linux学习笔记03

@[TOC] # 概括 今天的任务还是把昨天的知识进行实操练习 简要概括如下： 1. 运行表达定量比对的R程序 2. 继续进行批量操作 3. 将表达定量的结果合并成矩阵 4. 了解最后的定量矩阵、标准化矩阵、TMM算法调整的矩阵

主要程序过程如下：

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Rscript script/run-featurecounts.R -b ../1.Mapping/BLO_S1_LD1.bam -g ../ref/genes.gtf -o BLO_S1_LD1
awk '{print "Rscript script/run-featurecounts.R -b ../1.Mapping/"$2".bam  -g ../ref/genes.gtf -o "$2  }' ../data/samples.txt > codee.sh
sh codee.sh
#在3.目录下进行以下代码
ls ../2.Quantification/*.count >genes.quant_files.txt
perl script/abundance_estimates_to_matrix.pl --est_method featureCounts --quant_files genes.quant_files.txt --out_prefix genes
less -S genes.counts.matrix
less -S genes.TPM.not_cross_norm
less -S genes.TMM.EXPR.matrix
less -S genes.TPM.not_cross_norm.TMM_info.txt

遇到的问题

R包版本问题

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Error: package or namespace load failed for ‘edgeR’:
 package ‘edgeR’ was installed by an R version with different internals; it needs to be reinstalled for use with this R version
Execution halted

看到上述问题，可以判断为版本不匹配所致， 于是我尝试的解决方案是安装最新的R和最新的edgeR 主要原因是我的旧版本是3.6.3，更新的版本的是4.0.3。 强烈建议 先把旧版本的R包给删了 可以参考这里的教程 >Linux系统更新最新版R语言方法

我直接就更新了，导致安装了新版本的r之后，许许多多的r程序都需要更新。呜呜呜~

结果展示

R程序——Subread比对

把上述冲突处理完之后， 运行R程序：

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#!/usr/bin/env Rscript
# parse parameter ---------------------------------------------------------
library(argparser, quietly=TRUE)
# Create a parser
p <- arg_parser("run featureCounts and calculate FPKM/TPM")

# Add command line arguments
p <- add_argument(p, "--bam", help="input: bam file", type="character")
p <- add_argument(p, "--gtf", help="input: gtf file", type="character")
p <- add_argument(p, "--output", help="output prefix", type="character")

# Parse the command line arguments
argv <- parse_args(p)

library(Rsubread)
library(limma)
library(edgeR)

bamFile <- argv$bam
gtfFile <- argv$gtf
nthreads <- 1
outFilePref <- argv$output

outStatsFilePath  <- paste(outFilePref, '.log',  sep = ''); 
outCountsFilePath <- paste(outFilePref, '.count', sep = ''); 

fCountsList = featureCounts(bamFile, annot.ext=gtfFile, isGTFAnnotationFile=TRUE, nthreads=nthreads, isPairedEnd=TRUE)
dgeList = DGEList(counts=fCountsList$counts, genes=fCountsList$annotation)
fpkm = rpkm(dgeList, dgeList$genes$Length)
tpm = exp(log(fpkm) - log(sum(fpkm)) + log(1e6))

write.table(fCountsList$stat, outStatsFilePath, sep="\t", col.names=FALSE, row.names=FALSE, quote=FALSE)

featureCounts = cbind(fCountsList$annotation[,1], fCountsList$counts, fpkm, tpm)
colnames(featureCounts) = c('gene_id', 'counts', 'fpkm','tpm')
write.table(featureCounts, outCountsFilePath, sep="\t", col.names=TRUE, row.names=FALSE, quote=FALSE)

得到了以下结果(以KID_S4_LD3为例)： 

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        ==========     _____ _    _ ____  _____  ______          _____
        =====         / ____| |  | |  _ \|  __ \|  ____|   /\   |  __ \
          =====      | (___ | |  | | |_) | |__) | |__     /  \  | |  | |
            ====      \___ \| |  | |  _ <|  _  /|  __|   / /\ \ | |  | |
              ====    ____) | |__| | |_) | | \ \| |____ / ____ \| |__| |
        ==========   |_____/ \____/|____/|_|  \_\______/_/    \_\_____/
       Rsubread 2.4.3

//========================== featureCounts setting ===========================\\
||                                                                            ||
||             Input files : 1 BAM file                                       ||
||                                                                            ||
||                           KID_S4_LD3.bam                                   ||
||                                                                            ||
||              Paired-end : no                                               ||
||        Count read pairs : no                                               ||
||              Annotation : genes.gtf (GTF)                                  ||
||      Dir for temp files : .                                                ||
||                 Threads : 1                                                ||
||                   Level : meta-feature level                               ||
||      Multimapping reads : counted                                          ||
|| Multi-overlapping reads : not counted                                      ||
||   Min overlapping bases : 1                                                ||
||                                                                            ||
\\============================================================================//

