Need help in amplicon data analysis

Question

[rishav513]

Hi,

I am analyzing my data in R using the phyloseq package, but one thing I am unable to understand while performing this task.

> samples.out <- rownames(otu)
> rownames(sam) <- samples.out
Error in `.rowNamesDF<-`(x, value = value) : invalid 'row.names' length

After assigning "samples.out", the value of "rownames(otu)" , the output of "samples.out" is like this :

[1] "ASV_1"     "ASV_2"     "ASV_3"     "ASV_4"     "ASV_5"     "ASV_6"    
    [7] "ASV_7"     "ASV_8"     "ASV_9"     "ASV_10"    "ASV_11"    "ASV_12"   
   [13] "ASV_13"    "ASV_14"    "ASV_15"    "ASV_16"    "ASV_17"    "ASV_18"   
   [19] "ASV_19"    "ASV_20"    "ASV_21"    "ASV_22"    "ASV_23"    "ASV_24"   
   [25] "ASV_25"    "ASV_26"    "ASV_27"    "ASV_28"    "ASV_29"    "ASV_30"   
   [31] "ASV_31"    "ASV_32"    "ASV_33"    "ASV_34"    "ASV_35"    "ASV_36"   
   [37] "ASV_37"    "ASV_38"    "ASV_39"    "ASV_40"    "ASV_41"    "ASV_42"   
   [43] "ASV_43"    "ASV_44"    "ASV_45"    "ASV_46"    "ASV_47"    "ASV_48"   
   [49] "ASV_49"    "ASV_50"    "ASV_51"    "ASV_52"    "ASV_53"    "ASV_54"   
   [55] "ASV_55"    "ASV_56"    "ASV_57"    "ASV_58"    "ASV_59"    "ASV_60"   
   [61] "ASV_61"    "ASV_62"    "ASV_63"    "ASV_64"    "ASV_65"    "ASV_66"   
   [67] "ASV_67"    "ASV_68"    "ASV_69"    "ASV_70"    "ASV_71"    "ASV_72"   
   [73] "ASV_73"    "ASV_74"    "ASV_75"    "ASV_76"    "ASV_77"    "ASV_78"   
   [79] "ASV_79"    "ASV_80"    "ASV_81"    "ASV_82"    "ASV_83"    "ASV_84"   
   [85] "ASV_85"    "ASV_86"    "ASV_87"    "ASV_88"    "ASV_89"    "ASV_90"   
   [91] "ASV_91"    "ASV_92"    "ASV_93"    "ASV_94"    "ASV_95"    "ASV_96"   
   [97] "ASV_97"    "ASV_98"    "ASV_99"    "ASV_100"   "ASV_101"   "ASV_102"  
  [103] "ASV_103"   "ASV_104"   "ASV_105"   "ASV_106"   "ASV_107"   "ASV_108"  
  [109] "ASV_109"   "ASV_110"   "ASV_111"   "ASV_112"   "ASV_113"   "ASV_114"  
  [115] "ASV_115"   "ASV_116"   "ASV_117"   "ASV_118"   "ASV_119"   "ASV_120"  
  [121] "ASV_121"   "ASV_122"   "ASV_123"   "ASV_124"   "ASV_125"   "ASV_126"  
  [127] "ASV_127"   "ASV_128"   "ASV_129"   "ASV_130"   "ASV_131"   "ASV_132"  
  [133] "ASV_133"   "ASV_134"   "ASV_135"   "ASV_136"   "ASV_137"   "ASV_138"  
  [139] "ASV_139"   "ASV_140"   "ASV_141"   "ASV_142"   "ASV_143"   "ASV_144"  
  [145] "ASV_145"   "ASV_146"   "ASV_147"   "ASV_148"   "ASV_149"   "ASV_150"  
  [151] "ASV_151"   "ASV_152"   "ASV_153"   "ASV_154"   "ASV_155"   "ASV_156"  
  [157] "ASV_157"   "ASV_158"   "ASV_159"   "ASV_160"   "ASV_161"   "ASV_162"  
  [163] "ASV_163"   "ASV_164"   "ASV_165"   "ASV_166"   "ASV_167"   "ASV_168"  
  [169] "ASV_169"   "ASV_170"   "ASV_171"   "ASV_172"   "ASV_173"   "ASV_174"  
  [175] "ASV_175"   "ASV_176"   "ASV_177"   "ASV_178"   "ASV_179"   "ASV_180"  
  [181] "ASV_181"   "ASV_182"   "ASV_183"   "ASV_184"   "ASV_185"   "ASV_186"  
  [187] "ASV_187"   "ASV_188"   "ASV_189"   "ASV_190"   "ASV_191"   "ASV_192"  
  [193] "ASV_193"   "ASV_194"   "ASV_195"   "ASV_196"   "ASV_197"   "ASV_198"  
  [199] "ASV_199"   "ASV_200"   "ASV_201"   "ASV_202"   "ASV_203"   "ASV_204"  
  [205] "ASV_205"   "ASV_206"   "ASV_207"   "ASV_208"   "ASV_209"   "ASV_210"  
  [211] "ASV_211"   "ASV_212"   "ASV_213"   "ASV_214"   "ASV_215"   "ASV_216"  
  [217] "ASV_217"   "ASV_218"   "ASV_219"   "ASV_220"   "ASV_221"   "ASV_222"  
  [223] "ASV_223"   "ASV_224"   "ASV_225"   "ASV_226"   "ASV_227"   "ASV_228"  
  [229] "ASV_229"   "ASV_230"   "ASV_231"   "ASV_232"   "ASV_233"   "ASV_234"  
