Shoulder transcriptome analysis: comparing healed vs unhealed tendon

Import count data and sample sheet

Patients AD-CAB 3001-AD-CAB 3026.

They have both “tear” (tear edge) and “tendon” (tendon body) samples from intact and torn tissue.

x <- readRDS("counts.rds")

ss <- read.table("samplesheets/pheno_data.tsv",header=TRUE)

ss <- ss[grep("AD-CAB_3",ss$Participant_ID),]

ss <- ss[which(ss$Tendon_integrity_2_years_post.op != "NA"),]
ss <- ss[ss$Tissue != "capsule",]

head(ss,3)

##    Participant_ID Case Redcap_ID Tissue                        fastq pvt.pub
## 93    AD-CAB_3001   RC        53 tendon 1-2_S67_L001_R1_001.fastq.gz    <NA>
## 94    AD-CAB_3002   RC        54 tendon 2-2_S68_L001_R1_001.fastq.gz    <NA>
## 95    AD-CAB_3003   RC        55 tendon 3-2_S69_L001_R1_001.fastq.gz    <NA>
##    Age Sex Primary_OA Cuff_Arthropathy Prosthesis Affected_Side Height_cm
## 93  67   M       <NA>             <NA>       <NA>          Left     177.8
## 94  58   M       <NA>             <NA>       <NA>         Right     179.0
## 95  56   M       <NA>             <NA>       <NA>          Left     179.0
##    Weight_kg   BMI ASA      Smoking Diabetes Hypercholesterolaemia Hypertension
## 93       120 37.90   2  Ever_smoker       No                   Yes          Yes
## 94        79 25.00   1 Never_smoker       No                    No           No
## 95        85 26.52   1 Never_smoker       No                   Yes         <NA>
##    Thyroid CRP Creat eGFR Urea Fast_Glucose hba1c Metabolic_Syndrome
## 93      No 2.9    83   84  7.0          5.2   5.6                Yes
## 94      No 0.7    70   90  6.9          5.4   5.3                 No
## 95      No 2.9    72   90  4.3          5.2   5.4                 No
##    Tendon_integrity_2_years_post.op ASES_2_years_post.op_Pain
## 93                             Torn                        NA
## 94                             Torn                        NA
## 95                           Intact                        50
##    ASES_2_years_post.op_ADL ASES_2_years_post.op_Total Oxford_Baseline
## 93                       NA                         NA              25
## 94                       NA                         NA              33
## 95                       50                        100              38
##    Oxford_3_month Oxford_12_month Oxford_24_month QuickDASH_ QuickDASH_3_month
## 93             41              46              NA       38.6              13.6
## 94             NA              48              NA       29.5                NA
## 95             38              44              48       29.5              27.3
##    QuickDASH_12_month QuickDASH_24_month EQ.5D.3L_Baseline EQ.5D.3L_3_month
## 93               13.6                 NA             11222            11123
## 94                0.0                 NA             11111               NA
## 95                9.1                  0             11221            11121
##    EQ.5D.3L_12_month EQ.5D.3L_24_month EQVAS_Baseline EQVAS_3_month
## 93             21222                NA             90            50
## 94             11111                NA             90            NA
## 95             11111             22222             80            70
##    EQVAS_12_month EQVAS_24_month
## 93             60             NA
## 94             90             NA
## 95             80             60

table(ss$Tissue)

## 
##   tear tendon 
##     21     21

table(ss$Tendon_integrity_2_years_post.op)

## 
## Intact   Torn 
##     28     14

table(paste(ss$Tissue ,ss$Tendon_integrity_2_years_post.op))

## 
##   tear Intact     tear Torn tendon Intact   tendon Torn 
##            14             7            14             7

paste(ss$Participant_ID,ss$Tissue ,ss$Tendon_integrity_2_years_post.op)

##  [1] "AD-CAB_3001 tendon Torn"   "AD-CAB_3002 tendon Torn"  
##  [3] "AD-CAB_3003 tendon Intact" "AD-CAB_3004 tendon Intact"
##  [5] "AD-CAB_3005 tendon Torn"   "AD-CAB_3006 tendon Torn"  
##  [7] "AD-CAB_3007 tendon Intact" "AD-CAB_3008 tendon Intact"
##  [9] "AD-CAB_3009 tendon Intact" "AD-CAB_3010 tendon Intact"
## [11] "AD-CAB_3011 tendon Intact" "AD-CAB_3014 tendon Intact"
## [13] "AD-CAB_3015 tendon Intact" "AD-CAB_3016 tendon Torn"  
## [15] "AD-CAB_3017 tendon Torn"   "AD-CAB_3018 tendon Intact"
## [17] "AD-CAB_3019 tendon Intact" "AD-CAB_3020 tendon Intact"
## [19] "AD-CAB_3021 tendon Torn"   "AD-CAB_3023 tendon Intact"
## [21] "AD-CAB_3025 tendon Intact" "AD-CAB_3001 tear Torn"    
## [23] "AD-CAB_3002 tear Torn"     "AD-CAB_3003 tear Intact"  
## [25] "AD-CAB_3004 tear Intact"   "AD-CAB_3005 tear Torn"    
## [27] "AD-CAB_3006 tear Torn"     "AD-CAB_3007 tear Intact"  
## [29] "AD-CAB_3008 tear Intact"   "AD-CAB_3009 tear Intact"  
## [31] "AD-CAB_3010 tear Intact"   "AD-CAB_3011 tear Intact"  
## [33] "AD-CAB_3014 tear Intact"   "AD-CAB_3015 tear Intact"  
## [35] "AD-CAB_3016 tear Torn"     "AD-CAB_3017 tear Torn"    
## [37] "AD-CAB_3018 tear Intact"   "AD-CAB_3019 tear Intact"  
## [39] "AD-CAB_3020 tear Intact"   "AD-CAB_3021 tear Torn"    
## [41] "AD-CAB_3023 tear Intact"   "AD-CAB_3025 tear Intact"

sapply(strsplit(gsub("-",".",gsub("_L001","|",gsub("_R1","|",ss$fastq))),"\\|"),"[[",1) %in% colnames(x)

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

ss$data <- sapply(strsplit(gsub("-",".",gsub("_L001","|",gsub("_R1","|",ss$fastq))),"\\|"),"[[",1)

rownames(ss)<-ss$data

Now subset the data for the different comparisons.

Tendon v tear (intact) versus Tendon v tear (torn).

Comparisons:

Tendon v tear (intact): DE1.
Tendon v tear (torn): DE2.

Then run a mitch analysis to see the differences.

The other approach is to find the direct differences between intact and torn samples.

tendon (intact) vs tendon (torn).
tear (intact) vs tear (torn).

#de1
ss1 <- ss[ss$Tendon_integrity_2_years_post.op=="Intact",]
x1 <- x[,which(colnames(x) %in% ss1$data)]
ss1 %>% kbl(caption = "Intact tendon group") %>% kable_paper("hover", full_width = F)

Intact tendon group
	Participant_ID	Case	Redcap_ID	Tissue	fastq	pvt.pub	Age	Sex	Primary_OA	Cuff_Arthropathy	Prosthesis	Affected_Side	Height_cm	Weight_kg	BMI	ASA	Smoking	Diabetes	Hypercholesterolaemia	Hypertension	Thyroid	CRP	Creat	eGFR	Urea	Fast_Glucose	hba1c	Metabolic_Syndrome	Tendon_integrity_2_years_post.op	ASES_2_years_post.op_Pain	ASES_2_years_post.op_ADL	ASES_2_years_post.op_Total	Oxford_Baseline	Oxford_3_month	Oxford_12_month	Oxford_24_month	QuickDASH_	QuickDASH_3_month	QuickDASH_12_month	QuickDASH_24_month	EQ.5D.3L_Baseline	EQ.5D.3L_3_month	EQ.5D.3L_12_month	EQ.5D.3L_24_month	EQVAS_Baseline	EQVAS_3_month	EQVAS_12_month	EQVAS_24_month	data
3.2_S69	AD-CAB_3003	RC	55	tendon	3-2_S69_L001_R1_001.fastq.gz	NA	56	M	NA	NA	NA	Left	179.00	85	26.52	1	Never_smoker	No	Yes	NA	No	2.9	72	90	4.3	5.2	5.4	No	Intact	50	50	100	38	38	44	48	29.5	27.3	9.1	0.0	11221	11121	11111	22222	80	70	80	60	3.2_S69
4.2_S70	AD-CAB_3004	RC	56	tendon	4-2_S70_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Right	185.00	95	28.00	2	Ever_smoker	No	Yes	Yes	No	3.6	91	74	7.8	6.7	5.7	No	Intact	45	50	95	24	41	46	48	NA	18.2	13.6	0.0	11232	11121	11121	11111	NA	80	60	52	4.2_S70
7.2_S73	AD-CAB_3007	RC	59	tendon	7-2_S73_L001_R1_001.fastq.gz	NA	63	F	NA	NA	NA	Right	163.00	57	21.50	1	Ever_smoker	No	No	No	No	2.9	53	90	29.0	NA	5.3	No	Intact	50	50	100	37	38	46	48	36.4	18.2	11.4	2.3	11122	11111	11111	11111	93	97	100	100	7.2_S73
8.2_S74	AD-CAB_3008	RC	60	tendon	8-2_S74_L001_R1_001.fastq.gz	NA	70	M	NA	NA	NA	Left	167.00	71	25.50	2	Never_smoker	No	Yes	Yes	No	2.9	87	77	7.3	5.5	5.4	No	Intact	NA	NA	NA	24	25	47	NA	54.5	54.5	11.4	NA	22221	22221	11111	NA	90	90	92	NA	8.2_S74
9.2_S75	AD-CAB_3009	RC	61	tendon	9-2_S75_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Right	182.00	83	25.10	2	Ever_smoker	No	No	No	No	2.9	79	88	7.5	5.8	5.8	No	Intact	50	50	100	35	41	43	46	52.3	11.4	4.5	2.3	11211	11121	11111	11111	85	96	90	90	9.2_S75
10.2_S76	AD-CAB_3010	RC	62	tendon	10-2_S76_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Left	179.00	98	30.00	2	Never_smoker	No	No	No	Yes	2.9	82	90	5.6	4.9	5.1	No	Intact	45	17	62	21	45	43	44	45.5	4.5	11.4	20.5	11221	11111	11121	11221	70	70	75	85	10.2_S76
11.2_S77	AD-CAB_3011	RC	63	tendon	11-2_S77_L001_R1_001.fastq.gz	NA	50	M	NA	NA	NA	Right	180.00	82	25.00	1	Never_smoker	No	Yes	No	No	2.9	99	76	6.7	5.7	6.0	No	Intact	35	18	53	18	38	44	46	61.4	31.8	6.8	20.5	11221	11222	11111	11121	75	83	75	78	11.2_S77
14.2_S80	AD-CAB_3014	RC	66	tendon	14-2_S80_L001_R1_001.fastq.gz	NA	70	M	NA	NA	NA	Left	1.77	100	32.00	3	Ever_smoker	No	Yes	Yes	No	2.9	86	79	7.1	6.3	5.8	Yes	Intact	50	45	95	27	43	47	47	50.0	9.1	6.8	9.1	12221	11111	11111	11111	90	90	88	85	14.2_S80
15.2_S81	AD-CAB_3015	RC	69	tendon	15-2_S81_L001_R1_001.fastq.gz	NA	66	M	NA	NA	NA	Left	175.00	95	31.00	3	Never_smoker	No	Yes	Yes	No	2.9	53	90	4.1	5.5	5.2	Yes	Intact	50	47	97	25	NA	NA	48	40.9	NA	NA	13.6	11121	NA	NA	11121	78	NA	NA	93	15.2_S81
18.2_S84	AD-CAB_3018	RC	72	tendon	18-2_S84_L001_R1_001.fastq.gz	NA	72	M	NA	NA	NA	Right	170.00	85	29.00	2	Ever_smoker	No	Yes	Yes	No	3.2	82	82	6.4	5.5	5.5	No	Intact	50	50	100	36	37	48	48	31.8	13.6	9.1	0.0	11122	11112	11111	11111	90	82	92	90	18.2_S84
19.2_S85	AD-CAB_3019	RC	73	tendon	19-2_S85_L001_R1_001.fastq.gz	NA	45	F	NA	NA	NA	Right	163.00	98	36.90	2	Never_smoker	No	Yes	Yes	No	4.3	70	57	4.6	4.7	5.7	Yes	Intact	50	35	85	32	28	45	47	36.4	38.6	18.2	13.6	11221	11221	11121	11121	50	58	45	34	19.2_S85
20.2_S86	AD-CAB_3020	RC	67	tendon	20-2_S86_L001_R1_001.fastq.gz	NA	61	M	NA	NA	NA	Right	180.00	92	29.00	1	Ever_smoker	No	No	No	No	2.9	78	57	3.9	5.5	5.6	No	Intact	50	40	90	30	34	36	44	50.0	34.1	31.8	0.0	12321	12321	12211	12211	77	40	70	70	20.2_S86
23.2_S89	AD-CAB_3023	RC	75	tendon	23-2_S89_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Right	179.00	81	25.00	2	Ever_smoker	No	Yes	No	No	2.9	75	57	5.7	5.4	5.7	No	Intact	50	40	90	42	43	48	48	15.9	15.9	0.0	2.3	11211	11121	11111	11111	85	85	90	85	23.2_S89
25.2_S91	AD-CAB_3025	RC	77	tendon	25-2_S91_L001_R1_001.fastq.gz	NA	62	F	NA	NA	NA	Left	157.00	95	38.50	2	Never_smoker	No	Yes	Yes	No	12.0	72	57	6.6	6.2	5.7	Yes	Intact	45	43	88	26	36	38	41	59.1	29.5	31.8	18.2	12231	11221	11221	11121	82	80	60	80	25.2_S91
3.3_S95	AD-CAB_3003	RC	55	tear	3-3_S95_L001_R1_001.fastq.gz	NA	56	M	NA	NA	NA	Left	179.00	85	26.52	1	Never_smoker	No	Yes	NA	No	2.9	72	90	4.3	5.2	5.4	No	Intact	50	50	100	38	38	44	48	29.5	27.3	9.1	0.0	11221	11121	11111	22222	80	70	80	60	3.3_S95
4.3_S96	AD-CAB_3004	RC	56	tear	4-3_S96_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Right	185.00	95	28.00	2	Ever_smoker	No	Yes	Yes	No	3.6	91	74	7.8	6.7	5.7	No	Intact	45	50	95	24	41	46	48	NA	18.2	13.6	0.0	11232	11121	11121	11111	NA	80	60	52	4.3_S96
7.3_S99	AD-CAB_3007	RC	59	tear	7-3_S99_L001_R1_001.fastq.gz	NA	63	F	NA	NA	NA	Right	163.00	57	21.50	1	Ever_smoker	No	No	No	No	2.9	53	90	29.0	NA	5.3	No	Intact	50	50	100	37	38	46	48	36.4	18.2	11.4	2.3	11122	11111	11111	11111	93	97	100	100	7.3_S99
8.3_S100	AD-CAB_3008	RC	60	tear	8-3_S100_L001_R1_001.fastq.gz	NA	70	M	NA	NA	NA	Left	167.00	71	25.50	2	Never_smoker	No	Yes	Yes	No	2.9	87	77	7.3	5.5	5.4	No	Intact	NA	NA	NA	24	25	47	NA	54.5	54.5	11.4	NA	22221	22221	11111	NA	90	90	92	NA	8.3_S100
9.3_S101	AD-CAB_3009	RC	61	tear	9-3_S101_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Right	182.00	83	25.10	2	Ever_smoker	No	No	No	No	2.9	79	88	7.5	5.8	5.8	No	Intact	50	50	100	35	41	43	46	52.3	11.4	4.5	2.3	11211	11121	11111	11111	85	96	90	90	9.3_S101
10.3_S102	AD-CAB_3010	RC	62	tear	10-3_S102_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Left	179.00	98	30.00	2	Never_smoker	No	No	No	Yes	2.9	82	90	5.6	4.9	5.1	No	Intact	45	17	62	21	45	43	44	45.5	4.5	11.4	20.5	11221	11111	11121	11221	70	70	75	85	10.3_S102
11.3_S103	AD-CAB_3011	RC	63	tear	11-3_S103_L001_R1_001.fastq.gz	NA	50	M	NA	NA	NA	Right	180.00	82	25.00	1	Never_smoker	No	Yes	No	No	2.9	99	76	6.7	5.7	6.0	No	Intact	35	18	53	18	38	44	46	61.4	31.8	6.8	20.5	11221	11222	11111	11121	75	83	75	78	11.3_S103
14.3_S106	AD-CAB_3014	RC	66	tear	14-3_S106_L001_R1_001.fastq.gz	NA	70	M	NA	NA	NA	Left	1.77	100	32.00	3	Ever_smoker	No	Yes	Yes	No	2.9	86	79	7.1	6.3	5.8	Yes	Intact	50	45	95	27	43	47	47	50.0	9.1	6.8	9.1	12221	11111	11111	11111	90	90	88	85	14.3_S106
15.3_S107	AD-CAB_3015	RC	69	tear	15-3_S107_L001_R1_001.fastq.gz	NA	66	M	NA	NA	NA	Left	175.00	95	31.00	3	Never_smoker	No	Yes	Yes	No	2.9	53	90	4.1	5.5	5.2	Yes	Intact	50	47	97	25	NA	NA	48	40.9	NA	NA	13.6	11121	NA	NA	11121	78	NA	NA	93	15.3_S107
18.3_S110	AD-CAB_3018	RC	72	tear	18-3_S110_L001_R1_001.fastq.gz	NA	72	M	NA	NA	NA	Right	170.00	85	29.00	2	Ever_smoker	No	Yes	Yes	No	3.2	82	82	6.4	5.5	5.5	No	Intact	50	50	100	36	37	48	48	31.8	13.6	9.1	0.0	11122	11112	11111	11111	90	82	92	90	18.3_S110
19.3_S111	AD-CAB_3019	RC	73	tear	19-3_S111_L001_R1_001.fastq.gz	NA	45	F	NA	NA	NA	Right	163.00	98	36.90	2	Never_smoker	No	Yes	Yes	No	4.3	70	57	4.6	4.7	5.7	Yes	Intact	50	35	85	32	28	45	47	36.4	38.6	18.2	13.6	11221	11221	11121	11121	50	58	45	34	19.3_S111
20.3_S112	AD-CAB_3020	RC	67	tear	20-3_S112_L001_R1_001.fastq.gz	NA	61	M	NA	NA	NA	Right	180.00	92	29.00	1	Ever_smoker	No	No	No	No	2.9	78	57	3.9	5.5	5.6	No	Intact	50	40	90	30	34	36	44	50.0	34.1	31.8	0.0	12321	12321	12211	12211	77	40	70	70	20.3_S112
23.3_S115	AD-CAB_3023	RC	75	tear	23-3_S115_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Right	179.00	81	25.00	2	Ever_smoker	No	Yes	No	No	2.9	75	57	5.7	5.4	5.7	No	Intact	50	40	90	42	43	48	48	15.9	15.9	0.0	2.3	11211	11121	11111	11111	85	85	90	85	23.3_S115
25.3_S117	AD-CAB_3025	RC	77	tear	25-3_S117_L001_R1_001.fastq.gz	NA	62	F	NA	NA	NA	Left	157.00	95	38.50	2	Never_smoker	No	Yes	Yes	No	12.0	72	57	6.6	6.2	5.7	Yes	Intact	45	43	88	26	36	38	41	59.1	29.5	31.8	18.2	12231	11221	11221	11121	82	80	60	80	25.3_S117

