changes to address issue 8

R/ClassFilter.R

@@ -755,10 +755,10 @@ w4m_filter_by_sample_class <- function(
     }
     metadata_order <-
       Reduce(
-        function(a1,a2) {
+        function(a1, a2) {
           order(
-            metadata_df[metadata_names,a1],
-            metadata_df[metadata_names,a2]
+            metadata_df[metadata_names, a1],
+            metadata_df[metadata_names, a2]
           )
         },
         order_split
@@ -869,7 +869,6 @@ w4m_filter_by_sample_class <- function(
     treatments <- smpl_metadata[class_column][[1]]
     nrow_dm <- nrow(data_matrix)
     unitrts <- unique(treatments)
-    ntrts <- length(unitrts)
     smpl_metadata <- data.frame(
       trt = unitrts,
       n = sapply(X = unitrts, FUN = function(x) sum(x == treatments)),
@@ -893,7 +892,7 @@ w4m_filter_by_sample_class <- function(
       stringsAsFactors = FALSE
     )
     smpl_metadata_colnames <- colnames(smpl_metadata)
-    smpl_metadata$sampleMetadata <- smpl_metadata[,class_column]
+    smpl_metadata$sampleMetadata <- smpl_metadata[,"trt"]
     smpl_metadata <- smpl_metadata[c("sampleMetadata",smpl_metadata_colnames)]
     rownames(new_df) <- rownames(data_matrix)
     data_matrix <- as.matrix(new_df)
@@ -902,7 +901,6 @@ w4m_filter_by_sample_class <- function(
     treatments <- smpl_metadata[class_column][[1]]
     nrow_dm <- nrow(data_matrix)
     unitrts <- unique(treatments)
-    ntrts <- length(unitrts)
     smpl_metadata <- data.frame(
       trt = unitrts,
       n = sapply(X = unitrts, FUN = function(x) sum(x == treatments)),
@@ -926,7 +924,7 @@ w4m_filter_by_sample_class <- function(
       stringsAsFactors = FALSE
     )
     smpl_metadata_colnames <- colnames(smpl_metadata)
-    smpl_metadata$sampleMetadata <- smpl_metadata[,class_column]
+    smpl_metadata$sampleMetadata <- smpl_metadata[,"trt"]
     smpl_metadata <- smpl_metadata[c("sampleMetadata",smpl_metadata_colnames)]
     rownames(new_df) <- rownames(data_matrix)
     data_matrix <- as.matrix(new_df)
@@ -944,14 +942,14 @@ w4m_filter_by_sample_class <- function(
     my_pca <- prcomp(t(data_matrix), scale. = TRUE, tol = sqrt(.Machine$double.eps))
     # Extract eigenvalues to determine how many are < 1
     # ref for extraction: https://stat.ethz.ch/pipermail/r-help/2005-August/076610.html
-    ev <- my_pca$sdev^2
+    ev <- my_pca$sdev ^ 2
     # The cut-off for the scree is somewhat arbitrary,
     #   https://en.wikipedia.org/wiki/Scree_plot, which cites
     #   Norman and Steiner, Biostatistics: The Bare Essentials, p. 201
     #   (https://books.google.com/books?id=8rkqWafdpuoC&pg=PA201)
     # To be conservative, limit the number of PCs to twice the number of eigenvalues that are greater than 1.
     #   It might be better instead to keep adding components until the residual approaches some threshold.
-    my_rank <- min(length(ev),2*sum(ev > 1))
+    my_rank <- min(length(ev),2 * sum(ev > 1))
     my_scores <- my_pca$x
     my_scores <- my_scores[,1:min(ncol(my_scores),my_rank)]
     # For each treatment, calculate the medoid, i.e.,

inst/NEWS

@@ -1,9 +1,23 @@
+# CHANGES IN VERSION 0.98.18
+
+## SIGNIFICANT USER-VISIBLE CHANGES
+
+* Bug fix https://github.com/HegemanLab/w4mclassfilter/issues/8 - "centroid and median computation fails for some inputs"
+
+## INTERNAL CHANGES
+
+* Built with R 3.6.2 (2019-12-12) "Dark and Stormy Night"
+* Built with the command `R CMD build w4mclassfilter` to assure that vignettes are built as intended.
+
 # CHANGES IN VERSION 0.98.17
 
