- NEBULA v1.2.2
- Overview
- Installation
- Functions
- Basic usage
- Specifying scaling factors
- Difference between NEBULA-LN and NEBULA-HL
- Filtering low-expression genes
- Checking convergence for the summary statistics and quality control
- Using other mixed models
- Testing contrasts
- Extracting marginal and conditional Pearson residuals

The R package, *nebula*, provides fast algorithms for fitting
negative binomial and Poisson mixed models for analyzing large-scale
multi-subject single-cell data. The package *nebula* accounts for
the hierarchical structure of the data by decomposing the total
overdispersion into between-subject and within-subject components using
a negative binomial mixed model (NBMM). The package nebula can be used
for e.g., identifying marker genes, testing treatment effects, detecting
genes with differential expression, performing cell-level co-expression
analysis, and obtaining Pearson residuals for downstream analyses.

More details can be found in the manuscript “NEBULA: a fast negative binomial mixed model for differential expression and co-expression analyses of large-scale multi-subject single-cell data” (https://www.nature.com/articles/s42003-021-02146-6).

To install the latest version from github:

```
install.packages("devtools")
library(devtools)
install_github("lhe17/nebula")
```

Because the package *nebula* uses the R package
*Rfast*, the installation process may first install
*Rfast*, which requires that GSL is installed or available in the
environment. The installation also requires Rcpp-1.0.7 and has been
tested on R-4.1.0. Starting from this version, *nebula* does not
support R-3.6 or an older version of R. For R-3.6, the version 1.1.8 can
be installed via R-forge (https://r-forge.r-project.org/R/?group_id=2407) although
it is not recommended to use an older version.

Please contact liang.he@health.sdu.dk for more information.

The current version provides the following functions.

`nebula`

: performs an association analysis using NBMMs given a count matrix and subject IDs.`group_cell`

: reorders cells to group them by the subject IDs.`nbresidual`

: extracts Pearson residuals from the fitted model.

We use an example data set to illustrate how to use nebula to perform an association analysis of multi-subject single-cell data. The example data set attached to the R package can be loaded as follows.

```
library(nebula)
data(sample_data)
```

The example data set includes a count matrix of 6030 cells and 10 genes from 30 subjects.

```
dim(sample_data$count)
#> [1] 10 6176
```

The count matrix can be a matrix object or a sparse dgCMatrix object. The elements should be integers.

```
sample_data$count[1:5,1:5]
#> 5 x 5 sparse Matrix of class "dgCMatrix"
#>
#> A . . . . .
#> B . . . . .
#> C . 1 2 . .
#> D . . . . .
#> E . . . . .
```

The subject IDs of each cell are stored in
`sample_data$sid`

. The subject IDs can be a character or
numeric vector, the length of which should equal the number of
cells.

```
head(sample_data$sid)
#> [1] "1" "1" "1" "1" "1" "1"
table(sample_data$sid)
#>
#> 1 10 11 12 13 14 15 16 17 18 19 2 20 21 22 23 24 25 26 27
#> 187 230 185 197 163 216 211 195 200 239 196 223 198 202 213 210 199 214 237 200
#> 28 29 3 30 4 5 6 7 8 9
#> 205 183 222 191 205 225 211 197 215 207
```

The next step is to build a design matrix for the predictors. The
example data set includes a data frame consisting of three predictors
stored in `sample_data$pred`

. To build the design matrix, we
can use the function `model.matrix`

. The intercept term must
be included in the design matrix. Each column in the design matrix
should have a unique variable name.

```
head(sample_data$pred)
#> X1 X2 cc
#> 1 0.6155094 0.9759191 control
#> 2 1.4608092 0.9759191 case
#> 3 1.6675054 0.9759191 control
#> 4 -0.1717715 0.9759191 case
#> 5 0.2277492 0.9759191 control
#> 6 -0.2635516 0.9759191 control
df = model.matrix(~X1+X2+cc, data=sample_data$pred)
head(df)
#> (Intercept) X1 X2 cccontrol
#> 1 1 0.6155094 0.9759191 1
#> 2 1 1.4608092 0.9759191 0
#> 3 1 1.6675054 0.9759191 1
#> 4 1 -0.1717715 0.9759191 0
#> 5 1 0.2277492 0.9759191 1
#> 6 1 -0.2635516 0.9759191 1
```

