Human Neurogenesis

We will demonstrate the basic scone workflow by using an early scRNA-Seq data set (Pollen et al. 2014). We focus on a set of 65 human cells sampled from four biological conditions: Cultured neural progenitor cells (“NPC”) derived from pluripotent stem cells, primary cortical samples at gestation weeks 16 and 21 (“GW16” and “GW21” respectively) and late cortical samples cultured for 3 weeks (“GW21+3”). Gene-level expression data for these cells can be loaded directly from the scRNAseq package on Bioconductor.

library(scRNAseq)

## ----- Load Example Data -----
fluidigm <- ReprocessedFluidigmData(assays = "rsem_counts")
assay(fluidigm) <- as.matrix(assay(fluidigm))

The rsem_counts assay contains expected gene-level read counts via RSEM (Li and Dewey 2011) quantification of 130 single-cell libraries aligned to the hg38 RefSeq transcriptome. The data object also contains library transcriptome alignment metrics obtained from Picard and other basic tools.

## ----- List all QC fields -----

# List all qc fields (accessible via colData())
metadata(fluidigm)$which_qc

##  [1] "NREADS"                       "NALIGNED"                    
##  [3] "RALIGN"                       "TOTAL_DUP"                   
##  [5] "PRIMER"                       "INSERT_SZ"                   
##  [7] "INSERT_SZ_STD"                "COMPLEXITY"                  
##  [9] "NDUPR"                        "PCT_RIBOSOMAL_BASES"         
## [11] "PCT_CODING_BASES"             "PCT_UTR_BASES"               
## [13] "PCT_INTRONIC_BASES"           "PCT_INTERGENIC_BASES"        
## [15] "PCT_MRNA_BASES"               "MEDIAN_CV_COVERAGE"          
## [17] "MEDIAN_5PRIME_BIAS"           "MEDIAN_3PRIME_BIAS"          
## [19] "MEDIAN_5PRIME_TO_3PRIME_BIAS"

All cell-level metadata, such as cell origin and sequence coverage (“low” vs “high” coverage) can be accessed using colData():

# Joint distribution of "biological condition"" and "coverage type""
table(colData(fluidigm)$Coverage_Type,
      colData(fluidigm)$Biological_Condition)

##       
##        GW16 GW21 GW21+3 NPC
##   High   26    8     16  15
##   Low    26    8     16  15

Each cell had been sequenced twice, at different levels of coverage. In this vignette we will focus on the high-coverage data. Before we get started we will do some preliminary filtering to remove the low-coverage replicates and undetected gene features:

# Preliminary Sample Filtering: High-Coverage Only
is_select = colData(fluidigm)$Coverage_Type == "High"
fluidigm = fluidigm[,is_select]

# Retain only detected transcripts
fluidigm = fluidigm[which(apply(assay(fluidigm) > 0,1,any)),]

Visualizing Technical Variability and Batch Effects

One of our alignment quality readouts is the fraction of reads aligned to the transcriptome. We can use simple bar plots to visualize how this metric relates to the biological batch.

# Define a color scheme
cc <- c(brewer.pal(9, "Set1"))

# One batch per Biological Condition
batch = factor(colData(fluidigm)$Biological_Condition)

# Alignment Quality Metrics
qc = colData(fluidigm)[,metadata(fluidigm)$which_qc]

# Barplot of read proportion mapping to human transcriptome
ralign = qc$RALIGN
o = order(ralign)[order(batch[order(ralign)])] # Order by batch, then value

barplot(ralign[o], col=cc[batch][o], 
        border=cc[batch][o], main="Percentage of reads mapped")
legend("bottomleft", legend=levels(batch), fill=cc,cex=0.4)

We can see modest differences between batches, and we see that there is one GW21 cell with a particularly low alignment rate relative to the rest of the GW21 batch. These types of observations can inform us of “poor-quality” libraries or batches. We may alternatively consider the number of reads for each library:

# Barplot of total read number
nreads = qc$NREADS
o = order(nreads)[order(batch[order(nreads)])] # Order by batch, then value

barplot(nreads[o], col=cc[batch][o], 
        border=cc[batch][o], main="Total number of reads")
legend("topright", legend=levels(batch), fill=cc, cex=0.4)

We see that read coverage varies substantially between batches as well as within batches. These coverage differences and other technical features can induce non-intuitive biases upon expression estimates. Though some biases can be addressed with simple library-size normalization and cell-filtering, demand for greater cell numbers may require more sophisticated normalization methods in order to compare multiple batches of cells. Batch-specific biases are impossible to address directly in this study as biological origin and sample preparation are completely confounded.

