Loupe Cell Browser ATAC教程翻译-Part1

仅供学术交流，严禁转载及商用！原内容来自于10X Genomics官方支持文档。

什么是Loupe Cell Browser? - What is Loupe

简介 - Introduction

Loupe Cell Browser is a desktop application for Windows and MacOS that allows you to quickly and easily visualize and analyze 10x Chromium™ Single Cell ATAC data. It is optimized for finding significant peaks and distinguishing transcription factor motifs, identifying cell types, comparing chromatin accessibility between groups of cells, and exploring substructure within cell clusters. Loupe is named for a jeweler's loupe, which is used to inspect gems.

Loupe Cell Browser是一款能够在Windows和MacOS上运行的桌面应用，可以让你快速、方便的可视化和分析10x Chromium™ Single Cell ATAC数据。该软件用于寻找显著peaks，区分转录因子motif，识别细胞类型，比较细胞群间染色质可接近性，以及探索细胞群内的亚群。Loupe是根据珠宝商用来检查宝石的小型放大镜(Loupe)来命名的。

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Loupe Cell Browser opens .cloupe files generated by the Cell Ranger ATAC pipeline. Once your data is in Loupe Cell Browser, you can rapidly explore and gain insights from the data without writing a line of code.

Loupe Cell Browser的输入文件为Cell Ranger ATAC流程生成的 .cloupe 文件。当你把数据加载到Loupe Cell Browser中时，你就可以快速的探索数据，从中获得更多的见解，而不用写一行代码。

Loupe Cell Browser is built to accelerate the following applications:

Loupe Cell Browser为了加速以下应用而构建：

Identifying Cell Types

Find promoters and transcription factor motifs that differentiate distinct cell types and functional groups using Accessibility mode.

识别细胞类型

利用可接近性模式发现区别特异细胞类型及功能群组的启动子和转录因子motif。

Analyzing Differential Accessibility

Use the ATAC Peak Viewer to pinpoint the location of differentially accessible genomic regions with putative regulatory function.

分析差异可接近性

使用ATAC Peak Viewer精确查找差异可接近基因组区域的位置，推断其调控功能。

Finding Significant Features

Create custom clusters and use differential accessibility tools to identify precise cell groups within your data.

发现显著性特征

创建自定义分群并利用差异可接近性工具来识别数据集中精确细胞群组。

Sharing Results

Save features of interest, export data tables, and capture screenshots of your single-cell ATAC data.

分享结果

保存感兴趣的特征，导出数据表，保存single-cell ATAC数据的截图。

To walk through the features and uses of Loupe Cell Browser for single-cell ATAC data, start the ATAC tutorial.

为了更快的了解针对single-cell ATAC数据分析的Loupe Cell Browser的特性及使用方法，从ATAC教程开始吧！

Loupe Cell Browser can also be used to analyze Chromium™ Single Cell 5′ and 3′ gene expression and immune profiling data. To learn how, consult the Loupe Cell Browser for Gene Expression page.

Loupe Cell Browser也可以用来分析Chromium™ Single Cell 5′ 和 3′基因表达以及免疫分析数据。想了解更多？移步Loupe Cell Browser for Gene Expression页面

Finally, if you have already used Loupe Cell Browser to investigate gene expression, and are new to ATAC, you should read Differences from Gene Expression.

最后，如果你已经用Loupe Cell Browser分析过基因表达数据，但是刚了解ATAC，你应该读一读同“基因表达差异”小节。

下载

https://support.10xgenomics.com/single-cell-atac/software/downloads/latest

安装：Loupe Cell Browser

下载和安装

首先，从下载页下载Loupe Cell Browser。

windows

Loupe Cell Browser for Windows is distributed as a self-installing .exe file. Double-click on the downloaded file to install.

Loupe Cell Browser for Windows作为一个自安装的.exe文件进行分发。双击下载好的文件开始安装。

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You will then be prompted to choose an installation folder. After installation, you will be able to open Loupe Cell Browser by double-clicking on the desktop icon, or double-clicking on a .cloupe file in your file system.

接着会提示你选择要安装到的文件夹。安装结束后，你就可以通过双击桌面图标，或者双击一个.cloupe文件来打开Loupe Cell Browser。

macOS

Loupe Cell Browser for the Mac is distributed as a DMG file. Open this file by double-clicking on it. Then install Loupe by dragging the Loupe icon into the Applications folder. You can then start Loupe by double-clicking on its app icon.