  [235] "ASV_235"   "ASV_236"   "ASV_237"   "ASV_238"   "ASV_239"   "ASV_240"  
  [241] "ASV_241"   "ASV_242"   "ASV_243"   "ASV_244"   "ASV_245"   "ASV_246"  
  [247] "ASV_247"   "ASV_248"   "ASV_249"   "ASV_250"   "ASV_251"   "ASV_252"  
  [253] "ASV_253"   "ASV_254"   "ASV_255"   "ASV_256"   "ASV_257"   "ASV_258"  
  [259] "ASV_259"   "ASV_260"   "ASV_261"   "ASV_262"   "ASV_263"   "ASV_264"  
  [265] "ASV_265"   "ASV_266"   "ASV_267"   "ASV_268"   "ASV_269"   "ASV_270"  
  [271] "ASV_271"   "ASV_272"   "ASV_273"   "ASV_274"   "ASV_275"   "ASV_276"  
  [277] "ASV_277"   "ASV_278"   "ASV_279"   "ASV_280"   "ASV_281"   "ASV_282"  
  [283] "ASV_283"   "ASV_284"   "ASV_285"   "ASV_286"   "ASV_287"   "ASV_288"  
  [289] "ASV_289"   "ASV_290"   "ASV_291"   "ASV_292"   "ASV_293"   "ASV_294"  
  [295] "ASV_295"   "ASV_296"   "ASV_297"   "ASV_298"   "ASV_299"   "ASV_300"  
  [301] "ASV_301"   "ASV_302"   "ASV_303"   "ASV_304"   "ASV_305"   "ASV_306"  
  [307] "ASV_307"   "ASV_308"   "ASV_309"   "ASV_310"   "ASV_311"   "ASV_312"  
  [313] "ASV_313"   "ASV_314"   "ASV_315"   "ASV_316"   "ASV_317"   "ASV_318"  
  [319] "ASV_319"   "ASV_320"   "ASV_321"   "ASV_322"   "ASV_323"   "ASV_324"  
  [325] "ASV_325"   "ASV_326"   "ASV_327"   "ASV_328"   "ASV_329"   "ASV_330"  
  [331] "ASV_331"   "ASV_332"   "ASV_333"   "ASV_334"   "ASV_335"   "ASV_336"  
  [337] "ASV_337"   "ASV_338"   "ASV_339"   "ASV_340"   "ASV_341"   "ASV_342"  
  [343] "ASV_343"   "ASV_344"   "ASV_345"   "ASV_346"   "ASV_347"   "ASV_348"  
  [349] "ASV_349"   "ASV_350"   "ASV_351"   "ASV_352"   "ASV_353"   "ASV_354"  
  [355] "ASV_355"   "ASV_356"   "ASV_357"   "ASV_358"   "ASV_359"   "ASV_360"  
  [361] "ASV_361"   "ASV_362"   "ASV_363"   "ASV_364"   "ASV_365"   "ASV_366"  
  [367] "ASV_367"   "ASV_368"   "ASV_369"   "ASV_370"   "ASV_371"   "ASV_372"  
  [373] "ASV_373"   "ASV_374"   "ASV_375"   "ASV_376"   "ASV_377"   "ASV_378"  
  [379] "ASV_379"   "ASV_380"   "ASV_381"   "ASV_382"   "ASV_383"   "ASV_384"  
  [385] "ASV_385"   "ASV_386"   "ASV_387"   "ASV_388"   "ASV_389"   "ASV_390"  
  [391] "ASV_391"   "ASV_392"   "ASV_393"   "ASV_394"   "ASV_395"   "ASV_396"  
  [397] "ASV_397"   "ASV_398"   "ASV_399"   "ASV_400"   "ASV_401"   "ASV_402"  
  [403] "ASV_403"   "ASV_404"   "ASV_405"   "ASV_406"   "ASV_407"   "ASV_408"  
  [409] "ASV_409"   "ASV_410"   "ASV_411"   "ASV_412"   "ASV_413"   "ASV_414"  
  [415] "ASV_415"   "ASV_416"   "ASV_417"   "ASV_418"   "ASV_419"   "ASV_420"  
  [421] "ASV_421"   "ASV_422"   "ASV_423"   "ASV_424"   "ASV_425"   "ASV_426"  
  [427] "ASV_427"   "ASV_428"   "ASV_429"   "ASV_430"   "ASV_431"   "ASV_432"  
  [433] "ASV_433"   "ASV_434"   "ASV_435"   "ASV_436"   "ASV_437"   "ASV_438"  
  [439] "ASV_439"   "ASV_440"   "ASV_441"   "ASV_442"   "ASV_443"   "ASV_444"  
  [445] "ASV_445"   "ASV_446"   "ASV_447"   "ASV_448"   "ASV_449"   "ASV_450"  
  [451] "ASV_451"   "ASV_452"   "ASV_453"   "ASV_454"   "ASV_455"   "ASV_456"  
  [457] "ASV_457"   "ASV_458"   "ASV_459"   "ASV_460"   "ASV_461"   "ASV_462"  
  [463] "ASV_463"   "ASV_464"   "ASV_465"   "ASV_466"   "ASV_467"   "ASV_468"  
  [469] "ASV_469"   "ASV_470"   "ASV_471"   "ASV_472"   "ASV_473"   "ASV_474"  
  [475] "ASV_475"   "ASV_476"   "ASV_477"   "ASV_478"   "ASV_479"   "ASV_480"  
  [481] "ASV_481"   "ASV_482"   "ASV_483"   "ASV_484"   "ASV_485"   "ASV_486"  
  [487] "ASV_487"   "ASV_488"   "ASV_489"   "ASV_490"   "ASV_491"   "ASV_492"  
  [493] "ASV_493"   "ASV_494"   "ASV_495"   "ASV_496"   "ASV_497"   "ASV_498"  
  [499] "ASV_499"   "ASV_500"   "ASV_501"   "ASV_502"   "ASV_503"   "ASV_504" 