 ## SIGNIFICANT USER-VISIBLE CHANGES
 
 * Enhancement https://github.com/HegemanLab/w4mclassfilter/issues/7 - "enable sorting on multiple columns of metadata"
-* Built with R 3.6.1 (2019-07-05) "Action of the Toes"
+
+## INTERNAL CHANGES
+
+* Built with R 3.6.2 (2019-12-12) "Dark and Stormy Night"
 * Built with the command `R CMD build w4mclassfilter` to assure that vignettes are built as intended.
 
 # CHANGES IN VERSION 0.98.16

tests/testthat/exp_cent_centroid_dm.tsv

@@ -0,0 +1,16 @@
+	F	M
+HMDB00191	1055337.6	806733.222222222
+HMDB00208	6523832	2847488.88888889
+HMDB00251	309783.6	475958.333333333
+HMDB00299	603356.5	669856
+HMDB00512	592718.6	368961.222222222
+HMDB00518	150841.1	155697.111111111
+HMDB00715	2303879.4	2735121.22222222
+HMDB01032	6314400.9	17336157.4444444
+HMDB01101	2430552.8	1357405.88888889
+HMDB01101.1	1711126.7	2161230.22222222
+HMDB03193	179434	247385.777777778
+HMDB04824	788488.9	722462.666666667
+HMDB10348	257916.8	266074.666666667
+HMDB13189	1359519	2112920.22222222
+HMDB59717	603835.3	853206.111111111

tests/testthat/exp_cent_centroid_sm.tsv

@@ -0,0 +1,3 @@
+sampleMetadata	trt	n
+F	F	10
+M	M	9

tests/testthat/exp_cent_centroid_vm.tsv

@@ -0,0 +1,16 @@
+variableMetadata	name	mz	rt
+HMDB00191	loquor	650	600
+HMDB00208	loquimini	873	476
+HMDB00251	pasamur	500	423
+HMDB00299	bantur	700	500
+HMDB00512	pantur	900	543
+HMDB00518	loquoris	870	250
+HMDB00715	loquitur	725	900
+HMDB01032	loquimur	550	425
+HMDB01101	bar	150	300
+HMDB01101.1	baz	200	225
+HMDB03193	foo	100	200
+HMDB04824	loquantur	950	522
+HMDB10348	batur	300	275
+HMDB13189	baris	800	325
+HMDB59717	bamur	125	400

tests/testthat/exp_cent_median_dm.tsv

@@ -0,0 +1,16 @@
+	F	M
+HMDB03193	103479	242549
+HMDB59717	511026	871209
+HMDB01101	969821	608298
+HMDB01101.1	418933	976436
+HMDB10348	187730.5	176500
+HMDB00251	258349.5	352855
+HMDB01032	1212834	14989438
+HMDB00191	830941	645785
+HMDB00299	488968	614370
+HMDB00715	2421370	2658555
+HMDB13189	938163.5	1941527
+HMDB00518	53461	129886
+HMDB00208	7105002.5	3143654
+HMDB00512	431064	342532
+HMDB04824	901439.5	605191

tests/testthat/exp_cent_median_sm.tsv

@@ -0,0 +1,3 @@
+sampleMetadata	trt	n
+F	F	10
+M	M	9

tests/testthat/exp_cent_median_vm.tsv

@@ -0,0 +1,16 @@
+variableMetadata	name	mz	rt
+HMDB03193	foo	100	200
+HMDB59717	bamur	125	400
+HMDB01101	bar	150	300
+HMDB01101.1	baz	200	225
+HMDB10348	batur	300	275
+HMDB00251	pasamur	500	423
+HMDB01032	loquimur	550	425
+HMDB00191	loquor	650	600
+HMDB00299	bantur	700	500
+HMDB00715	loquitur	725	900
+HMDB13189	baris	800	325
+HMDB00518	loquoris	870	250
+HMDB00208	loquimini	873	476
+HMDB00512	pantur	900	543
+HMDB04824	loquantur	950	522