#de2
ss2 <- ss[ss$Tendon_integrity_2_years_post.op=="Torn",]
x2 <- x[,which(colnames(x) %in% ss2$data)]
ss2 %>% kbl(caption = "Torn tendon group") %>% kable_paper("hover", full_width = F)

Torn tendon group
	Participant_ID	Case	Redcap_ID	Tissue	fastq	pvt.pub	Age	Sex	Primary_OA	Cuff_Arthropathy	Prosthesis	Affected_Side	Height_cm	Weight_kg	BMI	ASA	Smoking	Diabetes	Hypercholesterolaemia	Hypertension	Thyroid	CRP	Creat	eGFR	Urea	Fast_Glucose	hba1c	Metabolic_Syndrome	Tendon_integrity_2_years_post.op	ASES_2_years_post.op_Pain	ASES_2_years_post.op_ADL	ASES_2_years_post.op_Total	Oxford_Baseline	Oxford_3_month	Oxford_12_month	Oxford_24_month	QuickDASH_	QuickDASH_3_month	QuickDASH_12_month	QuickDASH_24_month	EQ.5D.3L_Baseline	EQ.5D.3L_3_month	EQ.5D.3L_12_month	EQ.5D.3L_24_month	EQVAS_Baseline	EQVAS_3_month	EQVAS_12_month	EQVAS_24_month	data
1.2_S67	AD-CAB_3001	RC	53	tendon	1-2_S67_L001_R1_001.fastq.gz	NA	67	M	NA	NA	NA	Left	177.8	120	37.9	2	Ever_smoker	No	Yes	Yes	No	2.9	83	84	7.0	5.2	5.6	Yes	Torn	NA	NA	NA	25	41	46	NA	38.6	13.6	13.6	NA	11222	11123	21222	NA	90	50	60	NA	1.2_S67
2.2_S68	AD-CAB_3002	RC	54	tendon	2-2_S68_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Right	179.0	79	25.0	1	Never_smoker	No	No	No	No	0.7	70	90	6.9	5.4	5.3	No	Torn	NA	NA	NA	33	NA	48	NA	29.5	NA	0.0	NA	11111	NA	11111	NA	90	NA	90	NA	2.2_S68
5.2_S71	AD-CAB_3005	RC	57	tendon	5-2_S71_L001_R1_001.fastq.gz	NA	59	F	NA	NA	NA	Right	NA	NA	NA	1	Never_smoker	No	No	No	No	3.8	51	90	6.2	NA	5.2	NA	Torn	50	50	100	38	45	NA	48	25.0	4.5	NA	0.0	11121	11111	NA	11111	92	94	95	NA	5.2_S71
6.2_S72	AD-CAB_3006	RC	58	tendon	6-2_S72_L001_R1_001.fastq.gz	NA	65	M	NA	NA	NA	Left	172.0	78	26.0	1	Never_smoker	No	No	No	No	2.9	77	90	5.6	4.8	5.4	No	Torn	NA	NA	NA	35	31	37	NA	34.1	36.4	27.3	NA	12221	11221	11111	NA	82	40	80	NA	6.2_S72
16.2_S82	AD-CAB_3016	RC	71	tendon	16-2_S82_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Left	180.0	88	27.0	1	Ever_smoker	Yes	No	Yes	No	2.9	73	90	4.7	7.3	8.3	No	Torn	40	42	82	24	27	45	43	43.2	38.6	6.8	11.4	11122	11222	11112	11121	70	60	50	80	16.2_S82
17.2_S83	AD-CAB_3017	RC	70	tendon	17-2_S83_L001_R1_001.fastq.gz	NA	60	M	NA	NA	NA	Left	166.0	80	29.0	2	Ever_smoker	No	No	No	No	2.9	119	57	7.2	NA	5.7	No	Torn	NA	NA	NA	13	40	NA	NA	93.2	38.6	NA	NA	12221	21211	NA	NA	70	60	NA	NA	17.2_S83
21.2_S87	AD-CAB_3021	RC	68	tendon	21-2_S87_L001_R1_001.fastq.gz	NA	46	M	NA	NA	NA	Right	180.0	88	27.2	2	Never_smoker	No	Yes	No	No	2.9	77	57	5.9	5.9	5.3	No	Torn	NA	NA	NA	29	44	43	NA	45.5	20.5	11.4	NA	NA	11111	11121	NA	NA	80	65	NA	21.2_S87
1.3_S93	AD-CAB_3001	RC	53	tear	1-3_S93_L001_R1_001.fastq.gz	NA	67	M	NA	NA	NA	Left	177.8	120	37.9	2	Ever_smoker	No	Yes	Yes	No	2.9	83	84	7.0	5.2	5.6	Yes	Torn	NA	NA	NA	25	41	46	NA	38.6	13.6	13.6	NA	11222	11123	21222	NA	90	50	60	NA	1.3_S93
2.3_S94	AD-CAB_3002	RC	54	tear	2-3_S94_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Right	179.0	79	25.0	1	Never_smoker	No	No	No	No	0.7	70	90	6.9	5.4	5.3	No	Torn	NA	NA	NA	33	NA	48	NA	29.5	NA	0.0	NA	11111	NA	11111	NA	90	NA	90	NA	2.3_S94
5.3_S97	AD-CAB_3005	RC	57	tear	5-3_S97_L001_R1_001.fastq.gz	NA	59	F	NA	NA	NA	Right	NA	NA	NA	1	Never_smoker	No	No	No	No	3.8	51	90	6.2	NA	5.2	NA	Torn	50	50	100	38	45	NA	48	25.0	4.5	NA	0.0	11121	11111	NA	11111	92	94	95	NA	5.3_S97
6.3_S98	AD-CAB_3006	RC	58	tear	6-3_S98_L001_R1_001.fastq.gz	NA	65	M	NA	NA	NA	Left	172.0	78	26.0	1	Never_smoker	No	No	No	No	2.9	77	90	5.6	4.8	5.4	No	Torn	NA	NA	NA	35	31	37	NA	34.1	36.4	27.3	NA	12221	11221	11111	NA	82	40	80	NA	6.3_S98
16.3_S108	AD-CAB_3016	RC	71	tear	16-3_S108_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Left	180.0	88	27.0	1	Ever_smoker	Yes	No	Yes	No	2.9	73	90	4.7	7.3	8.3	No	Torn	40	42	82	24	27	45	43	43.2	38.6	6.8	11.4	11122	11222	11112	11121	70	60	50	80	16.3_S108
17.3_S109	AD-CAB_3017	RC	70	tear	17-3_S109_L001_R1_001.fastq.gz	NA	60	M	NA	NA	NA	Left	166.0	80	29.0	2	Ever_smoker	No	No	No	No	2.9	119	57	7.2	NA	5.7	No	Torn	NA	NA	NA	13	40	NA	NA	93.2	38.6	NA	NA	12221	21211	NA	NA	70	60	NA	NA	17.3_S109
21.3_S113	AD-CAB_3021	RC	68	tear	21-3_S113_L001_R1_001.fastq.gz	NA	46	M	NA	NA	NA	Right	180.0	88	27.2	2	Never_smoker	No	Yes	No	No	2.9	77	57	5.9	5.9	5.3	No	Torn	NA	NA	NA	29	44	43	NA	45.5	20.5	11.4	NA	NA	11111	11121	NA	NA	80	65	NA	21.3_S113

#de3
ss3 <- ss[ss$Tissue=="tendon",]
x3 <- x[,which(colnames(x) %in% ss3$data)]
ss3 %>% kbl(caption = "Tendon body samples") %>% kable_paper("hover", full_width = F)

