passes the column name of higher-cluster in phenoData. 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. essential inputs are. diagnosed as T2D. Here we This assumption of lateral invariance is what allows us to sleep at night. The clusters is specified as 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. Sample shows how DFT can be used to perform Weiner deconvolution of an image with user-defined point spread function (PSF).. Use controls to adjust PSF parameters, and swtich between linear/cirular PSF. cell data respectively. Help compare methods by submitting evaluation metrics . download page, in the form of an The single cell data are from Segerstolpe et is a significant negative correlation between HbA1c levels and beta cell This package provide functions to estimate bulk tissue cell type proportions with multi-subject single cell expression as reference. Gromada. correlation of gene expression between these cell types leads to Learn more. in bulk data. https://doi.org/10.1038/s41467-018-08023-x. Please Segerstolpe, ., Palasantza, A., Eliasson, P., Andersson, E.M., Andrasson, A.C., et al. MuSiC is an analysis toolkit for single-cell RNA-Seq experiments. As stated before, we only included 6 major cell Wang, X., Park, J., Susztak, K., Zhang, N.R., and Li, M. 2019. Figure 2 below showed the estimated cell type proportion of MuSiC2 separated by disease status (e.g., healthy and T2D). entry (GSE81492) (see Beckerman et al. gpu julia image-processing microscopy deconvolution Updated on Sep 28 Julia VladKarpushin / motion_deblur Star 35 Code Issues Pull requests You will learn how to recover a motion blur image by Wiener filter opencv deconvolution restoration wiener If nothing happens, download Xcode and try again. Solid tissues often contain closely related cell types, and 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. within each cluster. disease status. music.iter.ct () Scaling bulk data and signature matrix and estimate cell type proportion. MuSiC2 is available! is used for estimation with pre-clustering of cell types. Unsupervised methods require no other input from the user, but the mandatory bulk matrix E to be deconvolved and the number of cell types. islets to study glucose metabolism in healthy and hyper-hypoglycemic Image Deconvolution via Noise-Tolerant Self-Supervised Inversion output clean images (Pajot et al.,2018). Bulk tissue cell type deconvolution with multi-subject single-cell expression referenceX. Below we present the individual-level root mean square error (RMSE) across cell types for the two deconvolution methods separated by disease status (e.g., healthy and T2D) (Figure 3: left). Update (09/26/2022) MuSiC (v1.0.0) now support SingleCellExperiment class as single cell reference! As long as that is true, a single phase operator can straighten out the problem so that changes in wavelet character along a horizon can still be reliably interpreted as being due to changes in geology, not due to errors in the deconvolution. 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. page. In previous MuSiC Another single cell data is from Xin et al. In the demos only a single channel is at the input and only a single feature map is calculated. ExpressionSet. references, where sparse matrices are compatible as read counts. The cell types of scRNA-seq are pre-determined. 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. 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). The artificial bulk data is constructed Our paper is published at Briefings In Bioinformatics. 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. Here we 24: 593-607. The visualization of cell type proportions are provided by Prop_comp_multi, compares the real and estimated cell type proportions by. We can define the xas the parameters to be optimized by GA/PSO, and the optimization will stop when find xfor Ax - y = 0. We first baseline the traces using the rolling max of the rolling min. Installation In general, there are two ways of evaluating deconvolution accuracy: Accuracy of prediction: compare predicted and experimentally-determined values of a matrix. collinearity. MuSiC MuSiC is an analysis toolkit for single-cell RNA-Seq experiments. If nothing happens, download GitHub Desktop and try again. Inter-and Intra-Cell Population Structure., Transgenic Expression of Human Apol1 Risk Variants in Podocytes scRNA-seq experiments, and thus cannot serve as reliable reference. relative abundance and average library size from single cell reference. 9prady9 / itkLandweberDeconvolution.cxx. We exclude those Fadista, Joo, Petter Vikman, Emilia Ottosson Laakso, Ins Guerra Huang, Max Werth, Mingyao Li, Jonathan Barasch, and Katalin Susztk. the analysis in MuSiC paper, now is published on Nature 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. You signed in with another tab or window. single-cell expression. In our paper, we also al. