readme.md
In mrj31/immdonor: Search Immport for Donors
T Cell Cytometery Analysis
Immdonor is a GraphQL API of
Immport Organ Donor metadata hosted on
Hasura Cloud. This API lets you request exactly
what data you need from the Immport data
model and returns a JSON
output which can be converted to a R dataframe!
This is a fast way to link Flow Cytometry Standard
(.FCS) files to
their metadata and enables more robust analysis with
Cytoverse. You will need to have the FCS files
saved locally (see quick_fetch.R).
Connect to API
You can connect using your preferred GraphQL R client. Here we use
rOpenSci’s ghql package
library(ghql)
#By connecting to this API you agree to the Immport.org Terms of Service
con <- GraphqlClient$new(url = "https://resolved-lab-57.hasura.app/v1/graphql")
GET
qry <- Query$new()
studies<- qry$query('studies','{
shared_data_study {
study_accession
actual_enrollment
minimum_age
maximum_age
}
}')
x <- con$exec(qry$queries$studies)
ANSWER
studies<- as.data.frame (jsonlite::fromJSON(x))
study\_accession
actual\_enrollment
minimum\_age
maximum\_age
SDY702
56
3.00
73.00
SDY1041
20
21.00
70.00
SDY1097
43
.20
63.00
SDY1389
51
9.00
76.00
Organ Donor Metadata
The studies listed above feature cytometry data generously donated by
organ donors. We can build queries to return exactly what metadata we
would like to know about these organ donors.
qry <- Query$new()
files<- qry$query('files','{
resultFiles {
filePath
studyAccession
subjectAccession
maxSubjectAge
gender
biosampleType
parameters
panel_id
markers
}
}')
x <- con$exec(qry$queries$files)
files<- as.data.frame (jsonlite::fromJSON(x))
Biosample types
Primary Lymphoid Organs
Count
Bone Marrow
150
Thymus
31
Secondary Lymphoid Organs
Count
Inguinal lymph node
102
Lung lymph node
246
Mesenteric lymph node
120
Spleen
182
Tonsil
12
Mucosal
Count
Colon
76
Ileum
61
Jejunum
58
Lung
130
PBMC
136
Whole blood
31
Panels
Here are the top 5 staining panels with the most biosamples
panel\_id
parameters
markers
n
FCM-19
14
FITC-A-CD25|Qdot 605-A-CD45RA|Qdot 655-A-CD3|QDot
705-A-CD127|APC-A-CD19|PE-Cy7-A-CD4|PE-A-FOXP3
81
FCM-56
20
APC-A-CMV|BV570-A-CD3|Qdot 655-A-CD4|Qdot 605 UV-A-CD8|Qdot
605-A-CD45RA|Alexa Fluor 488-A-CCR7|QDot 705-A-NA|BV421-A-CD95|Alexa
Fluor 700-A-INFg|PerCP-Cy5-5-A-TNFa|PE-Cy7-A-IL2|PE-A-CD107a|DAPI-A-DEAD
78
FCM-21
18
FITC-A-CD25|PE-Cy5-A-CTLA-4|BV421-A-CD69|Qdot 605-A-CD45RA|Qdot
655-A-CD3|QDot 705-A-CD127|APC-A-CD103|Alexa Fluor
700-A-KI67|APC-Cy7-A-CD8|PE-Cy7-A-CD4|PE-A-FOX-p3
57
FCM-1
14
Alexa Fluor 488-A-CCR7|PerCP-Cy5-5-A-CD45RO|BV421-A-CD69|Qdot
605-A-CD45RA|Qdot 655-A-CD3|QDot
705-A-CD127|APC-A-CD31|APC-Cy7-A-CD8|PE-Texas
Red-A-CD19|PE-Cy7-A-CD4|PE-A-CD28
54
FCM-2
18
Alexa Fluor 488-A-CCR7|PerCP-Cy5-5-A-CD45RO|BV421-A-CD69|Qdot
605-A-CD45RA|Qdot 655-A-CD3|QDot 705-A-CD127|APC-A-CD31|APC-Cy7-A-CD8|PE
Alexa Fluor 610-A-CD19|PE-Cy7-A-CD4|PE-A-CD28
52
T Cell Surface Panel Analysis
panel\_id
parameters
markers
n
FCM-2
18
Alexa Fluor 488-A-CCR7|PerCP-Cy5-5-A-CD45RO|BV421-A-CD69|Qdot
605-A-CD45RA|Qdot 655-A-CD3|QDot 705-A-CD127|APC-A-CD31|APC-Cy7-A-CD8|PE
Alexa Fluor 610-A-CD19|PE-Cy7-A-CD4|PE-A-CD28
52
FCM-3
18
Alexa Fluor 488-A-NA|PerCP-Cy5-5-A-NA|BV421-A-NA|Qdot 605-A-NA|Qdot
655-A-NA|QDot 705-A-NA|APC-A-NA|APC-Cy7-A-NA|PE Alexa Fluor
610-A-NA|PE-Cy7-A-NA|PE-A-NA
27
Link cytoset to metadata
In order to use all of OpenCyto’s features like collapseDataForGating
we need to annotate the donor metadata into the cytoset’s pData.
