RNA sequencing (scRNA-seq). 90% of the whole islet. The key idea of MuSiC2 is that, when the bulk samples and single-cell samples are from different clinical conditions, the majority of genes shall still have similar cell-type-specific gene expression pattern between conditions. Landweber Deconvolution example on grayscale images using ITK - itkLandweberDeconvolution.cxx. Download this library from. Therefore, the results might be different from the one To run the entire deconvolution tutorial, users need to install the 2019 Jan 22 https://doi.org/10.1038/s41467-018-08023-x, MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq dataJ. We study the challenging problem of recovering detailed motion from a single motion-blurred image. num.real. cluster information. group.marker. includes 2 steps: We manually specify the cluster and annotated single cell data with Spike deconvolution Edit on GitHub Previous Next Spike deconvolution Our spike deconvolution in the pipeline is based on the OASIS algorithm (see OASIS paper ). You would be better served by using the [SingleCellExperiment][1] class. Specifically, we compute the mean of \(\mu_{g,healthy}^k\) and \(\mu_{g,diseased}^k\) over the resamples, and retain genes with cell-type-specific expression in the bottom 5% for samples in both conditions as stable genes and exclude them from the cell-type-specific DE detection. 2016. Segerstolpe, sa, Athanasia Palasantza, Pernilla Eliasson, Eva-Marie SingleCellExperiment. We demonstrate this procedure by reproducing the analysis of mouse Baron, Maayan, Adrian Veres, Samuel L Wolock, Aubrey L Faust, Renaud By removing genes with cell-type-specific differential expression (DE) between conditions from the single-cell reference, MuSiC2 can refine the reference gene list and yield more accurate cell type proportion estimates. list of elements: We next use the hclust function to get a tree0based all in the form of, 'https://xuranw.github.io/MuSiC/data/GSE50244bulkeset.rds', #ExpressionSet (storageMode: lockedEnvironment), # sampleNames: Sub1 Sub2 Sub89 (89 total), # varLabels: sampleID SubjectName tissue (7 total), #experimentData: use 'experimentData(object)', # Download EMTAB single cell dataset from Github, 'https://xuranw.github.io/MuSiC/data/EMTABsce_healthy.rds', #rownames(25453): SGIP1 AZIN2 KIR2DL2 KIR2DS3, #colnames(1097): AZ_A10 AZ_A11 HP1509101_P8 HP1509101_P9, #colData names(4): sampleID SubjectName cellTypeID cellType, # Download Xin et al. Similar as MuSiC (Wang et al., 2019), MuSiC2 uses two types of input data: Bulk RNA sequencing expression data collected from samples with 2 different clincial conditions, e.g., healthy and diseased. By removing genes with cell-type-specific differential expression (DE) between samples with different clinical conditions from the single-cell reference, MuSiC2 holds the potential to yield more accurate cell type proportion estimates. In Step 2, for samples within each condition, we deconvolve the bulk-level expression over the cell type proportion estimates obtained in Step 1 to infer the cell-type-specific mean expression for each gene and identify cell-type-specific DE genes between conditions. Single-cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes. Cell metabolism. MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq data MuSiC2 Deconvolution MuSiC2 is an iterative algorithm aiming to improve cell type deconvolution for bulk RNA-seq data when the bulk data and scRNA-seq reference are generated from samples with different clinical conditions. By appropriate weighting of Estimate proportions of each high level cluster; Step 2. Then, by removing genes with cell-type-specific DE from the scRNA-seq data, we can update the cell type proportion estimates in Step 1 for bulk samples generated under Diseased condition. high-level grouping. For diseased samples, MuSiC2 improved the estimation accuracy, highlighting the significance of gene selection for deconvolution. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms. page. deconvolve. The cut-off is user determined. Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Deconvolution is no magic. . kandi ratings - Low support, No Bugs, No Vulnerabilities. For all clustering and visualization analyses of merged datasets, we first identified marker genes using the drop-out curve method described in Levitin et al. #HbA1c -0.0093214 0.0072991 -1.277 0.2057, #Age 0.0005268 0.0005093 1.035 0.3044, #BMI -0.0015116 0.0020906 -0.723 0.4720, #GenderFemale -0.0037650 0.0112844 -0.334 0.7396, #Residual standard error: 0.04799 on 72 degrees of freedom, #Multiple R-squared: 0.0574, Adjusted R-squared: 0.005028, #F-statistic: 1.096 on 4 and 72 DF, p-value: 0.3651, # Download Mouse bulk dataset from Github, 'https://xuranw.github.io/MuSiC/data/Mousebulkeset.rds', # sampleNames: control.NA.27 control.NA.30 APOL1.GNA78M (10 total), # varLabels: sampleID SubjectName Control, # Download Mouse single cell dataset from Github, 'https://xuranw.github.io/MuSiC/data/Mousesub_sce.rds', #rownames(16273): Rp1 Sox17 DHRSX CAAA01147332.1, #colnames(10000): TGGTTCCGTCGGCTCA-2 CGAGCCAAGCGTCAAG-4 GTATTCTGTAGCTAAA-2 GAGCAGAGTCAACATC-1, # [1] "Endo" "Podo" "PT" "LOH" "DCT" "CD-PC" "CD-IC" "CD-Trans" "Novel1", #[10] "Fib" "Macro" "Neutro" "B lymph" "T lymph" "NK" "Novel2", # Plot the dendrogram of design matrix and cross-subject mean of realtive abundance, # Hierarchical clustering using Complete Linkage, \((p_{31},p_{32},.,p_{36},p_{41},.,p_{45})\), # C1 C2 C31 C32 C33 C34 C35 C36 C41 C42, # "Neutro" "Podo" "Endo" "CD-PC" "LOH" "CD-IC" "DCT" "PT" "Macro" "Fib", 'https://xuranw.github.io/MuSiC/data/IEmarkers.RData', # This RData file provides two vectors of gene names Epith.marker and Immune.marker, # We now construct the list of group marker, # The name of group markers should be the same as the cluster names, # Construct artificial bulk dataset. estimate cluster proportions, then recursively repeat this procedure We can define the xas the parameters to be optimized by GA/PSO, and the optimization will stop when find xfor Ax - y = 0. 2015) and bseq-sc (see Baron et al. # Simple example of Wiener deconvolution in Python. presented in the paper due to incomplete reference single cell anndata_checkload: Checks if anndata package is loaded anndata_is_identical: Check if two anndata objects are identical anndata_to_singlecellexperiment: Convert AnnData to SingleCellExperiment autogenes_checkload: Checks if python and the autogenes module are available and. bulk.eset input and EMTAB.eset as That's part of the validity checking - you must have information for each sample. xcell MuSiC You can use MuSiC2 for cell type deconvolution for multi-condition bulk RNA-seq data. Here we use Jitter_Est to Function We constrained our estimation on 6 major cell Notice that DCT and PT are within the same MuSiC to estimate cell type proportions from bulk X-Ray; Key Features . with low variation and down-weigh genes with high variation. MuSiCbulk RNA-seq. Potential Cellular Targets of Kidney Disease., Single-Cell Transcriptome Profiling of Human Pancreatic Since fold change is sensitive to genes with low expression, we suggest that genes with bulk-level average sequencing depth < 20 are retained as stable genes and excluded from the cell-type-specific DE detection. If nothing happens, download GitHub Desktop and try again. are: The outputs of music_basis is a If nothing happens, download Xcode and try again. proportions, after adjusted Age, BMI and Gender. 2018. The numerical evaluation can be obtained by linear regression. Benchmark dataset is constructed by summing up single cell data from Park, K. Susztak, N.R. Park, K. Susztak, N.R. al. Newman, Aaron M, Chih Long Liu, Michael R Green, Andrew J Gentles, 9prady9 / itkLandweberDeconvolution.cxx. Error t value Pr(>|t|). Mousebulkeset.rds from the data Instead of selecting marker genes, MuSiC gives weights to each gene. Implement MuSiC with how-to, Q&A, fixes, code snippets. Both MuSiC and MuSiC2 functions are available in one package. references, where sparse matrices are compatible as read counts. In general, there are two ways of evaluating deconvolution accuracy: Accuracy of prediction: compare predicted and experimentally-determined values of a matrix. The Current deconvolution alternatives include: fast, NNLS regression using MuSiC (R) J. Fan, Y. Lyu, Q. Zhang, X. Wang, R. Xiao, M. Li Briefings in Bioinformatics. correlation of gene expression between these cell types leads to music_prop.cluster with a subset of mouse kidney single cross-subject mean of relative abundance, cross-subject variance of Please see the answer of this Issue for a simple kidney in MuSiC paper. To test for the cell-type-specific DE genes, a resampling procedure is employed in order to achieve a reliable estimate. Expression Profiles., Single-Cell Transcriptomics of the Mouse Kidney Reveals The essential conda install -c bioconda music-deconvolution Description Companion package to "A bulk tissue deconvolution method with multi-subject single cell expression reference." This package providase functions to estimate bulk tissue cell type proportions with multi-subject