While it can be very helpful to visualize distributions of single quality metrics it should be noted that QC metrics are often correlated. In some cases it may be more useful to consider Principal Components (PCs) of the quality matrix, identifying latent factors of protocol variation:

## ----- PCA of QC matrix -----
qpc = prcomp(qc,center = TRUE,scale. = TRUE)
barplot((qpc$sdev^2)/sum(qpc$sdev^2), border="gray", 
        xlab="PC", ylab="Proportion of Variance", main="Quality PCA")

Even though 19 different QC metrics have been quantified in this analysis, PCA shows us that only a small number of PCs are needed to described a majority of the QC variance (e.g. 3 to explain 76%). We will now visualize the distribution of the first PC in the context of batch:

# Barplot of PC1 of the QC matrix
qc1 = as.vector(qpc$x[,1])
o = order(qc1)[order(batch[order(qc1)])]

barplot(qc1[o], col=cc[batch][o], 
        border=cc[batch][o], main="Quality PC1")
legend("bottomright", legend=levels(batch), 
       fill=cc, cex=0.8)

This first PC appears to represent both inter-batch and intra-batch sample heterogeneity, similar the the total number of reads. If this latent factor reflects variation in sample preparation, we may expect expression artifacts to trace this factor as well: in other words, we should be very skeptical of genes for which expression correlates strongly with the first PC of quality metrics. In this vignette we will show how latent factors like this can be applied to the normalization problem.

Drop-out Characteristics

Before we move on to normalization, let’s briefly consider a uniquely single-cell problem: “drop-outs.” One of the greatest challenges in modeling drop-out effects is modeling both i) technical drop-outs and ii) biological expression heterogeneity. One way to simplify the problem is to focus on genes for which we have strong prior belief in true expression. The scone package contains lists of genes that are believed to be ubiquitously and even uniformly expressed across human tissues. If we assume these genes are truly expressed in all cells, we can label all zero abundance observations as drop-out events. We model detection failures as a logistic function of mean expression, in line with the standard logistic model for drop-outs employed by the field:

# Extract Housekeeping Genes
data(housekeeping)
hk = intersect(housekeeping$V1,rownames(assay(fluidigm)))

# Mean log10(x+1) expression
mu_obs = rowMeans(log10(assay(fluidigm)[hk,]+1))

# Assumed False Negatives
drop_outs = assay(fluidigm)[hk,] == 0

# Logistic Regression Model of Failure
ref.glms = list()
for (si in 1:dim(drop_outs)[2]){
  fit = glm(cbind(drop_outs[,si],1 - drop_outs[,si]) ~ mu_obs,
            family=binomial(logit))
  ref.glms[[si]] = fit$coefficients
}

The list ref.glm contains the intercept and slope of each fit. We can now visualize the fit curves and the corresponding Area Under the Curves (AUCs):

par(mfrow=c(1,2))

# Plot Failure Curves and Calculate AUC
plot(NULL, main = "False Negative Rate Curves",
     ylim = c(0,1),xlim = c(0,6), 
     ylab = "Failure Probability", xlab = "Mean log10 Expression")
x = (0:60)/10
AUC = NULL
for(si in 1:ncol(assay(fluidigm))){
  y = 1/(exp(-ref.glms[[si]][1] - ref.glms[[si]][2] * x) + 1)
  AUC[si] = sum(y)/10
  lines(x, 1/(exp(-ref.glms[[si]][1] - ref.glms[[si]][2] * x) + 1),
        type = 'l', lwd = 2, col = cc[batch][si])
}

# Barplot of FNR AUC
o = order(AUC)[order(batch[order(AUC)])]

barplot(AUC[o], col=cc[batch][o], border=cc[batch][o], main="FNR AUC")
legend("topright", legend=levels(batch), fill=cc, cex=0.4)

Model-based metrics such as these may be more interpretable with respect to upstream sample preparation, and can be very useful for assessing single-cell library quality.

