16 CITE-Seq

In this lab, we will look at how single cell RNA-seq and single cell protein expression measurement datasets can be jointly analyzed, as part of a CITE-Seq experiment. To learn more about how the antibody barcode matrix is computationally generated from the sequencing data, please visit CITE-seq-Count. To learn more about CITE-Seq and feature barcoding, please visit the CITE-seq site.

This lab closely follows the official vignette available at Using Seurat with multi-modal data.

16.2 Load in the data

Here, we analyze a dataset of 8,617 cord blood mononuclear cells (CBMCs), produced with CITE-seq, where we simultaneously measure the single cell transcriptomes alongside the expression of 11 surface proteins, whose levels are quantified with DNA-barcoded antibodies. First, we load in two count matrices : one for the RNA measurements, and one for the antibody-derived tags (ADT).

# Load in the RNA UMI matrix

# Note that this dataset also contains ~5% of mouse cells, which we can use
# as negative controls for the protein measurements. For this reason, the
# gene expression matrix has HUMAN_ or MOUSE_ appended to the beginning of
# each gene.
cbmc.rna <- as.sparse(read.csv(paste0(mydir, "GSE100866_CBMC_8K_13AB_10X-RNA_umi.csv.gz"), sep = ",", header = TRUE, row.names = 1))
cbmc.rna[20400:20403,1:2]

# To make life a bit easier going forward, we're going to discard all but
# the top 100 most highly expressed mouse genes, and remove the 'HUMAN_'
# from the CITE-seq prefix
cbmc.rna <- CollapseSpeciesExpressionMatrix(cbmc.rna, prefix = "HUMAN_", controls = "MOUSE_")

# Load in the ADT UMI matrix
cbmc.adt <- as.sparse(read.csv(paste0(mydir, "GSE100866_CBMC_8K_13AB_10X-ADT_umi.csv.gz"), sep = ",", header = TRUE, row.names = 1))

# When adding multimodal data to Seurat, it's okay to have duplicate feature names. Each set of
# modal data (eg. RNA, ADT, etc.) is stored in its own Assay object.  One of these Assay objects
# is called the 'default assay', meaning it's used for all analyses and visualization.  To pull
# data from an assay that isn't the default, you can specify a key that's linked to an assay for
# feature pulling.  To see all keys for all objects, use the Key function.  

# Lastly, we observed poor enrichments for CCR5, CCR7, and CD10 - and therefore 
# remove them from the matrix (optional)
cbmc.adt <- cbmc.adt[setdiff(rownames(x = cbmc.adt), c("CCR5", "CCR7", "CD10")), ]

# Look at structure of ADT matrix.
cbmc.adt[1:10,1:3]

# What fraction of cells in the ADT and RNA matrix overlap?
length(intersect(colnames(cbmc.rna), colnames(cbmc.adt))) / length(union(colnames(cbmc.rna), colnames(cbmc.adt)))

# Save current progress.
save(cbmc.rna, cbmc.adt, file = Rda.quickload.path)

16.3 Setup a Seurat object, and cluster cells based on RNA expression

The steps below represent a quick clustering of the PBMCs based on the scRNA-seq data. For more detail on individual steps or more advanced options, see our PBMC clustering guided tutorial here

## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
## Centering and scaling data matrix

