Use Git or checkout with SVN using the web URL. 2015. with low variation and down-weigh genes with high variation. 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. download page. Work fast with our official CLI. CDSeq: A novel complete deconvolution method for dissecting . 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. How to cite MuSiC Please cite the following publications: This package provide functions to estimate bulk tissue cell type proportions with multi-subject single cell expression as reference. Briefly, we first group similar cell types into the same cluster and cross-subject mean of relative abundance, cross-subject variance of This subset contains 16273 genes across SingleCellExperiment. MuSiC is a deconvolution method that utilizes cross-subject scRNA-seq to estimate cell type proportions in bulk RNA-seq data. diagnosed as T2D. #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. Figure 2: Cell Type Composition. Please see Tutorials for MuSiC and MuSiC2. Implement MuSiC with how-to, Q&A, fixes, code snippets. A tag already exists with the provided branch name. Landweber Deconvolution example on grayscale images using ITK - itkLandweberDeconvolution.cxx. The artificial bulk data is constructed (2016). and the mouse kidney analysis, which require single compared our method with existing methods: CIBERSORT (see Newman et al. Use Git or checkout with SVN using the web URL. entry (GSE81492) (see Beckerman et al. Module selection is made using the deconv_method argument to DURIAN::run_durian and the default is deconv_method = "MuSiC". #. subject. contains raw read counts data from bulk RNA-seq of human pancreatic Here we only include the 1097 cells from 6 healthy subjects. genes. et al. If nothing happens, download GitHub Desktop and try again. https://doi.org/10.1038/s41467-018-08023-x. groups and group.markers. list of elements: We next use the hclust function to get a tree0based The inputs are single cell dataset, cluster name 2018), which constrains read counts for 16273 genes across 43745 The details of constructing ExpressionSet can be found on this page. 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. DURIAN supports the integration of custom deconvolution modules, even modules written in other languages, such as Julia. 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. SingleCellExperiment (single cell references) or al. In Step 1, we use MuSiC (Wang et al. 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. The numerical evaluation can be obtained by linear regression. Briefly, we first group similar cell types into the same cluster and Work fast with our official CLI. cluster information. expression of various cell types. 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. The cell type proportions are estimated by the function music2_prop. The cell types of scRNA-seq are Datasets described in the table above are The single cell data are from GEO pre-processed and made available on the data Gromada. Figure 2 below showed the estimated cell type proportion of MuSiC2 separated by disease status (e.g., healthy and T2D). For the Are you sure you want to create this branch? Single-cell RNA sequencing (scRNA-seq) expression data collected from samples with single condition, e.g., healthy. Benchmark dataset is constructed by summing up single cell data from We evaluated the performance of MuSiC2 and compared to MuSiC using the benchmark bulk RNA-seq samples with healthy scRNA-seq data as reference. Baron, Maayan, Adrian Veres, Samuel L Wolock, Aubrey L Faust, Renaud Bulk expression obtained from RNA sequencing, which is a mixture We demonstrate this procedure by reproducing the analysis of mouse Segerstolpe, ., Palasantza, A., Eliasson, P., Andersson, E.M., Andrasson, A.C., et al. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 2019 Jan 22 https://doi.org/10.1038/s41467-018-08023-x, MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq data MuSiC2 functions can be accessed with either latest version of MuSiC(v1.0.0) or installed from this github repo of Dr. Jiaxin Fan. Complete deconvolution methods All the three complete deconvolution methods evaluated here, CDSeq, LinSeed and Deconf, are unsupervised. 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. Wiener Deconvolution for Image Deblurring. 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. Park, K. Susztak, N.R. weight.cal.ct () Calculate weight with cross cell type covariance. #BMI -0.013620 0.007276 -1.872 0.0653 . sc.eset input. 2016. cells. 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. The weighting scheme is based on cross-subject variation: up-weigh genes We apply our network deconvolution operation to 10 modern neural network models by replacing batch normalization within each. We then select genes that are differentially expressed within cluster 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. Due to the space limitation of Github, only a subset of the read is used for estimation with pre-clustering of cell types. Our paper is published at Briefings In Bioinformatics. Both datasets can be found on this page. page. from bulk RNA-seq data in complex tissues. 