<p>I would recommend not to use <code>h</code> to cut the tree but to use <code>k</code>, as <code>k</code> instructs the number of clusters to obtain, and then leaves it to the function to find the correct height for that. From there on you can easily extract the genes per cluster, for example with this code obtaining the first three clusters:</p>
<pre class="lang-r prettyprint-override"><code>
heatof &lt;- 
pheatmap(logFC_mat, 
         show_rownames=F, cluster_cols=F, cluster_rows=T, scale=&quot;row&quot;,
         cex=1, clustering_distance_rows=&quot;euclidean&quot;, cex=1,
         clustering_distance_cols=&quot;euclidean&quot;, 
         clustering_method=&quot;complete&quot;, border_color=FALSE)

#/ extract the top three clusters
k &lt;- 3
sort(cutree(heatof$tree_row, k=3)) -&gt; hi_clusters

genes_per_cluster &lt;- do.call(rbind, lapply(unique(hi_clusters), function(x){
  
  data.frame(cluster=x, gene=names(hi_clusters[hi_clusters %in% x]))
             
  
}))

genes_per_cluster
#&gt;     cluster                  gene
#&gt; 1         1  ENSMUSG00000097417.1
#&gt; 2         1  ENSMUSG00000084950.7
#&gt; 3         1 ENSMUSG00000021824.12
#&gt; 4         1 ENSMUSG00000018381.15
#&gt; 5         1  ENSMUSG00000098716.1
#&gt; 6         1 ENSMUSG00000039206.13
#&gt; 7         1 ENSMUSG00000047880.14
#&gt; 8         1  ENSMUSG00000094127.1
#&gt; 9         1  ENSMUSG00000098609.1
#&gt; 10        1  ENSMUSG00000106093.1
#&gt; 11        1 ENSMUSG00000009585.17
#&gt; 12        1  ENSMUSG00000027950.7
#&gt; 13        1 ENSMUSG00000004071.12
#&gt; 14        1  ENSMUSG00000104208.1
#&gt; 15        1 ENSMUSG00000047369.15
#&gt; 16        1  ENSMUSG00000097660.1
#&gt; 17        1 ENSMUSG00000002109.14
#&gt; 18        1  ENSMUSG00000108524.1
#&gt; 19        1 ENSMUSG00000067786.16
#&gt; 20        1  ENSMUSG00000085437.8
#&gt; 21        1  ENSMUSG00000086220.1
#&gt; 22        1  ENSMUSG00000102299.1
#&gt; 23        1 ENSMUSG00000079293.11
#&gt; 24        1  ENSMUSG00000085425.1
#&gt; 25        1  ENSMUSG00000006517.5
#&gt; 26        1 ENSMUSG00000049775.16
#&gt; 27        1  ENSMUSG00000109881.1
#&gt; 28        1  ENSMUSG00000102349.1
#&gt; 29        1  ENSMUSG00000089636.1
#&gt; 30        1  ENSMUSG00000086360.1
#&gt; 31        1 ENSMUSG00000036138.16
#&gt; 32        1  ENSMUSG00000045382.6
#&gt; 33        1 ENSMUSG00000030720.16
#&gt; 34        1 ENSMUSG00000027398.13
#&gt; 35        1  ENSMUSG00000104823.1
#&gt; 36        1  ENSMUSG00000100783.1
#&gt; 37        1  ENSMUSG00000082848.1
#&gt; 38        1  ENSMUSG00000110148.1
#&gt; 39        1  ENSMUSG00000085754.7
#&gt; 40        1  ENSMUSG00000108321.1
#&gt; 41        1 ENSMUSG00000032363.15
#&gt; 42        1 ENSMUSG00000003868.14
#&gt; 43        1  ENSMUSG00000107075.1
#&gt; 44        1  ENSMUSG00000074136.9
#&gt; 45        1  ENSMUSG00000089875.1
#&gt; 46        1 ENSMUSG00000055725.11
#&gt; 47        1 ENSMUSG00000028451.12
#&gt; 48        1  ENSMUSG00000078786.9
#&gt; 49        1  ENSMUSG00000087442.1
#&gt; 50        1  ENSMUSG00000053442.4
#&gt; 51        1  ENSMUSG00000101674.6
#&gt; 52        1  ENSMUSG00000097748.1
#&gt; 53        1  ENSMUSG00000050010.8
#&gt; 54        1 ENSMUSG00000029623.14
#&gt; 55        1  ENSMUSG00000097060.1
#&gt; 56        1 ENSMUSG00000050627.13
#&gt; 57        1  ENSMUSG00000085471.1
#&gt; 58        1 ENSMUSG00000039753.16
#&gt; 59        1  ENSMUSG00000027706.8
#&gt; 60        1  ENSMUSG00000032487.8
#&gt; 61        1 ENSMUSG00000002985.16
#&gt; 62        1 ENSMUSG00000038274.11
#&gt; 63        1  ENSMUSG00000087206.1
#&gt; 64        1  ENSMUSG00000079355.3
#&gt; 65        1  ENSMUSG00000086693.1
#&gt; 66        1 ENSMUSG00000030579.10
#&gt; 67        1  ENSMUSG00000108235.1
#&gt; 68        1  ENSMUSG00000097077.1
#&gt; 69        1  ENSMUSG00000087367.1
#&gt; 70        1 ENSMUSG00000037982.16
#&gt; 71        1 ENSMUSG00000029422.14
#&gt; 72        1 ENSMUSG00000026749.11
#&gt; 73        1 ENSMUSG00000067288.12
#&gt; 74        1  ENSMUSG00000090330.2
#&gt; 75        1 ENSMUSG00000059791.14
#&gt; 76        1  ENSMUSG00000103698.1
#&gt; 77        1  ENSMUSG00000097472.1
#&gt; 78        1  ENSMUSG00000026156.8
#&gt; 79        1 ENSMUSG00000075297.10
#&gt; 80        1 ENSMUSG00000055692.18
#&gt; 81        1 ENSMUSG00000039307.16
#&gt; 82        1 ENSMUSG00000045576.16
#&gt; 83        1  ENSMUSG00000087486.1
#&gt; 84        1  ENSMUSG00000014470.4
#&gt; 85        1  ENSMUSG00000056078.5
#&gt; 86        1 ENSMUSG00000049928.15
#&gt; 87        1  ENSMUSG00000086714.1