Loupe Cell Browser for the Mac作为一个DMG文件进行分发。双击打开这个文件。接着拖动Loupe图标到Applications文件夹来开始安装。随后通过双击相应应用图标来启动Loupe。

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You will then be able to open Loupe Cell Browser within the Applications folder, or by double-clicking on a Loupe Cell Browser .cloupe file in your file system.

你可以在Application文件夹中，或双击一个Loupe Cell Browser .cloupe文件来打开Loupe Cell Browser。

与基因表达的区别 - Differences from Gene Expression

If you've used Loupe Cell Browser before to analyze gene expression, you will find exploring ATAC data familiar in some ways, and different in others. The Cell Ranger ATAC algorithm documentation covers algorithms and analysis in more detail, but in short, here are some key things to keep in mind when looking at ATAC data:

如果你之前使用过Loupe Cell Browser来分析基因表达，你会发现在探索ATAC数据时，一些方式上两者相似，其他方面则略有不同。Cell Ranger ATAC算法文档包含了算法和分析方面的更多细节，但是简短来讲，以下是在查看ATAC数据时一些关键性的东西：

UMI count per cell is the unit of gene expression. Cut sites per cell is the unit of accessibility.

UMI count per cell是基因表达的单位。Cut sites per cell是可接近性的单位。

Genes are the rows of a gene expression matrix. Peaks are the rows of a chromatin accessibility matrix.

基因表达矩阵中每一行是基因。染色质可接近性矩阵中每一行是peak。

Peaks are genomic regions where there were significant upticks in fragment cut sites, which indicate regions of open chromatin. They are named by their location (e.g., "chr1:10244-10510")

Peaks是基因组区域，这些区域在片段切割位点(fragment cut sites)具有显著提升，表明为开放染色质区域。它们通过其位置来命名（例如“Chr1:10244-10510”）。

Unlike genes, peaks are likely to be different between different datasets.

不同于基因，peaks在不同数据集间更倾向于不同。

There are typically more distinct peaks in an ATAC dataset than there are genes in a reference.

通常，ATAC数据集中的峰数要比参考基因组中的基因多

The dynamic range of gene expression per cell is typically much wider than the dynamic range of cut sites per peak per cell. This means that you will often use aggregate features (see below) to separate data.

Gene expression per cell的动态范围相比cut sites per peak per cell的动态范围要更宽。这意味着你需要经常使用累加特性（见下文）来分离数据。

In addition to peaks, there are several aggregate feature types which can be also used to differentiate cells:

除了peaks之外，还有一些其他累加特性类型可用来进行细胞区分：

Promoter sums, which are the sums of cut sites per cell (within peaks) which are close to one of the transcription start sites for that gene. These features are named "(Gene) Sum". Not all peaks are associated with a gene.

启动子总和，是接近该基因的转录起始位点之一的cut sites per cell (within peaks)的总和。这些特征被命名为"(Gene)Sum"。并非所有peaks都被关联到了一个基因。

Transcription factor motifs, which are the sums of cut sites per cell which fall within peaks associated with a motif by the Cell Ranger ATAC pipeline. Motif features are named after the motifs themselves (e.g., "SPI1"). A peak is usually associated with multiple motifs.

转录因子motif，是位于被Cell Ranger ATAC流程关联了motif的peak中的cut sites per cell的总和。Motif特征是以motif本身命名的（例如“SPI1”）。一个peak通常会关联多个motifs。

An ATAC dataset will take up several times as much disk space (per cell) than a gene expression dataset.

一个ATAC数据集会占用基因表达数据集几倍的磁盘空间。

To see fragment locations per cluster in high resolution, you will need access to the fragments.tsv.gz file for that run, generated by the Cell Ranger ATAC pipeline. These files are typically several times larger than the .cloupe file, which is why they are not bundled. You can either specify the location of this file on a locally mounted file system, or on the web via a URL.

在高分辨率下观察每个分群片段位置，需要适用到当次Cell Ranger ATAC流程分析生成 fragments.tsv.gz 的文件。这些文件通常比 .cloupe 文件大几倍，这也是为什么它没有被捆绑在其中的原因。你可以指定该文件在本地挂载的文件系统中的位置，或通过URL访问web上的位置。

Loupe Cell Browser ATAC 教程

Welcome to the Loupe Cell Browser ATAC tutorial. In the next few pages, we will be finding significant features, analyzing differential accessibility patterns, and exploring substructure within a real-world dataset. Along the way, we'll touch on most of the features of Loupe Cell Browser.