//================================= Running ==================================\\
||                                                                            ||
|| Load annotation file genes.gtf ...                                         ||
||    Features : 246042                                                       ||
||    Meta-features : 44677                                                   ||
||    Chromosomes/contigs : 18                                                ||
||                                                                            ||
|| Process BAM file KID_S4_LD3.bam...                                         ||
||    Single-end reads are included.                                          ||
||    Total alignments : 556850                                               ||
||    Successfully assigned alignments : 334274 (60.0%)                       ||
||    Running time : 0.02 minutes                                             ||
||                                                                            ||
|| Write the final count table.                                               ||
|| Write the read assignment summary.                                         ||
||                                                                            ||
\\============================================================================//


我们从上面的结果可以看到，其实成功比对的真的很少很少，只有60%，这次主要是练习，数据文件本身就有残缺，所以结果就会如此

经过subread处理后会得到俩个文件，一个是count文件，记录基因对应的read关系，一个是log文件，尝试打开一个（KID_S1_LD1.count）： 里面记录了每个基因的表达量，使用的是不同的标准化的模式（FPKM和TPM） 

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gene_id counts  fpkm    tpm
HF32875 0       0       0
HF32876 15      25.4415259802859        23.9388625965012
HF32877 3       2.03342463708189        1.91332363574273
HF32878 25      29.7651040853782        28.0070754176678
HF32879 18      42.955539269997 40.4184384670904
HF32880 0       0       0
HF32881 57      153.870852921363        144.782715020522
HF32882 6       28.953807443051 27.2436967248677
HF32883 11      28.7686512004096        27.0694764420548
HF32884 17      59.0448663461795        55.5574749559139
HF32885 5       39.2299789096495        36.9129224209838
HF32886 4       26.4386282106244        24.8770725649297
HF32887 18      24.5690631369069        23.1179303873095
HF32888 1       3.79997705117859        3.57553743311272
HF32889 0       0       0
HF32890 0       0       0
HF32891 0       0       0
HF32892 5       6.82473976025192        6.4216473298082
HF32893 0       0       0
HF32894 3       3.67203169592005        3.45514896735134
HF32895 6       31.308901828371 29.4596911953114
HF32896 32      127.835125414008        120.284746467792
HF32897 6       18.0761339285346        17.0084957453594
HF32898 6       9.26849926647243        8.72107005640241
HF32899 3       7.59113745026628        7.14277895570777
HF32900 34      27.6717731831349        26.0373837853089
HF32901 83      32.8682837095589        30.9269706587429
HF32902 0       0       0
HF32903 8       10.6790052747932        10.0482667642923
HF32904 18      50.9887829776328        47.9772113752216
HF32905 30      87.4807551086836        82.3138430457231
HF32906 0       0       0

再打开这个转录组log文件： 比对上的一共305644 未比对成功的，其中unmapped未匹配上注释文件的一共有119800个，Unassigned_NoFeatures(未映射到注释文件中注释的片段)（130553），说明注释文件没有给出较为全面的注释，581个都是比对时模糊不清的。 

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Assigned        305644
Unassigned_Unmapped     119800
Unassigned_Read_Type    0
Unassigned_Singleton    0
Unassigned_MappingQuality       0
Unassigned_Chimera      0
Unassigned_FragmentLength       0
Unassigned_Duplicate    0
Unassigned_MultiMapping 0
Unassigned_Secondary    0
Unassigned_NonSplit     0
Unassigned_NoFeatures   130553
Unassigned_Overlapping_Length   0
Unassigned_Ambiguity    581

参考网上的说法 Assigned: The read (or fragment) was assigned to a gene feature in the annotation file provided with option -a Ambiguity: Section 5.3 of the paper. The fragment might originate from gene A or gene B, and it is not clear which gene it originated from. MultiMapping: The fragment maps to multiple different positions. Will a read with multiple alignments be assigned or unassigned if I use the --primary option? NoFeatures: The fragment mapped to a region that is not annotated in the annotation file. Unmapped: The fragment is not mapped to the reference at all. MappingQuality: The fragment’s mapping quality is below the threshold I set with option -Q. FragementLength: The insert size between the two read mates is larger or smaller than the options set with -d and -D. Chimera: ??? I’m guessing that the fragment’s mates are mapped to different chromosomes. Secondary: ??? Nonjunction: ??? Duplicate: The fragment is duplicated in the data, so it was not assigned.