# Load cytoset
fcs<- files %>% filter(data.resultFiles.panel_id == "FCM-2"|data.resultFiles.panel_id == "FCM-3")
fn<- fcs$data.resultFiles.filePath
cs<- load_cytoset_from_fcs(files = fn)
#Annotate immdonor metadata to pData
p<- pData(cs)
m<- cbind(p,fcs)
pData(cs)<- m
# Harmonize marker names
channels <- colnames(cs)
markers <- as.vector(pData(parameters(cs[[1]]))$desc)
names(markers)<- channels
markernames(cs) <- markers
Compensate and Transform
# Apply file internal compensation
comps <- lapply(cs, function(cf) spillover(cf)$SPILL)
cs_comp <- compensate(cs, comps)
# Transform fluorescent channels with FCSTrans
channels_to_exclude <- c(grep(colnames(cs), pattern="FSC"),
grep(colnames(cs), pattern="SSC"),
grep(colnames(cs), pattern="Time"))
chnls <- colnames(cs)[-channels_to_exclude]
fcstrans<- FCSTransTransform(transformationId = "defaultFCSTransTransform", channelrange = 2^18, channeldecade = 4.5, range = 4096, cutoff = -111, w = 0.5, rescale = TRUE)
transList <- transformList(chnls, fcstrans)
cs_trans<- transform(cs_comp,transList)
cs<- save_cytoset(cs_trans, "cytosets/tcell)
Build Autogating strategy
Since we annotated the cytoset with each file’s age, gender and
biosample type we can use the collapseDataForGating feature and
groupby biosampleType to improve our autogating.
cs<- load_cytoset(path = "cytosets/tcell")
gs<- GatingSet(cs)
gs_add_gating_method(gs, alias = "nonDebris",
pop = "+",
parent = "root",
dims = "FSC-A",
gating_method = "mindensity",
gating_args = "min = 20000, max=50000",
collapseDataForGating = "TRUE",
groupBy = "data.resultFiles.biosampleType")
## ...
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## done
gs_add_gating_method(gs, alias = "singlets",
pop = "+",
parent = "nonDebris",
dims = "FSC-A,FSC-H",
gating_method = "singletGate")
## ...
## done
gs_add_gating_method(gs, alias = "bcells",
pop = "+/-",
parent = "singlets",
dims = "CD19",
gating_method = "mindensity",
collapseDataForGating = "TRUE",
groupBy = "data.resultFiles.biosampleType")
## ...
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## done
gs_add_gating_method(gs, alias = "CD4",
pop = "+/-+/-",
parent = "CD19-",
dims = "CD3,CD4",
gating_method = "mindensity",
gating_args = "min = 1500, max=2500",
collapseDataForGating = "TRUE",
groupBy = "data.resultFiles.biosampleType")
## ...
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## done
gs_add_gating_method(gs, alias = "Tmem",
pop = "+/-+/-",
parent = "CD4+",
dims = "CCR7,CD45RA",
gating_method = "mindensity",
gating_args = "min = 1750, max=2500",
collapseDataForGating = "TRUE",
groupBy = "data.resultFiles.biosampleType")
