Identify marker feats for selected clusters.
findMarkers(
gobject,
spat_unit = NULL,
feat_type = NULL,
expression_values = c("normalized", "scaled", "custom"),
cluster_column = NULL,
method = c("scran", "gini", "mast"),
subset_clusters = NULL,
group_1 = NULL,
group_2 = NULL,
min_expr_gini_score = 0.5,
min_det_gini_score = 0.5,
detection_threshold = 0,
rank_score = 1,
min_feats = 4,
min_genes = NULL,
group_1_name = NULL,
group_2_name = NULL,
adjust_columns = NULL,
...
)giotto object
spatial unit
feature type
feat expression values to use
clusters to use
method to use to detect differentially expressed feats
selection of clusters to compare
group 1 cluster IDs from cluster_column for pairwise comparison
group 2 cluster IDs from cluster_column for pairwise comparison
gini: filter on minimum gini coefficient for expression
gini: filter minimum gini coefficient for detection
gini: detection threshold for feat expression
gini: rank scores to include
minimum number of top feats to return (for gini)
deprecated, use min_feats
mast: custom name for group_1 clusters
mast: custom name for group_2 clusters
mast: column in pDataDT to adjust for (e.g. detection rate)
additional parameters for the findMarkers function in scran or zlm function in MAST
data.table with marker feats
Wrapper for all individual functions to detect marker feats for clusters.
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
#> cell_spatInfo_spatVector.shp
#> cell
#>
#> 3.2 read Giotto spatial centroid information
#> cell
#>
#> 3.3 read Giotto spatial overlap information
#> No overlaps were found, overlap loading will be
#> skipped
#>
#> 4. read Giotto image information
#> a giotto python environment was found
#> Using python path:
#> "/Users/yuanlab/Library/r-miniconda/envs/giotto_env/bin/pythonw"
findMarkers(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
#>