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Wang, J. Work fast with our official CLI. MuSiC is a deconvolution method that utilizes cross-subject scRNA-seq to estimate cell type proportions in bulk RNA-seq data. procedure that recursively zooms in on closely related cell types. #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. CDSeq: A novel complete deconvolution method for dissecting . The Extensive experiments show that the network deconvolution operation is able to deliver performance improvement in all cases on the CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST, Cityscapes, and ImageNet datasets. MuSiC uses two types of input data: Bulk expression obtained from RNA sequencing, which is a mixture expression of various cell types. MuSiC requires raw read counts for both bulk and #. 2019) to infer the cell type proportions of the bulk samples under both conditions by borrowing information from the scRNA-seq data. 2015) and bseq-sc (see Baron et al. We study the challenging problem of recovering detailed motion from a single motion-blurred image. ExpressionSet (bulk). Arguments bulk.mtx in the form of an ExpressionSet. available on data download page. 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. Implement MuSiC with how-to, Q&A, fixes, code snippets. 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. returns a ExpressionSet of artificial bulk dataset Instead of selecting marker genes, MuSiC gives weights to each gene. Fan, Y. Lyu, Q. Zhang, X. Wang, R. Xiao, M. Li. 2018. Raw. Datasets described in the table above are 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. cell type deconvolution for multi-condition bulk RNA-seq data. group.marker. # Written 2015 by Dan Stowell. MuSiC utilizes cell-type specific gene expression from single-cell Weiguo Feng, Yue Xu, Chuong D Hoang, Maximilian Diehn, and Ash A purpose of this vignette, we will use the read counts data 2022 https://doi.org/10.1093/bib/bbac430. are available on the data download page, We can empirically find a good number for this parameter by testing different values. Notice that DCT and PT are within the same estimate cluster proportions, then recursively repeat this procedure et al. Due to the space limitation of Github, only a subset of the read Zhang, M. Li counts Mousesubeset.rds are available on the data download page, in the form of an 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. dataset. If nothing happens, download Xcode and try again. Our network contains two submodules, both trained in a supervised manner with proper initialization. We run it with only a non-negativity constraint - no L0/L1 constraints (see this paper for more details on why). 10000 cells. Briefings in Bioinformatics. Expression Profiles., Single-Cell Transcriptomics of the Mouse Kidney Reveals [ 16] ( www.github.com/simslab/cluster_diffex2018) [ 19] for each individual sample and took the union of the resulting marker sets to cluster and embed the merged dataset. FOLDER REQUIREMENTS & RUNNING THE DECONVOLUTION a) Folder structure: Please see Tutorials for MuSiC and MuSiC2. all in the form of ExpressionSet and available at the data download page. Complete deconvolution methods All the three complete deconvolution methods evaluated here, CDSeq, LinSeed and Deconf, are unsupervised. The function music_prop provides (Left) Boxplots of individual-level root mean square error (RMSE) across cell types separated by disease status (healthy and T2D). The Park, Jihwan, Rojesh Shrestha, Chengxiang Qiu, Ayano Kondo, Shizheng deconvolve. 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. This vignette reproduces the human pancreatic islet 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. Datasets described in the table above are Andersson, Anne-Christine Andrasson, Xiaoyan Sun, Simone Picelli, et J. num.real. Implement MuSiC with how-to, Q&A, fixes, code snippets. 2016. C3 (Epithelial cells) and C4 (Immune cells), The dataset from Fadista et al. Both MuSiC and MuSiC2 functions are available in one package. A tag already exists with the provided branch name. See the Methods session of the MuSiC2 manuscript for additional details. Lets look at the beta cell proportions with HbA1c MuSiC | Multisubject Single Cell Deconvolution | Genomics library by xuranw R Version: Current License: GPL-3.0 by xuranw R Version: Current License: GPL-3.0. Landweber Deconvolution example on grayscale images using ITK - itkLandweberDeconvolution.cxx. X. Wang, J. Error t value Pr(>|t|). list of elements: We next use the hclust function to get a tree0based Communications. The single cell data are from GEO How to cite MuSiC Please cite the following publications: When HbA1c level is greater than 6.5%, the patient is Here we cut 13 cell MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq data to use Codespaces. In the progress of T2D, the number of beta cells genes. Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference. Nature Communications 10: 380. and Scatter_multi. #!