added headers for all scripts

IzarLab · Nov 9, 2022 · 0d368b8 · 0d368b8
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diff --git a/QC_metrics/QC_vln_plots.R b/QC_metrics/QC_vln_plots.R
@@ -4,6 +4,9 @@ library(stringr)
 library(rlang)
 library(rlist)
 
+### title: Plotting violin plots of quality metrics for all datasets
+### author: Yiping Wang date: 11/08/2022
+
 #define list of s3 folders to download data from, names of each dataset, and alternate names that replace dashes with underscores
 foldersList = c("s3://fresh-vs-frozen-comparison-ohio/BI5/scrna-seq",
   "s3://fresh-vs-frozen-comparison-ohio/BI5/snrna-seq",

diff --git a/QC_metrics/load_stress_sigs.R b/QC_metrics/load_stress_sigs.R
@@ -1,3 +1,6 @@
+### title: Load lists of stress signature genes to calculate strength of these signatures across all datasets
+### author: Yiping Wang date: 11/08/2022
+
 #hardcoded list of stress signature genes, from either van den Brink 2017 or Denisenko 2020 papers
 stress_sig_dysfunctional_cd8 = c("LAG3","HAVCR2","PDCD1","PTMS","FAM3C","IFNG","AKAP5","CD7","PHLDA1","ENTPD1","SNAP47","TNS3","CXCL13","RDH10","DGKH","KIR2DL4","LYST","MIR155HG","RAB27A","CSF1","CTLA4","TNFRSF9","CD27","CCL3","ITGAE","PAG1","TNFRSF1B","GALNT1","GBP2","MYO7A")
 stress_sig_nmeth_celseq = c("FOS","HSPA1A","JUN","FOSB","JUNB","EGR1","HSPA1B","UBC","ZFP36","HSPB1","HSP90AA1","MT2","DNAJB1","BTG2","NR4A1","CEBPD","HSPA8","MT1","IER2","DNAJA1","SOCS3","ATF3","JUND","CEBPB","ID3","PPP1R15A","HSPE1","CXCL1","DUSP1","HSP90AB1","NFKBIA","HSPH1")

diff --git a/QC_metrics/rename_sample_IDs.R b/QC_metrics/rename_sample_IDs.R
@@ -1,3 +1,6 @@
+### title: Rename sample IDs in rds objects across all datasets to human-readable format for plotting
+### author: Yiping Wang date: 11/08/2022
+
 #given a Seurat object integrated_rds and the name of a dataset, rename the orig.ident field of integrated_rds to more human-readable names
 rename_IDs <- function(integrated_rds, dataset_name) {
   if (dataset_name=="Mel" || dataset_name=="BI5")

diff --git a/TCR_analyses/TCR_circos.R b/TCR_analyses/TCR_circos.R
@@ -11,6 +11,9 @@ library(limma)
 library(reshape2)
 library(circlize)
 
+### title: Create circos plots showing overlap in TCR clonotypes between fresh and frozen samples in cutaneous melanoma, NSCLC, and uveal primary datasets
+### author: Yiping Wang date: 11/08/2022
+
 #load in sample names for either ribas dataset, or cutaneous melanoma, uveal primary, and NSCLC datasets
 #create array of colors corresponding to either fresh or frozen samples to label circos plots
 useRibas = FALSE

diff --git a/TCR_analyses/TCR_clonality_umaps.R b/TCR_analyses/TCR_clonality_umaps.R
@@ -4,6 +4,9 @@ library(Seurat)
 library(infercnv)
 library(stringr)
 
+### title: Create umaps of T cells with TCR clonality plotted in different colors according to frequency, for cutaneous melanoma, uveal melanoma primary, uveal melanoma metastasis, and sequential cutaneous melanoma samples under pembrolizumab therapy
+### author: Yiping Wang date: 11/08/2022
+
 #load list of rds objects corresponding to each dataset
 foldersList = c("",
   "",

diff --git a/TCR_analyses/assign_TCR_clonality.R b/TCR_analyses/assign_TCR_clonality.R
@@ -1,3 +1,6 @@
+### title: Assign TCR clonality information to T cells to rds files for a particular dataset
+### author: Yiping Wang date: 11/08/2022
+
 assign_TCR_clonality <- function(seu, dataset_name) {
   #rename orig.ident to more human-readable names
   seu$orig.ident[seu$orig.ident=="ribas_310_on"] = "ribas1_on_tcr_S36_L004"

diff --git a/cell_type_annotation/cell_type_annotation.R b/cell_type_annotation/cell_type_annotation.R
@@ -5,6 +5,9 @@ library(rlang)
 library(rlist)
 library(dplyr)
 