The association analysis between the gene expression and the
predictors can then be conducted using the function `nebula`

as follows. The count matrix is an *M* by *N* matrix,
where *M* is the number of genes, and *N* is the number of
cells. The function by default fitted the negative binomial gamma mixed
model (NBGMM) for each of the genes, and return a list of summary
statistics including the fold change, p-values, and both subject-level
and cell-level overdispersions (\(\sigma^2\) and \(\phi^{-1}\)). The p-values returned by
`nebula`

are raw p-values (not adjusted for multiple
testing).

```
re = nebula(sample_data$count,sample_data$sid,pred=df)
#> Remove 0 genes having low expression.
#> Analyzing 10 genes with 30 subjects and 6176 cells.
re
#> $summary
#> logFC_(Intercept) logFC_X1 logFC_X2 logFC_cccontrol se_(Intercept)
#> 1 -1.902455 -0.016755225 -0.097867225 0.047278197 0.06335820
#> 2 -2.046638 -0.002679074 -0.053812464 -0.022293899 0.06181112
#> 3 -2.033211 0.017954707 0.002398445 -0.048296661 0.08695028
#> 4 -2.008542 -0.005698984 -0.027780387 0.077357703 0.05509711
#> 5 -1.979437 0.011557090 -0.025198987 0.032890493 0.06155853
#> 6 -1.949991 0.013483039 -0.012548791 -0.031590577 0.07440949
#> 7 -1.969248 -0.003531361 0.075230699 -0.009075031 0.06185028
#> 8 -1.964371 0.013639930 -0.061302756 -0.059284665 0.07786361
#> 9 -2.072699 -0.017372176 -0.043828288 0.026624998 0.05737632
#> 10 -2.045646 0.030742876 0.022260805 -0.025516032 0.06842796
#> se_X1 se_X2 se_cccontrol p_(Intercept) p_X1 p_X2
#> 1 0.03534659 0.06449424 0.06879634 4.362617e-198 0.6354810 0.1291514
#> 2 0.03787429 0.06255849 0.07385888 2.052788e-240 0.9436079 0.3896819
#> 3 0.03696089 0.09238230 0.07258521 6.275230e-121 0.6271261 0.9792875
#> 4 0.03704556 0.05624824 0.07252600 5.822948e-291 0.8777381 0.6213846
#> 5 0.03750948 0.06101307 0.07331551 7.432319e-227 0.7579977 0.6795995
#> 6 0.03623477 0.07321208 0.07087566 2.257914e-151 0.7098168 0.8639067
#> 7 0.03631619 0.06068697 0.07133730 1.872102e-222 0.9225364 0.2151043
#> 8 0.03551903 0.07955877 0.06969748 1.957495e-140 0.7009654 0.4409831
#> 9 0.03816039 0.05767972 0.07453316 9.307495e-286 0.6489358 0.4473406
#> 10 0.03798694 0.06917485 0.07374591 2.292903e-196 0.4183419 0.7476005
#> p_cccontrol gene_id gene
#> 1 0.4919443 1 A
#> 2 0.7627706 2 B
#> 3 0.5058082 3 C
#> 4 0.2861434 4 D
#> 5 0.6537089 5 E
#> 6 0.6558008 6 F
#> 7 0.8987718 7 G
#> 8 0.3949916 8 H
#> 9 0.7209245 9 I
#> 10 0.7293432 10 J
#>
#> $overdispersion
#> Subject Cell
#> 1 0.08125256 0.8840821
#> 2 0.07102681 0.9255032
#> 3 0.17159404 0.9266395
#> 4 0.05026165 0.8124118
#> 5 0.07075366 1.2674146
#> 6 0.12086392 1.1096065
#> 7 0.07360445 0.9112956
#> 8 0.13571262 0.7549629
#> 9 0.05541398 0.8139652
#> 10 0.09496649 0.9410035
#>
#> $convergence
#> [1] 1 1 1 1 1 1 1 1 1 1
#>
#> $algorithm
#> [1] "NBGMM (LN)" "NBGMM (LN)" "NBGMM (LN)" "NBGMM (LN)" "NBGMM (LN)"
#> [6] "NBGMM (LN)" "NBGMM (LN)" "NBGMM (LN)" "NBGMM (LN)" "NBGMM (LN)"
#>
#> $covariance
#> NULL
#>
#> $random_effect
#> NULL
```

The cells in the count matrix need to be grouped by the subjects
(that is, the cells of the same subject should be placed consecutively)
before using as the input to the function `nebula`

. If the
cells are not grouped, the function `group_cell`

can be used
to first reorder the cells, as shown below. If a scaling factor is
specified by the user, it should also be included in
`group_cell`

. If the cells are already grouped,
`group_cell`

will return *NULL*.

```
data_g = group_cell(count=sample_data$count,id=sample_data$sid,pred=df)
re = nebula(data_g$count,data_g$id,pred=data_g$pred)
```

If `pred`

is not specified, `nebula`

will fit
the model with an intercept term by default. This can be used when only
the overdispersions are of interest.