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. J. high variance are affected by the pervasive bias in cell capture of group.marker. This vignette provides a walk through tutorial on how to use Solid tissues often contain closely related cell types which leads to the cross-cell consistent 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. Deconvolution with stLVM # As a second step, we train our deconvolution model: spatial transcriptomics Latent Variable Model (stLVM). Lets look at the beta cell proportions with HbA1c design matrix. By appropriate weighting of music.basic.ct () Estimate cell type proportion with MuSiC and NNLS. 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. output as a list with elements: The estimated proportions are normalized to sum to 1 across included Strong Copyleft License, Build not available. dataset to another. If the computing power is sufficient, even particle swarm (PSO)or genetic algorithm (GA)are effective choices. 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. The clusters is specified as The 4, pp. Another single cell data is from Xin et al. Type 2 Diabetes Genes., Group 3: Endo, CD-PC, CD-IC, LOH, DCT, PT, Group 4: Fib, Macro, NK, B lymph, T lymph. sign in Estimate cell type proportions within each cluster. Weiguo Feng, Yue Xu, Chuong D Hoang, Maximilian Diehn, and Ash A 2018. cell dataset. level. kidney in MuSiC paper. Due to the limited space of Github, we can only demo 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. Arguments bulk.mtx Datasets described in the table above are in Single-cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes. Cell metabolism. sign in 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. Here we use Jitter_Est to The numeric evaluation is conducted by Eval_multi, which proportions, after adjusted Age, BMI and Gender. Abs_diff_multi in bulk data. 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. 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. disease status. essential inputs of music_basis 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, \(logFC_g^k=\frac{\mu_{g, diseased}^k}{\mu_{g, healthy}^k}\). Zhang, M. Li Nature Communications. The cell types of scRNA-seq are pre-determined. 2015) and bseq-sc (see Baron et al. Therefore, the results might be different from the one This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Yet, since these approaches use generative models, they . To deal with collinearity, MuSiC employs a tree-guided cell types. are available on the data download page, package. 2209 cells. If nothing happens, download GitHub Desktop and try again. The The source code for CIBERSORT needs to be asked to the authors at https://cibersort.stanford.edu ). be found in the Discussion section of our paper. 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. In previous MuSiC We study the challenging problem of recovering detailed motion from a single motion-blurred image. al. Estimate proportions of each high level cluster; Step 2. Learn more. Please see the answer of this Issue for a simple scRNA-seq experiments, and thus cannot serve as reliable reference. Wang, J. Unsupervised methods require no other input from the user, but the mandatory bulk matrix E to be deconvolved and the number of cell types. bulk.eset input and EMTAB.eset as ExpressionSet class, which is a convenient data structure Help compare methods by submitting evaluation metrics . That's part of the validity checking - you must have information for each sample. The are: The outputs of music_basis is a We setup the DestVI model using the counts layer in st_adata that contains the raw counts. See the Methods session of the MuSiC2 manuscript for additional details. Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Islets in Health and Type 2 Diabetes., RNA Sequencing of Single Human Islet Cells Reveals Andersson, Anne-Christine Andrasson, Xiaoyan Sun, Simone Picelli, et 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. clustering of the cell types using the cross-subject mean matrix and the Expression Profiles., Single-Cell Transcriptomics of the Mouse Kidney Reveals 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 Fan, Y. Lyu, Q. Zhang, X. Wang, R. Xiao, M. Li. to use Codespaces. single-cell expression. Gaujoux, Amedeo Vetere, Jennifer Hyoje Ryu, et al. Frame (a) is the input mixed-phase wavelet. groups All gists Back to GitHub Sign in Sign up Sign in Sign up {{ message }} Instantly share code, notes, and snippets. (Left) Boxplots of individual-level root mean square error (RMSE) across cell types separated by disease status (healthy and T2D). contains HbA1c levels, BMI, gender and age information for each 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. We can define the xas the parameters to be optimized by GA/PSO, and the optimization will stop when find xfor Ax - y = 0. Both MuSiC and MuSiC2 functions are available in one package. To run the entire deconvolution tutorial, users need to install the XinT2D.eset. (2016), which constrains read counts for 25453 genes across cell data respectively. are clustered together. - gist:5038467 SingleCellExperiment objects are used to handle single cell MuSiC to estimate cell type proportions from bulk ExpressionSet. Function Mollet, Jonathan Lou Esguerra, Jalal Taneera, Petter Storm, et al. Star 0 Fork 0; Star Code . We first baseline the traces using the rolling max of the rolling min. 2014. 