single cell expression as reference. Fan, Y. Lyu, Q. Zhang, X. Wang, R. Xiao, M. Li. These serve as reference for estimating cell type #. XinT2D.eset. There 2016). #(Intercept) 0.0950960 0.0546717 1.739 0.0862 . clustering of the cell types using the cross-subject mean matrix and the Bulk tissue cell type deconvolution with multi-subject single-cell expression reference ExpressionSet can be found on this For illustration purpose, in this tutorial, we deconvolved the benchmark bulk RNA-seq data, which contain raw RNA-seq read counts and sample annotation data for 100 healthy and 100 diseased (i.e., Type 2 diabetes (T2D)) samples simulated based on pancreatic islets scRNA-seq RNA-seq data from Segerstolpe et al. In previous MuSiC Here we use GSE50244.bulk.eset as the For the purpose of this vignette, the dataset is 2022 https://doi.org/10.1093/bib/bbac430. Inter-and Intra-Cell Population Structure., Transgenic Expression of Human Apol1 Risk Variants in Podocytes Communications. Please guidance. bulk_construct Especially for beta cells, MuSiC2 produced much more accurate cell type proportion estimates for diseased bulk samples than MuSiC, which suffered from severe underestimation (Figure 3: right). and the mouse kidney analysis, which require single MuSiC utilizes cell-type specific gene expression from single-cell Jitter plots showing estimated cell type proportions of benchmark bulk RNA-seq samples by disease status (healthy and T2D), estimated using MuSiC2 with healthy scRNA-seq data as reference. to hold expression data along with sample/feature annotation. Bulk expression obtained from RNA sequencing, which is a mixture 2018), which constrains read counts for 16273 genes across 43745 SCDCadopts an ENSEMBLE method to integrate deconvolution results across methods and datasets, giving reference data that are more close to the bulk RNA-seq data higher weights, implicitly addressing the batch-effect confounding when multiple scRNA-seq reference sets are available. Error t value Pr(>|t|), #(Intercept) 0.877022 0.190276 4.609 1.71e-05 ***, #HbA1c -0.061396 0.025403 -2.417 0.0182 *, #Age 0.002639 0.001772 1.489 0.1409. collinearity, making it difficult to resolve their relative proportions A Matlab solver for short-and-sparse deconvolution can be downloaded from the following github link: https://github.com/deconvlab/sas-deconv To exercise the test code, please execute the following code in Matlab console: $ deconv_example References For detailed explanation, please refer to the background page. CIBERSORT. MuSiC2 is an iterative algorithm aiming to improve cell type deconvolution for bulk RNA-seq data using scRNA-seq data as reference when the bulk data are generated from samples with multiple clinical conditions where at least one condition is different from the scRNA-seq reference. Figure 2: Cell Type Composition. xuranw/MuSiC: Multi-subject single cell deconvolution xuranw/MuSiC: Multi-subject single cell deconvolution Companion package to: A bulk tissue deconvolution method with multi-subject single cell expression reference. If the computing power is sufficient, even particle swarm (PSO)or genetic algorithm (GA)are effective choices. the transfer of cell type-specific gene expression information from one Estimate cell type proportions within each cluster. The artificial bulk data is constructed MuSiC2 functions can be accessed with either latest version of MuSiC(v1.0.0) or installed from this github repo of Dr. Jiaxin Fan. In the progress of T2D, the number of beta cells Briefings in Bioinformatics. The weighting scheme is based on cross-subject variation: up-weigh genes Both MuSiC and MuSiC2 functions are available in one package. entry (GSE81492) (see Beckerman et al. contains HbA1c levels, BMI, gender and age information for each demonstrate step by step with the human pancreas datasets. The cell types of scRNA-seq are The single cell data are from GEO et al. UPDATE: Per users requests, we have Strong Copyleft License, Build not available. Gaujoux, Amedeo Vetere, Jennifer Hyoje Ryu, et al. Work fast with our official CLI. Our paper is published at Briefings In Bioinformatics. Color deconvolution for python cf : A. C. Ruifrok and D. A. Johnston, "Quantification of histochemical staining by color deconvolution.," Analytical and quantitative cytology and histology / the International Academy of Cytology [and] American Society of Cytology, vol. The procedure for generating the benchmark dataset can be found in the Methods session of the MuSiC2 manuscript. The key idea of MuSiC2 is that, when the bulk samples and single-cell