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README.md

singleCellNet

Table of content

  1. Introduction
  2. Data
  3. Train SCN claissfier
  4. Assess SCN claissfier with heldout data
  5. Query
  6. Visualization
  7. Train cross-species SCN classifier
  8. Query for cross-species data
  9. Assess SCN claissfier with external dataset
  10. More detailed visualization examples
  11. Explore important celltype-specific top-pairs
  12. SCN score calibration
  13. Loom integration
  14. Seurat integration
  15. SCE integration
  16. Available training datasets

Introduction

SingleCellNet enables the classifcation of single cell RNA-Seq data across species and platforms. See our recent publication for more details. Additionally, we have a vignette to guide you through the steps as well.

Here, we illustrate ...

  • how to build and assess single cell classifiers

  • how to build and assess cross-species single cell classifiers

  • how to use these classifiers to quantify 'cell identity' from query scRNA-Seq data

If you want to use the bulk RNA-Seq version of CellNet, go to bulk CellNet.

Our singleCellNet is available on Python pySCN which is Scanpy and AnnData compatible.

Data

In this example, we use a subset of the Tabula Muris data to train singleCellNet. To learn more about the Tabula Muris project, see the manuscript. As query data, we use scRNA-Seq of kidney cells as reported in Park et al 2018. We also provide an example of classifying human, bead enriched PBMCs (from https://www.ncbi.nlm.nih.gov/pubmed/28091601). You can download this data here:

APPLICATION METADATA EXPRESSION
Query metadata expression data
Training metadata expression data
cross-species human-mouse orthologs
cross-species metadata expression data

*more training datasets (metadata and expression data) are provided at the bottom of the page.

Training

Setup

install.packages("devtools")
devtools::install_github("pcahan1/singleCellNet")
library(singleCellNet)

Optional set up if you are working with loom files

devtools::install_github(repo = "hhoeflin/hdf5r")
devtools::install_github(repo = "mojaveazure/loomR", ref = "develop")
library(loomR)

Fetch the data if you have not already done so

download.file("https://s3.amazonaws.com/cnobjects/singleCellNet/examples/sampTab_Park_MouseKidney_062118.rda", "sampTab_Park_MouseKidney_062118.rda")

download.file("https://s3.amazonaws.com/cnobjects/singleCellNet/examples/expMatrix_Park_MouseKidney_Oct_12_2018.rda", "expMatrix_Park_MouseKidney_Oct_12_2018.rda")

download.file("https://s3.amazonaws.com/cnobjects/singleCellNet/examples/expMatrix_TM_Raw_Oct_12_2018.rda", "expMatrix_TM_Raw_Oct_12_2018.rda")

download.file("https://s3.amazonaws.com/cnobjects/singleCellNet/examples/sampTab_TM_053018.rda", "sampTab_TM_053018.rda")

## For cross-species analyis:
download.file("https://s3.amazonaws.com/cnobjects/singleCellNet/examples/human_mouse_genes_Jul_24_2018.rda", "human_mouse_genes_Jul_24_2018.rda")

download.file("https://s3.amazonaws.com/cnobjects/singleCellNet/examples/6k_beadpurfied_raw.rda", "6k_beadpurfied_raw.rda")

download.file("https://s3.amazonaws.com/cnobjects/singleCellNet/examples/stDat_beads_mar22.rda", "stDat_beads_mar22.rda")

## To demonstrate how to integrate loom files to SCN
download.file("https://s3.amazonaws.com/cnobjects/singleCellNet/examples/pbmc_6k.loom", "pbmc_6k.loom")

Load query data

stPark = utils_loadObject("sampTab_Park_MouseKidney_062118.rda")
expPark = utils_loadObject("expMatrix_Park_MouseKidney_Oct_12_2018.rda")
dim(expPark)
[1] 16272 43745

genesPark = rownames(expPark)

rm(expPark)
gc()

Load the training data

expTMraw = utils_loadObject("expMatrix_TM_Raw_Oct_12_2018.rda")
dim(expTMraw)
[1] 23433 24936

stTM = utils_loadObject("sampTab_TM_053018.rda")
dim(stTM)
[1] 24936    17

stTM<-droplevels(stTM)

Find genes in common to the data sets and limit analysis to these

commonGenes = intersect(rownames(expTMraw), genesPark)
length(commonGenes)
[1] 13831

expTMraw = expTMraw[commonGenes,]

