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Type: Package
Title: Metagenomic Clustering
Version: 0.1.1
Author: Dipro Sinha [aut, cre], Sayanti Guha Majumdar [aut], Anu Sharma [aut], Dwijesh Chandra Mishra [aut], Md Yeasin [aut]
Maintainer: Dipro Sinha <diprosinha@gmail.com>
Description: Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.2
Imports: factoextra, cluster, dbscan, dplyr, seqinr, Biostrings
Depends: R (≥ 3.6)
NeedsCompilation: no
Packaged: 2024-06-20 05:07:17 UTC; YEASIN
Repository: CRAN
Date/Publication: 2024-06-20 05:20:02 UTC

Calculation of GC content

Description

This function will calculate GC content from each sequence or contigs of a FASTA file.

Usage

GC.content(fasta_file)

Arguments

fasta_file

Name of the fasta or multifasta file

Value

Value of the GC content of each sequence or contig.

Author(s)

Dipro Sinha <diprosinha@gmail.com>,Sayanti Guha Majumdar, Anu Sharma, Dwijesh Chandra Mishra

Examples

library(metaCluster)
library(seqinr)
sample_data <- read.fasta(file = system.file("extdata/sample1.fasta", package = "metaCluster"),
seqtype = "DNA")
gc <- GC.content(sample_data)

Determination of Suitable Clustering Algorithm for Metagenomics Data

Description

This function will give the best clustering algorithm for a given metagenomics data based on silhouette index for kmeans clustering, kmedoids clustering, fuzzy kmeans clsutering, DBSCAN clustering and hierarchical clsutering.

Usage

clust.suite(data, k, eps, minpts)

Arguments

data

Feature matrix consisting of different genomic features.Each row represents features corresponding to a particular individual or contig and each column represents different genomic features.

k

Optimum number of clusters

eps

Radius value for DBSCAN clustering

minpts

Minimum point value of DBSCAN clustering

Value

kmeans

Output of kmeans clustering

kmedoids

Output of kmedoids clustering

fkmeans

Output of fuzzy kmeans clustering

dbscan

Output of dbscan clustering

hierarchical

Output of hierarchical clustering

silhouette.kmeans

Silhouette plot of kmeans clustering

silhouette.kmedoids

Silhouette plot of kmedoids clustering

silhouette.fkmeans

Silhouette plot of fuzzy kmeans clustering

silhouette.dbscan

Silhouette plot of dbscan clustering

silhouette.hierarchical

Silhouette plot of hierarchical clustering

best.clustering.method

Best clustering algorithm based on silhouette index

silhouette.summary

Average silhouette width of each clustering algorithm

Author(s)

Dipro Sinha <diprosinha@gmail.com>,Sayanti Guha Majumdar, Anu Sharma, Dwijesh Chandra Mishra

Examples


library(metaCluster)
data(metafeatures)
result <- clust.suite(metafeatures[1:200,],8,0.5,10)

Metagenomic data

Description

Feature matrix consisting of different genomic features.Each row represents features corresponding to a particular individual or contig and each column represents different genomic features.

Usage

data("metafeatures")

Format

A data frame with 1196 observations on the following 8 variables.