欢迎来到Loupe Cell Browser ATAC教程部分。在接下来的几页中，我们将寻找显著特征，分析差异可接近性模式，以及探索真实数据中的亚群。通过学习本教程，我们将接触到Loupe Cell Browser的大部分功能。

环境建立 - Setup

Before beginning the tutorial, make sure you have downloaded Loupe Cell Browser. If this is your first time working with Loupe Cell Browser, you can access the ATAC tutorial dataset by clicking on the "ATACTutorial.cloupe" link on the Recent Files page:

在开始学习教程之前，请确定你已经下载并安装了Loupe Cell Browser。如果这是你第一次使用Loupe Cell Browser，你可以通过点击“近期文件”页面中的“ATACTutorial.cloupe”来获取ATAC教程数据集。

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You can also access the tutorial dataset by clicking on 'Load ATAC Tutorial Dataset' from the Help menu.

你也可以通过点击帮助菜单中的“Load ATAC Tutorial Dataset”来获得教程数据集。

About the ATAC Tutorial Dataset

The ATAC tutorial dataset contains the results of a cellranger-atac run over a set of human peripheral blood mononuclear cells, with the standard Chromium™ Single-Cell ATAC protocol. The targeted cell count was 5,000; the observed barcode count from the pipeline was 5,335.

ATAC教程数据集包含了一个使用cellranger-atac分析人外周血单核细胞（根据标准Chromium™ Single-Cell ATAC protocol）的结果。靶细胞计数为5000，流程分析观测到的barcode计数为5335。

Now that you've loaded the file and familiarized yourself with some ATAC data concepts, click here to tour the user interface.

现在你已经加载了文件，了解了一些ATAC数据的概念，可以开始学习用户界面了。

Loupe Cell Browser: 用户界面

With the ATACTutorial dataset loaded, let's take a quick tour of the Loupe Cell Browser user interface for ATAC datasets.

加载好ATACTutorial数据集，我们来快速浏览一下Loupe Cell Browser的用户界面。

Barcode Plot

The workspace is centered around the barcode plot, in which single points representing cell barcodes are shown in a variety of projections. Each point represents a single barcode, the vast majority of which represent a single cell. The default projection is the t-SNE plot created by the Cell Ranger ATAC pipeline. Cell Ranger ATAC generates this plot by identifying the most significant peak vectors using dimensionality reduction techniques, and then processing the lower-dimension matrix through t-SNE to produce a two-dimensional scatter plot. You may also view a projection that plots cut site counts in a peak, near a gene promoter, or within a certain motif on two-dimensional axes. The selector at the upper right of the barcode plot allows you to toggle between projections.

整个工作空间以barcode图为中心，其中代表细胞barcode的单个点在各种投射中展示。每一个点代编一个单一barcode，绝大多数代表一个单一细胞。默认的投射是Cell Ranger ATAC流程生成的t-SNE图。Cell Ranger ATAC通过使用降维手段识别最显著的peak向量，接着使用t-SNE来处理低维矩阵以生成二维散点图。你也可以在一个二维图中查看peak中，临近基因启动子，或在特定motif中的切割位点计数投射。

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You can click-and-drag the mouse over the cells to reposition the plot, and use the mouse wheel or track pad to smoothly zoom in and out. You'll see cluster labels as you move your mouse over the plot, which is useful for data that has a high number of precomputed clusters. Cells are colored by the active legend in the sidebar.

你可以在图上点击并拖动鼠标来改变展示位置，使用鼠标滚轮或触摸板平滑的放大或缩小。当你鼠标在图上滑动时会看到分群标识，这对有大量预计算的分群的数据很有帮助。细胞根据侧边栏中活动图例进行着色。

工具盒 - The Toolbox

On the left side of the window is the toolbox. When you move your mouse over the toolbox buttons, you will see an explanation of what each button does. Use the toolbox to open files, save your work, control zoom, select cells for manual categorization, export screenshots of the current plot view, and split the barcode plot into individual clusters. Clicking the 10x button will return you to the home screen and Recent Files list.