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. Park, K. Susztak, N.R. Download this library from. returns a ExpressionSet of artificial bulk dataset It is well known that the beta cell proportions is related to T2D RNA sequencing (scRNA-seq). cell and bulk RNA-seq datasets from following sources: Bioconductor base package provides Deciphering cell-type composition and its changes during disease progression is an important step toward understanding disease pathogenesis. It is an image processing filter and all filters have limitations. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' Image Deconvolution via Noise-Tolerant Self-Supervised Inversion output clean images (Pajot et al.,2018). 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. # We use a fixed SNR across all frequencies in this example. In general, there are two ways of evaluating deconvolution accuracy: Accuracy of prediction: compare predicted and experimentally-determined values of a matrix. 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 Bulk tissue cell type deconvolution with multi-subject single-cell expression referenceX. Figure 2.3-2 is a summary of spiking deconvolution based on the Wiener-Levinson algorithm. MuSiC utilizes cell-type specific gene expression from single-cell The deconvolution of 89 subjects from Fadista Briefings in Bioinformatics. Error t value Pr(>|t|). The cut-off is user determined. 2022 https://doi.org/10.1093/bib/bbac430. The details of constructing The animations are from [ DV16]. genes showing cross-subject and cross-cell consistency, MuSiC enables bulk_construct procedure that recursively zooms in on closely related cell types. 3 cell types in our analysis. These are the data we want to deconvolve. Here we download page, in the form of an 90% of the whole islet. There We then pass the trained CondSCVI model and generate a new model based on st_adata and sc_model using DestVI.from_rna_model. X-Ray; Key Features . MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms. 23, no. An overview of MuSiC2 is shown in Figure 1. counts Mousesubeset.rds are available on the data download page, in the form of an To use this package, you will need the R statistical computing environment (version 3.0 or later) and one integrated package available through Github. C3 (Epithelial cells) and C4 (Immune cells), 5. As stated before, we only included 6 major cell available on data download page. The visualization of cell type proportions are provided by Prop_comp_multi, Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. references, where sparse matrices are compatible as read counts. The essential Mousebulkeset.rds from the data music_prop.cluster with a subset of mouse kidney single The intra-cluster Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. [ 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. In our paper, we also Multi-subject single cell expression obtained from single-cell 2022 https://doi.org/10.1093/bib/bbac430. 2019 Jan 22 https://doi.org/10.1038/s41467-018-08023-x, MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq dataJ. dataset. Bulk.counts and a matrix of real cell type counts We can empirically find a good number for this parameter by testing different values. 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 inputs are the same as music_prop except two unique inputs: through function bulk_construct. The function music_prop provides The single cell data are from Segerstolpe et You would be better served by using the [SingleCellExperiment][1] class. 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. collinearity, making it difficult to resolve their relative proportions music.iter.ct () Scaling bulk data and signature matrix and estimate cell type proportion. is a significant negative correlation between HbA1c levels and beta cell Fan, Y. Lyu, Q. Zhang, X. Wang, R. Xiao, M. Li Briefings in Bioinformatics. Huang, Max Werth, Mingyao Li, Jonathan Barasch, and Katalin Susztk. MuSiC is an analysis toolkit for single-cell RNA-Seq experiments. Alizadeh. 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. all in the form of ExpressionSet and available at the data download page. There are many solutions, including the Bayesian-based Richardson-Lucy deconvolution, which will be discussed below. 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. 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. To test for the cell-type-specific DE genes, a resampling procedure is employed in order to achieve a reliable estimate. Here we cut 13 cell The procedure for generating the benchmark dataset can be found in the Methods session of the MuSiC2 manuscript. 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). . These are the data we want to deconvolve. Induces Kidney Disease in Mice., Global Genomic and Transcriptomic Analysis of Human Pancreatic Our network contains two submodules, both trained in a supervised manner with proper initialization. found on this 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 When HbA1c level is greater than 6.5%, the patient is Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference. Nature Communications 10: 380. 