+### title: Annotate cell types manually across all datasets based on Seurat clustering and marker gene expression
+### author: Yiping Wang date: 11/08/2022
+
 #load in BI5 cutaneous melanoma scrna and snrna-seq samples, integrate, cluster, and store in rds file
 integrate_BI5 = TRUE
 if (integrate_BI5) {

diff --git a/cell_type_annotation/cell_type_annotation_heatmap.R b/cell_type_annotation/cell_type_annotation_heatmap.R
@@ -5,6 +5,9 @@ library(rlang)
 library(rlist)
 library(dplyr)
 
+### title: Create heatmap of top 10 markers in each seurat cluster for each dataset
+### author: Yiping Wang date: 11/08/2022
+
 #load list of folders for each datasets, depending on whether samples were processed by slyper protocol or not
 using_slyper = FALSE
 if (using_slyper) {

diff --git a/cell_type_annotation/cell_type_barplots.R b/cell_type_annotation/cell_type_barplots.R
@@ -6,6 +6,9 @@ library(viridis)
 library(seriation)
 library(RColorBrewer)
 
+### title: Create barplots of cell type frequency, immune and non-immune simpson diversity, and malignant cell fractions
+### author: Yiping Wang date: 11/08/2022
+
 #load in folder names for each dataset
 useRibas = FALSE
 if (useRibas)

diff --git a/cell_type_annotation/reannotate_BI5_and_uveal_primary_tcells.R b/cell_type_annotation/reannotate_BI5_and_uveal_primary_tcells.R
@@ -9,6 +9,9 @@ library(grid)
 library(stringr)
 library(dplyr)
 
+### title: Annotate T cells in cutaneous melanoma sequential samples more finely
+### author: Yiping Wang date: 11/08/2022
+
 #define folder locations for cutaneous melanoma and uveal primary data
 foldersList = c("",
   "s3://fresh-vs-frozen-comparison-ohio/cpoi-uvealprimarydata")

diff --git a/cell_type_annotation/reannotate_ribas_melanoma_tcells.R b/cell_type_annotation/reannotate_ribas_melanoma_tcells.R
@@ -8,6 +8,9 @@ library(scales)
 library(grid)
 library(stringr)
 
+### title: Annotate T cells in cutaneous melanoma sequential samples more finely
+### author: Yiping Wang date: 11/08/2022
+
 recluster_ribas_tcells = TRUE
 recluster_ribas_myeloid = FALSE
 

diff --git a/cell_type_annotation/reannotate_uveal_melanoma_tcells.R b/cell_type_annotation/reannotate_uveal_melanoma_tcells.R
@@ -8,6 +8,9 @@ library(scales)
 library(grid)
 library(stringr)
 
+### title: Annotate T cells in uveal melanoma metastatic samples more finely
+### author: Yiping Wang date: 11/08/2022
+
 #load uveal melanoma data, subset to t-cells, and save to rds file
 seu = readRDS("/data/um_all_integrated.rds")
 seu <- subset(seu, manual_annotation_label %in% c("T-cells"))

diff --git a/cell_type_annotation/reannotate_uveal_melanoma_tcells_nk.R b/cell_type_annotation/reannotate_uveal_melanoma_tcells_nk.R
@@ -8,6 +8,9 @@ library(scales)
 library(grid)
 library(stringr)
 
+### title: Check whether two clusters in integrated uveal melanoma metastatic T-cells data are NK cells, by reclustering them
+### author: Yiping Wang date: 11/08/2022
+
 #load reintegrated data for uveal melanoma t cells, select 2 clusters with possible nk cell gene expression
 seu = readRDS(paste0("/data/reannotate_uveal_melanoma_tcells_reintegrated_with_1_subclustered_dim_num_25_then_15.rds"))
 

diff --git a/cell_type_annotation/reannotate_uveal_melanoma_tcells_vln_plots.R b/cell_type_annotation/reannotate_uveal_melanoma_tcells_vln_plots.R
@@ -7,6 +7,9 @@ library(viridis)
 library(scales)
 library(grid)
 
