80234 rows × 4 columns
\n", "" ], "text/plain": [ - " peak gene distance peak_type\n", - "0 chr1_565113_565543 OR4F16 56510 distal\n", - "1 chr1_569179_569635 OR4F16 52418 distal\n", - "2 chr1_713534_714806 AL669831.1 24331 distal\n", - "3 chr1_752436_753020 AL669831.1 -13300 distal\n", - "4 chr1_762144_763353 AL669831.1 -23008 distal\n", - "... ... ... ... ...\n", - "80229 chrY_23418918_23419001 PRORY 129245 distal\n", - "80230 chrY_23422186_23422618 PRORY 125628 distal\n", - "80231 chrY_23584049_23584422 PRORY -35804 distal\n", - "80232 chrY_28816422_28818023 NaN NaN intergenic\n", - "80233 chrY_58855905_58856257 NaN NaN intergenic\n", + " seqname source feature start end score strand attribute \\\n", + "0 chr1 HAVANA gene 11869 14409 . + . \n", + "12 chr1 HAVANA gene 14404 29570 . - . \n", + "28 chr1 HAVANA gene 29554 31109 . + . \n", + "39 chr1 HAVANA gene 34554 36081 . - . \n", + "47 chr1 HAVANA gene 52473 53312 . + . \n", + "\n", + " gene_id gene_type \\\n", + "0 \"ENSG00000223972.5\" \"transcribed_unprocessed_pseudogene\" \n", + "12 \"ENSG00000239906.1\" \"lncRNA\" \n", + "28 \"ENSG00000225972.1\" \"unprocessed_pseudogene\" \n", + "39 \"ENSG00000229905.1\" \"lncRNA\" \n", + "47 \"ENSG00000230368.2\" \"lncRNA\" \n", "\n", - "[80234 rows x 4 columns]" + " gene_name level hgnc_id havana_gene tag \n", + "0 \"DDX11L1\" 2 \"HGNC:37102\" \"OTTHUMG00000000961.2\" NaN \n", + "12 \"RP11-34P13.14\" 2 NaN \"OTTHUMG00000002481.1\" NaN \n", + "28 \"MTND1P23\" 2 \"HGNC:42092\" \"OTTHUMG00000002338.1\" NaN \n", + "39 \"RP11-206L10.4\" 2 NaN \"OTTHUMG00000002408.1\" NaN \n", + "47 \"FAM41C\" 2 \"HGNC:27635\" \"OTTHUMG00000002469.1\" NaN " ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "metadata = pd.read_csv(\"../data/10xGenomics_ATACseq/atac_v1_pbmc_10k_peak_annotation.tsv\", index_col=None, header=0, delimiter='\\t')\n", - "metadata" + "feature_type='gene'\n", + "annotation_source = 'HAVANA'\n", + "\n", + "gtf = get_gtf_annotations(\"../data/10kPBMC_scATAC/gencode.v38.annotation.gtf.gz\",\n", + " feature_type=feature_type,\n", + " annotation=annotation_source)\n", + "gtf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "d624060d-8b40-430d-95ba-5394e0b46f15", + "metadata": {}, + "source": [ + "## Match peaks to genes.\n", + "\n", + "We can then give the identified peaks a gene annotation by finding genes that overlap with the identified peaks. This is done with the `match_genes_to_peaks` function." ] }, { "cell_type": "code", - "execution_count": 47, - "id": "89dc3fad", + "execution_count": 7, + "id": "6e7d1547-9478-46e6-8bc1-b56effb205cf", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/emj760/miniconda3/envs/EMBEDR/lib/python3.9/site-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " self._setitem_single_block(indexer, value, name)\n" + ] + }, { "data": { "text/html": [ @@ -434,1304 +346,453 @@ " \n", "144023 rows × 1 columns
\n", + "144023 rows × 4 columns