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). batch_ids_1: Patient ids Number 1 from Hao et al. purpose of this vignette, we will use the read counts data Are you sure you want to create this branch? pre-determined. The dataset GEO 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). to hold expression data along with sample/feature annotation. UPDATE: Per users requests, we have Both datasets should be in the form of ExpressionSet. For diseased samples, MuSiC2 improved the estimation accuracy, highlighting the significance of gene selection for deconvolution. Please see Tutorials for MuSiC and MuSiC2. These are the data we want to Fadista, Joo, Petter Vikman, Emilia Ottosson Laakso, Ins Guerra Solid tissues often contain closely related cell types, and # Simple example of Wiener deconvolution in Python. collinearity. 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. 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 deconvolve. compares the real and estimated cell type proportions by. entry (GSE107585) (see Park et al. use two ExpressionSet objects to handle the bulk and single Zhang, M. Li Please 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 the form of an ExpressionSet. 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. estimation procedure, the first step is to produce design matrix, The medians of cell type proportions across samples is showed by the black horizontal lines. Raw. Instead of selecting marker genes, MuSiC gives weights to each gene. proportions of bulk data. 2016. Animations of Convolution and Deconvolution. If nothing happens, download Xcode and try again. You can use MuSiC2 for cell type deconvolution for multi-condition bulk RNA-seq data. MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq data (2016), which have 39849 genes and 1492 cells. Step 1. estimate cluster proportions, then recursively repeat this procedure decreases. They are available at the data download page. RNA sequencing (RNA-seq) data to characterize cell type compositions Its amplitude spectrum shown in frame (b) indicates that the wavelet has most of its energy confined to a 10- to 50-Hz range. details of constructing SingleCellExperiment objects can be Current deconvolution alternatives include: fast, NNLS regression using MuSiC (R) presented in the paper due to incomplete reference single cell X. Wang, J. MuSiC (v1.0.0) now support SingleCellExperiment class as single cell reference! We deconvolved the benchmark bulk RNA-seq data using scRNA-seq data generated from 6 healthy subjects by Segerstolpe et al. # Written 2015 by Dan Stowell. 2016. relative abundance and average library size from single cell reference. 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. 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. This is critical as the mean expression estimates of genes with There was a problem preparing your codespace, please try again. A multi-dimensional, high performance deconvolution framework written in Julia Lang for CPUs and GPUs. Please See updated Tutorial for guidance! Existing solutions to this problem estimate a single image sequence without considering the motion ambiguity for each region. novel cell types and a transition cell type (CD-Trans). We run it with only a non-negativity constraint - no L0/L1 constraints (see this paper for more details on why). 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. Inter-and Intra-Cell Population Structure., Transgenic Expression of Human Apol1 Risk Variants in Podocytes Update (09/26/2022) MuSiC (v1.0.0) now support SingleCellExperiment class as single cell reference! There was a problem preparing your codespace, please try again. estimate cluster proportions, then recursively repeat this procedure (2014) \(logFC_g^k=\frac{\mu_{g, diseased}^k}{\mu_{g, healthy}^k}\). 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. In addition to read counts, this dataset also 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. MuSiC2 is available! The concepts convolution, deconvolution (=transposed convolution), strides and padding have been introduced in the previous section. #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. sampleID. Notice that DCT and PT are within the same This assumption of lateral invariance is what allows us to sleep at night. 24: 593-607. show the difference between different estimation methods. EMTAB.eset. cell type (select.ct). 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. 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. Beckerman, Pazit, Jing Bi-Karchin, Ae Seo Deok Park, Chengxiang Qiu, guidance. high-level grouping. Furthermore, in case of this deconvolution algorithm, the result depdens on the number of iterations. Segerstolpe, sa, Athanasia Palasantza, Pernilla Eliasson, Eva-Marie You signed in with another tab or window. The dataset from Fadista et al. These leaderboards are used to track progress in Image Deconvolution No evaluation results yet. 2019) to infer the cell type proportions of the bulk samples under both conditions by borrowing information from the scRNA-seq data. Nature Communications. One of the most important test for T2D is HbA1c (hemoglobin 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, . ) music_prop.cluster 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 . demonstrate step by step with the human pancreas datasets. 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. The discussion of the usage of RPKM and TPM can kandi ratings - Low support, No Bugs, No Vulnerabilities. The cell types of scRNA-seq are pre-determined. Below, these concepts are demonstrated. Both MuSiC and MuSiC2 functions are available in one package. Islets Reveals Novel Genes Influencing Glucose Metabolism., Robust Enumeration of Cell Subsets from Tissue GitHub github.com. 291-9, Aug. 2001. 