+### title: Plotting violin plots of T cell exhaustion signatures in integrated uveal melanoma metastatic T cell data
+### author: Yiping Wang date: 11/08/2022
+
 # colBP <- c('#A80D11', '#008DB8')
 # colSCSN <- c('#E1AC24', '#288F56')
 colDC <- c('#DE8C00', '#F564E3', '#7CAE00', '#00B4F0', '#00C08B')

diff --git a/correlation_with_wgs/correlate_wgs_and_infercnv.R b/correlation_with_wgs/correlate_wgs_and_infercnv.R
@@ -7,6 +7,9 @@ library(stringr)
 library(ggplot2)
 library(infercnv)
 
+### title: Correlate average arm-level infercnv and whole-genome sequencing copy number alterations in uveal melanoma metastatic dataset
+### author: Yiping Wang date: 11/08/2022
+
 useRollingAverage = FALSE
 flattenToMedian = FALSE
 skipIndividualCalc = FALSE

diff --git a/geo_submission/geo_submission.R b/geo_submission/geo_submission.R
@@ -5,6 +5,9 @@ library(rlang)
 library(rlist)
 library(dplyr)
 
+### title: Print out csv files of count matrices for single-nucleus and single-cell sequencing data across all datasets
+### author: Yiping Wang date: 11/08/2022
+
 # rename_tcr_arr = c("Mel_sc_5_CD45+","Mel_sn_5","UM_sc_5","UM_sc_5_CD45+","UM_sn_5_inhib","NSCLC_sc_5","NSCLC_sn_5","NSCLC_sn_5_inhib","ribas_pre","ribas_on","ribas_on_later","UMEL_1_1","UMEL_1_2","UMEL_2_1","UMEL_2_2","UMEL_2_3","UMEL_3_1","UMEL_3_2","UMEL_3_3","UMEL_4_1","UMEL_4_2","UMEL_5_1","UMEL_5_2","UMEL_5_3","UMEL_6_1","UMEL_6_2","UMEL_6_3","UMEL_7_1","UMEL_7_2","UMEL_7_3")
 
 # names(rename_tcr_arr) = c("TCRBI5_S1_L001","bi005-skcm-5snseq-TCR","UMEL-CUUM1-SCRNA-5P-NA-PRIMARY-TCR-F2","UMEL-CUUM1-SCRNA-5P-NA-PRIMARY-TCR-F3","UMEL-CUUM1-SNRNA-5P-WI-PRIMARY-TCR-F9","NSCL_NR001_SCRNA_5P_NA_BRAIN_TCR","NSCL_NR001_SNSEQ_5P_NI_BRAIN_TCR","NSCL_NR001_SNSEQ_5P_WI_BRAIN_TCR","ribas1_pre_tcr_S35_L004","ribas1_on_tcr_S36_L004","ribas_310_on_later_previd_3_TCR","um_07_gk_pre_S4_L001","um_07_gk_on_S8_L001","um_08_ar_pre_S1_L001","um_08_ar_on_S2_L001","um_08_ar_post_S3_L001","um_09_mw_pre_S5_L001","um_09_mw_on_S6_L001","um_09_mw_post_S7_L001","um_11_lc_pre_S12_L002","um_11_lc_on_S16_L002","um_12_ml_pre_S9_L002","um_12_ml_on_S10_L002","um_12_ml_post_S11_L002","um_15_lm_pre_S13_L002","um_15_lm_on_S14_L002","um_15_lm_post_S15_L002","um_16_rs_pre_S17_L003","um_16_rs_on_S18_L003","um_16_rs_post_S19_L003")

diff --git a/geo_submission/mbpm_puckdata_printout_info_for_geo_fresh_vs_frozen_comparison.R b/geo_submission/mbpm_puckdata_printout_info_for_geo_fresh_vs_frozen_comparison.R
@@ -3,6 +3,9 @@ library(ggplot2)
 library(rlist)
 library(grid)
 