tests/testthat/exp_cent_medoid_dm.tsv

@@ -0,0 +1,16 @@
+	F	M
+HMDB03193	3732	173175
+HMDB01101.1	608298	4763576
+HMDB00518	0	129886
+HMDB10348	16262	168264
+HMDB01101	831937	229568
+HMDB13189	2003278	2755434
+HMDB59717	289677	1028110
+HMDB00251	167246	293988
+HMDB01032	263055	26222916
+HMDB00208	5402629	3143654
+HMDB00299	242085	808657
+HMDB04824	477259	959381
+HMDB00512	198512	556003
+HMDB00191	910201	785428
+HMDB00715	859466	5140022

tests/testthat/exp_cent_medoid_sm.tsv

@@ -0,0 +1,3 @@
+sampleMetadata	medoid	injectionOrder	mode	age	bmi	gender
+F	HU_110	53	pos	50	20.9	F
+M	HU_078	34	pos	46	25.18	M

tests/testthat/exp_cent_medoid_vm.tsv

@@ -0,0 +1,16 @@
+variableMetadata	name	mz	rt
+HMDB03193	foo	100	200
+HMDB01101.1	baz	200	225
+HMDB00518	loquoris	870	250
+HMDB10348	batur	300	275
+HMDB01101	bar	150	300
+HMDB13189	baris	800	325
+HMDB59717	bamur	125	400
+HMDB00251	pasamur	500	423
+HMDB01032	loquimur	550	425
+HMDB00208	loquimini	873	476
+HMDB00299	bantur	700	500
+HMDB04824	loquantur	950	522
+HMDB00512	pantur	900	543
+HMDB00191	loquor	650	600
+HMDB00715	loquitur	725	900

tests/testthat/exp_cent_none_dm.tsv

@@ -0,0 +1,16 @@
+	HU_028	HU_051	HU_060	HU_110	HU_149	HU_152	HU_175	HU_178	HU_185	HU_208	HU_017	HU_034	HU_078	HU_091	HU_093	HU_099	HU_130	HU_134	HU_138
+HMDB03193	412165	27242	436566	3732	127285	451270	212500	79673	0	43907	76043	44943	173175	242549	57066	559869	339188	471368	262271
+HMDB59717	1030464	67604	306862	289677	895435	715190	1563158	784738	146195	239030	357351	301983	1028110	1530493	270027	1378535	808334	1132813	871209
+HMDB01101	6877586	3158	10789748	831937	442510	1107705	1464339	31250	2724553	32742	30689	52217	229568	4763576	3878773	976436	608298	1605075	72021
+HMDB01101.1	52217	10789748	229568	608298	1107705	1464339	31250	2724553	72900	30689	6877586	3158	4763576	3878773	976436	831937	1605075	72021	442510
+HMDB10348	544877	34582	529874	16262	591487	433529	161069	214392	13781	39315	47259	60885	168264	176500	76457	610110	279156	524468	451573
+HMDB00251	616555	622468	180988	167246	10985	335711	403815	80614	63393	616061	368600	94936	293988	352855	767894	268331	310918	1248919	577184
+HMDB01032	26023086	430453	8103558	263055	1554658	20249262	5588731	871010	15920	44276	2569205	1604999	26222916	257139	675754	59906109	31151730	18648127	14989438
+HMDB00191	771533	392284	888498	910201	2292023	1246459	1945577	710519	773384	622898	560002	575790	785428	645785	591569	960658	639437	1092885	1409045
+HMDB00299	1046138	159386	1013302	242085	1057660	1110050	566050	411886	142233	284775	250551	456162	808657	614370	250403	768004	504108	1014041	1362408
+HMDB00715	2547452	371059	4983588	859466	2615560	3820724	3577833	2295288	625924	1341900	1252089	905408	5140022	2658555	814523	2558923	4184204	3865723	3236644
+HMDB13189	727587	619181	136278	2003278	113937	3132404	2893445	2092753	1034666	841661	2644620	1661412	2755434	593863	837865	3526136	1608814	3446611	1941527
+HMDB00518	58249	0	342102	0	419508	48673	28361	514579	23108	73831	0	85944	129886	175800	13154	230242	440223	315368	10657
+HMDB00208	13420742	1172376	7172632	5402629	7621236	8960828	10335722	7037373	1574738	2540044	747080	595872	3143654	4059767	1433702	5593888	2477288	3346077	4230072
+HMDB00512	319783	85009	1333877	198512	1140422	542345	1171008	827723	222953	85554	0	280560	556003	590779	209285	342532	569970	525240	246282
+HMDB04824	1144386	178517	1046190	477259	1089284	1411802	1020206	782673	346761	387811	374028	539206	959381	605191	310260	1253319	477995	825691	1157093