And the output of "rownames(sam)" is

[1] "1"   "2"   "3"   "4"   "5"   "6"   "7"   "8"   "9"   "10"  "11"  "12" 
 [13] "13"  "14"  "15"  "16"  "17"  "18"  "19"  "20"  "21"  "22"  "23"  "24" 
 [25] "25"  "26"  "27"  "28"  "29"  "30"  "31"  "32"  "33"  "34"  "35"  "36" 
 [37] "37"  "38"  "39"  "40"  "41"  "42"  "43"  "44"  "45"  "46"  "47"  "48" 
 [49] "49"  "50"  "51"  "52"  "53"  "54"  "55"  "56"  "57"  "58"  "59"  "60" 
 [61] "61"  "62"  "63"  "64"  "65"  "66"  "67"  "68"  "69"  "70"  "71"  "72" 
 [73] "73"  "74"  "75"  "76"  "77"  "78"  "79"  "80"  "81"  "82"  "83"  "84" 
 [85] "85"  "86"  "87"  "88"  "89"  "90"  "91"  "92"  "93"  "94"  "95"  "96" 
 [97] "97"  "98"  "99"  "100" "101" "102" "103" "104" "105" "106" "107" "108"
[109] "109" "110" "111" "112" "113" "114" "115" "116"

please help me.

[Matthias Zepper]

Just run length(rownames(sam)) and length(sample.out) and you will see what the problem is.

[rishav513]

length(rownames(sam))
[1] 116
length(samples.out)
[1] 11288

Yaa, I know this is problem but is there any way to solve this problem ?

