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# MingPCACluster
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A new simple and efficient software to PCA and Cluster For popolation VCF File or STOmics gem File
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A new simple and efficient software to PCA and Cluster For popolation VCF File or STOmics gem File
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### 1 Introduction
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<b>MingPCACluster</b> 是于基于VCF开发的PCA分析和聚类软件，同时兼并了Genotype 等格式软件，同时开发针对时空单细胞表达量的格式（xx.gem.gz）文件（beta功能）。
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即只要对应的一个输入文件进来，这PCA和作图分组等一位到位。
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</br>
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</br> keyword : VCF2PCA ;&nbsp;&nbsp;&nbsp;&nbsp; VCF2Kinship ;&nbsp;&nbsp;&nbsp;&nbsp; cluster;&nbsp;&nbsp;&nbsp;&nbsp; k-means ;&nbsp;&nbsp;&nbsp;&nbsp; cellbin ;&nbsp;&nbsp;&nbsp;&nbsp;STOmics
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</br>亮点：
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</br>1 The result is the same with [tassel](https://www.maizegenetics.net/tassel),[gapit](https://zzlab.net/GAPIT/) and [gcta](https://yanglab.westlake.edu.cn/software/gcta/#Overview) , just the difference in precision.
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</br>2 功能有 1 多种kinship矩阵 2 PCA结果 3 聚类结果 和4 以cluster染色并作图。
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</br>3 一个VCF输入，一步到位，方便用户使用.
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</br>4 边读边算，内存剥离受位点多少的影响（时空组是剥离受基因数量多少的影响），内存只受样品量影响，故上100k的样品当也行，在这个基础上开发时空细胞PCA和聚类,虽然时空组学上主要是样品多。（80K 60G内存）
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</br>5 Kmean聚类分析，并找出最佳K值，和Structure和K值一样. 作图以此染色。
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</br>6 提作作图小脚本，可以用这个脚本优化作图等。
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</br>
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</br>程序是给一些有基础的生信朋友用的，若是小白看不懂就算了。
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</br>
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</br><b>MingPCACluster</b> MingPCAC is a PCA analysis software format developed based on VCF. It also incorporates Genotype, etc., and develops a file (beta function) for the expression of spatiotemporal cells. That is, as long as the input is satisfied, the PCA and the cluster group are of the same output.
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### 2 Download and Install
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------------
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The <b>new version</b> will be updated and maintained in <b>[hewm2008/MingPCACluster](https://github.com/hewm2008/MingPCACluster)</b>, please click below website to download the latest version
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</br><p align="center"><b>[hewm2008/MingPCACluster](https://github.com/hewm2008/MingPCACluster)</b></p>
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<b> 2.1. linux/MaxOS&nbsp;&nbsp;&nbsp; [Download](https://github.com/hewm2008/MingPCACluster/archive/v1.00.tar.gz)</b>
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</br> <b>2.2 Pre-install</b>
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</br> MingPCACluster is for Linux/Unix/macOS only.
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</br>Before installing,please make sure the following pre-requirements are ready to use.