## ...
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## done
lln<- subset(gs, data.resultFiles.biosampleType == "Lung lymph node")
lung<- subset(gs, data.resultFiles.biosampleType == "Lung")
spleen<- subset(gs, data.resultFiles.biosampleType == "Spleen")
blood<- subset(gs, data.resultFiles.biosampleType == "Whole blood")
CD3+CD4 T cells
Lung Lymph node
ggcyto(lln, aes(x = "CD4", y = "CD3")) + geom_gate("CD4+") + geom_hex(bins = 128)+ ggcyto_par_set(limits = "instrument")
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Lung
ggcyto(lung, aes(x = "CD4", y = "CD3")) + geom_gate("CD4+") + geom_hex(bins = 128)+ ggcyto_par_set(limits = "instrument")
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Spleen
ggcyto(spleen, aes(x = "CD4", y = "CD3")) + geom_gate("CD4+") + geom_hex(bins = 128)+ ggcyto_par_set(limits = "instrument")
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
CD4 memory subsets
Lung Lymph Node
ggcyto(gs_pop_get_data(lln ,"CD4+"), aes(x = "CCR7", y = "CD45RA")) + geom_hex(bins = 128)
Lung
ggcyto(gs_pop_get_data(lung ,"CD4+"), aes(x = "CCR7", y = "CD45RA")) + geom_hex(bins = 128)
Spleen
ggcyto(gs_pop_get_data(spleen ,"CD4+"), aes(x = "CCR7", y = "CD45RA")) + geom_hex(bins = 128)
Metacyto analysis
work in progress…
fcs<- files %>% filter(data.resultFiles.panel_id == "FCM-2"|data.resultFiles.panel_id == "FCM-3")
fcs<- fcs %>%
dplyr::filter(data.resultFiles.biosampleType == "Lung lymph node"|data.resultFiles.biosampleType == "Lung"|data.resultFiles.biosampleType == "Spleen"|data.resultFiles.biosampleType == "PBMC"|data.resultFiles.biosampleType == "Whole blood"|data.resultFiles.biosampleType == "Inguinal lymph node"|data.resultFiles.biosampleType == "Colon"|data.resultFiles.biosampleType == "Ileum"|data.resultFiles.biosampleType == "Jejunum")
fcs$fcs_files<- fcs$data.resultFiles.filePath
fcs$study_id <- fcs$data.resultFiles.biosampleType
fcs_info<- fcs %>%
dplyr::select(fcs_files, study_id)
sample_info <- fcs %>%
dplyr::select(fcs_files, data.resultFiles.maxSubjectAge,data.resultFiles.biosampleType, data.resultFiles.gender)
preprocessing.batch(inputMeta = fcs_info,
assay = "FCM",
outpath = "metacyto/panel/preprocess_output",
b = 1/150,
excludeTransformParameters=c("FSC-A","FSC-W","FSC-H","SSC-A","SSC-W","SSC-H","Time"))
## Study ID = Colon Preprocessing
## Study ID = Ileum Preprocessing
## Study ID = Jejunum Preprocessing
## Study ID = Whole blood Preprocessing
## Study ID = Lung lymph node Preprocessing
## Study ID = Inguinal lymph node Preprocessing
## Study ID = Lung Preprocessing
## Study ID = Spleen Preprocessing
## Study ID = PBMC Preprocessing
## Preprocess result stored in the folder: metacyto/panel/preprocess_output
files=list.files("metacyto/panel",pattern="processed_sample",recursive=TRUE,full.names=TRUE)
cs<- load_cytoset(path = "cytosets/tcell")
new<- markernames(cs)
channels_to_exclude <- c(grep(colnames(cs), pattern="FSC"),
grep(colnames(cs), pattern="SSC"),
grep(colnames(cs), pattern="Time"))
old <- colnames(cs)[-channels_to_exclude]
old<- toupper(old)
oldplus<-paste0(old,"+")
oldminus<-paste0(old,"-")
nameUpdator(oldNames=old, newNames=new, files=files)
nameUpdator(oldNames=oldplus, newNames=new, files=files)
nameUpdator(oldNames=oldminus, newNames=new, files=files)
#define parameters that we don't want to cluster
excludeClusterParameters=c("FSC-A","FSC-W","FSC-H","SSC-A","SSC-W","SSC-H","Time")
cluster_label=autoCluster.batch(preprocessOutputFolder="metacyto/panel/preprocess_output",
excludeClusterParameters=excludeClusterParameters,
labelQuantile=0.95,
minPercent = 0.05,
clusterFunction=flowSOM.MC)
## Clustering , study ID = Colon
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Ileum
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Jejunum
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Whole blood
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Lung lymph node
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Inguinal lymph node
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Lung
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Spleen
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = PBMC
## Building SOM
## Mapping data to SOM
## Building MST
searchCluster.batch(preprocessOutputFolder="metacyto/panel/preprocess_output",
outpath="metacyto/panel/search_output",
clusterLabel=cluster_label)
## Searching , study ID = Colon
## Searching , study ID = Ileum
## Searching , study ID = Jejunum
## Searching , study ID = Whole blood
## Searching , study ID = Lung lymph node
## Searching , study ID = Inguinal lymph node
## Searching , study ID = Lung
## Searching , study ID = Spleen
## Searching , study ID = PBMC
files=list.files("metacyto/panel/search_output",pattern="cluster_stats_in_each_sample",recursive=TRUE,full.names=TRUE)
fcs_stats=collectData(files,longform=TRUE)
# join the cluster summary statistics with sample information
all_data=inner_join(fcs_stats,sample_info,by="fcs_files")
# See the fraction of what clusters are affected by Gender (while controlling for age)
GA=glmAnalysis(value="value",
variableOfInterst="data.resultFiles.gender",
parameter="fraction",
otherVariables=c("data.resultFiles.maxSubjectAge"),
studyID="study_id",label="label",
data=all_data,CILevel=0.95,ifScale=c(TRUE,FALSE))
GA=GA[order(GA$`Effect_size`),]
GA$`label`=as.character(GA$`label`)
w = which((GA$`label`)>15)
