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自定义组件

20字小于1分钟

预览组件

2024-10-30

贡献者: Hughes
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734字约2分钟

markdown

2024-10-30

标题 2

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标题 5
标题 6

加粗:加粗文字

斜体: 斜体文字

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内容 标记

数学表达式: (2n1)-(2^{n-1}) ~ 2n112^{n-1} -1

rωr(yωω)=(yωω){(logy)r+i=1r(1)Ir(ri+1)(logy)riωi}\frac {\partial^r} {\partial \omega^r} \left(\frac {y^{\omega}} {\omega}\right) = \left(\frac {y^{\omega}} {\omega}\right) \left\{(\log y)^r + \sum_{i=1}^r \frac {(-1)^ Ir \cdots (r-i+1) (\log y)^{ri}} {\omega^i} \right\}

19th

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内容右对齐

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  1. 有序列表1
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TablesAreCool
col 3 isright-aligned$1600
col 2 iscentered$12
zebra stripesare neat$1

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代码:

const a = 1
+const b = 2
+const c = a + b
+
+const obj = {
+  toLong: {
+    deep: {
+      deep: {
+        deep: {
+          value: 'this is to long text. this is to long text. this is to long text. this is to long text.',
+        }
+      }
+    }
+  }
+}

代码分组:

tab1
const a = 1
+const b = 2
+const c = a + b

代码块高亮:

function foo() {
+  const a = 1
+
+  console.log(a)
+
+  const b = 2
+  const c = 3
+
+  console.log(a + b + c)
+  console.log(a + b)
+}

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function foo() {
+  const a = 1
+}

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注释内容 link inline code

const a = 1
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<h1>Hello Word!</h1>
+<p><span id="very">非常</span>强大!</p>
document.querySelector('#very').addEventListener('click', () => {
+  alert('非常强大')
+})
span {
+  color: red;
+}

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贡献者: Hughes
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10X Genomics scRNA-seq 数据分析

1427字约5分钟

2024-10-30

为了便于工作流的可重复性,我们推荐您使用json文件来描述和存档您的实验参数。

写在前面

这里会涉及到BioOS文件管理的相关内容,请参考BioOS文件管理

在这里,我们假设您已经对平台的使用有了基础的了解,并创建了必要的文件夹。如果还没有,请参考动手学BioOS计算

请注意,我们不要求您对json文件、WDL文件或者云计算有深入的了解,您只需要知道如何使用json文件来描述您的实验参数。我们的目标是您只需要知道如何“复制、粘贴”就能完成您的实验。

让我们开始吧!

对于一个典型的10X Genomics scRNA-seq实验,我们推荐使用如下的json文件:

{
+  "cellranger_count_workflow.chemistry": "auto",
+  "cellranger_count_workflow.cpu": 32,
+  "cellranger_count_workflow.disk_space": "300 GB",
+  "cellranger_count_workflow.fastq_file_paths": null,
+  "cellranger_count_workflow.memory": "225 GB",
+  "cellranger_count_workflow.no_bam": "False",
+  "cellranger_count_workflow.reference_genome_tar_gz": "s3://bioos-wcnjupodeig44rr6t02v0/Example_10X_data/RAW/refdata-cellranger-GRCh38-3.0.0.tar.gz",
+  "cellranger_count_workflow.run_id": null,
+  "cellranger_count_workflow.sample": null,
+  "cellranger_count_workflow.secondary": "False"
+}

看起来很复杂,但没关系。仔细观察,您会发现,这个json文件的部分参数已经自动设置好了,在大部分情况下,您只需要依次填写您自己的参数即可。

(markdown格式 引用 可折叠 或者 展开上标引用)注释:作为快速上手教程,我们不对具体的参数做出解释,具体的参数的解释请参考10X Genomics官方文档,在支持文档,我们也会对一些关键参数做出解释。

这里我们给出了填写示例:

注意这几个存在内容新增的部分

{
+  "cellranger_count_workflow.chemistry": "auto",
+  "cellranger_count_workflow.cpu": 32,
+  "cellranger_count_workflow.disk_space": "300 GB",
+  "cellranger_count_workflow.fastq_file_paths": [ 
+    "s3://bioos-wcnjupodeig44rr6t02v0/Example_10X_data/ERR8048237/5891STDY8062334_S1_L001_I1_001.fastq.gz",
+    "s3://bioos-wcnjupodeig44rr6t02v0/Example_10X_data/ERR8048237/5891STDY8062334_S1_L001_R1_001.fastq.gz",
+    "s3://bioos-wcnjupodeig44rr6t02v0/Example_10X_data/ERR8048237/5891STDY8062334_S1_L001_R2_001.fastq.gz"
+  ],
+  "cellranger_count_workflow.memory": "225 GB",
+  "cellranger_count_workflow.no_bam": "False",
+  "cellranger_count_workflow.reference_genome_tar_gz": "s3://bioos-wcnjupodeig44rr6t02v0/Example_10X_data/RAW/refdata-cellranger-GRCh38-3.0.0.tar.gz",
+  "cellranger_count_workflow.run_id": "ERR8048237",
+  "cellranger_count_workflow.sample": "5891STDY8062334",
+  "cellranger_count_workflow.secondary": "False"
+}

是的,就是这么简单。我们添加了文件路径,并填写了run_id和sample。这和您在本地计算的参数填写逻辑是一样的。我们已经准备好了,现在就提交任务吧!