窗口左侧是工具盒。当你滑动鼠标到工具盒相应按钮上，你会看到关于这个按钮的功能解释。使用工具盒中的工具，你可以打开文件，保存工作，控制放大缩小，选择细胞进行手动分类，导出当前展示图的截屏，将barcode图按分群分隔。点击10x按钮会返回起始页和近期文件列表。

模式选择器&侧边栏 - Mode Selector & Sidebar

Use the mode selector at the top right corner of the workspace to switch between Loupe Cell Browser's different modes. There are two modes in Loupe Cell Browser 3.0 for ATAC data: Categories mode, where you can see the different cluster assignments for all the cells, and Accessibility mode, where you can overlay quantitative cut site count information atop the barcodes. Switching between modes will apply mode-specific coloring to the graph, and change the sidebar to reveal mode-specific functionality.

使用工作区右上角的模式选择器来切换Loupe Cell Browser的不同模式。对于ATAC数据，Loupe Cell Browser 3.0有两种模式：分类模式，在此模式下你可以看到对全部细胞进行的不同分群；可接近性模式，在此模式下你可以在badrcodes之上叠加定量切割位点计数信息。切换不同的模式会对图应用模式特异着色，且改变侧边栏来展示模式特有功能。

分类模式 - Categories Mode

Cell Ranger ATAC pipelines compute and produce clusterings from two algorithms: a graph-based clustering algorithm and by either K-Means or K-Medoids clustering. A selector at the top of the Categories sidebar allows you to switch between these clusterings, or other clusterings that you can create yourself within Loupe Cell Browser.

Cell Ranger ATAC流程通过两种算法计算和生成聚类：基于图的聚类算法，K-Means或K-Medoids。分类侧边栏顶部的选择器可以让你在这些聚类分群中切换，或在Loupe Cell Browser中创建其他自定义分群。

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You may hide, show and highlight individual clusters within a category by using the sidebar. To highlight a cluster, click on the cluster name within the legend. To toggle the appearance of a cluster, click on the checkbox next to the cluster name. Finally, you may hide or show all clusters within a category by clicking on the menu with three dots, to the right of the category selector.

利用侧边栏，你也可以对一个分类中的单个分群进行隐藏，展示，高亮。想要高亮一个分群，单击图例中相应的分群名称。决定是否展示一个分群，单击分群名称边上的复选框。最后，你也可以通过点击分类选择器右边三个点的菜单来隐藏或展示一个分类中的所有分群。

You also may also rename and recolor clusters by right-clicking on a cluster name or color, and selecting the desired option from the pop-up menu.

你也可以通过右键点击分群名称，从弹出的菜单中选择期望的选项对分群进行重命名或重新着色。

可接近性模式 - Accessibility Mode

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In Accessibility mode, you see a graphical representation of chromatin accessibility across your dataset. You can view the number of cut sites detected per cell within individual peaks, near the promoter regions of particular genes, or in total across the entire genome, through the Peak Sum feature. You may also view Z-scores of transcription factor motif counts per barcode, look at one or more features at a time, load and save lists of features for analyzing across multiple datasets, and look at the density of cut site counts across your data. We will explore Accessibility mode more in-depth when looking for cell types.

在可接近性模式下，你可以看到一个整个数据集染色质可接近性的图像化展示。你可以查看Peak Sum特性查看单个peak内、特定基因启动子区域附近或整个基因组范围内每个细胞检测到的切割位点的数量。还可以查看每个barcode的转录因子motif计数的Z-score值，一次查看一个或多个特性，加载和保存用于跨多个数据集分析的特性列表，并查看整个数据的切割位点计数的密度。在寻找细胞类型时，我们将更深入地探讨可接近性模式。

特征表&Peak查看器 - Feature Table & Peak Viewer

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The panel on the bottom of the workspace does double duty for ATAC data in Loupe Cell Browser. The Feature Table shows information about differentially accessible peaks, transcription factor motifs, or promoter sums between clusters. The Peak Viewer shows the differential distribution of peaks and cut sites per cluster within the genome. You can use the toolbar at bottom left to toggle between the two panels. When you first load the ATAC dataset, you will see the feature table, preloaded with the transcription factor motifs that are most significantly different between the clusters in the active category. Selecting the Peak Viewer will by default show the first five peaks in the genome, and their distribution within the active set of clusters. We will explore the Feature Table in depth in Significant Features, and cover how to hone in on regions of interest on the Peak Viewer page.