## Warning: Use of `GA$label` is discouraged. Use `label` instead.
## Warning: Use of `GA$Effect_size` is discouraged. Use `Effect_size` instead.
Top 20 largest effect size
label
Effect\_size
SE
t\_value
p\_value
lower
upper
N
47
CD28+|CD3+|CD4+|CD45RO+
0.8031651
0.2278789
3.524526
0.0008629
0.3464856
1.2598446
66
184
CD28+|CD4+|CD45RO+
0.7426466
0.2428331
3.058260
0.0034352
0.2559982
1.2292949
66
85
CCR7+|CD19+|CD31+|CD4+|CD45RO+|CD8+
0.7358015
0.2545410
2.890700
0.0056343
0.2247892
1.2468138
61
128
CCR7+|CD19+|CD3+|CD31+|CD4+|CD45RO+|CD8+
0.7305022
0.2551306
2.863248
0.0060710
0.2183061
1.2426982
61
205
CD4+|CD45RO+
0.7237409
0.2444161
2.961102
0.0045181
0.2339201
1.2135616
66
203
CD28+|CD3+|CD4+|CD45RO+|CD8+
0.7039717
0.2343360
3.004113
0.0041215
0.2335226
1.1744207
61
30
CCR7+|CD19+
0.6178449
0.2168472
2.849217
0.0061552
0.1832733
1.0524165
66
78
CCR7+|CD19+|CD45RO+
0.6075617
0.2304315
2.636626
0.0108627
0.1457666
1.0693569
66
103
CCR7+|CD19+|CD31+|CD4+|CD45RO+
0.5967158
0.2418360
2.467439
0.0167492
0.1120655
1.0813660
66
11
CD127-|CD19+|CD4+|CD45RO+|CD8+
0.5930820
0.2192813
2.704663
0.0095541
0.1516916
1.0344725
55
104
CCR7+|CD19+|CD31+|CD4+
0.5903663
0.2410285
2.449363
0.0175242
0.1073343
1.0733983
66
132
CCR7+|CD3+|CD45RO+
0.5903451
0.2647291
2.229997
0.0298485
0.0598160
1.1208741
66
74
CCR7+|CD19+|CD28+|CD31+|CD4+|CD45RA+|CD45RO+|CD8+
0.5826280
0.2760517
2.110576
0.0397328
0.0284311
1.1368248
61
213
CD19+|CD3+|CD45RA+|CD45RO+
0.5573624
0.2245925
2.481660
0.0161614
0.1072689
1.0074559
66
151
CD4+
0.5528965
0.2464158
2.243754
0.0288942
0.0590682
1.0467248
66
17
CCR7+|CD19+|CD28+|CD45RO+|CD8+
0.5446699
0.2295150
2.373134
0.0214444
0.0838993
1.0054405
61
95
CCR7+|CD19+|CD28+|CD31+|CD4+|CD45RA+|CD8+
0.5394233
0.2465378
2.187994
0.0329366
0.0453505
1.0334961
66
120
CCR7+|CD19+|CD28+|CD3+|CD4+|CD45RA+
0.5315369
0.2320631
2.290484
0.0258510
0.0664721
0.9966016
66
136
CD19+|CD45RA+
0.5297685
0.2182681
2.427146
0.0185209
0.0923495
0.9671875
66
226
CCR7+|CD19+|CD28+|CD3+|CD31+|CD4+|CD45RA+|CD45RO+|CD69+|CD8+
0.5258602
0.2900366
1.813083
0.0763471
\-0.0579531
1.1096735
55
- Coming soon:
- CyTOF data support
- Please contact me with ANY questions, comments or suggestions!!!
- Slack: Join the immunespace
slack!