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</br> 1) [convert](https://linux.die.net/man/1/convert) command is recommended to be pre-installed, although it is not required
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</br> 2) g++ : g++ with [--std=c++11](https://gcc.gnu.org/) > 4.8+ is recommended
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 </br> 3) zlib : [zlib](https://zlib.net/) > 1.2.3 is recommended
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 </br> 4) R : [R](https://www.r-project.org/) with [ggplot](http://ggplot.yhathq.com/) is recommended
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</br> <b>2.3 Install</b>
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</br> Users can install it with the following options:
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</br> Option 1:
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<pre>
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git clone https://github.com/hewm2008/MingPCACluster.git
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cd MingPCACluster; chmod 755 -R bin/*
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./bin/MingPCACluster -h
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</pre>
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### 3 Parameter description
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------------
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</br><b>3.1 MingPCACluster</b>
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</br><b>3.1.1 Main parameter</b>
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```php
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Usage: Ming2PCACluster -InVCF <in.vcf.gz> -OutPut <outPrefix>
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-InVCF <str> Input SNP VCF Format
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-InGenotype <str> InPut Genotype File
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-InSTOgem <str> InPut STOmics gem File of MIDCounts(beta)
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-InKinship <str> Input SNP K Kinship File Format
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-OutPut <str> OutPut File Prefix(Kinship PCA etc)
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-SubPop <str> SubGroup Sample File List[ALLsample]
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-Method <int> Method of Kinship [1-4],defaut [1]
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1:BaldingNicolsKinship(VanRaden/Normalized_IBS)
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2:IBSKinshipImpute 3:IBSKinship 4:p_dis
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-help Show more Parameters and help [hewm2008]
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```
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</br> brief description for function:
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<pre>
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# 用法一看即明，最基础的为 一个输入和输出即可
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# 输入文件基因组格式见 pdf.主要为VCF和gem文件
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# 更多说明后面将在知乎更新
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Ming2PCACluster -InSTOgem Test.gem.gz -OutPut Test -CellBin 100
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### run without pop.info
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# Ming2PCACluster -InVCF Khuman.vcf.gz -OutPut OUT
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### run with pop.info
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Ming2PCACluster -InVCF Khuman.vcf.gz -OutPut OUT -InSampleGroup pop.info
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</pre>
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</br><b>3.1.2 Detail parameters</b>
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```php
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Usage: Ming2PCACluster -InVCF <in.vcf.gz> -OutPut <outPrefix>
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-InVCF <str> Input SNP VCF Format
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-InGenotype <str> InPut Genotype File
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-InSTOgem <str> InPut STOmics gem File of MIDCounts(beta)
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-InKinship <str> Input SNP K Kinship File Format
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-OutPut <str> OutPut File Prefix(Kinship PCA etc)
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-SubPop <str> SubGroup Sample File List[ALLsample]
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-Method <int> Method of Kinship [1-4],defaut [1]
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1:BaldingNicolsKinship(VanRaden/Normalized_IBS)
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2:IBSKinshipImpute 3:IBSKinship 4:p_dis
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-help Show more Parameters and help [hewm2008]
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-MAF <float> Min minor allele frequency filter [0.001]
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-Fchr <str> Filter the chrX chr[chrX,chrY,X,Y]
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-Miss <float> Max ratio of miss allele filter [0.25]
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-Het <float> Max ratio of het allele filter [1.00]
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-HWE <float> Exact test of Hardy-Weinberg Equilibrium for SNP Pvalue[0]
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-CellBin <int> STOmics cell bin[50]
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-KeepRemainVCF keep the VCF after filter
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-InSampleGroup <string> In File of sampleGroup info,format(sample groupA)
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-PCANum <int> Num of PCA eig [10]
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-MaxCluNum <int> Max Cluster Num to find Best K [12]
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-BestKRatio <float> Get the best K Cluster by deta-SSE Ratio[0.1]
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-STOName <string> STOmics Sample Name STOName
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```
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</br><b>3.2.2 Other parameters</b>
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</br>程序也提供了作图软件perl 作图脚本（这个脚本后面将会优化更动较大，主要是最近时间较忙），作图脚本的简要参数说明如下：
