FiNeMo hit calling report

TF-MoDISco seqlet comparisons

The following figures and statistics compare the called hits with the seqlets used by TF-MoDISco to construct each motif.

Hit vs. seqlet counts

This figure shows the number of hits called vs. the number of TF-MoDISco seqlets identified for each motif. The hit counts should be greater than the corresponding seqlet counts, since TF-MoDISco stringently filters seqlets and usually uses a smaller input window. The dashed line is the identity line.

CWMs and seqlet recall

For each motif, this table examines the consistency between hits and TF-MoDISco seqlets.

The following statistics report the number of hits, seqlets, and their relationships:

Hits: The number of hits called by FiNeMo
Restricted Hits: The number of FiNeMo hits within the TF-MoDISco input regions
Seqlets: The number of unique TF-MoDISco seqlets
Hit/Seqlet Overlaps: The number of hits that coincide with TF-MoDISco seqlets
Missed Seqlets: The number of TF-MoDISco seqlets not called as hits
Additional Restricted Hits: The number of hits within the TF-MoDISco input regions that were not identified as seqlets by TF-MoDISco
Seqlet Recall: The fraction of seqlets that are called as hits
Hit-Seqlet Correlation: The Pearson correlation between the additional-restricted-hits CWM and the seqlet CWM

Note that the seqlet counts here may be lower than those shown in the tfmodisco-lite report due to double-counting in overlapping regions. The seqlet counts shown here are after de-duplication, while the counts in the tfmodisco-lite report are not de-duplicated.

Note that palindromic motifs may have lower recall due to disagreements on orientation. If seqlet recall is near zero for all motifs, the -W/--modisco-region-width argument is likely incorrect.

CWMs (contribution weight matrices) are average contribution scores over a set of regions. The CWMs shown here are:

Hit CWM (FC): The forward-strand CWM of all hits
Hit CWM (RC): The reverse-strand CWM of all hits
Seqlet CWM: The CWM of all TF-MoDISco seqlets
Missed-Seqlet-Only CWM: The CWM of all TF-MoDISco seqlets that were not called as hits
Additional-Restricted-Hit CWM: The CWM of all hits within the TF-MoDISco input regions that were not identified as seqlets by TF-MoDISco

The hit-seqlet correlation is the Pearson correlation between the additional-restricted-hits CWM and the seqlet CWM. This statistic measures the similarity between hits that were missed by TF-MoDISco and the seqlets used to construct the motif.

Motif Name	Seqlet Recall	Hit-Seqlet Correlation	Hits	Restricted Hits	Seqlets	Hit/Seqlet Overlaps	Missed Seqlets	Additional Restricted Hits
`pos_patterns.pattern_0`	0.834	0.999	124123	107322	13313	11109	2027	96213
`pos_patterns.pattern_1`	0.941	1.001	42696	39469	9469	8912	543	30557
`pos_patterns.pattern_2`	0.854	0.999	64693	56185	6315	5394	867	50791
`pos_patterns.pattern_3`	0.803	0.965	42406	35685	3397	2728	630	32957
`pos_patterns.pattern_4`	0.823	0.996	27447	24221	2864	2358	476	21863
`pos_patterns.pattern_5`	0.792	1.000	40993	31472	2119	1678	390	29794
`pos_patterns.pattern_6`	0.869	0.998	17513	14764	2032	1765	253	12999
`pos_patterns.pattern_7`	0.914	1.000	21379	18294	2003	1831	162	16463
`pos_patterns.pattern_8`	0.868	0.994	19398	15838	1618	1404	195	14434
`pos_patterns.pattern_9`	0.872	0.901	43748	38554	1558	1358	190	37196
`pos_patterns.pattern_10`	0.951	1.000	12275	10791	1530	1455	66	9336
`pos_patterns.pattern_11`	0.786	0.956	37260	30486	1506	1184	297	29302
`pos_patterns.pattern_12`	0.836	0.935	48679	36408	1471	1230	191	35178
`pos_patterns.pattern_13`	0.722	0.998	11608	9091	1074	775	276	8316
`pos_patterns.pattern_14`	0.535	0.996	17620	14929	837	448	375	14481
`pos_patterns.pattern_15`	0.887	0.996	7504	6265	822	729	86	5536
`pos_patterns.pattern_16`	0.829	1.000	5372	4483	805	667	123	3816
`pos_patterns.pattern_17`	0.890	0.997	9148	7639	803	715	75	6924
`pos_patterns.pattern_18`	0.905	0.988	8878	7597	695	629	63	6968
`pos_patterns.pattern_19`	0.767	0.991	8103	6804	686	526	143	6278
`pos_patterns.pattern_20`	0.315	0.983	17097	9490	492	155	317	9335
`pos_patterns.pattern_21`	0.951	0.980	9186	8312	470	447	20	7865
`pos_patterns.pattern_22`	0.908	0.936	7408	6128	445	404	37	5724
`pos_patterns.pattern_23`	0.167	0.969	6222	3282	402	67	322	3215
`pos_patterns.pattern_24`	0.346	1.017	163770	120114	384	133	241	119981
`pos_patterns.pattern_25`	0.942	0.984	7157	6307	311	293	16	6014
`pos_patterns.pattern_26`	0.303	0.960	126664	94085	310	94	206	93991
`pos_patterns.pattern_27`	0.956	0.999	2064	1881	297	284	11	1597
`pos_patterns.pattern_28`	0.716	0.873	21509	17674	278	199	71	17475
`pos_patterns.pattern_29`	0.867	0.992	3187	2649	241	209	32	2440
`pos_patterns.pattern_30`	0.969	1.000	972	942	226	219	6	723
`pos_patterns.pattern_31`	0.957	0.999	2311	1859	161	154	5	1705
`pos_patterns.pattern_32`	0.918	0.948	2675	2165	98	90	8	2075
`pos_patterns.pattern_33`	0.843	0.990	4296	2534	89	75	11	2459
`pos_patterns.pattern_34`	0.855	0.938	2320	1853	62	53	8	1800
`pos_patterns.pattern_35`	0.903	0.978	2656	2053	62	56	4	1997
`pos_patterns.pattern_36`	0.981	0.974	1681	1346	52	51	1	1295
`pos_patterns.pattern_37`	0.885	0.949	4180	3190	52	46	6	3144
`pos_patterns.pattern_38`	0.474	0.826	28668	22408	38	18	20	22390
`pos_patterns.pattern_39`	0.618	0.913	5249	4240	34	21	13	4219
`pos_patterns.pattern_40`	0.957	0.950	2419	1958	23	22	1	1936
`pos_patterns.pattern_41`	1.000	0.924	2071	1605	21	21	0	1584
`neg_patterns.pattern_0`	0.904	0.995	55202	17259	1563	1413	126	15846
`neg_patterns.pattern_1`	0.914	0.998	25838	5738	267	244	22	5494
`neg_patterns.pattern_2`	0.894	1.004	8966	2413	208	186	20	2227
`neg_patterns.pattern_3`	0.944	0.976	26389	4761	126	119	5	4642
`neg_patterns.pattern_4`	0.909	0.912	9624	2878	121	110	11	2768
`neg_patterns.pattern_5`	0.940	0.997	6888	1497	100	94	6	1403
`neg_patterns.pattern_6`	0.306	0.900	130536	104032	98	30	68	104002
`neg_patterns.pattern_7`	0.500	1.009	47930	37631	92	46	44	37585
`neg_patterns.pattern_8`	0.519	0.966	95275	75261	81	42	38	75219
`neg_patterns.pattern_9`	0.545	0.977	126314	100280	66	36	28	100244
`neg_patterns.pattern_10`	0.774	0.965	841	291	62	48	14	243
`neg_patterns.pattern_11`	0.705	0.985	3221	893	61	43	18	850
`neg_patterns.pattern_12`	0.821	0.986	15051	5758	39	32	6	5726
`neg_patterns.pattern_13`	0.143	0.816	13149	10430	35	5	30	10425
`neg_patterns.pattern_14`	0.970	0.921	11397	3163	33	32	1	3131
`neg_patterns.pattern_15`	0.286	0.856	8468	7027	28	8	20	7019
`neg_patterns.pattern_16`	0.783	0.973	1044	300	23	18	4	282
`neg_patterns.pattern_17`	0.619	0.883	2276	631	21	13	8	618