- iMessage: mrjaffery at icloud dot com
- email: mrjaffery at gmail dot com
mrj31/immdonor documentation built on Nov. 1, 2021, 4:29 a.m.
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T Cell Cytometery Analysis
Immdonor is a GraphQL API of Immport Organ Donor metadata hosted on Hasura Cloud. This API lets you request exactly what data you need from the Immport data model and returns a JSON output which can be converted to a R dataframe!
This is a fast way to link Flow Cytometry Standard (.FCS) files to their metadata and enables more robust analysis with Cytoverse. You will need to have the FCS files saved locally (see quick_fetch.R).
Connect to API
You can connect using your preferred GraphQL R client. Here we use rOpenSci’s ghql package
library(ghql)
#By connecting to this API you agree to the Immport.org Terms of Service
con <- GraphqlClient$new(url = "https://resolved-lab-57.hasura.app/v1/graphql")
GET
qry <- Query$new()
studies<- qry$query('studies','{
shared_data_study {
study_accession
actual_enrollment
minimum_age
maximum_age
}
}')
x <- con$exec(qry$queries$studies)
ANSWER
studies<- as.data.frame (jsonlite::fromJSON(x))
Organ Donor Metadata
The studies listed above feature cytometry data generously donated by organ donors. We can build queries to return exactly what metadata we would like to know about these organ donors.
qry <- Query$new()
files<- qry$query('files','{
resultFiles {
filePath
studyAccession
subjectAccession
maxSubjectAge
gender
biosampleType
parameters
panel_id
markers
}
}')
x <- con$exec(qry$queries$files)
files<- as.data.frame (jsonlite::fromJSON(x))
Biosample types
Primary Lymphoid Organs Count Bone Marrow 150 Thymus 31 Secondary Lymphoid Organs Count Inguinal lymph node 102 Lung lymph node 246 Mesenteric lymph node 120 Spleen 182 Tonsil 12 Mucosal Count Colon 76 Ileum 61 Jejunum 58 Lung 130 PBMC 136 Whole blood 31Panels
Here are the top 5 staining panels with the most biosamples
panel\_id parameters markers n FCM-19 14 FITC-A-CD25|Qdot 605-A-CD45RA|Qdot 655-A-CD3|QDot 705-A-CD127|APC-A-CD19|PE-Cy7-A-CD4|PE-A-FOXP3 81 FCM-56 20 APC-A-CMV|BV570-A-CD3|Qdot 655-A-CD4|Qdot 605 UV-A-CD8|Qdot 605-A-CD45RA|Alexa Fluor 488-A-CCR7|QDot 705-A-NA|BV421-A-CD95|Alexa Fluor 700-A-INFg|PerCP-Cy5-5-A-TNFa|PE-Cy7-A-IL2|PE-A-CD107a|DAPI-A-DEAD 78 FCM-21 18 FITC-A-CD25|PE-Cy5-A-CTLA-4|BV421-A-CD69|Qdot 605-A-CD45RA|Qdot 655-A-CD3|QDot 705-A-CD127|APC-A-CD103|Alexa Fluor 700-A-KI67|APC-Cy7-A-CD8|PE-Cy7-A-CD4|PE-A-FOX-p3 57 FCM-1 14 Alexa Fluor 488-A-CCR7|PerCP-Cy5-5-A-CD45RO|BV421-A-CD69|Qdot 605-A-CD45RA|Qdot 655-A-CD3|QDot 705-A-CD127|APC-A-CD31|APC-Cy7-A-CD8|PE-Texas Red-A-CD19|PE-Cy7-A-CD4|PE-A-CD28 54 FCM-2 18 Alexa Fluor 488-A-CCR7|PerCP-Cy5-5-A-CD45RO|BV421-A-CD69|Qdot 605-A-CD45RA|Qdot 655-A-CD3|QDot 705-A-CD127|APC-A-CD31|APC-Cy7-A-CD8|PE Alexa Fluor 610-A-CD19|PE-Cy7-A-CD4|PE-A-CD28 52T Cell Surface Panel Analysis
panel\_id parameters markers n FCM-2 18 Alexa Fluor 488-A-CCR7|PerCP-Cy5-5-A-CD45RO|BV421-A-CD69|Qdot 605-A-CD45RA|Qdot 655-A-CD3|QDot 705-A-CD127|APC-A-CD31|APC-Cy7-A-CD8|PE Alexa Fluor 610-A-CD19|PE-Cy7-A-CD4|PE-A-CD28 52 FCM-3 18 Alexa Fluor 488-A-NA|PerCP-Cy5-5-A-NA|BV421-A-NA|Qdot 605-A-NA|Qdot 655-A-NA|QDot 705-A-NA|APC-A-NA|APC-Cy7-A-NA|PE Alexa Fluor 610-A-NA|PE-Cy7-A-NA|PE-A-NA 27Link cytoset to metadata
In order to use all of OpenCyto’s features like collapseDataForGating
we need to annotate the donor metadata into the cytoset’s pData.
