RNA_seq表达分析
输入文件
input_v1.0.txt （三列，分别是 *.1.fastq.gz,*2.fastq.gz , *.sam）
hisat2运行参数与流程（hisat2_IWGSCv1.0.py）
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'shengwei ma'
__author_email__ = '[email protected]'
import subprocess
with open('input_v1.0_1.txt', 'r') as f:
for line in f:
lin = line.strip().split()
fq1, fq2, sam = lin[0], lin[1], lin[2]
proc = subprocess.Popen(
['hisat2', '-p', '20', '--dta', '-x', '/data2/Fshare/IWGSCv1.0_hisat2/IWGSCv1.0_hiast2', '--known-splicesite-infile',
'/data2/Fshare/IWGSCv1.0_hisat2/TGACv1.ss', '--novel-splicesite-infile', '/data2/masw_data/rna_seq/all.ss',
'-t', '--no-discordant', '--no-mixed', '-1', fq1, '-2', fq2, '-S', sam], shell=False)
proc.wait()
print sam
new = sam[:-3] + 'unmap.txt'
mat = sam[:-3] + 'match.sam'
mis = sam[:-3] + 'mismatch.sam'
unmap = open(new, 'w')
mat1 = open(mat, 'w')
mis1 = open(mis, 'w')
with open(sam, 'r') as f1:
for (num, value) in enumerate(f1):
lin = value.strip().split()
if value.startswith('@'):
mat1.writelines(value)
mis1.writelines(value)
else:
if '*' in lin[2]:
unmap.writelines(lin[0])
else:
if 'I' not in lin[5] and 'D' not in lin[5] and 'XM:i:0' in value: #筛选完全匹配的reads，但是对于softclip 无效
mat1.writelines(value)
if 'I' not in lin[5] and 'D' not in lin[5] and 'XM:i:0' not in value:
mis1.writelines(value)
mat1.close()
unmap.close()
mis1.close()
proc = subprocess.Popen(['samtools', 'view', '-@', '10', '-b', '-o', mat[:-3] + 'bam', mat], shell=False)
proc.wait()
proc = subprocess.Popen(['samtools', 'sort', '-@', '10', '-o', mat[:-3] + 'sorted.bam', mat[:-3] + 'bam'], shell=False)
proc.wait()
proc = subprocess.Popen(['shred', '-u', '-z', mat, sam, mat[:-3] + 'bam'], shell=False)
proc.wait()hisat2输出信息，也可见该目录下的mapping_information.txt
| Sample | total_reads | unmapped_reads | uniquely-mapped_reads | Multimapped_reads |
|---|---|---|---|---|
| ATW_AAOSW_6 | 45401788(100.00%) | 1625896(3.58%) | 36054911(79.41%) | 7720981(17.01%) |
| ATW_ABOSW_7 | 43317025(100.00%) | 3384472(7.81%) | 32613798(75.29%) | 7318755(16.90%) |
| ATW_ACOSW | 51224256(100.00%) | 3682442(7.19%) | 37964008(74.11%) | 9577806(18.70%) |
| ATW_ADOSW | 85237299(100.00%) | 5327852(6.25%) | 63649972(74.67%) | 16259475(19.08%) |
| ATW_AEOSW | 44470405(100.00%) | 3714550(8.35%) | 33422930(75.16%) | 7332925(16.49%) |
| ATW_AFOSW_2 | 38815740(100.00%) | 2344586(6.04%) | 30028428(77.36%) | 6442726(16.60%) |
| ATW_AGOSW_2 | 35749803(100.00%) | 3298492(9.23%) | 26017323(72.78%) | 6433988(18.00%) |
| ATW_AHOSW_3 | 52146021(100.00%) | 4219037(8.09%) | 39229076(75.23%) | 8697908(16.68%) |
| ATW_AIOSW_2 | 67283195(100.00%) | 19946431(29.65%) | 31218902(46.40%) | 16117862(23.96%) |
| ATW_AKOSW_2 | 75347431(100.00%) | 24014018(31.87%) | 33383763(44.31%) | 17949650(23.82%) |
| ATW_ALOSW_3 | 42039096(100.00%) | 2031197(4.83%) | 32613783(77.58%) | 7394116(17.59%) |
| ATW_AMOSW_4 | 38844640(100.00%) | 7661962(19.72%) | 25025383(64.42%) | 6157295(15.85%) |
| ATW_ANOSW | 89075171(100.00%) | 11783105(13.23%) | 63333349(71.10%) | 13958717(15.67%) |
| ATW_AOSW | 50836846(100.00%) | 1967671(3.87%) | 40375224(79.42%) | 8493951(16.71%) |
| ATW_COSW | 45388739(100.00%) | 4336069(9.55%) | 33513277(73.84%) | 7539393(16.61%) |
| ATW_DOSW_2 | 48400597(100.00%) | 1782615(3.68%) | 38184280(78.89%) | 8433702(17.42%) |
| ATW_FOSW_2 | 47627837(100.00%) | 11084697(23.27%) | 29081034(61.06%) | 7462106(15.67%) |
| ATW_GOSW_3 | 47851480(100.00%) | 2025594(4.23%) | 38041640(79.50%) | 7784246(16.27%) |
| ATW_HOSW_3 | 46349244(100.00%) | 3243306(7.00%) | 35294358(76.15%) | 7811580(16.85%) |
| ATW_IOSW_4 | 53653235(100.00%) | 2427235(4.52%) | 42707453(79.60%) | 8518547(15.88%) |
| ATW_KOSW_4 | 39894644(100.00%) | 3043191(7.63%) | 30655324(76.84%) | 6196129(15.53%) |
| ATW_LOSW_5 | 40476784(100.00%) | 2375565(5.87%) | 31157278(76.98%) | 6943941(17.16%) |
| ATW_MOSW_5 | 49643196(100.00%) | 6008219(12.10%) | 34849848(70.20%) | 8785129(17.70%) |
| ATW_NOSW_6 | 45463315(100.00%) | 5519168(12.14%) | 32357850(71.17%) | 7586297(16.69%) |
| ATW_POSW_6 | 42820604(100.00%) | 4444437(10.38%) | 31740451(74.12%) | 6635716(15.50%) |