Split for training and assessment, and transform training data

set.seed(100) #can be any random seed number
stList = splitCommon(sampTab=stTM, ncells=100, dLevel="newAnn")
stTrain = stList[[1]]
expTrain = expTMraw[,rownames(stTrain)]

Train the classifier

- If you increase nTopGenes and nTopGenePairs, you may get a even better classifier performance on query data!
system.time(class_info<-scn_train(stTrain = stTrain, expTrain = expTrain, nTopGenes = 10, nRand = 70, nTrees = 1000, nTopGenePairs = 25, dLevel = "newAnn", colName_samp = "cell"))
   user  system elapsed 
 476.839  25.809 503.351

Assessing the classifier with heldout data

Apply to held out data

#validate data
stTestList = splitCommon(sampTab=stList[[2]], ncells=100, dLevel="newAnn") #normalize validation data so that the assessment is as fair as possible
stTest = stTestList[[1]]
expTest = expTMraw[commonGenes,rownames(stTest)]

#predict
classRes_val_all = scn_predict(cnProc=class_info[['cnProc']], expDat=expTest, nrand = 50)

Assess classifier

tm_heldoutassessment = assess_comm(ct_scores = classRes_val_all, stTrain = stTrain, stQuery = stTest, dLevelSID = "cell", classTrain = "newAnn", classQuery = "newAnn", nRand = 50)

plot_PRs(tm_heldoutassessment)

plot_metrics(tm_heldoutassessment)

Classification result heatmap

#Create a name vector label used later in classification heatmap where the values are cell types/ clusters and names are the sample names
 
nrand = 50
sla = as.vector(stTest$newAnn)
names(sla) = as.vector(stTest$cell)
slaRand = rep("rand", nrand) 
names(slaRand) = paste("rand_", 1:nrand, sep='')
sla = append(sla, slaRand) #include in the random cells profile created

sc_hmClass(classMat = classRes_val_all,grps = sla, max=300, isBig=TRUE)

Attribution plot

plot_attr(classRes=classRes_val_all, sampTab=stTest, nrand=nrand, dLevel="newAnn", sid="cell")

Viusalize average top pairs genes expression for training data

gpTab = compareGenePairs(query_exp = expTest, training_exp = expTrain, training_st = stTrain, classCol = "newAnn", sampleCol = "cell", RF_classifier = class_info$cnProc$classifier, numPairs = 20, trainingOnly= TRUE)

train = findAvgLabel(gpTab = gpTab, stTrain = stTrain, dLevel = "newAnn")

hm_gpa_sel(gpTab, genes = class_info$cnProc$xpairs, grps = train, maxPerGrp = 50)

Query

Apply to Park et al query data

expPark = utils_loadObject("expMatrix_Park_MouseKidney_Oct_12_2018.rda") 
  
nqRand = 50
system.time(crParkall<-scn_predict(class_info[['cnProc']], expPark, nrand=nqRand))
   user  system elapsed 
 89.633   5.010  95.041 

Visualization

sgrp = as.vector(stPark$description1)
names(sgrp) = as.vector(stPark$sample_name)
grpRand =rep("rand", nqRand)
names(grpRand) = paste("rand_", 1:nqRand, sep='')
sgrp = append(sgrp, grpRand)

# heatmap classification result
sc_hmClass(crParkall, sgrp, max=5000, isBig=TRUE, cCol=F, font=8)

Classification annotation assignment

# This classifies a cell with  the catgory with the highest classification score or higher than a classification score threshold of your choosing.
# The annotation result can be found in a column named category in the query sample table.

stPark <- get_cate(classRes = crParkall, sampTab = stPark, dLevel = "description1", sid = "sample_name", nrand = nqRand)

Classification result violin plot

sc_violinClass(sampTab = stPark, classRes = crParkall, sid = "sample_name", dLevel = "description1", addRand = nqRand)

Skyline plot of classification results

library(viridis)
stKid2 = addRandToSampTab(crParkall, stPark, "description1", "sample_name")
skylineClass(crParkall, "T cell", stKid2, "description1",.25, "sample_name")