+### title: Print out csv files of count matrices for spatial sequencing data for sequential cutaneous melanoma samples
+### author: Yiping Wang date: 11/08/2022
+
 #pats = c("MPM08_pre_slide","MPM08_on_slide","MPM08_on_later_slide")
 pats = c("MPM08_on_slide","MPM08_on_later_slide")
 

diff --git a/geo_submission/mbpm_puckdata_printout_info_for_geo_fresh_vs_frozen_comparison.R~ b/geo_submission/mbpm_puckdata_printout_info_for_geo_fresh_vs_frozen_comparison.R~
@@ -0,0 +1,40 @@
+library(Seurat)
+library(ggplot2)
+library(rlist)
+library(grid)
+
+pats = c("MBM05_rep1_slide","MBM06_slide","MBM07_slide","MBM08_slide","MBM11_rep1_slide","MBM18_slide","MBM13_slide","MPM08_pre_slide","MPM10_slide","MPM06_slide","MBM05_rep2_slide","MBM11_rep2_slide","puck5","puck6final","puck7_20_feature_threshold","puck8_20_feature_threshold")
+
+for (pat in pats) {
+  system(paste0("aws s3 cp s3://uveal-melanoma/figurefolder/mbpm_puckdata_add_rctd_and_sigs/",pat,"_with_rctd_sigs.rds /data/mbpm_puckdata_add_rctd_and_sigs/",pat,"_with_rctd_sigs.rds"))
+  puck = readRDS(paste0("/data/mbpm_puckdata_add_rctd_and_sigs/",pat,"_with_rctd_sigs.rds"))
+  system(paste0("rm /data/mbpm_puckdata_add_rctd_and_sigs/",pat,"_with_rctd_sigs.rds"))
+  out_pat = pat
+  if (pat=="puck5")
+  {
+    output_pat="MPM01_rep1_slide"
+  }
+  if (pat=="puck6final")
+  {
+    output_pat="MPM01_rep2_slide"
+  }
+  if (pat=="puck7_20_feature_threshold")
+  {
+    output_pat="MBM05_rep3_slide"
+  }
+  if (pat=="puck8_20_feature_threshold")
+  {
+    output_pat="MBM11_rep3_slide"
+  }
+  out_pat = str_replace(out_pat, "MPM", "ECM")
+
+  spatialdf = data.frame(barcode = colnames(puck), xcoord = puck$image@coordinates$x, ycoord = puck$image@coordinates$y)
+
+  write.csv(spatialdf, paste0("/data/mbpm_puckdata_printout_info_for_geo/",out_pat,"_spatial_coordinates.csv"))
+  system(paste0("aws s3 cp /data/mbpm_puckdata_printout_info_for_geo/",out_pat,"_spatial_coordinates.csv s3://uveal-melanoma/figurefolder/mbpm_puckdata_printout_info_for_geo/",out_pat,"_spatial_coordinates.csv"))
+  system(paste0("rm /data/mbpm_puckdata_printout_info_for_geo/",out_pat,"_spatial_coordinates.csv"))
+
+  write.csv(as.data.frame(puck@assays$Spatial@counts),paste0("/data/mbpm_puckdata_printout_info_for_geo/",out_pat,"_raw_counts.csv"))
+  system(paste0("aws s3 cp /data/mbpm_puckdata_printout_info_for_geo/",out_pat,"_raw_counts.csv s3://uveal-melanoma/figurefolder/mbpm_puckdata_printout_info_for_geo/",out_pat,"_raw_counts.csv"))
+  system(paste0("rm /data/mbpm_puckdata_printout_info_for_geo/",out_pat,"_raw_counts.csv"))
+}
diff --git a/infercnv_analyses/fresh_vs_frozen_infercnv_comparison.R b/infercnv_analyses/fresh_vs_frozen_infercnv_comparison.R
@@ -2,6 +2,9 @@ library(rlist)
 library(ggplot2)
 library(stringr)
 