#&gt; 88        1  ENSMUSG00000109274.1
#&gt; 89        1  ENSMUSG00000109854.1
#&gt; 90        1  ENSMUSG00000060802.8
#&gt; 91        2  ENSMUSG00000085505.1
#&gt; 92        2  ENSMUSG00000092134.1
#&gt; 93        2  ENSMUSG00000097272.1
#&gt; 94        2  ENSMUSG00000087700.1
#&gt; 95        2  ENSMUSG00000101162.2
#&gt; 96        2  ENSMUSG00000107317.1
#&gt; 97        2  ENSMUSG00000102451.1
#&gt; 98        2  ENSMUSG00000109669.1
#&gt; 99        2  ENSMUSG00000086836.1
#&gt; 100       2  ENSMUSG00000092545.7
#&gt; 101       2  ENSMUSG00000090625.1
#&gt; 102       2  ENSMUSG00000047988.1
#&gt; 103       2  ENSMUSG00000086430.1
#&gt; 104       2  ENSMUSG00000105941.1
#&gt; 105       2  ENSMUSG00000022223.8
#&gt; 106       2 ENSMUSG00000031145.15
#&gt; 107       2  ENSMUSG00000086680.2
#&gt; 108       2 ENSMUSG00000021485.13
#&gt; 109       2  ENSMUSG00000097768.1
#&gt; 110       2 ENSMUSG00000075576.12
#&gt; 111       2  ENSMUSG00000090952.2
#&gt; 112       2 ENSMUSG00000040723.14
#&gt; 113       2  ENSMUSG00000027329.9
#&gt; 114       2  ENSMUSG00000106005.1
#&gt; 115       2 ENSMUSG00000046027.17
#&gt; 116       2 ENSMUSG00000004221.16
#&gt; 117       2  ENSMUSG00000023988.8
#&gt; 118       2  ENSMUSG00000087611.1
#&gt; 119       2  ENSMUSG00000092103.2
#&gt; 120       2  ENSMUSG00000090081.1
#&gt; 121       2  ENSMUSG00000107999.1
#&gt; 122       2 ENSMUSG00000041774.14
#&gt; 123       2  ENSMUSG00000090246.1
#&gt; 124       2  ENSMUSG00000032528.4
#&gt; 125       2 ENSMUSG00000021250.13
#&gt; 126       2 ENSMUSG00000009292.17
#&gt; 127       2  ENSMUSG00000109491.1
#&gt; 128       2  ENSMUSG00000090252.1
#&gt; 129       2  ENSMUSG00000087443.1
#&gt; 130       2  ENSMUSG00000064339.1
#&gt; 131       2  ENSMUSG00000091160.1
#&gt; 132       2  ENSMUSG00000108332.1
#&gt; 133       2  ENSMUSG00000048721.2
#&gt; 134       2  ENSMUSG00000102961.1
#&gt; 135       2  ENSMUSG00000096957.1
#&gt; 136       2  ENSMUSG00000093006.2
#&gt; 137       2 ENSMUSG00000027774.16
#&gt; 138       2 ENSMUSG00000012126.16
#&gt; 139       2  ENSMUSG00000092190.1
#&gt; 140       2  ENSMUSG00000074213.2
#&gt; 141       2  ENSMUSG00000086564.9
#&gt; 142       2  ENSMUSG00000092655.1
#&gt; 143       2  ENSMUSG00000098482.1
#&gt; 144       2  ENSMUSG00000108574.1
#&gt; 145       2  ENSMUSG00000087669.1
#&gt; 146       2  ENSMUSG00000078653.4
#&gt; 147       2  ENSMUSG00000108353.1
#&gt; 148       2  ENSMUSG00000092386.1
#&gt; 149       2  ENSMUSG00000087607.1
#&gt; 150       2 ENSMUSG00000061607.14
#&gt; 151       2  ENSMUSG00000026729.9
#&gt; 152       2  ENSMUSG00000064330.9
#&gt; 153       2  ENSMUSG00000103864.1
#&gt; 154       2  ENSMUSG00000085208.3
#&gt; 155       2  ENSMUSG00000073628.5
#&gt; 156       2  ENSMUSG00000102776.1
#&gt; 157       2  ENSMUSG00000086998.1
#&gt; 158       2 ENSMUSG00000006728.12
#&gt; 159       2  ENSMUSG00000021273.9
#&gt; 160       2  ENSMUSG00000062591.5
#&gt; 161       2 ENSMUSG00000040936.14
#&gt; 162       2  ENSMUSG00000046145.7
#&gt; 163       2  ENSMUSG00000087366.7
#&gt; 164       2  ENSMUSG00000039452.5
#&gt; 165       2  ENSMUSG00000087660.1
#&gt; 166       2  ENSMUSG00000084878.1
#&gt; 167       2  ENSMUSG00000101438.1
#&gt; 168       2  ENSMUSG00000064351.1
#&gt; 169       2  ENSMUSG00000086312.1
#&gt; 170       2  ENSMUSG00000097221.1
#&gt; 171       2  ENSMUSG00000107090.1
#&gt; 172       2  ENSMUSG00000095280.1
#&gt; 173       2 ENSMUSG00000017837.12
#&gt; 174       2  ENSMUSG00000109251.1
#&gt; 175       2  ENSMUSG00000086600.8
#&gt; 176       2  ENSMUSG00000090009.1
#&gt; 177       2  ENSMUSG00000101695.1
#&gt; 178       2 ENSMUSG00000084128.10
#&gt; 179       2 ENSMUSG00000038502.16
#&gt; 180       2  ENSMUSG00000015942.9
#&gt; 181       2  ENSMUSG00000106478.1
#&gt; 182       2  ENSMUSG00000054556.6
#&gt; 183       2  ENSMUSG00000082180.1
#&gt; 184       2 ENSMUSG00000013997.10
#&gt; 185       2  ENSMUSG00000100548.1
#&gt; 186       2  ENSMUSG00000053706.2
#&gt; 187       2  ENSMUSG00000093598.1
#&gt; 188       3  ENSMUSG00000097860.1
#&gt; 189       3  ENSMUSG00000079179.9
#&gt; 190       3  ENSMUSG00000097347.1
#&gt; 191       3  ENSMUSG00000092371.1
#&gt; 192       3  ENSMUSG00000109754.1
#&gt; 193       3  ENSMUSG00000090016.1
#&gt; 194       3  ENSMUSG00000097854.1
#&gt; 195       3  ENSMUSG00000029360.3
#&gt; 196       3  ENSMUSG00000102564.1
#&gt; 197       3  ENSMUSG00000025353.4
#&gt; 198       3  ENSMUSG00000052214.9
#&gt; 199       3 ENSMUSG00000037936.15
#&gt; 200       3  ENSMUSG00000105198.1

