Identify marker genes for all or selected clusters based on scran's implementation of findMarkers.
findScranMarkers(
gobject,
spat_unit = NULL,
feat_type = NULL,
expression_values = c("normalized", "scaled", "custom"),
cluster_column,
subset_clusters = NULL,
group_1 = NULL,
group_1_name = NULL,
group_2 = NULL,
group_2_name = NULL,
verbose = TRUE,
...
)giotto object
spatial unit
feature type
gene expression values to use
clusters to use
selection of clusters to compare
group 1 cluster IDs from cluster_column for pairwise comparison
custom name for group_1 clusters
group 2 cluster IDs from cluster_column for pairwise comparison
custom name for group_2 clusters
be verbose (default = FALSE)
additional parameters for the findMarkers function in scran
data.table with marker genes
This is a minimal convenience wrapper around
the findMarkers function from the scran package.
To perform differential expression between custom selected groups of cells you need to specify the cell_ID column to parameter cluster_column and provide the individual cell IDs to the parameters group_1 and group_2
By default group names will be created by pasting the different id names within each selected group. When you have many different ids in a single group it is recommend to provide names for both groups to group_1_name and group_2_name
g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> python already initialized in this session
#> active environment : '/usr/bin/python3'
#> python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/bin/python3"
findScranMarkers(g, cluster_column = "leiden_clus")
#> Using 'Scran' to detect marker genes. If used in published
#> research, please cite:
#> Lun ATL, McCarthy DJ, Marioni JC (2016).
#> 'A step-by-step workflow for low-level analysis of single-cell RNA-seq
#> data with Bioconductor.'
#> F1000Res., 5, 2122. doi: 10.12688/f1000research.9501.2.
#> [[1]]
#> Top p.value FDR summary.logFC logFC.2 logFC.3
#> <int> <num> <num> <num> <num> <num>
#> 1: 1 2.304174e-04 6.573796e-03 0.90645500 0.37802208 0.906455001
#> 2: 1 2.488503e-04 6.573796e-03 0.73714285 0.35023787 0.285465533
#> 3: 1 3.573331e-02 2.824072e-01 -0.65006774 -0.65006774 -0.310767600
#> 4: 1 5.986157e-17 3.795223e-14 1.29002259 0.11152501 -0.003955259
#> 5: 1 9.144098e-03 1.183134e-01 0.71476651 -0.15878804 0.086193959
#> ---
#> 630: 392 8.170461e-01 9.963036e-01 0.12019791 0.12019791 0.066849413
#> 631: 409 8.469325e-01 9.963036e-01 0.28779748 0.04379729 0.099088405
#> 632: 410 9.981090e-01 9.981090e-01 -0.11016023 0.03910845 -0.110160233
#> 633: 460 9.958793e-01 9.981090e-01 -0.05961090 -0.05961090 0.007542745
#> 634: 471 9.542557e-01 9.963036e-01 -0.03719674 -0.03719674 -0.033661719
#> logFC.4 logFC.5 logFC.6 logFC.7 feats cluster
#> <num> <num> <num> <num> <char> <char>
#> 1: 0.2628823782 0.45234522 0.3483568624 0.861221868 Arpp19 1
#> 2: 0.1417528118 0.25891761 0.7371428514 0.743099124 Nr4a1 1
#> 3: 0.1593223354 -0.59127151 -0.2332950800 -0.172305095 Grb14 1
#> 4: -0.0549709160 -0.20970272 -0.3019308099 1.290022593 F3 1
#> 5: 0.1355646082 0.71476651 0.4749149442 1.159030178 Mgp 1
#> ---
#> 630: -0.1191742596 -0.11774369 -0.1085689549 -0.132165513 Ly6c1 1