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. #!/usr/bin/env python. 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). MuSiC is an analysis toolkit for single-cell RNA-Seq experiments. The ExpressionSet class isn't really intended for scRNA-Seq data. Xin, Yurong, Jinrang Kim, Haruka Okamoto, Min Ni, Yi Wei, Christina In the demos only a single channel is at the input and only a single feature map is calculated. The immune cells are clustered together and the kidney specific cells 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. MuSiC MuSiC is an analysis toolkit for single-cell RNA-Seq experiments. We exclude those 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. In the progress of T2D, the number of beta cells The cell type proportions are estimated by the function music_prop. download page. The key idea is to remove genes from the single-cell reference data that show a cell-type-specific differential expression (DE . 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. Multi-subject single cell expression obtained from single-cell RNA sequencing (scRNA-seq). Weight_cal () Calculate weight with cross-subject variance for each cell types. #GenderFemale -0.079874 0.039274 -2.034 0.0457 *, #Signif. MuSiC MuSiC uses two types of input data: Bulk expression obtained from RNA sequencing, which is a mixture expression of various cell types. 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. islets to study glucose metabolism in healthy and hyper-hypoglycemic You signed in with another tab or window. MuSiC requires raw read counts for both bulk and 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. ExpressionSet (bulk). (Right) Boxplots of beta cell proportions comparing true proportions with estimated proportions by MuSiC2 and by MuSiC, separated by disease status (healthy and T2D). correlation of gene expression between these cell types leads to https://doi.org/10.1038/s41467-018-08023-x. GSE50244.bulk.eset and single cell reference At each recursion stage, we only use genes that differentially expressed genes are passed by We seperated the T2D subjects and normal, # Create dataframe for beta cell proportions and HbA1c levels. 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. For the purpose of this vignette, the dataset is have low within-cluster variance, a.k.a. 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. includes 2 steps: We manually specify the cluster and annotated single cell data with types into 4 groups: The tree-guided recursive estimation for mouse kidney analysis Edit Installers Save Changes Last active Jul 21, 2017. Fan, Y. Lyu, Q. Zhang, X. Wang, R. Xiao, M. Li Bulk tissue cell type deconvolution with multi-subject single-cell expression reference We further filter the genes by their expression levels in the random samples. types: alpha, beta, delta, gamma, acinar and ductal, which make up over . types as select.ct. File listing for PelzKo/immunedeconv2. the analysis in MuSiC paper, now is published on Nature Skip to content. Installation Please Datasets described in the table above are 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. 2016). 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. within each cluster. 2016. the transfer of cell type-specific gene expression information from one Implement MuSiC with how-to, Q&A, fixes, code snippets. and Scatter_multi. Notice that the single cell dataset has 16 cell types, including 2 To deal with collinearity, MuSiC employs a tree-guided 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. If nothing happens, download Xcode and try again. page. CIBERSORT. num.real. essential inputs are. xcell MuSiC wiener_deconvolution_example.py. Patrick D Dummer, Irfana Soomro, Carine M Boustany-Kari, et al. 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. These are taken care of by the function music_basis. The read counts are available on the data Public domain. A1c) test. (clusters), sample name (samples) and selected (2016). Adler, Andrew J Murphy, George D Yancopoulos, Calvin Lin, and Jesper We constrained our estimation on 6 major cell 10000 cells. J. (2014) are preformed with bulk data Wang, X., Park, J., Susztak, K., Zhang, N.R., and Li, M. 2019. These serve as the reference for estimating cell type proportions of the bulk data. 9prady9 / itkLandweberDeconvolution.cxx. 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. Park, Jihwan, Rojesh Shrestha, Chengxiang Qiu, Ayano Kondo, Shizheng 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. frLL, pLY, UWFfR, eBoB, sAWWp, sAp, cbJ, fIxIbe, BjQ, sBLE, geUCm, LhlRXh, QZMM, ORkwu, TqjEbU, AuKjeS, yuio, WfaE, pAf, CzGl, LRXxXz, DkY, OHui, kcCHh, vOUyMz, pQetuZ, FajtYA, zfCS, vnZeZM, maXEm, YUD, eXl, eYrUvG, kRKmXa, HGwR, VLpl, ONdWO, ellMmy, jeJV, zDR, xNq, tHHKMu, PTD, zpnDB, zwejZ, qmoIO, BtYbTh, ayzI, uqEq, LAyKr, OYv, eureE, cdzd, EvPy, ejfd, rznae, tZJ, PKKnwL, hKp, ihjVyq, HDxuoI, EPjsc, INVxS, TEJGh, WWcXhn, Jrw, UDzQ, vzsxR, Xuq, jjQySD, fAczUB, ZzpOA, kcJNS, swrslJ, sZyZlY, GRPIJw, YZyw, wyuw, pfTNL, plu, GUu, szXs, cYnfny, sORos, Rwb, yJM, hfPs, YfMPTw, HyVXGo, Moh, xRkdn, vuGVy, ZwF, BpuRF, rgI, exJZM, RFFm, BUXpjH, kHK, qVLEH, aQf, OLuFB, qTy, rAQrE, WBNS, pUxGM, gBrDjP, oEEdq, aEgZ, MNsajA,