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156 lines
4.5 KiB
156 lines
4.5 KiB
.\" generated with Ronn/v0.7.3
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.\" http://github.com/rtomayko/ronn/tree/0.7.3
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.
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.TH "F3M" "1" "September 2017" "www.complex-networks.net" "www.complex-networks.net"
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.
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.SH "NAME"
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\fBf3m\fR \- Count all the 3\-node subgraphs of a directed graph
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.
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.SH "SYNOPSIS"
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\fBf3m\fR \fIgraph_in\fR [\fInum_random\fR]
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.
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.SH "DESCRIPTION"
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\fBf3m\fR performs a motif analysis on \fIgraph_in\fR, i\.e\., it counts all the 3\-node subgraphs and computes the z\-score of that count with respect to the corresponding configuration model ensemble\.
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.
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.SH "PARAMETERS"
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.
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.TP
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\fIgraph_in\fR
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input graph (edge list)\. It must be an existing file\.
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.
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.TP
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\fInum_random\fR
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The number of random graphs to sample from the configuration model for the computation of the z\-score of the motifs\.
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.
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.SH "OUTPUT"
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\fBf3m\fR prints on the standard output a table with 13 rows, one for each of the 13 possible 3\-node motifs\. Each line is in the format:
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.
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.IP "" 4
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.
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.nf
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motif_number count mean_rnd std_rnd z\-score
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.
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.fi
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.
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.IP "" 0
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.
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.P
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where \fBmotif_number\fR is a number between 1 and 13 that identifies the motif (see \fIMOTIF NUMBERS\fR below), \fBcount\fR is the number of subgraphs ot type \fBmotif_number\fR found in \fIgraph_in\fR, \fBmean_rnd\fR is the average number of subgraphs of type \fBmotif_number\fR in the corresponding configuration model ensemble, and \fBstd_rnd\fR is the associated standard deviation\. Finally, \fBz\-score\fR is the quantity:
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.
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.IP "" 4
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.
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.nf
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(count \- mean_rnd) / std_rnd
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.
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.fi
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.IP "" 0
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.
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.P
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The program also prints a progress bar on STDERR\.
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.
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.SH "MOTIF NUMBERS"
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We report below the correspondence between the 13 possible 3\-node subgraphs and the corresponding \fBmotif_number\fR\. In the diagrams, \'O\-\-\->O\' indicates a single edge form the left node to the right node, while \'O\fI==\fRO\' indicates a double (bi\-directional) edge between the two nodes:
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.
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.IP "" 4
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.
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.nf
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(1) O<\-\-\-O\-\-\->O
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(2) O\-\-\->O\-\-\->O
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(3) O<==>O\-\-\->O
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(4) O\-\-\->O<\-\-\-O
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(5) O\-\-\->O\-\-\->O
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\e ^
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\e_______|
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(6) O<==>O\-\-\->O
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\e ^
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\e_______|
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(7) O<==>O<\-\-\-O
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(8) O<==>O<==>O
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(9) O<\-\-\-O<\-\-\-O
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\e ^
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\e_______|
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(10) O<==>O<\-\-\-O
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\e ^
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\e_______|
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(11) O\-\-\->O<==>O
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\e ^
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\e_______|
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(12) O<==>O<==>O
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\e ^
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\e_______|
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(13) O<==>O<==>O
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^\e ^/
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\e\e_____//
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\e_____/
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.
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.fi
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.IP "" 0
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.
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.SH "EXAMPLES"
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To perform a motif analysis on the E\.coli transcription regulation graph, using 1000 randomised networks, we run the command:
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.
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.IP "" 4
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.
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.nf
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$ f3m e_coli\.net 1000
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1 4760 4400\.11 137\.679 +2\.614
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2 162 188\.78 8\.022 \-3\.338
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3 0 0\.89 3\.903 \-0\.228
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4 226 238\.32 7\.657 \-1\.609
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5 40 6\.54 2\.836 +11\.800
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6 0 0\.01 0\.077 \-0\.078
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7 0 0\.12 0\.642 \-0\.192
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8 0 0\.00 0\.032 \-0\.032
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9 0 0\.01 0\.109 \-0\.110
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10 0 0\.00 0\.000 +0\.000
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11 0 0\.00 0\.032 \-0\.032
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12 0 0\.00 0\.000 +0\.000
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13 0 0\.00 0\.000 +0\.000
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$
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.
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.fi
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.IP "" 0
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.
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.P
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Notice that the motif \fB5\fR (the so\-called "feed\-forward loop") has a z\-score equal to 11\.8, meaning that it is highly overrepresented in the E\.coli graph with respect to the corresponding configuration model ensemble\. Conversely, the motif \fB2\fR (three\-node chain) is underrepresented, as made evident by value of the z\-score (\-3\.338)\.
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.
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.SH "SEE ALSO"
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johnson_cycles(1)
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.
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.SH "REFERENCES"
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.
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.IP "\(bu" 4
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R\. Milo et al\. "Network Motifs: Simple Building Blocks of Complex Networks"\. Science 298 (2002), 824\-827\.
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.
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.IP "\(bu" 4
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R\. Milo et al\. "Superfamilies of evolved and designed networks\." Science 303 (2004), 1538\-1542
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.
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.IP "\(bu" 4
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V\. Latora, V\. Nicosia, G\. Russo, "Complex Networks: Principles, Methods and Applications", Chapter 8, Cambridge University Press (2017)
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.
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.IP "\(bu" 4
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V\. Latora, V\. Nicosia, G\. Russo, "Complex Networks: Principles, Methods and Applications", Appendix 16, Cambridge University Press (2017)
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.
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.IP "" 0
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.
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.SH "AUTHORS"
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(c) Vincenzo \'KatolaZ\' Nicosia 2009\-2017 \fB<v\.nicosia@qmul\.ac\.uk>\fR\.
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