Created on 2022-07-07 by the reprex package (v2.0.1)

<span class="math-container">```</span>
</code></pre>

CC BY-SA 4.0
2022-07-07T18:12:35.477Z
Thanks for the answer. I have no problem retrieving the genes in the clusters, just identifying the genes associated with the clusters observed in the heatmap. E.g. in my example, the first cluster (cluster 1) is the first cluster in the heatmap (on the top), however my second cluster is not my second cluster (cluster 2)...
h3ab74
CC BY-SA 4.0
2022-07-07T10:21:11.580Z
2022-07-07T10:21:11.580Z
God of Heatmapzzz
<p>I have been attempting to extract particular clusters, given by hierarchical clustering outputs from the <code>pheatmap</code> function (in R).</p>
<p>Please find the <code>dput</code> output of the dataset in question below:</p>
<pre><code>structure(list(`72v24.logFC` = c(-1.47511280138092, -1.10386310802134, 
-1.14476608274903, -1.63563574551239, -0.895377907265352, -1.35216488439027, 
-2.04949381135359, -1.0344565728341, -0.967008696515716, -1.11233305998901, 
-2.02663681865178, -1.74756562789736, -1.02076838668592, -1.27981009782201, 
-1.13181804725663, -1.19537930856639, -2.23476188503608, -1.67500131867632, 
-0.950059974858215, -2.3124598902615, -1.93757457440581, -3.24602437386808, 
-0.839869104053795, -1.40329424193006, -1.63484798229567, -0.912148120310492, 
-1.33467209346367, -1.27031027753064, -0.925498608058671, -0.802956638081314, 
-0.85457796397113, -1.0591348681193, -0.825278459132589, -0.671570119524199, 
-1.8439768319437, -0.947033608766851, -1.63849733567277, -1.40584263305038, 
-1.1317958487115, -1.40724695083764, -1.21842141154588, -2.29248800050516, 
-1.41699787526721, -0.879218050664484, -2.25064962332145, 1.4685855647974, 
-2.02553848518381, 1.32808748053321, -2.46062484389627, -2.45830217362141, 
-0.880536960021976, -0.689060222340137, -0.863262774594, -1.64961312646758, 
1.10572346456398, -0.891760767302047, -1.22716047163938, -0.680697741299578, 
-1.24551344420046, -0.891918251648935, -1.64722787848732, -0.832845262108387, 
-0.937129577660045, -0.891684122691785, 1.96452911491088, -1.59420664438654, 
-0.921295293242016, -1.8304480291305, -0.632859040349798, 1.69982819847455, 
-0.982668795391973, -1.61816842714933, -1.23602172818196, -2.15356245206668, 
-1.67961932563564, 1.15478114880933, 1.32665444151868, -1.04590896017973, 
-2.29770026795487, -1.27498150009975, -1.36310217581123, -1.09169550738617, 
1.5349413586602, -1.66842984591719, -0.760122936706606, -1.10054393155051, 
-1.00344072779106, -0.993259123487476, -1.43136824963825, -1.07214189069438, 
-0.990813303020613, -1.51324812740189, -1.23465012540084, -1.00098231964124, 
-1.92195764952845, -1.00139179559519, -0.940118366042393, -2.35969198434574, 
-0.574626090826994, -0.884870428965888, -0.87601460107501, 1.61336157755029, 
-0.777670642457121, -0.7312214522498, -0.812926777785119, -1.13443421373561, 
-1.56498420350444, -0.69126972981765, -1.44351026281231, -1.21962275860236, 
-2.21155555407497, -1.02015250027788, -0.604620179871691, -1.53013572266942, 
-0.790050747397837, -1.17950736026735, -0.781075150161975, -0.888554144538286, 
3.59172865862026, -0.8665071699621, -2.97701792242157, -1.44424941630738, 
-1.50528882788757, -0.772465957539335, -1.57664185213987, -1.02371443439054, 
-0.931807409493221, 1.77728969821973, -1.05455101137715, -1.09765136526264, 
-0.881436416213335, -0.985605985458853, -1.31644334991692, 2.26917914828263, 
-1.43844640540614, -1.75106843701475, -1.22483991752173, -1.65032906954133, 
-0.686319914242903, 2.45473055753775, -1.15594379837188, 1.46397122502826, 
-0.91260428248088, -0.726540148411507, -1.18227838019011, -0.940722690371935, 
-1.28074043595918, -0.629033607437977, 1.01053893402133, -0.735590704487842, 
-2.0620695625823, -0.692251638781327, -0.717818968339447, -0.564023101160995, 
1.51541391124355, -1.89444914881727, -0.737931396419173, -1.17763728235316, 
-0.887605568116903, -2.8392997898765, -1.48018352596355, 1.60698486226092, 
-1.96804216410227, -0.881437183709851, 3.08730764755417, -0.600282670155725, 
-0.905719394102218, -1.05553935946213, -1.13508709782814, -0.613381276596874, 
-0.642647139688194, -0.861929974132332, -1.30652597213552, -1.73647794867498, 
-0.619909931049252, -0.880145154869759, -0.60084263914673, -1.16825573255754, 
-0.942919502394457, -1.3709930239358, -0.80595477526712, -0.628115199442632, 
-1.11502998682131, -1.01274335471793, -1.16595378001606, -3.36098944183559, 
-1.19617345920841, -2.10811718229285, 3.33752122936, -1.67696701892834, 
-1.90639199489612, -0.574737209544065, -1.66853541929234, -0.595093777485911, 
-0.7964813653225, -0.791102813817634, -0.601987407016077, -1.52559881980829, 