## Computing nearest neighbor graph
## Computing SNN
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 8617
## Number of edges: 347548
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8592
## Number of communities: 19
## Elapsed time: 1 seconds
## Calculating cluster 0
## Calculating cluster 1
## Calculating cluster 2
## Calculating cluster 3
## Calculating cluster 4
## Calculating cluster 5
## Calculating cluster 6
## Calculating cluster 7
## Calculating cluster 8
## Calculating cluster 9
## Calculating cluster 10
## Calculating cluster 11
## Calculating cluster 12
## Calculating cluster 13
## Calculating cluster 14
## Calculating cluster 15
## Calculating cluster 16
## Calculating cluster 17
## Calculating cluster 18
##            p_val avg_logFC pct.1 pct.2    p_val_adj cluster            gene
## 1   2.589983e-38 3.0688532 1.000 0.082 5.309724e-34       5     MOUSE-Shfm1
## 2   3.343974e-38 3.3214918 1.000 0.065 6.855481e-34       5     MOUSE-Eef1g
## 3   4.576942e-38 3.0566728 1.000 0.087 9.383188e-34       5      MOUSE-Rtn4
## 4   5.060526e-38 3.3736882 1.000 0.092 1.037459e-33       5      MOUSE-Npm1
## 5   5.853288e-38 3.1075365 1.000 0.075 1.199983e-33       5   MOUSE-S100a11
## 6   7.309965e-38 3.1210730 1.000 0.137 1.498616e-33       5       MOUSE-Bgn
## 7   7.854611e-38 3.1823668 1.000 0.048 1.610274e-33       5  MOUSE-Atp6v1g1
## 8   8.078132e-38 3.1757854 1.000 0.079 1.656098e-33       5    MOUSE-Serbp1
## 9   1.205463e-37 3.5136647 1.000 0.081 2.471320e-33       5   MOUSE-Gm10260
## 10  1.309584e-37 3.0016168 1.000 0.065 2.684778e-33       5     MOUSE-Rpl22
## 11  1.358667e-37 3.2116968 1.000 0.081 2.785404e-33       5    MOUSE-Ndufa4
## 12  1.451626e-37 3.4523548 1.000 0.121 2.975979e-33       5     MOUSE-Rpl32
## 13  1.548045e-37 3.0793534 1.000 0.055 3.173646e-33       5    MOUSE-Rps27l
## 14  1.891767e-37 3.2763202 1.000 0.073 3.878312e-33       5     MOUSE-Rplp2
## 15  1.918593e-37 3.2224149 1.000 0.131 3.933307e-33       5   MOUSE-Slc25a4
## 16  2.230479e-37 3.4298554 1.000 0.137 4.572705e-33       5      MOUSE-Rps9
## 17  2.262278e-37 3.2949243 1.000 0.144 4.637896e-33       5  MOUSE-Hsp90ab1
## 18  2.436807e-37 3.0609232 1.000 0.128 4.995698e-33       5      MOUSE-Nme1
## 19  2.576793e-37 3.3457217 1.000 0.096 5.282684e-33       5    MOUSE-Tmsb10
## 20  2.599760e-37 3.1809764 1.000 0.149 5.329768e-33       5     MOUSE-Tubb5
## 21  2.835723e-37 2.9497869 1.000 0.067 5.813516e-33       5      MOUSE-Calr
## 22  2.845096e-37 3.5141515 1.000 0.085 5.832732e-33       5      MOUSE-Ybx1
## 23  2.861704e-37 3.0508286 1.000 0.098 5.866779e-33       5    MOUSE-Arpc1b
## 24  2.943740e-37 3.0414266 1.000 0.113 6.034962e-33       5      MOUSE-Lmna
## 25  3.141150e-37 2.8791432 1.000 0.104 6.439672e-33       5      MOUSE-Ctsl
## 26  3.205286e-37 2.9776496 