# Load cytoset
fcs<- files %>% filter(data.resultFiles.panel_id == "FCM-2"|data.resultFiles.panel_id == "FCM-3")
fn<- fcs$data.resultFiles.filePath
cs<- load_cytoset_from_fcs(files = fn)
#Annotate immdonor metadata to pData
p<- pData(cs)
m<- cbind(p,fcs)
pData(cs)<- m
# Harmonize marker names
channels <- colnames(cs)
markers <- as.vector(pData(parameters(cs[[1]]))$desc)
names(markers)<- channels
markernames(cs) <- markers
Compensate and Transform
# Apply file internal compensation
comps <- lapply(cs, function(cf) spillover(cf)$SPILL)
cs_comp <- compensate(cs, comps)
# Transform fluorescent channels with FCSTrans
channels_to_exclude <- c(grep(colnames(cs), pattern="FSC"),
grep(colnames(cs), pattern="SSC"),
grep(colnames(cs), pattern="Time"))
chnls <- colnames(cs)[-channels_to_exclude]
fcstrans<- FCSTransTransform(transformationId = "defaultFCSTransTransform", channelrange = 2^18, channeldecade = 4.5, range = 4096, cutoff = -111, w = 0.5, rescale = TRUE)
transList <- transformList(chnls, fcstrans)
cs_trans<- transform(cs_comp,transList)
cs<- save_cytoset(cs_trans, "cytosets/tcell)
Build Autogating strategy
Since we annotated the cytoset with each file’s age, gender and
biosample type we can use the collapseDataForGating feature and
groupby biosampleType to improve our autogating.
cs<- load_cytoset(path = "cytosets/tcell")
gs<- GatingSet(cs)
gs_add_gating_method(gs, alias = "nonDebris",
pop = "+",
parent = "root",
dims = "FSC-A",
gating_method = "mindensity",
gating_args = "min = 20000, max=50000",
collapseDataForGating = "TRUE",
groupBy = "data.resultFiles.biosampleType")
## ...
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## done
gs_add_gating_method(gs, alias = "singlets",
pop = "+",
parent = "nonDebris",
dims = "FSC-A,FSC-H",
gating_method = "singletGate")
## ...
## done
gs_add_gating_method(gs, alias = "bcells",
pop = "+/-",
parent = "singlets",
dims = "CD19",
gating_method = "mindensity",
collapseDataForGating = "TRUE",
groupBy = "data.resultFiles.biosampleType")
## ...
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## done
gs_add_gating_method(gs, alias = "CD4",
pop = "+/-+/-",
parent = "CD19-",
dims = "CD3,CD4",
gating_method = "mindensity",
gating_args = "min = 1500, max=2500",
collapseDataForGating = "TRUE",
groupBy = "data.resultFiles.biosampleType")
## ...
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## done
gs_add_gating_method(gs, alias = "Tmem",
pop = "+/-+/-",
parent = "CD4+",
dims = "CCR7,CD45RA",
gating_method = "mindensity",
gating_args = "min = 1750, max=2500",
collapseDataForGating = "TRUE",
groupBy = "data.resultFiles.biosampleType")