The scaling factor for each cell is specified in `nebula`

using the argument `offset`

. The argument `offset`

has to be a vector of length *N* containing positive values. Note
that log(`offset`

) will be the offset term in the NBMM.
Common scaling factors can be the library size of a cell or a
normalizing factor adjusted using e.g., TMM. If not specified,
`nebula`

will set `offset`

as one by default,
which means that each cell is treated equally. If the input count matrix
is already normalized by another tool, e.g., scTransform, then you
should not specify `offset`

. However, since
`nebula`

directly models the raw counts, it is not
recommended to use a normalized count matrix for
`nebula`

.

`re = nebula(sample_data$count,sample_data$sid,pred=df,offset=sample_data$offset)`

In *nebula*, a user can choose one of the two algorithms to
fit an NBGMM. NEBULA-LN uses an approximated likelihood based on the law
of large numbers, and NEBULA-HL uses an h-likelihood. A user can select
these methods through `method='LN'`

or
`method='HL'`

. NEBULA-LN is faster and performs particularly
well when the number of cells per subject (CPS) is large. In addition,
NEBULA-LN is much more accurate in estimating a very large subject-level
overdispersion. In contrast, NEBULA-HL is slower but more accurate in
estimating the cell-level overdispersion.

In the following analysis of the example data set comprising ~200 cells per subject, the difference of the estimated cell-level overdispersions between NEBULA-LN and NEBULA-HL is ~5% for most genes.

```
re_ln = nebula(sample_data$count,sample_data$sid,pred=df,offset=sample_data$offset,method='LN')
#> Remove 0 genes having low expression.
#> Analyzing 10 genes with 30 subjects and 6176 cells.
re_hl = nebula(sample_data$count,sample_data$sid,pred=df,offset=sample_data$offset,method='HL')
#> Remove 0 genes having low expression.
#> Analyzing 10 genes with 30 subjects and 6176 cells.
## compare the estimated overdispersions
cbind(re_hl$overdispersion,re_ln$overdispersion)
#> Subject Cell Subject Cell
#> 1 0.08432321 0.9284703 0.08125256 0.8840821
#> 2 0.07455464 0.9726512 0.07102681 0.9255032
#> 3 0.17403276 0.9817570 0.17159404 0.9266395
#> 4 0.05352148 0.8516682 0.05026165 0.8124118
#> 5 0.07480033 1.3254379 0.07075366 1.2674146
#> 6 0.12372426 1.1653128 0.12086392 1.1096065
#> 7 0.07724824 0.9578169 0.07360445 0.9112956
#> 8 0.13797646 0.7991954 0.13571262 0.7549629
#> 9 0.05879492 0.8568854 0.05541398 0.8139652
#> 10 0.09782335 0.9940223 0.09496649 0.9410035
```

Such difference has little impact on testing fixed-effects predictors under this sample size.

```
## compare the p-values for testing the predictors using NEBULA-LN and NEBULA-HL
cbind(re_hl$summary[,10:12],re_ln$summary[,10:12])
#> p_X1 p_X2 p_cccontrol p_X1 p_X2 p_cccontrol
#> 1 0.6373037 0.1346298 0.4950795 0.6354810 0.1291514 0.4919443
#> 2 0.9444825 0.3977109 0.7626827 0.9436079 0.3896819 0.7627706
#> 3 0.6282384 0.9787882 0.5087304 0.6271261 0.9792875 0.5058082
#> 4 0.8786074 0.6278826 0.2868256 0.8777381 0.6213846 0.2861434
#> 5 0.7596198 0.6872259 0.6544751 0.7579977 0.6795995 0.6537089
#> 6 0.7134192 0.8656686 0.6576835 0.7098168 0.8639067 0.6558008
#> 7 0.9216994 0.2230964 0.8977251 0.9225364 0.2151043 0.8987718
#> 8 0.7017083 0.4443604 0.3955343 0.7009654 0.4409831 0.3949916
#> 9 0.6505414 0.4561469 0.7238323 0.6489358 0.4473406 0.7209245
#> 10 0.4199828 0.7510837 0.7308108 0.4183419 0.7476005 0.7293432
```

The bias of NEBULA-LN in estimating the cell-level overdispersion
gets larger when the CPS value becomes lower or the gene expression is
more sparse. If the CPS value is <30, `nebula`

will set
`method='HL'`

regardless of the user’s input.