Tendon body samples
	Participant_ID	Case	Redcap_ID	Tissue	fastq	pvt.pub	Age	Sex	Primary_OA	Cuff_Arthropathy	Prosthesis	Affected_Side	Height_cm	Weight_kg	BMI	ASA	Smoking	Diabetes	Hypercholesterolaemia	Hypertension	Thyroid	CRP	Creat	eGFR	Urea	Fast_Glucose	hba1c	Metabolic_Syndrome	Tendon_integrity_2_years_post.op	ASES_2_years_post.op_Pain	ASES_2_years_post.op_ADL	ASES_2_years_post.op_Total	Oxford_Baseline	Oxford_3_month	Oxford_12_month	Oxford_24_month	QuickDASH_	QuickDASH_3_month	QuickDASH_12_month	QuickDASH_24_month	EQ.5D.3L_Baseline	EQ.5D.3L_3_month	EQ.5D.3L_12_month	EQ.5D.3L_24_month	EQVAS_Baseline	EQVAS_3_month	EQVAS_12_month	EQVAS_24_month	data
1.2_S67	AD-CAB_3001	RC	53	tendon	1-2_S67_L001_R1_001.fastq.gz	NA	67	M	NA	NA	NA	Left	177.80	120	37.90	2	Ever_smoker	No	Yes	Yes	No	2.9	83	84	7.0	5.2	5.6	Yes	Torn	NA	NA	NA	25	41	46	NA	38.6	13.6	13.6	NA	11222	11123	21222	NA	90	50	60	NA	1.2_S67
2.2_S68	AD-CAB_3002	RC	54	tendon	2-2_S68_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Right	179.00	79	25.00	1	Never_smoker	No	No	No	No	0.7	70	90	6.9	5.4	5.3	No	Torn	NA	NA	NA	33	NA	48	NA	29.5	NA	0.0	NA	11111	NA	11111	NA	90	NA	90	NA	2.2_S68
3.2_S69	AD-CAB_3003	RC	55	tendon	3-2_S69_L001_R1_001.fastq.gz	NA	56	M	NA	NA	NA	Left	179.00	85	26.52	1	Never_smoker	No	Yes	NA	No	2.9	72	90	4.3	5.2	5.4	No	Intact	50	50	100	38	38	44	48	29.5	27.3	9.1	0.0	11221	11121	11111	22222	80	70	80	60	3.2_S69
4.2_S70	AD-CAB_3004	RC	56	tendon	4-2_S70_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Right	185.00	95	28.00	2	Ever_smoker	No	Yes	Yes	No	3.6	91	74	7.8	6.7	5.7	No	Intact	45	50	95	24	41	46	48	NA	18.2	13.6	0.0	11232	11121	11121	11111	NA	80	60	52	4.2_S70
5.2_S71	AD-CAB_3005	RC	57	tendon	5-2_S71_L001_R1_001.fastq.gz	NA	59	F	NA	NA	NA	Right	NA	NA	NA	1	Never_smoker	No	No	No	No	3.8	51	90	6.2	NA	5.2	NA	Torn	50	50	100	38	45	NA	48	25.0	4.5	NA	0.0	11121	11111	NA	11111	92	94	95	NA	5.2_S71
6.2_S72	AD-CAB_3006	RC	58	tendon	6-2_S72_L001_R1_001.fastq.gz	NA	65	M	NA	NA	NA	Left	172.00	78	26.00	1	Never_smoker	No	No	No	No	2.9	77	90	5.6	4.8	5.4	No	Torn	NA	NA	NA	35	31	37	NA	34.1	36.4	27.3	NA	12221	11221	11111	NA	82	40	80	NA	6.2_S72
7.2_S73	AD-CAB_3007	RC	59	tendon	7-2_S73_L001_R1_001.fastq.gz	NA	63	F	NA	NA	NA	Right	163.00	57	21.50	1	Ever_smoker	No	No	No	No	2.9	53	90	29.0	NA	5.3	No	Intact	50	50	100	37	38	46	48	36.4	18.2	11.4	2.3	11122	11111	11111	11111	93	97	100	100	7.2_S73
8.2_S74	AD-CAB_3008	RC	60	tendon	8-2_S74_L001_R1_001.fastq.gz	NA	70	M	NA	NA	NA	Left	167.00	71	25.50	2	Never_smoker	No	Yes	Yes	No	2.9	87	77	7.3	5.5	5.4	No	Intact	NA	NA	NA	24	25	47	NA	54.5	54.5	11.4	NA	22221	22221	11111	NA	90	90	92	NA	8.2_S74
9.2_S75	AD-CAB_3009	RC	61	tendon	9-2_S75_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Right	182.00	83	25.10	2	Ever_smoker	No	No	No	No	2.9	79	88	7.5	5.8	5.8	No	Intact	50	50	100	35	41	43	46	52.3	11.4	4.5	2.3	11211	11121	11111	11111	85	96	90	90	9.2_S75
10.2_S76	AD-CAB_3010	RC	62	tendon	10-2_S76_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Left	179.00	98	30.00	2	Never_smoker	No	No	No	Yes	2.9	82	90	5.6	4.9	5.1	No	Intact	45	17	62	21	45	43	44	45.5	4.5	11.4	20.5	11221	11111	11121	11221	70	70	75	85	10.2_S76
11.2_S77	AD-CAB_3011	RC	63	tendon	11-2_S77_L001_R1_001.fastq.gz	NA	50	M	NA	NA	NA	Right	180.00	82	25.00	1	Never_smoker	No	Yes	No	No	2.9	99	76	6.7	5.7	6.0	No	Intact	35	18	53	18	38	44	46	61.4	31.8	6.8	20.5	11221	11222	11111	11121	75	83	75	78	11.2_S77
14.2_S80	AD-CAB_3014	RC	66	tendon	14-2_S80_L001_R1_001.fastq.gz	NA	70	M	NA	NA	NA	Left	1.77	100	32.00	3	Ever_smoker	No	Yes	Yes	No	2.9	86	79	7.1	6.3	5.8	Yes	Intact	50	45	95	27	43	47	47	50.0	9.1	6.8	9.1	12221	11111	11111	11111	90	90	88	85	14.2_S80
15.2_S81	AD-CAB_3015	RC	69	tendon	15-2_S81_L001_R1_001.fastq.gz	NA	66	M	NA	NA	NA	Left	175.00	95	31.00	3	Never_smoker	No	Yes	Yes	No	2.9	53	90	4.1	5.5	5.2	Yes	Intact	50	47	97	25	NA	NA	48	40.9	NA	NA	13.6	11121	NA	NA	11121	78	NA	NA	93	15.2_S81
16.2_S82	AD-CAB_3016	RC	71	tendon	16-2_S82_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Left	180.00	88	27.00	1	Ever_smoker	Yes	No	Yes	No	2.9	73	90	4.7	7.3	8.3	No	Torn	40	42	82	24	27	45	43	43.2	38.6	6.8	11.4	11122	11222	11112	11121	70	60	50	80	16.2_S82
17.2_S83	AD-CAB_3017	RC	70	tendon	17-2_S83_L001_R1_001.fastq.gz	NA	60	M	NA	NA	NA	Left	166.00	80	29.00	2	Ever_smoker	No	No	No	No	2.9	119	57	7.2	NA	5.7	No	Torn	NA	NA	NA	13	40	NA	NA	93.2	38.6	NA	NA	12221	21211	NA	NA	70	60	NA	NA	17.2_S83
18.2_S84	AD-CAB_3018	RC	72	tendon	18-2_S84_L001_R1_001.fastq.gz	NA	72	M	NA	NA	NA	Right	170.00	85	29.00	2	Ever_smoker	No	Yes	Yes	No	3.2	82	82	6.4	5.5	5.5	No	Intact	50	50	100	36	37	48	48	31.8	13.6	9.1	0.0	11122	11112	11111	11111	90	82	92	90	18.2_S84
19.2_S85	AD-CAB_3019	RC	73	tendon	19-2_S85_L001_R1_001.fastq.gz	NA	45	F	NA	NA	NA	Right	163.00	98	36.90	2	Never_smoker	No	Yes	Yes	No	4.3	70	57	4.6	4.7	5.7	Yes	Intact	50	35	85	32	28	45	47	36.4	38.6	18.2	13.6	11221	11221	11121	11121	50	58	45	34	19.2_S85
20.2_S86	AD-CAB_3020	RC	67	tendon	20-2_S86_L001_R1_001.fastq.gz	NA	61	M	NA	NA	NA	Right	180.00	92	29.00	1	Ever_smoker	No	No	No	No	2.9	78	57	3.9	5.5	5.6	No	Intact	50	40	90	30	34	36	44	50.0	34.1	31.8	0.0	12321	12321	12211	12211	77	40	70	70	20.2_S86
21.2_S87	AD-CAB_3021	RC	68	tendon	21-2_S87_L001_R1_001.fastq.gz	NA	46	M	NA	NA	NA	Right	180.00	88	27.20	2	Never_smoker	No	Yes	No	No	2.9	77	57	5.9	5.9	5.3	No	Torn	NA	NA	NA	29	44	43	NA	45.5	20.5	11.4	NA	NA	11111	11121	NA	NA	80	65	NA	21.2_S87
23.2_S89	AD-CAB_3023	RC	75	tendon	23-2_S89_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Right	179.00	81	25.00	2	Ever_smoker	No	Yes	No	No	2.9	75	57	5.7	5.4	5.7	No	Intact	50	40	90	42	43	48	48	15.9	15.9	0.0	2.3	11211	11121	11111	11111	85	85	90	85	23.2_S89
25.2_S91	AD-CAB_3025	RC	77	tendon	25-2_S91_L001_R1_001.fastq.gz	NA	62	F	NA	NA	NA	Left	157.00	95	38.50	2	Never_smoker	No	Yes	Yes	No	12.0	72	57	6.6	6.2	5.7	Yes	Intact	45	43	88	26	36	38	41	59.1	29.5	31.8	18.2	12231	11221	11221	11121	82	80	60	80	25.2_S91

#de4
ss4 <- ss[ss$Tissue=="tear",]
x4 <- x[,which(colnames(x) %in% ss4$data)]
ss4 %>% kbl(caption = "Tear edge samples") %>% kable_paper("hover", full_width = F)

