from biopsies. We applied this pipeline on bulk iPSC-derived kidney and brain organoid transcriptome data to identify cell types employing two scRNA-seq kidney datasets and one brain dataset. Relevant cells present in kidney (eg. proximal tubules, distal convoluted tubules and podocytes) and brain (eg. neurons, astrocytes, oligodendrocytes and microglia) with obligatory endothelial and immune-related cells were identified. We anticipate that this pipeline will also enable estimation of cell type fractions in organoids of other tissues.

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InstaPrism: an R package for fast implementation of BayesPrism

Cited by 6 publications

References 12 publications

Missing cell types in single-cell references impact deconvolution of bulk data but are detectable

Missing cell types in single-cell references impact deconvolution of bulk data but are detectable

Cellular and transcriptional profiles of peripheral blood mononuclear cells pre-vaccination predict immune response to preventative MUC1 vaccine

Single cell data enables dissecting cell types present in bulk transcriptome data

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