+### title: Correlate average arm-level infercnv copy numter alterations in fresh vs. Frozen samples in cutaneous melanoma, uveal primary, and NSCLC datasets
+### author: Yiping Wang date: 11/08/2022
+
 #define lists of sample names, folder locations, cancer cell type annotations, and fresh/frozen status
 patslist = list(c("CD45negGEXBI5_S1_L001_final_thresh","bi005-skcm-5snseq_final_thresh","skcm-bi005-5pv2-snseq_final_thresh"),c("UMEL-CUUM1-SCRNA-5P-NA-PRIMARY-GEX-E12_final_thresh","UMEL-CUUM1-SCRNA-5P-NA-PRIMARY-GEX-F1_final_thresh","UMEL-CUUM1-SNRNA-5P-WI-PRIMARY-GEX-F12_final_thresh"),c("NSCL_NR001_SCRNA_5P_NA_BRAIN_GEX_final_thresh","NSCL_NR001_SNSEQ_5P_NI_BRAIN_GEX_final_thresh","NSCL_NR001_SNSEQ_5P_WI_BRAIN_GEX_final_thresh","NSCL-NR001-5pv2-snseq_final_thresh"),c("Sarcoma167GEX_final_thresh","Sarcoma322GEX_final_thresh","Sarcoma559GEX_final_thresh","Sarcoma708GEX_final_thresh"))
 

diff --git a/infercnv_analyses/make_infercnv_plots2.R b/infercnv_analyses/make_infercnv_plots2.R
@@ -6,6 +6,9 @@ library(grid)
 library(rlist)
 library(stringr)
 
+### title: Make heatmap plots of infercnv results for all datasets
+### author: Yiping Wang date: 11/08/2022
+
 setwd("/data/")
 
 selectstep = 20

diff --git a/infercnv_analyses/r310_infercnv_and_resistance_sig.R b/infercnv_analyses/r310_infercnv_and_resistance_sig.R
@@ -10,6 +10,9 @@ library(infercnv)
 library(ggpubr)
 library(rlist)
 
+### title: Print heatmap of infercnv copy number changes, with histogram of immune resistance signature gene density overtop, for each sample in cutaneous melanoma sequential treatment dataset
+### author: Yiping Wang date: 11/08/2022
+
 #download object containing all ribas data from s3, subset to data for 310 sample
 system("aws s3 cp s3://melanoma-ribas/ribas1/Seurat/integrated/ribas_integrated_titrate_thresh_integrated.rds /data/ribas_integrated_titrate_thresh_integrated.rds")
 integrated_rds = readRDS("/data/ribas_integrated_titrate_thresh_integrated.rds")

diff --git a/initial_processing/Seurat_cellbender_fresh_vs_frozen_comparison.R b/initial_processing/Seurat_cellbender_fresh_vs_frozen_comparison.R
@@ -20,6 +20,9 @@ library(scater)
 library(pheatmap)
 library(grid)
 
+### title: Creation of Seurat object from Cellbender output, and doublet and QC thresholding, for cutaneous melanoma, uveal melanoma primary, and nsclc samples
+### author: Yiping Wang date: 11/08/2022
+
 #setwd("/home/ubuntu/")
 
 # foldersList = c("s3://fresh-vs-frozen-comparison/BI5/scrna-seq",

diff --git a/initial_processing/Seurat_cellbender_ribas_melanoma.R b/initial_processing/Seurat_cellbender_ribas_melanoma.R
@@ -19,6 +19,9 @@ library(SingleCellExperiment)
 library(scater)
 library(pheatmap)
 
+### title: Creation of Seurat object from Cellbender output, and doublet and QC thresholding, for cutaneous melanoma samples under pembrolizumab treatment
+### author: Yiping Wang date: 11/08/2022
+
 #setwd("/home/ubuntu/")
 
 #pats = c("ribas1_on_5pv2_S27_L004","ribas1_pre_5pv2_S26_L004")

diff --git a/initial_processing/Seurat_cellbender_uveal_melanoma_new_samples.R b/initial_processing/Seurat_cellbender_uveal_melanoma_new_samples.R
@@ -19,6 +19,9 @@ library(SingleCellExperiment)
 library(scater)
 library(pheatmap)
 
+### title: Creation of Seurat object from Cellbender output, and doublet and QC thresholding, for uveal melanoma liver metastasis samples
+### author: Yiping Wang date: 11/08/2022
+
 #setwd("/home/ubuntu/")
 
 pats = c("uv003-uvme-snseq-3p-post")

diff --git a/initial_processing/Seurat_integrate_fresh_vs_frozen_comparison.R b/initial_processing/Seurat_integrate_fresh_vs_frozen_comparison.R
@@ -10,6 +10,9 @@ library(cowplot)
 library(stringr)
 #library(celldex)
 