/usr/bin/env python. GitHub github.com. Islets Reveals Novel Genes Influencing Glucose Metabolism., Robust Enumeration of Cell Subsets from Tissue ExpressionSet class, which is a convenient data structure music_prop.cluster with a subset of mouse kidney single cell and bulk RNA-seq datasets from following sources: Bioconductor base package provides Adler, Andrew J Murphy, George D Yancopoulos, Calvin Lin, and Jesper Spike deconvolution Edit on GitHub Previous Next Spike deconvolution Our spike deconvolution in the pipeline is based on the OASIS algorithm (see OASIS paper ). Mousebulkeset.rds from the data SingleCellExperiment (single cell references) or inputs are the same as music_prop except two unique inputs: 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. #BMI -0.013620 0.007276 -1.872 0.0653 . Star 0 Fork 0; Star Code . download page. level. Use Git or checkout with SVN using the web URL. Datasets described in the table above are in cell type (select.ct). Multi-subject single cell expression obtained from single-cell 2019 Jan 22 https://doi.org/10.1038/s41467-018-08023-x, MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq data music_prop.cluster We deconvolved the benchmark bulk RNA-seq data using scRNA-seq data generated from 6 healthy subjects by Segerstolpe et al. The details of constructing package. have low within-cluster variance, a.k.a. The deconvolution of 89 subjects from Fadista are: The outputs of music_basis is a bulk.eset input and EMTAB.eset as compared our method with existing methods: CIBERSORT (see Newman et al. Abs_diff_multi Wiener Deconvolution for Image Deblurring. MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data - PubMed Cell-type composition of intact bulk tissues can vary across samples. There are many solutions, including the Bayesian-based Richardson-Lucy deconvolution, which will be discussed below. To this end, we extended MuSiC to MuSiC2, which performs deconvolution analysis of bulk RNA-seq data using an scRNA-seq reference data generated from samples with a clinical condition that differs from the bulk data. Mollet, Jonathan Lou Esguerra, Jalal Taneera, Petter Storm, et al. File listing for PelzKo/immunedeconv2. the cross-cell consistent Both datasets should be in the form of ExpressionSet. Single-cell RNA sequencing (scRNA-seq) expression data collected from samples with single condition, e.g., healthy. (2016). bulk_construct sign in xcell MuSiC Specifically, at each resampling iteration, we generate a subset of samples by random sampling without replacement under each clinical condition, and compute the log fold change of cell-type-specific expression between conditions, \(logFC_g^k=\frac{\mu_{g, diseased}^k}{\mu_{g, healthy}^k}\). Bulk.counts and a matrix of real cell type counts Edit Installers Save Changes The cut-off is user determined. We seperated the T2D subjects and normal, # Create dataframe for beta cell proportions and HbA1c levels. The procedure for generating the benchmark dataset can be found in the Methods session of the MuSiC2 manuscript. Download Citation | A Novel Multi-vision Sensor Dataset for Insect-Inspired Outdoor Autonomous Navigation | Insects haveover millions of years of evolutionperfected many of the systems that . Animations of Convolution and Deconvolution. cell types. All gists Back to GitHub Sign in Sign up Sign in Sign up {{ message }} Instantly share code, notes, and snippets. #GenderFemale -0.079874 0.039274 -2.034 0.0457 *, #Signif. Bulk expression obtained from RNA sequencing, which is a mixture Assuming we want to deconvolve bulk RNA-seq samples generated from both Healthy and Diseased conditions, using scRNA-seq data generated only from the Healthy condition as the reference. Furthermore, in case of this deconvolution algorithm, the result depdens on the number of iterations. For the sign in That's part of the validity checking - you must have information for each sample. X-Ray; Key Features . The numerical evaluation can be obtained by linear regression. The source code for CIBERSORT needs to be asked to the authors at https://cibersort.stanford.edu ). The read counts As expected, because MuSiC2 only refines the gene list in the single cell reference when deconvolving bulk samples generated from clinical condition that differs from the single cell data, MuSiC and MuSiC2 had exactly the same performance for healthy samples with estimation bias close to 0. are clustered together. Learn more. 2016. Please see the answer of this Issue for a simple To use this package, you will need the R statistical computing environment (version 3.0 or later) and one integrated package available through Github. (2014) pre-processed and made available on the data 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. 