1.000 0.103 6.571158e-33       5      MOUSE-Gas5
## 27  3.248892e-37 2.9783581 1.000 0.141 6.660554e-33       5     MOUSE-Atp5b
## 28  3.248892e-37 2.9437600 1.000 0.102 6.660554e-33       5   MOUSE-Slc25a3
## 29  3.373363e-37 3.3672873 1.000 0.095 6.915732e-33       5     MOUSE-Hmga2
## 30  3.486852e-37 3.1325081 1.000 0.045 7.148395e-33       5      MOUSE-Eno1
## 31  3.709120e-37 3.3965827 1.000 0.113 7.604067e-33       5     MOUSE-H3f3b
## 32  3.731361e-37 3.3445044 1.000 0.109 7.649664e-33       5     MOUSE-Eif5a
## 33  3.766307e-37 3.2020434 0.998 0.060 7.721307e-33       5    MOUSE-mt-Nd2
## 34  4.115629e-37 3.1411789 1.000 0.099 8.437452e-33       5       MOUSE-Pkm
## 35  4.300905e-37 2.8335714 1.000 0.064 8.817285e-33       5     MOUSE-Rps26
## 36  4.511666e-37 3.2189211 1.000 0.077 9.249367e-33       5     MOUSE-Rps24
## 37  4.661889e-37 3.0882543 1.000 0.107 9.557339e-33       5      MOUSE-Ldha
## 38  4.914987e-37 3.2250975 1.000 0.103 1.007622e-32       5     MOUSE-Calm1
## 39  5.008380e-37 3.7123031 1.000 0.157 1.026768e-32       5     MOUSE-Rplp0
## 40  5.175830e-37 2.9014521 1.000 0.089 1.061097e-32       5       MOUSE-Esd
## 41  5.244907e-37 3.4640366 1.000 0.094 1.075258e-32       5     MOUSE-Rpl26
## 42  5.524036e-37 3.4322609 1.000 0.134 1.132483e-32       5     MOUSE-Rpl14
## 43  5.604675e-37 3.1056839 1.000 0.085 1.149014e-32       5    MOUSE-Eef1b2
## 44  5.709515e-37 3.2070806 1.000 0.111 1.170508e-32       5      MOUSE-Eef2
## 45  6.051582e-37 2.8562423 1.000 0.099 1.240635e-32       5    MOUSE-Cox6b1
## 46  6.276767e-37 2.9754960 1.000 0.076 1.286800e-32       5     MOUSE-Arpc2
## 47  6.294522e-37 2.9709200 1.000 0.102 1.290440e-32       5    MOUSE-Ppp1ca
## 48  6.603459e-37 3.4895906 1.000 0.087 1.353775e-32       5      MOUSE-Cfl1
## 49  6.726146e-37 3.1145355 0.998 0.048 1.378927e-32       5   MOUSE-Slc25a5
## 50  7.176881e-37 3.5936267 1.000 0.126 1.471332e-32       5     MOUSE-Rps11
## 51  8.111934e-37 3.1933176 1.000 0.093 1.663028e-32       5     MOUSE-Rps19
## 52  8.378419e-37 2.9673217 1.000 0.109 1.717660e-32       5 MOUSE-Tnfrsf12a
## 53  8.654506e-37 3.5547613 1.000 0.154 1.774260e-32       5      MOUSE-Pfn1
## 54  8.654506e-37 3.2416146 1.000 0.136 1.774260e-32       5      MOUSE-Txn1
## 55  9.162208e-37 3.2749753 1.000 0.077 1.878344e-32       5      MOUSE-Naca
## 56  9.605768e-37 3.3640993 1.000 0.118 1.969279e-32       5     MOUSE-Rplp1
## 57  9.781641e-37 3.0361351 1.000 0.098 2.005334e-32       5    MOUSE-Tm4sf1
## 58  1.121385e-36 3.2047609 1.000 0.112 2.298952e-32       5    MOUSE-Ranbp1
## 59  1.238798e-36 2.9237221 1.000 0.122 2.539659e-32       5     MOUSE-Anxa2
## 60  1.374845e-36 3.0152863 1.000 0.125 2.818570e-32       5       MOUSE-B2m
## 61  1.486569e-36 3.0540241 1.000 0.164 3.047615e-32       5     MOUSE-Hint1
## 62  