The scone Workflow

So far we have only described potential problems with single-cell expression data. Now we will take steps to address problems with our example data set. The basic QC and normalization pipeline we will use in this vignette allows us to:

• Filter out poor libraries using the metric_sample_filter function.
• Run and score many different normalization workflows (different combinations of normalization modules) using the main scone function.
• Browse top-ranked methods and visualize trade-offs with the biplot_color and sconeReport function.

In order to run many different workflows, SCONE relies on a normalization workflow template composed of 3 modules:

1. Data imputation: replacing zero-abundance values with expected values under a prior drop-out model. As we will see below, this module may be used as a modifier for module 2, without passing imputed values forward to downstream analyses. 2) Scaling or quantile normalization: either i) normalization that scales each sample’s transcriptome abundances by a single factor or ii) more complex offsets that match quantiles across samples. Examples: TMM or DESeq scaling factors, upper quartile normalization, or full-quantile normalization.
2. Regression-based approaches for removing unwanted correlated variation from the data, including batch effects. Examples: RUVg (Risso et al. 2014) or regression on Quality Principal Components described above.

On Threshold Selection

Let’s take a closer look at the computation behind selecting the ralign filter. In choosing a threshold value 67.7, metric_sample_filter is taking 4 values into account:

1. hard_ralign, the default “hard” threshold at 15 - rather forgiving… 2) 3 (zcut) times the standard deviation below the mean ralign value. 3) 3 (zcut) times the MAD below the median ralign value. 4) suff_ralign, the sufficient threshold set to NULL by default.

hist(qc$RALIGN, breaks = 0:100)
# Hard threshold
abline(v = 15, col = "yellow", lwd = 2)
# 3 (zcut) standard deviations below the mean ralign value
abline(v = mean(qc$RALIGN) - 3*sd(qc$RALIGN), col = "green", lwd = 2)
# 3 (zcut) MADs below the median ralign value
abline(v = median(qc$RALIGN) - 3*mad(qc$RALIGN), col = "red", lwd = 2)
# Sufficient threshold
abline(v = NULL, col = "grey", lwd = 2)

# Final threshold is the minimum of 
# 1) the sufficient threshold and 
# 2) the max of all others
thresh = min(NULL,
             max(c(15,mean(qc$RALIGN) - 3*sd(qc$RALIGN),
                   median(qc$RALIGN) - 3*mad(qc$RALIGN))))
abline(v = thresh, col = "blue", lwd = 2, lty = 2)

legend("topleft",legend = c("Hard","SD","MAD","Sufficient","Final"),
       lwd = 2, col = c("yellow","green","red","grey","blue"),
       lty = c(1,1,1,1,2), cex = .5)

We see here that the 3rd “MAD” threshold exceeds the first two thresholds (“Hard” and “SD”), and as the “Sufficient” threshold is NULL metric_sample_filter settles for the the third threshold. If the “Sufficient” threshold was not NULL and was exceeded by any of the other three thresholds (“Hard”,“SD”,“MAD”), metric_sample_filter would settle for the “Sufficient” threshold. Note also that if mixture=TRUE an additional criterion is considered: distributions may be fit to a two-component mixture model, and a threshold is defined with respect to the mean and standard deviation of the “best” component.

As metric_sample_filter relies on a maximum of candidate thresholds, we recommend users treat this function as a stringent sample filter.