Loupe Cell Browser工作区下方的面板在分析ATAC数据时具有双重功能。特征表展示了关于差异可接近性peaks，转录因子motifs，分群之间启动子总和的相关信息。Peak Viewer展示了基因组中peaks的差异分布以及cut sites per cluster的差异分布。你可以使用左下方的工具条来切换两个面板。当你第一次载入ATAC数据集时，你将看到feature表，其中预加载了在活动类别中的分群之间差异最为显著的转录因子motifs。选择Peak Viewer初始后默认会显示基因组中前五个peaks，以及这些peaks在活动分群集中的分布。我们将在显著特征一节进一步深入探索特征表，介绍如何在Peak Viewer页面上找到感兴趣的区域。

Now that you are familiar with the user interface, let's explore the data.

现在你已经熟悉了用户界面，我们开始探索数据吧。

细胞类型识别 - Identifying Cell Types

Goal: To locate known cell types within the dataset.

目标：查找数据集中已知细胞类型。

Identifying cell types from known markers is straightforward and fast in Loupe Cell Browser. We'll do this two ways, first through looking at quantitative accessibility, then by importing feature lists.

使用Loupe Cell Browser根据已知markers识别细胞类型非常直接，快速。我们将通过两种方式实现，首先是关注量化的可接近性，接着是通过导入特征列表。

特征搜索 - Feature Search

Let's get a feel for the t-SNE plot for this dataset. We're looking at a PBMC sample, so we would hope to see relatively clear clustering of common cell types. As every t-SNE plot is different, we need to use feature markers to orient ourselves. Let's start by using promoter markers to find cellular types.

我们首先来感受一下这个数据集的t-SNE图。我们正在查看一个PBMC样本，因此我们希望能够相对清晰的看到常见细胞类型分群。由于每一个t-SNE图都是不一样的，我们需要利用特征标记(feature markers)来为我们定向。让我们先以启动子标记来寻找细胞类型作为开始吧。

利用启动子总和确定细胞类型 - Using Promoter Sums to Determine Cell Type

The Cell Ranger ATAC pipeline labels individual peaks as promoters for a particular gene if the peak falls 1000 bases upstream from a gene's transcription start site, or 100 bases downstream from a gene's transcription start site. A promoter sum for a given gene is the number of cut sites per cell that fall within all the peaks labeled as promoters for that gene. Even though most cell type specific chromatin accessibility happens distal from promoters (a well known phenomenon that we observe in our data), we have found that promoter sums are sufficient to identify cell subtypes, as genes known to be upregulated in those subtypes will likely be more accessible at the promoter. Let's use some T-cell and B-cell gene markers to test this.

如果peak落在一个基因的转录起始位点上下游1000bp范围内，Cell Ranger ATAC流程就将单个(?)peaks标记为特定基因的启动子。对于一个给定基因的启动子总和就是落在该基因所有被标记为启动子的peaks内的每个细胞切割位点的数量。尽管大多数细胞类型特异性的染色质可接近性发生在启动子远端（我们在我们的数据中观测到的一个常见现象），我们发现由于这些亚型中已知会上调的基因在启动子区域更倾向于可接近，启动子总和足以用来识别细胞亚型。让我们用一些T细胞和B细胞的基因markers来检验一下。

First, select Accessibility mode from the Mode Selector. You will see an Active Feature List. This is like a scratch pad for exploring markers and motifs in your dataset. You can type in the name of a gene, transcription factor motif, or even a peak genomic region into the search box. Let's look for B cells first. Type MS4A1 (CD20) into the search box to bring up the "MS4A1 Sum" feature. Press Tab or Enter to add the promoter sum to the active feature list, and calculate cut site counts for that promoter across the dataset.

首先，在模式选择器中选择可接近性模式。你会看得到一个活动特征列表。在你探索你数据集中的markers和motifs时，这就像一个便签本一样。你可以在搜索框中输入一个基因的名称，转录因子基序，或者是一个peak的基因组区域。让我们先来看一下B细胞。在搜索框中输入MS4A1(CD20)，检索得到“MS4A1 Sum”特征。按下Tab键或回车键来将启动子总和加入活动特征列表，并计算整个数据集中该启动子的切割位点数。

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Right away, you should see that cells where the MS4A1 transcription start site was accessible are neatly packed into one of the t-SNE clusters. To confirm, you can try some other B-cell markers: CD19 and IGKC. It is clear that distinct region represents B cells.