[h.mon]

We don't have any idea of what you are doing, as you didn't show much code, nor described in detail the analyses you are performing. Why the lengths differ? Did you perform some kind of ASV filtering or clustering?

As a extreme example, you could be trying to transfer the ASV names from one experiment (like microbiome from human gut) to another completely different experiment (microbiome from soil).

What exactly is your intention? What are the sam and otu objects?

[rishav513]

OTU is the output of dada2 that contains ASV information, here is an overview of the file:

C_1b_L  C_1a_S  C_1a_R  C_1a_N  C_1a_L  F_96e_S F_96e_R F_96e_N F_96e_L F_96d_S F_96d_R F_96d_N F_96d_L F_96c_S F_96c_R F_96c_N F_96c_L F_96b_S F_96b_R F_96b_N F_96b_L F_96a_S F_96a_R F_96a_N F_96a_L F_27e_S F_27e_R F_27e_N F_27e_L F_27d_S F_27d_R F_27d_N F_27d_L F_27b_S F_27b_R C_96d_R C_96d_N C_96d_L C_96c_S C_96c_R C_96c_N C_96c_L C_96b_S C_96b_R C_96b_N C_96b_L C_96a_S C_96a_R C_96a_N C_96a_L C_27e_S C_27e_R C_27e_N C_27e_L C_27d_S C_27d_R C_27d_N C_27d_L C_27c_S C_27c_R C_27c_N F_1e_S  F_1e_R  F_1e_N  F_1e_L  F_1d_S  F_1d_R  F_1d_N  F_1d_L  F_1c_S  F_1c_R  F_1c_N  F_1c_L  F_1b_S  F_1b_R  F_1b_N  F_1b_L  F_1a_S  F_1a_R  F_1a_N  F_1a_L  C_96e_S C_96e_R C_96e_N C_96e_L C_96d_S F_27b_N F_27b_L F_27a_S F_27a_R F_27a_N F_27a_L C_27c_L C_27b_S C_27b_R C_27b_N C_27b_L C_27a_S C_27a_R C_27a_N C_27a_L C_1e_S  C_1e_R  C_1e_N  C_1e_L  C_1d_S  C_1d_R  C_1d_N  C_1d_L  C_1c_S  C_1c_R  C_1c_N  C_1c_L  C_1b_S  C_1b_R  C_1b_N
ASV_1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   736 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_2   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   703 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_3   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   678 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_4   1   1   1   1   1   1   1   1   1   1   387 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   281 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_5   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   640 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_6   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   483 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   138 1   1   1   1
ASV_7   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   521 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   73  1   1   1   1   1   1   1   1   1   1
ASV_8   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   586 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_9   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   584 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_10  1   1   1   1   1   1   1   1   1   1   319 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   240 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_11  1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   460 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   64  1   1   1   1   1   1   1   1   1   1
ASV_12  1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   513 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_13  1   1   1   375 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   134 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_14  1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   440 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   68  1   1   1   1   1   1   1   1   1   1
ASV_15  1   1   1   1   1   1   1   1   1   1   320 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   187 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_16  1   1   1   1   1   1   1   1   1   1   287 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   220 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_17  1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   500 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
ASV_18  1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   174 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   319 1   1   1   1   1
ASV_19  1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   432 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   56  1   1   1   1   1   1   1   1   1   1
ASV_20  399 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   87  1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1

and sam contains metadata information, here is an overview of the file

Soil_origin Plant_genotype Plant_compartments Sample_Name
1           C             G1                  L      C_1b_L
2           C             G1                  N      C_1a_S
3           C             G1                  R      C_1a_R
4           C             G1                  S      C_1a_N
5           C             G1                  L      C_1a_L
6           C             G1                  N     F_96e_S

[rishav513]

I want to merge my data using this command :

ps <- phyloseq(otu_table(otu, taxa_are_rows=FALSE), 
               sample_data(sam), 
               tax_table(taxa))

for alpha diversity analysis

[h.mon]

Why taxa_are_rows=FALSE if you taxa are rows?

[rishav513]

Actually, I am following standard workflow to construct a phyloseq object directly from the dada2 outputs, and in that workflow, this code was written for constructing a phyloseq object.

ps <- phyloseq(otu_table(otu, taxa_are_rows=FALSE), 
               sample_data(sam), 
               tax_table(taxa))

Here, otu contains count values, sam contains metadata information of the sample and taxa contains taxonomical information. I think "taxa_are_rows=FALSE" is kept so that only count values of otu would be considered.