Applying the sample filter

With the metric_sample_filter output in hand, it is fairly straightforward to remove the one “poor” sample from our study:

goodDat = fluidigm[,mfilt]

# Final Gene Filtering: Highly expressed in at least 5 cells
num_reads = quantile(assay(fluidigm)[assay(fluidigm) > 0])[4]
num_cells = 5
is_quality = rowSums(assay(fluidigm) >= num_reads ) >= num_cells

Creating a SconeExperiment Object

Prior to running main scone function we will want to define a SconeExperiment object that contains the primary expression data, experimental metadata, and control gene sets.

# Expression Data (Required)
expr = assay(goodDat)[is_quality,]

# Biological Origin - Variation to be preserved (Optional)
bio = factor(colData(goodDat)$Biological_Condition)

# Processed Alignment Metrics - Variation to be removed (Optional)
qc = colData(goodDat)[,metadata(goodDat)$which_qc]
ppq = scale(qc[,apply(qc,2,sd) > 0],center = TRUE,scale = TRUE)

# Positive Control Genes - Prior knowledge of DE (Optional)
poscon = intersect(rownames(expr),strsplit(paste0("ALS2, CDK5R1, CYFIP1,",
                                                  " DPYSL5, FEZ1, FEZ2, ",
                                                  "MAPT, MDGA1, NRCAM, ",
                                                  "NRP1, NRXN1, OPHN1, ",
                                                  "OTX2, PARD6B, PPT1, ",
                                                  "ROBO1, ROBO2, RTN1, ",
                                                  "RTN4, SEMA4F, SIAH1, ",
                                                  "SLIT2, SMARCA1, THY1, ",
                                                  "TRAPPC4, UBB, YWHAG, ",
                                                  "YWHAH"),split = ", ")[[1]])

# Negative Control Genes - Uniformly expressed transcripts (Optional)
negcon = intersect(rownames(expr),hk)

# Creating a SconeExperiment Object
my_scone <- SconeExperiment(expr,
                qc=ppq, bio = bio,
                negcon_ruv = rownames(expr) %in% negcon,
                poscon = rownames(expr) %in% poscon
)

Defining Normalization Modules

Before we can decide which workflows (normalizations) we will want to compare, we will also need to define the types of scaling functions we will consider in the comparison of normalizations:

## ----- User-defined function: Dividing by number of detected genes -----

EFF_FN = function (ei)
{
  sums = colSums(ei > 0)
  eo = t(t(ei)*sums/mean(sums))
  return(eo)
}

## ----- Scaling Argument -----

scaling=list(none=identity, # Identity - do nothing

             eff = EFF_FN, # User-defined function

             sum = SUM_FN, # SCONE library wrappers...
             tmm = TMM_FN, 
             uq = UQ_FN,
             fq = FQT_FN,
             psi = PSINORM_FN,
             deseq = DESEQ_FN)

If imputation is to be included in the comparison, imputation arguments must also be provided by the user:

# Simple FNR model estimation with SCONE::estimate_ziber
fnr_out = estimate_ziber(x = expr, bulk_model = TRUE,
                         pos_controls = rownames(expr) %in% hk,
                         maxiter = 10000)

## ----- Imputation List Argument -----
imputation=list(none=impute_null, # No imputation
                expect=impute_expectation) # Replace zeroes

## ----- Imputation Function Arguments -----
# accessible by functions in imputation list argument
impute_args = list(p_nodrop = fnr_out$p_nodrop, mu = exp(fnr_out$Alpha[1,]))

my_scone <- scone(my_scone,
                imputation = imputation, impute_args = impute_args,
                scaling=scaling,
                k_qc=3, k_ruv = 3,
                adjust_bio="no",
                run=FALSE)

Note, that because the imputation step is quite slow, we do not run it here, but will run scone without imputation.