#> 631: -0.1038916802 -0.08677730 0.1468124449 0.287797484 Cadps2 1
#> 632: -0.0370309299 0.08793704 -0.0007125669 0.160973282 Zfhx3 1
#> 633: 0.0008977208 0.01361647 0.0494467014 0.002896235 Ube2l6 1
#> 634: -0.0066309208 -0.02467083 -0.0237548120 0.020868744 Tie1 1
#>
#> [[2]]
#> Top p.value FDR summary.logFC logFC.1 logFC.3
#> <int> <num> <num> <num> <num> <num>
#> 1: 1 4.493099e-04 1.238533e-02 0.98709500 0.51179478 0.287395704
#> 2: 1 1.686308e-02 1.979851e-01 0.80939008 0.65006774 0.339300141
#> 3: 1 4.882728e-12 3.095650e-09 1.17849759 -0.11152501 -0.115480266
#> 4: 1 1.685305e-03 3.561612e-02 0.87355455 0.15878804 0.244982000
#> 5: 1 1.435589e-02 1.857476e-01 0.75902888 0.59003057 0.759028878
#> ---
#> 630: 384 8.843225e-01 9.954992e-01 0.10905760 0.05961090 0.067153648
#> 631: 390 9.681886e-01 9.954992e-01 -0.18631844 -0.13596775 -0.011582149
#> 632: 397 8.170828e-01 9.954992e-01 0.15793618 -0.06470923 0.086129945
#> 633: 403 8.469325e-01 9.954992e-01 -0.14768897 -0.04379729 0.055291116
#> 634: 471 9.780374e-01 9.954992e-01 0.03719674 0.03719674 0.003535019
#> logFC.4 logFC.5 logFC.6 logFC.7 feats cluster
#> <num> <num> <num> <num> <char> <char>
#> 1: 0.98709500 0.36235238 0.37944760 0.68255672 Carhsp1 2
#> 2: 0.80939008 0.05879623 0.41677266 0.47776265 Grb14 2
#> 3: -0.16649592 -0.32122773 -0.41345582 1.17849759 F3 2
#> 4: 0.29435265 0.87355455 0.63370298 1.31781822 Mgp 2
#> 5: 0.77054700 0.36936712 0.06646966 0.95356486 Prr18 2
#> ---
#> 630: 0.06050862 0.07322737 0.10905760 0.06250714 Ube2l6 2
#> 631: -0.18631844 0.13414898 0.07512455 0.13773810 Neurod6 2
#> 632: 0.15793618 0.06086162 0.11227616 0.23929012 Hlf 2
#> 633: -0.14768897 -0.13057459 0.10301516 0.24400019 Cadps2 2
#> 634: 0.03056582 0.01252591 0.01344193 0.05806548 Tie1 2
#>
#> [[3]]
#> Top p.value FDR summary.logFC logFC.1 logFC.2
#> <int> <num> <num> <num> <num> <num>
#> 1: 1 2.041188e-02 2.696069e-01 0.50498456 0.504984557 0.402682581
#> 2: 1 2.304174e-04 1.327988e-02 -0.90645500 -0.906455001 -0.528432923
#> 3: 1 4.477933e-03 8.871905e-02 0.30442144 0.205947496 0.176755272
#> 4: 1 1.758868e-11 1.115122e-08 1.29397785 0.003955259 0.115480266
#> 5: 1 1.186323e-02 1.979287e-01 -0.85347407 -0.739522029 -0.511888427
#> ---
#> 630: 401 9.681886e-01 9.955840e-01 -0.17473629 -0.124385599 0.011582149
#> 631: 413 8.946801e-01 9.955840e-01 -0.12170101 -0.121701007 -0.078984900
#> 632: 420 9.915440e-01 9.955840e-01 -0.07998884 -0.046764443 -0.079988838
#> 633: 436 9.913842e-01 9.955840e-01 -0.06715365 -0.007542745 -0.067153648
#> 634: 500 9.780374e-01 9.955840e-01 0.03366172 0.033661719 -0.003535019
#> logFC.4 logFC.5 logFC.6 logFC.7 feats cluster
#> <num> <num> <num> <num> <char> <char>
#> 1: 0.169203980 0.493700297 0.017217739 0.68881115 Bcam 3
#> 2: -0.643572623 -0.454109779 -0.558098138 -0.04523313 Arpp19 3
#> 3: 0.241349413 0.237414552 0.304421441 -0.04306167 Aldh1a2 3
#> 4: -0.051015657 -0.205747465 -0.297975551 1.29397785 F3 3
#> 5: -0.853474068 -0.630817196 -0.669008913 -0.70085278 Hpcal4 3
#> ---
#> 630: -0.174736288 0.145731128 0.086706697 0.14932025 Neurod6 3
#> 631: 0.088504889 0.101981067 -0.048113349 0.23111255 Rab3c 3
#> 632: -0.002089627 -0.030261178 0.091251923 -0.09796174 Cpne8 3