提交任务

让我们回到BioOS平台,来到我们的cellrangerTest页面。试试看,找到页面上的"运行参数"选项卡>输入参数>"上传JSON文件",将您的json文件上传。

然后,点击页面上的绿色按钮"开始分析",等待任务完成。

注意右上角的绿色按钮

注意右上角的绿色按钮

注意右上角的绿色按钮,点击即可开始分析,并在3秒后自动跳转到分析历史界面。

查看结果

任务完成后,您可以在分析历史中看到您的任务。点击任务名称,进入任务详情页面。在任务详情页面,您可以查看/下载结果。

任务分析历史

任务分析历史

现在这张图片展示了任务分析历史的详情,选中本次进行的实验。新的页面展示了本次实验的所有状态信息,你可以在这里再次查阅输入和输出参数。当然你也可以在这里查看或下载结果。

点击"查看",让我们来看看结果吧!所有的结果文件都会列出在这里,除了结果之外,也包括运行日志等文件,这取决于WDL文件的具体设置。

# 在本例中,你的文件结构是这个样子:
+全部文件/analysis/$投递任务ID/$WDL_Task_ID/$workflow标识ID/call-$WDL_Task_ID/
+├── execution
+   ├── PRJNA693201_dlst000564_SRR13482541
+   └── outs
+       ├── PRJNA693201_dlst000564_SRR13482541_filtered_feature_bc_matrix.h5ad
+       ├── metrics_summary.csv
+       ├── possorted_genome_bam.bam
+       └── web_summary.html
+   ├── PRJNA693201_dlst000564_SRR13482541_outs.tar.gz
+   ├── log # cromwell 日志文件
+   ├── rc # cromwell 运行时文件
+   ├── script # cromwell 脚本文件
+   ├── stderr # cromwell 标准错误文件
+   └── stdout # cromwell 标准输出文件

我们在这里展示的是一个多样本的分析结果(如果您按照上图的方法点击查看结果),所以您会看到多个样本的结果文件夹。让我们逐级打开文件夹,找到我们需要的文件。所有的日志以及结果文件都会列出在这里。

在这个示例中,我们需要的文件在 "outs" 文件夹中,让我们逐渐深入文件夹,找到我们需要的文件。

查看工作流的计算结果

查看工作流的计算结果

让我们逐级打开文件夹,找到我们需要的文件。所有的日志以及结果文件都会列出在这里。

🤔 如果你想在本地查看结果?

我们需要这里的 $ID_filtered_feature_bc_matrix.h5ad 文件,让我们点击下载。

下载我们感兴趣的结果

下载我们感兴趣的结果

这里我们展示了如何下载我们感兴趣的结果。我们逐级打开目录,到最后一层。然后在我们感兴趣的结果后面点击下载。

🎊Bravo!🎊 到此为止,您已经掌握了BioOS的基本使用方法,并成功完成了一次10X Genomics scRNA-seq的分析。👏👏👏

如果我有很多数据呢?

🤔好,那么好,这时候可能就会有人问了,如果我们有很多数据,也要像这样一个一个点击吗?

非常好的问题!当你尝试把一件简单的事情重复做上一万遍的时候,其复杂度将会指数增加📈。

当然不是,我们在这里只展示了BioOS的冰山一角,BioOS的真正能力将在您尝试构建数据模型/实体之后展现。下面,让我们从一个稍微复杂的例子开始,一步一步的学习如何调度BioOS强大的计算能力。

贡献者: SunHao, Hughes
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this.combineResults(g,p.combineWith)}const{tokenize:n,processTerm:s,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:n,processTerm:s},r),t),{tokenize:a,processTerm:c}=i,f=a(e).flatMap(p=>c(p)).filter(p=>!!p).map(bn(i)).map(p=>this.executeQuerySpec(p,i));return this.combineResults(f,i.combineWith)}executeQuerySpec(e,t){const n=Object.assign(Object.assign({},this._options.searchOptions),t),s=(n.fields||this._options.fields).reduce((y,_)=>Object.assign(Object.assign({},y),{[_]:Te(n.boost,_)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:a,bm25:c}=n,{fuzzy:l,prefix:h}=Object.assign(Object.assign({},Ge.weights),i),f=this._index.get(e.term),p=this.termResults(e.term,e.term,1,e.termBoost,f,s,r,c);let g,b;if(e.prefix&&(g=this._index.atPrefix(e.term)),e.fuzzy){const y=e.fuzzy===!0?.2:e.fuzzy,_=y<1?Math.min(a,Math.round(e.term.length*y)):y;_&&(b=this._index.fuzzyGet(e.term,_))}if(g)for(const[y,_]of g){const E=y.length-e.term.length;if(!E)continue;b==null||b.delete(y);const 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n)s[r]=Object.fromEntries(i);e.push([t,s])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,n,s,r,i,a,c,l=new Map){if(r==null)return l;for(const h of Object.keys(i)){const f=i[h],p=this._fieldIds[h],g=r.get(p);if(g==null)continue;let b=g.size;const y=this._avgFieldLength[p];for(const _ of g.keys()){if(!this._documentIds.has(_)){this.removeTerm(p,_,t),b-=1;continue}const E=a?a(this._documentIds.get(_),t,this._storedFields.get(_)):1;if(!E)continue;const I=g.get(_),M=this._fieldLength.get(_)[p],P=gn(I,b,this._documentCount,M,y,c),D=n*s*f*E*P,z=l.get(_);if(z){z.score+=D,wn(z.terms,e);const O=Te(z.match,t);O?O.push(h):z.match[t]=[h]}else l.set(_,{score:D,terms:[e],match:{[t]:[h]}})}}return 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