1.709022e-36 3.1125583 1.000 0.165 3.503666e-32       5   MOUSE-S100a10
## 63  2.391987e-36 3.2185589 1.000 0.110 4.903812e-32       5     MOUSE-Cox8a
## 64  2.546516e-36 3.4041833 1.000 0.197 5.220612e-32       5      MOUSE-Rpl4
## 65  2.694724e-36 3.4068076 1.000 0.113 5.524454e-32       5     MOUSE-Rpl23
## 66  2.782618e-36 3.0687226 1.000 0.167 5.704646e-32       5    MOUSE-Gnb2l1
## 67  2.803277e-36 2.9466599 1.000 0.071 5.746997e-32       5     MOUSE-Cald1
## 68  3.060359e-36 3.0983460 1.000 0.120 6.274042e-32       5       MOUSE-Mt1
## 69  3.061720e-36 3.5504399 1.000 0.085 6.276832e-32       5    MOUSE-Rps3a1
## 70  3.089689e-36 3.0538578 1.000 0.110 6.334172e-32       5      MOUSE-Cdk4
## 71  3.162101e-36 2.9919371 1.000 0.107 6.482624e-32       5     MOUSE-Gsto1
## 72  3.194002e-36 3.2865393 1.000 0.187 6.548023e-32       5      MOUSE-Dlk1
## 73  3.483292e-36 3.1971727 1.000 0.123 7.141097e-32       5     MOUSE-Prdx1
## 74  3.818679e-36 3.2985227 1.000 0.224 7.828673e-32       5      MOUSE-Rps3
## 75  4.225164e-36 3.3243282 1.000 0.209 8.662009e-32       5       MOUSE-Vim
## 76  4.389534e-36 3.5227154 1.000 0.236 8.998984e-32       5    MOUSE-Rpl13a
## 77  4.799768e-36 1.9191170 0.979 0.024 9.840005e-32       5            MYL3
## 78  4.817738e-36 3.2396729 1.000 0.146 9.876844e-32       5     MOUSE-Anxa1
## 79  5.263533e-36 3.4278589 1.000 0.126 1.079077e-31       5     MOUSE-Rps15
## 80  5.521946e-36 2.8645123 1.000 0.114 1.132054e-31       5      MOUSE-Cstb
## 81  5.736456e-36 3.1156562 1.000 0.148 1.176031e-31       5    MOUSE-Myl12a
## 82  7.180006e-36 2.9175274 1.000 0.183 1.471973e-31       5    MOUSE-Cox4i1
## 83  8.161440e-36 3.0344876 1.000 0.108 1.673177e-31       5     MOUSE-Lamp1
## 84  8.360053e-36 3.2601292 1.000 0.205 1.713894e-31       5      MOUSE-Actb
## 85  9.018525e-36 3.2825215 1.000 0.269 1.848888e-31       5   MOUSE-mt-Cytb
## 86  1.661694e-35 3.3308664 1.000 0.295 3.406639e-31       5      MOUSE-Rpl8
## 87  1.755974e-35 3.1615591 1.000 0.124 3.599922e-31       5      MOUSE-Gnas
## 88  1.844947e-35 3.2151653 0.998 0.145 3.782325e-31       5    MOUSE-mt-Nd4
## 89  2.160878e-35 3.5516310 1.000 0.144 4.430015e-31       5      MOUSE-Cd63
## 90  2.272852e-35 3.3854796 1.000 0.178 4.659574e-31       5     MOUSE-Rps14
## 91  2.334724e-35 2.9695641 1.000 0.171 4.786418e-31       5       MOUSE-Bsg
## 92  3.138487e-35 3.3949580 1.000 0.378 6.434213e-31       5      MOUSE-Rps5
## 93  4.384772e-35 3.1478765 1.000 0.237 8.989221e-31       5    MOUSE-Lgals3
## 94  7.608819e-35 3.5809744 0.995 0.238 1.559884e-30       5    MOUSE-Malat1
## 95  1.535971e-34 3.1143600 1.000 0.331 3.148895e-30       5    MOUSE-S100a4
## 96  3.969846e-34 3.2843640 1.000 0.549 8.138582e-30       5    MOUSE-S100a6
## 97  7.554328e-34 3.3319006 1.000 0.355 1.548713e-29       5    