Tear edge samples
	Participant_ID	Case	Redcap_ID	Tissue	fastq	pvt.pub	Age	Sex	Primary_OA	Cuff_Arthropathy	Prosthesis	Affected_Side	Height_cm	Weight_kg	BMI	ASA	Smoking	Diabetes	Hypercholesterolaemia	Hypertension	Thyroid	CRP	Creat	eGFR	Urea	Fast_Glucose	hba1c	Metabolic_Syndrome	Tendon_integrity_2_years_post.op	ASES_2_years_post.op_Pain	ASES_2_years_post.op_ADL	ASES_2_years_post.op_Total	Oxford_Baseline	Oxford_3_month	Oxford_12_month	Oxford_24_month	QuickDASH_	QuickDASH_3_month	QuickDASH_12_month	QuickDASH_24_month	EQ.5D.3L_Baseline	EQ.5D.3L_3_month	EQ.5D.3L_12_month	EQ.5D.3L_24_month	EQVAS_Baseline	EQVAS_3_month	EQVAS_12_month	EQVAS_24_month	data
1.3_S93	AD-CAB_3001	RC	53	tear	1-3_S93_L001_R1_001.fastq.gz	NA	67	M	NA	NA	NA	Left	177.80	120	37.90	2	Ever_smoker	No	Yes	Yes	No	2.9	83	84	7.0	5.2	5.6	Yes	Torn	NA	NA	NA	25	41	46	NA	38.6	13.6	13.6	NA	11222	11123	21222	NA	90	50	60	NA	1.3_S93
2.3_S94	AD-CAB_3002	RC	54	tear	2-3_S94_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Right	179.00	79	25.00	1	Never_smoker	No	No	No	No	0.7	70	90	6.9	5.4	5.3	No	Torn	NA	NA	NA	33	NA	48	NA	29.5	NA	0.0	NA	11111	NA	11111	NA	90	NA	90	NA	2.3_S94
3.3_S95	AD-CAB_3003	RC	55	tear	3-3_S95_L001_R1_001.fastq.gz	NA	56	M	NA	NA	NA	Left	179.00	85	26.52	1	Never_smoker	No	Yes	NA	No	2.9	72	90	4.3	5.2	5.4	No	Intact	50	50	100	38	38	44	48	29.5	27.3	9.1	0.0	11221	11121	11111	22222	80	70	80	60	3.3_S95
4.3_S96	AD-CAB_3004	RC	56	tear	4-3_S96_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Right	185.00	95	28.00	2	Ever_smoker	No	Yes	Yes	No	3.6	91	74	7.8	6.7	5.7	No	Intact	45	50	95	24	41	46	48	NA	18.2	13.6	0.0	11232	11121	11121	11111	NA	80	60	52	4.3_S96
5.3_S97	AD-CAB_3005	RC	57	tear	5-3_S97_L001_R1_001.fastq.gz	NA	59	F	NA	NA	NA	Right	NA	NA	NA	1	Never_smoker	No	No	No	No	3.8	51	90	6.2	NA	5.2	NA	Torn	50	50	100	38	45	NA	48	25.0	4.5	NA	0.0	11121	11111	NA	11111	92	94	95	NA	5.3_S97
6.3_S98	AD-CAB_3006	RC	58	tear	6-3_S98_L001_R1_001.fastq.gz	NA	65	M	NA	NA	NA	Left	172.00	78	26.00	1	Never_smoker	No	No	No	No	2.9	77	90	5.6	4.8	5.4	No	Torn	NA	NA	NA	35	31	37	NA	34.1	36.4	27.3	NA	12221	11221	11111	NA	82	40	80	NA	6.3_S98
7.3_S99	AD-CAB_3007	RC	59	tear	7-3_S99_L001_R1_001.fastq.gz	NA	63	F	NA	NA	NA	Right	163.00	57	21.50	1	Ever_smoker	No	No	No	No	2.9	53	90	29.0	NA	5.3	No	Intact	50	50	100	37	38	46	48	36.4	18.2	11.4	2.3	11122	11111	11111	11111	93	97	100	100	7.3_S99
8.3_S100	AD-CAB_3008	RC	60	tear	8-3_S100_L001_R1_001.fastq.gz	NA	70	M	NA	NA	NA	Left	167.00	71	25.50	2	Never_smoker	No	Yes	Yes	No	2.9	87	77	7.3	5.5	5.4	No	Intact	NA	NA	NA	24	25	47	NA	54.5	54.5	11.4	NA	22221	22221	11111	NA	90	90	92	NA	8.3_S100
9.3_S101	AD-CAB_3009	RC	61	tear	9-3_S101_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Right	182.00	83	25.10	2	Ever_smoker	No	No	No	No	2.9	79	88	7.5	5.8	5.8	No	Intact	50	50	100	35	41	43	46	52.3	11.4	4.5	2.3	11211	11121	11111	11111	85	96	90	90	9.3_S101
10.3_S102	AD-CAB_3010	RC	62	tear	10-3_S102_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Left	179.00	98	30.00	2	Never_smoker	No	No	No	Yes	2.9	82	90	5.6	4.9	5.1	No	Intact	45	17	62	21	45	43	44	45.5	4.5	11.4	20.5	11221	11111	11121	11221	70	70	75	85	10.3_S102
11.3_S103	AD-CAB_3011	RC	63	tear	11-3_S103_L001_R1_001.fastq.gz	NA	50	M	NA	NA	NA	Right	180.00	82	25.00	1	Never_smoker	No	Yes	No	No	2.9	99	76	6.7	5.7	6.0	No	Intact	35	18	53	18	38	44	46	61.4	31.8	6.8	20.5	11221	11222	11111	11121	75	83	75	78	11.3_S103
14.3_S106	AD-CAB_3014	RC	66	tear	14-3_S106_L001_R1_001.fastq.gz	NA	70	M	NA	NA	NA	Left	1.77	100	32.00	3	Ever_smoker	No	Yes	Yes	No	2.9	86	79	7.1	6.3	5.8	Yes	Intact	50	45	95	27	43	47	47	50.0	9.1	6.8	9.1	12221	11111	11111	11111	90	90	88	85	14.3_S106
15.3_S107	AD-CAB_3015	RC	69	tear	15-3_S107_L001_R1_001.fastq.gz	NA	66	M	NA	NA	NA	Left	175.00	95	31.00	3	Never_smoker	No	Yes	Yes	No	2.9	53	90	4.1	5.5	5.2	Yes	Intact	50	47	97	25	NA	NA	48	40.9	NA	NA	13.6	11121	NA	NA	11121	78	NA	NA	93	15.3_S107
16.3_S108	AD-CAB_3016	RC	71	tear	16-3_S108_L001_R1_001.fastq.gz	NA	68	M	NA	NA	NA	Left	180.00	88	27.00	1	Ever_smoker	Yes	No	Yes	No	2.9	73	90	4.7	7.3	8.3	No	Torn	40	42	82	24	27	45	43	43.2	38.6	6.8	11.4	11122	11222	11112	11121	70	60	50	80	16.3_S108
17.3_S109	AD-CAB_3017	RC	70	tear	17-3_S109_L001_R1_001.fastq.gz	NA	60	M	NA	NA	NA	Left	166.00	80	29.00	2	Ever_smoker	No	No	No	No	2.9	119	57	7.2	NA	5.7	No	Torn	NA	NA	NA	13	40	NA	NA	93.2	38.6	NA	NA	12221	21211	NA	NA	70	60	NA	NA	17.3_S109
18.3_S110	AD-CAB_3018	RC	72	tear	18-3_S110_L001_R1_001.fastq.gz	NA	72	M	NA	NA	NA	Right	170.00	85	29.00	2	Ever_smoker	No	Yes	Yes	No	3.2	82	82	6.4	5.5	5.5	No	Intact	50	50	100	36	37	48	48	31.8	13.6	9.1	0.0	11122	11112	11111	11111	90	82	92	90	18.3_S110
19.3_S111	AD-CAB_3019	RC	73	tear	19-3_S111_L001_R1_001.fastq.gz	NA	45	F	NA	NA	NA	Right	163.00	98	36.90	2	Never_smoker	No	Yes	Yes	No	4.3	70	57	4.6	4.7	5.7	Yes	Intact	50	35	85	32	28	45	47	36.4	38.6	18.2	13.6	11221	11221	11121	11121	50	58	45	34	19.3_S111
20.3_S112	AD-CAB_3020	RC	67	tear	20-3_S112_L001_R1_001.fastq.gz	NA	61	M	NA	NA	NA	Right	180.00	92	29.00	1	Ever_smoker	No	No	No	No	2.9	78	57	3.9	5.5	5.6	No	Intact	50	40	90	30	34	36	44	50.0	34.1	31.8	0.0	12321	12321	12211	12211	77	40	70	70	20.3_S112
21.3_S113	AD-CAB_3021	RC	68	tear	21-3_S113_L001_R1_001.fastq.gz	NA	46	M	NA	NA	NA	Right	180.00	88	27.20	2	Never_smoker	No	Yes	No	No	2.9	77	57	5.9	5.9	5.3	No	Torn	NA	NA	NA	29	44	43	NA	45.5	20.5	11.4	NA	NA	11111	11121	NA	NA	80	65	NA	21.3_S113
23.3_S115	AD-CAB_3023	RC	75	tear	23-3_S115_L001_R1_001.fastq.gz	NA	58	M	NA	NA	NA	Right	179.00	81	25.00	2	Ever_smoker	No	Yes	No	No	2.9	75	57	5.7	5.4	5.7	No	Intact	50	40	90	42	43	48	48	15.9	15.9	0.0	2.3	11211	11121	11111	11111	85	85	90	85	23.3_S115
25.3_S117	AD-CAB_3025	RC	77	tear	25-3_S117_L001_R1_001.fastq.gz	NA	62	F	NA	NA	NA	Left	157.00	95	38.50	2	Never_smoker	No	Yes	Yes	No	12.0	72	57	6.6	6.2	5.7	Yes	Intact	45	43	88	26	36	38	41	59.1	29.5	31.8	18.2	12231	11221	11221	11121	82	80	60	80	25.3_S117

QC analysis

First to look at the number of reads per data set.

par(mar=c(5,8,3,1))
barplot(colSums(x1),horiz=TRUE,las=1,xlab="Intact tendon group reads")
abline(v=4e6,col="red",lty=2)

barplot(colSums(x2),horiz=TRUE,las=1,xlab="Torn tendon group reads")
abline(v=4e6,col="red",lty=2)

barplot(colSums(x3),horiz=TRUE,las=1,xlab="Tendon body reads")
abline(v=4e6,col="red",lty=2)

barplot(colSums(x4),horiz=TRUE,las=1,xlab="Tear edge reads")
abline(v=4e6,col="red",lty=2)

Filter out low reads

x1 <- x1[,colSums(x1)>4e6]
x2 <- x2[,colSums(x2)>4e6]
x3 <- x3[,colSums(x3)>4e6]
x4 <- x4[,colSums(x4)>4e6]

ss1 <- ss1[which(ss1$data %in% colnames(x1)),]
ss2 <- ss2[which(ss2$data %in% colnames(x2)),]
ss3 <- ss3[which(ss3$data %in% colnames(x3)),]
ss4 <- ss4[which(ss4$data %in% colnames(x4)),]

ss1 <- ss1[order(rownames(ss1)),]
x1 <- x1[,order(colnames(x1))]

ss2 <- ss2[order(rownames(ss2)),]
x2 <- x2[,order(colnames(x2))]

ss3 <- ss3[order(rownames(ss3)),]
x3 <- x3[,order(colnames(x3))]

ss4 <- ss4[order(rownames(ss4)),]
x4 <- x4[,order(colnames(x4))]

rownames(ss1) == colnames(x1)

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

rownames(ss2) == colnames(x2)

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

rownames(ss3) == colnames(x3)

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE

rownames(ss4) == colnames(x4)

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE

MDS plot

Multidimensional scaling plot to show the variation between all samples, very similar to PCA.

xx <- cbind(x3,x4)
ssx <- rbind(ss3,ss4)

mds <- cmdscale(dist(t(xx)))

plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=19, cex=4 ,col="gray")
text(mds, labels=rownames(mds) )

cols <- as.numeric(as.factor(ssx$Tendon_integrity_2_years_post.op))+1 #red=intact,green=torn
shapes <- as.numeric(as.factor(ssx$Tissue))+14

plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
  type = "p",bty="n",pch=shapes, cex=2 ,col=cols)
text(mds, labels=rownames(mds) )

# Add a legend
legend("top",
  legend = c("tear intact", "tendon intact", "tear torn", "tendon torn"),
  col= c("red","red","green","green"),
  pch = c(15,16,15,16),
  pt.cex = 2,
  cex = 1.2 )

Prep pathways

genesets <- gmt_import("ReactomePathways_2023-09-01.gmt")

# gene table
gt <- as.data.frame(rownames(x))
#gt$gene <- sapply(strsplit(gt[,1]," "),"[[",2)
gt[,1] <- sub("@",".",sub("\\."," ",(sub("\\.","@",gt[,1]))))
gt$gene <- sapply(strsplit(gt[,1]," "),"[[",2)
gt[,1] <- rownames(x)

DE1: Tendon v tear (intact)

colnames(x1) == rownames(ss1)

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

x1f <- x1[which(rowMeans(x1)>10),]
dim(x1)

## [1] 60651    26

dim(x1f)

## [1] 16573    26

ss1$tissue <- factor(ss1$Tissue,levels=c("tendon","tear"))
ss1$redcap <- as.character(ss1$Redcap_ID,ordered=FALSE) # unfortunately not possible to correct for patient of origin.

dds <- DESeqDataSetFromMatrix(countData = x1f , colData = ss1, design = ~ Age + Sex + tissue )

## converting counts to integer mode

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

##   the design formula contains one or more numeric variables that have mean or
##   standard deviation larger than 5 (an arbitrary threshold to trigger this message).
##   Including numeric variables with large mean can induce collinearity with the intercept.
##   Users should center and scale numeric variables in the design to improve GLM convergence.