#> 633: -0.006645024 0.006073720 0.041903956 -0.00464651 Ube2l6 3
#> 634: 0.027030799 0.008990887 0.009906907 0.05453046 Tie1 3
#>
#> [[4]]
#> Top p.value FDR summary.logFC logFC.1 logFC.2
#> <int> <num> <num> <num> <num> <num>
#> 1: 1 4.493099e-04 2.589659e-02 -0.98709500 -0.4753002269 -0.98709500
#> 2: 1 5.078518e-03 1.341575e-01 0.63286874 0.2471766190 0.03863261
#> 3: 1 2.625294e-10 1.664436e-07 1.34499351 0.0549709160 0.16649592
#> 4: 1 6.317306e-03 1.540451e-01 0.77121823 0.7712182292 0.63261566
#> 5: 1 5.436870e-03 1.378790e-01 -0.49047596 -0.2909354996 -0.49047596
#> ---
#> 630: 375 9.718823e-01 9.963231e-01 0.25621852 -0.0073971898 -0.07536530
#> 631: 388 9.359774e-01 9.963231e-01 -0.18941904 -0.1371096029 -0.18941904
#> 632: 464 9.915440e-01 9.963231e-01 -0.07789921 -0.0446748158 -0.07789921
#> 633: 498 9.963231e-01 9.963231e-01 -0.06050862 -0.0008977208 -0.06050862
#> 634: 532 9.542557e-01 9.963231e-01 -0.03056582 0.0066309208 -0.03056582
#> logFC.3 logFC.5 logFC.6 logFC.7 feats cluster
#> <num> <num> <num> <num> <char> <char>
#> 1: -0.699699298 -0.62474263 -0.60764740 -0.304538282 Carhsp1 4
#> 2: 0.287423926 0.23942405 0.63286874 0.254403628 Pdyn 4
#> 3: 0.051015657 -0.15473181 -0.24695989 1.344993509 F3 4
#> 4: 0.497192512 0.41653024 0.81838388 0.173456117 Pcdh8 4
#> 5: -0.326046063 -0.55954648 -0.63876080 -0.022109950 Galnt6 4
#> ---
#> 630: -0.105810362 0.07316508 -0.01604273 0.256218524 Shox2 4
#> 631: 0.049030572 0.02565367 -0.11877136 0.160893184 Pvalb 4
#> 632: 0.002089627 -0.02817155 0.09334155 -0.095872118 Cpne8 4
#> 633: 0.006645024 0.01271874 0.04854898 0.001998515 Ube2l6 4
#> 634: -0.027030799 -0.01803991 -0.01712389 0.027499665 Tie1 4
#>
#> [[5]]
#> Top p.value FDR summary.logFC logFC.1 logFC.2
#> <int> <num> <num> <num> <num> <num>
#> 1: 1 5.593922e-02 6.149128e-01 -0.49370030 0.01128426 -0.09101772
#> 2: 1 6.920551e-03 2.437572e-01 0.39256404 0.17396056 0.16375244
#> 3: 1 1.123889e-11 7.125455e-09 1.49972532 0.20970272 0.32122773
#> 4: 1 1.685305e-03 7.632025e-02 -0.87355455 -0.71476651 -0.87355455
#> 5: 1 1.087374e-02 3.133614e-01 0.55954648 0.26861098 0.06907052
#> ---
#> 630: 389 9.306563e-01 9.968179e-01 -0.12557085 -0.12557085 -0.06086162
#> 631: 400 9.322696e-01 9.968179e-01 0.12151310 -0.01650327 -0.04972766
#> 632: 425 9.594927e-01 9.968179e-01 0.11239339 0.11239339 -0.01440080
#> 633: 448 9.803600e-01 9.968179e-01 -0.07322737 -0.01361647 -0.07322737
#> 634: 540 9.959609e-01 9.968179e-01 0.02467083 0.02467083 -0.01252591
#> logFC.3 logFC.4 logFC.6 logFC.7 feats cluster
#> <num> <num> <num> <num> <char> <char>
#> 1: -0.493700297 -0.32449632 -0.4764825581 0.19511086 Bcam 5
#> 2: 0.141852032 0.10981295 0.3925640424 0.14215547 Cp 5
#> 3: 0.205747465 0.15473181 -0.0922280856 1.49972532 F3 5
#> 4: -0.628572547 -0.57920190 -0.2398515625 0.44426367 Mgp 5
#> 5: 0.233500418 0.55954648 -0.0792143163 0.53743653 Galnt6 5
#> ---
#> 630: 0.025268327 0.09707457 0.0514145381 0.17842850 Hlf 5
#> 631: 0.030261178 0.02817155 0.1215131007 -0.06770057 Cpne8 5
#> 632: -0.020039113 -0.04836938 0.0446992547 0.11626795 Vstm2a 5
#> 633: -0.006073720 -0.01271874 0.0358302361 -0.01072023 Ube2l6 5