+### title: Seurat integration of cutaneous melanoma, uveal melanoma primary, and nsclc samples
+### author: Yiping Wang date: 11/08/2022
+
 # foldersList = c("s3://fresh-vs-frozen-comparison-ohio/BI5/scrna-seq",
 #   "s3://fresh-vs-frozen-comparison-ohio/BI5/snrna-seq",
 #   "s3://fresh-vs-frozen-comparison-ohio/cpoi-uvealprimarydata",

diff --git a/initial_processing/Seurat_integrate_uveal_melanoma_new_samples.R b/initial_processing/Seurat_integrate_uveal_melanoma_new_samples.R
@@ -9,6 +9,8 @@ library(purrr)
 library(cowplot)
 library(stringr)
 
+### title: Seurat integration of uveal melanoma liver metastasis samples
+### author: Yiping Wang date: 11/08/2022
 
 outputnamearr = c("um_all_merged")#,"um_on_integrated","um_post_integrated","um_pre_integrated")
 pat_list_arr = list(c("um_07_gk_on_S8_L001","um_07_gk_pre_S4_L001","um_08_ar_on_S2_L001","um_08_ar_post_S3_L001","um_08_ar_pre_S1_L001","um_09_mw_on_S6_L001","um_09_mw_post_S7_L001","um_09_mw_pre_S5_L001","um_11_lc_on_S16_L002","um_11_lc_pre_S12_L002","um_12_ml_on_S10_L002","um_12_ml_post_S11_L002","um_12_ml_pre_S9_L002","um_15_lm_on_S14_L002","um_15_lm_post_S15_L002","um_15_lm_pre_S13_L002","um_16_rs_on_S18_L003","um_16_rs_post_S19_L003","um_16_rs_pre_S17_L003"))#,c("um_07_gk_on_S8_L001","um_08_ar_on_S2_L001","um_09_mw_on_S6_L001","um_11_lc_on_S16_L002","um_12_ml_on_S10_L002","um_15_lm_on_S14_L002","um_16_rs_on_S18_L003"),c("uv003_uvme_snseq_3p_post","um_08_ar_post_S3_L001","um_09_mw_post_S7_L001","um_12_ml_post_S11_L002","um_15_lm_post_S15_L002","um_16_rs_post_S19_L003"),c("um_07_gk_pre_S4_L001","um_08_ar_pre_S1_L001","um_09_mw_pre_S5_L001","um_11_lc_pre_S12_L002","um_12_ml_pre_S9_L002","um_15_lm_pre_S13_L002","um_16_rs_pre_S17_L003"))

diff --git a/sampling_to_uniform_saturation/sampling_to_uniform_saturation.R b/sampling_to_uniform_saturation/sampling_to_uniform_saturation.R
@@ -3,6 +3,9 @@ library(cellranger)
 library(rlist)
 library(Seurat)
 
+### title: Downsample UMI counts in cutaneous melanoma and NSCLC datasets, across both slyper and non-slyper protocols, to obtain comparable UMI counts across samples
+### author: Yiping Wang date: 11/08/2022
+
 patsList = list(c("CD45negGEXBI5_S1_L001","CD45posGEXBI5_S1_L001"),
   c("bi005-skcm-5snseq","bi005-skcm","skcm-bi005-5pv2-snseq"),
   c("UMEL-CUUM1-SCRNA-5P-NA-PRIMARY-GEX-E12","UMEL-CUUM1-SCRNA-5P-NA-PRIMARY-GEX-F1","UMEL-CUUM1-SNRNA-5P-WI-PRIMARY-GEX-F12"),

diff --git a/sampling_to_uniform_saturation/sampling_to_uniform_saturation_vlnplots.R b/sampling_to_uniform_saturation/sampling_to_uniform_saturation_vlnplots.R
@@ -4,6 +4,9 @@ library(stringr)
 library(rlang)
 library(rlist)
 
+### title: Plotting violin plots of quality metrics for cutaneous melanoma and NSCLC datasets, using data downsampled to obtain comparable UMI counts across samples
+### author: Yiping Wang date: 11/08/2022
+
 integrated_name_arr_underscore = c("BI5","NR1")
 integrated_name_arr = c("BI5","NR1")
 fresh_idents = c("CD45negGEXBI5_S1_L001","CD45posGEXBI5_S1_L001","NSCL_NR001_SCRNA_5P_NA_BRAIN_GEX")

diff --git a/scenic_analyses/r310_scenic_and_infercnv.R b/scenic_analyses/r310_scenic_and_infercnv.R
@@ -7,6 +7,9 @@ library(rlist)
 library(fgsea)
 library(stringr)
 