When NEBULA-LN is used, the user can opt for better accuracy of estimating a smaller subject-level overdispersion through the argument \(\kappa\). NEBULA first fits the data using NEBULA-LN. If the estimated \(\kappa\) for a gene is smaller than the user-defined value, NEBULA-HL will be used to estimate the subject-level overdispersion for the gene. The default value of \(\kappa\) is 800, which can provide a good estimate of the subject-level overdispersion as low as ~0.005. Our simulation results suggest that \(\kappa=200\) is often sufficient for achieving a well controlled false positive rate of testing a cell-level predictor. We do not recommend using a smaller \(\kappa\) than 200. Specifying a larger \(\kappa\) can obtain a more accurate estimate of a smaller subject-level overdispersion when the cell-level overdispersion is large, but will be computationally slower. On the other hand, testing a subject-level predictor (i.e., a variable whose values are shared across all cells from a subject, such as age, sex, treatment, genotype, etc) is more sensitive to the accuracy of the estimated subject-level overdispersion. So we recommend using \(\kappa=800\) (as default) or even larger when testing a subject-level predictor. Another option to testing a subject-level predictor is to use a Poisson gamma mixed model, which is extremely fast (>50x faster than NEBULA-LN) and will be described below.

NEBULA-HL automatically uses a higher-order Laplace approximation for
low-expressed genes of which the average count per subject is less than
3. The higher-order Laplace approximation substantially increases the
accuracy for estimating the subject-level overdispersion for
low-expressed genes and controls the false positive rate. Nevertheless,
we recommend removing genes with very low expression from the analysis
because there is little statistical power for these genes. Filtering out
low-expressed genes can be specified by `cpc=0.005`

(i.e.,
counts per cell<0.5%). The argument `cpc`

is defined by
the ratio between the total count of the gene and the number of
cells.

*nebula* reports convergence information about the estimation
algorithm for each gene along with the summary statistics. This is
useful and important information for quality control to filter out genes
of which the estimation procedure potentially does not converge.
Generally, a convergence code \(\leq\)
-20 suggests that the algorithm does not converge well. If the
convergence code is -30, which indicates a failure of convergence, their
summary statistics should NOT be used. If the convergence code is -20 or
-40, it indicates that the optimization algorithm stops at the maximum
step limit before the complete convergence. The results should be
interpreted with caution in this case. The failure of convergence may
occur when the sample size is very small, there are too few positive
counts, or the gene has huge overdispersions, in which case the
likelihood is flat or the optimization is sensitive to the initial
values. For those genes that have a bad convergence code, in many cases,
trying a different negative binomial mixed model (e.g., NBLMM, see below
for more details) may solve the problem.

Depending on the concrete application, the estimated gene-specific overdispersions can also be taken into consideration in quality control. For example, when testing differential expression for a variable, genes with a very large estimated cell-level overdispersion should be filtered out because such genes have huge unexplained noises. A large cell-level overdispersion is generally rare in UMI-based single cell data, especially among abundantly expressed genes, but more common in e.g., SMART-seq2 as PCR duplicates introduce substantial noises. It might be hard to give a precise cut-off for a large overdispersion because it also depends on the sample size of the data. Based on the empirical simulation study in (https://www.nature.com/articles/s42003-021-02146-6), genes with an estimated cell-level overdispersion >100 should be removed for a data set with at least 50 cells per subject. On the other hand, if the purpose is to extract residuals for downstream analysis such as clustering, genes with a large cell-level overdispersion might be preferable because they have large variations.

If the variable of interest is subject-level, genes with a very large
subject-level overdispersion (>1) should be removed or interpreted
cautiously as well. In addition, at least a moderate number of subjects
(>30) are required for testing a subject-level variable using
`nebula`

simply because a small number of subjects are not
enough to accurately estimate the subject-level overdispersion. As shown
in the original article, even 30 subjects lead to mild inflated type I
errors in most simulated scenarios. If the number of subjects is small,
methods accounting for small sample size (e.g., DESeq2, edgeR) should be
used for testing subject-level variables.