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge,20) %>%
  kbl(caption = "Top gene expression differences between tendon (ctrl) and tear (case) of intact tissues") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences between tendon (ctrl) and tear (case) of intact tissues
	baseMean	log2FoldChange	lfcSE	stat	10.2_S76	10.3_S102	11.3_S103	14.2_S80	14.3_S106	15.2_S81	15.3_S107	18.2_S84	18.3_S110	19.2_S85	19.3_S111	20.2_S86	20.3_S112	23.2_S89	23.3_S115	25.2_S91	3.2_S69	3.3_S95	4.2_S70	4.3_S96	7.2_S73	7.3_S99	8.2_S74	8.3_S100	9.2_S75	9.3_S101
ENSG00000183091.20.NEB	13657.7361	-9.475428	0.9227078	-10.269153	6.353477	5.839636	5.004771	12.278959	4.242025	10.039295	5.035579	6.803017	4.644989	16.62250	5.501492	5.328767	4.830237	16.10258	5.549527	6.814874	16.77936	4.993240	7.533251	4.649284	5.733830	6.768170	15.95547	3.796932	9.884009	5.776340
ENSG00000125414.19.MYH2	13405.4771	-11.379104	1.2336425	-9.223988	3.579296	5.602947	3.374098	12.696551	2.402798	12.475248	3.676300	8.247780	4.201051	16.26222	3.257371	3.830172	2.967203	15.59144	4.306145	8.121821	16.62435	2.402798	4.025040	2.402798	4.366180	8.236892	16.67722	3.228088	10.589621	2.916621
ENSG00000214872.8.SMTNL1	645.9268	-7.849999	0.8692640	-9.030628	4.346995	3.661730	4.319108	6.922830	3.456243	5.992967	3.514157	4.491470	3.642287	12.36789	3.594889	3.061752	2.967203	11.69789	4.256090	3.574614	11.92321	3.705787	4.493695	3.453480	4.303668	4.229608	11.96570	3.228088	6.248074	3.414995
ENSG00000248746.6.ACTN3	856.1159	-9.658681	1.0824791	-8.922742	3.245903	3.389835	4.182927	10.513053	3.269095	5.872795	3.317428	3.788352	2.402798	12.95766	3.442176	3.326941	2.402798	11.84239	3.969240	3.000658	12.52041	3.243242	6.067308	2.402798	2.965404	3.015252	11.62193	2.402798	5.898464	3.283727
ENSG00000163126.15.ANKRD23	2844.8682	-7.377102	0.8339746	-8.845715	4.956198	4.529208	5.281521	10.275355	4.820276	7.452137	5.305138	5.174880	5.465236	14.60713	4.568389	5.446665	4.371707	13.87089	5.697412	5.880867	14.09786	5.076633	5.632072	4.761920	5.164282	5.985143	13.81208	4.081205	7.954863	5.360366
ENSG00000164309.15.CMYA5	5110.9744	-6.894549	0.7988693	-8.630384	6.636348	6.133897	5.589753	11.006170	5.101029	9.705686	5.725240	6.819959	6.959593	15.26670	6.712524	6.318777	4.694398	14.46122	6.784913	7.446223	15.26851	6.132839	7.341005	5.832437	5.921527	7.109321	14.70139	4.561838	8.960604	6.933152
ENSG00000173641.18.HSPB7	1566.0350	-6.561147	0.7815509	-8.395034	4.815775	4.737931	5.943931	9.453523	4.320721	7.661171	4.334767	7.503921	4.513370	13.07901	4.136097	6.417201	4.488425	12.98875	6.879994	6.691404	13.30924	4.596238	5.703048	4.390036	5.940929	5.690275	13.54240	6.362034	7.564130	4.323863
ENSG00000164122.9.ASB5	402.6146	-8.895785	1.0740731	-8.282290	3.003142	2.979987	3.200716	7.623332	2.402798	4.344425	3.514157	3.788352	3.036669	12.25419	3.726875	3.061752	2.402798	10.68651	3.097302	3.242462	10.89687	2.402798	3.514469	3.018228	3.631351	3.262673	10.82238	2.990288	5.236594	2.916621
ENSG00000154358.23.OBSCN	4076.3400	-6.277968	0.7581847	-8.280262	5.796891	6.004100	6.654357	10.626725	6.303581	8.781143	5.869028	7.822023	6.621622	15.02425	6.801899	6.436099	5.948783	14.13468	6.535001	6.556303	15.09230	5.927943	6.793443	6.433955	6.412422	7.149254	13.94461	5.644660	8.980641	5.683286
ENSG00000140986.8.RPL3L	819.5918	-8.079442	0.9804515	-8.240532	4.346995	3.773880	3.642101	8.636915	3.019892	6.587514	4.493168	4.941270	3.778847	12.63360	4.136097	3.326941	3.368493	11.77585	3.673230	3.820070	12.70770	2.402798	4.025040	3.453480	3.631351	4.229608	12.00814	2.402798	6.732035	3.630668
ENSG00000112183.15.RBM24	427.0572	-6.509227	0.7926673	-8.211802	3.709761	4.054277	3.944622	8.142925	3.019892	5.253024	4.565606	5.374631	4.201051	11.84991	4.440006	4.067517	3.844831	10.95243	5.008824	5.153207	11.08341	3.575675	4.493695	3.453480	5.059943	4.229608	11.42637	2.990288	5.606079	4.093698
ENSG00000072954.7.TMEM38A	558.8802	-6.327064	0.7715832	-8.200106	4.197081	3.968298	4.382000	7.307271	4.067618	5.768748	3.816101	5.003512	4.009117	12.06416	4.984724	5.057925	3.844831	11.43763	5.065183	4.267384	12.17671	3.575675	5.108688	4.390036	4.482660	4.307898	11.21873	2.990288	6.466711	4.472301
ENSG00000104369.5.JPH1	331.6083	-7.319780	0.8959116	-8.170204	4.274299	2.402798	3.517710	7.903766	2.402798	5.400793	3.939906	4.298983	3.642287	11.21941	4.219341	3.061752	3.196057	10.54606	4.306145	3.704622	11.19856	3.001221	3.940320	3.266792	3.931643	3.448539	11.00862	3.228088	5.174805	3.529009
ENSG00000160678.12.S100A1	453.2676	-5.571715	0.6830624	-8.156964	4.274299	4.584577	4.382000	7.370139	4.767255	7.136680	4.761637	7.277654	4.513370	11.70575	4.297294	6.739520	4.099519	11.29838	5.008824	4.705245	11.59436	4.759198	4.959646	4.589152	4.303668	4.697394	11.00325	4.081205	6.075657	4.093698
ENSG00000185739.13.SRL	796.6372	-6.957381	0.8548156	-8.139043	3.825602	3.968298	3.374098	8.438711	3.019892	5.872795	4.334767	5.909826	3.036669	12.64168	4.740048	3.525540	3.511348	11.72237	6.195492	5.577781	12.59817	3.425213	5.108688	3.859118	5.409288	4.952188	11.96791	2.990288	6.721464	4.093698
ENSG00000164708.6.PGAM2	941.9428	-7.835474	0.9676494	-8.097431	4.026106	3.213800	3.374098	8.503187	2.402798	6.460414	3.054860	6.391347	3.778847	12.76107	4.628125	6.025763	3.196057	11.96509	4.790873	5.227556	12.88297	2.402798	5.336079	4.316124	3.365487	5.717331	12.34087	3.407010	6.955882	4.214179
ENSG00000116748.21.AMPD1	610.2067	-10.976434	1.3651242	-8.040612	3.709761	2.402798	2.970559	8.690198	2.402798	5.534424	2.402798	3.938570	2.402798	12.18398	3.257371	2.402798	2.402798	11.26987	3.485922	3.000658	11.89366	2.402798	3.940320	2.402798	3.840527	3.602093	12.11655	2.402798	6.262763	2.402798
ENSG00000163380.16.LMOD3	845.7891	-6.889563	0.8576780	-8.032808	4.114646	3.389835	3.944622	8.344091	2.402798	6.264901	4.985034	5.003512	4.286703	12.84045	5.028777	4.585259	4.309062	11.80976	4.685454	4.339876	12.60251	3.705787	4.178095	4.237574	4.237863	4.307898	12.04467	4.561838	6.375138	3.722904
ENSG00000117115.13.PADI2	567.0307	-6.933673	0.8652876	-8.013142	4.026106	3.389835	4.029536	8.182844	4.920438	5.713711	3.514157	7.879622	4.580834	11.74462	4.297294	3.955009	3.844831	11.37450	4.400631	3.242462	11.97760	3.243242	5.177555	2.402798	3.931643	5.042045	11.92653	3.555044	6.262763	3.722904
ENSG00000124701.6.APOBEC2	665.7476	-7.527524	0.9469407	-7.949309	4.197081	3.661730	3.517710	7.804887	2.402798	5.922090	3.676300	5.227575	4.009117	12.51201	4.792667	2.402798	3.635105	11.66065	4.685454	4.190388	11.74044	3.575675	4.548408	3.965866	4.237863	4.381573	12.09198	3.228088	6.277302	3.529009

dge1 <- dge
write.table(dge1,file="tear1.tsv",quote=FALSE,sep='\t')

# mitch
m1 <- mitch_import(dge1, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 16573

## Note: no. genes in output = 16550

## Note: estimated proportion of input genes in output = 0.999

mres1 <- mitch_calc(m1, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres1$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences between tendon (ctrl) and tear (case) of intact tissues") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences between tendon (ctrl) and tear (case) of intact tissues
	set	setSize	pANOVA	s.dist	p.adjustANOVA
1443	mitochondrial fatty acid beta-oxidation of saturated fatty acids	10	0.0000102	-0.8056106	0.0001781
197	Citric acid cycle (TCA cycle)	22	0.0000000	-0.7666989	0.0000000
494	Glyoxylate metabolism and glycine degradation	23	0.0000000	-0.7666164	0.0000000
45	Activation of PPARGC1A (PGC-1alpha) by phosphorylation	10	0.0001117	-0.7055260	0.0012754
123	Branched-chain amino acid catabolism	21	0.0000001	-0.6868188	0.0000014
1429	Zinc transporters	10	0.0001695	0.6867110	0.0016855
214	Complex I biogenesis	51	0.0000000	-0.6736724	0.0000000
718	Mucopolysaccharidoses	11	0.0001164	0.6709376	0.0012941
319	Dissolution of Fibrin Clot	10	0.0002836	0.6628295	0.0024639
1074	Respiratory electron transport	93	0.0000000	-0.6422518	0.0000000
302	Diseases associated with N-glycosylation of proteins	20	0.0000009	0.6349788	0.0000192
406	Formation of ATP by chemiosmotic coupling	16	0.0000115	-0.6333313	0.0001957
1075	Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins.	113	0.0000000	-0.6289399	0.0000000
1324	The citric acid (TCA) cycle and respiratory electron transport	160	0.0000000	-0.6139208	0.0000000
143	CS/DS degradation	11	0.0004299	0.6130688	0.0034531
1323	The activation of arylsulfatases	10	0.0011064	0.5956832	0.0073180
490	Glycogen synthesis	13	0.0002094	-0.5938153	0.0019624
915	Pyruvate metabolism and Citric Acid (TCA) cycle	51	0.0000000	-0.5910911	0.0000000
1256	Syndecan interactions	26	0.0000004	0.5745955	0.0000093
161	Carnitine metabolism	14	0.0002139	-0.5713940	0.0019921

m1top <- subset(mres1$enrichment_result,p.adjustANOVA<0.05)
m1up <- subset(m1top,s.dist>0)$set
m1dn <- subset(m1top,s.dist<0)$set

if (! file.exists("tear_mitchres1.html") ) {
  mitch_report(mres1,outfile="tear_mitchres1.html",overwrite=FALSE)
}

DE2: Tendon v tear (torn)

colnames(x2) == rownames(ss2)

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

x2f <- x2[which(rowMeans(x2)>10),]
dim(x2)

## [1] 60651    12

dim(x2f)

## [1] 16590    12

ss2$tissue <- factor(ss2$Tissue,levels=c("tendon","tear"))
ss2$redcap <- as.character(ss2$Redcap_ID,ordered=FALSE) # unfortunately not possible to correct for patient of origin.

dds <- DESeqDataSetFromMatrix(countData = x2f , colData = ss2, design = ~ Age + Sex + tissue )

## converting counts to integer mode

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

##   the design formula contains one or more numeric variables that have mean or
##   standard deviation larger than 5 (an arbitrary threshold to trigger this message).
##   Including numeric variables with large mean can induce collinearity with the intercept.
##   Users should center and scale numeric variables in the design to improve GLM convergence.

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge,20) %>%
  kbl(caption = "Top gene expression differences between tendon (ctrl) and tear (case) of torn tissues") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences between tendon (ctrl) and tear (case) of torn tissues
	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj	1.3_S93	16.2_S82	16.3_S108	17.2_S83	17.3_S109	2.2_S68	21.2_S87	21.3_S113	5.2_S71	5.3_S97	6.2_S72	6.3_S98
ENSG00000159251.8.ACTC1	1096.92105	-7.850215	1.3686937	-5.735553	0e+00	0.0001612	5.434797	13.242506	2.324036	5.431931	3.973187	6.624198	9.355165	3.202362	11.371636	3.818985	4.856548	3.778394
ENSG00000274276.4.CBSL	26.53041	2.748503	0.4964329	5.536505	0e+00	0.0001618	5.078619	3.005120	4.420193	4.249733	6.430281	3.718052	4.370129	6.348402	4.159482	6.411412	3.674782	4.596819
ENSG00000162267.12.ITIH3	85.27914	-4.518586	0.8174848	-5.527426	0e+00	0.0001618	4.792380	7.158375	4.527603	5.886580	4.198942	9.151117	5.569679	2.949825	7.521479	3.928078	4.254272	4.196300
ENSG00000162998.5.FRZB	49.83554	-4.070802	0.7407816	-5.495279	0e+00	0.0001618	4.698151	7.225747	3.673819	5.667609	4.198942	6.609311	7.370305	2.949825	7.035782	2.956933	4.465615	3.778394
ENSG00000196557.13.CACNA1H	105.20850	-6.041900	1.1128431	-5.429247	1e-07	0.0001819	5.078619	4.889082	3.773936	7.725333	3.520144	9.266824	6.109321	2.949825	8.392529	2.324036	4.529021	2.324036
ENSG00000198768.11.APCDD1L	98.62085	3.786230	0.7008539	5.402310	1e-07	0.0001819	5.041208	3.278690	8.042205	5.284509	7.836187	4.527866	4.455275	6.331292	4.065862	8.877099	3.674782	6.556856
ENSG00000258227.7.CLEC5A	79.36328	4.152771	0.7785411	5.334041	1e-07	0.0002110	3.478634	4.494723	8.200848	4.002078	7.176471	2.966670	3.678965	6.524250	4.404316	8.547718	3.901512	5.549560
ENSG00000104951.16.IL4I1	58.29778	4.364987	0.8209387	5.317069	1e-07	0.0002110	3.720953	4.331861	8.697712	4.132160	6.804925	2.966670	3.019909	5.819243	3.233675	6.427970	3.999880	5.365597
ENSG00000121068.14.TBX2	184.33772	-3.688689	0.6957024	-5.302107	1e-07	0.0002110	6.288720	7.944740	4.029754	8.471152	5.441103	9.551445	8.038406	3.683482	8.570970	6.234360	6.476076	3.934810
ENSG00000250945.1.AC096773.1	30.86213	-1.879814	0.3558985	-5.281882	1e-07	0.0002121	5.041208	6.304619	4.673866	5.360159	4.198942	6.467880	5.195676	4.265908	5.439124	4.027783	5.827013	4.507695
ENSG00000286208.1.AC093838.1	31.72052	-2.282873	0.4359259	-5.236837	2e-07	0.0002464	4.792380	6.103639	4.240153	6.243700	4.556011	6.667955	5.374307	3.803289	4.246486	2.324036	5.752566	5.209809
ENSG00000072840.13.EVC	184.75271	1.651327	0.3167304	5.213667	2e-07	0.0002496	7.120977	6.150807	8.253792	7.092872	8.804750	6.830757	7.033114	8.004625	6.136553	8.218287	6.521389	8.099035
ENSG00000139330.6.KERA	60.93028	-5.564602	1.0694044	-5.203459	2e-07	0.0002496	3.824033	8.908761	2.324036	5.384494	3.520144	6.134673	6.711413	2.324036	6.003304	3.212191	2.945596	3.778394
ENSG00000278969.1.AC026310.2	20.66536	3.707920	0.7193917	5.154244	3e-07	0.0002867	3.478634	3.005120	6.384411	3.688023	5.831402	3.419806	3.956625	5.867699	2.324036	4.894080	3.674782	5.413895
ENSG00000211451.12.GNRHR2	26.52316	-1.508742	0.2929067	-5.150932	3e-07	0.0002867	4.880463	6.029829	4.719254	5.687205	4.198942	5.581157	5.455741	4.101570	5.254073	4.119796	5.375155	4.970508
ENSG00000171551.12.ECEL1	18.29803	-4.957437	0.9660194	-5.131819	3e-07	0.0002905	2.912772	6.587294	2.324036	4.068872	3.309590	4.592543	3.507573	2.324036	6.748344	2.956933	3.384893	3.073929
ENSG00000231290.6.APCDD1L.DT	54.34127	2.928137	0.5729635	5.110512	3e-07	0.0002905	5.376433	5.147927	8.029247	4.249733	6.953951	4.388050	4.073806	5.914558	4.065862	6.840100	3.999880	5.153752
ENSG00000110786.18.PTPN5	38.19020	-4.297182	0.8412363	-5.108175	3e-07	0.0002905	5.315533	5.720823	3.295228	7.571859	3.309590	5.781675	5.494753	2.324036	5.539470	2.324036	6.296472	4.412055
ENSG00000088340.17.FER1L4	285.04857	3.713531	0.7275814	5.103939	3e-07	0.0002905	4.487007	5.841102	10.085815	6.474234	9.746425	5.193830	5.331745	7.187214	4.842303	8.718440	4.702777	9.496921
ENSG00000184166.2.OR1D2	71.97979	-1.980638	0.3984338	-4.971058	7e-07	0.0005524	5.718611	7.075569	5.293520	6.906434	4.556011	7.833395	6.499901	5.423759	5.979833	3.928078	6.621865	6.111590

dge2 <- dge
write.table(dge2,file="tear2.tsv",quote=FALSE,sep='\t')