| ATW_QOSW_7 | 45189058(100.00%) | 2519770(5.58%) | 35464036(78.48%) | 7205252(15.94%) |
| ATW_ROSW_7 | 41964678(100.00%) | 2418292(5.76%) | 32377059(77.15%) | 7169327(17.08%) |
| ATW_SOSW_8 | 46010346(100.00%) | 2071554(4.50%) | 36664030(79.69%) | 7274762(15.81%) |
| ATW_TOSW_8 | 41117096(100.00%) | 3337900(8.12%) | 30307865(73.71%) | 7471331(18.17%) |
| ATW_VOSW_6 | 44532829(100.00%) | 1723299(3.87%) | 34529586(77.54%) | 8279944(18.59%) |
| ERR392055 | 26786162(100.00%) | 4047245(15.11%) | 15990303(59.70%) | 6748614(25.19%) |
| ERR392056 | 29879250(100.00%) | 4692482(15.70%) | 17363629(58.11%) | 7823139(26.18%) |
| ERR392057 | 30226502(100.00%) | 3185007(10.54%) | 19814488(65.55%) | 7227007(23.91%) |
| ERR392058 | 18558499(100.00%) | 1998085(10.77%) | 12203151(65.76%) | 4357263(23.48%) |
| ERR392059 | 30793173(100.00%) | 2344455(7.61%) | 22029424(71.54%) | 6419294(20.85%) |
| ERR392060 | 22417889(100.00%) | 2366252(10.56%) | 14884436(66.40%) | 5167201(23.05%) |
| ERR392061 | 20355570(100.00%) | 3719356(18.27%) | 11386270(55.94%) | 5249944(25.79%) |
| ERR392062 | 25108363(100.00%) | 3797487(15.12%) | 14548403(57.94%) | 6762473(26.93%) |
| ERR392063 | 29965698(100.00%) | 4989790(16.65%) | 16957287(56.59%) | 8018621(26.76%) |
| ERR392064 | 34565714(100.00%) | 4811540(13.92%) | 20727298(59.96%) | 9026876(26.12%) |
| ERR392064 | 34565714(100.00%) | 4811540(13.92%) | 20727298(59.96%) | 9026876(26.12%) |
| ERR392065 | 27177427(100.00%) | 4520724(16.63%) | 15412488(56.71%) | 7244215(26.66%) |
| ERR392066 | 37515837(100.00%) | 7292639(19.44%) | 19656276(52.39%) | 10566922(28.17%) |
| ERR392067 | 29548095(100.00%) | 5994021(20.29%) | 15715972(53.19%) | 7838102(26.53%) |
| ERR392068 | 30476750(100.00%) | 3991164(13.10%) | 18606572(61.05%) | 7879014(25.85%) |
| ERR392069 | 31424506(100.00%) | 2679321(8.53%) | 21520594(68.48%) | 7224591(22.99%) |
| ERR392070 | 29660313(100.00%) | 4427930(14.93%) | 17592218(59.31%) | 7640165(25.76%) |
| ERR392071 | 29373530(100.00%) | 4610961(15.70%) | 17098379(58.21%) | 7664190(26.09%) |
| ERR392072 | 33407315(100.00%) | 4025192(12.05%) | 21596921(64.65%) | 7785202(23.30%) |
| ERR392073 | 28004777(100.00%) | 3852619(13.76%) | 17378994(62.06%) | 6773164(24.19%) |
| ERR392074 | 26658599(100.00%) | 5465146(20.50%) | 13906279(52.16%) | 7287174(27.34%) |
| ERR392075 | 27595485(100.00%) | 4936815(17.89%) | 15247427(55.25%) | 7411243(26.86%) |
| ERR392076 | 29674240(100.00%) | 5400293(18.20%) | 16416891(55.32%) | 7857056(26.48%) |
| ERR392077 | 31078820(100.00%) | 3574955(11.50%) | 20081821(64.62%) | 7422044(23.88%) |
| ERR392078 | 32327629(100.00%) | 2421810(7.49%) | 23264665(71.97%) | 6641154(20.54%) |
| ERR392079 | 23704655(100.00%) | 3131080(13.21%) | 14112639(59.54%) | 6460936(27.26%) |
| ERR392080 | 27746131(100.00%) | 2992584(10.79%) | 18525078(66.77%) | 6228469(22.45%) |
| ERR392081 | 31358914(100.00%) | 3746073(11.95%) | 20709901(66.04%) | 6902940(22.01%) |
| ERR392082 | 32871524(100.00%) | 2346792(7.14%) | 23727550(72.18%) | 6797182(20.68%) |
| ERR392083 | 32240850(100.00%) | 3128224(9.70%) | 21232287(65.86%) | 7880339(24.44%) |
| ERR392084 | 32641566(100.00%) | 2632926(8.07%) | 23252740(71.24%) | 6755900(20.70%) |
| NG-5789_1A_lib7482 | 34356521(100.00%) | 2743533(7.99%) | 26100505(75.97%) | 5512483(16.04%) |
| NG-5789_1B_lib7486 | 42444661(100.00%) | 2276170(5.36%) | 32538608(76.66%) | 7629883(17.98%) |
| NG-5789_2A_lib7483 | 58377229(100.00%) | 1980165(3.39%) | 46325570(79.36%) | 10071494(17.25%) |
| NG-5789_2B_lib7487 | 45374776(100.00%) | 1459680(3.22%) | 35121823(77.40%) | 8793273(19.38%) |
| NG-5789_3A_lib7484 | 39082313(100.00%) | 2105348(5.39%) | 30799509(78.81%) | 6177456(15.81%) |
| NG-5789_3B_lib7488 | 39159546(100.00%) | 1543503(3.94%) | 30324689(77.44%) | 7291354(18.62%) |
| NG-5789_4A_lib7485 | 28215511(100.00%) | 1319564(4.68%) | 22135951(78.45%) | 4759996(16.87%) |
| NG-5789_4B_lib7489 | 34385209(100.00%) | 2265183(6.59%) | 26117900(75.96%) | 6002126(17.46%) |
| SRR1175868 | 62225856(100.00%) | 4434070(7.13%) | 46741787(75.12%) | 11049999(17.76%) |