tests/testthat/exp_cent_none_sm.tsv

@@ -0,0 +1,20 @@
+sampleMetadata	injectionOrder	mode	age	bmi	gender
+HU_028	7	pos	41	23.92	F
+HU_051	20	pos	24	23.23	F
+HU_060	24	pos	55	28.72	F
+HU_110	53	pos	50	20.9	F
+HU_149	72	pos	35	19.49	F
+HU_152	75	pos	26	17.58	F
+HU_175	87	pos	35	21.26	F
+HU_178	88	pos	60	32.87	F
+HU_185	95	pos	42	21.09	F
+HU_208	106	pos	27	18.61	F
+HU_017	2	pos	41	23.03	M
+HU_034	9	pos	52	23.37	M
+HU_078	34	pos	46	25.18	M
+HU_091	42	pos	61	26.12	M
+HU_093	43	pos	53	21.71	M
+HU_099	46	pos	23	21.3	M
+HU_130	63	pos	33	26.06	M
+HU_134	67	pos	48	22.89	M
+HU_138	68	pos	42	21.88	M

tests/testthat/exp_cent_none_vm.tsv

@@ -0,0 +1,16 @@
+variableMetadata	name	mz	rt
+HMDB03193	foo	100	200
+HMDB59717	bamur	125	400
+HMDB01101	bar	150	300
+HMDB01101.1	baz	200	225
+HMDB10348	batur	300	275
+HMDB00251	pasamur	500	423
+HMDB01032	loquimur	550	425
+HMDB00191	loquor	650	600
+HMDB00299	bantur	700	500
+HMDB00715	loquitur	725	900
+HMDB13189	baris	800	325
+HMDB00518	loquoris	870	250
+HMDB00208	loquimini	873	476
+HMDB00512	pantur	900	543
+HMDB04824	loquantur	950	522