# mitch
m2 <- mitch_import(dge2, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 16590

## Note: no. genes in output = 16566

## Note: estimated proportion of input genes in output = 0.999

mres2 <- mitch_calc(m2, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres2$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences between tendon (ctrl) and tear (case) of torn tissues") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences between tendon (ctrl) and tear (case) of torn tissues
	set	setSize	pANOVA	s.dist	p.adjustANOVA
374	Expression and translocation of olfactory receptors	21	0.0000000	-0.7671113	0.0000001
1430	Zinc transporters	10	0.0000819	0.7192196	0.0010779
804	Olfactory Signaling Pathway	25	0.0000000	-0.6910271	0.0000001
45	Activation of PPARGC1A (PGC-1alpha) by phosphorylation	10	0.0001571	-0.6901667	0.0017544
1344	Trafficking and processing of endosomal TLR	12	0.0001176	0.6419999	0.0014699
382	FCGR activation	10	0.0004769	0.6379319	0.0045899
615	Keratan sulfate degradation	13	0.0001005	0.6229547	0.0012896
197	Citric acid cycle (TCA cycle)	22	0.0000016	-0.5907992	0.0000412
718	Mucopolysaccharidoses	11	0.0008750	0.5794514	0.0073575
856	Phase 0 - rapid depolarisation	27	0.0000006	-0.5553898	0.0000178
1015	Regulation of CDH11 gene transcription	10	0.0023704	0.5550495	0.0164358
689	Metal ion SLC transporters	18	0.0000651	0.5436441	0.0008988
1083	Retrograde neurotrophin signalling	13	0.0007301	0.5410732	0.0063584
776	Nitric oxide stimulates guanylate cyclase	17	0.0001331	-0.5351914	0.0015580
319	Dissolution of Fibrin Clot	10	0.0034828	0.5335347	0.0223925
494	Glyoxylate metabolism and glycine degradation	24	0.0000068	-0.5304881	0.0001409
143	CS/DS degradation	10	0.0042307	0.5223726	0.0260065
214	Complex I biogenesis	51	0.0000000	-0.5163422	0.0000000
1252	Striated Muscle Contraction	35	0.0000002	-0.5116569	0.0000056
1324	The activation of arylsulfatases	10	0.0054442	0.5075985	0.0317149

m2top <- subset(mres2$enrichment_result,p.adjustANOVA<0.05)
m2up <- subset(m2top,s.dist>0)$set
m2dn <- subset(m2top,s.dist<0)$set

if (! file.exists("tear_mitchres2.html") ) {
  mitch_report(mres2,outfile="tear_mitchres2.html",overwrite=FALSE)
}

Now run a mitch analysis to see the similarities/differences between tendon-tear for intact vs torn

l1 <- list("intact"=dge1,"torn"=dge2)
mm <- mitch_import(l1, DEtype="deseq2",geneTable=gt)

## Note: Mean no. genes in input = 16581.5

## Note: no. genes in output = 16178

## Note: estimated proportion of input genes in output = 0.976

mmres1 <- mitch_calc(mm, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be 
##             statistically significant.

head(mmres1$enrichment_result,20) %>%
  kbl(caption = "Top tendon-tear pathways in intact and torn samples") %>%
  kable_paper("hover", full_width = F)

Top tendon-tear pathways in intact and torn samples
	set	setSize	pMANOVA	s.intact	s.torn	p.intact	p.torn	s.dist	SD	p.adjustMANOVA
1426	Zinc transporters	10	0.0001508	0.6848095	0.7194582	0.0001767	0.0000815	0.9932694	0.0245003	0.0011316
45	Activation of PPARGC1A (PGC-1alpha) by phosphorylation	10	0.0001731	-0.7032905	-0.6919842	0.0001174	0.0001509	0.9866405	0.0079948	0.0012561
197	Citric acid cycle (TCA cycle)	22	0.0000000	-0.7640899	-0.5928898	0.0000000	0.0000015	0.9671358	0.1210568	0.0000001
1440	mitochondrial fatty acid beta-oxidation of saturated fatty acids	10	0.0000572	-0.8036244	-0.4966353	0.0000108	0.0065392	0.9447004	0.2170741	0.0005182
494	Glyoxylate metabolism and glycine degradation	23	0.0000000	-0.7634008	-0.5289815	0.0000000	0.0000112	0.9287637	0.1657595	0.0000000
717	Mucopolysaccharidoses	11	0.0003995	0.6686291	0.5789740	0.0001229	0.0008838	0.8844636	0.0633958	0.0024906
319	Dissolution of Fibrin Clot	10	0.0011594	0.6626423	0.5332756	0.0002848	0.0034989	0.8505749	0.0914760	0.0057931
214	Complex I biogenesis	51	0.0000000	-0.6701695	-0.5185884	0.0000000	0.0000000	0.8473848	0.1071841	0.0000000
382	FCGR activation	10	0.0015636	0.5507546	0.6376423	0.0025625	0.0004797	0.8425665	0.0614389	0.0072651
1340	Trafficking and processing of endosomal TLR	12	0.0004732	0.5246299	0.6419337	0.0016504	0.0001178	0.8290449	0.0829463	0.0028411
143	CS/DS degradation	10	0.0023950	0.6205715	0.5214621	0.0006781	0.0042980	0.8105750	0.0700809	0.0102173
374	Expression and translocation of olfactory receptors	21	0.0000000	-0.2445085	-0.7685273	0.0524491	0.0000000	0.8064853	0.3705372	0.0000000
1073	Respiratory electron transport	93	0.0000000	-0.6391709	-0.4871091	0.0000000	0.0000000	0.8036260	0.1075239	0.0000000
302	Diseases associated with N-glycosylation of proteins	20	0.0000050	0.6326773	0.4936750	0.0000010	0.0001323	0.8024933	0.0982895	0.0000678
406	Formation of ATP by chemiosmotic coupling	16	0.0000609	-0.6303211	-0.4945938	0.0000127	0.0006141	0.8012039	0.0959737	0.0005364
615	Keratan sulfate degradation	13	0.0004343	0.4950629	0.6233743	0.0019972	0.0000994	0.7960420	0.0907299	0.0026738
1074	Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins.	113	0.0000000	-0.6259785	-0.4794334	0.0000000	0.0000000	0.7884830	0.1036231	0.0000000
1321	The activation of arylsulfatases	10	0.0038148	0.5938521	0.5069644	0.0011462	0.0055029	0.7808157	0.0614389	0.0155036
1249	Striated Muscle Contraction	34	0.0000000	-0.5653166	-0.5215669	0.0000000	0.0000001	0.7691651	0.0309357	0.0000003
123	Branched-chain amino acid catabolism	21	0.0000001	-0.6844593	-0.3284291	0.0000001	0.0091819	0.7591773	0.2517514	0.0000021

if (! file.exists("tear_multimitchres1.html") ) {
  mitch_report(mmres1,outfile="tear_multimitchres1.html",overwrite=FALSE)
}

## Dataset saved as " /tmp/RtmpUPjaUw/tear_multimitchres1.rds ".

## 
## 
## processing file: mitch.Rmd

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## 17/34                             
## 18/34 [echart1d]                  
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## 34/34

## output file: /mnt/data/mdz/projects/shoulder/shoulder-instability-osteroarthritis/mitch.knit.md

## /home/mdz/anaconda3/bin/pandoc +RTS -K512m -RTS /mnt/data/mdz/projects/shoulder/shoulder-instability-osteroarthritis/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpUPjaUw/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpUPjaUw/rmarkdown-str14d65b2074b384.html

## 
## Output created: /tmp/RtmpUPjaUw/mitch_report.html

## [1] TRUE

# prioritise by SD to get the discordant pathways
mmres1 <- mitch_calc(mm, genesets, priority="SD")

## Note: Prioritisation by SD after selecting sets with 
##             p.adjustMANOVA<=0.05.

head(mmres1$enrichment_result,20) %>%
  kbl(caption = "Top tendon-tear pathways in intact and torn samples (discordant only)") %>%
  kable_paper("hover", full_width = F)

Top tendon-tear pathways in intact and torn samples (discordant only)
	set	setSize	pMANOVA	s.intact	s.torn	p.intact	p.torn	s.dist	SD	p.adjustMANOVA
374	Expression and translocation of olfactory receptors	21	0.0000000	-0.2445085	-0.7685273	0.0524491	0.0000000	0.8064853	0.3705372	0.0000000
802	Olfactory Signaling Pathway	25	0.0000000	-0.2076568	-0.6924113	0.0723481	0.0000000	0.7228795	0.3427732	0.0000000
1015	Regulation of FOXO transcriptional activity by acetylation	10	0.0016650	-0.4900915	-0.0217838	0.0072833	0.9050626	0.4905754	0.3311436	0.0076391
114	Beta-oxidation of very long chain fatty acids	10	0.0016810	-0.5153018	-0.0617887	0.0047773	0.7351367	0.5189931	0.3206822	0.0076644
504	HDMs demethylate histones	21	0.0000170	-0.0725176	0.3769294	0.5651727	0.0027896	0.3838418	0.3178070	0.0001959
393	FOXO-mediated transcription of cell death genes	15	0.0002053	-0.4846171	-0.0573697	0.0011553	0.7005038	0.4880011	0.3021095	0.0014560
1430	cGMP effects	14	0.0003608	-0.0712430	-0.4931240	0.6444591	0.0013998	0.4982438	0.2983150	0.0023398
188	Chk1/Chk2(Cds1) mediated inactivation of Cyclin B:Cdk1 complex	11	0.0046890	0.4235620	0.0126014	0.0149997	0.9423158	0.4237494	0.2905930	0.0185905
1196	Signaling by Leptin	11	0.0017309	-0.5212920	-0.1119396	0.0027556	0.5203670	0.5331752	0.2894558	0.0078187
932	RHO GTPases activate CIT	19	0.0016437	0.2330948	-0.1409513	0.0786214	0.2875688	0.2723976	0.2644905	0.0075893
123	Branched-chain amino acid catabolism	21	0.0000001	-0.6844593	-0.3284291	0.0000001	0.0091819	0.7591773	0.2517514	0.0000021
292	Deposition of new CENPA-containing nucleosomes at the centromere	30	0.0000306	0.3480555	-0.0028528	0.0009697	0.9784305	0.3480672	0.2481296	0.0003169
792	Nucleosome assembly	30	0.0000306	0.3480555	-0.0028528	0.0009697	0.9784305	0.3480672	0.2481296	0.0003169
774	Nitric oxide stimulates guanylate cyclase	17	0.0001308	-0.1862545	-0.5369972	0.1837300	0.0001263	0.5683808	0.2480125	0.0009989
289	Degradation of cysteine and homocysteine	14	0.0014468	-0.4908880	-0.1432902	0.0014718	0.3533218	0.5113738	0.2457888	0.0068095
273	Defective HDR through Homologous Recombination Repair (HRR) due to PALB2 loss of BRCA1 binding function	24	0.0000431	0.4456481	0.0986288	0.0001573	0.4030330	0.4564317	0.2453797	0.0004196
274	Defective HDR through Homologous Recombination Repair (HRR) due to PALB2 loss of BRCA2/RAD51/RAD51C binding function	24	0.0000431	0.4456481	0.0986288	0.0001573	0.4030330	0.4564317	0.2453797	0.0004196
278	Defective homologous recombination repair (HRR) due to BRCA1 loss of function	24	0.0000431	0.4456481	0.0986288	0.0001573	0.4030330	0.4564317	0.2453797	0.0004196
280	Defective homologous recombination repair (HRR) due to PALB2 loss of function	24	0.0000431	0.4456481	0.0986288	0.0001573	0.4030330	0.4564317	0.2453797	0.0004196
548	Impaired BRCA2 binding to PALB2	23	0.0000695	0.4507125	0.1102445	0.0001827	0.3601656	0.4639996	0.2407472	0.0005828

if (! file.exists("tear_multmitchres1_discord.html") ) {
  mitch_report(mmres1,outfile="tear_multmitchres1_discord.html",overwrite=FALSE)
}

## Dataset saved as " /tmp/RtmpUPjaUw/tear_multmitchres1_discord.rds ".
## 
## 
## processing file: mitch.Rmd

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## 21/34 [heatmap]

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## 33/34 [session_info]              
## 34/34

## output file: /mnt/data/mdz/projects/shoulder/shoulder-instability-osteroarthritis/mitch.knit.md

## /home/mdz/anaconda3/bin/pandoc +RTS -K512m -RTS /mnt/data/mdz/projects/shoulder/shoulder-instability-osteroarthritis/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmpUPjaUw/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpUPjaUw/rmarkdown-str14d65b52a75977.html

## 
## Output created: /tmp/RtmpUPjaUw/mitch_report.html

## [1] TRUE

The other approach is to find the direct differences between intact and torn samples.