| SRR1177760 | 68599252(100.00%) | 4784428(6.97%) | 51327798(74.82%) | 12487026(18.20%) |
| SRR1177761 | 77573636(100.00%) | 5418773(6.99%) | 58236525(75.07%) | 13918338(17.94%) |
| SRR1460549 | 30618243(100.00%) | 2210006(7.22%) | 20459229(66.82%) | 7949008(25.96%) |
| SRR1460550 | 72955159(100.00%) | 5074113(6.96%) | 48678341(66.72%) | 19202705(26.32%) |
| SRR1460551 | 27179120(100.00%) | 2914078(10.72%) | 17292077(63.62%) | 6972965(25.66%) |
| SRR1460552 | 37693050(100.00%) | 2304774(6.11%) | 25576360(67.85%) | 9811916(26.03%) |
| SRR1460553 | 23942473(100.00%) | 1441952(6.02%) | 16313271(68.14%) | 6187250(25.84%) |
| SRR1460554 | 17985663(100.00%) | 1158728(6.44%) | 12218492(67.93%) | 4608443(25.62%) |
| Wheat_Room1_AL_20DPA_RNA_Extra2 | 32685090(100.00%) | 3056423(9.35%) | 21550138(65.93%) | 8078529(24.72%) |
| Wheat_Room_SE_30DPA_RNA | 23711650(100.00%) | 3477993(14.67%) | 13233266(55.81%) | 7000391(29.52%) |
使用featurecount计算reads数
其中-a 是输入文件，-o 是输出结果，每次运行注意修改。
featureCounts -T 20 -t exon -g Name --readExtension5 70 --readExtension3 70 -p -O --donotsort -C -a /data2/masw_data/transcript/TGACv1.cdna.gff3 -o /data2/masw_data/transcript/TGACv1.cdna.reformat_expression_new.txt ATW_AOSW.match.sorted.bam ATW_AAOSW_6.match.sorted.bam ATW_ANOSW.match.sorted.bam ATW_LOSW_5.match.sorted.bam ATW_ADOSW.match.sorted.bam ATW_AEOSW.match.sorted.bam ATW_DOSW_2.match.sorted.bam ATW_POSW_6.match.sorted.bam ATW_IOSW_4.match.sorted.bam ATW_KOSW_4.match.sorted.bam ATW_ROSW_7.match.sorted.bam ATW_ALOSW_3.match.sorted.bam ATW_TOSW_8.match.sorted.bam ATW_VOSW_6.match.sorted.bam ATW_MOSW_5.match.sorted.bam ATW_NOSW_6.match.sorted.bam ATW_COSW.match.sorted.bam ATW_AGOSW_2.match.sorted.bam ATW_GOSW_3.match.sorted.bam ATW_HOSW_3.match.sorted.bam ATW_ABOSW_7.match.sorted.bam ATW_ACOSW.match.sorted.bam ATW_QOSW_7.match.sorted.bam ATW_AHOSW_3.match.sorted.bam SRR1175868.match.sorted.bam SRR1177760.match.sorted.bam SRR1177761.match.sorted.bam NG-5789_1A_lib7482.match.sorted.bam NG-5789_1B_lib7486.match.sorted.bam NG-5789_2A_lib7483.match.sorted.bam NG-5789_2B_lib7487.match.sorted.bam NG-5789_3A_lib7484.match.sorted.bam NG-5789_3B_lib7488.match.sorted.bam NG-5789_4A_lib7485.match.sorted.bam NG-5789_4B_lib7489.match.sorted.bam ATW_SOSW_8.match.sorted.bam ATW_AFOSW_2.match.sorted.bam ATW_AIOSW_2.match.sorted.bam ATW_AKOSW_2.match.sorted.bam ATW_FOSW_2.match.sorted.bam ATW_AMOSW_4.match.sorted.bam ERR392061.match.sorted.bam ERR392055.match.sorted.bam ERR392057.match.sorted.bam ERR392072.match.sorted.bam ERR392082.match.sorted.bam ERR392059.match.sorted.bam ERR392080.match.sorted.bam ERR392081.match.sorted.bam ERR392078.match.sorted.bam ERR392084.match.sorted.bam ERR392063.match.sorted.bam ERR392076.match.sorted.bam ERR392074.match.sorted.bam ERR392075.match.sorted.bam ERR392058.match.sorted.bam ERR392077.match.sorted.bam ERR392056.match.sorted.bam ERR392070.match.sorted.bam ERR392064.match.sorted.bam ERR392068.match.sorted.bam ERR392073.match.sorted.bam ERR392083.match.sorted.bam ERR392079.match.sorted.bam ERR392065.match.sorted.bam ERR392066.match.sorted.bam ERR392062.match.sorted.bam ERR392069.match.sorted.bam ERR392060.match.sorted.bam ERR392067.match.sorted.bam ERR392071.match.sorted.bam SRR1460549.match.sorted.bam SRR1460550.match.sorted.bam SRR1460551.match.sorted.bam SRR1460552.match.sorted.bam SRR1460553.match.sorted.bam SRR1460554.match.sorted.bam计算FPKM。使用 fpkm.py .输入文件为上述featurecount输出文件
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'shengwei ma'
__author_email__ = '[email protected]'
import numpy as np
raw_total = [('root_Z10_rep1', 48869175), ('root_Z10_rep2', 43775892), ('root_Z13_rep1', 78098556),
('root_Z13_rep2', 38101219), ('root_Z39_rep1', 79909447), ('root_Z39_rep2', 40755855),
('stem_Z30_rep1', 46617982), ('stem_Z30_rep2', 38376167), ('stem_Z32_rep1', 51226000),
('stem_Z32_rep2', 36851453), ('stem_Z65_rep1', 39546386), ('stem_Z65_rep2',40007899),
('leaf_Z10_rep1', 37779196), ('leaf_Z10_rep2', 42809530), ('leaf_Z23_rep1', 43634977),
('leaf_Z23_rep2', 39944147), ('leaf_Z71_rep1', 41052670), ('leaf_Z71_rep2', 32451311),
('spike_Z32_rep1', 45825886), ('spike_Z32_rep2', 43105938), ('spike_Z39_rep1', 39932553),