## ...
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## Warning in .gating_gtMethod(x, y, ...): NAs introduced by coercion
## done
lln<- subset(gs, data.resultFiles.biosampleType == "Lung lymph node")
lung<- subset(gs, data.resultFiles.biosampleType == "Lung")
spleen<- subset(gs, data.resultFiles.biosampleType == "Spleen")
blood<- subset(gs, data.resultFiles.biosampleType == "Whole blood")
CD3+CD4 T cells
Lung Lymph node
ggcyto(lln, aes(x = "CD4", y = "CD3")) + geom_gate("CD4+") + geom_hex(bins = 128)+ ggcyto_par_set(limits = "instrument")
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Lung
ggcyto(lung, aes(x = "CD4", y = "CD3")) + geom_gate("CD4+") + geom_hex(bins = 128)+ ggcyto_par_set(limits = "instrument")
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
Spleen
ggcyto(spleen, aes(x = "CD4", y = "CD3")) + geom_gate("CD4+") + geom_hex(bins = 128)+ ggcyto_par_set(limits = "instrument")
## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
CD4 memory subsets
Lung Lymph Node
ggcyto(gs_pop_get_data(lln ,"CD4+"), aes(x = "CCR7", y = "CD45RA")) + geom_hex(bins = 128)
Lung
ggcyto(gs_pop_get_data(lung ,"CD4+"), aes(x = "CCR7", y = "CD45RA")) + geom_hex(bins = 128)
Spleen
ggcyto(gs_pop_get_data(spleen ,"CD4+"), aes(x = "CCR7", y = "CD45RA")) + geom_hex(bins = 128)
Metacyto analysis
work in progress…
fcs<- files %>% filter(data.resultFiles.panel_id == "FCM-2"|data.resultFiles.panel_id == "FCM-3")
fcs<- fcs %>%
dplyr::filter(data.resultFiles.biosampleType == "Lung lymph node"|data.resultFiles.biosampleType == "Lung"|data.resultFiles.biosampleType == "Spleen"|data.resultFiles.biosampleType == "PBMC"|data.resultFiles.biosampleType == "Whole blood"|data.resultFiles.biosampleType == "Inguinal lymph node"|data.resultFiles.biosampleType == "Colon"|data.resultFiles.biosampleType == "Ileum"|data.resultFiles.biosampleType == "Jejunum")
fcs$fcs_files<- fcs$data.resultFiles.filePath
fcs$study_id <- fcs$data.resultFiles.biosampleType
fcs_info<- fcs %>%
dplyr::select(fcs_files, study_id)
sample_info <- fcs %>%
dplyr::select(fcs_files, data.resultFiles.maxSubjectAge,data.resultFiles.biosampleType, data.resultFiles.gender)
preprocessing.batch(inputMeta = fcs_info,
assay = "FCM",
outpath = "metacyto/panel/preprocess_output",
b = 1/150,
excludeTransformParameters=c("FSC-A","FSC-W","FSC-H","SSC-A","SSC-W","SSC-H","Time"))
## Study ID = Colon Preprocessing
## Study ID = Ileum Preprocessing
## Study ID = Jejunum Preprocessing
## Study ID = Whole blood Preprocessing
## Study ID = Lung lymph node Preprocessing
## Study ID = Inguinal lymph node Preprocessing
## Study ID = Lung Preprocessing
## Study ID = Spleen Preprocessing
## Study ID = PBMC Preprocessing
## Preprocess result stored in the folder: metacyto/panel/preprocess_output
files=list.files("metacyto/panel",pattern="processed_sample",recursive=TRUE,full.names=TRUE)
cs<- load_cytoset(path = "cytosets/tcell")
new<- markernames(cs)
channels_to_exclude <- c(grep(colnames(cs), pattern="FSC"),
grep(colnames(cs), pattern="SSC"),
grep(colnames(cs), pattern="Time"))
old <- colnames(cs)[-channels_to_exclude]
old<- toupper(old)
oldplus<-paste0(old,"+")
oldminus<-paste0(old,"-")
nameUpdator(oldNames=old, newNames=new, files=files)
nameUpdator(oldNames=oldplus, newNames=new, files=files)
nameUpdator(oldNames=oldminus, newNames=new, files=files)
#define parameters that we don't want to cluster
excludeClusterParameters=c("FSC-A","FSC-W","FSC-H","SSC-A","SSC-W","SSC-H","Time")
cluster_label=autoCluster.batch(preprocessOutputFolder="metacyto/panel/preprocess_output",
excludeClusterParameters=excludeClusterParameters,
labelQuantile=0.95,
minPercent = 0.05,
clusterFunction=flowSOM.MC)
## Clustering , study ID = Colon
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Ileum
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Jejunum
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Whole blood
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Lung lymph node
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Inguinal lymph node
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Lung
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = Spleen
## Building SOM
## Mapping data to SOM
## Building MST
## Clustering , study ID = PBMC
## Building SOM
## Mapping data to SOM
## Building MST
searchCluster.batch(preprocessOutputFolder="metacyto/panel/preprocess_output",
outpath="metacyto/panel/search_output",
clusterLabel=cluster_label)
## Searching , study ID = Colon
## Searching , study ID = Ileum
## Searching , study ID = Jejunum
## Searching , study ID = Whole blood
## Searching , study ID = Lung lymph node
## Searching , study ID = Inguinal lymph node
## Searching , study ID = Lung
## Searching , study ID = Spleen
## Searching , study ID = PBMC
files=list.files("metacyto/panel/search_output",pattern="cluster_stats_in_each_sample",recursive=TRUE,full.names=TRUE)
fcs_stats=collectData(files,longform=TRUE)
# join the cluster summary statistics with sample information
all_data=inner_join(fcs_stats,sample_info,by="fcs_files")
# See the fraction of what clusters are affected by Gender (while controlling for age)
GA=glmAnalysis(value="value",
variableOfInterst="data.resultFiles.gender",
parameter="fraction",
otherVariables=c("data.resultFiles.maxSubjectAge"),
studyID="study_id",label="label",
data=all_data,CILevel=0.95,ifScale=c(TRUE,FALSE))
GA=GA[order(GA$`Effect_size`),]
GA$`label`=as.character(GA$`label`)
w = which((GA$`label`)>15)