\n", "" ], "text/plain": [ - " name\n", - "id \n", - "chr1:9781-10672 chr1:9781-10672\n", - "chr1:180678-181311 chr1:180678-181311\n", - "chr1:184004-184867 chr1:184004-184867\n", - "chr1:186550-187463 chr1:186550-187463\n", - "chr1:191198-192095 chr1:191198-192095\n", - "... ...\n", - "KI270713.1:21358-22260 KI270713.1:21358-22260\n", - "KI270713.1:25966-26842 KI270713.1:25966-26842\n", - "KI270713.1:29713-30529 KI270713.1:29713-30529\n", - "KI270713.1:34051-35030 KI270713.1:34051-35030\n", - "KI270713.1:36930-37826 KI270713.1:36930-37826\n", + " chromosome start stop gene_name\n", + "peak_ID \n", + "chr1:9781-10672 chr1 9781 10672 DDX11L1\n", + "chr1:180678-181311 chr1 180678 181311 RP4-740C4.7\n", + "chr1:184004-184867 chr1 184004 184867 RP4-740C4.7\n", + "chr1:186550-187463 chr1 186550 187463 HES5\n", + "chr1:191198-192095 chr1 191198 192095 HES5\n", + "... ... ... ... ...\n", + "KI270713.1:21358-22260 KI270713.1 21358 22260 NaN\n", + "KI270713.1:25966-26842 KI270713.1 25966 26842 NaN\n", + "KI270713.1:29713-30529 KI270713.1 29713 30529 NaN\n", + "KI270713.1:34051-35030 KI270713.1 34051 35030 NaN\n", + "KI270713.1:36930-37826 KI270713.1 36930 37826 NaN\n", "\n", - "[144023 rows x 1 columns]" + "[144023 rows x 4 columns]" ] }, - "execution_count": 47, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "adata_2 = adata.copy()\n", - "adata_2.var" + "upstream, downstream = 2000, 0\n", + "\n", + "match_genes_to_peaks(adata,\n", + " gtf,\n", + " upstream=upstream,\n", + " downstream=downstream,\n", + " verbose=False)\n", + "\n", + "adata.var" + ] + }, + { + "cell_type": "markdown", + "id": "45b9043a-9682-45a3-a13c-bc25c76d1310", + "metadata": {}, + "source": [ + "## Filter cells and peaks\n", + "\n", + "Now that we have the data loaded, we can perform quality control filtering. Specifically, we're going to filter out peaks that don't appear in enough cells and cells that don't have enough reads or peaks." ] }, { "cell_type": "code", - "execution_count": 45, - "id": "367ef1d5", + "execution_count": 8, + "id": "409f6e06-ef77-4898-ba8a-0eb940ac883b", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "\n", - " | peak | \n", - "name | \n", - "
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---|---|---|---|---|---|---|---|---|---|---|---|
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---|---|---|---|---|
id | \n", - "\n", - " | \n", - " | \n", - " | \n", - " |
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---|---|---|---|---|---|
id | \n", - "\n", - " | \n", - " | \n", - " | \n", - " | \n", - " |
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144023 rows × 5 columns
\n", - "5 rows × 25 columns
\n", "" ], "text/plain": [ - " plate_barcode mouse_id tissue FACS_selection mouse_sex \\\n", - "cell_id \n", - "A22.D042044.3_9_M.1.1 D042044 3_9_M Marrow Multiple M \n", - "C5.D042044.3_9_M.1.1 D042044 3_9_M Marrow Multiple M \n", - "D10.D042044.3_9_M.1.1 D042044 3_9_M Marrow Multiple M \n", - "E13.D042044.3_9_M.1.1 D042044 3_9_M Marrow Multiple M \n", - "F19.D042044.3_9_M.1.1 D042044 3_9_M Marrow Multiple M \n", - "\n", - " subtissue cell_ontology_class cell_ontology_id \\\n", - "cell_id \n", - "A22.D042044.3_9_M.1.1 B-cells