MOUSE-mt-Nd1
## 98  8.353188e-34 1.4166049 0.984 0.093 1.712487e-29       5          DYNLL2
## 99  1.504607e-33 3.3243385 1.000 0.446 3.084595e-29       5      MOUSE-Fth1
## 100 2.099049e-33 3.1753011 1.000 0.400 4.303261e-29       5    MOUSE-Tmsb4x
## 101 7.464377e-32 2.6376435 0.963 0.098 1.530272e-27       5     MOUSE-Acta2
## 102 3.566992e-29 1.3863978 0.972 0.062 7.312689e-25       5   CTD-2090I13.1
## 103 1.946493e-23 0.9060487 0.810 0.013 3.990505e-19       5       HIST2H2AB
##            p_val avg_logFC pct.1 pct.2    p_val_adj cluster            gene
## 1   5.694937e-32  2.261027 1.000 0.146 1.167519e-27      13    MOUSE-Ranbp1
## 2   6.122860e-32  2.499240 0.991 0.159 1.255248e-27      13     MOUSE-Rps15
## 3   7.231469e-32  2.668083 0.991 0.152 1.482523e-27      13     MOUSE-Rplp1
## 4   1.174562e-31  2.778360 1.000 0.357 2.407970e-27      13    MOUSE-S100a4
## 5   1.209176e-31  2.730176 1.000 0.423 2.478932e-27      13    MOUSE-Tmsb4x
## 6   1.797643e-31  2.408277 0.991 0.156 3.685348e-27      13     MOUSE-Anxa2
## 7   1.936705e-31  2.542341 0.972 0.109 3.970440e-27      13     MOUSE-Rplp2
## 8   2.159518e-31  2.481118 0.991 0.145 4.427228e-27      13     MOUSE-Cox8a
## 9   2.985211e-31  2.723078 0.981 0.157 6.119982e-27      13     MOUSE-Prdx1
## 10  3.172531e-31  2.901085 1.000 0.566 6.504006e-27      13    MOUSE-S100a6
## 11  7.106704e-31  3.160532 0.991 0.236 1.456945e-26      13      MOUSE-Actb
## 12  8.246008e-31  2.495074 0.981 0.147 1.690514e-26      13     MOUSE-Rpl23
## 13  9.223015e-31  2.471669 0.981 0.117 1.890810e-26      13   MOUSE-Gm10260
## 14  9.371755e-31  2.452599 0.981 0.160 1.921303e-26      13     MOUSE-Rps11
## 15  9.779046e-31  2.620856 0.953 0.113 2.004802e-26      13     MOUSE-Rps24
## 16  1.251003e-30  2.465479 0.972 0.129 2.564682e-26      13     MOUSE-Rpl26
## 17  1.756273e-30  2.535067 0.953 0.101 3.600535e-26      13     MOUSE-Rpl22
## 18  2.034570e-30  2.482089 0.991 0.155 4.171071e-26      13     MOUSE-Rpl32
## 19  2.379570e-30  2.559327 0.972 0.167 4.878356e-26      13     MOUSE-Rpl14
## 20  2.548928e-30  2.753512 0.943 0.111 5.225558e-26      13   MOUSE-S100a11
## 21  2.681390e-30  2.301315 0.981 0.197 5.497117e-26      13   MOUSE-S100a10
## 22  3.546009e-30  2.573062 0.991 0.186 7.269673e-26      13      MOUSE-Pfn1
## 23  3.734094e-30  2.449508 0.991 0.209 7.655267e-26      13     MOUSE-Rps14
## 24  4.606642e-30  2.482054 0.981 0.266 9.444077e-26      13    MOUSE-Rpl13a
## 25  5.136327e-30  2.335140 0.972 0.121 1.052998e-25      13    MOUSE-Rps3a1
## 26  5.280799e-30  2.215450 0.981 0.197 1.082617e-25      13     MOUSE-Hint1
## 27  5.595216e-30  2.314418 0.953 0.113 1.147075e-25      13      MOUSE-Naca
## 28  7.937246e-30  2.479286 0.953 0.135 1.627215e-25      13    MOUSE-Cox6b1
## 29  1.203382e-29  2.444119 0.953 0.169 