![]() This usually comes with a warranty and is a great option if you don’t want to use Scotch Guard. We used seven cans of Scotch Guard the day it arrived to cover every inch of the couch and it has made a huge difference! You can also check in your area for professionals to come in and treat your couch. ![]() We decided we would take the risk only if we were willing to stain treat it immediately and take the extra time to care for it. So if that isn’t your vibe, I recommend getting the standard depth (41″) because it is still deeper than most!Ī white sofa is a risky decision to most. That is why I love a deep couch: it invites. My favorite thing is when a friend leans back onto the giant couch with a cup of tea and stays for a while to chat. When we have friends over, I offer them extra pillows to put behind them if they aren’t as comfortable settling in at first. It is very deep (47″), and I love that because it is for lounging, living, and a kick-off-your-shoes-and-curl-up-with-a-book type home. When ordering in the store, they tripled checked that I wanted the extra deep option. The first thing to know is I love a deep sofa. The exact sofa we have is the Harmony Extra Deep 82″ Yarn Dyed Linen in Stone White. That’s my goal in this post – is to help you decide if it is right for you! Before we decided on this sofa, I looked around the internet for other opinions since it was an investment. Reproducing results from manuscriptĬode to reproduce Harmony results from the Korsunsky et al 2019 manuscript will be made available on /immunogenomics/harmony2019.It has been one year with our West Elm’s Harmony Sofa so I wanted to give an honest, not sponsored review. We created a more advanced tutorial that explores the internal data structures used in the Harmony algorithm. The examples above all return integrated PCA embeddings. my_harmony_embeddings <- HarmonyMatrix(my_pca_embeddings, meta_data, c("dataset", "donor", "batch_id"), do_pca=FALSE)ĭo the same with your Seurat object: seuratObject <- RunHarmony(seuratObject, c("dataset", "donor", "batch_id")) Advanced To do this, specify a vector covariates to integrate. Harmony can integrate over multiple covariates. For more, details, check out this vignette. You can run Harmony with functions from the MUDAN package. SeuratObj <- RunUMAP(seuratObj, reduction = "harmony") seuratObj <- RunHarmony(seuratObj, "dataset") In downstream analyses, use the Harmony embeddings instead of PCA.įor example, run Harmony and then UMAP in two lines.Run Harmony with the RunHarmony() function. ![]() You'll only need to make two changes to your code. You can run Harmony within your Seurat workflow. My_harmony_embeddings <- HarmonyMatrix(normalized_counts, meta_data, "dataset") Seurat Harmony will scale these counts, run PCA, and finally perform integration. You can also run Harmony on a sparse matrix of library size normalized expression counts. ![]() My_harmony_embeddings <- HarmonyMatrix(my_pca_embeddings, meta_data, "dataset", do_pca=FALSE) Normalized gene matrix Harmony is packaged with a small dataset library(harmony) To input your own low dimensional embeddings directly, set do_pca=FALSE. The Harmony algorithm iteratively corrects PCA embeddings. Quick StartĬheck out this vignette for a quick start tutorial. We made it easy to run Harmony in most common R analysis pipelines. Installation may include compiling C++ code from source, so it can take a few minutes. To run Harmony, open R and install directly from github using the following commands: library(devtools) Harmony has been tested on Linux, OS X, and Windows platforms. Please consult the DESCRIPTION file for more details on required R packages. Harmony has been tested on R versions >= 3.4. Fast, sensitive and accurate integration of single-cell data with HarmonyĬheck out the manuscript in Nature Methods:įor Python users, check out the harmonypy package by Kamil Slowikowski. ![]()
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