Cross-species classification

Load the mouse training and human query data

stQuery = utils_loadObject("stDat_beads_mar22.rda")
expQuery = utils_loadObject("6k_beadpurfied_raw.rda") # use Matrix if RAM low
dim(expQuery)
[1] 32643  6000

stTM = utils_loadObject("sampTab_TM_053018.rda")
expTMraw = utils_loadObject("expMatrix_TM_Raw_Oct_12_2018.rda") # reload training

Load the ortholog table and convert human gene names to mouse ortholog names, and limit analysis to genes in common between the training and query data.

oTab = utils_loadObject("human_mouse_genes_Jul_24_2018.rda")
dim(oTab)
[1] 16688     3

aa = csRenameOrth(expQuery, expTMraw, oTab)
expQueryOrth = aa[['expQuery']]
expTrainOrth = aa[['expTrain']]

Limit anlaysis to a subset of the TM cell types

cts = c("B cell",  "cardiac muscle cell", "endothelial cell", "erythroblast", "granulocyte", "hematopoietic precursor cell", "late pro-B cell", "limb_mesenchymal", "macrophage", "mammary_basal_cell", "monocyte", "natural killer cell", "T cell", "trachea_epithelial", "trachea_mesenchymal")

stTM2 = filter(stTM, newAnn %in% cts)
stTM2 = droplevels(stTM2)
rownames(stTM2) = as.vector(stTM2$cell) # filter strips rownames

expTMraw2 = expTrainOrth[,rownames(stTM2)]
dim(expTMraw2)
[1] 14550 15161

Train Classifier

stList = splitCommon(stTM2, ncells=100, dLevel="newAnn")
stTrain = stList[[1]]
expTrain = expTMraw2[,rownames(stTrain)]

system.time(class_info2<-scn_train(stTrain = stTrain, expTrain = expTrain, nTopGenes = 10, nRand = 70, nTrees = 1000, nTopGenePairs = 25, dLevel = "newAnn", colName_samp = "cell"))
   user  system elapsed 
 41.029   6.747  47.963 

Apply to held out data

#validate data
stTestList = splitCommon(stList[[2]], ncells=100, dLevel="newAnn") 
stTest = stTestList[[1]]
expTest = expTMraw2[,rownames(stTest)]

#predict
system.time(classRes_val_all2 <- scn_predict(class_info2[['cnProc']], expTest, nrand = 50))
   user  system elapsed 
  0.691   0.032   0.724 

Assess classifier

tm_heldoutassessment = assess_comm(ct_scores = classRes_val_all2, stTrain = stTrain, stQuery = stTest, dLevelSID = "cell", classTrain = "newAnn", classQuery = "newAnn", nRand = 50)

plot_PRs(tm_heldoutassessment)

plot_metrics(tm_heldoutassessment)

Classification result heatmap

nrand=50
sla = as.vector(stTest$newAnn)
names(sla) = as.vector(stTest$cell)
slaRand = rep("rand", nrand)
names(slaRand) = paste("rand_", 1:nrand, sep='')
sla = append(sla, slaRand)

# heatmap classification result
sc_hmClass(classRes_val_all2, sla, max=300, font=7, isBig=TRUE)

Attribute plot

plot_attr(classRes_val_all2, stTest, nrand=nrand, dLevel="newAnn", sid="cell")

Apply to human query data

stQuery$description = as.character(stQuery$description)
stQuery[which(stQuery$description == "NK cell"), "description"] = "natural killer cell"

nqRand = 50
system.time(crHS <- scn_predict(class_info2[['cnProc']], expQueryOrth, nrand=nqRand))
   user  system elapsed 
  3.566   0.548   4.166 

Assess classifier with external dataset

tm_pbmc_assessment = assess_comm(ct_scores = crHS, stTrain = stTrain, stQuery = stQuery, classTrain = "newAnn",classQuery="description",dLevelSID="sample_name")
plot_PRs(tm_pbmc_assessment)

plot_metrics(tm_pbmc_assessment)

More visualization

Classification result heatmap

sgrp = as.vector(stQuery$prefix)
names(sgrp) = as.vector(stQuery$sample_name)
grpRand = rep("rand", nqRand)
names(grpRand) = paste("rand_", 1:nqRand, sep='')
sgrp = append(sgrp, grpRand)

sc_hmClass(crHS, sgrp, max=5000, isBig=TRUE, cCol=F, font=8)

Note that the macrophage category seems to be promiscuous in the mouse held out data, too.