In addition to the NBGMM, the *nebula* package provides
efficient estimation implementation for a Poisson gamma mixed model and
a negative binomial lognormal mixed model (NBLMM). This can be specified
through `model="PMM"`

and `model="NBLMM"`

,
respectively. The NBLMM is the same model as that adopted in the
`glmer.nb`

function in the *lme4* R package, but is
computationally much more efficient by setting `method='LN'`

.
The only difference between NBGMM and NBLMM is that NBGMM uses a gamma
distribution for the random effects while the NBLMM uses a lognormal
distribution. The PMM is the fastest among these models. Note that the
Poisson mixed model (PMM) should not be used to test a cell-level
predictor because it only estimates the subject-level overdispersion.
Here is an example of using the PMM to fit the example data set.

```
re = nebula(sample_data$count,sample_data$sid,pred=df,offset=sample_data$offset,model='PMM')
#> Remove 0 genes having low expression.
#> Analyzing 10 genes with 30 subjects and 6176 cells.
```

logFC_(Intercept) | logFC_X1 | logFC_X2 | logFC_cccontrol | se_(Intercept) | se_X1 | se_X2 | se_cccontrol | p_(Intercept) | p_X1 | p_X2 | p_cccontrol | gene_id | gene |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

-1.903571 | -0.0155809 | -0.0976660 | 0.0511060 | 0.0661297 | 0.0329115 | 0.0655553 | 0.0642299 | 0 | 0.6359142 | 0.1362700 | 0.4262222 | 1 | A |

-2.047864 | -0.0032670 | -0.0536887 | -0.0189269 | 0.0644332 | 0.0355074 | 0.0635450 | 0.0694853 | 0 | 0.9266904 | 0.3981703 | 0.7853239 | 2 | B |

-2.032645 | 0.0179777 | 0.0009387 | -0.0505390 | 0.0908196 | 0.0345496 | 0.0932449 | 0.0676706 | 0 | 0.6028248 | 0.9919678 | 0.4551611 | 3 | C |

-2.009746 | -0.0054963 | -0.0278602 | 0.0782074 | 0.0573209 | 0.0350745 | 0.0574459 | 0.0686939 | 0 | 0.8754792 | 0.6276888 | 0.2549156 | 4 | D |

-1.980528 | 0.0106338 | -0.0248791 | 0.0312190 | 0.0644287 | 0.0343355 | 0.0621576 | 0.0671645 | 0 | 0.7567865 | 0.6889656 | 0.6420644 | 5 | E |

-1.950451 | 0.0160341 | -0.0134775 | -0.0345244 | 0.0778198 | 0.0333858 | 0.0738508 | 0.0650410 | 0 | 0.6310363 | 0.8551928 | 0.5955505 | 6 | F |

-1.970271 | -0.0026753 | 0.0750060 | -0.0063677 | 0.0645989 | 0.0341936 | 0.0615160 | 0.0668723 | 0 | 0.9376369 | 0.2227329 | 0.9241391 | 7 | G |

-1.964311 | 0.0141532 | -0.0610984 | -0.0578672 | 0.0809943 | 0.0336579 | 0.0800990 | 0.0656800 | 0 | 0.6741201 | 0.4455910 | 0.3782927 | 8 | H |

-2.074031 | -0.0178190 | -0.0436094 | 0.0259745 | 0.0597947 | 0.0362203 | 0.0587679 | 0.0707912 | 0 | 0.6227459 | 0.4580494 | 0.7136813 | 9 | I |

-2.046055 | 0.0307026 | 0.0227238 | -0.0246112 | 0.0714158 | 0.0354844 | 0.0702268 | 0.0691813 | 0 | 0.3869068 | 0.7462578 | 0.7220276 | 10 | J |

In some situations, a user may want to test a combination (contrast)
of the log(FC) or perform a global test for multiple variables or
levels. For example, a user may want to test whether the log(FC) of two
variables are the same. Here, we show how `nebula`

can be
used for this kind of analysis.