('spike_Z39_rep2', 47541814), ('spike_Z65_rep1', 42669288), ('spike_Z65_rep2', 47926984),
('carpel', 47926984), ('carpel-like structure', 63914055), ('stamen', 72154863),
('latent_lepto_rep1', 31612988), ('latent_lepto_rep2', 40168491), ('diplo_dia_rep1', 56397064),
('diplo_dia_rep2', 43915096), ('zygo_pachy_rep1', 36976965), ('zygo_pachy_rep2', 37616043),
('metaphaseI_rep1', 26895947), ('metaphaseI_rep2', 32120026), ('grain_Z71_rep1', 43938792),
('grain_Z71_rep2', 36471154), ('grain_Z75_rep1', 47336764), ('grain_Z75_rep2', 51333413),
('grain_Z85_rep1', 36543140), ('grain_Z85_rep2', 31182678), ('Wheat_Room1_10DPA', 16712256),
('Wheat_Room1_10DPA_Rep', 22819483), ('Wheat_Room2_10DPA', 27121510), ('Wheat_Room2_10DPA_Rep', 29453109),
('Wheat_Room1_AL_20DPA', 30598515), ('Wheat_Room1_AL_20DPA_Rep', 28518937), ('Wheat_Room2_AL_20DPA', 24838220),
('Wheat_Room2_AL_20DPA_Rep', 27715580), ('Wheat_Room1_AL_20DPA_Extra1', 29978007), ('Wheat_Room1_AL_20DPA_Extra2', 30079461),
('Wheat_Room1_SE_20DPA', 25140145), ('Wheat_Room1_SE_20DPA_Rep', 24446796), ('Wheat_Room2_SE_20DPA', 21339690),
('Wheat_Room2_SE_20DPA_Rep', 22815780),
('Wheat_Room1_TC_20DPA', 16629117), ('Wheat_Room1_TC_20DPA_Rep', 27612315), ('Wheat_Room2_TC_20DPA', 25304622),
('Wheat_Room2_TC_20DPA_Rep', 25352139), ('Wheat_Room1_REF_20DPA', 29929219), ('Wheat_Room1_REF_20DPA_Rep', 26636425),
('Wheat_Room2_REF_20DPA', 24316737), ('Wheat_Room2_REF_20DPA_Rep', 29330096), ('Wheat_Room1_SE_30DPA', 20661506),
('Wheat_Room1_SE_30DPA_Rep', 22777481), ('Wheat_Room2_SE_30DPA', 30513836), ('Wheat_Room2_SE_30DPA_Rep', 21486098),
('Wheat_Room1_AL_SE_30DPA', 28821672), ('Wheat_Room1_AL_SE_30DPA_Rep', 20134665), ('Wheat_Room2_AL_SE_30DPA', 23721856),
('Wheat_Room2_AL_SE_30DPA_Rep', 24896811), ('wheat_23_1', 28444918), ('wheat_23_2', 67968193),
('wheat_23_3', 24321425), ('wheat_4_1', 35430306), ('wheat_4_2', 22527710), ('wheat_4_3', 16848204)]
organs = open('1.txt', 'w')
organs.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % ('Geneid', 'Chr', 'root_max', 'stem_max', 'leaf_max', 'spike_max', 'grain_max'
, 'stamen_max', 'new_carpel'))
with open('specific_gene_expression_new.txt', 'r') as f: # 此处注意修改输入文件
print "%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \
"\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \
"\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \
"\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t" % \
('Geneid', 'Chr', 'Start', 'End', 'Strand', 'Length', 'root_Z10', 'root_Z13','root_Z39',
'stem_Z30', 'stem_Z32', 'stem_Z65', 'leaf_Z10', 'leaf_Z23', 'leaf_Z71',
'spike_Z32', 'spike_Z39', 'spike_Z65', 'carpel', 'carpel_like_structure',
'stamen', 'latet_lepto', 'diplo_dia', 'zygo_pachy', 'metaphaseI',
'grain_Z71', 'grain_Z75', 'grain_Z85', 'Wheat_10DPA', 'Wheat_AL_20DPA',
'Wheat_SE_20DPA', 'Wheat_TC_20DPA', 'Wheat_REF_20DPA', 'Wheat_SE_30DPA',
'Wheat_AL.SE_30DPA', 'wheat_23', 'wheat_4', 'root_Z10_std', 'root_Z13_std', 'root_Z39_std',
'stem_Z30_std', 'stem_Z32_std', 'stem_Z65_std', 'leaf_Z10_std', 'leaf_Z23_std', 'leaf_Z71_std',
'spike_Z32_std', 'spike_Z39_std', 'spike_Z65_std', 'carpel_std', 'carpel-like_std', 'stamen_std',
'latet_lepto_std', 'diplo_dia_std', 'zygo_pachy_std', 'metaphaseI_std', 'grain_Z71_std',
'grain_Z75_std', 'grain_Z85_std','Wheat_10DPA_std', 'Wheat_AL_20DPA_std','Wheat_SE_20DPA_std',
'Wheat_TC_20DPA_std', 'Wheat_REF_20DPA_std', 'Wheat_SE_30DPA_std',
'Wheat_AL.SE_30DPA_std', 'wheat_23_std', 'wheat_4_std')
for line in f:
if line.startswith('#') or line.startswith('Geneid'):
pass
else:
new = line.strip().split('\t')
(Geneid, Chr, Start, End, Strand, Length, root_Z10_rep1, root_Z10_rep2, root_Z13_rep1, root_Z13_rep2,
root_Z39_rep1, root_Z39_rep2, stem_Z30_rep1, stem_Z30_rep2, stem_Z32_rep1, stem_Z32_rep2, stem_Z65_rep1,
stem_Z65_rep2, leaf_Z10_rep1, leaf_Z10_rep2, leaf_Z23_rep1, leaf_Z23_rep2, leaf_Z71_rep1, leaf_Z71_rep2,
spike_Z32_rep1, spike_Z32_rep2, spike_Z39_rep1, spike_Z39_rep2, spike_Z65_rep1, spike_Z65_rep2, carpel,
carpel_like_structure, stamen, latet_lepto_rep1, latent_lepto_rep2, diplo_dia_rep1, diplo_dia_rep2,