2.467054e-25      13      MOUSE-Txn1
## 30  1.260502e-29  2.399652 1.000 0.189 2.584156e-25      13     MOUSE-Rplp0
## 31  1.296573e-29  2.369831 0.962 0.149 2.658103e-25      13      MOUSE-Cstb
## 32  1.409398e-29  2.398371 0.991 0.323 2.889407e-25      13      MOUSE-Rpl8
## 33  1.501281e-29  2.156191 0.953 0.133 3.077776e-25      13    MOUSE-Arpc1b
## 34  1.713709e-29  2.675714 0.925 0.101 3.513275e-25      13     MOUSE-Rps26
## 35  1.713709e-29  2.368067 0.953 0.117 3.513275e-25      13    MOUSE-Ndufa4
## 36  1.781970e-29  2.380462 0.953 0.102 3.653216e-25      13     MOUSE-Eef1g
## 37  2.110199e-29  2.402753 0.991 0.402 4.326119e-25      13      MOUSE-Rps5
## 38  2.136048e-29  2.620474 0.962 0.177 4.379112e-25      13  MOUSE-Hsp90ab1
## 39  2.270318e-29  2.698158 0.962 0.131 4.654380e-25      13    MOUSE-Tmsb10
## 40  2.298364e-29  2.360640 0.981 0.254 4.711877e-25      13      MOUSE-Rps3
## 41  2.774051e-29  2.405205 0.953 0.120 5.687082e-25      13    MOUSE-Eef1b2
## 42  2.971166e-29  2.403258 0.962 0.146 6.091187e-25      13      MOUSE-Eef2
## 43  3.217869e-29  2.255929 0.972 0.143 6.596953e-25      13     MOUSE-Eif5a
## 44  6.052367e-29  2.390698 0.962 0.129 1.240796e-24      13     MOUSE-Rps19
## 45  6.446107e-29  2.381902 0.943 0.118 1.321516e-24      13     MOUSE-Shfm1
## 46  8.392692e-29  2.522999 0.991 0.468 1.720586e-24      13      MOUSE-Fth1
## 47  1.039199e-28  2.490092 0.953 0.181 2.130462e-24      13    MOUSE-Myl12a
## 48  1.059617e-28  2.363141 0.962 0.174 2.172321e-24      13     MOUSE-Atp5b
## 49  1.136848e-28  2.420211 0.962 0.170 2.330652e-24      13      MOUSE-Rps9
## 50  1.677680e-28  2.231629 0.953 0.121 3.439412e-24      13      MOUSE-Ybx1
## 51  1.797144e-28  2.131666 0.953 0.122 3.684326e-24      13      MOUSE-Cfl1
## 52  2.217540e-28  3.140499 0.972 0.297 4.546179e-24      13   MOUSE-mt-Cytb
## 53  2.236480e-28  2.200847 0.953 0.127 4.585009e-24      13      MOUSE-Npm1
## 54  3.684323e-28  2.280569 0.972 0.240 7.553230e-24      13       MOUSE-Vim
## 55  4.076600e-28  2.343247 0.953 0.134 8.357438e-24      13       MOUSE-Pkm
## 56  4.636754e-28  2.218386 0.981 0.228 9.505810e-24      13      MOUSE-Rpl4
## 57  6.749065e-28  2.132095 0.962 0.200 1.383626e-23      13    MOUSE-Gnb2l1
## 58  7.778351e-28  2.210371 0.934 0.163 1.594640e-23      13      MOUSE-Nme1
## 59  8.741573e-28  2.341123 0.943 0.215 1.792110e-23      13    MOUSE-Cox4i1
## 60  2.222504e-27  2.114141 0.962 0.138 4.556355e-23      13     MOUSE-Calm1
## 61  2.860745e-27  2.205576 0.962 0.158 5.864813e-23      13      MOUSE-Gnas
## 62  4.016601e-27  2.146927 0.962 0.267 8.234433e-23      13    MOUSE-Lgals3
## 63  4.338376e-27  3.106415 0.925 0.179 8.894105e-23      13    MOUSE-mt-Nd4
## 64  4.581561e-27  2.038114 0.896 0.086 9.392658e-23      13   MOUSE-Slc25a5
## 65  5.536735e-27  