我们打开count矩阵，可以看到不同基因下匹配到的reads

reads count矩阵

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        BLO_S1_LD1      BLO_S1_LD2      BLO_S1_LD3      BLO_S2_LD1      BLO_S2_LD2      BLO_S2_LD3      BLO_S3_LD1      BLO_S3_LD2      BLO_S3_LD3      BLO_S4_LD1      BLO_S4_
HF18496 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF16431 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF07143 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF39048 10      0       1       0       0       0       0       3       1       1       0       0       0       1       0       1       0       0       0       1       0
HF32055 2       1       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       1
HF35981 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF31129 197     12      4       15      11      12      16      9       4       9       13      10      8       12      24      13      13      11      15      13      14
HF14726 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF30136 397     28      16      17      20      18      20      18      28      27      26      26      21      23      29      19      17      15      28      25      20
HF01285 144     5       11      7       7       6       10      11      10      6       3       4       4       5       6       6       10      6       12      8       16
HF15351 146     7       11      6       3       6       6       9       11      5       2       9       9       6       10      4       5       2       6       12      6
HF28792 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF42099 175     9       11      4       4       12      17      11      15      75      79      67      7       14      15      4       10      6       15      21      30
HF12561 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF21569 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF05175 67      3       4       2       2       3       1       1       3       4       2       5       3       1       0       2       3       3       5       4       0
HF24422 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF02960 110     4       5       4       15      6       14      17      15      14      14      15      6       5       6       8       6       9       12      7       10
HF03959 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF11853 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF20463 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF30006 4       2       0       0       0       0       0       0       0       0       0       0       0       1       0       0       0       0       0       0       0
HF25733 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF19008 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF42715 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0

对上述矩阵进行标准化后：（方法是TPM） TPM矩阵 

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        BLO_S1_LD1      BLO_S1_LD2      BLO_S1_LD3      BLO_S2_LD1      BLO_S2_LD2      BLO_S2_LD3      BLO_S3_LD1      BLO_S3_LD2      BLO_S3_LD3      BLO_S4_LD1      BLO_S4_
HF18496 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF16431 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF07143 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF39048 3.64430415937764        0       6.43321840995913        0       0       0       0       16.4326267389083        5.64536361832566        5.9040116944613 0       0
HF32055 0.728860831875528       5.97388663516309        0       0       0       0       0       0       0       0       0       0       0       6.79366266459393        0
HF35981 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF31129 39.2324922495974        39.1744833621847        14.0622023236281        52.9458320708972        40.927037081633 44.2142685920757        49.040039090221 26.939733798730
HF14726 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF30136 34.1377121086869        39.4679251851224        24.2872065814187        25.9091803167698        32.1300831021721        28.636368181224 26.468239396608 23.264168192162
HF01285 15.7755890482016        8.97915475837397        21.273035110233 13.5919566175187        14.3271260131465        12.1611843060004        16.8606473865918        18.1128
HF15351 12.8641539257505        10.1104101111559        17.1093983041183        9.37001896517854        4.93841491101642        9.78095250926674        8.13637161714809
HF28792 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF42099 14.9759943290718        12.6253227129725        16.6174347746069        6.06706233315074        6.39522101362723        18.9994221266268        22.3901855790316
HF12561 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF21569 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF05175 2.61418438934525        1.91877931981567        2.75508552443989        1.38309578315818        1.4579054459325 2.16562968886897        0.600499386164851       0.58645
HF24422 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0
HF02960 19.3843415319528        11.5547544127497        15.5539984582893        12.493358620238 49.3841491101642        19.5619050185335        37.9697342133578        45.0275
HF03959 0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0       0

与此同时TPM矩阵相应有个校正因子的信息，我们可以发现因子大部分是大于80%，可见校正幅度不大。 norm factors校正因子 
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group   lib.size        norm.factors    eff.lib.size
BLO_S1_LD1      999623  1.19835285824697        1197901.07921941
BLO_S1_LD2      1000024 1.14444978101036        1144477.24780511
BLO_S1_LD3      1000092 1.17712340823365        1177231.70358721
BLO_S2_LD1      1000051 0.790137765984441       790178.063010506
BLO_S2_LD2      1000037 0.920995506851478       921029.583685232
BLO_S2_LD3      999995  1.0840994226589 1084094.00216179
BLO_S3_LD1      999966  0.971096339652144       971063.322376596
BLO_S3_LD2      1000002 0.897106990690095       897108.784904077
BLO_S3_LD3      999976  0.887155199786789       887133.908061994
BLO_S4_LD1      999972  0.874964482098764       874939.983093265
BLO_S4_LD2      1000096 0.929513259538841       929602.492811757
BLO_S4_LD3      1000037 0.875277014429481       875309.399679015
KID_S1_LD1      999994  1.266534203831  1266526.60462578
KID_S1_LD2      999939  1.21755253986366        1217478.26915873
KID_S1_LD3      1000050 1.24861324728186        1248675.67794423
KID_S2_LD1      999975  0.968264483401714       968240.276789629
KID_S2_LD2      999949  1.08012898371   1080073.89713183
KID_S2_LD3      1000066 1.11022114688009        1110294.42147579
KID_S3_LD1      999960  1.00488458514679        1004844.38976338
KID_S3_LD2      999942  0.898797372534519       898745.242286912
KID_S3_LD3      999931  0.974514145521912       974446.904045871
KID_S4_LD1      1000018 0.87262090923823        872636.616414597
KID_S4_LD2      999966  0.892593891696263       892563.543503945
KID_S4_LD3      999965  0.931133742026098       931101.152345127