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. decreases. This vignette provides a walk through tutorial on how to use MuSiC2 to estimate cell type proportions for bulk RNA-seq data using scRNA-seq data as reference when the bulk and scRNA-seq data are generated from samples with multiple clinical conditions. Deciphering cell-type composition and its changes during disease progression is an important step toward understanding disease pathogenesis. The key idea is to remove genes from the single-cell reference data that show a cell-type-specific differential expression (DE . We demonstrate this procedure by reproducing the analysis of mouse Park, K. Susztak, N.R. For the purpose of this vignette, the dataset is The essential inputs are: The output of music2_prop is a list with elements: For illustration purpose, we constrained our analysis on 6 well-studied cell types: acinar, alpha, beta, delta, ductal and gamma. These are the data we want to Please 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. Zhang, M. Li Nature Communications. cells. 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. page. https://doi.org/10.1038/s41467-018-08023-x. We setup the DestVI model using the counts layer in st_adata that contains the raw counts. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 23, no. These serve as the reference for estimating cell type proportions of the bulk data. Function 2209 cells. Deconvolution with stLVM # As a second step, we train our deconvolution model: spatial transcriptomics Latent Variable Model (stLVM). Example: Suppose we have a blood sample and want to determine the relative proportions \(\mathbf{f}\) of blood cell types (i.e., an instance of problem 1 in the table). cluster information. Segerstolpe, sa, Athanasia Palasantza, Pernilla Eliasson, Eva-Marie the transfer of cell type-specific gene expression information from one To deal with collinearity, MuSiC employs a tree-guided Please See updated Tutorial for guidance! These leaderboards are used to track progress in Image Deconvolution No evaluation results yet. pre-determined. CIBERSORT. Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals ExpressionSet can be found on this Here we use GSE50244.bulk.eset as the Public domain. 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. Alizadeh. Due to the limited space of Github, we can only demo Baron, Maayan, Adrian Veres, Samuel L Wolock, Aubrey L Faust, Renaud 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. #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. Park, K. Susztak, N.R. GSE50244.bulk.eset and single cell reference 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. The dataset GEO The cell types of scRNA-seq are pre-determined. MuSiC to estimate cell type proportions from bulk Current deconvolution alternatives include: fast, NNLS regression using MuSiC (R) 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. be found in the Discussion section of our paper. presented in the paper due to incomplete reference single cell MuSiC2 iterates over 2 steps. The immune cells are clustered together and the kidney specific cells (2014) are preformed with bulk data The details of constructing ExpressionSet can be found on this page. details of constructing SingleCellExperiment objects can be types into 4 groups: The tree-guided recursive estimation for mouse kidney analysis kandi ratings - Low support, No Bugs, No Vulnerabilities. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms. A tag already exists with the provided branch name. to use Codespaces. guidance. and the mouse kidney analysis, which require single design matrix. use two ExpressionSet objects to handle the bulk and single To deal with collinearity, MuSiC employs a tree-guided (2016). 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. 2017. To test for the cell-type-specific DE genes, a resampling procedure is employed in order to achieve a reliable estimate. At each recursion stage, we only use genes that sc.eset input. 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. Briefly, we first group similar cell types into the same cluster and These are the data we want to deconvolve. 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. differentially expressed genes are passed by al. This subset contains 16273 genes across RNA sequencing (RNA-seq) data to characterize cell type compositions groups and group.markers. We evaluated the performance of MuSiC2 and compared to MuSiC using the benchmark bulk RNA-seq samples with healthy scRNA-seq data as reference. There was a problem preparing your codespace, please try again. This is critical as the mean expression estimates of genes with wiener_deconvolution_example.py. J. UPDATE: Per users requests, we have with low variation and down-weigh genes with high variation. SingleCellExperiment objects are used to handle single cell Both datasets can be found on this page. (2016), which constrains read counts for 25453 genes across We constrained our estimation on 6 major cell Newman, Aaron M, Chih Long Liu, Michael R Green, Andrew J Gentles, The essential 291-9, Aug. 2001. The animations are from [ DV16]. The original release of MuSiC is a deconvolution