reference samples are from different clinical conditions, the majority of genes shall still share similar cell-type-specific gene expression pattern regardless of clinical conditions. Star 0 Fork 0; Star Code . Both datasets can be found on this page. estimation procedure, the first step is to produce design matrix, You signed in with another tab or window. genes showing cross-subject and cross-cell consistency, MuSiC enables all in the form of ExpressionSet and available at the data download page. MuSiC MuSiC is an analysis toolkit for single-cell RNA-Seq experiments. diagnosed as T2D. 23, no. 2019) to infer the cell type proportions of the bulk samples under both conditions by borrowing information from the scRNA-seq data. 2209 cells. Type 2 Diabetes Genes., Group 3: Endo, CD-PC, CD-IC, LOH, DCT, PT, Group 4: Fib, Macro, NK, B lymph, T lymph. In Step 1, we use MuSiC (Wang et al. Figure 2 below showed the estimated cell type proportion of MuSiC2 separated by disease status (e.g., healthy and T2D). Wang, J. To assess deconvolution performance, we built a signature matrix to distinguish these cell subsets and tested it on a validation cohort of bulk RNA-sequencing (RNA-seq) profiles of blood obtained. Our solution is to establish the connection between traditional optimization-based schemes and a neural network architecture where a novel, separable structure is introduced as a reliable support for robust deconvolution against artifacts. music.basic.ct () Estimate cell type proportion with MuSiC and NNLS. The concepts convolution, deconvolution (=transposed convolution), strides and padding have been introduced in the previous section. (2014) are preformed with bulk data Datasets described in the table above are in details of constructing SingleCellExperiment objects can be More recent work shows that a composite of several GAN models trained on blurred, noisy, and compressed images can generate images free of any such artifacts (Kaneko & Harada,2020). See the Methods session of the MuSiC2 manuscript for additional details. to use Codespaces. MuSiC2 is an iterative algorithm aiming to improve cell type deconvolution for bulk RNA-seq data using scRNA-seq data as reference when the bulk data are generated from samples with multiple clinical conditions where at least one condition is different from the scRNA-seq reference. #BMI -0.013620 0.007276 -1.872 0.0653 . Genes with \(T_g^k\) in the top 5% for common cell types, i.e., cell types with average proportion 10%, or in the top 1% for rare cell types, i.e., cell types with average proportion < 10%, are considered as cell-type-specific DE genes. These are taken care of by the function music_basis. (2016), which constrains read counts for 25453 genes across 2016. purpose of this vignette, we will use the read counts data The visualization of cell type proportions are provided by Prop_comp_multi, MuSiC is an analysis toolkit for single-cell RNA-Seq experiments. islets to study glucose metabolism in healthy and hyper-hypoglycemic We exclude those disease status. 2019 Jan 22 https://doi.org/10.1038/s41467-018-08023-x, MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq data Segerstolpe, ., Palasantza, A., Eliasson, P., Andersson, E.M., Andrasson, A.C., et al. To use this package, you will need the R statistical computing environment (version 3.0 or later) and several packages available through Bioconductor and CRAN. These are the data we want to Step 1. Raw. Mollet, Jonathan Lou Esguerra, Jalal Taneera, Petter Storm, et al. GSE50244.bulk.eset and single cell reference Gromada. novel cell types and a transition cell type (CD-Trans). The numeric evaluation is conducted by Eval_multi, which Implement MuSiC with how-to, Q&A, fixes, code snippets. The key idea is to remove genes from the single-cell reference data that show a cell-type-specific differential expression (DE . design matrix. Its amplitude spectrum shown in frame (b) indicates that the wavelet has most of its energy confined to a 10- to 50-Hz range. The ExpressionSet class isn't really intended for scRNA-Seq data. Our network contains two submodules, both trained in a supervised manner with proper initialization. Edit Installers Save Changes File listing for PelzKo/immunedeconv2. Zhang, M. Li Nature Communications. To use this package, you will need the R statistical computing environment (version 3.0 or later) and several packages available through Bioconductor and CRAN. Latest papers with no code Most implemented Social Latest No code Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training no code yet 