Selecting SCONE Workflows

The main scone method arguments allow for a lot of flexibility, but a user may choose to run very specific combinations of modules. For this purpose, scone can be run in run=FALSE mode, generating a list of workflows to be performed and storing this list within a SconeExperiment object. After running this command the list can be extracted using the get_params method.

my_scone <- scone(my_scone,
                scaling=scaling,
                k_qc=3, k_ruv = 3,
                adjust_bio="no",
                run=FALSE)

head(get_params(my_scone))

##                                 imputation_method scaling_method uv_factors
## none,none,no_uv,no_bio,no_batch              none           none      no_uv
## none,eff,no_uv,no_bio,no_batch               none            eff      no_uv
## none,sum,no_uv,no_bio,no_batch               none            sum      no_uv
## none,tmm,no_uv,no_bio,no_batch               none            tmm      no_uv
## none,uq,no_uv,no_bio,no_batch                none             uq      no_uv
## none,fq,no_uv,no_bio,no_batch                none             fq      no_uv
##                                 adjust_biology adjust_batch
## none,none,no_uv,no_bio,no_batch         no_bio     no_batch
## none,eff,no_uv,no_bio,no_batch          no_bio     no_batch
## none,sum,no_uv,no_bio,no_batch          no_bio     no_batch
## none,tmm,no_uv,no_bio,no_batch          no_bio     no_batch
## none,uq,no_uv,no_bio,no_batch           no_bio     no_batch
## none,fq,no_uv,no_bio,no_batch           no_bio     no_batch

In the call above, we have set the following parameter arguments:

• k_ruv = 3. The maximum number of RUVg factors to consider.
• k_qc = 3. The maximum number of quality PCs (QPCs) to be included in a linear model, analogous to RUVg normalization. The qc argument must be provided.
• adjust_bio = “no.” Biological origin will NOT be included in RUVg or QPC regression models. The bio argument will be provided for evaluation purposes.

These arguments translate to the following set of options:

apply(get_params(my_scone),2,unique)

## $imputation_method
## [1] "none"
## 
## $scaling_method
## [1] "none"  "eff"   "sum"   "tmm"   "uq"    "fq"    "psi"   "deseq"
## 
## $uv_factors
## [1] "no_uv"   "ruv_k=1" "ruv_k=2" "ruv_k=3" "qc_k=1"  "qc_k=2"  "qc_k=3" 
## 
## $adjust_biology
## [1] "no_bio"
## 
## $adjust_batch
## [1] "no_batch"

Some scaling methods, such as scaling by gene detection rate (EFF_FN()), will not make sense within the context of imputed data, as imputation replaces zeroes with non-zero values. We can use the select_methods method to produce a SconeExperiment object initialized to run only meaningful normalization workflows.

is_screened = ((get_params(my_scone)$imputation_method == "expect") &
                 (get_params(my_scone)$scaling_method %in% c("none",
                                                             "eff")))

my_scone = select_methods(my_scone,
                          rownames(get_params(my_scone))[!is_screened ])

Calling scone with run=TRUE

Now that we have selected our workflows, we can run scone in run=TRUE mode. As well as arguments used in run=FALSE mode, this mode relies on a few additional arguments. In order to understand these arguments, we must first understand the 8 metrics used to evaluate each normalization. The first 6 metrics rely on a reduction of the normalized data down to 3 dimensions via PCA (default). Each metric is taken to have a positive (higher is better) or negative (lower is better) signature.

• BIO_SIL: Preservation of Biological Difference. The average silhouette width of clusters defined by bio, defined with respect to a Euclidean distance metric over the first 3 expression PCs. Positive signature.
• BATCH_SIL: Removal of Batch Structure. The average silhouette width of clusters defined by batch, defined with respect to a Euclidean distance metric over the first 3 expression PCs. Negative signature.
• PAM_SIL: Preservation of Single-Cell Heterogeneity. The maximum average silhouette width of clusters defined by PAM clustering, defined with respect to a Euclidean distance metric over the first 3 expression PCs. Positive signature.
• EXP_QC_COR: Removal of Alignment Artifacts. R^2 measure for regression of first 3 expression PCs on first k_qc QPCs. Negative signature.
• EXP_UV_COR: Removal of Expression Artifacts. R^2 measure for regression of first 3 expression PCs on first 3 PCs of the negative control (specified by eval_negcon or ruv_negcon by default) sub-matrix of the original (raw) data. Negative signature.
• EXP_WV_COR: Preservation of Biological Variance. R^2 measure for regression of first 3 expression PCs on first 3 PCs of the positive control (specified by eval_poscon) sub-matrix of the original (raw) data. Positive signature.
• RLE_MED: Reduction of Global Differential Expression. The mean squared-median Relative Log Expression (RLE). Negative signature.
• RLE_IQR: Reduction of Global Differential Variability. The variance of the inter-quartile range (IQR) of the RLE. Negative signature.