class

a factor with levels contig-0 contig-1000000 contig-10000000 contig-100000000 contig-1000000000 contig-1001000000 contig-1002000000 contig-1003000000 contig-1004000000 contig-1005000000 contig-1006000000 contig-1007000000 contig-1008000000 contig-1009000000 contig-101000000 contig-1010000000 contig-1011000000 contig-1012000000 contig-1013000000 contig-1014000000 contig-1015000000 contig-1016000000 contig-1017000000 contig-1018000000 contig-1019000000 contig-102000000 contig-1020000000 contig-1021000000 contig-1022000000 contig-1023000000 contig-1024000000 contig-1025000000 contig-1026000000 contig-1027000000 contig-1028000000 contig-1029000000 contig-103000000 contig-1030000000 contig-1031000000 contig-1032000000 contig-1033000000 contig-1034000000 contig-1035000000 contig-1036000000 contig-1037000000 contig-1038000000 contig-1039000000 contig-104000000 contig-1040000000 contig-1041000000 contig-1042000000 contig-1043000000 contig-1044000000 contig-1045000000 contig-1046000000 contig-1047000000 contig-1048000000 contig-1049000000 contig-105000000 contig-1050000000 contig-1051000000 contig-1052000000 contig-1053000000 contig-1054000000 contig-1055000000 contig-1056000000 contig-1057000000 contig-1058000000 contig-1059000000 contig-106000000 contig-1060000000 contig-1061000000 contig-1062000000 contig-1063000000 contig-1064000000 contig-1065000000 contig-1066000000 contig-1067000000 contig-1068000000 contig-1069000000 contig-107000000 contig-1070000000 contig-1071000000 contig-1072000000 contig-1073000000 contig-1074000000 contig-1075000000 contig-1076000000 contig-1077000000 contig-1078000000 contig-1079000000 contig-108000000 contig-1080000000 contig-1081000000 contig-1082000000 contig-1083000000 contig-1084000000 contig-1085000000 contig-1086000000 contig-1087000000 contig-1088000000 contig-1089000000 contig-109000000 contig-1090000000 contig-1091000000 contig-1092000000 contig-1093000000 contig-1094000000 contig-1095000000 contig-1096000000 contig-1097000000 contig-1098000000 contig-1099000000 contig-11000000 contig-110000000 contig-1100000000 contig-1101000000 contig-1102000000 contig-1103000000 contig-1104000000 contig-1105000000 contig-1106000000 contig-1107000000 contig-1108000000 contig-1109000000 contig-111000000 contig-1110000000 contig-1111000000 contig-1112000000 contig-1113000000 contig-1114000000 contig-1115000000 contig-1116000000 contig-1117000000 contig-1118000000 contig-1119000000 contig-112000000 contig-1120000000 contig-1121000000 contig-1122000000 contig-1123000000 contig-1124000000 contig-1125000000 contig-1126000000 contig-1127000000 contig-1128000000 contig-1129000000 contig-113000000 contig-1130000000 contig-1131000000 contig-1132000000 contig-1133000000 contig-1134000000 contig-1135000000 contig-1136000000 contig-1137000000 contig-1138000000 contig-1139000000 contig-114000000 contig-1140000000 contig-1141000000 contig-1142000000 contig-1143000000 contig-1144000000 contig-1145000000 contig-1146000000 contig-1147000000 contig-1148000000 contig-1149000000 contig-115000000 contig-1150000000 contig-1151000000 contig-1152000000 contig-1153000000 contig-1154000000 contig-1155000000 contig-1156000000 contig-1157000000 contig-1158000000 contig-1159000000 contig-116000000 contig-1160000000 contig-1161000000 contig-1162000000 contig-1163000000 contig-1164000000 contig-1165000000 contig-1166000000 contig-1167000000 contig-1168000000 contig-1169000000 contig-117000000 contig-1170000000 contig-1171000000 contig-1172000000 contig-1173000000 contig-1174000000 contig-1175000000 contig-1176000000 contig-1177000000 contig-1178000000 contig-1179000000 contig-118000000 contig-1180000000 contig-1181000000 contig-1182000000 contig-1183000000 contig-1184000000 contig-1185000000 contig-1186000000 contig-1187000000 contig-1188000000 contig-1189000000 contig-119000000 contig-1190000000 contig-1191000000 contig-1192000000 contig-1193000000 contig-1194000000 contig-1195000000 contig-1196000000 contig-1197000000 contig-1198000000 contig-1199000000 contig-12000000 contig-120000000 contig-1200000000 contig-1201000000 contig-1202000000 contig-1203000000 contig-1204000000 contig-1205000000 contig-1206000000 contig-1207000000 contig-1208000000 contig-1209000000 contig-121000000 contig-1210000000 contig-1211000000 contig-1212000000 contig-1213000000 contig-1214000000 contig-1215000000 contig-1216000000 contig-1217000000 contig-1218000000 contig-1219000000 contig-122000000 contig-1220000000 contig-1221000000 contig-1222000000 contig-1223000000 contig-1224000000 contig-1225000000 contig-1226000000 contig-1227000000 contig-1228000000 contig-1229000000 contig-123000000 contig-1230000000 contig-1231000000 contig-1232000000 contig-1233000000 contig-1234000000 contig-1235000000 contig-1236000000 contig-1237000000 contig-1238000000 contig-1239000000 contig-124000000 contig-1240000000 contig-1241000000 contig-1242000000 contig-1243000000 contig-1244000000 contig-1245000000 contig-1246000000 contig-1247000000 contig-1248000000 contig-1249000000 contig-125000000 contig-1250000000 contig-1251000000 contig-1252000000 contig-1253000000 contig-1254000000 contig-1255000000 contig-1256000000 contig-1257000000 contig-1258000000 contig-1259000000 contig-126000000 contig-1260000000 contig-1261000000 contig-1262000000 contig-1263000000 contig-1264000000 contig-1265000000 contig-1266000000 contig-1267000000 contig-1268000000 contig-1269000000 contig-127000000 contig-1270000000 contig-1271000000 contig-1272000000 contig-1273000000 contig-1274000000 contig-1275000000 contig-1276000000 contig-1277000000 contig-1278000000 contig-1279000000 contig-128000000 contig-1280000000 contig-1281000000 contig-1282000000 contig-1283000000 contig-1284000000 contig-1285000000 contig-1286000000 contig-1287000000 contig-1288000000 contig-1289000000 contig-129000000 contig-1290000000 contig-1291000000 contig-1292000000 contig-1293000000 contig-1294000000 contig-1295000000 contig-1296000000 contig-1297000000 contig-1298000000 contig-1299000000 contig-13000000 contig-130000000 contig-1300000000 contig-1301000000 contig-1302000000 contig-1303000000 contig-1304000000 contig-1305000000 contig-1306000000 contig-1307000000 contig-1308000000 contig-1309000000 contig-131000000 contig-1310000000 contig-1311000000 contig-1312000000 contig-1313000000 contig-1314000000 contig-1315000000 contig-1316000000 contig-1317000000 contig-1318000000 contig-1319000000 contig-132000000 contig-1320000000 contig-1321000000 contig-1322000000 contig-1323000000 contig-1324000000 contig-1325000000 contig-1326000000 contig-1327000000 contig-1328000000 contig-1329000000 contig-133000000 contig-1330000000 contig-1331000000 contig-1332000000 contig-1333000000 contig-1334000000 contig-1335000000 contig-1336000000 contig-1337000000 contig-1338000000 contig-1339000000 contig-134000000 contig-1340000000 contig-1341000000 contig-1342000000 contig-1343000000 contig-1344000000 contig-1345000000 contig-1346000000 contig-1347000000 contig-1348000000 contig-1349000000 contig-135000000 contig-1350000000 contig-1351000000 contig-1352000000 contig-1353000000 contig-1354000000 contig-1355000000 contig-1356000000 contig-1357000000 contig-1358000000 contig-1359000000 contig-136000000 contig-1360000000 contig-1361000000 contig-1362000000 contig-1363000000 contig-1364000000 contig-1365000000 contig-1366000000 contig-1367000000 contig-1368000000 contig-1369000000 contig-137000000 contig-1370000000 contig-1371000000 contig-1372000000 contig-1373000000 contig-1374000000 contig-1375000000 contig-1376000000 contig-1377000000 contig-1378000000 contig-1379000000 contig-138000000 contig-1380000000 contig-1381000000 contig-1382000000 contig-1383000000 contig-1384000000 contig-1385000000 contig-1386000000 contig-1387000000 contig-1388000000 contig-1389000000 contig-139000000 contig-1390000000 contig-1391000000 contig-1392000000 contig-1393000000 contig-1394000000 contig-1395000000 contig-1396000000 contig-1397000000 contig-1398000000 contig-1399000000 contig-14000000 contig-140000000 contig-1400000000 contig-1401000000 contig-1402000000 contig-1403000000 contig-1404000000 contig-1405000000 contig-1406000000 contig-1407000000 contig-1408000000 contig-1409000000 contig-141000000 contig-1410000000 contig-1411000000 contig-1412000000 contig-1413000000 contig-1414000000 contig-1415000000 contig-1416000000 contig-1417000000 contig-1418000000 contig-1419000000 contig-142000000 contig-1420000000 contig-1421000000 contig-1422000000 contig-1423000000 contig-1424000000 contig-1425000000 contig-1426000000 contig-1427000000 contig-1428000000 contig-1429000000 contig-143000000 contig-1430000000 contig-1431000000 contig-1432000000 contig-1433000000 contig-1434000000 contig-1435000000 contig-1436000000 contig-1437000000 contig-1438000000 contig-1439000000 contig-144000000 contig-1440000000 contig-1441000000 contig-1442000000 contig-1443000000 contig-1444000000 contig-1445000000 contig-1446000000 contig-1447000000