zygo_pachy_rep1, zygo_pachy_rep2, metaphaseI_rep1, metaphaseI_rep2, grain_Z71_rep1, grain_Z71_rep2,
grain_Z75_rep1, grain_Z75_rep2, grain_Z85_rep1, grain_Z85_rep2, Wheat_Room1_10DPA, Wheat_Room1_10DPA_Rep,
Wheat_Room2_10DPA, Wheat_Room2_10DPA_Rep, Wheat_Room1_AL_20DPA, Wheat_Room1_AL_20DPA_Rep,
Wheat_Room2_AL_20DPA, Wheat_Room2_AL_20DPA_Rep, Wheat_Room1_AL_20DPA_Extra1, Wheat_Room1_AL_20DPA_Extra2,
Wheat_Room1_SE_20DPA, Wheat_Room1_SE_20DPA_Rep, Wheat_Room2_SE_20DPA, Wheat_Room2_SE_20DPA_Rep,
Wheat_Room1_TC_20DPA, Wheat_Room1_TC_20DPA_Rep, Wheat_Room2_TC_20DPA, Wheat_Room2_TC_20DPA_Rep,
Wheat_Room1_REF_20DPA, Wheat_Room1_REF_20DPA_Rep, Wheat_Room2_REF_20DPA, Wheat_Room2_REF_20DPA_Rep,
Wheat_Room1_SE_30DPA, Wheat_Room1_SE_30DPA_Rep, Wheat_Room2_SE_30DPA, Wheat_Room2_SE_30DPA_Rep,
Wheat_Room1_AL_SE_30DPA, Wheat_Room1_AL_SE_30DPA_Rep, Wheat_Room2_AL_SE_30DPA, Wheat_Room2_AL_SE_30DPA_Rep,
wheat_23_1, wheat_23_2, wheat_23_3, wheat_4_1, wheat_4_2, wheat_4_3) = new
new_root_Z10_rep1 = int(root_Z10_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[0][-1]))
new_root_Z10_rep2 = int(root_Z10_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[1][-1]))
new_root_Z13_rep1 = int(root_Z13_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[2][-1]))
new_root_Z13_rep2 = int(root_Z13_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[3][-1]))
new_root_Z39_rep1 = int(root_Z39_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[4][-1]))
new_root_Z39_rep2 = int(root_Z39_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[5][-1]))
new_stem_Z30_rep1 = int(stem_Z30_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[6][-1]))
new_stem_Z30_rep2 = int(stem_Z30_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[7][-1]))
new_stem_Z32_rep1 = int(stem_Z32_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[8][-1]))
new_stem_Z32_rep2 = int(stem_Z32_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[9][-1]))
new_stem_Z65_rep1 = int(stem_Z65_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[10][-1]))
new_stem_Z65_rep2 = int(stem_Z65_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[11][-1]))
new_leaf_Z10_rep1 = int(leaf_Z10_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[12][-1]))
new_leaf_Z10_rep2 = int(leaf_Z10_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[13][-1]))
new_leaf_Z23_rep1 = int(leaf_Z23_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[14][-1]))
new_leaf_Z23_rep2 = int(leaf_Z23_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[15][-1]))
new_leaf_Z71_rep1 = int(leaf_Z71_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[16][-1]))
new_leaf_Z71_rep2 = int(leaf_Z71_rep2) * pow(10.0 , 9) / (int(Length) * int(raw_total[17][-1]))
new_spike_Z32_rep1 = int(spike_Z32_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[18][-1]))
new_spike_Z32_rep2 = int(spike_Z32_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[19][-1]))
new_spike_Z39_rep1 = int(spike_Z39_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[20][-1]))
new_spike_Z39_rep2 = int(spike_Z39_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[21][-1]))
new_spike_Z65_rep1 = int(spike_Z65_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[22][-1]))
new_spike_Z65_rep2 = int(spike_Z65_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[23][-1]))
new_carpel = int(carpel) * pow(10.0, 9) / (int(Length) * int(raw_total[24][-1]))
new_carpel_like_structure = int(carpel_like_structure) * pow(10.0, 9) / (int(Length) * int(raw_total[25][-1]))
new_stamen = int(stamen) * pow(10.0, 9) / (int(Length) * int(raw_total[26][-1]))
new_latet_lepto_rep1 = int(latet_lepto_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[27][-1]))
new_latet_lepto_rep2 = int(latent_lepto_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[28][-1]))
new_diplo_dia_rep1 = int(diplo_dia_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[29][-1]))
new_diplo_dia_rep2 = int(diplo_dia_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[30][-1]))
new_zygo_pachy_rep1 = int(zygo_pachy_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[31][-1]))