+### title: Determine regulators from scenic analysis that are differentially expressed in clone 2 of sequential cutaneous melanoma dataset
+### author: Yiping Wang date: 11/08/2022
+
 pats = c("ribas_310_on_GEX_5pv2_S27_L004_titrate_thresh_kmeans","ribas_310_on_later_previd_3_GEX_titrate_thresh_kmeans","ribas_310_pre_GEX_5pv2_S26_L004_titrate_thresh_kmeans")
 cbpats = c("ribas_310_on_GEX_5pv2_S27_L004_titrate_thresh","ribas_310_on_later_previd_3_GEX_titrate_thresh","ribas_310_pre_GEX_5pv2_S26_L004_titrate_thresh")
 shortpats = c("on","on_later","pre")

diff --git a/scenic_analyses/scenic_prep.R b/scenic_analyses/scenic_prep.R
@@ -1,3 +1,6 @@
+### title: Print out csv files of count matrices for sequential cutaneous melanoma samples
+### author: Yiping Wang date: 11/08/2022
+
 system("aws s3 cp s3://melanoma-ribas/ribas1/Seurat/integrated/ribas_integrated_titrate_thresh_integrated.rds /data/ribas_integrated_titrate_thresh_integrated.rds")
 integrated_rds = readRDS("/data/ribas_integrated_titrate_thresh_integrated.rds")
 system("rm /data/ribas_integrated_titrate_thresh_integrated.rds")

diff --git a/scenic_analyses/scenic_run.py b/scenic_analyses/scenic_run.py
@@ -18,6 +18,9 @@
 
 from distributed import Client, LocalCluster
 
+### title: Run scenic for each sample in sequential cutaneous melanoma dataset
+### author: Yiping Wang date: 11/08/2022
+
 if __name__=="__main__":
 
     tf_names = load_tf_names("hs_hgnc_curated_tfs.txt")

diff --git a/trajectory_analysis/t_cell_destiny.R b/trajectory_analysis/t_cell_destiny.R
@@ -10,6 +10,9 @@ library(stringr)
 library(rlist)
 library(scRepertoire)
 
+### title: Perform diffusion analysis for T-cells from cutaneous melanoma samples under therapy and uveal melanoma metastatic samples, and plot signatures of T-cell activation and exhaustion on diffusion maps
+### author: Yiping Wang date: 11/08/2022
+
 colDC <- c('#DE8C00', '#F564E3', '#7CAE00', '#00B4F0', '#00C08B')
 
 prefix_arr = c("cd8")#"treg_and_tfh","treg","tfh","cd4")#,"cd8")

diff --git a/wes_analyses/splitChromosomes.py b/wes_analyses/splitChromosomes.py
@@ -1,3 +1,6 @@
+### title: Split a fastq file containing entire hg19 sequence into fastq files for each chromosome, for use by controlFREEC program
+### author: Yiping Wang date: 11/08/2022
+
 inFI = open("/data/Homo_sapiens_assembly19.fasta")
 firstline = True
 for line in inFI:

diff --git a/wes_analyses/whole_exome_pipeline.sh b/wes_analyses/whole_exome_pipeline.sh
@@ -1,3 +1,6 @@
+### title: Download reference files for whole exome analysis, and whole exome sequencing fastq files for cutaneous melanoma sequential therapy samples, map fastq files to generate bam files, and run controlFREEC with these inputs
+### author: Yiping Wang date: 11/08/2022
+
 wget https://storage.googleapis.com/gatk-best-practices/somatic-b37/Homo_sapiens_assembly19.fasta Homo_sapiens_assembly19.fasta
 wget https://storage.googleapis.com/gatk-best-practices/somatic-b37/Homo_sapiens_assembly19.fasta.fai Homo_sapiens_assembly19.fasta.fai
 ~/hisat2-2.2.1/hisat2-build -p 16 Homo_sapiens_assembly19.fastq Homo_sapiens_assembly19