2.718182 0.915 0.097 1.135086e-22      13    MOUSE-mt-Nd2
## 66  1.000179e-26  2.182786 0.915 0.137 2.050467e-22      13   MOUSE-Slc25a3
## 67  2.126742e-26  3.054986 0.953 0.380 4.360033e-22      13    MOUSE-mt-Nd1
## 68  2.555881e-26  2.306473 0.915 0.160 5.239812e-22      13       MOUSE-B2m
## 69  2.753783e-26  2.265517 0.915 0.108 5.645531e-22      13     MOUSE-Cald1
## 70  5.021616e-26  2.158415 0.887 0.083 1.029482e-21      13      MOUSE-Eno1
## 71  6.477556e-26  1.876609 0.934 0.145 1.327964e-21      13      MOUSE-Cdk4
## 72  9.132195e-26  1.929906 0.925 0.148 1.872191e-21      13      MOUSE-Lmna
## 73  1.443189e-25  2.028266 0.896 0.142 2.958682e-21      13     MOUSE-Gsto1
## 74  2.187135e-25  2.573234 0.906 0.139 4.483845e-21      13      MOUSE-Gas5
## 75  9.676047e-25  2.057407 0.896 0.125 1.983686e-20      13       MOUSE-Esd
## 76  1.163687e-24  1.963349 0.915 0.180 2.385675e-20      13     MOUSE-Anxa1
## 77  1.249671e-24  1.814887 0.934 0.165 2.561951e-20      13   MOUSE-Slc25a4
## 78  5.351638e-24  2.017758 0.906 0.178 1.097139e-19      13      MOUSE-Cd63
## 79  9.051252e-24  1.716038 0.858 0.116 1.855597e-19      13    MOUSE-Serbp1
## 80  1.035274e-23  1.886684 0.877 0.143 2.122415e-19      13      MOUSE-Ldha
## 81  6.074209e-23  2.021360 0.858 0.093 1.245274e-18      13    MOUSE-Rps27l
## 82  1.256351e-22  1.796896 0.877 0.113 2.575645e-18      13     MOUSE-Arpc2
## 83  7.286297e-22  1.796396 0.821 0.087 1.493764e-17      13  MOUSE-Atp6v1g1
## 84  4.131820e-21  2.009562 0.849 0.156 8.470644e-17      13       MOUSE-Mt1
## 85  4.918820e-20  1.713497 0.830 0.139 1.008407e-15      13    MOUSE-Ppp1ca
## 86  5.324099e-20  2.763313 0.755 0.134 1.091493e-15      13     MOUSE-Acta2
## 87  4.312371e-19  1.870316 0.802 0.173 8.840792e-15      13       MOUSE-Bgn
## 88  8.315943e-19  1.833178 0.811 0.221 1.704851e-14      13      MOUSE-Dlk1
## 89  3.248015e-18  1.842319 0.764 0.145 6.658756e-14      13     MOUSE-Lamp1
## 90  4.798035e-18  1.818716 0.821 0.205 9.836452e-14      13       MOUSE-Bsg
## 91  5.757352e-18  2.126146 0.792 0.184 1.180315e-13      13     MOUSE-Tubb5
## 92  1.243481e-16  1.983483 0.717 0.142 2.549260e-12      13      MOUSE-Ctsl
## 93  2.451551e-16  1.886574 0.717 0.106 5.025925e-12      13      MOUSE-Calr
## 94  7.392626e-14  1.794081 0.585 0.065 1.515562e-09      13            MYL3
## 95  7.932037e-13  1.738518 0.632 0.135 1.626147e-08      13     MOUSE-Hmga2
## 96  4.127263e-12  1.526832 0.660 0.137 8.461302e-08      13    MOUSE-Tm4sf1
## 97  1.305564e-11  1.661696 0.623 0.127 2.676536e-07      13      MOUSE-Rtn4
## 98  5.333310e-09  1.413978 0.585 0.152 1.093382e-04      13     MOUSE-H3f3b
## 99  7.207313e-08  1.132159 0.434 0.103 1.477571e-03      13   CTD-2090I13.1
## 100 9.026752e-07  1.263586 0.509 0.149 1.850574e-02      13 MOUSE-Tnfrsf12a