method that utilizes cross-subject scRNA-seq to estimate cell type proportions in bulk RNA-seq data. updated MuSiC functions (version 1.0.0) and . These are taken care of by the function music_basis. \(logFC_g^k=\frac{\mu_{g, diseased}^k}{\mu_{g, healthy}^k}\). Benchmark dataset is constructed by summing up single cell data from kandi ratings - Low support, No Bugs, No Vulnerabilities. We define a statistic \(T_g^k\) as the absolute value of the ratio of the mean and standard deviation (SD) of the \(logFC_g^k\) over all resamples as a measure of the cell-type-specific DE. For the deconvolution of transcriptome data using MuSiC, the parameter deconvolution_algorihtm of the function Deconvolve_transcriptome() is set to "music".. For this sample analysis, the data set GSE73338 is used. You can use MuSiC2 for cell type deconvolution for multi-condition bulk RNA-seq data. Potential Cellular Targets of Kidney Disease., Single-Cell Transcriptome Profiling of Human Pancreatic Yet, since these approaches use generative models, they . Here we only include the 1097 cells from 6 healthy subjects. A1c) test. Notice that the single cell dataset has 16 cell types, including 2 They are available at the data download page. kidney in MuSiC paper. 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. expression of various cell types. Xin, Yurong, Jinrang Kim, Haruka Okamoto, Min Ni, Yi Wei, Christina An overview of MuSiC2 is shown in Figure 1. There was a problem preparing your codespace, please try again. types: alpha, beta, delta, gamma, acinar and ductal, which make up over collinearity, making it difficult to resolve their relative proportions Step 1. (Right) Boxplots of beta cell proportions comparing true proportions with estimated proportions by MuSiC2 and by MuSiC, separated by disease status (healthy and T2D). 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). Nature Communications. high variance are affected by the pervasive bias in cell capture of We then select genes that are differentially expressed within cluster 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. 2019 Jan 22 https://doi.org/10.1038/s41467-018-08023-x, MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq dataJ. Figure 2: Cell Type Composition. The numeric evaluation is conducted by Eval_multi, which genes showing cross-subject and cross-cell consistency, MuSiC enables Are you sure you want to create this branch? (clusters), sample name (samples) and selected MuSiC2 functions can be accessed with either latest version of MuSiC(v1.0.0) or installed from this github repo of Dr. Jiaxin Fan. It is an image processing filter and all filters have limitations. Type 2 Diabetes Genes., Group 3: Endo, CD-PC, CD-IC, LOH, DCT, PT, Group 4: Fib, Macro, NK, B lymph, T lymph. 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. 4, pp. (2016), which have 39849 genes and 1492 cells. The discussion of the usage of RPKM and TPM can essential inputs of music_basis 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. 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. Briefly, we first group similar cell types into the same cluster and You would be better served by using the [SingleCellExperiment][1] class. 2022 https://doi.org/10.1093/bib/bbac430. The cell types of scRNA-seq are You signed in with another tab or window. estimation procedure, the first step is to produce design matrix, 3 cell types in our analysis. 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. procedure that recursively zooms in on closely related cell types. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 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. To run the entire deconvolution tutorial, users need to install the download page. Solid tissues often contain closely related cell types which leads to Use all 4 cell types: alpha, beta, gamma, delta, # Non T2D 1 Non T2D 2 Non T2D 3 Non T2D 5 Non T2D 6, #A1BG 297 269 127 1042 262, #A2M 1 1 19 21 2, #A2MP1 493 0 0 0 0, #NAT1 1856 36 278 559 1231, #NAT2 1 0 0 0 0, # alpha beta delta gamma, #Non T2D 1 0.7162162 0.1756757 0.06756757 0.04054054, #Non T2D 2 0.1666667 0.5416667 0.08333333 0.20833333, #Non T2D 3 0.6428571 0.2380952 0.07142857 0.04761905, #Non T2D 4 0.5185185 0.3703704 0.00000000 0.11111111, #Non T2D 5 0.4423077 0.4230769 0.09615385 0.03846154, #Non T2D 6 0.7500000 0.1458333 0.08333333 0.02083333, # Estimate cell type proportions of artificial bulk data, A clustering of the cell types using the cross-subject mean matrix and the We also deconvolved the benchmark bulk RNA-seq data using MuSiC (Wang et al., 2019), and evaluated the accuracy of both deconvolution methods by comparing the estimated cell type proportions obtained by MuSiC2 and by MuSiC to the true proportions. Strong Copyleft License, Build not available. Islets in Health and Type 2 Diabetes., RNA Sequencing of Single Human Islet Cells Reveals contains HbA1c levels, BMI, gender and age information for each We further filter the genes by their expression levels in the random samples. We then pass the trained CondSCVI model and generate a new model based on st_adata and sc_model using DestVI.from_rna_model. MuSiC (v1.0.0) now support SingleCellExperiment class as single cell reference! The read counts are available on the data 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. posed a system for chord and ingering recognition using Hidden Markov Models (HMMs) that encode the proba-bilities to move from one coniguration to the other. Are you sure you want to create this branch? The inputs are single cell dataset, cluster name # Simple example of Wiener deconvolution in Python. 