new_zygo_pachy_rep2 = int(zygo_pachy_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[32][-1]))
new_metaphaseI_rep1 = int(metaphaseI_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[33][-1]))
new_metaphaseI_rep2 = int(metaphaseI_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[34][-1]))
new_grain_Z71_rep1 = int(grain_Z71_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[35][-1]))
new_grain_Z71_rep2 = int(grain_Z71_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[36][-1]))
new_grain_Z75_rep1 = int(grain_Z75_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[37][-1]))
new_grain_Z75_rep2 = int(grain_Z75_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[38][-1]))
new_grain_Z85_rep1 = int(grain_Z85_rep1) * pow(10.0, 9) / (int(Length) * int(raw_total[39][-1]))
new_grain_Z85_rep2 = int(grain_Z85_rep2) * pow(10.0, 9) / (int(Length) * int(raw_total[40][-1]))
Wheat_Room1_10DPA = int(Wheat_Room1_10DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[41][-1]))
Wheat_Room1_10DPA_Rep = int(Wheat_Room1_10DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[42][-1]))
Wheat_Room2_10DPA = int(Wheat_Room2_10DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[43][-1]))
Wheat_Room2_10DPA_Rep = int(Wheat_Room2_10DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[44][-1]))
Wheat_Room1_AL_20DPA = int(Wheat_Room1_AL_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[45][-1]))
Wheat_Room1_AL_20DPA_Rep = int(Wheat_Room1_AL_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[46][-1]))
Wheat_Room2_AL_20DPA = int(Wheat_Room2_AL_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[47][-1]))
Wheat_Room2_AL_20DPA_Rep = int(Wheat_Room2_AL_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[48][-1]))
Wheat_Room1_AL_20DPA_Extra1 = int(Wheat_Room1_AL_20DPA_Extra1) * pow(10.0, 9) / (int(Length) * int(raw_total[49][-1]))
Wheat_Room1_AL_20DPA_Extra2 = int(Wheat_Room1_AL_20DPA_Extra2) * pow(10.0, 9) / (int(Length) * int(raw_total[50][-1]))
Wheat_Room1_SE_20DPA = int(Wheat_Room1_SE_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[51][-1]))
Wheat_Room1_SE_20DPA_Rep = int(Wheat_Room1_SE_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[52][-1]))
Wheat_Room2_SE_20DPA = int(Wheat_Room2_SE_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[53][-1]))
Wheat_Room2_SE_20DPA_Rep = int(Wheat_Room2_SE_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[54][-1]))
Wheat_Room1_TC_20DPA = int(Wheat_Room1_TC_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[55][-1]))
Wheat_Room1_TC_20DPA_Rep = int(Wheat_Room1_TC_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[56][-1]))
Wheat_Room2_TC_20DPA = int(Wheat_Room2_TC_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[57][-1]))
Wheat_Room2_TC_20DPA_Rep = int(Wheat_Room2_TC_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[58][-1]))
Wheat_Room1_REF_20DPA = int(Wheat_Room1_REF_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[59][-1]))
Wheat_Room1_REF_20DPA_Rep = int(Wheat_Room1_REF_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[60][-1]))
Wheat_Room2_REF_20DPA = int(Wheat_Room2_REF_20DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[61][-1]))
Wheat_Room2_REF_20DPA_Rep = int(Wheat_Room2_REF_20DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[62][-1]))
Wheat_Room1_SE_30DPA = int( Wheat_Room1_SE_30DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[63][-1]))
Wheat_Room1_SE_30DPA_Rep = int(Wheat_Room1_SE_30DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[64][-1]))
Wheat_Room2_SE_30DPA = int(Wheat_Room2_SE_30DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[65][-1]))
Wheat_Room2_SE_30DPA_Rep = int(Wheat_Room2_SE_30DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[66][-1]))
Wheat_Room1_AL_SE_30DPA = int(Wheat_Room1_AL_SE_30DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[67][-1]))
Wheat_Room1_AL_SE_30DPA_Rep = int(Wheat_Room1_AL_SE_30DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[68][-1]))
Wheat_Room2_AL_SE_30DPA = int(Wheat_Room2_AL_SE_30DPA) * pow(10.0, 9) / (int(Length) * int(raw_total[69][-1]))