Hit distributions

The following figures visualize the distribution of hits across motifs and peaks.

Overall distribution of hits per peak

This plot shows the distribution of hit counts per peak for any motif. The number of peaks with no hits should be near zero.

Per-motif distributions of hits per peak

These plots show the distribution of hit counts per peak for each motif.

Motif Name	Hits Per Peak
`pos_patterns.pattern_0`
`pos_patterns.pattern_1`
`pos_patterns.pattern_2`
`pos_patterns.pattern_3`
`pos_patterns.pattern_4`
`pos_patterns.pattern_5`
`pos_patterns.pattern_6`
`pos_patterns.pattern_7`
`pos_patterns.pattern_8`
`pos_patterns.pattern_9`
`pos_patterns.pattern_10`
`pos_patterns.pattern_11`
`pos_patterns.pattern_12`
`pos_patterns.pattern_13`
`pos_patterns.pattern_14`
`pos_patterns.pattern_15`
`pos_patterns.pattern_16`
`pos_patterns.pattern_17`
`pos_patterns.pattern_18`
`pos_patterns.pattern_19`
`pos_patterns.pattern_20`
`pos_patterns.pattern_21`
`pos_patterns.pattern_22`
`pos_patterns.pattern_23`
`pos_patterns.pattern_24`
`pos_patterns.pattern_25`
`pos_patterns.pattern_26`
`pos_patterns.pattern_27`
`pos_patterns.pattern_28`
`pos_patterns.pattern_29`
`pos_patterns.pattern_30`
`pos_patterns.pattern_31`
`pos_patterns.pattern_32`
`pos_patterns.pattern_33`
`pos_patterns.pattern_34`
`pos_patterns.pattern_35`
`pos_patterns.pattern_36`
`pos_patterns.pattern_37`
`pos_patterns.pattern_38`
`pos_patterns.pattern_39`
`pos_patterns.pattern_40`
`pos_patterns.pattern_41`
`neg_patterns.pattern_0`
`neg_patterns.pattern_1`
`neg_patterns.pattern_2`
`neg_patterns.pattern_3`
`neg_patterns.pattern_4`
`neg_patterns.pattern_5`
`neg_patterns.pattern_6`
`neg_patterns.pattern_7`
`neg_patterns.pattern_8`
`neg_patterns.pattern_9`
`neg_patterns.pattern_10`
`neg_patterns.pattern_11`
`neg_patterns.pattern_12`
`neg_patterns.pattern_13`
`neg_patterns.pattern_14`
`neg_patterns.pattern_15`
`neg_patterns.pattern_16`
`neg_patterns.pattern_17`

Motif co-occurrence

This heatmap shows the co-occurrence of motifs across peaks. The color intensity here represents the pearson correlation between the motifs' occurrence across peaks, where occurence is defined as the presence of a hit for a motif in a peak.