## Warning: Use of `GA$label` is discouraged. Use `label` instead.
## Warning: Use of `GA$Effect_size` is discouraged. Use `Effect_size` instead.
Top 20 largest effect size
label Effect\_size SE t\_value p\_value lower upper N 47 CD28+|CD3+|CD4+|CD45RO+ 0.8031651 0.2278789 3.524526 0.0008629 0.3464856 1.2598446 66 184 CD28+|CD4+|CD45RO+ 0.7426466 0.2428331 3.058260 0.0034352 0.2559982 1.2292949 66 85 CCR7+|CD19+|CD31+|CD4+|CD45RO+|CD8+ 0.7358015 0.2545410 2.890700 0.0056343 0.2247892 1.2468138 61 128 CCR7+|CD19+|CD3+|CD31+|CD4+|CD45RO+|CD8+ 0.7305022 0.2551306 2.863248 0.0060710 0.2183061 1.2426982 61 205 CD4+|CD45RO+ 0.7237409 0.2444161 2.961102 0.0045181 0.2339201 1.2135616 66 203 CD28+|CD3+|CD4+|CD45RO+|CD8+ 0.7039717 0.2343360 3.004113 0.0041215 0.2335226 1.1744207 61 30 CCR7+|CD19+ 0.6178449 0.2168472 2.849217 0.0061552 0.1832733 1.0524165 66 78 CCR7+|CD19+|CD45RO+ 0.6075617 0.2304315 2.636626 0.0108627 0.1457666 1.0693569 66 103 CCR7+|CD19+|CD31+|CD4+|CD45RO+ 0.5967158 0.2418360 2.467439 0.0167492 0.1120655 1.0813660 66 11 CD127-|CD19+|CD4+|CD45RO+|CD8+ 0.5930820 0.2192813 2.704663 0.0095541 0.1516916 1.0344725 55 104 CCR7+|CD19+|CD31+|CD4+ 0.5903663 0.2410285 2.449363 0.0175242 0.1073343 1.0733983 66 132 CCR7+|CD3+|CD45RO+ 0.5903451 0.2647291 2.229997 0.0298485 0.0598160 1.1208741 66 74 CCR7+|CD19+|CD28+|CD31+|CD4+|CD45RA+|CD45RO+|CD8+ 0.5826280 0.2760517 2.110576 0.0397328 0.0284311 1.1368248 61 213 CD19+|CD3+|CD45RA+|CD45RO+ 0.5573624 0.2245925 2.481660 0.0161614 0.1072689 1.0074559 66 151 CD4+ 0.5528965 0.2464158 2.243754 0.0288942 0.0590682 1.0467248 66 17 CCR7+|CD19+|CD28+|CD45RO+|CD8+ 0.5446699 0.2295150 2.373134 0.0214444 0.0838993 1.0054405 61 95 CCR7+|CD19+|CD28+|CD31+|CD4+|CD45RA+|CD8+ 0.5394233 0.2465378 2.187994 0.0329366 0.0453505 1.0334961 66 120 CCR7+|CD19+|CD28+|CD3+|CD4+|CD45RA+ 0.5315369 0.2320631 2.290484 0.0258510 0.0664721 0.9966016 66 136 CD19+|CD45RA+ 0.5297685 0.2182681 2.427146 0.0185209 0.0923495 0.9671875 66 226 CCR7+|CD19+|CD28+|CD3+|CD31+|CD4+|CD45RA+|CD45RO+|CD69+|CD8+ 0.5258602 0.2900366 1.813083 0.0763471 \-0.0579531 1.1096735 55- Coming soon:
- CyTOF data support
- Please contact me with ANY questions, comments or suggestions!!!
- Slack: Join the immunespace slack!
- iMessage: mrjaffery at icloud dot com
- email: mrjaffery at gmail dot com
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