1.15480324851026, -0.830167728660421), `72v0.logFC` = c(-5.10937036648522, 
-8.41670756910048, -8.18671783299995, -2.79415692883273, -3.62090016917187, 
-8.37523504570539, -3.79100494829738, -7.20659380156151, -5.46135634576091, 
3.43493215839382, -1.67650075643956, -2.99391647274475, -5.77357941089076, 
-5.22675557853878, 3.26436094268851, -5.98693257837598, -5.85864442290848, 
-2.39995052889286, -5.19221784614079, -4.72634343497625, -7.46407809296449, 
-3.04588366694082, -3.85014181238163, 4.44756662720351, -4.42031795110716, 
-2.58478993868624, -5.46121416816988, -2.09364835701952, -5.42397023596482, 
-5.26192471384563, -4.48979100280513, -5.14171942073048, -5.5267515390142, 
3.88762033289334, -5.88489034628499, -2.85490179670763, -5.31586479048071, 
-2.64877712735579, -6.24766069294439, -5.35826818217055, -4.97204642950896, 
-2.82989963758335, -4.75016577988789, -3.51684070536132, -3.20931950649022, 
-3.62394584217955, -2.81449967053025, -5.68308664428684, -3.11500402887324, 
-4.08965188145047, -5.43176189260205, -4.30737780091769, 2.52666133143524, 
-3.88213803962076, -5.517506523237, -3.27502948978619, -5.37724717693793, 
2.77090845859761, -4.22554364178978, -5.81944792042417, -2.23700806354102, 
-1.81804660477942, -3.45613077773548, -4.88444075082709, -5.48877199958173, 
-4.90077849970695, -3.79304730634383, -0.953597409660203, -5.573920008042, 
-1.83250444732307, -1.73248469438834, -2.35460087734472, -4.43029236446579, 
-3.37654864236982, -3.63410171518379, -2.78276966423079, -2.01664452557169, 
-3.54834187597395, -2.04395822264753, -2.15693095907293, -4.04759665569609, 
-4.87928960250415, 1.1358557257212, -4.89657506999093, -2.42698466771808, 
-4.55703295523896, -2.53230009478764, -1.74571568381108, 1.93875826054123, 
-2.29178704384166, -1.88631492984573, -4.17811684330152, -4.55155213716622, 
1.93093495783903, -3.66429213603731, 2.73817157445051, -3.96977406502995, 
-4.18487497756956, 2.17512996773018, -4.41168757499049, -2.96267804428878, 
-4.60683474159913, -3.0573109426718, -1.59500391193586, -4.65882130908176, 
-2.68903554919404, -4.44676809434802, 1.60275224879078, -3.83760729167497, 
-3.09907199540008, -2.37905633686461, -4.29650784478684, -1.88615561094275, 
-4.44026540480975, -2.01996858091239, -2.46842498723876, -4.24053886261736, 
-4.32561109033911, -2.96097578889787, -3.76708395833968, -2.45229520135946, 
-2.33856203921674, -4.23241140779399, -3.48627475173498, 2.25888018977625, 
-3.85271202182208, -4.61317704601435, -1.95186634348091, 1.3205477461731, 
-2.51561846705872, -1.7031812569795, -4.44717736673796, -3.88478215160905, 
-3.27547652595564, -2.81690169904308, -4.83981492357284, -2.4486942125238, 
-4.31360736969916, -4.22122930700826, -3.49045260391997, -2.87862947524065, 
-4.14268228866617, -2.62252967420329, -4.23902905919912, -4.14777621540367, 
-2.86056694414781, -1.70668584273452, 1.3624660569909, -2.59153842674144, 
-4.12393693326418, -2.12959469550212, 1.46923996462969, -1.98633421258236, 
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1.53128139579193, 2.65531461899762, -1.33692502254506, -1.40309746138702, 
7.1503230589606, -3.03232696595877, -3.99799016693196, 1.36106448869886, 
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2.25817917663687, -4.47531796954113, -3.0627832955887), `24v0.logFC` = c(-5.63038870666508, 
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&quot;ENSMUSG00000053706.2&quot;, &quot;ENSMUSG00000109854.1&quot;, &quot;ENSMUSG00000060802.8&quot;, 
&quot;ENSMUSG00000093598.1&quot;), class = &quot;data.frame&quot;)
</code></pre>
<p>Please note, I have checked out many posts regarding this, most notably: <a href="https://www.biostars.org/p/287512/#287518" rel="nofollow noreferrer">https://www.biostars.org/p/287512/#287518</a>, which highlights how to perform this procedure. Regardless, there are some unanswered questions.</p>
<p>The problem is the following: After running the code suggested in the post, I do indeed obtain x number of clusters, depending on the height I decide to cut the tree at. However, they are in an incomprehensible order, making it extremely difficult to extract the clusters as they are observed in the heatmap. Please note, the dataframe I have uploaded is only a snippet of the true data - and is called logFC_mat, in the code below:</p>
<pre><code>pheatmap(logFC_mat, 
         show_rownames=F, cluster_cols=F, cluster_rows=T, scale=&quot;row&quot;,
         cex=1, clustering_distance_rows=&quot;euclidean&quot;, cex=1,
         clustering_distance_cols=&quot;euclidean&quot;, 
         clustering_method=&quot;complete&quot;, border_color=FALSE) -&gt; heatof