在TPM算法下，使用TMM修正后的矩阵：（一般我们使用的就是这个矩阵） TMM校正依赖这个perl程序 script/support_scripts/run_TMM_scale_matrix.pl

TPM+TMM矩阵 

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        BLO_S1_LD1      BLO_S1_LD2      BLO_S1_LD3      BLO_S2_LD1      BLO_S2_LD2      BLO_S2_LD3      BLO_S3_LD1      BLO_S3_LD2      BLO_S3_LD3      BLO_S4_LD1      BLO_S4_
HF18496 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF16431 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF07143 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF39048 3.041   0.000   5.465   0.000   0.000   0.000   0.000   18.317  6.363   6.748   0.000   0.000   0.000   5.580   0.000   7.018   0.000   0.000   0.000   6.191   0.000
HF32055 0.608   5.220   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   5.580   0.000   0.000   0.000   0.000   0.000   0.000   5.755
HF35981 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF31129 32.739  34.230  11.946  67.008  44.438  40.784  50.500  30.030  13.910  33.187  44.993  38.408  24.096  36.590  70.466  49.855  50.071  41.555  50.901  43.980  44.029
HF14726 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF30136 28.487  34.486  20.633  32.791  34.886  26.415  27.256  25.932  42.042  42.988  38.854  43.118  27.311  30.281  36.764  31.462  28.272  24.467  41.026  36.519  27.158
HF01285 13.164  7.846   18.072  17.202  15.556  11.218  17.362  20.190  19.129  12.171  5.712   8.451   6.628   8.387   9.691   12.658  21.188  12.469  22.401  14.888  27.680
HF15351 10.735  8.834   14.535  11.859  5.362   9.022   8.379   13.286  16.924  8.157   3.063   15.294  11.993  8.094   12.990  6.787   8.520   3.343   9.008   17.961  8.348
HF28792 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF42099 12.497  11.032  14.117  7.678   6.944   17.526  23.057  15.772  22.414  118.840 117.491 110.580 9.060   18.344  18.925  6.592   16.551  9.740   21.873  30.529  40.542
HF12561 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF21569 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF05175 2.181   1.677   2.341   1.750   1.583   1.998   0.618   0.654   2.044   2.890   1.356   3.762   1.770   0.597   0.000   1.503   2.264   2.220   3.324   2.651   0.000
HF24422 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF02960 16.176  10.096  13.214  15.812  53.620  18.044  39.100  50.192  46.156  45.680  42.875  50.979  15.991  13.491  15.588  27.148  20.449  30.085  36.033  20.955  27.828
HF03959 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF11853 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF20463 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF30006 0.351   3.015   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   1.611   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF25733 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF19008 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF42715 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF19277 26.619  28.132  35.548  21.269  19.234  18.493  21.467  21.559  23.652  16.302  22.363  28.736  30.729  27.998  27.958  16.954  13.098  23.125  23.080  17.257  14.973
HF34254 2.451   5.802   4.556   4.543   4.109   3.457   3.210   1.697   0.000   0.000   3.520   0.000   1.532   3.101   1.493   1.950   5.876   0.000   5.177   3.441   3.198
HF27633 6.529   7.067   11.628  1.581   4.290   4.812   3.351   5.905   6.154   2.610   3.675   5.438   8.529   6.475   6.235   4.072   2.727   4.011   3.603   3.592   2.226
HF30992 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF35339 26.367  39.055  21.648  17.989  19.522  19.161  20.336  32.247  19.605  17.819  33.449  27.840  31.536  36.837  28.376  24.709  37.224  18.255  16.398  27.247  22.796
HF06789 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
HF33530 0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000

目前记录表达量有俩种手段，一种是基于比对，一种基于kmer，使用比对方法好处就是能了解到reads的情况（有没有污染啥的） edgeR可以没有生物学重复

#linux
linux学习笔记03
http://example.com/2022/07/28/Linux学习日记03/
作者
Brianyjh
发布于
2022年7月28日
许可协议