Classification violin plot

sc_violinClass(sampTab = stQuery, classRes = crHS, sid = "sample_name", dLevel = "description")

Classification violin plot with adjusted width

sc_violinClass(sampTab = stQuery,classRes = crHS, sid = "sample_name", dLevel = "description", ncol = 12)

Classification violin plot with selected cluster

sc_violinClass(stQuery, crHS, sid = "sample_name", dLevel = "description", ncol = 12, sub_cluster = "B cell")

Attribution plot

plot_attr(crHS, stQuery, nrand=nqRand, sid="sample_name", dLevel="description")

Attribution plot with subcluster focus

plot_attr(sampTab = stQuery, classRes = crHS, sid = "sample_name", dLevel = "description", nrand = 50, sub_cluster = c("B cell", "T cell"))

UMAP by category

system.time(umPrep_HS<-prep_umap_class(crHS, stQuery, nrand=nqRand, dLevel="description", sid="sample_name", topPC=5))
  user  system elapsed 
 25.703   0.740  26.450 
plot_umap(umPrep_HS)

Heatmap top pairs genes for training sample average

system.time(gpTab2 <- compareGenePairs(query_exp = expQueryOrth, training_exp = expTrainOrth, training_st = stTrain, classCol = "newAnn", sampleCol = "cell", RF_classifier = class_info2$cnProc$classifier, numPairs = 20, trainingOnly = FALSE))
   user  system elapsed 
 84.130   0.677  84.826

sgrp = as.vector(stQuery$prefix)
names(sgrp) = rownames(stQuery)
train2 = findAvgLabel(gpTab2, stTrain = stTrain, dLevel = "newAnn")
sgrp = append(sgrp, train2)

hm_gpa_sel(gpTab2, genes = class_info2$cnProc$xpairs, grps = sgrp, maxPerGrp = 5)

How to calibrate/make sense of a given SCN score

#this function aims to give you a sense of how precise/sensitive SCN is with the assigned score of a given cell type for a cell

#tm_assess_matrix = tm_heldoutassessment$nonNA_PR

#tm_assess_matrix is a held_out assessment metric extracted from tm_heldoutassessment, which is already stored in SCN.
#e_assess_matrix is also provided for a gastrulation SCN classifier 

score = 0.6
celltype = "B cell"

calibration = scn_calibration(score = score, celltype = celltype, matrix=tm_assess_matrix)
#[1] "SCN score of 0.6 for cell type B cell has precision of 0.979 ~ 0.979 and sensitivity of 0.93 ~ 0.93"

calibration

#$score
#[1] 0.6

#$celltype
#[1] "B cell"

#$precision
#[1] 0.979 0.979

#$recall
#[1] 0.93 0.93

How to integrate loom file to SCN

lfile = loadLoomExpCluster("pbmc_6k.loom", cellNameCol = "obs_names", xname = "description")
stQuery = lfile$sampTab
dim(stQuery)
[1] 6000    2

expQuery = lfile$expDat
dim(expQuery)
[1] 32643  6000

#With this you can rerun the cross-species analysis and follow the exact same steps

Integrate Seurat object to SCN analysis

#exp_type options can be: counts, normcounts, and logcounts, if they are available in your sce object
seuratfile = extractSeurat(seurat_object, exp_slot_name = "counts")
sampTab = seuratfile$sampTab
expDat = seuratfile$expDat

Integrate SCE object to SCN analysis

#exp_type options can be: counts, data, and scale.data if they are available in your sce object
scefile = extractSCE(sce_object, exp_slot_name = "counts") 
sampTab = scefile$sampTab
expDat = scefile$expDat

More training data for your own analysis

study species organ/tissue seq method data
Baron mouse pancreas inDrop data
Baron human pancreas inDrop data
Murano* human pancreas Cel-Seq2 data
Segerstolp human pancreas Smart-Seq data
Park human kidney 10x data
Haber mouse intestine Smart-Seq2 data
TM10x mouse atlas subset 10x data
TM10x mouse atlas 10x data
TMfacs mouse atlas subset Smart-Seq data
TMfacs mouse atlas Smart-Seq data
MWS mouse atlas microwell-seq data
Zeisel mouse barin altas 10x data
Loo mouse cortex(e14.5) Dropseq data
Darmanis human cortex C1 data
Gokce* human striatum C1 and Smart-Seq2 data

*the expresion data is log-transformed.

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SingleCellNet: classify single cells across species and platforms

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