#make clusters based on h=1.5
sort(cutree(heatof$tree_row, h=1.5)) -&gt; hi_clusters

#check if clusters are in same order as in heatmap
hi_clusters[hi_clusters==1] %&gt;% names() -&gt; clust1
hi_clusters[hi_clusters==2] %&gt;% names() -&gt; clust2

length(clust1) #56
length(clust2) #78
all(clust1 %in% 
      rownames(logFC_mat[heatof<span class="math-container">$tree_row$</span>order[1:56],])) #TRUE

all(clust2 %in%
      rownames(logFC_mat[heatof<span class="math-container">$tree_row$</span>order[57:134],])) #FALSE
</code></pre>
<p>Now, from visual inspection of the heatmap, it becomes clear that cluster 2 is in fact the last 78 genes rather than the middle cluster. The problem is, why is it labeled as cluster 2 then !? In addition, this is a snippet of the data. The real data contains in fact thousands of rows, and so such visual deductions can simply be impossible.</p>
<p>Now for the question: How to extract the clusters, such that they are in the SAME ORDER, as what is seen in the heatmap (Top to bottom)?</p>
<p>Thanks.</p>

<r><rna-seq><clustering><unsupervised-learning>
Extracting clusters, hierarchical clustering - bulk RNASeq
# Extracting clusters, hierarchical clustering - bulk RNASeq

(Asked by: username_0 on 2022-07-06)