tests/testthat/in_cent_dm.tsv

@@ -0,0 +1,17 @@
+dataMatrix	HU_017	HU_028	HU_034	HU_051	HU_060	HU_078	HU_091	HU_093	HU_099	HU_110	HU_130	HU_134	HU_138	HU_149	HU_152	HU_175	HU_178	HU_185	HU_204	HU_208
+HMDB03193	76043	412165	44943	27242	436566	173175	242549	57066	559869	3732	339188	471368	262271	127285	451270	212500	79673	NA	891129	43907
+HMDB01101	30689	6877586	52217	3158	10789748	229568	4763576	3878773	976436	831937	608298	1605075	72021	442510	1107705	1464339	31250	2724553	891129	32742
+HMDB01101	6877586	52217	3158	10789748	229568	4763576	3878773	976436	831937	608298	1605075	72021	442510	1107705	1464339	31250	2724553	72900	891129	30689
+HMDB10348	47259	544877	60885	34582	529874	168264	176500	76457	610110	16262	279156	524468	451573	591487	433529	161069	214392	13781	891129	39315
+HMDB59717	357351	1030464	301983	67604	306862	1028110	1530493	270027	1378535	289677	808334	1132813	871209	895435	715190	1563158	784738	146195	891129	239030
+HMDB00822	14627	14627	14627	14627	14627	14627	14627	14627	14627	14627	14627	14627	14627	14627	14627	14627	14627	14627	14627	14627
+HMDB13189	2644620	727587	1661412	619181	136278	2755434	593863	837865	3526136	2003278	1608814	3446611	1941527	113937	3132404	2893445	2092753	1034666	891129	841661
+HMDB00299	250551	1046138	456162	159386	1013302	808657	614370	250403	768004	242085	504108	1014041	1362408	1057660	1110050	566050	411886	142233	891129	284775
+HMDB00191	560002	771533	575790	392284	888498	785428	645785	591569	960658	910201	639437	1092885	1409045	2292023	1246459	1945577	710519	773384	891129	622898
+HMDB00518	-34236	58249	85944	NA	342102	129886	175800	13154	230242	NA	440223	315368	10657	419508	48673	28361	514579	23108	891129	73831
+HMDB00715	1252089	2547452	905408	371059	4983588	5140022	2658555	814523	2558923	859466	4184204	3865723	3236644	2615560	3820724	3577833	2295288	625924	891129	1341900
+HMDB01032	2569205	26023086	1604999	430453	8103558	26222916	257139	675754	59906109	263055	31151730	18648127	14989438	1554658	20249262	5588731	871010	15920	891129	44276
+HMDB00208	747080	13420742	595872	1172376	7172632	3143654	4059767	1433702	5593888	5402629	2477288	3346077	4230072	7621236	8960828	10335722	7037373	1574738	891129	2540044
+HMDB04824	374028	1144386	539206	178517	1046190	959381	605191	310260	1253319	477259	477995	825691	1157093	1089284	1411802	1020206	782673	346761	891129	387811
+HMDB00512	NA	319783	280560	85009	1333877	556003	590779	209285	342532	198512	569970	525240	246282	1140422	542345	1171008	827723	222953	891129	85554
+HMDB00251	368600	616555	94936	622468	180988	293988	352855	767894	268331	167246	310918	1248919	577184	10985	335711	403815	80614	63393	891129	616061

tests/testthat/in_cent_sm.tsv

@@ -0,0 +1,21 @@
+sampleMetadata	injectionOrder	mode	age	bmi	gender
+HU_017	2	pos	41	23.03	M
+HU_028	7	pos	41	23.92	F
+HU_034	9	pos	52	23.37	M
+HU_051	20	pos	24	23.23	F
+HU_060	24	pos	55	28.72	F
+HU_078	34	pos	46	25.18	M
+HU_091	42	pos	61	26.12	M
+HU_093	43	pos	53	21.71	M
+HU_099	46	pos	23	21.3	M
+HU_110	53	pos	50	20.9	F
+HU_130	63	pos	33	26.06	M
+HU_134	67	pos	48	22.89	M
+HU_138	68	pos	42	21.88	M
+HU_149	72	pos	35	19.49	F
+HU_152	75	pos	26	17.58	F
+HU_175	87	pos	35	21.26	F
+HU_178	88	pos	60	32.87	F
+HU_185	95	pos	42	21.09	F
+HU_204	104	pos	31	29.06	M
+HU_208	106	pos	27	18.61	F

tests/testthat/in_cent_vm.tsv

@@ -0,0 +1,17 @@
+variable	name	mz	rt
+HMDB03193	foo	100	200
+HMDB01101	bar	150	300
+HMDB01101	baz	200	225
+HMDB00208	loquimini	873	476
+HMDB10348	batur	300	275
+HMDB00299	bantur	700	500
+HMDB00191	loquor	650	600
+HMDB00518	loquoris	870	250
+HMDB59717	bamur	125	400
+HMDB00822	bamini	300	199
+HMDB13189	baris	800	325
+HMDB00715	loquitur	725	900
+HMDB01032	loquimur	550	425
+HMDB04824	loquantur	950	522
+HMDB00512	pantur	900	543
+HMDB00251	pasamur	500	423