Dim.1

a numeric vector

Dim.2

a numeric vector

Dim.3

a numeric vector

Dim.4

a numeric vector

Dim.5

a numeric vector

Dim.6

a numeric vector

gc

a numeric vector

Oligonucleotide Frequency

Description

This function will calculate oligonucleotide frequency of each sequence or contig from a FASTA file.

Usage

oligo.freq(fasta_file, f)

Arguments

fasta_file

Name of the fasta or multifasta file

f

Length of the oligonucleotide

Value

Frequency value of each oligonucleotide of length specified by the user

Author(s)

Dipro Sinha <diprosinha@gmail.com>,Sayanti Guha Majumdar, Anu Sharma, Dwijesh Chandra Mishra

Examples

library(metaCluster)
freq <- oligo.freq(fasta_file = system.file("extdata/sample1.fasta", package = "metaCluster"),4)

Finding Optimum Number of Cluster for Metagenomics Data

Description

This function will give optimum number of clusters based on Within Sum of Squares (wss) plot.

Usage

opt.clust.num(data, nc, seed = 1234)

Arguments

data

Feature matrix consisting of different genomic features.Each row represents features corresponding to a particular individual or contig and each column represents different genomic features.

nc

Probable number of clusters

seed

Seed value for iteration

Value

WSS plot

Author(s)

Dipro Sinha <diprosinha@gmail.com>,Sayanti Guha Majumdar, Anu Sharma, Dwijesh Chandra Mishra

Examples

library(metaCluster)
data(metafeatures)
wss_plot <- opt.clust.num(metafeatures[1:200,], nc=10, seed = 1234)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.