BiocParallel::register(
  BiocParallel::SerialParam()
) # Register BiocParallel Serial Execution

my_scone <- scone(my_scone,
                  scaling=scaling,
                  run=TRUE,
                  eval_kclust = 2:6,
                  stratified_pam = TRUE,
                  return_norm = "in_memory",
                  zero = "postadjust")

In the call above, we have set the following parameter arguments:

• eval_kclust = 2:6. For PAM_SIL, range of k (# of clusters) to use when computing maximum average silhouette width of PAM clusterings.
• stratified_pam = TRUE. For PAM_SIL, apply separate PAM clusterings to each biological batch rather than across all batches. Average is weighted by batch group size.
• return_norm = “in_memory”. Store all normalized matrices in addition to evaluation data. Otherwise normalized data is not returned in the resulting object.
• zero = “postadjust”. Restore data entries that are originally zeroes / negative after normalization to zero after the adjustment step.

The output will contain various updated elements:

# View Metric Scores
head(get_scores(my_scone))

##                                    BIO_SIL   PAM_SIL EXP_QC_COR EXP_UV_COR
## none,uq,ruv_k=1,no_bio,no_batch  0.2999009 0.6027087 -0.7687707 -0.4064851
## none,fq,no_uv,no_bio,no_batch    0.3019147 0.5558012 -0.8327434 -0.6553034
## none,uq,ruv_k=2,no_bio,no_batch  0.2335280 0.5106264 -0.7438718 -0.4022547
## none,fq,ruv_k=1,no_bio,no_batch  0.2798231 0.4621010 -0.7798794 -0.4685371
## none,psi,ruv_k=2,no_bio,no_batch 0.2639432 0.4550877 -0.7466728 -0.3252813
## none,uq,ruv_k=3,no_bio,no_batch  0.2456939 0.4646160 -0.7579988 -0.3475772
##                                  EXP_WV_COR      RLE_MED     RLE_IQR
## none,uq,ruv_k=1,no_bio,no_batch   0.4026451 -0.059523383 -0.10170726
## none,fq,no_uv,no_bio,no_batch     0.5878819 -0.013334022 -0.04482276
## none,uq,ruv_k=2,no_bio,no_batch   0.4238010 -0.058967735 -0.09460102
## none,fq,ruv_k=1,no_bio,no_batch   0.4430293 -0.005462639 -0.07016376
## none,psi,ruv_k=2,no_bio,no_batch  0.4399643 -0.057854898 -0.19168375
## none,uq,ruv_k=3,no_bio,no_batch   0.4175758 -0.058506600 -0.10121989

# View Mean Score Rank
head(get_score_ranks(my_scone))

##  none,uq,ruv_k=1,no_bio,no_batch    none,fq,no_uv,no_bio,no_batch 
##                         40.85714                         39.28571 
##  none,uq,ruv_k=2,no_bio,no_batch  none,fq,ruv_k=1,no_bio,no_batch 
##                         39.14286                         37.42857 
## none,psi,ruv_k=2,no_bio,no_batch  none,uq,ruv_k=3,no_bio,no_batch 
##                         36.85714                         36.71429

# Extract normalized data from top method
out_norm = get_normalized(my_scone,
                          method = rownames(get_params(my_scone))[1])

get_scores returns the 8 raw metrics for each normalization multiplied by their signature - or “scores.” get_score_ranks returns the mean score rank for each normalization. Both of these are sorted in decreasing order by mean score rank. Finally get_normalized returns the normalized expression data for the requested method. If the normalized data isn’t stored in the object it will be recomputed.