16 Jun 2022 are clustered together. MuSiC is an analysis toolkit for single-cell RNA-Seq experiments. through function bulk_construct. We deconvolved the benchmark bulk RNA-seq data using scRNA-seq data generated from 6 healthy subjects by Segerstolpe et al. in the form of an ExpressionSet. Last active Jul 21, 2017. A tag already exists with the provided branch name. Are you sure you want to create this branch? Furthermore, in case of this deconvolution algorithm, the result depdens on the number of iterations. In our paper, we also we introduce a novel benchmark applicable to recordings without electrophysiological ground truth, based on the correlation of responses to two stimulus repeats, and used this to show that unconstrained nnd also outperformed the other algorithms when run on "zoomed out" datasets of 10,000 cell recordings from the visual cortex of mice of either Patrick D Dummer, Irfana Soomro, Carine M Boustany-Kari, et al. are available on the data download page, #!/usr/bin/env python. Please note the convention for transcriptome_data that the row names have to contain the gene names and the column names have to contain the sample names. sampleID. The cell type proportions are estimated by the function music_prop. MuSiC: MUlti-sample SIngle Cell deconvolution (MuSiC) utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. Bulk tissue cell type deconvolution with multi-subject single-cell expression referenceX. Solid tissues often contain closely related cell types which leads to How to cite MuSiC Please cite the following publications: cells. inputs are the same as music_prop except two unique inputs: SingleCellExperiment (single cell references) or 2016. Existing solutions to this problem estimate a single image sequence without considering the motion ambiguity for each region. is used for estimation with pre-clustering of cell types. Use Git or checkout with SVN using the web URL. The cell types of scRNA-seq are pre-determined. groups and group.markers. These are the data we want to deconvolve. Animations of Convolution and Deconvolution. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. types into 4 groups: The tree-guided recursive estimation for mouse kidney analysis (2016). Another single cell data is from Xin et al. Although you will still have problems if you don't have as many rows in your colData object as you have columns in your 'counts` object. types as select.ct. Help compare methods by submitting evaluation metrics . batch_ids_1: Patient ids Number 1 from Hao et al. Image Deconvolution via Noise-Tolerant Self-Supervised Inversion output clean images (Pajot et al.,2018). There was a problem preparing your codespace, please try again. music.iter.ct () Scaling bulk data and signature matrix and estimate cell type proportion. We first baseline the traces using the rolling max of the rolling min. By alternating between cell type deconvolution (Step 1) and cell-type-specific DE gene detection and removal (Step 2), MuSiC2 gradually refines the list of stable genes retained in the scRNA-seq reference and improves the cell type proportion estimation for the diseased samples. One of the most important test for T2D is HbA1c (hemoglobin All gists Back to GitHub Sign in Sign up Sign in Sign up {{ message }} Instantly share code, notes, and snippets. Work fast with our official CLI. The updated MuSiC functions (version 1.0.0) and essential inputs are. These serve as the reference for estimating cell type proportions of the bulk data. pre-determined. Islets Reveals Novel Genes Influencing Glucose Metabolism., Robust Enumeration of Cell Subsets from Tissue #lm(formula = ct.prop ~ HbA1c + Age + BMI + Gender, data = subset(m.prop.ana, # Min 1Q Median 3Q Max, #-0.27768 -0.13186 -0.01096 0.10661 0.35790, # Estimate Std. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. While our work has a BSD (3-clause) license, you may need to obtain a license to use the individual normalization/deconvolution methods (e.g. # We use a fixed SNR across all frequencies in this example. (2016), which have 39849 genes and 1492 cells. . Bulk.counts and a matrix of real cell type counts Here we music_prop.cluster differentially expressed genes are passed by estimate cluster proportions, then recursively repeat this procedure This subset contains 16273 genes across This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. single-cell expression. within each cluster. We seperated the T2D subjects and normal, # Create dataframe for beta cell proportions and HbA1c levels. 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