16.4 Add the protein expression levels to the Seurat object

Seurat v3.0 allows you to store information from multiple assays in the same object, as long as the data is multi-modal (collected on the same set of cells). You can use the SetAssayData and GetAssayData accessor functions to add and fetch data from additional assays.

## 3 x 3 sparse Matrix of class "dgCMatrix"
##     CTGTTTACACCGCTAG CTCTACGGTGTGGCTC AGCAGCCAGGCTCATT
## CD3               60               52               89
## CD4               72               49              112
## CD8               76               59               61
## 3 x 3 sparse Matrix of class "dgCMatrix"
##     CTGTTTACACCGCTAG CTCTACGGTGTGGCTC AGCAGCCAGGCTCATT
## CD3               60               52               89
## CD4               72               49              112
## CD8               76               59               61
## Normalizing across features
## Centering and scaling data matrix

16.5 Visualize protein levels on RNA clusters

You can use the names of any ADT markers, (i.e. “adt_CD4”), in FetchData, FeaturePlot, RidgePlot, FeatureScatter, DoHeatmap, or any other visualization features

## Picking joint bandwidth of 0.0848
## Picking joint bandwidth of 0.1
## Picking joint bandwidth of 0.142
## Picking joint bandwidth of 0.0862

## [1] 0.8291063

## [1] 0.0102657

16.6 Identify differentially expressed proteins between clusters

## Calculating cluster Memory CD4 T
## Calculating cluster CD14+ Mono
## Calculating cluster Naive CD4 T
## Calculating cluster NK
## Calculating cluster Mouse
## Calculating cluster B
## Calculating cluster CD8 T
## Calculating cluster CD16+ Mono
## Calculating cluster T/Mono doublets
## Calculating cluster CD34+
## Calculating cluster Multiplets
## Calculating cluster Eryth
## Calculating cluster Mk
## Calculating cluster DC
## Calculating cluster pDCs

16.7 Cluster directly on protein levels

You can also run dimensional reduction and graph-based clustering directly on CITE-seq data

## Warning in irlba(A = t(x = object), nv = npcs, ...): You're computing too large
## a percentage of total singular values, use a standard svd instead.
## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from pca_adt_ to pcaadt_
## Warning: All keys should be one or more alphanumeric characters followed by an
## underscore '_', setting key to pcaadt_

## Building SNN based on a provided distance matrix
## Computing SNN
## Warning: Adding a Graph without an assay associated with it
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 7895
## Number of edges: 258146
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9491
## Number of communities: 11
## Elapsed time: 0 seconds
## Warning: Adding a command log without an assay associated with it
##     
##      Memory CD4 T CD14+ Mono Naive CD4 T   NK    B CD8 T CD16+ Mono
##   0          1754          0        1217   29    0    27          0
##   1             0       2189           0    4    0     0         30
##   2             3          0           2  890    3     1          0
##   3             0          4           0    2  319     0          2
##   4            24          0          18    4    1   243          0
##   5             1         27           4  157    2     2         10
##   6             4          5           0    1    0     0          0
##   7             4         59           4    0    0     0          9
##   8             0          9           0    2    0     0        179
##   9             0          0           1    0    0     0          0
##   10            1          0           2    0   25     0          0
##     
##      T/Mono doublets CD34+ Eryth   Mk   DC pDCs
##   0                5     2     4   24    1    2
##   1                1     1     5   25   55    0
##   2                0     1     3    7    2    1
##   3                0     2     2    3    0    0
##   4                0     0     1    2    0    0
##   5               56     0     9   16    6    2
##   6                1   113    81   16    5    0
##   7              117     0     0    2    0    1
##   8                0     0     0    1    0    0
##   9                0     0     0    0    1   43
##   10               2     0     0    0    0    0

The ADT-based clustering yields similar results, but with a few differences

  • Clustering is improved for CD4/CD8 T cell populations, based on the robust ADT data for CD4, CD8, CD14, and CD45RA
  • However, some clusters for which the ADT data does not contain good distinguishing protein markers (i.e. Mk/Ery/DC) lose separation
  • You can verify this using FindMarkers at the RNA level, as well

16.8 Additional exploration: another example of multi-modal analysis

For another nice example of multi-modal analysis, please explore this single cell ATAC-Seq vignette and this scATAC-seq data integration.

16.9 Acknowledgements

This document is largely a tutorial from Seurat website, with some small modifications. The official vignette is available at Using Seurat with multi-modal data.