#> 634: -0.008990887 0.01803991 0.0009160208 0.04553958 Tie1 5
#>
#> [[6]]
#> Top p.value FDR summary.logFC logFC.1 logFC.2
#> <int> <num> <num> <num> <num> <num>
#> 1: 1 3.397885e-03 0.161814023 -0.21860348 -0.2186034850 -0.228811601
#> 2: 1 4.477933e-03 0.161814023 -0.30442144 -0.0984739449 -0.127666169
#> 3: 1 2.488503e-04 0.026295183 -0.73714285 -0.7371428514 -0.386904982
#> 4: 1 2.214999e-03 0.142773240 -0.59423614 -0.3856921243 -0.594236137
#> 5: 1 5.527494e-06 0.003504431 1.59195340 0.3019308099 0.413455817
#> ---
#> 630: 439 9.954992e-01 0.999269911 -0.10316260 -0.1031626015 0.001392101
#> 631: 441 8.607175e-01 0.999269911 0.15481148 -0.0555131172 -0.061205578
#> 632: 475 9.981090e-01 0.999269911 -0.10944767 0.0007125669 0.039821013
#> 633: 522 9.167732e-01 0.999269911 -0.09306864 0.0676941335 -0.059100050
#> 634: 557 9.959609e-01 0.999269911 0.02375481 0.0237548120 -0.013441926
#> logFC.3 logFC.4 logFC.5 logFC.7 feats cluster
#> <num> <num> <num> <num> <char> <char>
#> 1: -0.250712011 -0.28275109 -0.3925640424 -0.250408572 Cp 6
#> 2: -0.304421441 -0.06307203 -0.0670068886 -0.347483107 Aldh1a2 6
#> 3: -0.451677319 -0.59539004 -0.4782252394 0.005956272 Nr4a1 6
#> 4: -0.345444817 -0.63286874 -0.3934446919 -0.378465115 Pdyn 6
#> 5: 0.297975551 0.24695989 0.0922280856 1.591953403 F3 6
#> ---
#> 630: -0.006672761 0.07105764 -0.0320322801 -0.125109657 Tmem100 6
#> 631: 0.099145148 0.15481148 0.1291902545 -0.143563712 Sox10 6
#> 632: -0.109447666 -0.03631836 0.0886496029 0.161685849 Zfhx3 6
#> 633: -0.064738368 -0.09306864 -0.0446992547 0.071568694 Vstm2a 6
#> 634: -0.009906907 0.01712389 -0.0009160208 0.044623556 Tie1 6
#>
#> [[7]]
#> Top p.value FDR summary.logFC logFC.1 logFC.2
#> <int> <num> <num> <num> <num> <num>
#> 1: 1 5.986157e-17 3.795223e-14 -1.29002259 -1.290022593 -1.17849759
#> 2: 2 2.601482e-11 4.123350e-09 -0.80632925 -0.806329248 -0.85701198
#> 3: 2 6.222905e-13 1.315107e-10 -0.91299388 -0.912993882 -0.95549842
#> 4: 2 1.632817e-13 5.176031e-11 -1.07608973 -1.076089728 -0.84140367
#> 5: 3 4.570002e-10 5.794763e-08 -0.75975639 -0.759756385 -0.72428750
#> ---
#> 630: 515 9.951443e-01 9.995295e-01 -0.16463733 -0.003874561 -0.13066874
#> 631: 516 9.437524e-01 9.995295e-01 0.14414795 0.090391089 -0.04581802
#> 632: 526 9.016343e-01 9.995295e-01 0.16876674 -0.147756604 0.09518521
#> 633: 543 9.442058e-01 9.995295e-01 -0.05806548 -0.020868744 -0.05806548
#> 634: 564 9.963231e-01 9.995295e-01 -0.06250714 -0.002896235 -0.06250714
#> logFC.3 logFC.4 logFC.5 logFC.6 feats cluster
#> <num> <num> <num> <num> <char> <char>
#> 1: -1.29397785 -1.344993509 -1.49972532 -1.59195340 F3 7
#> 2: -0.91264376 -0.953070757 -0.92818993 -1.24705822 Phkg1 7
#> 3: -1.06086870 -0.487032401 -0.97877848 -1.00052882 Pde8a 7
#> 4: -0.75568738 -0.540543087 -0.69790200 -0.97585842 Dkkl1 7
#> 5: -0.67456185 -0.822037085 -0.61893080 -1.19424107 Npas3 7
#> ---
#> 630: -0.13630706 -0.164637330 -0.11626795 -0.07156869 Vstm2a 7
#> 631: -0.05162772 0.059370626 -0.13158806 0.14414795 Cd82 7
#> 632: 0.16876674 -0.106073111 -0.09744106 0.11742053 Lmo3 7
#> 633: -0.05453046 -0.027499665 -0.04553958 -0.04462356 Tie1 7
#> 634: 0.00464651 -0.001998515 0.01072023 0.04655047 Ube2l6 7
#>