紧接着，你应该看到那些MS4A1转录起始位点可接近的细胞被整洁的划归到了t-SNE分群之一。为了确认，你可以尝试一些其他的B细胞markers：CD19和IGKC。很明显那个独特的区域代表B细胞。

As you add multiple features to a list, the coloring will represent a combination of cut site counts per cell among all features in the list. With MS4A1, CD19 and IGKC in the list and 'Feature Max' as the selected attribute, the coloring of the plot reflects the maximum cut site count per cell among the selected features. Clicking on a single peak or promoter sum within a list shows the number of cut site counts per cell for that feature. Finally, hovering over a promoter sum or peak will reveal a graph and a trash icon; clicking the graph icon will highlight that feature in the peak viewer (see below), and clicking the trash icon will remove that feature from the current list.

当你向一个列表中加入多个特征时，着色将表示列表中所有特征的每细胞切割位点计数的组合。当使用MS4A1，CD19和IGKC特征，并选择“Feature Max”属性。图的着色反映了所选特征中每细胞最大切割点计数。单击一个列表内的一个peak或启动子总和将展示该特征的每细胞切割位点计数的数量。最后，鼠标悬停在一个启动子总和或peak将显示一个图标和一个垃圾箱图标；单击图表图标将在peak viewer中高亮该特征（见下文），单击垃圾箱图标将在当前列表中删除该特征。

bcell_peakviewer.png

利用转录因子motifs确定细胞类型 - Using Transcription Factor Motifs to Determine Cell Type

Transcription factor motif patterns may also yield insights about cell type. Motifs are different from peak and promoter sums. For each transcription factor motif, Cell Ranger computes a z-score for each barcode, which represents the relative accessibility of all peaks containing that motif within that cell, compared to the entire dataset. When selected, motif z-scores are displayed in the t-SNE plot, rather than absolute cut site counts. In addition, motifs may only be selected one at a time.

转录因子motif模式也可以获得一些细胞类型方面的信息。Motifs不同于peak和启动子总和。对于每一个转录因子motif，Cell Ranger对每一个barcode计算一个z-score，这些z-score代表了与整个数据集相比，一个细胞中所有包括该motif的peaks的相对可接近性。

Let's reset the Active Feature List by clicking the trash icon for each feature currently in the list. Next, select SPI1 from the feature search box. The SPI1 (PU.1) transcription factor has been shown to play a key role in monocyte function [1]. Selecting SPI1 shows the cells with higher z-scores in red, indicating higher relative accessibility among all peaks that have the SPI1 motif. SPI1 also has a role in B-cell regulation; since we already identified the B-cell cluster through gene markers, it should follow that the large cluster at upper left are monocytes.

让我们通过点击垃圾箱图标删除当前列表中的所有特征来重置活动特征列表。接下来，在特征搜索框中选择SPI1。研究表明，SPI1(PU.1)转录因子在单核细胞功能中扮演了关键角色[1]。选择SPI1将高z-score的细胞显示为红色，表明在所有具有SPI1 motif的peak之间具有较高的相对可达性。SPI1在B细胞调节中也具有一定作用；由于我们已经通过基因标记识别了B细胞分群，因此，左上方的大群应该是单核细胞。

spi1.png

导入和导出特征列表 - Importing and Exporting Feature Lists

Another way to find cell subtypes is to import a CSV file which contains markers for cell types of interest. You can download one such file here: ATACBloodCell.csv. After downloading, click on the three dots to the right of the current feature list, and select 'Import Lists' from the dropdown menu. Select the file you just downloaded. You can find more information about how to generate feature lists on the Sharing Results page.

另一种发现细胞亚型的方式是导入一个包含所有感兴趣细胞类型marker的CSV文件。你可以从这里下载一个这样的文件：ATACBloodCell.csv。下载后，点击当前特征列表右上方的三个点按钮，在弹出菜单中选择“导入列表”。选择你刚刚下载好的文件。你可以在分享结果部分找到更多关于如何生成特征列表的信息。

import-list.png

After import, you should now be able to select from one of five sets of markers by selecting from the feature list selector (click the toggle next to the current feature list).