Wheat_Room2_AL_SE_30DPA_Rep = int(Wheat_Room2_AL_SE_30DPA_Rep) * pow(10.0, 9) / (int(Length) * int(raw_total[70][-1]))
wheat_23_1 = int(wheat_23_1) * pow(10.0, 9) / (int(Length) * int(raw_total[71][-1]))
wheat_23_2 = int(wheat_23_2) * pow(10.0, 9) / (int(Length) * int(raw_total[72][-1]))
wheat_23_3 = int(wheat_23_3) * pow(10.0, 9) / (int(Length) * int(raw_total[73][-1]))
wheat_4_1 = int(wheat_4_1) * pow(10.0, 9) / (int(Length) * int(raw_total[74][-1]))
wheat_4_2 = int(wheat_4_2) * pow(10.0, 9) / (int(Length) * int(raw_total[75][-1]))
wheat_4_3 = int(wheat_4_3) * pow(10.0, 9) / (int(Length) * int(raw_total[76][-1]))
root_Z10_mean = np.mean(np.array([new_root_Z10_rep1, new_root_Z10_rep2]))
root_Z10_std = np.std(np.array([new_root_Z10_rep1, new_root_Z10_rep2]))
root_Z13_mean = np.mean(np.array([new_root_Z13_rep1, new_root_Z13_rep2]))
root_Z13_std = np.std(np.array([new_root_Z13_rep1, new_root_Z13_rep2]))
root_Z39_mean = np.mean(np.array([new_root_Z39_rep1, new_root_Z39_rep2]))
root_Z39_std = np.std(np.array([new_root_Z39_rep1, new_root_Z39_rep2]))
stem_Z30_mean = np.mean(np.array([new_stem_Z30_rep1, new_stem_Z30_rep2]))
stem_Z30_std = np.std(np.array([new_stem_Z30_rep1, new_stem_Z30_rep2]))
stem_Z32_mean = np.mean(np.array([new_stem_Z32_rep1, new_stem_Z32_rep2]))
stem_Z32_std = np.std(np.array([new_stem_Z32_rep1, new_stem_Z32_rep2]))
stem_Z65_mean = np.mean(np.array([new_stem_Z65_rep1, new_stem_Z65_rep2]))
stem_Z65_std = np.std(np.array([new_stem_Z65_rep1, new_stem_Z65_rep2]))
leaf_Z10_mean = np.mean(np.array([new_leaf_Z10_rep1, new_leaf_Z10_rep2]))
leaf_Z10_std = np.std(np.array([new_leaf_Z10_rep1, new_leaf_Z10_rep2]))
leaf_Z23_mean = np.mean(np.array([new_leaf_Z23_rep1, new_leaf_Z23_rep2]))
leaf_Z23_std = np.std(np.array([new_leaf_Z23_rep1, new_leaf_Z23_rep2]))
leaf_Z71_mean = np.mean(np.array([new_leaf_Z71_rep1, new_leaf_Z71_rep2]))
leaf_Z71_std = np.std(np.array([new_leaf_Z71_rep1, new_leaf_Z71_rep2]))
spike_Z32_mean = np.mean(np.array([new_spike_Z32_rep1, new_spike_Z32_rep2]))
spike_Z32_std = np.std(np.array([new_spike_Z32_rep1, new_spike_Z32_rep2]))
spike_Z39_mean = np.mean(np.array([new_spike_Z39_rep1, new_spike_Z39_rep2]))
spike_Z39_std = np.std(np.array([new_spike_Z39_rep1, new_spike_Z39_rep2]))
spike_Z65_mean = np.mean(np.array([new_spike_Z65_rep1, new_spike_Z65_rep2]))
spike_Z65_std = np.std(np.array([new_spike_Z65_rep1, new_spike_Z65_rep2]))
latet_lepto_mean = np.mean(np.array([new_latet_lepto_rep1, new_latet_lepto_rep2]))
latet_lepto_std = np.std(np.array([new_latet_lepto_rep1, new_latet_lepto_rep2]))
diplo_dia_mean = np.mean(np.array([new_diplo_dia_rep1, new_diplo_dia_rep2]))
diplo_dia_std = np.std(np.array([new_diplo_dia_rep1, new_diplo_dia_rep2]))
zygo_pachy_mean = np.mean(np.array([new_zygo_pachy_rep1, new_zygo_pachy_rep2]))
zygo_pachy_std = np.std(np.array([new_zygo_pachy_rep1, new_zygo_pachy_rep2]))
metaphaseI_mean = np.mean(np.array([new_metaphaseI_rep1, new_metaphaseI_rep2]))
metaphaseI_std = np.std(np.array([new_metaphaseI_rep1, new_metaphaseI_rep2]))
grain_Z71_mean = np.mean(np.array([new_grain_Z71_rep1, new_grain_Z71_rep2]))
grain_Z71_std = np.std(np.array([new_grain_Z71_rep1, new_grain_Z71_rep2]))
grain_Z75_mean = np.mean(np.array([new_grain_Z75_rep1, new_grain_Z75_rep2]))
grain_Z75_std = np.std(np.array([new_grain_Z75_rep1, new_grain_Z75_rep2]))
grain_Z85_mean = np.mean(np.array([new_grain_Z85_rep1, new_grain_Z85_rep2]))
grain_Z85_std = np.std(np.array([new_grain_Z85_rep1, new_grain_Z85_rep2]))
root_max = np.max(np.array([new_root_Z10_rep1, new_root_Z10_rep2, new_root_Z13_rep1, new_root_Z13_rep2, new_root_Z39_rep1, new_root_Z39_rep2]))
stem_max = np.max(np.array([new_stem_Z30_rep1, new_stem_Z30_rep2, new_stem_Z32_rep1, new_stem_Z32_rep2, new_stem_Z65_rep1, new_stem_Z65_rep2]))
leaf_max = np.max(np.array([new_leaf_Z10_rep1, new_leaf_Z10_rep2, new_leaf_Z23_rep1, new_leaf_Z23_rep2, new_leaf_Z71_rep1, new_leaf_Z71_rep2]))
spike_max = np.max(np.array([new_spike_Z32_rep1, new_spike_Z32_rep2, new_spike_Z39_rep1, new_spike_Z39_rep2, new_spike_Z65_rep1, new_spike_Z65_rep2]))