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```php
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ploteig -h
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Version:1.0 hewm2008@gmail.com
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Usage: ploteig -InPCA pca.eigenvec -OutPrefix Fig
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Options
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-InPCA <s> : InPut File of PCA
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-OutPrefix <s> : OutPut file prefix
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-BinDir <s> : The Bin Dir of gnuplot/R/ps2pdf/convert [$PATH]
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-help : Show more help [hewm2008]
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-columns <s> : the columns to plot a:b [3:4]
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-pops <s> : Populations to plot, eg -p GA:GB:GC [ALL]
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-border <i> : how to plot the border (1,2,4,8,3,31 ) [3]
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-title <s> : title (legend) [PCA]
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-keystyle <s> : put key at top right default(in) [outside]box [outside]
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-pointsize <i> : point size for plot [3]
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```
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</br><b>3.3 Output files</b>
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|Module | outFlie | Description |
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|:-----:|:-------------------|:------------------------------------------------------------|
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| List | | |
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| |out.kinship |输出的亲缘矩阵，各样品的两两关系 |
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| |out.eigenvec |输出最优聚类和pca结果 |
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| |out.eigenval |输出最优聚类和pca结果 |
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| |out.PCA1_PCA2.pdf |输出按cluster染色后的pca 1 2图 |
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| |out.K.pdf |输出cluster K图 |
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| |out.cluster |输出的各种K的cluster聚类结果 |
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| |Out.cellbin.gz |输出bin50 cell的结果，若是 -InSTOgem |
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| |Out.cluster pdf/png |输出坐标cluester图，若是-InSTOgem |
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示例图见上面应用场景给的图。示例图和格式当一看即明，相关图可以见example 1 和2
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### 4 Example
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------------
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</br>See more detailed usage in the&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <b>[Chinese Documentation](https://github.com/hewm2008/MingPCACluster/blob/main/Ming2PCACluster使用手册_manual_chinese.pdf)</b>
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</br>See more detailed usage in the&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; <b>[English Documentation](https://github.com/hewm2008/MingPCACluster/blob/main/Ming2PCACluster使用手册_manual_chinese.pdf)</b>
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</br>See the example directory and Manual.pdf for more detail.
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</br>具体见这儿 Manual.pdf for more detail 里面的示意数据和脚本，后期将在某些网址释放一些教程
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</br></br>
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../../bin/MingPCACluster -InVCF in.vcf.gz -OutPut outPrefix
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</br> 目录 Example/example*/　里面有输入和输出和脚本用法。
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* Example 1)千人VCF重测序SNP基因型
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</br> 共从K 人数据chr22 dbsnp里面随机挑出了3492个位点，挑 CEU（49） ， CHB（46） ， JPT（56）和 YRI （52）共203 个样品来分析。
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</br>聚类走势，best K
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</br>![K_SSE.png](https://github.com/hewm2008/MingPCACluster/blob/main/xxample/Example1/K_SSE.png)
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</br>PCA结果
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</br>![PCA.png](https://github.com/hewm2008/MingPCACluster/blob/main/xxample/Example1/PCA.png)
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* Example 2) cellbin时空细胞表达量pca和聚类
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</br>时空分析我初了解主要是：seurat ，我很浅淡的了解，这个包用到的n*m (n是样品，m是位点)的稀疏矩阵，好像周边的做时空的人总说内存很大，我这没有对时空数据敏感，对表达量进行了取log10. 也用了稀疏矩阵 和 n*n, 由于时空n是样品量很大，怕难下降。
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</br>初以 我这用了文件大于(File.gem.gz : 177M )， 范围： XXmin: 4975 XXmax: 23374 YYmin: 2525 YYmax: 20724 )。取bin 50， n达到的88507，即主要88507*88507的矩阵double上，占用60.742G (稀疏矩阵5G 矩阵:55G) 。
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</br> PCA K Fig
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</br>![out1.png](https://github.com/hewm2008/MingPCACluster/blob/main/example/Example2/OUT1.png)
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</br> PCA plot Fig
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</br>![out2.png](https://github.com/hewm2008/MingPCACluster/blob/main/example/Example2/OUT2.png)
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</br> STOmics Cluster plot Fig
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</br>![out3.png](https://github.com/hewm2008/MingPCACluster/blob/main/example/Example2/OUT3.png)
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### 5 Advantages
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</br>速度快，少内存 fast speed, low memory
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</br>简明易用 Simple and easy to use
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</br>免安装 Free installation
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### 6 Discussing
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------------
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- [:email:](https://github.com/hewm2008/MingPCACluster) hewm2008@gmail.com / hewm2008@qq.com
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- join the<b><i> QQ Group : 125293663</b></i>
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######################swimming in the sky and flying in the sea #############################
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