DE3. tendon (intact) vs tendon (torn).

colnames(x3) == rownames(ss3)

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE

x3f <- x3[which(rowMeans(x3)>10),]
dim(x3)

## [1] 60651    19

dim(x3f)

## [1] 16536    19

ss3$tissue <- factor(ss3$Tissue,levels=c("tendon","tear"))
ss3$integrity <- factor(ss3$Tendon_integrity_2_years_post.op)

dds <- DESeqDataSetFromMatrix(countData = x3f , colData = ss3, design = ~ Age + Sex + integrity )

## converting counts to integer mode

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

##   the design formula contains one or more numeric variables that have mean or
##   standard deviation larger than 5 (an arbitrary threshold to trigger this message).
##   Including numeric variables with large mean can induce collinearity with the intercept.
##   Users should center and scale numeric variables in the design to improve GLM convergence.

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

## 1 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTest

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge,20) %>%
  kbl(caption = "Top gene expression differences between intact (ctrl) and torn (case) tendon.") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences between intact (ctrl) and torn (case) tendon.
	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj	10.2_S76	14.2_S80	15.2_S81	16.2_S82	17.2_S83	18.2_S84	19.2_S85	2.2_S68	20.2_S86	21.2_S87	23.2_S89	25.2_S91	3.2_S69	4.2_S70	5.2_S71	6.2_S72	7.2_S73	8.2_S74	9.2_S75
ENSG00000108405.4.P2RX1	41.21403	4.3413409	0.9823026	4.419556	0.0000099	0.1006945	3.255370	3.885790	4.443246	3.671201	4.290304	6.271105	1.915476	8.759992	2.669663	3.403286	4.376566	2.863137	3.586673	3.898479	7.374338	3.109345	3.788873	2.781987	5.454967
ENSG00000255769.7.GOLGA2P10	16.18043	-2.4280355	0.5644333	-4.301722	0.0000169	0.1006945	3.255370	4.046465	4.676274	2.668721	3.198832	1.915476	6.246798	3.583503	4.283121	4.144979	5.391856	4.573979	5.908772	3.537995	3.605618	3.282044	4.425996	4.882578	4.596984
ENSG00000134917.10.ADAMTS8	44.63816	3.4303461	0.8005346	4.285069	0.0000183	0.1006945	4.104454	3.701675	4.378192	9.330768	3.510394	4.479089	3.554327	4.003720	1.915476	3.702948	3.876923	3.993704	3.814767	3.427855	6.228635	3.977773	3.512536	4.735653	3.854440
ENSG00000162852.14.CNST	175.51083	-0.7802754	0.2058750	-3.790044	0.0001506	0.6226657	7.474345	7.222186	6.959817	6.980698	6.506107	6.989153	8.685764	7.097355	7.434936	7.442272	8.134954	7.690348	8.506628	7.111960	7.440062	6.525901	7.651766	7.710144	6.892307
ENSG00000227827.3.PKD1P2	66.04932	-3.1708756	0.8872557	-3.573801	0.0003518	0.9215750	5.204110	7.662761	7.501349	4.196915	2.504424	4.763028	6.920423	5.356804	4.109923	6.315477	8.350164	5.294121	1.915476	6.724714	1.915476	4.143746	6.117823	5.742922	5.011279
ENSG00000273066.5.AL355987.4	12.13401	-1.3820308	0.3876083	-3.565534	0.0003631	0.9215750	4.317294	4.255494	3.802930	3.795263	3.755181	4.763028	4.843324	3.911130	4.757901	4.799120	4.198760	3.993704	4.795113	4.298916	2.927189	2.617642	3.512536	4.384472	4.506250
ENSG00000120693.14.SMAD9	98.94344	1.2294166	0.3466388	3.546679	0.0003901	0.9215750	5.567825	5.960168	6.607501	8.604941	7.027494	6.570295	6.139789	7.126734	5.278600	6.757063	6.891459	6.506179	5.862599	6.321890	7.348897	5.672774	6.698430	6.559717	6.333705
ENSG00000122042.10.UBL3	289.30915	-0.4758400	0.1389598	-3.424300	0.0006164	0.9999176	8.396061	8.245663	8.156159	7.906647	8.070629	7.541399	8.940188	7.734578	8.524283	8.000441	8.345029	8.368836	8.271668	8.470747	7.768950	7.986200	8.252087	8.378637	8.028581
ENSG00000263276.1.AC020978.7	11.21707	-1.6121027	0.4772870	-3.377638	0.0007311	0.9999176	4.317294	2.850282	4.080897	3.671201	3.415976	3.457650	5.760422	3.300721	4.757901	3.562659	3.748702	3.993704	4.795113	3.638623	2.927189	3.679636	4.425996	4.481074	4.458519
ENSG00000225783.8.MIAT	226.49307	2.1919115	0.6716071	3.263681	0.0010997	0.9999176	7.277072	6.981102	5.957138	10.373561	5.766977	7.494778	3.969024	5.484189	6.616130	7.423980	7.397557	8.124700	5.066314	5.493698	10.249400	7.302258	7.537217	4.481074	6.613693
ENSG00000228716.7.DHFR	109.31693	-0.6409490	0.1989584	-3.221523	0.0012751	0.9999176	6.776544	6.943115	7.113019	5.954547	6.641428	7.269581	7.229113	6.777383	7.247293	7.106004	7.284027	6.558848	7.746789	6.765465	6.301307	6.375359	6.381335	5.887241	6.686046
ENSG00000138829.12.FBN2	12.72584	2.2872322	0.7107843	3.217899	0.0012913	0.9999176	3.085671	3.885790	5.241285	6.774140	5.055372	3.457650	1.915476	4.319711	3.673207	2.683679	3.252399	3.377566	1.915476	3.427855	4.117123	3.561678	3.703592	4.167603	2.540207
ENSG00000000005.6.TNMD	250.62746	2.6208379	0.8228680	3.185004	0.0014475	0.9999176	5.133254	7.449118	5.670393	11.511902	6.969918	5.708015	2.894475	7.662883	6.832078	6.771613	7.831373	7.781693	2.915085	5.913338	8.068087	4.708157	4.963498	5.921156	7.674865
ENSG00000215424.10.MCM3AP.AS1	31.28213	-0.7800067	0.2468202	-3.160222	0.0015765	0.9999176	5.540829	5.490212	6.139853	4.359410	4.876313	4.825751	5.552982	5.141293	5.531626	5.061830	5.127730	4.625413	5.489322	5.493698	4.718563	4.943848	5.376546	5.352296	5.277691
ENSG00000116396.15.KCNC4	132.81346	1.0654126	0.3396946	3.136384	0.0017105	0.9999176	6.918901	6.714121	6.893662	8.699243	6.909945	7.117832	5.171001	7.662883	5.658273	7.237539	7.465408	6.479103	6.819663	7.111960	7.903149	6.738127	7.262890	6.352104	6.725814
ENSG00000274810.4.NPHP3.ACAD11	16.35090	-1.2720445	0.4076874	-3.120146	0.0018076	0.9999176	4.979858	3.598092	5.377657	3.795263	3.959221	5.152446	4.134502	3.811263	4.574173	4.144979	4.919420	3.993704	5.146198	5.134751	4.348612	3.109345	4.819151	4.571277	4.248745
ENSG00000083635.8.NUFIP1	26.50502	-0.8498701	0.2726964	-3.116543	0.0018299	0.9999176	4.807586	4.644792	4.728848	4.011653	4.338252	5.051766	5.983432	4.171147	4.970251	5.241519	5.648388	5.056001	5.862599	5.165836	4.718563	5.107418	4.928785	5.193916	5.043493
ENSG00000197978.9.GOLGA6L9	92.96471	-0.8726635	0.2800262	-3.116363	0.0018310	0.9999176	6.426414	5.516474	7.073732	5.929771	6.367914	6.570295	7.581668	6.550328	7.066006	6.869547	6.877288	6.423371	7.271051	6.899639	6.007433	4.899790	6.698430	7.047007	5.847820
ENSG00000187609.16.EXD3	203.10779	0.5618165	0.1828105	3.073219	0.0021176	0.9999176	7.301262	7.495767	7.938462	7.557649	7.796364	7.777810	7.263434	8.088059	7.777797	7.744521	7.240420	7.307863	7.528458	7.423227	8.093832	8.658078	7.570885	7.192513	7.955279
ENSG00000115457.10.IGFBP2	123.57068	1.4688315	0.4869031	3.016682	0.0025556	0.9999176	5.335983	7.291830	5.796926	9.616355	7.193878	7.002551	5.941524	6.315066	4.507041	6.650868	7.183988	6.609656	6.232722	6.251836	7.251573	6.224910	6.525868	5.921156	7.095623

dge3 <- dge
write.table(dge3,file="tear3.tsv",quote=FALSE,sep='\t')

# mitch
m3 <- mitch_import(dge3, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 16536

## Note: no. genes in output = 16514

## Note: estimated proportion of input genes in output = 0.999

mres3 <- mitch_calc(m3, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres3$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences between intact (ctrl) and torn (case) tendon.") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences between intact (ctrl) and torn (case) tendon.
	set	setSize	pANOVA	s.dist	p.adjustANOVA
206	Cohesin Loading onto Chromatin	10	1.15e-05	-0.8011512	0.0001067
424	Formation of the ternary complex, and subsequently, the 43S complex	51	0.00e+00	-0.7765875	0.0000000
416	Formation of a pool of free 40S subunits	100	0.00e+00	-0.7415316	0.0000000
1093	SARS-CoV-1 modulates host translation machinery	36	0.00e+00	-0.7333583	0.0000000
1086	Ribosomal scanning and start codon recognition	58	0.00e+00	-0.7279035	0.0000000
1416	Viral mRNA Translation	88	0.00e+00	-0.7252485	0.0000000
848	Peptide chain elongation	88	0.00e+00	-0.7213675	0.0000000
461	GTP hydrolysis and joining of the 60S ribosomal subunit	111	0.00e+00	-0.7199721	0.0000000
1369	Translation initiation complex formation	58	0.00e+00	-0.7160432	0.0000000
1138	Selenocysteine synthesis	92	0.00e+00	-0.7141361	0.0000000
621	L13a-mediated translational silencing of Ceruloplasmin expression	110	0.00e+00	-0.7133881	0.0000000
52	Activation of the mRNA upon binding of the cap-binding complex and eIFs, and subsequent binding to 43S	59	0.00e+00	-0.7112021	0.0000000
373	Eukaryotic Translation Termination	92	0.00e+00	-0.7103501	0.0000000
371	Eukaryotic Translation Elongation	93	0.00e+00	-0.7030815	0.0000000
368	Establishment of Sister Chromatid Cohesion	10	1.23e-04	-0.7011997	0.0008410
780	Nonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC)	94	0.00e+00	-0.6945513	0.0000000
154	Cap-dependent Translation Initiation	118	0.00e+00	-0.6893703	0.0000000
372	Eukaryotic Translation Initiation	118	0.00e+00	-0.6893703	0.0000000
828	PINK1-PRKN Mediated Mitophagy	21	1.00e-07	-0.6784032	0.0000011
1079	Response of EIF2AK4 (GCN2) to amino acid deficiency	100	0.00e+00	-0.6691056	0.0000000

m3top <- subset(mres3$enrichment_result,p.adjustANOVA<0.05)
m3up <- subset(m3top,s.dist>0)$set
m3dn <- subset(m3top,s.dist<0)$set

if (! file.exists("tear_mitchres3.html") ) {
  mitch_report(mres3,outfile="tear_mitchres3.html",overwrite=FALSE)
}

DE4. tear (intact) vs tear (torn).

colnames(x4) == rownames(ss4)

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE

x4f <- x4[which(rowMeans(x4)>10),]
dim(x4)

## [1] 60651    19

dim(x4f)

## [1] 16462    19

ss4$tissue <- factor(ss4$Tissue,levels=c("tendon","tear"))
ss4$integrity <- factor(ss4$Tendon_integrity_2_years_post.op)

dds <- DESeqDataSetFromMatrix(countData = x4f , colData = ss4, design = ~ Age + Sex + integrity )

## converting counts to integer mode

## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors

##   the design formula contains one or more numeric variables with integer values,
##   specifying a model with increasing fold change for higher values.
##   did you mean for this to be a factor? if so, first convert
##   this variable to a factor using the factor() function

##   the design formula contains one or more numeric variables that have mean or
##   standard deviation larger than 5 (an arbitrary threshold to trigger this message).
##   Including numeric variables with large mean can induce collinearity with the intercept.
##   Users should center and scale numeric variables in the design to improve GLM convergence.

res <- DESeq(dds)

## estimating size factors

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

## final dispersion estimates

## fitting model and testing

z <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge,20) %>%
  kbl(caption = "Top gene expression differences between intact (ctrl) and torn (case) tear.") %>%
  kable_paper("hover", full_width = F)