I have been attempting to extract particular clusters, given by hierarchical clustering outputs from the pheatmap function (in R).
Please find the dput output of the dataset in question below:
structure(list(`72v24.logFC` = c(-1.47511280138092, -1.10386310802134, 
-1.14476608274903, -1.63563574551239, -0.895377907265352, -1.35216488439027, 
-2.04949381135359, -1.0344565728341, -0.967008696515716, -1.11233305998901, 
-2.02663681865178, -1.74756562789736, -1.02076838668592, -1.27981009782201, 
-1.13181804725663, -1.19537930856639, -2.23476188503608, -1.67500131867632, 
-0.950059974858215, -2.3124598902615, -1.93757457440581, -3.24602437386808, 
-0.839869104053795, -1.40329424193006, -1.63484798229567, -0.912148120310492, 
-1.33467209346367, -1.27031027753064, -0.925498608058671, -0.802956638081314, 
-0.85457796397113, -1.0591348681193, -0.825278459132589, -0.671570119524199, 
-1.8439768319437, -0.947033608766851, -1.63849733567277, -1.40584263305038, 
-1.1317958487115, -1.40724695083764, -1.21842141154588, -2.29248800050516, 
-1.41699787526721, -0.879218050664484, -2.25064962332145, 1.4685855647974, 
-2.02553848518381, 1.32808748053321, -2.46062484389627, -2.45830217362141, 
-0.880536960021976, -0.689060222340137, -0.863262774594, -1.64961312646758, 
1.10572346456398, -0.891760767302047, -1.22716047163938, -0.680697741299578, 
-1.24551344420046, -0.891918251648935, -1.64722787848732, -0.832845262108387, 
-0.937129577660045, -0.891684122691785, 1.96452911491088, -1.59420664438654, 
-0.921295293242016, -1.8304480291305, -0.632859040349798, 1.69982819847455, 
-0.982668795391973, -1.61816842714933, -1.23602172818196, -2.15356245206668, 
-1.67961932563564, 1.15478114880933, 1.32665444151868, -1.04590896017973, 
-2.29770026795487, -1.27498150009975, -1.36310217581123, -1.09169550738617, 
1.5349413586602, -1.66842984591719, -0.760122936706606, -1.10054393155051, 
-1.00344072779106, -0.993259123487476, -1.43136824963825, -1.07214189069438, 
-0.990813303020613, -1.51324812740189, -1.23465012540084, -1.00098231964124, 
-1.92195764952845, -1.00139179559519, -0.940118366042393, -2.35969198434574, 
-0.574626090826994, -0.884870428965888, -0.87601460107501, 1.61336157755029, 
-0.777670642457121, -0.7312214522498, -0.812926777785119, -1.13443421373561, 
-1.56498420350444, -0.69126972981765, -1.44351026281231, -1.21962275860236, 
-2.21155555407497, -1.02015250027788, -0.604620179871691, -1.53013572266942, 
-0.790050747397837, -1.17950736026735, -0.781075150161975, -0.888554144538286, 
3.59172865862026, -0.8665071699621, -2.97701792242157, -1.44424941630738, 
-1.50528882788757, -0.772465957539335, -1.57664185213987, -1.02371443439054, 
-0.931807409493221, 1.77728969821973, -1.05455101137715, -1.09765136526264, 
-0.881436416213335, -0.985605985458853, -1.31644334991692, 2.26917914828263, 
-1.43844640540614, -1.75106843701475, -1.22483991752173, -1.65032906954133, 
-0.686319914242903, 2.45473055753775, -1.15594379837188, 1.46397122502826, 
-0.91260428248088, -0.726540148411507, -1.18227838019011, -0.940722690371935, 
-1.28074043595918, -0.629033607437977, 1.01053893402133, -0.735590704487842, 
-2.0620695625823, -0.692251638781327, -0.717818968339447, -0.564023101160995, 
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-3.18620970041199, -3.80052092618969, 1.59131320240714)), row.names = c("ENSMUSG00000097417.1", 
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"ENSMUSG00000053706.2", "ENSMUSG00000109854.1", "ENSMUSG00000060802.8", 
"ENSMUSG00000093598.1"), class = "data.frame")

Please note, I have checked out many posts regarding this, most notably: https://www.biostars.org/p/287512/#287518, which highlights how to perform this procedure. Regardless, there are some unanswered questions.
The problem is the following: After running the code suggested in the post, I do indeed obtain x number of clusters, depending on the height I decide to cut the tree at. However, they are in an incomprehensible order, making it extremely difficult to extract the clusters as they are observed in the heatmap. Please note, the dataframe I have uploaded is only a snippet of the true data - and is called logFC_mat, in the code below:
pheatmap(logFC_mat, 
         show_rownames=F, cluster_cols=F, cluster_rows=T, scale="row",
         cex=1, clustering_distance_rows="euclidean", cex=1,
         clustering_distance_cols="euclidean", 
         clustering_method="complete", border_color=FALSE) -> heatof

#make clusters based on h=1.5
sort(cutree(heatof$tree_row, h=1.5)) -> hi_clusters

#check if clusters are in same order as in heatmap
hi_clusters[hi_clusters==1] %>% names() -> clust1
hi_clusters[hi_clusters==2] %>% names() -> clust2

length(clust1) #56
length(clust2) #78
all(clust1 %in% 
      rownames(logFC_mat[heatof$tree_row$order[1:56],])) #TRUE

all(clust2 %in%
      rownames(logFC_mat[heatof$tree_row$order[57:134],])) #FALSE

Now, from visual inspection of the heatmap, it becomes clear that cluster 2 is in fact the last 78 genes rather than the middle cluster. The problem is, why is it labeled as cluster 2 then !? In addition, this is a snippet of the data. The real data contains in fact thousands of rows, and so such visual deductions can simply be impossible.
Now for the question: How to extract the clusters, such that they are in the SAME ORDER, as what is seen in the heatmap (Top to bottom)?
Thanks.

**Tags:** r, rna-seq, clustering, unsupervised-learning

---

## 1 answers

### Answer by username_1 on 2022-07-07. Score: 1

I would recommend not to use h to cut the tree but to use k, as k instructs the number of clusters to obtain, and then leaves it to the function to find the correct height for that. From there on you can easily extract the genes per cluster, for example with this code obtaining the first three clusters:

heatof <- 
pheatmap(logFC_mat, 
         show_rownames=F, cluster_cols=F, cluster_rows=T, scale="row",
         cex=1, clustering_distance_rows="euclidean", cex=1,
         clustering_distance_cols="euclidean", 
         clustering_method="complete", border_color=FALSE)

#/ extract the top three clusters
k <- 3
sort(cutree(heatof$tree_row, k=3)) -> hi_clusters

genes_per_cluster <- do.call(rbind, lapply(unique(hi_clusters), function(x){
  
  data.frame(cluster=x, gene=names(hi_clusters[hi_clusters %in% x]))
             