grain_max = np.max(np.array([new_grain_Z71_rep1, new_grain_Z71_rep2, Wheat_Room2_10DPA, Wheat_Room2_10DPA_Rep,
new_grain_Z85_rep1, new_grain_Z85_rep2]))
stamen_max = np.mean(np.array([new_stamen, new_latet_lepto_rep1, new_latet_lepto_rep2, new_diplo_dia_rep1, new_diplo_dia_rep2,
new_zygo_pachy_rep1, new_zygo_pachy_rep2, new_metaphaseI_rep1, new_metaphaseI_rep2]))
organs.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % (Geneid, Chr, root_max, stem_max, leaf_max, spike_max, grain_max
, stamen_max, new_carpel))
Wheat_10DPA_mean = np.mean(np.array([Wheat_Room1_10DPA, Wheat_Room1_10DPA_Rep,Wheat_Room2_10DPA, Wheat_Room2_10DPA_Rep]))
Wheat_10DPA_std = np.std(np.array([Wheat_Room1_10DPA, Wheat_Room1_10DPA_Rep,Wheat_Room2_10DPA, Wheat_Room2_10DPA_Rep]))
Wheat_AL_20DPA_mean = np.mean(np.array([Wheat_Room1_AL_20DPA, Wheat_Room1_AL_20DPA_Rep,Wheat_Room2_AL_20DPA, Wheat_Room2_AL_20DPA_Rep, Wheat_Room1_AL_20DPA_Extra1, Wheat_Room1_AL_20DPA_Extra2]))
Wheat_AL_20DPA_std = np.std(np.array([Wheat_Room1_AL_20DPA, Wheat_Room1_AL_20DPA_Rep,Wheat_Room2_AL_20DPA, Wheat_Room2_AL_20DPA_Rep, Wheat_Room1_AL_20DPA_Extra1, Wheat_Room1_AL_20DPA_Extra2]))
Wheat_SE_20DPA_mean = np.mean(np.array([Wheat_Room1_SE_20DPA, Wheat_Room1_SE_20DPA_Rep, Wheat_Room2_SE_20DPA, Wheat_Room2_SE_20DPA_Rep]))
Wheat_SE_20DPA_std = np.std(np.array([Wheat_Room1_SE_20DPA, Wheat_Room1_SE_20DPA_Rep, Wheat_Room2_SE_20DPA, Wheat_Room2_SE_20DPA_Rep]))
Wheat_TC_20DPA_mean = np.mean(np.array([Wheat_Room1_TC_20DPA, Wheat_Room1_TC_20DPA_Rep, Wheat_Room2_TC_20DPA, Wheat_Room2_TC_20DPA_Rep]))
Wheat_TC_20DPA_std = np.std(np.array([Wheat_Room1_TC_20DPA, Wheat_Room1_TC_20DPA_Rep, Wheat_Room2_TC_20DPA, Wheat_Room2_TC_20DPA_Rep]))
Wheat_REF_20DPA_mean = np.mean(np.array([Wheat_Room1_REF_20DPA, Wheat_Room1_REF_20DPA_Rep, Wheat_Room2_REF_20DPA, Wheat_Room2_REF_20DPA_Rep]))
Wheat_REF_20DPA_std = np.std(np.array([Wheat_Room1_REF_20DPA, Wheat_Room1_REF_20DPA_Rep, Wheat_Room2_REF_20DPA, Wheat_Room2_REF_20DPA_Rep]))
Wheat_SE_30DPA_mean = np.mean(np.array([Wheat_Room1_SE_30DPA, Wheat_Room1_SE_30DPA_Rep, Wheat_Room2_SE_30DPA, Wheat_Room2_SE_30DPA_Rep]))
Wheat_SE_30DPA_std = np.std(np.array([Wheat_Room1_SE_30DPA, Wheat_Room1_SE_30DPA_Rep, Wheat_Room2_SE_30DPA, Wheat_Room2_SE_30DPA_Rep]))
Wheat_AL_SE_30DPA_mean = np.mean(np.array([Wheat_Room1_AL_SE_30DPA, Wheat_Room1_AL_SE_30DPA_Rep, Wheat_Room2_AL_SE_30DPA, Wheat_Room2_AL_SE_30DPA_Rep]))
Wheat_AL_SE_30DPA_std = np.std(np.array([Wheat_Room1_AL_SE_30DPA, Wheat_Room1_AL_SE_30DPA_Rep, Wheat_Room2_AL_SE_30DPA, Wheat_Room2_AL_SE_30DPA_Rep]))
wheat_23_mean = np.mean(np.array([wheat_23_1, wheat_23_2, wheat_23_3]))
wheat_23_std = np.std(np.array([wheat_23_1, wheat_23_2, wheat_23_3]))
wheat_4_mean = np.mean(np.array([wheat_4_1, wheat_4_2, wheat_4_3]))
wheat_4_std = np.std(np.array([wheat_4_1, wheat_4_2, wheat_4_3]))
print "%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \
"\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \
"\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" \
"\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s" % \
(Geneid, Chr, Start, End, Strand, Length, root_Z10_mean, root_Z13_mean,root_Z39_mean, stem_Z30_mean,
stem_Z32_mean, stem_Z65_mean, leaf_Z10_mean, leaf_Z23_mean, leaf_Z71_mean, spike_Z32_mean,
spike_Z39_mean, spike_Z65_mean, new_carpel, new_carpel_like_structure, new_stamen, latet_lepto_mean,
diplo_dia_mean, zygo_pachy_mean, metaphaseI_mean, grain_Z71_mean, grain_Z75_mean, grain_Z85_mean,
Wheat_10DPA_mean, Wheat_AL_20DPA_mean, Wheat_SE_20DPA_mean, Wheat_TC_20DPA_mean, Wheat_REF_20DPA_mean,
Wheat_SE_30DPA_mean, Wheat_AL_SE_30DPA_mean, wheat_23_mean, wheat_4_mean,
root_Z10_std, root_Z13_std, root_Z39_std, stem_Z30_std, stem_Z32_std, stem_Z65_std, leaf_Z10_std,
leaf_Z23_std, leaf_Z71_std, spike_Z32_std, spike_Z39_std, spike_Z65_std, 'null', 'null', 'null',
latet_lepto_std, diplo_dia_std, zygo_pachy_std, metaphaseI_std, grain_Z71_std, grain_Z75_std,
grain_Z85_std, Wheat_10DPA_std, Wheat_AL_20DPA_std, Wheat_SE_20DPA_std, Wheat_TC_20DPA_std,
Wheat_REF_20DPA_std, Wheat_SE_30DPA_std, Wheat_AL_SE_30DPA_std, wheat_23_std, wheat_4_std)
organs.close()