immature B cell CL:0000816 \n", - "C5.D042044.3_9_M.1.1 B-cells late pro-B cell CL:0002048 \n", - "D10.D042044.3_9_M.1.1 B-cells precursor B cell CL:0000817 \n", - "E13.D042044.3_9_M.1.1 B-cells macrophage CL:0000235 \n", - "F19.D042044.3_9_M.1.1 B-cells late pro-B cell CL:0002048 \n", - "\n", - " free_annotation cluster_ids \\\n", - "cell_id \n", - "A22.D042044.3_9_M.1.1 NaN 6.0 \n", - "C5.D042044.3_9_M.1.1 Dntt- late pro-B cell 8.0 \n", - "D10.D042044.3_9_M.1.1 pre-B cell (Philadelphia nomenclature) 2.0 \n", - "E13.D042044.3_9_M.1.1 NaN 10.0 \n", - "F19.D042044.3_9_M.1.1 Dntt- late pro-B cell 8.0 \n", - "\n", - " ... subsetA subsetA_cluster_ids subsetB \\\n", - "cell_id ... \n", - "A22.D042044.3_9_M.1.1 ... False NaN False \n", - "C5.D042044.3_9_M.1.1 ... False NaN False \n", - "D10.D042044.3_9_M.1.1 ... False NaN False \n", - "E13.D042044.3_9_M.1.1 ... False NaN False \n", - "F19.D042044.3_9_M.1.1 ... False NaN False \n", - "\n", - " subsetB_cluster_ids subsetC subsetC_cluster_ids \\\n", - "cell_id \n", - "A22.D042044.3_9_M.1.1 NaN False NaN \n", - "C5.D042044.3_9_M.1.1 NaN True 2.0 \n", - "D10.D042044.3_9_M.1.1 NaN False NaN \n", - "E13.D042044.3_9_M.1.1 NaN False NaN \n", - "F19.D042044.3_9_M.1.1 NaN True 2.0 \n", + " plate_barcode mouse_id tissue FACS_selection \\\n", + "cell_id \n", + "A8.D042105.3_11_M.1.1 D042105 3_11_M Limb_Muscle Multiple \n", + "K10.D042105.3_11_M.1.1 D042105 3_11_M Limb_Muscle Multiple \n", + "L13.D042105.3_11_M.1.1 D042105 3_11_M Limb_Muscle Multiple \n", + "M15.D042105.3_11_M.1.1 D042105 3_11_M Limb_Muscle Multiple \n", + "N17.D042105.3_11_M.1.1 D042105 3_11_M Limb_Muscle Multiple \n", "\n", - " subsetD subsetD_cluster_ids subsetE \\\n", - "cell_id \n", - "A22.D042044.3_9_M.1.1 False NaN False \n", - "C5.D042044.3_9_M.1.1 False NaN False \n", - "D10.D042044.3_9_M.1.1 False NaN False \n", - "E13.D042044.3_9_M.1.1 False NaN False \n", - "F19.D042044.3_9_M.1.1 False NaN False \n", + " mouse_sex subtissue \\\n", + "cell_id \n", + "A8.D042105.3_11_M.1.1 M Diaphragm \n", + "K10.D042105.3_11_M.1.1 M Diaphragm \n", + "L13.D042105.3_11_M.1.1 M Diaphragm \n", + "M15.D042105.3_11_M.1.1 M Diaphragm \n", + "N17.D042105.3_11_M.1.1 M Diaphragm \n", "\n", - " subsetE_cluster_ids \n", - "cell_id \n", - "A22.D042044.3_9_M.1.1 NaN \n", - "C5.D042044.3_9_M.1.1 NaN \n", - "D10.D042044.3_9_M.1.1 NaN \n", - "E13.D042044.3_9_M.1.1 NaN \n", - "F19.D042044.3_9_M.1.1 NaN \n", + " cell_ontology_class cell_ontology_id \\\n", + "cell_id \n", + "A8.D042105.3_11_M.1.1 skeletal muscle satellite stem cell CL:0008011 \n", + "K10.D042105.3_11_M.1.1 mesenchymal stem cell CL:0000134 \n", + "L13.D042105.3_11_M.1.1 mesenchymal stem cell CL:0000134 \n", + "M15.D042105.3_11_M.1.1 endothelial cell CL:0000115 \n", + "N17.D042105.3_11_M.1.1 skeletal muscle satellite stem cell CL:0008011 \n", "\n", - "[5 rows x 25 columns]" + " free_annotation cluster_ids tSNE_1 tSNE_2 \n", + "cell_id \n", + "A8.D042105.3_11_M.1.1 NaN 0.0 -8.882184 -10.730514 \n", + "K10.D042105.3_11_M.1.1 NaN 1.0 5.435094 34.060681 \n", + "L13.D042105.3_11_M.1.1 NaN 1.0 -0.888768 27.308671 \n", + "M15.D042105.3_11_M.1.1 NaN 3.0 -23.209078 2.695871 \n", + "N17.D042105.3_11_M.1.1 NaN 0.0 -20.972265 2.364300 " ] }, "execution_count": 3, @@ -378,14 +292,21 @@ " \"FACS.selection\": \"FACS_selection\",\n", " \"mouse.sex_x\": \"mouse_sex\",\n", " \"subtissue_y\": \"subtissue\"})\n", + "raw_data.obs = raw_data.obs.rename(columns=col_renames)\n", + "\n", "## We also need to do some type coercion\n", "dtypes = {\"subsetA\": bool,\n", " \"subsetB\": bool,\n", " \"subsetC\": bool,\n", " \"subsetD\": bool,\n", " \"subsetE\": bool,}\n", + "dtypes = {key: val for key, val in dtypes.items() if key in raw_data.obs.columns}\n", + "raw_data.obs = raw_data.obs.astype(dtypes)\n", + "\n", "## Drop some redundant columns as well!\n", - "raw_data.obs = raw_data.obs.drop(columns=[\"tissue_y\", \"subtissue_x\"]).rename(columns=col_renames).astype(dtypes)\n", + "drop_cols = [\"subtissue_x\"] + [col for col in raw_data.obs.columns if col[-2:] == '_y']\n", + "raw_data.obs = raw_data.obs.drop(columns=drop_cols)\n", + "\n", "raw_data.obs.head()" ] }, @@ -414,8 +335,6 @@ } ], "source": [ - "import re\n", - "\n", "## Find any spike-ins and remove them\n", "raw_data.var['is_ERCC'] = [re.search('^ERCC-', gene) is not None for gene in raw_data.var.index]\n", "raw_data.obs['perc_ERCC'] = raw_data.X[:, raw_data.var.is_ERCC].sum(axis=1) / raw_data.X.sum(axis=1)\n", @@ -424,10 +343,21 @@ "## Count the number of reads in each cell\n", "raw_data.obs['n_reads'] = raw_data.X.sum(axis=1)\n", "\n", - "\n", "## Find any ribosomal genes\n", "raw_data.var['is_ribo'] = [re.search(\"^Rp[sl][0-9]\", gene) is not None for gene in raw_data.var.index]\n", - "raw_data.obs['perc_ribo'] = raw_data.X[:, raw_data.var.is_ribo].sum(axis=1) / raw_data.obs.n_reads" + "raw_data.obs['perc_ribo'] = raw_data.X[:, raw_data.var.is_ribo].sum(axis=1) / raw_data.obs.n_reads\n", + "\n", + "## Determine the fraction of Rn45s...\n", + "raw_data.var['is_Rn45s'] = [re.search(\"^Rn45s\", gene) is not None for gene in raw_data.var.index]\n", + "raw_data.obs['perc_Rn45s'] = raw_data.X[:, raw_data.var.is_Rn45s].sum(axis=1) / raw_data.obs.n_reads" + ] + }, + { + "cell_type": "markdown", + "id": "92b8dd02-669c-4df5-a7a4-173f0f3d39f0", + "metadata": {}, + "source": [ + "Check that there are no ERCCs (spike-ins) in the data!" ] }, { @@ -437,18 +367,23 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Index([], dtype='object', name='gene')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 0 spike-ins in the data!\n" + ] } ], "source": [ - "raw_data.var.index[raw_data.var.is_ERCC]" + "print(f\"There are {len(raw_data.var.index[raw_data.var.is_ERCC])} spike-ins in the data!\")" + ] + }, + { + "cell_type": "markdown", + "id": "9ca472cc-70a9-4ad6-9a36-ceb0687985a6", + "metadata": {}, + "source": [ + "Now we can do some quality control and assess whether we need to filter based on these metadata..." ] }, { @@ -459,9 +394,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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