The first step is to tell `nebula`

to output the
covariance matrix of the estimated log(FC). This can be done by
specifying `covariance=TRUE`

in `nebula`

. To save
storage, the covariance returned by `nebula`

only contains
the elements in the lower triangular part including the diagonal. Here
is an example to recover the covariance matrix from the output of
`nebula`

.

```
df = model.matrix(~X1+X2+cc, data=sample_data$pred)
re_ln = nebula(sample_data$count,sample_data$sid,pred=df,offset=sample_data$offset,method='LN',covariance=TRUE)
#> Remove 0 genes having low expression.
#> Analyzing 10 genes with 30 subjects and 6176 cells.
cov= matrix(NA,4,4)
cov[lower.tri(cov,diag=T)] = as.numeric(re_ln$covariance[1,])
cov[upper.tri(cov)] = t(cov)[upper.tri(cov)]
cov
#> [,1] [,2] [,3] [,4]
#> [1,] 4.014261e-03 2.499051e-05 1.384999e-04 -5.197643e-05
#> [2,] 2.499051e-05 1.249382e-03 9.212341e-06 -1.167080e-05
#> [3,] 1.384999e-04 9.212341e-06 4.159507e-03 5.142249e-05
#> [4,] -5.197643e-05 -1.167080e-05 5.142249e-05 4.732936e-03
```

Note that if there are *K* variables, the covariance table in
the output will have *(K+1)K/2* columns. So, for a large
*K*, substantial increase of computational intensity should be
expected.

The second step is to build the contrast vector for your hypothesis.
In this example, we want to test whether the log(FC) of *X1* and
*X2* are equal for the first gene. This hypothesis leads to the
contrast vector `(0 1 -1 0)`

. Thus, the test can be performed
using the following code.

```
df = model.matrix(~X1+X2+cc, data=sample_data$pred)
## the gene to test
gene_i = 1
## output covariance
re_ln = nebula(sample_data$count,sample_data$sid,pred=df,offset=sample_data$offset,method='LN',covariance=TRUE)
#> Remove 0 genes having low expression.
#> Analyzing 10 genes with 30 subjects and 6176 cells.
## recover the covariance matrix
cov= matrix(NA,4,4)
cov[lower.tri(cov,diag=T)] = as.numeric(re_ln$covariance[gene_i,])
cov[upper.tri(cov)] = t(cov)[upper.tri(cov)]
## build the contrast vector
contrast = c(0,1,-1,0)
## testing the hypothesis
eff = sum(contrast*re_ln$summary[gene_i,1:4])
p = pchisq(eff^2/(t(contrast)%*%cov%*%contrast),1,lower.tail=FALSE)
p
#> [,1]
#> [1,] 0.2692591
```

Pearson residuals are the distances between the raw count and its expected value standardized by its standard deviation. Pearson residuals obtained from fitting the NBMM can be used for normalization and downstream analyses. The marginal Pearson residuals are obtained by removing from the raw count the contribution from all fixed-effect variables included in the model. The conditional Pearson residuals further remove the subject-level random effects, which capture the contribution of all other potential subject-level variables that are not explicitly included in the model. Therefore, the conditional Pearson residuals are very useful in a situation where one needs to remove the subject-level batch effects from the normalized residuals for downstream analyses.

Both Pearson residuals can be easily extracted by using the
`nbresidual`

function after successfully running the
`nebula`

function. To extract the marginal Pearson residuals,
one provides in `nbresidual`

the object returned by
`nebula`

together with the same arguments including the count
matrix, `id`

, `pred`

and `offset`

used
in running the `nebula`

function. Here is an example.

```
re = nebula(sample_data$count,sample_data$sid,pred=df,offset=sample_data$offset)
pres = nbresidual(re,count=sample_data$count,id=sample_data$sid,pred=df,offset=sample_data$offset)
```

The parameters `count`

, `id`

, `pred`

and `offset`

should be the same in these two functions. Then,
the marginal Pearson residuals are available in the matrix
`pres$residuals`

. The rows in `pres$residuals`

correspond to the genes in the output of `nebula`

, and the
columns are the cells in `count`

.

To extract the conditional Pearson residuals, we need to first let
`nebula`

output subject-level random effects by setting
`output_re=TRUE`

when running `nebula`

as shown
below.

`re = nebula(sample_data$count,sample_data$sid,pred=df,offset=sample_data$offset,output_re=TRUE)`

The returned object will include an *M* by *L* matrix
of the random effects, where *L* is the number of subjects. In
the current version, this option does NOT support
`model="PMM"`

. Then, the conditional Pearson residuals can be
extracted by running `nbresidual`

with
`conditional=TRUE`

.

`pres = nbresidual(re,count=sample_data$count,id=sample_data$sid,pred=df,offset=sample_data$offset,conditional=TRUE)`