  
}))

genes_per_cluster
#>     cluster                  gene
#> 1         1  ENSMUSG00000097417.1
#> 2         1  ENSMUSG00000084950.7
#> 3         1 ENSMUSG00000021824.12
#> 4         1 ENSMUSG00000018381.15
#> 5         1  ENSMUSG00000098716.1
#> 6         1 ENSMUSG00000039206.13
#> 7         1 ENSMUSG00000047880.14
#> 8         1  ENSMUSG00000094127.1
#> 9         1  ENSMUSG00000098609.1
#> 10        1  ENSMUSG00000106093.1
#> 11        1 ENSMUSG00000009585.17
#> 12        1  ENSMUSG00000027950.7
#> 13        1 ENSMUSG00000004071.12
#> 14        1  ENSMUSG00000104208.1
#> 15        1 ENSMUSG00000047369.15
#> 16        1  ENSMUSG00000097660.1
#> 17        1 ENSMUSG00000002109.14
#> 18        1  ENSMUSG00000108524.1
#> 19        1 ENSMUSG00000067786.16
#> 20        1  ENSMUSG00000085437.8
#> 21        1  ENSMUSG00000086220.1
#> 22        1  ENSMUSG00000102299.1
#> 23        1 ENSMUSG00000079293.11
#> 24        1  ENSMUSG00000085425.1
#> 25        1  ENSMUSG00000006517.5
#> 26        1 ENSMUSG00000049775.16
#> 27        1  ENSMUSG00000109881.1
#> 28        1  ENSMUSG00000102349.1
#> 29        1  ENSMUSG00000089636.1
#> 30        1  ENSMUSG00000086360.1
#> 31        1 ENSMUSG00000036138.16
#> 32        1  ENSMUSG00000045382.6
#> 33        1 ENSMUSG00000030720.16
#> 34        1 ENSMUSG00000027398.13
#> 35        1  ENSMUSG00000104823.1
#> 36        1  ENSMUSG00000100783.1
#> 37        1  ENSMUSG00000082848.1
#> 38        1  ENSMUSG00000110148.1
#> 39        1  ENSMUSG00000085754.7
#> 40        1  ENSMUSG00000108321.1
#> 41        1 ENSMUSG00000032363.15
#> 42        1 ENSMUSG00000003868.14
#> 43        1  ENSMUSG00000107075.1
#> 44        1  ENSMUSG00000074136.9
#> 45        1  ENSMUSG00000089875.1
#> 46        1 ENSMUSG00000055725.11
#> 47        1 ENSMUSG00000028451.12
#> 48        1  ENSMUSG00000078786.9
#> 49        1  ENSMUSG00000087442.1
#> 50        1  ENSMUSG00000053442.4
#> 51        1  ENSMUSG00000101674.6
#> 52        1  ENSMUSG00000097748.1
#> 53        1  ENSMUSG00000050010.8
#> 54        1 ENSMUSG00000029623.14
#> 55        1  ENSMUSG00000097060.1
#> 56        1 ENSMUSG00000050627.13
#> 57        1  ENSMUSG00000085471.1
#> 58        1 ENSMUSG00000039753.16
#> 59        1  ENSMUSG00000027706.8
#> 60        1  ENSMUSG00000032487.8
#> 61        1 ENSMUSG00000002985.16
#> 62        1 ENSMUSG00000038274.11
#> 63        1  ENSMUSG00000087206.1
#> 64        1  ENSMUSG00000079355.3
#> 65        1  ENSMUSG00000086693.1
#> 66        1 ENSMUSG00000030579.10
#> 67        1  ENSMUSG00000108235.1
#> 68        1  ENSMUSG00000097077.1
#> 69        1  ENSMUSG00000087367.1
#> 70        1 ENSMUSG00000037982.16
#> 71        1 ENSMUSG00000029422.14
#> 72        1 ENSMUSG00000026749.11
#> 73        1 ENSMUSG00000067288.12
#> 74        1  ENSMUSG00000090330.2
#> 75        1 ENSMUSG00000059791.14
#> 76        1  ENSMUSG00000103698.1
#> 77        1  ENSMUSG00000097472.1
#> 78        1  ENSMUSG00000026156.8
#> 79        1 ENSMUSG00000075297.10
#> 80        1 ENSMUSG00000055692.18
#> 81        1 ENSMUSG00000039307.16
#> 82        1 ENSMUSG00000045576.16
#> 83        1  ENSMUSG00000087486.1
#> 84        1  ENSMUSG00000014470.4
#> 85        1  ENSMUSG00000056078.5
#> 86        1 ENSMUSG00000049928.15
#> 87        1  ENSMUSG00000086714.1
#> 88        1  ENSMUSG00000109274.1
#> 89        1  ENSMUSG00000109854.1
#> 90        1  ENSMUSG00000060802.8
#> 91        2  ENSMUSG00000085505.1
#> 92        2  ENSMUSG00000092134.1
#> 93        2  ENSMUSG00000097272.1
#> 94        2  ENSMUSG00000087700.1
#> 95        2  ENSMUSG00000101162.2
#> 96        2  ENSMUSG00000107317.1
#> 97        2  ENSMUSG00000102451.1
#> 98        2  ENSMUSG00000109669.1
#> 99        2  ENSMUSG00000086836.1
#> 100       2  ENSMUSG00000092545.7
#> 101       2  ENSMUSG00000090625.1
#> 102       2  ENSMUSG00000047988.1
#> 103       2  ENSMUSG00000086430.1
#> 104       2  ENSMUSG00000105941.1
#> 105       2  ENSMUSG00000022223.8
#> 106       2 ENSMUSG00000031145.15