Top gene expression differences between intact (ctrl) and torn (case) tear.
	baseMean	log2FoldChange	lfcSE	stat	pvalue	padj	1.3_S93	10.3_S102	11.3_S103	14.3_S106	15.3_S107	16.3_S108	17.3_S109	18.3_S110	19.3_S111	20.3_S112	21.3_S113	23.3_S115	3.3_S95	4.3_S96	5.3_S97	6.3_S98	7.3_S99	8.3_S100	9.3_S101
ENSG00000160808.11.MYL3	292.03470	5.640668	0.7882517	7.155922	0e+00	0.0000000	12.015251	5.040094	5.447762	4.602719	4.765705	5.348479	10.151130	5.039428	4.686916	5.584445	5.858599	5.369543	4.893233	5.470333	5.455282	5.824088	5.107987	4.749442	4.352538
ENSG00000154358.23.OBSCN	535.03337	4.291105	0.6509957	6.591602	0e+00	0.0000003	12.715101	6.278577	6.855044	6.537729	6.192394	8.402272	10.817667	6.855135	7.047653	6.223473	6.227622	6.777732	6.212417	6.673548	7.273327	7.700741	7.372039	5.956808	5.997233
ENSG00000130598.16.TNNI2	188.59511	5.274715	0.8063469	6.541496	0e+00	0.0000003	11.282418	5.353189	3.870466	5.461623	4.600731	5.560983	9.802406	5.241439	4.612155	4.578215	5.178233	5.054253	4.646470	4.682696	4.894613	4.922054	5.591792	5.229181	4.700957
ENSG00000108515.18.ENO3	426.53843	4.585168	0.7448044	6.156205	0e+00	0.0000030	12.572473	6.261282	5.747611	6.553861	6.065882	6.388926	10.349496	6.355950	5.767234	5.738964	6.227622	5.900326	5.755349	5.871079	6.094762	5.216327	6.282344	5.509201	5.740993
ENSG00000163126.15.ANKRD23	281.00240	4.723716	0.8017283	5.891916	0e+00	0.0000123	11.955352	5.040094	5.646383	5.267410	5.703239	6.007473	9.901292	5.826145	5.099072	4.912275	5.595665	6.028537	5.484306	5.232525	5.417251	4.739598	6.302268	4.687394	5.720663
ENSG00000164309.15.CMYA5	571.59174	4.506435	0.7758916	5.808073	0e+00	0.0000170	12.983189	6.393962	5.908083	5.497114	6.065882	6.724226	10.603104	7.168243	6.964710	5.167364	7.216138	7.007944	6.394160	6.135957	6.641707	6.081237	7.334429	5.060725	7.122765
ENSG00000036448.10.MYOM2	176.05466	5.574517	0.9765226	5.708539	0e+00	0.0000254	11.488184	4.375692	4.357373	4.334825	5.091668	4.704909	8.479627	4.368493	4.531013	4.033026	5.346580	5.264143	4.495288	4.608167	4.450246	4.217480	5.412364	4.749442	5.031172
ENSG00000185482.8.STAC3	239.25638	3.901258	0.6854444	5.691575	0e+00	0.0000254	11.702834	6.393962	5.478086	6.147056	4.972494	6.491167	9.202979	5.883791	5.193673	5.128558	6.451212	5.264143	5.980211	6.113895	5.813077	5.452487	5.591792	5.790366	6.495200
ENSG00000214872.8.SMTNL1	59.41365	5.106018	0.9334413	5.470101	0e+00	0.0000807	9.626334	4.375692	4.869442	4.221376	4.278829	4.159550	8.260970	4.368493	4.342185	3.868646	4.220404	4.837829	4.408910	4.225095	4.096129	4.000754	4.829174	4.055743	4.193812
ENSG00000165887.12.ANKRD2	70.68061	4.454290	0.8182982	5.443358	1e-07	0.0000844	9.858939	4.605613	4.265873	5.180258	4.278829	4.159550	8.380178	5.287039	3.467455	4.702193	4.084522	4.710819	4.713946	4.225095	4.450246	5.340015	4.232306	4.472996	4.277470
ENSG00000101306.11.MYLK2	70.38820	5.092071	0.9503123	5.358313	1e-07	0.0001233	9.931606	4.375692	5.095141	4.221376	4.132793	4.264141	8.260970	3.467455	3.467455	5.046878	4.220404	4.325823	3.894437	4.225095	5.112265	4.516848	4.094349	4.185524	5.100391
ENSG00000163157.15.TMOD4	70.37804	4.699162	0.8809796	5.334020	1e-07	0.0001292	10.054092	4.459244	4.035576	4.522187	5.146958	4.815391	7.669797	4.860623	4.441855	4.157969	4.675422	4.392411	4.573950	4.608167	4.349771	4.000754	5.004587	3.467455	4.651254
ENSG00000196218.13.RYR1	387.75695	3.931141	0.7446007	5.279528	1e-07	0.0001522	12.319139	6.500519	4.869442	6.537729	5.770913	6.477011	10.241977	6.975951	6.456296	4.758803	6.144280	6.102935	7.038689	5.997906	5.563009	7.255457	6.587491	6.487995	6.879343
ENSG00000163395.17.IGFN1	68.20521	5.131902	0.9726908	5.275985	1e-07	0.0001522	9.655233	4.671000	3.870466	4.433714	4.278829	4.159550	8.733481	4.250861	3.467455	4.033026	4.432486	4.876976	3.467455	4.438414	4.234462	4.922054	5.778761	4.550098	4.277470
ENSG00000072954.7.TMEM38A	66.01269	4.138900	0.8229364	5.029428	5e-07	0.0005294	9.861859	4.605613	4.918370	4.676928	4.506211	5.091861	7.552611	4.645852	5.439987	4.509528	4.520856	5.489914	4.312410	4.935983	4.349771	5.002966	4.890813	3.884867	4.994936
ENSG00000124701.6.APOBEC2	49.38139	4.486345	0.8978211	4.996925	6e-07	0.0005688	9.573879	4.375692	4.265873	3.467455	4.400404	4.159550	6.664095	4.645852	5.281360	4.353450	4.333715	5.178672	4.312410	4.608167	4.697205	4.834731	4.949151	4.055743	4.277470
ENSG00000156219.17.ART3	35.24470	5.530251	1.1079266	4.991532	6e-07	0.0005688	9.247553	4.175519	4.161064	3.906010	3.939985	4.034329	5.931348	4.368493	4.227819	3.868646	3.467455	4.392411	3.467455	3.908207	4.234462	4.634654	4.693868	3.467455	3.467455
ENSG00000078814.17.MYH7B	197.54998	3.277687	0.6585170	4.977376	6e-07	0.0005780	11.175604	5.245074	6.776412	5.779147	6.435626	6.965037	9.042489	6.594262	6.621182	6.080328	6.122631	6.353801	6.047549	5.574314	5.868859	6.856111	6.554860	4.807947	5.980461
ENSG00000259768.6.AC004943.2	372.17415	4.429451	0.8945773	4.951446	7e-07	0.0006258	5.607887	5.420368	5.507706	6.309092	5.516991	5.767493	5.473861	6.068129	5.238308	6.594197	6.673589	4.797190	7.365020	6.157671	8.352681	12.559802	5.890203	5.629012	5.657723
ENSG00000140986.8.RPL3L	88.55836	5.731181	1.1604278	4.938852	8e-07	0.0006343	10.410749	4.459244	4.357373	3.906010	5.033628	4.264141	8.089585	4.470947	4.756437	4.157969	3.467455	4.392411	3.467455	4.225095	4.450246	5.216327	4.829174	3.467455	4.352538

dge4 <- dge
write.table(dge4,file="tear4.tsv",quote=FALSE,sep='\t')

# mitch
m4 <- mitch_import(dge4, DEtype="deseq2",geneTable=gt)

## The input is a single dataframe; one contrast only. Converting
##         it to a list for you.

## Note: Mean no. genes in input = 16462

## Note: no. genes in output = 16438

## Note: estimated proportion of input genes in output = 0.999

mres4 <- mitch_calc(m4, genesets, priority="effect")

## Note: Enrichments with large effect sizes may not be
##             statistically significant.

head(mres4$enrichment_result,20) %>%
  kbl(caption = "Top gene pathway differences between intact (ctrl) and torn (case) tear.") %>%
  kable_paper("hover", full_width = F)

Top gene pathway differences between intact (ctrl) and torn (case) tear.
	set	setSize	pANOVA	s.dist	p.adjustANOVA
405	Folding of actin by CCT/TriC	10	0.0002124	-0.6763331	0.0014929
367	Establishment of Sister Chromatid Cohesion	11	0.0002019	-0.6471220	0.0014326
1443	mitochondrial fatty acid beta-oxidation of saturated fatty acids	10	0.0004410	0.6417336	0.0030129
120	Biotin transport and metabolism	11	0.0010470	0.5706957	0.0066843
205	Cohesin Loading onto Chromatin	10	0.0019163	-0.5666423	0.0110302
481	Gluconeogenesis	29	0.0000002	0.5608634	0.0000018
187	Chk1/Chk2(Cds1) mediated inactivation of Cyclin B:Cdk1 complex	12	0.0008126	-0.5582309	0.0052333
1245	Somitogenesis	50	0.0000000	-0.5547327	0.0000000
1061	Regulation of ornithine decarboxylase (ODC)	48	0.0000000	-0.5545302	0.0000000
489	Glycogen breakdown (glycogenolysis)	14	0.0003853	0.5479438	0.0026568
1047	Regulation of activated PAK-2p34 by proteasome mediated degradation	48	0.0000000	-0.5474502	0.0000000
762	Negative regulation of NOTCH4 signaling	54	0.0000000	-0.5372360	0.0000000
1113	SLBP independent Processing of Histone Pre-mRNAs	10	0.0036516	-0.5308376	0.0187982
100	Autodegradation of the E3 ubiquitin ligase COP1	49	0.0000000	-0.5282525	0.0000000
1400	Ubiquitin-dependent degradation of Cyclin D	50	0.0000000	-0.5272126	0.0000000
1399	Ubiquitin Mediated Degradation of Phosphorylated Cdc25A	49	0.0000000	-0.5268180	0.0000000
1448	p53-Independent DNA Damage Response	49	0.0000000	-0.5268180	0.0000000
1449	p53-Independent G1/S DNA damage checkpoint	49	0.0000000	-0.5268180	0.0000000
460	GSK3B and BTRC:CUL1-mediated-degradation of NFE2L2	52	0.0000000	-0.5262771	0.0000000
1112	SLBP Dependent Processing of Replication-Dependent Histone Pre-mRNAs	11	0.0026876	-0.5226152	0.0145531

m4top <- subset(mres4$enrichment_result,p.adjustANOVA<0.05)
m4up <- subset(m4top,s.dist>0)$set
m4dn <- subset(m4top,s.dist<0)$set

if (! file.exists("tear_mitchres4.html") ) {
  mitch_report(mres4,outfile="tear_mitchres4.html",overwrite=FALSE)
}

Session information

For reproducibility.

sessionInfo()

## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
##  [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
##  [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] pkgload_1.3.3               GGally_2.2.0               
##  [3] beeswarm_0.4.0              gtools_3.9.5               
##  [5] echarts4r_0.4.5             kableExtra_1.3.4           
##  [7] topconfects_1.18.0          limma_3.58.1               
##  [9] eulerr_7.0.0                mitch_1.14.0               
## [11] MASS_7.3-60                 fgsea_1.28.0               
## [13] gplots_3.1.3                DESeq2_1.42.0              
## [15] SummarizedExperiment_1.32.0 Biobase_2.62.0             
## [17] MatrixGenerics_1.14.0       matrixStats_1.2.0          
## [19] GenomicRanges_1.54.1        GenomeInfoDb_1.38.5        
## [21] IRanges_2.36.0              S4Vectors_0.40.2           
## [23] BiocGenerics_0.48.1         reshape2_1.4.4             
## [25] lubridate_1.9.3             forcats_1.0.0              
## [27] stringr_1.5.1               dplyr_1.1.4                
## [29] purrr_1.0.2                 readr_2.1.4                
## [31] tidyr_1.3.0                 tibble_3.2.1               
## [33] ggplot2_3.4.4               tidyverse_2.0.0            
## [35] zoo_1.8-12                  R.utils_2.12.3             
## [37] R.oo_1.25.0                 R.methodsS3_1.8.2          
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7            gridExtra_2.3           rlang_1.1.2            
##  [4] magrittr_2.0.3          compiler_4.3.2          systemfonts_1.0.5      
##  [7] vctrs_0.6.5             rvest_1.0.3             pkgconfig_2.0.3        
## [10] crayon_1.5.2            fastmap_1.1.1           XVector_0.42.0         
## [13] ellipsis_0.3.2          labeling_0.4.3          caTools_1.18.2         
## [16] utf8_1.2.4              promises_1.2.1          rmarkdown_2.25         
## [19] tzdb_0.4.0              xfun_0.41               cachem_1.0.8           
## [22] zlibbioc_1.48.0         jsonlite_1.8.8          highr_0.10             
## [25] later_1.3.2             DelayedArray_0.28.0     BiocParallel_1.36.0    
## [28] parallel_4.3.2          R6_2.5.1                bslib_0.6.1            
## [31] stringi_1.8.3           RColorBrewer_1.1-3      jquerylib_0.1.4        
## [34] assertthat_0.2.1        Rcpp_1.0.11             knitr_1.45             
## [37] httpuv_1.6.13           Matrix_1.6-4            timechange_0.2.0       
## [40] tidyselect_1.2.0        yaml_2.3.8              rstudioapi_0.15.0      
## [43] abind_1.4-5             codetools_0.2-19        lattice_0.22-5         
## [46] plyr_1.8.9              shiny_1.8.0             withr_2.5.2            
## [49] evaluate_0.23           ggstats_0.5.1           xml2_1.3.6             
## [52] pillar_1.9.0            KernSmooth_2.23-22      generics_0.1.3         
## [55] RCurl_1.98-1.13         hms_1.1.3               munsell_0.5.0          
## [58] scales_1.3.0            xtable_1.8-4            glue_1.6.2             
## [61] tools_4.3.2             data.table_1.14.10      webshot_0.5.5          
## [64] locfit_1.5-9.8          fastmatch_1.1-4         cowplot_1.1.2          
## [67] grid_4.3.2              colorspace_2.1-0        GenomeInfoDbData_1.2.11
## [70] cli_3.6.2               fansi_1.0.6             viridisLite_0.4.2      
## [73] S4Arrays_1.2.0          svglite_2.1.3           gtable_0.3.4           
## [76] sass_0.4.8              digest_0.6.33           SparseArray_1.2.3      
## [79] farver_2.1.1            htmlwidgets_1.6.4       htmltools_0.5.7        
## [82] lifecycle_1.0.4         httr_1.4.7              statmod_1.5.0          
## [85] mime_0.12