导入之后，现在你应该能够通过从特征列表选择器（单击当前特征列表旁边的切换按钮）中选择五组标记之一了。

You can use the feature list menu to create and export your own sets of markers, and rename and delete lists from your workspace. Choosing Export Lists from the feature menu will write all the currently active lists in your dataset to a CSV file, which you can import into other datasets. NOTE: If you wish to save the features in the Active Feature List, be sure to rename the list to something else prior to export.

你可以使用特征列表菜单来创建和导出你自己的marker集，在你的工作空间中重命名及删除列表。从功能菜单中选择导出列表将把数据集中所有当前活动的列表写到CSV文件中，你可以将该文件导入到其他数据集中。注意：如果你希望保存活动特征列表中的特征，请务必将列表在到处之前重命名为任意其他名称。

保存细胞类型分群 - Saving Cell Type Clusters

Now that we've gotten our bearings in the dataset, we can save cell types for later. We can create new clusters corresponding to our cell types either by manual selection, or by quantitative filtering.

现在我们已经在数据集中找到了（感兴趣细胞类型）大概的位置，我们可以保存细胞类型以便后续使用。我们可以通过人工选择或根据定量过滤来创建新的细胞类型分群。

Let's first create a B-cell cluster. Select the rectangular lasso tool from the toolbox, and drag a box around the cluster we found through B-cell marker (MS4A1, CD19) accessibility:

让我们先来创建一个B细胞分群。在工具箱中选择矩形套索工具，拖动选框选中我们通过B细胞marker(MS4A1, CD19)可接近性发现的分群：

select.png

When you finish dragging, a dialog box will appear, prompting you to type the name of a new or existing category, and a cluster name. Create a new "Cell Types" category, and call the cells "B Cells". Press the Save button, and a new Cell Types category will appear.

当你完成选中后，会弹出一个对话框，提示你输入一个新的或已存在的分类名称，以及一个分群名称。创建一个新的“细胞类型”分类，将这些细胞命名为“B细胞”。按下保存按钮，一个新的细胞类型分类就创建成功了。

Next, use the freehand lasso tool from the toolbox to draw an area around the monocytes, at the upper left of the t-SNE projection. Reuse the Cell Types category by typing it in the Category box, and create a new "Monocytes" cluster.

接下来，在工具箱中选择自由套索工具在t-SNE投射图左上方选中单核细胞区域。在分类框中选择输入细胞类型，创建一个新的“单核细胞”分群。

You may also create clusters quantitatively. Switch back to Accessibility mode, and select the All T Cells list that was just imported from ATACBloodCell.csv, or choose "CD3D Sum" from the feature search box. Click on the CD3D Sum feature in the list, and then find the input box under "Select by Count - CD3D Sum". Enter zero, and then press the filter button next to the input field. This will highlight every cell for which there was a fragment within a CD3D promoter peak, and bring up the cluster assignment box. Select "Cell Types" as the category, and add these cells to the "T Cells" cluster.

你也可以定量的创建分群。切换回可接近性模式，选择刚刚从 ATACBloodCell.csv 中导入的全部T细胞，或在特征搜索框中选择“CD3D Sum”特征。单击列表中的CD3D Sum特征，接着找到“Select by Count - CD3D Sum”下方的输入框。输入“0”，接着点击输入框旁边的过滤按钮。这将会高亮CD3D启动子peak内有fragment的每一个细胞，并弹出指定分群对话框。选择“细胞类型”作为分类，将这些细胞加入到“T细胞”分群中。

We'll stop here, but you can use additional genes and motifs of interest to further divide T cells into cytotoxic T cells, helper T cells, and memory T cells.

到这里我们将告一段落，当然你可以利用其他感兴趣的基因或者Motifs来进一步的将T细胞划分为杀伤性T细胞，辅助性T细胞，以及记忆T细胞。

tcell-select.png

Before proceeding, please save these cluster assignments by clicking on the Save icon in the toolbar. The ATAC Tutorial file bundled with Loupe Cell Browser is read-only, so you will be prompted to save a copy somewhere on your file system.

在进一步开始前，请点击工具箱中的保存按钮来保存这些指定好的分群。Loupe Cell Browser附带的ATAC教程文件的权限为只读，因此你将被提示将文件另存为其他副本来保存到系统上。

With feature markers and clusters saved, let's delve further into this ATAC data through the Peak Viewer.

保存好特征markers及分群后，让我们使用Peak Viewer进一步深入挖掘ATAC数据。

[1] https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0033474

结束语

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