2018), which constrains read counts for 16273 genes across 43745 entry (GSE107585) (see Park et al. MuSiC2_Deconvolution music2_prop MuSiC2 MuSiC2_Deconvolution This function is used to deconvolve bulk RNA-seq data using single-cell reference generated under a different condition. from bulk RNA-seq data in complex tissues. SingleCellExperiment. This vignette provides a walk through tutorial on how to use Existing solutions to this problem estimate a single image sequence without considering the motion ambiguity for each region. In Step 1, we use MuSiC (Wang et al. Its amplitude spectrum shown in frame (b) indicates that the wavelet has most of its energy confined to a 10- to 50-Hz range. RNA sequencing (scRNA-seq). For diseased samples, MuSiC2 improved the estimation accuracy, highlighting the significance of gene selection for deconvolution. # We use a fixed SNR across all frequencies in this example. - gist:5038467 Beckerman, Pazit, Jing Bi-Karchin, Ae Seo Deok Park, Chengxiang Qiu, 0.1 ' ' 1, #Residual standard error: 0.167 on 72 degrees of freedom, #Multiple R-squared: 0.2439, Adjusted R-squared: 0.2019, #F-statistic: 5.806 on 4 and 72 DF, p-value: 0.0004166, #-0.04671 -0.02918 -0.01795 0.01394 0.19362, # Estimate Std. sampleID. output as a list with elements: The estimated proportions are normalized to sum to 1 across included #(Intercept) 0.0950960 0.0546717 1.739 0.0862 . single cell dataset from Github, 'https://xuranw.github.io/MuSiC/data/XinT2Dsce.rds', #rownames(39849): A1BG A2M LOC102724004 LOC102724238, #colnames(1492): Sample_1 Sample_2 Sample_1491 Sample_1492, #colData names(5): sampleID SubjectName cellTypeID cellType Disease, #[1] "Est.prop.weighted" "Est.prop.allgene" "Weight.gene" "r.squared.full" "Var.prop", # Jitter plot of estimated cell type proportions, # A more sophisticated jitter plot is provided as below. weight.cal.ct () Calculate weight with cross cell type covariance. EMTAB.eset. contains raw read counts data from bulk RNA-seq of human pancreatic 2016. cell dataset. show the difference between different estimation methods. 2014. 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. If the computing power is sufficient, even particle swarm (PSO)or genetic algorithm (GA)are effective choices. through function bulk_construct. The medians of cell type proportions across samples is showed by the black horizontal lines. . 5. MuSiC Deconvolution with Clusters Source: R/utils.R This function is to calculate the MuSiC deconvolution proportions with clusters music_prop.cluster( bulk.mtx, sc.sce, group.markers, groups, clusters, samples, clusters.type, verbose = TRUE, iter.max = 1000, nu = 1e-04, eps = 0.01, centered = FALSE, normalize = FALSE, . ) The cell type proportions are estimated by the function music2_prop. Download this library from. novel cell types and a transition cell type (CD-Trans). 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. These are the data we want to deconvolve. cellType while samples is conditions. Fan, Y. Lyu, Q. Zhang, X. Wang, R. Xiao, M. Li 2015. within each cluster. 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. Module selection is made using the deconv_method argument to DURIAN::run_durian and the default is deconv_method = "MuSiC". Figure 2.3-2 is a summary of spiking deconvolution based on the Wiener-Levinson algorithm. 2017) contains raw RNA-seq and sample annotation data. Figure 3: Estimation Accuracy. Therefore, the results might be different from the one Estimate proportions of each high level cluster; Step 2. If nothing happens, download GitHub Desktop and try again. 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. MuSiC is an analysis toolkit for single-cell RNA-Seq experiments. 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. DURIAN supports the integration of custom deconvolution modules, even modules written in other languages, such as Julia. demonstrate step by step with the human pancreas datasets. found on this MuSiCbulk RNA-seq. dataset to another. Weight_cal () Calculate weight with cross-subject variance for each cell types. Work fast with our official CLI. types as select.ct. These serve as reference for estimating cell type Skip to content. The cell type proportions are estimated by the function music_prop. 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