#> 107       2  ENSMUSG00000086680.2
#> 108       2 ENSMUSG00000021485.13
#> 109       2  ENSMUSG00000097768.1
#> 110       2 ENSMUSG00000075576.12
#> 111       2  ENSMUSG00000090952.2
#> 112       2 ENSMUSG00000040723.14
#> 113       2  ENSMUSG00000027329.9
#> 114       2  ENSMUSG00000106005.1
#> 115       2 ENSMUSG00000046027.17
#> 116       2 ENSMUSG00000004221.16
#> 117       2  ENSMUSG00000023988.8
#> 118       2  ENSMUSG00000087611.1
#> 119       2  ENSMUSG00000092103.2
#> 120       2  ENSMUSG00000090081.1
#> 121       2  ENSMUSG00000107999.1
#> 122       2 ENSMUSG00000041774.14
#> 123       2  ENSMUSG00000090246.1
#> 124       2  ENSMUSG00000032528.4
#> 125       2 ENSMUSG00000021250.13
#> 126       2 ENSMUSG00000009292.17
#> 127       2  ENSMUSG00000109491.1
#> 128       2  ENSMUSG00000090252.1
#> 129       2  ENSMUSG00000087443.1
#> 130       2  ENSMUSG00000064339.1
#> 131       2  ENSMUSG00000091160.1
#> 132       2  ENSMUSG00000108332.1
#> 133       2  ENSMUSG00000048721.2
#> 134       2  ENSMUSG00000102961.1
#> 135       2  ENSMUSG00000096957.1
#> 136       2  ENSMUSG00000093006.2
#> 137       2 ENSMUSG00000027774.16
#> 138       2 ENSMUSG00000012126.16
#> 139       2  ENSMUSG00000092190.1
#> 140       2  ENSMUSG00000074213.2
#> 141       2  ENSMUSG00000086564.9
#> 142       2  ENSMUSG00000092655.1
#> 143       2  ENSMUSG00000098482.1
#> 144       2  ENSMUSG00000108574.1
#> 145       2  ENSMUSG00000087669.1
#> 146       2  ENSMUSG00000078653.4
#> 147       2  ENSMUSG00000108353.1
#> 148       2  ENSMUSG00000092386.1
#> 149       2  ENSMUSG00000087607.1
#> 150       2 ENSMUSG00000061607.14
#> 151       2  ENSMUSG00000026729.9
#> 152       2  ENSMUSG00000064330.9
#> 153       2  ENSMUSG00000103864.1
#> 154       2  ENSMUSG00000085208.3
#> 155       2  ENSMUSG00000073628.5
#> 156       2  ENSMUSG00000102776.1
#> 157       2  ENSMUSG00000086998.1
#> 158       2 ENSMUSG00000006728.12
#> 159       2  ENSMUSG00000021273.9
#> 160       2  ENSMUSG00000062591.5
#> 161       2 ENSMUSG00000040936.14
#> 162       2  ENSMUSG00000046145.7
#> 163       2  ENSMUSG00000087366.7
#> 164       2  ENSMUSG00000039452.5
#> 165       2  ENSMUSG00000087660.1
#> 166       2  ENSMUSG00000084878.1
#> 167       2  ENSMUSG00000101438.1
#> 168       2  ENSMUSG00000064351.1
#> 169       2  ENSMUSG00000086312.1
#> 170       2  ENSMUSG00000097221.1
#> 171       2  ENSMUSG00000107090.1
#> 172       2  ENSMUSG00000095280.1
#> 173       2 ENSMUSG00000017837.12
#> 174       2  ENSMUSG00000109251.1
#> 175       2  ENSMUSG00000086600.8
#> 176       2  ENSMUSG00000090009.1
#> 177       2  ENSMUSG00000101695.1
#> 178       2 ENSMUSG00000084128.10
#> 179       2 ENSMUSG00000038502.16
#> 180       2  ENSMUSG00000015942.9
#> 181       2  ENSMUSG00000106478.1
#> 182       2  ENSMUSG00000054556.6
#> 183       2  ENSMUSG00000082180.1
#> 184       2 ENSMUSG00000013997.10
#> 185       2  ENSMUSG00000100548.1
#> 186       2  ENSMUSG00000053706.2
#> 187       2  ENSMUSG00000093598.1
#> 188       3  ENSMUSG00000097860.1
#> 189       3  ENSMUSG00000079179.9
#> 190       3  ENSMUSG00000097347.1
#> 191       3  ENSMUSG00000092371.1
#> 192       3  ENSMUSG00000109754.1
#> 193       3  ENSMUSG00000090016.1
#> 194       3  ENSMUSG00000097854.1
#> 195       3  ENSMUSG00000029360.3
#> 196       3  ENSMUSG00000102564.1
#> 197       3  ENSMUSG00000025353.4
#> 198       3  ENSMUSG00000052214.9
#> 199       3 ENSMUSG00000037936.15
#> 200       3  ENSMUSG00000105198.1

Created on 2022-07-07 by the reprex package (v2.0.1)

```

> **Comments:**
> **username_0**: Thanks for the answer. I have no problem retrieving the genes in the clusters, just identifying the genes associated with the clusters observed in the heatmap. E.g. in my example, the first cluster (cluster 1) is the first cluster in the heatmap (on the top), however my second cluster is not my second cluster (cluster 2)...  

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